Datasets:

ppxscal

/

aminer-citation-graphv14-jaccard

like 0

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

61dfffc75244ab9dcb244803

Hesitant fuzzy information allows clustering data with multiple possible membership values for a single item in a reference set. Hesitant fuzzy sets have been applied in many decision-making problems, obtaining better results against others kinds of fuzzy sets. So, in this paper a method for image segmentation based on the hesitant fuzzy set theory is investigated. Additionally, processing time is sped up with a hardware-level parallelization technique using OpenMP. Comparing the experimental results, it can be seen that the segmentation by the propose algorithm is superior, compared to some of the state of the art. The most striking feature to emerge from this algorithm is its ability to preserve the details of the boundaries of the region, in addition to the fact that the regions are more homogeneous.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

623b16ee5aee126c0fdd843c

Partitional clustering-based image segmentation is one of the most significant approaches. K-means is the conventional clustering techniques even though very sensitive to noise and easy convergences to local optima depending on the initial cluster centers. In addition, the computational time of K-means algorithm is also very high due to the repetitive distance calculation between pixels and cluster centers. In order to solve these problems, this paper presents a Histogram-based Fast and Robust Crisp Image Clustering (HFRCIC) technique. Local spatial information is often introduced to an objective function to improve the noise robustness of the clustering technique. At first, the local spatial information has been introduced into HFRCIC by incorporating morphological reconstruction which assures noise-immunity as well as image detail-preservation. Then clustering has been executed depending on gray levels in the place of pixels of the image. As result, the execution time is low as the number of gray levels is usually much smaller than the number of pixels in the image. Due to the random initialization of centers, HFRCIC easily stuck into local optima as HFRCIC is greedy in nature and an efficient local optimizer. Therefore, Nature-Inspired Optimization Algorithms (NIOA) are successfully employed to overcome the problem within reasonable computational time. Here, Stochastic Fractal Search (SFS) has been employed to find the optimal cluster centers. The experimental study has been performed over synthetic images, real-world images and white the gray level conversion of RGB imaged for white blood cell (WBC) segmentation. Visual and numerical results indicate the superiority of the proposed HFRCIC with SFS(HFRCIC-SFS) over state-of-the-art image segmentation algorithms and NIOA-based crisp image clustering techniques.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

623c111dd18a2b5b405a6657

Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that the introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that the incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy STUDENT’S t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this article, we make two significant contributions. The first is that both the narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under STUDENT’S t-distribution ensures that spatial information is introduced into the updated parameters of FRSC, which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information, and provides better segmentation results than state-of-the-art clustering algorithms.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d16345aee126c0f313241

Based on psychologists’ theories, an individual’s handwriting somehow symbolizes a type of personality trait that can be a projection of the person’s innate. A person’s handwriting is the result of an organized system and has scientific bases that make it possible to analyze and specify individuals’ nature. This paper presents a novel real-time model based on handwriting samples collected from Persian-speaking people, which predicts their personality traits for the first time. Initially, 400 handwriting samples with a repetition of four different texts and psychological questionnaires and three psychologists’ comments have been collected. The pre-processing step is applied to the image samples and the decision-maker model was designed using a lightweight deep convolutional neural network (LWDCNN) structure. The texts were selected based on the psychologists’ guidance. The meaningful relation between the personality trait characters extracted from Persian handwriting and each of the personality traits of the person under-study is matched to a magnificent extent. Finally, the LWDCNN structure is evaluated based on the training samples. The proposed convolutional neural network provides reasonable accuracy for six different and three overlapping personality traits. Despite computational complexity and little time spent by the designed pre-train network to respond, the deep structure’s error level with limited layers is estimated smaller than 10%. The proposed algorithm’s efficiency has been proved by repeating the experiment and assessing measures such as accuracy and mean squared error (MSE).

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d1cfe5aee126c0f3bc5ac

To detect spacecraft damage caused by hypervelocity impact, we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection. The Gaussian mixture model (GMM) is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image (IRRI) reflecting the defect characteristics. The designed segmentation objective function is used to ensure the effectiveness of image segmentation results for noise removal and detail preservation, while taking into account the complexity of IRRI (that is, the required trade-offs are different). A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used for optimization to ensure damage segmentation accuracy. Experimental results verify the effectiveness of the proposed algorithm.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d20385aee126c0f40a3b1

FCM algorithm is one of the well-known techniques for image segmentation; it is based on an imprecise decision by using the membership function. However, FCM algorithm fails to proceed well enough in the presence of imaging artifacts due to its performance without any consideration of spatial information. In this paper, we propose two crucial modifications to the conventional FCM algorithm to tackle its sensitivity against noise. Firstly, the proposed algorithm provides full consideration of the spatial constraint, wherein the influence of neighboring pixels is defined according to two proposed terms, a fuzzy similarity measure as well as the level of noise. Secondly, we adopt a strategy to select the optimal pixel between the central pixel and its neighboring pixels that can better influence the segmentation performance in terms of compactness and separation information. The proposed algorithm is compared qualitatively and quantitatively with five existing clustering methods in terms of cluster validity functions, segmentation accuracy, tissue segmentation accuracy, and computational time.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d24105aee126c0f47a4f6

The financial industry is a key to promoting the development of the national economy, and the risk it takes is also the largest hidden risk in the financial market. Therefore, the risk existing in the current financial market should be deeply explored under blockchain technology (BT) to ensure the functions of financial markets. The risk of financial markets is analyzed using machine learning (ML) and random forest (RF). First, the clustering method is introduced, and an example is given to illustrate the RF classification model. The collected data sets are divided into test sets and training sets, the corresponding rules are formulated and generated, and the branches of the decision tree (DT) are constructed according to the optimization principle. Finally, the steps of constructing the branches of DT are repeated until they are not continued. The results show that the three major industries of the regional economy account for 3.5%, 51.8%, 3.2%, 3.4%, and 3.8% of the regional GDP, respectively, the secondary industry makes up 44.5%, 43%, 45.1%, 44.8%, and 43.6%, respectively, and the tertiary industry occupies 20%, 3.7%, 52.3%, 52.9%, 54%, and 54.6%, respectively. This shows that with the development of the industrial structure under BT, the economic subject gradually shifts from the primary industry to the tertiary industry; BT can improve the efficiency of the financial industry and reduce operating costs and dependence on media. Meanwhile, the financial features of BT can provide a good platform for business expansion. The application of BT to the supply chain gives a theoretical reference for promoting the synergy between companies.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

619bbb8c1c45e57ce97b67d8

Aiming at existing symmetric regularized picture fuzzy clustering with weak robustness, and it is difficult to meet the need for image segmentation in the presence of high noise. Hence, a robust dynamic semi-supervised symmetric regularized picture fuzzy clustering with KL-divergence and spatial information constraints is presented in this paper. Firstly, a weighted squared Euclidean distance from current pixel value, its neighborhood mean and median to clustering center is firstly proposed, and it is embedded into the objective function of symmetric regularized picture fuzzy clustering to obtain spatial picture fuzzy clustering. Secondly, the idea of maximum entropy fuzzy clustering is introduced into picture fuzzy clustering, and an entropy-based picture fuzzy clustering with clear physical meaning is constructed to avoid the problem of selecting weighted factors. Subsequently, the prior information of the current pixel is obtained by means of weighted local membership of neighborhood pixels, and it is embedded into the objective function of maximum entropy picture fuzzy clustering with multiple complementary spatial information constraints through KL-divergence, a robust dynamic semi-supervised picture fuzzy clustering optimization model and its iterative algorithm are given. In the end, this proposed algorithm is strictly proved to be convergent by Zangwill theorem. The experiments on various images and standard datasets illustrate how our proposed algorithm works. This proposed algorithm has excellent segmentation performance and anti-noise robustness, and outperforms eight state-of-the-art fuzzy or picture fuzzy clustering-related algorithms in the presence of high noise.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

62f4db8990e50fcafda4e63c

Aiming at the shortcoming of existing fuzzy clustering in noise suppression, the combination of fuzzy clustering and guided filtering can improve the ability of noise suppression to some extent. Although image guided membership filter achieves better anti-noise performance, it still does not meet the needs of images with high noise. In this paper, membership guided image filter is firstly introduced into kernel-based fuzzy local information clustering (KWFLICM), and a novel multi-objective optimization model for robust fuzzy clustering is constructed. Then least square method is used to solve the optimization model, and the iterative algorithm for noisy image segmentation is obtained. Experimental results show that the proposed algorithm has better segmentation performance and robustness compared with existing many robust fuzzy clustering algorithms and fuzzy clustering algorithms which use image to guide membership filtering.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d208d5aee126c0f411f0d

Fuzzy local information clustering is the most widely robust segmentation methods, but it is only suitable for image corrupted by certain intensity noise. Later, although fuzzy local information clustering integrated guided filter is improved the ability of suppressing noise, it still cannot meet the needs of image with high noise. This paper proposed a novel robust fuzzy local information clustering combined kernel metric with guided filter. Firstly, guided filter is introduced into fuzzy local information clustering with kernel metric (KWFLICM), and a novel multiple objective optimization model for fuzzy clustering is constructed. Secondly, the optimization model is solved by Lagrange multiplier method, and the iterative algorithm for image segmentation is presented. Experimental results show that the proposed algorithm has better segmentation performance and robustness than existing state of the art guided filter-driven fuzzy clustering with local information.

5bdc31af17c44a1f58a0b052

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spat...

628d26ee5aee126c0f4c2006

To solve the problem of high computational complexity and the lack of local similarity information in constructing similarity matrix, an algorithm combining intrinsic dimension and local tangent space (IDLTS) for manifold spectral clustering image segmentation is proposed. Firstly, considering the manifold structure of image feature space, local linear reconstruction in manifold learning is introduced, and the local tangent spatial similarity of image data is obtained. Secondly, performing the local PCA (Pincipal Components Analysis) in the K-nearest neighbor region of data points, the relationship between intrinsic dimensions of image data is calculated. Combining it with the local tangent spatial similarity, the similarity function and its related similarity matrix of the spectral clustering can be constructed. Thirdly, by sampling points and sampling points, as well as sampling points and non-sampling points, two similarity matrices are constructed with Nyström approximation strategy and used to approximate the eigenvectors for image segmentation. Finally the IDLTS manifold spectral clustering image segmentation is accomplished based on the constructed k principal eigenvectors. Berkeley Segmentation Dataset and eight evaluation metrics are selected to compare the proposed algorithm with some existing image segmentation algorithms. Experimental results show that the IDLTS has good performance in terms of segmentation accuracy and time consumption.

5bdc319c17c44a1f58a09c91

In many applications in engineering, one is interested in tracking a dynamic system whose state evolves on a manifold. Solutions to such problems frequently must resort to nonlinear filtering techniques as many manifolds can be described as equality restrictions on higher-dimensional embedding spaces. We propose in this paper a new particle filtering (PF) method to track the states of dynamic systems that evolve according to a random walk on the unit sphere. We derive an approximation to the intractable optimal importance function and develop a Markov Chain Monte Carlo (MCMC) method to sample from it. The system state variable is then estimated via a Monte Carlo approximation of its intrinsic mean on the sphere, obtained from the Karcher mean of the particle set. As we verify via computer simulations, the proposed method shows improved performance compared to previous Constrained Extended Kalman filters and Bootstrap PF solutions.

5ff682c5d4150a363cbb0428

We applied residual deep neural network for high noise power region uplink channel estimation. Proposed residual deep neural network channel estimation show more 1 dB gain under EPA/ETU channels compare to MMSE channel estimation and ability to work on wireless channels that have not previously been trained. Based on fundamental group topological data analysis, we obtained the reason for such transfer learning property in the problem of channel estimation, as well as in machine learning in general. Using achievements of deductive Langlands program, inductive Forneys program and dialectic Topological programs of Frobenius und Weierstrass(students Ajtai, Tyrtyshnikov-Zamarashkin, Oseledets, GelfandBernstein-Frenkel, Pontryagin-Anosov, Berezin, Müller, Bugaev-Kolmogorov-Arnold-Gusein-Zade, Lyusternik-SigalovGlebskiy-Shevchenko, Sinai-Margulis)-Hermite(Poincaré)Rossetti(Witten)-Chebyshev(Manin, Korkin-Zolotarev-Voronoy- Delaunay-Aleksandrov-Ryshkov, Baranovskii, Perelman, Fadeev-Wenkow, Stark-Lagarias)-Poisson(Schnorr, Gallager, Robinson, Boumal)-Möbius(Andronov-Aranson-GrinesPochinka, Gurevich)-Duffin(Bott-Smale)-Kronecker(Rokhlin- Gromov)-Neumann (Ghrist, Hilbert-Moser-Mehrmann, Sloane, Nebe, Hopf-Stiefel-Gander, Gunning-Hamilton, Zagier, Katz, Sturmfels-Siegal) schools which combine: Margulis-Gromov's dynamic theory of Hyperbolic group, Lattice/Ricci-Flow dynamic, Computation Theory: limitation of quantum and other models for solving hidden subgroup problem, Geometric integration theory, methods to singularities of differentiable maps, Information Theory, Non-linear optimization was shown that the main problem of Data-Driven and Model-Driven signal processing approaches is to preserve the topological properties of the input data: compensate for the space's curvature using the correct geometry, choose a suitable bundle with the trade-off between error and complexity, make prior on weight (regularization), solve the problem of nonlinear optimization.

5bdc319c17c44a1f58a09c95

The brain-like functionality of the artificial neural networks besides their great performance in various areas of scientific applications, make them a reliable tool to be employed in Audio-Visual Speech Recognition (AVSR) systems. The applications of such networks in the AVSR systems extend from the preliminary stage of feature extraction to the higher levels of information combination and speech modeling. In this paper, some carefully designed deep autoencoders are proposed to produce efficient bimodal features from the audio and visual stream inputs. The basic proposed structure is modified in three proceeding steps to make better usage of the presence of the visual information from the speakers' lips Region of Interest (ROI). The performance of the proposed structures is compared to both the unimodal and bimodal baselines in a professional phoneme recognition task, under different noisy audio conditions. This is done by employing a state-of-the-art DNN-HMM hybrid as the speech classifier. In comparison to the MFCC audio-only features, the finally proposed bimodal features cause an average relative reduction of 36.9% for a range of different noisy conditions, and also, a relative reduction of 19.2% for the clean condition in terms of the Phoneme Error Rates (PER).

5c6d2963f56def979812d948

•A comprehensive review on abnormal crowd behaviour detection methods.•Gaussian Mixture Model (GMM) is used to model features distribution of the crowd.•Hidden Markov Model (HMM) represents the normal event and detects the outlier.•Optical Flow (OF) method analyzes the motion patterns for behavior understanding.•Spatio-Temporal Technique (STT) combines features from the space and time dimension.

5bdc319c17c44a1f58a09c95

The brain-like functionality of the artificial neural networks besides their great performance in various areas of scientific applications, make them a reliable tool to be employed in Audio-Visual Speech Recognition (AVSR) systems. The applications of such networks in the AVSR systems extend from the preliminary stage of feature extraction to the higher levels of information combination and speech modeling. In this paper, some carefully designed deep autoencoders are proposed to produce efficient bimodal features from the audio and visual stream inputs. The basic proposed structure is modified in three proceeding steps to make better usage of the presence of the visual information from the speakers' lips Region of Interest (ROI). The performance of the proposed structures is compared to both the unimodal and bimodal baselines in a professional phoneme recognition task, under different noisy audio conditions. This is done by employing a state-of-the-art DNN-HMM hybrid as the speech classifier. In comparison to the MFCC audio-only features, the finally proposed bimodal features cause an average relative reduction of 36.9% for a range of different noisy conditions, and also, a relative reduction of 19.2% for the clean condition in terms of the Phoneme Error Rates (PER).

6319c80590e50fcafdc72344

The development of big data, machine learning, and the Internet of Things has led to rapid advances in the research field of Active and Assisted Living (AAL). A human is placed in the center of such an environment, interacting with different modalities while using the system. Although video still plays a dominant role in AAL technologies, audio, as the most natural means of interaction, is also used commonly, either as a single source of information, or in combination with other modalities. Despite the rapidly increased research efforts in the last decade, there is a lack of systematic overview of audio based technologies and applications in AAL. This review tries to fill this gap, and identifies five major topics where audio is an essential AAL building block: Physiological monitoring, emotion recognition in the context of AAL, human activity recognition, fall detection, and food intake monitoring. We address the data work flow and standard sensing technologies for capturing audio in the AAL environment, provide a comprehensive overview of audio-based AAL applications, and identify datasets available to the research community. Finally, we address the main challenges that should be handled in the upcoming years, and try to identify the potential future trends in audio-based AAL.

5bdc319c17c44a1f58a09c96

•In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF.•The bilinear term is defined with respect to the impulse responses of a spatiotemporal model.•Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method.•A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR.•Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method.

5a9cb63417c44a376ffb6163

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes.

5bdc319c17c44a1f58a09c96

•In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF.•The bilinear term is defined with respect to the impulse responses of a spatiotemporal model.•Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method.•A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR.•Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method.

5c4880dd7301396d1ffc809b

The system identification problem becomes more challenging when the parameter space increases. Recently, several works have focused on the identification of bilinear forms, which are related to the impulse responses of a spatiotemporal model, in the context of a multiple-input/single-output system. In this framework, the problem was addressed in terms of the Wiener filter and different basic adaptive algorithms. This paper studies two types of algorithms tailored for the identification of such bilinear forms, i.e., the Kalman filter (along with its simplified version) and an optimized least-mean-square (LMS) algorithm. Also, a comparison between them is performed, which shows interesting similarities. In addition to the mathematical derivation of the algorithms, we also provide extensive experimental results, which support the theoretical findings and indicate the good performance of the proposed solutions.

5bdc319c17c44a1f58a09c96

•In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF.•The bilinear term is defined with respect to the impulse responses of a spatiotemporal model.•Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method.•A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR.•Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method.

5c9dedda3cb210d271be7fde

•We introduce computationally efficient beamformers for large-scale uniform planar arrays.•Our simulation results show that the proposed methods require fewer computations than the benchmark method.•The reduction can be as large as one order of magnitude.•The source recovery performance of the proposed methods is slightly inferior to that of the benchmark.

5bdc319c17c44a1f58a09c96

•In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF.•The bilinear term is defined with respect to the impulse responses of a spatiotemporal model.•Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method.•A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR.•Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method.

5c9f430c3cb210d271811d78

The recursive least-squares (RLS) adaptive filter is an appealing choice in many system identification problems. The main reason behind its popularity is its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the identification of long length impulse responses, like in echo cancellation. Computationally efficient versions of the RLS algorithm, like those based on the dichotomous coordinate descent (DCD) iterations or QR decomposition techniques, reduce the complexity, but still have to face the challenges related to long length adaptive filters (e.g., convergence/tracking capabilities). In this paper, we focus on a different approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. In other words, a high-dimension system identification problem is reformulated in terms of low-dimension problems, which are combined together. This approach was recently addressed in terms of the Wiener filter, showing appealing features for the identification of low-rank systems, like real-world echo paths. In this paper, besides the development of the RLS algorithm based on this approach, we also propose a variable regularized version of this algorithm (using the DCD method to reduce the complexity), with improved robustness to double-talk. Simulations are performed in the context of echo cancellation and the results indicate the good performance of these algorithms.

5bdc319c17c44a1f58a09c96

•In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF.•The bilinear term is defined with respect to the impulse responses of a spatiotemporal model.•Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method.•A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR.•Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method.

5d440a9a3a55acddd2fdad11

Multidimensional system identification problems can be encountered in many important fields, such as big data, machine learning, and source separation. Nevertheless, a large parameter space usually raises additional challenges in terms of system identification. In this context, there is a huge interest in exploiting the methods based on tensor decompositions and modelling. Recently, we focused on bilinear forms (i.e., two-dimensional decomposition), in the framework of identifying spatiotemporal models. Following this approach, we developed several solutions based on the Wiener filter and different adaptive algorithms. In this paper, in order to further exploit the decomposition of the global impulse response, we propose an iterative Wiener filter tailored for the identification of trilinear forms (where third-order tensors are involved). Simulation results indicate the good performance of the proposed solution, as compared to the conventional Wiener approach.

5bdc319c17c44a1f58a09c98

The affine projection (AP) algorithm is one of the most celebrated adaptive filtering algorithms, and it achieves a good tradeoff between the convergence rate and computational complexity. However, the complexity of the AP algorithm increases with the projection order. A wealth of fast AP algorithms have been proposed to reduce the complexity in the last two decades. However, those low-complexity methods have not been well analyzed and compared. To fill this gap, this paper reviews the fast AP algorithms, including both fast filtering approaches and efficient solutions of the linear system of equations. The advantages and disadvantages of each fast implementation version are clarified based on an extensive performance evaluation, which could be useful to engineers when selecting a suitable algorithm for their specific applications and could also be a starting point for experts in this field to develop better solutions.

5a9cb5f517c44a376ffb3072

This paper presents a stable approach to solve the linear system of equations associated with the affine projection (AP) algorithm. Previously, the correlation matrix was approximated as a Toeplitz matrix and computationally efficient methods can be used to solve the inverse of the matrix. However, it was also found that a relatively large regularization parameter should he employed to ensure the algorithm's stability even for a float-point implementation, which also slows down the algorithm's convergence rate. We point out that the instability of the algorithm is mainly attributed to that the condition number of the Toeplitz matrix is much larger than that of the original correlation matrix. Motivated by the schemes used in speech coders, three approaches are proposed to improve the stability of the inverse of the Toeplitz matrix. Simulation results confirm that the time-windowing method achieves the best performance.

5bdc319c17c44a1f58a09c98

The affine projection (AP) algorithm is one of the most celebrated adaptive filtering algorithms, and it achieves a good tradeoff between the convergence rate and computational complexity. However, the complexity of the AP algorithm increases with the projection order. A wealth of fast AP algorithms have been proposed to reduce the complexity in the last two decades. However, those low-complexity methods have not been well analyzed and compared. To fill this gap, this paper reviews the fast AP algorithms, including both fast filtering approaches and efficient solutions of the linear system of equations. The advantages and disadvantages of each fast implementation version are clarified based on an extensive performance evaluation, which could be useful to engineers when selecting a suitable algorithm for their specific applications and could also be a starting point for experts in this field to develop better solutions.

5dfe172ddf1a9c0c41659ba6

This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.

5bdc319c17c44a1f58a09c98

The affine projection (AP) algorithm is one of the most celebrated adaptive filtering algorithms, and it achieves a good tradeoff between the convergence rate and computational complexity. However, the complexity of the AP algorithm increases with the projection order. A wealth of fast AP algorithms have been proposed to reduce the complexity in the last two decades. However, those low-complexity methods have not been well analyzed and compared. To fill this gap, this paper reviews the fast AP algorithms, including both fast filtering approaches and efficient solutions of the linear system of equations. The advantages and disadvantages of each fast implementation version are clarified based on an extensive performance evaluation, which could be useful to engineers when selecting a suitable algorithm for their specific applications and could also be a starting point for experts in this field to develop better solutions.

633dc27690e50fcafd9c52ce

•A novel affine projection tanh algorithm (APTA) for adaptive filter is proposed by solving the constraint tanh function optimization model in the projection subspace.•Steady-state performance, mean and mean-square stability and transient performance of APTA are analyzed by using a vector-formed Taylor series expansion and weight energy conservation relation.•Simulations on highly colored input signals validate the superiority of APTA on filtering accuracy and robustness

5bdc319c17c44a1f58a09c9c

High speed small target detection is a challenging problem for ground-based radar due to its maneuverability and low radar cross section (RCS). The range migration (RM) and Doppler frequency migration (DFM) will occur during the coherent integration period, which makes it difficult to improve the coherent integration ability and radar detection performance. In this study, a novel algorithm based on Keystone transform (KT) and linear canonical transform (LCT) for high speed small target detection with narrowband radar is proposed. Firstly, it employs KT to eliminate RM. Thereafter, the LCT is applied to compensate DFM and realize coherent integration for the target in the LCT domain. Two typical forms of LCT are given for easy realization and good detection performance. Finally, the constant false alarm ratio (CFAR) detector is performed to confirm a target and motion parameters are then estimated. Moreover, in order to realize fast compensation for velocity ambiguity effect, an improved method is proposed based on coarse and fine search. Compared with the generalized Radon Fourier transform (GRFT), the proposed method can acquire a close detection performance but with relatively low computational cost. Simulation results are provided to demonstrate the validity of proposed method.

5ff87636d4150a363c7b0c59

This paper focuses on the weak maneuvering target detection problem in the alpha-stable noise (ASN) environment. A novel coherent integration framework is proposed, where the range migration (RM) is first removed via approximate linear methods to make the noise distribution character remain unchanged, and then the ASN suppression, Doppler frequency migration (DFM) compensation, and coherent integration are simultaneously achieved in the proper transform domain. Based on this framework, a robust detection method is developed. First, the second-order keystone transform (SoKT) is employed to correct the range curve (RC) induced by the target's acceleration. Thereafter, the centroid axis rotation (CAR) is presented to remove the residual range walk (RW) by rotating the slow time axis parallel to the RW line in the fast-time and slow-time domain. Finally, the phase fractional lower-order Lv's distribution (PFLOLVD) is proposed to compensate the DFM and realize the energy accumulation of the target in the ASN environment. Furthermore, the performance of proposed algorithm in aspects of coherent integration time, computational complexity, and multiple targets detection are analyzed in detail. Compared with the existing coherent integration detection methods, the proposed method is both robust for ASN environments and superior in the detection performance. (C) 2020 Elsevier Inc. All rights reserved.

5bdc319c17c44a1f58a09c9d

In this paper, selection combiner output (SCO) signal-to-noise ratio (SNR) cumulative distribution functions (cdfs) of multivariate correlated and identically distributed, and exponentially-correlated Nakagami-m fading environments for integer and non-integer fading parameter m are rigorously surveyed, and mathematically reviewed. Simulation results are obtained under two scenarios: (i) equal-channel-gain, and (ii) unequal-channel-gain. Numerical simulation, only under specific scenarios, appears to be effective to verify findings from different fading environments and different specific contributions. Schematic diagrams are employed to show (i) top-down view for progress on SCO cdfs of multivariate correlated Nakagami-m fading, and (ii) mathematical relations among existing results, from which knowledge gaps can systematically be identified. Cross-verification revealing mathematical links among the published results is shown in detail, so that unidentified special cases can be mathematically unified to their corresponding generalised cases. Useful applications of correlated fading environments are also proposed. Not just focusing on reviewing correlated fading contributions, this paper also serves as a comprehensive tutorial, consolidating key findings on this topic.

5ac1826c17c44a1fda914ef8

Obtaining tractable and compact expressions for cumulative distribution functions (cdfs) of multivariate correlated fading has long been difficult in wireless communications. Selection combiner output (SCO) single-user cdfs of multivariate correlated and identically distributed (c.i.d.) or equally-correlated Rayleigh, Rician, and Nakagami-m fading for only even integer n-degree-of-freedom (DoF) no...

5bdc319c17c44a1f58a09c9f

The existing off-grid sparse Bayesian learning (SBL) DOA estimation method for nested arrays suffers from two major drawbacks: reduced array aperture and high modeling error. To solve these issues, a new data model formulation is first presented in this paper, in which we take the noise variance as a part of the unknown signal of interest, so as to learn its value by the Bayesian inference inherently. Then, we provide a novel grid refining procedure to eliminate the modeling error caused by off-grid gap, where we consider the locations of grid points as adjustable parameters and proceed to refine the grid point iteratively. Simulation results demonstrate that our method significantly improves the DOA estimation performance especially using a coarse grid.

5ff68107d4150a363cb5bf52

In this letter, we focus on the problem of direction-of-arrival (DOA) estimation with a nested array in the presence of unknown mutual coupling. Firstly, the measurement model of the nested array with mutual coupling is established, and the properties of the corresponding array covariance matrix are derived. Based on the obtained properties, the coarray output signal is then reconstructed with reduced mutual coupling. Finally, joint sparse recovery technique is employed to extract the DOAs. Our algorithm can achieve underdetermined DOA estimation with satisfactory performance under unknown mutual coupling. Numerical results demonstrate the effectiveness of the proposed algorithm.

5bdc319c17c44a1f58a09ca0

Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.

6125b0135244ab9dcb38b4dd

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

5bdc319c17c44a1f58a09ca0

Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.

62d172745aee126c0fdcfbba

To enhance the bit error rate (BER) performance for the dual-function radar-communication (DFRC), we investigate the problem of adaptive bit/power allocation with beamforming. Firstly, we formulate the bit/power allocation problem as a BER minimization problem subject to the radar signal-to-interference-plus-noise ratio (SINR), sidelobe information (SLI), and available resource constraints. Secondly, we devise an alternation optimization algorithm that divides the non-convex minimization problem into several low-complexity and iteratively updated subproblems. Finally, simulation results show that better communication performance, in terms of BER, can be obtained compared to state-of-the-art algorithms.

5bdc319c17c44a1f58a09ca0

Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.

632630cb90e50fcafdf5985f

Dual-function radar-communication (DFRC) based on frequency hopping (FH) MIMO radar (FH-MIMO DFRC) achieves symbol rate much higher than radar pulse repetition frequency. Such DFRC, however, is prone to eavesdropping due to the spatially uniform illumination of an FH-MIMO radar. In this paper, we reveal the potential of using permutations of hopping frequencies to achieve secure and high-speed FH-MIMO DFRC. Specifically, we identify the angle-dependent issue in detecting permutations and develop an element-wise phase compensation (EPC) to solve the issue for a legitimate user (Bob). EPC makes the demodulation at an eavesdropper (Eve) conditioned on knowing the angle-of-departure (AoD) of Bob. We also propose the random sign reversal (RSR) technique which randomly selects several antennas over hops and reverses their signs. Owing to EPC, there is a sign rule available for Bob. We employ the rule and develop a low-complexity algorithm for Bob to remove RSR. We further prove that, given the same signal-to-noise ratio, RSR plus EPC make the demodulation performance of Eve inferior to that of Bob in most angular regions. Confirmed by simulation, our design achieves substantially high physical layer security for FH-MIMO DFRC, improves demodulation performance compared with existing designs, and reduces mutual interference among radar targets.

5bdc319c17c44a1f58a09ca7

In this paper, we investigate the physical layer secrecy (PLS) problem in the multiple-input single-output (MISO) visible light communication (VLC) system aided by the spatial modulation (SM), which is termed as the MISO SM-VLC system. With addressing the characteristics of channel input signals and MISO SM-VLC scheme, the secrecy performance of MISO SM-VLC system is investigated in detail for the first time, when the input signals obey finite discrete distributions. Specifically, aided by the distribution information of the system's input and output signals, the average mutual information of MISO SM-VLC system is derived. The lower bound and an accurate closed-form expression for approximation of the average mutual information are also obtained, which can be utilized for estimating the achievable secrecy rate of MISO SM-VLC systems efficiently. Moreover, we analyzed the pairwise error probability and bit error rate (BER) of MISO SM-VLC systems, and additionally, some closed-form expressions about the BER and pairwise error probability are obtained. Numerical and simulation results verify the accuracy of the derived theoretical results of the secrecy performance for MISO SM-VLC systems.

599c77bc601a182cd25749ec

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the p...

5bdc319c17c44a1f58a09ca7

In this paper, we investigate the physical layer secrecy (PLS) problem in the multiple-input single-output (MISO) visible light communication (VLC) system aided by the spatial modulation (SM), which is termed as the MISO SM-VLC system. With addressing the characteristics of channel input signals and MISO SM-VLC scheme, the secrecy performance of MISO SM-VLC system is investigated in detail for the first time, when the input signals obey finite discrete distributions. Specifically, aided by the distribution information of the system's input and output signals, the average mutual information of MISO SM-VLC system is derived. The lower bound and an accurate closed-form expression for approximation of the average mutual information are also obtained, which can be utilized for estimating the achievable secrecy rate of MISO SM-VLC systems efficiently. Moreover, we analyzed the pairwise error probability and bit error rate (BER) of MISO SM-VLC systems, and additionally, some closed-form expressions about the BER and pairwise error probability are obtained. Numerical and simulation results verify the accuracy of the derived theoretical results of the secrecy performance for MISO SM-VLC systems.

5a0f9fe59ed5dbea8e77c928

We consider the secrecy rate for the multiple-input single-output optical wireless scattering communication, where the detected signal can be characterized by discrete photoelectrons. Assuming a legitimate receiver and an eavesdropper, we propose two secure communication protocols, the non-jamming protocol, and the cooperative jamming protocol, where for the former the transmitter does not send ja...

5bdc319c17c44a1f58a09ca7

In this paper, we investigate the physical layer secrecy (PLS) problem in the multiple-input single-output (MISO) visible light communication (VLC) system aided by the spatial modulation (SM), which is termed as the MISO SM-VLC system. With addressing the characteristics of channel input signals and MISO SM-VLC scheme, the secrecy performance of MISO SM-VLC system is investigated in detail for the first time, when the input signals obey finite discrete distributions. Specifically, aided by the distribution information of the system's input and output signals, the average mutual information of MISO SM-VLC system is derived. The lower bound and an accurate closed-form expression for approximation of the average mutual information are also obtained, which can be utilized for estimating the achievable secrecy rate of MISO SM-VLC systems efficiently. Moreover, we analyzed the pairwise error probability and bit error rate (BER) of MISO SM-VLC systems, and additionally, some closed-form expressions about the BER and pairwise error probability are obtained. Numerical and simulation results verify the accuracy of the derived theoretical results of the secrecy performance for MISO SM-VLC systems.

5aed14b617c44a443815699c

This paper investigates the physical layer security problem of visible light communication (VLC) systems relying on generalized space-shift keying (GSSK) termed as GSSK-VLC. The GSSK-VLC system considered is assumed to be comprised of three nodes: a transmitter equipped with multiple light-emitting diodes, a legitimate receiver, and a passive eavesdropper. Each of them is equipped with a single photo-detector. Specifically, the average mutual information (AMI) of a GSSK-VLC system is derived. We also obtain both a lower bound and an accurate closed-form expression of the approximate AMI, which can be employed for efficiently estimating the achievable secrecy rate of GSSK-VLC systems. Furthermore, the pairwise error probability and bit error rate of GSSK-VLC systems are analyzed, and again some closed-form expressions are obtained. Additionally, in order to enhance the secrecy performance of the GSSK-VLC system, an optimal LED pattern selection algorithm is proposed under the minimax criterion. We show that the proposed LED pattern selection algorithm is capable of enhancing both the AMI between the transmitter and legitimate user and the achievable secrecy rate of the GSSK-VLC system.

599c77bc601a182cd25749ec

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the p...

5c5ce4fd17c44a400fc38455

•A friendly optical jamming aided secrecy enhancement scheme is designed for the proposed SM-VLC system.•The secrecy performance of the SM-VLC system with optical jamming is analyzed, which includes the average mutual information (AMI), lower bound on AMI and its closed-form expression approximation and achievable secrecy rate.•Closed-form approximations for the AMI of Alice-to-Bob and Alice-to-Eve are derived.•The power allocation strategy for the proposed optical jamming based SM-VLC system is considered.•The pairwise error probability and bit error rate (BER) of the proposed friendly optical jamming aided secrecy enhancement SM-VLC system are derived.

599c77bc601a182cd25749ec

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the p...

5e09ab2fdf1a9c0c416d4f31

This paper studies the secrecy performance of multiple-input multiple-output (MIMO) wiretap channels, also termed as multiple-input multiple-output multiple-eavesdropper (MIMOME) channels, under transmit antenna selection (TAS) and BPSK/QPSK modulations. In the main channel between the transmitter and the legitimate receiver, a single transmit antenna is selected to maximize the instantaneous Signal to Noise Ratio (SNR) at the receiver. At the receiver and the eavesdropper, selection combining (SC) is utilized. Additionally, suppose that the transmitted message is modulated by BPSK/QPSK modes. We first derive the closed-form approximated expression for the ergodic secrecy rate under Rayleigh fading, assuming that the channel state information of the eavesdropper (CSIE) is available at the transmitter. Next, analytical formulas for the approximated and asymptotic secrecy outage probability are also developed when CSIE is unavailable (NCSIE). Simulation results are provided to demonstrate the approximation precision of the derived results above. Furthermore, the asymptotic results reveal that the secrecy diversity order degrades into 0 due to the finite-alphabet inputs, which is totally different from that driven by the Gaussian inputs.

599c77bc601a182cd25749ec

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the p...

600fe7b7d4150a363c22b1c8

Spatial modulation (SM) and its power to address physical layer security (PLS) issues have been under vibrant investigation. In this paper, we present an overview of PLS with SM in a methodological manner. The advantages and drawbacks of these techniques are specified. Next, we propose a cost- and energy-efficient secure SM scheme based on the concepts of the reconfigurable antenna (RA) and multiplicative noise. Specifically, the transmitter is equipped with a single RA and the receiver has multiple conventional antennas. In each transmission, one symbol duration is equally divided into multiple sub-symbol periods. During each sub-symbol period, a specific radiation pattern is activated and a weighted sub-symbol is sent through the RA. The receiver sums up the received signals in all sub-symbol periods to demodulate the information. The weighting coefficients are properly designed such that the summed received signals have a single non-zero element, of which the position delivers additional spatial information. In addition, the weighting coefficients serve as a source of multiplicative interference to unintended eavesdroppers. The proposed scheme does not require knowledge of the wiretap channel and, in contrast to conventional jamming-aided schemes, all transmit power is saved for useful signals. Finally, several prospective directions are identified for future research along the line of PLS-SM.

599c77bc601a182cd25749ec

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the p...

629364078c0a46000c95be7a

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

5a0f9fe59ed5dbea8e77c928

We consider the secrecy rate for the multiple-input single-output optical wireless scattering communication, where the detected signal can be characterized by discrete photoelectrons. Assuming a legitimate receiver and an eavesdropper, we propose two secure communication protocols, the non-jamming protocol, and the cooperative jamming protocol, where for the former the transmitter does not send ja...

5dce785d3a55ac93ec834a99

In free space optical (FSO) communication, jamming can cause inaccuracy in the received information. In this paper, we investigate the impact of jamming over atmospheric turbulence (AT) fading channels. To describe the AT, negative exponential (NE) and Gamma-Gamma (ΓΓ) fading channel models are considered. The effect of jamming is studied over a single-input single-output (SISO) FSO link. We introduce a theoretical framework to acquire a closed-form expression of the average bit error rate (ABER) under jamming-leading to an additive non-Gaussian noise channel. It is observed by thorough analysis that a 2 x 1 FSO system performs better than SISO FSO system in presence of jamming signal. It is realized that under jamming the performance of FSO link is dependent on different AT conditions. Furthermore, increasing number of transmit apertures allows FSO system to significantly overcome the effect of jamming, in order to obtain an improved ABER.

5a0f9fe59ed5dbea8e77c928

We consider the secrecy rate for the multiple-input single-output optical wireless scattering communication, where the detected signal can be characterized by discrete photoelectrons. Assuming a legitimate receiver and an eavesdropper, we propose two secure communication protocols, the non-jamming protocol, and the cooperative jamming protocol, where for the former the transmitter does not send ja...

5ff8827791e011c83266fa0e

Relay-assisted cooperative free space optical (FSO) communication system is a powerful technique, which offers advantages like fading mitigation and significant gain in spatial diversity due to shorter transmission hops. Even so, it can be hampered by jamming activities which in turn degrade the overall system performance. In this paper, the effect of relay jamming in the decode-and-forward (DF) protocol based cooperative FSO communication system, is introduced and studied thoroughly. The probability density function (pdf) of a random variable containing mixture of the negative exponential and Gaussian random variables is derived. The error performance of the considered FSO system is analysed over this new kind of additive mixture noise for different probabilities of jamming. By utilizing the derived pdf, a closed-form expression of the average bit error rate (BER) of the DF FSO relaying is obtained. A comparison of the BER performance of maximum-likelihood (ML) detector and a derived threshold based detector is also provided. It is observed that the derived threshold based detector offers almost similar performance as that of the ML detector. A thorough numerical study is also provided by considering different pointing error parameters of jammer's and user's channels.

5a0f9fe59ed5dbea8e77c928

We consider the secrecy rate for the multiple-input single-output optical wireless scattering communication, where the detected signal can be characterized by discrete photoelectrons. Assuming a legitimate receiver and an eavesdropper, we propose two secure communication protocols, the non-jamming protocol, and the cooperative jamming protocol, where for the former the transmitter does not send ja...

6338911690e50fcafd80486d

Security issue in underwater visible light communication (UVLC) arises mainly due to the scattering effect wherein numerous photons are statistically generated when a light beam strikes a water molecule. This paper considers an underwater communication scenario wherein a floating vehicle (FV) transmitter that is equipped with multiple light-emitting diodes (LEDs) communicates with the two legitimate near-end and far-end underwater vehicles (UVs) in presence of an eavesdropper. In particular, two non-orthogonal multiple access (NOMA) technology-based optimal LED selection (OLS) and suboptimal LED selection (SLS) schemes are proposed to select a LED that can transmit the information with the highest secrecy rate against active/passive eavesdropping attacks. Furthermore, the FVT transmits the information to both UVs with the selected LED only. Utilizing the successive interference cancellation (SIC) characteristic, this paper derives the closed-form secrecy outage probability expressions for both single-LED and multi-LED transmission strategies for both known and unknown CSI. The security performance of the proposed multi-LED NOMA-UVLC is compared with the conventional single-LED NOMA-UVLC under the effects of air bubbles for both fresh and salty water types. In addition, the validity of the numerical results is verified through Monte-carlo simulation analysis.

5aed14b617c44a443815699c

This paper investigates the physical layer security problem of visible light communication (VLC) systems relying on generalized space-shift keying (GSSK) termed as GSSK-VLC. The GSSK-VLC system considered is assumed to be comprised of three nodes: a transmitter equipped with multiple light-emitting diodes, a legitimate receiver, and a passive eavesdropper. Each of them is equipped with a single photo-detector. Specifically, the average mutual information (AMI) of a GSSK-VLC system is derived. We also obtain both a lower bound and an accurate closed-form expression of the approximate AMI, which can be employed for efficiently estimating the achievable secrecy rate of GSSK-VLC systems. Furthermore, the pairwise error probability and bit error rate of GSSK-VLC systems are analyzed, and again some closed-form expressions are obtained. Additionally, in order to enhance the secrecy performance of the GSSK-VLC system, an optimal LED pattern selection algorithm is proposed under the minimax criterion. We show that the proposed LED pattern selection algorithm is capable of enhancing both the AMI between the transmitter and legitimate user and the achievable secrecy rate of the GSSK-VLC system.

5c5ce4fd17c44a400fc38455

•A friendly optical jamming aided secrecy enhancement scheme is designed for the proposed SM-VLC system.•The secrecy performance of the SM-VLC system with optical jamming is analyzed, which includes the average mutual information (AMI), lower bound on AMI and its closed-form expression approximation and achievable secrecy rate.•Closed-form approximations for the AMI of Alice-to-Bob and Alice-to-Eve are derived.•The power allocation strategy for the proposed optical jamming based SM-VLC system is considered.•The pairwise error probability and bit error rate (BER) of the proposed friendly optical jamming aided secrecy enhancement SM-VLC system are derived.

5aed14b617c44a443815699c

This paper investigates the physical layer security problem of visible light communication (VLC) systems relying on generalized space-shift keying (GSSK) termed as GSSK-VLC. The GSSK-VLC system considered is assumed to be comprised of three nodes: a transmitter equipped with multiple light-emitting diodes, a legitimate receiver, and a passive eavesdropper. Each of them is equipped with a single photo-detector. Specifically, the average mutual information (AMI) of a GSSK-VLC system is derived. We also obtain both a lower bound and an accurate closed-form expression of the approximate AMI, which can be employed for efficiently estimating the achievable secrecy rate of GSSK-VLC systems. Furthermore, the pairwise error probability and bit error rate of GSSK-VLC systems are analyzed, and again some closed-form expressions are obtained. Additionally, in order to enhance the secrecy performance of the GSSK-VLC system, an optimal LED pattern selection algorithm is proposed under the minimax criterion. We show that the proposed LED pattern selection algorithm is capable of enhancing both the AMI between the transmitter and legitimate user and the achievable secrecy rate of the GSSK-VLC system.

5f51b90d9fced0a24bdc7f66

Visible light communication (VLC) is gaining a significant amount of interest as a new paradigm to meet rapidly increasing demands on wireless capacity required by a digitalized world. VLC is considered as a secure wireless communication scheme because VLC signals can be easily constrained within physical boundaries. In this paper, for the first time, we show that VLC is not as secure as people thought: VLC can be sniffed through walls! The key principle behind this is that in VLC transmissions, a VLC transmitter not only emits visible light signals but also leaks out 'side channel RF signals'. The leaked RF signals can be sniffed by a receiver to decode the VLC transmissions even the receiver is blocked (e.g., by walls) from the VLC transmitter. In this work, we establish a theoretical model to quantify the amplitude of the leaked RF signal and verify the model with comprehensive experiments. We design and implement a VLC sniffing system including receiver coil design, signal processing and frame decoding, spanning across hardware and software. Field studies show that with a cheap receiver design, our system can simultaneously sniff transmissions from multiple VLC transmitters 6.4 meters away with a 14 cm concrete wall in between, where the distance exceeds the communication range of most state-of-the-art VLC systems. By simply twining a wired earphone on the arm, we can sniff the VLC transmission 1.9 meters away.

5bdc319c17c44a1f58a09caa

Dual-function radar communications (DFRC) systems have recently been proposed to enable the coexistence of radar and wireless communications, which in turn alleviates the increased spectrum congestion crisis. In this paper, we consider the problem of sparse transmit array design for DFRC systems by antenna selection where same or different antennas are assigned to different functions. We consider three different types of DFRC systems which implement different simultaneous beamformers associated with single and different sparse arrays with shared aperture. We utilize the array configuration as an additional spatial degree of freedom (DoF) to suppress the cross-interference and facilitate the cohabitation of the two system functions. It is shown that the use of sparse arrays adds to improved angular resolution with well-controlled sidelobes on DFRC system paradigm. The utilization of sparse arrays in DFRC systems is validated using simulation examples.

5f1ffa6591e011d50a621af2

Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar ...

5bdc319c17c44a1f58a09caa

Dual-function radar communications (DFRC) systems have recently been proposed to enable the coexistence of radar and wireless communications, which in turn alleviates the increased spectrum congestion crisis. In this paper, we consider the problem of sparse transmit array design for DFRC systems by antenna selection where same or different antennas are assigned to different functions. We consider three different types of DFRC systems which implement different simultaneous beamformers associated with single and different sparse arrays with shared aperture. We utilize the array configuration as an additional spatial degree of freedom (DoF) to suppress the cross-interference and facilitate the cohabitation of the two system functions. It is shown that the use of sparse arrays adds to improved angular resolution with well-controlled sidelobes on DFRC system paradigm. The utilization of sparse arrays in DFRC systems is validated using simulation examples.

6218a92c5aee126c0f597118

Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, ...

5bdc319c17c44a1f58a09caa

Dual-function radar communications (DFRC) systems have recently been proposed to enable the coexistence of radar and wireless communications, which in turn alleviates the increased spectrum congestion crisis. In this paper, we consider the problem of sparse transmit array design for DFRC systems by antenna selection where same or different antennas are assigned to different functions. We consider three different types of DFRC systems which implement different simultaneous beamformers associated with single and different sparse arrays with shared aperture. We utilize the array configuration as an additional spatial degree of freedom (DoF) to suppress the cross-interference and facilitate the cohabitation of the two system functions. It is shown that the use of sparse arrays adds to improved angular resolution with well-controlled sidelobes on DFRC system paradigm. The utilization of sparse arrays in DFRC systems is validated using simulation examples.

6245498a9e795e3f7256c3f8

To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.

5bdc319c17c44a1f58a09caf

Cooperative transmissions for radar and communication tasks have been recently studied with the goal of addressing the problem of the increasingly crowded RF spectrum. In this paper, we propose a dual-function radar communication (DFRC) system in which the radar platform and radar resources are used for simultaneous target probing and communication symbol embedding. The proposed DFRC system is based on the concept of multiple-input multiple-output (MIMO) in tandem with transmit beamforming where a number of transmit beams, which can be larger than the number of transmit antennas, are formed. We show that the transmit beamforming weight vectors may be designed to achieve transmit processing gain, embed communication symbols intended for a communication receiver, and prevent eavesdroppers from intercepting the communication message. Our proposed method for embedding communication symbols into the MIMO radar emissions is based on changing the order of the orthogonal waveforms along the transmit beams from one pulse repetition period to another. In so doing, the communication symbols are represented by different associations of the orthogonal radar waveforms and transmit beams. We show that high data rate, in the megabits per seconds, is achievable for moderate number of transmit antennas, and is proportional to the factorial of the number of transmit beams. The error probability is analyzed and the bounds on the symbol error rate are derived. We show that changing the number of transmit beams leads to a tradeoff between the data rate and target detection accuracy. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.

5a9cb66717c44a376ffb8e39

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

5bdc319c17c44a1f58a09caf

Cooperative transmissions for radar and communication tasks have been recently studied with the goal of addressing the problem of the increasingly crowded RF spectrum. In this paper, we propose a dual-function radar communication (DFRC) system in which the radar platform and radar resources are used for simultaneous target probing and communication symbol embedding. The proposed DFRC system is based on the concept of multiple-input multiple-output (MIMO) in tandem with transmit beamforming where a number of transmit beams, which can be larger than the number of transmit antennas, are formed. We show that the transmit beamforming weight vectors may be designed to achieve transmit processing gain, embed communication symbols intended for a communication receiver, and prevent eavesdroppers from intercepting the communication message. Our proposed method for embedding communication symbols into the MIMO radar emissions is based on changing the order of the orthogonal waveforms along the transmit beams from one pulse repetition period to another. In so doing, the communication symbols are represented by different associations of the orthogonal radar waveforms and transmit beams. We show that high data rate, in the megabits per seconds, is achievable for moderate number of transmit antennas, and is proportional to the factorial of the number of transmit beams. The error probability is analyzed and the bounds on the symbol error rate are derived. We show that changing the number of transmit beams leads to a tradeoff between the data rate and target detection accuracy. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.

5ac1824c17c44a1fda9139d5

In this paper we consider the effects of radar and communications signals operating on the same frequency band on the detection performance of pulsed radar. Previous research has shown that it is possible to recover and demodulate communications signals that are coincident with radar pulses in time, frequency, and location, while assuming these communications signals have negligible effect on radar performance. We now validate that assumption by determining the effects of these embedded communications signals on detection performance of pulsed radar. We use both software simulation and hardware verification to model probability of detection with embedded communications. In this analysis, we define and vary two radar-communications signal parameters, the radar-to-communications power ratio (RCR) and symbol-rate-to-bandwidth ratio (SRBR), to assess how different communications signal parameters affect radar performance. We confirm that at a sufficiently low communications signal power and a sufficiently high number of communications symbols transmitted within each radar pulse, the effect of embedded communications on radar probability of detection is negligible.

5bdc319c17c44a1f58a09caf

Cooperative transmissions for radar and communication tasks have been recently studied with the goal of addressing the problem of the increasingly crowded RF spectrum. In this paper, we propose a dual-function radar communication (DFRC) system in which the radar platform and radar resources are used for simultaneous target probing and communication symbol embedding. The proposed DFRC system is based on the concept of multiple-input multiple-output (MIMO) in tandem with transmit beamforming where a number of transmit beams, which can be larger than the number of transmit antennas, are formed. We show that the transmit beamforming weight vectors may be designed to achieve transmit processing gain, embed communication symbols intended for a communication receiver, and prevent eavesdroppers from intercepting the communication message. Our proposed method for embedding communication symbols into the MIMO radar emissions is based on changing the order of the orthogonal waveforms along the transmit beams from one pulse repetition period to another. In so doing, the communication symbols are represented by different associations of the orthogonal radar waveforms and transmit beams. We show that high data rate, in the megabits per seconds, is achievable for moderate number of transmit antennas, and is proportional to the factorial of the number of transmit beams. The error probability is analyzed and the bounds on the symbol error rate are derived. We show that changing the number of transmit beams leads to a tradeoff between the data rate and target detection accuracy. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.

5f1ffa6591e011d50a621af2

Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar ...

5bdc319c17c44a1f58a09caf

Cooperative transmissions for radar and communication tasks have been recently studied with the goal of addressing the problem of the increasingly crowded RF spectrum. In this paper, we propose a dual-function radar communication (DFRC) system in which the radar platform and radar resources are used for simultaneous target probing and communication symbol embedding. The proposed DFRC system is based on the concept of multiple-input multiple-output (MIMO) in tandem with transmit beamforming where a number of transmit beams, which can be larger than the number of transmit antennas, are formed. We show that the transmit beamforming weight vectors may be designed to achieve transmit processing gain, embed communication symbols intended for a communication receiver, and prevent eavesdroppers from intercepting the communication message. Our proposed method for embedding communication symbols into the MIMO radar emissions is based on changing the order of the orthogonal waveforms along the transmit beams from one pulse repetition period to another. In so doing, the communication symbols are represented by different associations of the orthogonal radar waveforms and transmit beams. We show that high data rate, in the megabits per seconds, is achievable for moderate number of transmit antennas, and is proportional to the factorial of the number of transmit beams. The error probability is analyzed and the bounds on the symbol error rate are derived. We show that changing the number of transmit beams leads to a tradeoff between the data rate and target detection accuracy. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.

632630cb90e50fcafdf5985f

Dual-function radar-communication (DFRC) based on frequency hopping (FH) MIMO radar (FH-MIMO DFRC) achieves symbol rate much higher than radar pulse repetition frequency. Such DFRC, however, is prone to eavesdropping due to the spatially uniform illumination of an FH-MIMO radar. In this paper, we reveal the potential of using permutations of hopping frequencies to achieve secure and high-speed FH-MIMO DFRC. Specifically, we identify the angle-dependent issue in detecting permutations and develop an element-wise phase compensation (EPC) to solve the issue for a legitimate user (Bob). EPC makes the demodulation at an eavesdropper (Eve) conditioned on knowing the angle-of-departure (AoD) of Bob. We also propose the random sign reversal (RSR) technique which randomly selects several antennas over hops and reverses their signs. Owing to EPC, there is a sign rule available for Bob. We employ the rule and develop a low-complexity algorithm for Bob to remove RSR. We further prove that, given the same signal-to-noise ratio, RSR plus EPC make the demodulation performance of Eve inferior to that of Bob in most angular regions. Confirmed by simulation, our design achieves substantially high physical layer security for FH-MIMO DFRC, improves demodulation performance compared with existing designs, and reduces mutual interference among radar targets.

5a9cb66717c44a376ffb8e39

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

6048a98291e0115491a5cc72

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar’s waveform catalog.

5a9cb66717c44a376ffb8e39

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

62d172725aee126c0fdcf57f

A spectrum sharing radar can be guided by a cognitive decision process to determine the optimal radar operating frequency as the spectral environment changes. This decision process utilizes spectrum sensing or spectral prediction to determine the optimal radar transmission for a given situation. The radar transmitter power amplifier performance varies with frequency and bandwidth of the applied waveform, thus adaptive impedance tuners are useful in maximizing the transmitted power and radar range as the transmission frequency range is varied. Since high power handling is required in radar transmissions, and mechanically actuated impedance tuners presently demonstrate the best power handling, the time required to tune is often orders of magnitude greater than the pulse repetition interval. As such, the relatively lengthy impedance tuning operations should be guided to maximize the average output power as the system transitions between different center frequencies, bandwidths, and waveforms over time. This article presents an algorithm that performs impedance tuning with an evanescent-mode cavity tuner based on an average performance gradient computed for multiple transmit pulses. Comparison of test results with traditionally measured amplifier load-pull data shows that the transmitter is effectively optimized for maximum average output power.

5a9cb66717c44a376ffb8e39

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

634687f790e50fcafd9ca015

Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity.

5bdc319c17c44a1f58a09cb5

In this paper, we propose a beamspace-based method for nominal direction-of-arrival (DOA) and angular spread estimation of incoherently distributed (ID) sources using a uniform linear array (ULA). Firstly, with generalized array manifold of the ULA, we obtain the beamspace array manifold by performing beamspace transformation on the received vector of two overlapping subarrays, and further derive the beamspace shift invariance structure via designing appropriate beamforming matrix. Next, the total least squares approach is used to estimate the nominal DOAs of ID sources. Finally, with the DOA estimates, the corresponding angular spreads are obtained by means of the central moments of the angular distribution. The proposed method does not involve any spectral search and reduces the dimension of matrix operations, thus it is obviously more efficient than the traditional algorithms. Simulation results indicate that our proposed algorithm is comparable to the existing algorithms when the number of sensors is large.

6283780c5aee126c0f2d5b70

This paper presents an effective angular parameter estimation method based on the manifold separation technique (MST) for incoherently distributed sources, named as MST-ID algorithm. In the proposed method, at first, a mathematical model is established through the first-order Taylor expansion of the steering vector, in which the nominal direction of arrival (DOA) can be decoupled from the angular spread. Then, the decoupled steering vector is divided into two sub-steering vectors with equal dimensions, and further the nominal DOA is estimated by the shift invariant structure between the sub-steering vectors. Finally, the MST is used to separate the antenna array structure from the nominal DOA in the array steering vector, such that the signal covariance matrix can be easily obtained according to the antenna array structure and the estimated nominal DOA. On this basis, a spectrum search function is given to estimate the angular spread. Compared with the previous works, the presented method can not only improve the angular parameter estimation accuracy but also be suitable for arbitrary line array structures. Theoretical analysis and simulation results confirm the effectiveness of the proposed method.

5bdc319c17c44a1f58a09cb7

Singular spectral analysis (SSA) is a nonparametric spectral estimation method for performing the time series analysis. It represents a signal as the sum of its components. In this manuscript, a nearly cyclostationary signal is considered. The signal is quantized and the SSA is employed to reconstruct the original signal based on the quantized signal. First, the reconstructed signal is modeled as the weighted sum of the SSA components. In order to estimate the weights, each quantization level is considered as a class. Different signal values are associated with different probabilities of the corresponding classes via the sigmoid functions defined based on the distances between the signal values and the corresponding quantized levels. Therefore, our proposed method provides the optimal estimate of a given signal in the minimum cross entropy sense. Computer numerical simulation results show that our proposed method can reduce the quantization error and reconstruct the original signal more accurately compared to some existing algorithms.

61dffe155244ab9dcb223fb3

This paper proposes a fractional singular spectrum analysis (SSA)-based method for performing the fractional delay. First, the input sequence is divided into two overlapping sequences with the first sequence being the input sequence without its last point and the second sequence being the input sequence without its first point. Then, the singular value decompositions (SVD) are performed on the trajectory matrices constructed based on these two sequences. Next, the designs of both the right unitary matrix and the left unitary matrix for generating the new trajectory matrix are formulated as the quadratically constrained quadratic programing problems. The analytical solutions of these quadratically constrained quadratic programing problems are derived via the SVD approach. Finally, the fractional SSA components are obtained by performing the diagonal averaging operation, and the fractional delay sequence is obtained by summing up all the fractional SSA components together. Since the fractional SSA operations are nonlinear and adaptive, our proposed method is a kind of nonlinear and adaptive approach for performing the fractional delay. Besides, by discarding some fractional SSA components, the joint fractional delay operation and the denoising operation can be performed simultaneously.

5bdc319c17c44a1f58a09cbb

The linear canonical transform (LCT) has been shown to be a useful and powerful tool in optics and signal processing. In this paper, a new uncertainty relation in the LCT domain has been obtained at first. It shows that nonzero signal's energy in two arbitrary LCT domains cannot be arbitrarily large simultaneously, which is the generalization of the uncertainty principle for signal concentrations in the Fourier domain. Meanwhile, the signals which are the best in achieving simultaneous concentration in two arbitrary LCT domains are also proposed. In addition, some potential applications are presented to show the effectiveness of the theorems.

628d1eeb5aee126c0f3ef7ed

The Stockwell transform (ST), which is a reversible method of time-frequency spectral representation, is an extension of the ideas of the wavelet transform (WT) and short-time Fourier transform (STFT). And yet, it represents the signal just in the time-frequency plane, which is unfavorable for nonstationary signals. In this paper, a new linear canonical Stockwell transform (LCST) is proposed based on the specific convolution structure in linear canonical transform (LCT) domain, which is a combination of the merits of ST and LCT to address this problem. It not only characterizes the signal in the time-linear canonical frequency plane, but more importantly, inherits the advantages of ST with a clear physical meaning. First, the theories about the continuous LCST are described at length, including its definition, basic properties and the time-LCT domain-frequency analysis. Next, the convolution theorem and cross-correlation theorem constructed in LCST domain are considered. Further, the discretization algorithm of the LCST is explored in order to realize it in the physical system. Finally, based on the proposed LCST, we study and discuss several applications of it, including time-frequency analysis and filtering of chirp signals. The rationality and validity of the work is verified out by simulations.

5bdc319c17c44a1f58a09cb8

Time difference of arrival (TDOA) positioning is one of the widely used techniques for locating an emitter. Besides TDOA measurement errors, clock synchronization bias is an important factor that can degrade the localization accuracy. This paper focuses on TDOA localization using a set of receivers, where timing synchronization offsets exist among different receiver groups. A theoretical analysis is conducted and a new localization solution is developed for the case of imperfect time synchronization. The analysis starts with the Cramér–Rao bound (CRB) for the problem and derives the estimation bias and mean square error (MSE) using the classical quadratic constraint weighted least-squares (QCWLS) estimator, which does not consider synchronization errors. Additionally, an alternative performance measure, namely the localization success probability (SP), is introduced to evaluate the location accuracy. An explicit formula for calculating the localization success probability is presented. In addition, an improved quadratic constraint weighted least-squares estimator that accounts for synchronization errors is proposed to reduce the positioning errors. The Lagrange multiplier technique is used to solve this estimator. As a byproduct, a closed-form solution for the estimation of clock bias is also provided. First-order perturbation analysis reveals that the performance of the proposed estimates achieves the Cramér–Rao bound. Simulations corroborate the theoretical results and the good performance of the proposed method.

5a9cb63417c44a376ffb5d2f

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged timestamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér-Rao lower bound (CRLB). An analysis of the advantages ...

5bdc319c17c44a1f58a09cb8

Time difference of arrival (TDOA) positioning is one of the widely used techniques for locating an emitter. Besides TDOA measurement errors, clock synchronization bias is an important factor that can degrade the localization accuracy. This paper focuses on TDOA localization using a set of receivers, where timing synchronization offsets exist among different receiver groups. A theoretical analysis is conducted and a new localization solution is developed for the case of imperfect time synchronization. The analysis starts with the Cramér–Rao bound (CRB) for the problem and derives the estimation bias and mean square error (MSE) using the classical quadratic constraint weighted least-squares (QCWLS) estimator, which does not consider synchronization errors. Additionally, an alternative performance measure, namely the localization success probability (SP), is introduced to evaluate the location accuracy. An explicit formula for calculating the localization success probability is presented. In addition, an improved quadratic constraint weighted least-squares estimator that accounts for synchronization errors is proposed to reduce the positioning errors. The Lagrange multiplier technique is used to solve this estimator. As a byproduct, a closed-form solution for the estimation of clock bias is also provided. First-order perturbation analysis reveals that the performance of the proposed estimates achieves the Cramér–Rao bound. Simulations corroborate the theoretical results and the good performance of the proposed method.

62d172745aee126c0fdcfc13

In civil or military applications, a transmitter on Earth may utilize different means of communications. A typical example is the naval craft, which often radiates very high frequency signals received by a satellite system and high frequency signals received by land-based observers. This article concentrates on the combination of land-based and satellite-based over-the-horizon (OTH) geolocations. A quadratic constrained weighted least-squares optimization model for the combined geolocation is established and a differentiable exact penalty solution is developed to locate the OTH transmitter in an iterative manner, which is shown to have robust convergence performance. Theoretical analysis is presented, which involves the Cramér–Rao bound for combined geolocation under the constraint of ellipsoidal Earth model and the closed-form expression of mean square error for the proposed method. Numerical examples are provided to validate our theoretical analysis and illustrate that the proposed method outperforms other geolocation methods in a wide range of scenarios.

5a9cb63417c44a376ffb5d2f

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged timestamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér-Rao lower bound (CRLB). An analysis of the advantages ...

62d16bc05aee126c0fd17bd3

Locating a device is a basic element for many Internet of Things (IoT) applications. In particular, it often demands an algorithm having low complexity to limit the energy consumption and most important, sufficient robustness without knowing the device in the near-field for point localization or in the far-field for direction of arrival (DOA) estimation. This article proposes a new localization algorithm that can achieve the two purposes, with the theoretical analysis to validate the optimal accuracy and the real data experiment to support the promising performance. The first objective is achieved by a closed-form solution and the second is accomplished by using the modified polar representation (MPR) of the source position, based on a new formulation for the localization problem. While the MPR localization method has been introduced before, it is not sufficiently robust for IoT application to handle the large equal radius (LER) scenario or the presence of sensor position errors. The proposed algorithm uses a different MPR formulation, which is able to handle the LER scenario, sensor position errors, and has low computational complexity.

5a9cb63417c44a376ffb5d2f

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged timestamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér-Rao lower bound (CRLB). An analysis of the advantages ...

6338bdbe90e50fcafd9f0566

Data gathering using mobile sink (MS) based on rendezvous points (RPs) is a need in several Internet of Things (IoT) applications. However, devising energy-efficient and reliable tour planning strategies for MS is a challenging issue, considering that sensors have finite buffer space and disparate sensing rates. This is even more challenging in delay-tolerant networks, where it is more desirable to select the shortest traveling path. There exist several algorithms on MS scheduling, which are based on hierarchical protocols for data forwarding and data collection. These algorithms are lacking efficient tradeoff between the Quality-of-Service (QoS) requirements in terms of energy efficiency, reliability, and computational cost. Besides, these algorithms have shown high packet losses while jointly performing MS tour planning and buffer overflow management. To address these limitations, we propose EE-MSWSN, an energy-efficient MS wireless sensor network that reliably collects data by implementing efficient buffer management. It forms novel clustered tree-based structures to cover all the network, and select each RP based on 1) hop count; 2) number of accumulated data in each clustered tree; and 3) distance to the stationary sink. The extensive simulation results verify that the EE-MSWSN minimizes tour length for various network configurations and incurs less energy consumption while reliably gathering data without packet losses as compared with existing protocols.

5bdc319c17c44a1f58a09cbd

Based on a two-dimensional phase coding, a novel range ambiguity suppression technique is proposed in this paper. By transmitting two-dimensional phase coded signals, the two-dimensional spectrum of the range ambiguous signals will be shifted along both range and azimuth directions compared with that of desired signals. Then, part of the two-dimensional spectrum of the range ambiguous signals will be located outside the two-dimensional spectral support, which is known in priori, of the desired signals. Considering the range frequency and Doppler oversamplings, a filter corresponding to the two-dimensional spectral support of the desired signals is applied, which suppresses the range ambiguity. Simulation results of both point targets and distributed targets validate the effectiveness of the proposed method.

5e74959f91e01118d9548529

In order to accurately identify the authenticity of cigarette products and improve consumers' awareness of cigarette brand, a two-dimensional code-based cigarette retail and regulatory model is proposed, and its system implementation method is discussed. In the proposed system, the two-dimensional code contains order information, brand information, etc. The two-dimensional code preservation information is designed as a service platform website with parameters, and the messages obtained by different users are distinguished by parameters. In addition, APP client is designed for retailers, customer managers and specialists, and the scanner in the APP is used to identify these three types of users, and other tools are used to identify users by default. Furthermore, the proposed system implements a hierarchical management mode, which can be divided into five roles: consumers, retailers, customer managers, monopolists and system administrators. The purpose of the system is to improve the efficiency of cigarette distribution, strengthen the management of cigarette monopoly and strengthen the contact ability with consumers. Through the practical application test of this system, we find that the proposed system can fully utilize the technical advantages of two-dimensional code, and then provide an effective solution to improve the information management level of tobacco industry.

5bdc319c17c44a1f58a09cbf

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

5e64d0c991e0110a9152946d

Ontology-based information integration is a useful method to integrate heterogeneous data at the semantic level. However, there are some bottlenecks of the traditional method for constructing ontology, i.e., time-consuming, error-prone, and semantic loss. Ontology learning is a kind of ontology construction approach based on machine learning, it provides a new opportunity to tackle the above bottlenecks. Especially, it could be employed to construct ontologies and integrate large-scale and heterogeneous data from various information systems. This paper surveys the latest developments of ontology learning and highlights how they could be adopted and play a vital role in the integration of information systems. The recent techniques and tools of ontology learning from text and relational database are reviewed, the possibility of using ontology learning in information integration were discussed based on the mapping results of the aforementioned bottlenecks and features of ontology learning. The potential directions for using ontology learning in information systems integration were given.

5bdc319c17c44a1f58a09cbf

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

5ef4a2ba9e795e13834b1dea

Ontologies are playing an increasingly important role in knowledge management, and their functions have been appreciated and exploited by a broad range of communities, including systems engineering researchers and practitioners. Encompassing domain-related vocabularies, concepts, concept hierarchy, along with the properties and relationships, domain ontologies are becoming a promising medium for knowledge sharing and exchange. With the emergence of the semantic web and big data, learning domain ontologies from text is becoming a cutting-edge technique as it is an automatic process of deriving ontological knowledge. Specifically, a set of representative concepts and semantic relations can be rapidly derived from unstructured text documents in a hierarchical structure to model a domain. In this paper, we aim at exploiting the ontology learning approach to extract a domain ontology from systems engineering handbooks. An approach is proposed for learning terms, concepts, taxonomic and non-taxonomic relations. By incorporating both linguistic-based and statistical-based natural language processing techniques, we realized an automatic detection of complex domain terms and conceptualized the systems engineering body of knowledge in a semantic fashion. To evaluate the proposed approach, a case study is conducted, wherein the hybrid approach is applied with template-driven and machine learning algorithms. The result shows that the proposed approach has a robust performance in decreasing ontology development costs. This paper contributes to a good starting point for learning systems engineering ontologies to enhance knowledge acquisition and management.

5bdc319c17c44a1f58a09cbf

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

5ff8834291e011c832672431

Most of the knowledge achieved from research activities are available as computer-like unstructured data written in natural-language papers. Automatically retrieving and representing knowledge from natural-language papers as input for computer processing is complex and challenging. In this paper, we propose a novel syntactic-relationship approach based on natural language processing, efficiently applying clustering algorithms to generate knowledge taxonomies about specific domain texts automatically. The approach considers a cloud computing case study through the collection and analysis of a set of recent publications. To assess our proposal, we conducted a quantitative comparison between different clustering-intrinsic metrics. Results showed higher popularity and coverage of the present proposal than the state-of-the-art, especially when using hierarchical clustering. The differential of our proposal lies in building a well-informative representation of knowledge with only three-quarters of the original textual data, and without any ground truth labeling.

5bdc319c17c44a1f58a09cc0

Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.

5ec507ac9fced0a24b78eaea

The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.

5bdc319c17c44a1f58a09cc2

Small interfering RNA (siRNA) is widely used to specifically silence target gene expression, but its microRNA (miRNA)-like function inevitably suppresses hundreds of off-targets. Recently, complete elimination of the off-target repression has been achieved by introducing an abasic nucleotide to the pivot (position 6; siRNA-6O), of which impaired base pairing destabilizes transitional nucleation (positions 2-6). However, siRNA-6O varied in its conservation of on-target activity (similar to 80-100%), demanding bioinformatics to discover the principles underlying its on-target efficiency. Analyses of miRNA-target interactions (Ago HITS-CLIP) showed that the stability of transitional nucleation correlated with the target affinity of RNA interference. Furthermore, interrogated analyses of siRNA screening efficiency, experimental data and broadly conserved miRNA sequences showed that the free energy of transitional nucleation (positions 2-5) in siRNA-6O required the range of stability for effective on-target activity (-6 <= Delta G[2: 5] <= -3.5 kcal mol(-1)). Taking into consideration of these features together with locations, guanine-cytosine content (GC content), nucleotide stretches, single nucleotide polymorphisms and repetitive elements, we implemented a database named 'siAbasic' that provided the list of potent siRNA-6O sequences for most of human and mouse genes (>= similar to 95%), wherein we experimentally validated some of their therapeutic potency. siAbasic will aid to ensure potency of siRNA-6O sequences without concerning off-target effects for experimental and clinical purposes.

5c0f914ada562944aca93198

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA-protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

5c0f914ada562944aca93198

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA-protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

5c04961717c44a2c74706343

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. Results: In this study, we reported the design and implementation of DeepMir Tar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including highlevel expert-designed, low-level expert-designed and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMir Tar improved overall predictive performance.

5bdc319c17c44a1f58a09cc6

Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering.

59ae3c592bbe271c4c7211a6

5bdc319c17c44a1f58a09cc6

Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering.

5e6dfcda93d709897c04556b

Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which loses essential contexts for knowledge exploration and inference. We consider both facts and their conditions in biomedica...

5bdc319c17c44a1f58a09cc6

Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering.

5fc22312d4150a363cd5491f

Background Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions. Results We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types. Conclusions We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.

5bdc319c17c44a1f58a09cc6

Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering.

602655cbaf79179a99d6df33

Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form-SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively.

5bdc319c17c44a1f58a09cca

There is emerging evidence showing that lncRNAs can be involved in various critical biological processes. Zebrafish is a fully developed model system being used in a variety of basic research and biomedical studies. Hence, it is an ideal model organism to study the functions and mechanisms of lncRNAs. Here, we constructed ZFLNC-a comprehensive database of zebrafish lncRNA that is dedicated to providing a zebrafishbased platform for deep exploration of zebrafish lncRNAs and their mammalian counterparts to the relevant academic communities. The main data resources of lncRNAs in this database come from the NCBI, Ensembl, NONCODE, zflncRNApedia and literature. We also obtained lncRNAs as a supplement by analysing RNA-Seq datasets from SRA database. With these IncRNAs, we further carried out expression profiling, co-expression network prediction, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/Online Mendelian Inheritance in Man (OMIM) annotation and conservation analysis. As far as we know, ZFLNC is the most comprehensive and well-annotated database for zebrafish lncRNA.

5c0f8046da562944ac8555c9

By integrative data analysis and construction of coding-lncRNA gene co-expression network, we gained the most comprehensive dataset of zebrafish lncRNAs up to present, as well as their systematic annotations and comprehensive analyses on function and conservation. Our study provides a reliable zebrafish-based platform to deeply explore lncRNA function and mechanism, as well as the lncRNA commonality between zebrafish and human.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5bdc319c17c44a1f58a09cd4

Air pollution continues to pose a major threat to human health in China and its neighbor countries including Japan. Although the general clean air trend in China is to replace older coal-fired generation with clean energy, \\(60\\%\\) coal consumption in the overall energy use still makes it the most coal-dependent country. Improvement of the coal-fired generation efficiency is fully expected to play a vital role for the foreseeable future. Focusing on the main air pollutants, this paper presents a comprehensive study from the target region selection in China to the benefit allocation analysis for a potential collaboration between China and Japan in Integrated coal Gasification Combined Cycle (IGCC). Both radial and non-radial two-stage data envelopment analysis models with undesirable intermediate measures are applied first to evaluate the regional air quality improvement efficiencies in China (2005–2014). Due to the extremely high initial and dynamic investment for IGCC, target regions with the highest priorities to be installed with this technology are selected by k-means clustering method based on the results of two-stage data transformation model under variable returns to scale. Finally, a benefit analysis by multi-criteria allocation game with the principal performance indices from two representative IGCC power plants in China and Japan provides further insights for the mutual motivations of this kind of collaboration and uncovers some international policy implications.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5bdc319c17c44a1f58a09cd8

In data envelopment analysis for environmental performance measurement the undesirable outputs are taken into account. Ones of the standard approaches for dealing with the undesirable outputs are the hyperbolic and the directional distance measures. They both allow a simultaneous expansion of desirable outputs and a contraction of undesirable outputs by means of a single parameter. To meet environmental requirements, a technology with no disposability of undesirable outputs is often considered and the outputs are assumed to be only weakly disposable. We show that the combination of this type of technology with the hyperbolic measure, (or with its linearization, which is a special type of the directional distance model) may lead to a misleading efficiency score of the unit under evaluation. We derive the dual of the hyperbolic model under the environmental technology and describe some of its properties. Then, we use the hyperbolic and directional distance dual models for developing a second-phase method. This enables to detect the misleading scores of the decision making units. We illustrate the results on a real world data set.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5c7575b0f56def9798a1c7cb

This paper seeks to propose a network data envelopment analysis (DEA) framework for analysis of heterogeneous systems. The paper introduces the dummy connector so that every network structure can be transformed into the sun network structure. In his case, the dummy connector allows for heterogeneity of the decision making units (DMUs) in terms of their inner structure. Based on the sun network structure, the static and dynamic network DEA models are established. Thus, DMUs with different structures can be evaluated according to the static and dynamic network DEA models. The efficiency of each sub-unit, each period and each sub-unit in each period can also be obtained. Two simulated examples are presented using the static and dynamic DEA models.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5c8f9ef44895d9cbc6565d2b

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

5cc704e66558b90bfa0342af

In this paper, we proposed a new DEA approach to allocate the resource in branch network system which is not covered by the existing resource allocation works under a centralized decision-making environment. The branch network system is typically appears in multi-national or multi-regional corporations, which has many branches across multiple locations. Given the spatial distribution of the production, we imposed additional restrictions on resource allocation and divided the resource inputs into three groups: fixed inputs, regional inputs that allocated to the branches in the same area and common resource that an additional resource allocated to all the branches. Then, we generalize the model further to accommodate technological heterogeneity due to the difference in the geographical locations of the branches. And the objective of the proposed models is to maximize the gross profits of the entire organization, which is a natural assumption for a for-profit organization. Finally, an example was presented to illustrate the proposed approach with heterogeneous technology is more practically feasible and superior than the prior approach with homogeneous technology.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

62442e6b5aee126c0f5bdc9c

The paper aims at the evaluation of efficiency in sports. Many articles are dealing with the application of data envelopment analysis (DEA) models in this area. They are mainly oriented on efficiency evaluation of teams and not the individual players. On the contrary, the main aim of this paper is to combine both approaches and investigate the relation between individual efficiency of the players and the efficiency of the teams. The first step is the evaluation of individual efficiencies, and the second one is its aggregation into the teams' performance within a competition (League). The idea is to evaluate the efficiency of individual players in certain positions and explore how the individual efficiencies contribute to the efficiency of the teams. Individual efficiency is measured using traditional radial and slacks-based measure DEA models. Team efficiency is derived in several ways—traditional DEA models with the variables describing the true achievements of the teams, parallel DEA models that consider all positions and players, and actual results of the teams in the League, which is the true performance of the team. The study is based on the Canadian-American National Hockey League (NHL) statistics in 2019/2020. The results of the analysis are compared and discussed. They show that the true performance of the team is not always directly dependent on individual performances of the members of the team.

5bdc319c17c44a1f58a09ccd

After important sport events as the Summer Olympic Games (SOG) are, the participating countries are ranked according to the number of gold, silver and bronze medals. A lexicographic ranking is usually applied in official reports which leads to higher ranking of countries with one gold and no other medals comparing to countries without any gold but with several silver or bronze medals. Moreover, this ranking does not take into account the specific conditions of the countries (population, economic strength measured by gross domestic product and tradition in sports). The aim of the paper is not only to evaluate the absolute achievements of the countries but evaluate their performance with respect to the resources they can spent. A two-stage data envelopment analysis model is formulated and solved by an original slack-based measure procedure. The first stage evaluates the performance of the countries in training of athletes and the second stage evaluates the achievements of the nominated athletes. The models with variable returns to scale and weight restrictions are applied. The models and their results are illustrated on the case of Olympic Games 2016 and compared with results given by traditional approaches.

62442e6b5aee126c0f5bdc79

This paper deals with the dynamic efficiency analysis based on Data Envelopment Analysis (DEA) models. Our aim is to formulate new dynamic DEA models with time series that, compute the overall efficiency of the units with respect to all their inputs and outputs in all periods. The proposed models are compared with those previously set forth by Park and Park (Eur J Oper Res 193(2):567–580, 2009). We introduce six new dynamic DEA models with the quadratic objective function and nonlinear constraints; they differ in time weights of the units in every year. The first proposed model has a decreasing vector of the weights; for the second model, this vector is convex, and the third model uses the weights' ratio scale. These models are alternatives to already known models. The proposed models give quick results in one stage. We cannot rank the efficiency units by the efficiency scores obtained within the proposed models, three super-efficiency models are therefore proposed. The super-efficiency models compute the super-efficiency scores, greater for the efficient units, which can thus be ranked according to this score. All models are illustrated on a selected dataset, and then their results are discussed. The dataset contains 38 German NUTS 2 (Nomenclature of Units for Territorial Statistics) regions. The aim is to find the most efficient regions and their ranking between the years 2008 and 2016. Two inputs are used– employment (in thousands of hours worked) and gross fixed capital formation (in millions EUR) and one output—gross domestic product (in millions EUR). All calculations are carried out using our original procedures written in the LINGO modelling language.

5bdc319c17c44a1f58a09cce

In a rapidly evolving economic world, projects become tools to support organization goals. Project portfolio is set of all projects that are implemented in the organisation at a time. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realisation and outputs measure multiple goals of the organisation. The data envelopment analysis (DEA) is an appropriate approach to select efficient projects. The organisation has its total resources in limited quantities. Designing a portfolio of efficient projects not exceeding the limited resources does not always lead to the most efficient portfolio. De Novo optimisation is an approach for designing optimal systems by reshaping the feasible set. The paper proposes a new approach for project portfolio designing based on a systemic combination of DEA model and De Novo optimisation approach. A total available budget is a restriction on project portfolio. The proposed concept provides designing of optimal project portfolio with the minimal budget. Performance measures of the designed project portfolio are the efficiency of the portfolio and the effectiveness of outputs. Possible extensions of the concept are formulated and discussed.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5bdc319c17c44a1f58a09cce

In a rapidly evolving economic world, projects become tools to support organization goals. Project portfolio is set of all projects that are implemented in the organisation at a time. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realisation and outputs measure multiple goals of the organisation. The data envelopment analysis (DEA) is an appropriate approach to select efficient projects. The organisation has its total resources in limited quantities. Designing a portfolio of efficient projects not exceeding the limited resources does not always lead to the most efficient portfolio. De Novo optimisation is an approach for designing optimal systems by reshaping the feasible set. The paper proposes a new approach for project portfolio designing based on a systemic combination of DEA model and De Novo optimisation approach. A total available budget is a restriction on project portfolio. The proposed concept provides designing of optimal project portfolio with the minimal budget. Performance measures of the designed project portfolio are the efficiency of the portfolio and the effectiveness of outputs. Possible extensions of the concept are formulated and discussed.

5c8f9ef44895d9cbc6565d2b

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5c860e7a4895d9cbc60f36a6

This article contributes to the efficiency literature by defining, in the context of the data envelopment analysis framework, the directional distance function approach for measuring both technical and scale inefficiencies with regard to the use of individual inputs. The input-specific technical and scale inefficiencies are then aggregated in order to calculate the overall inefficiency measures. Empirical application focuses on a large dataset of Spanish and Portuguese construction companies between 2002 and 2010 and accounts for three inputs: materials, labor and fixed assets. The results show, first, that for both Spanish and Portuguese construction companies, fixed assets are the most technically inefficient input. Second, the most inefficient scale concerns the utilization of material input in both samples; the reason for this inefficiency is that firms tend to operate in the increasing returns to scale portion of technology set. Third, in both samples, large firms have the lowest input-specific technical inefficiencies, but the highest input-specific scale inefficiencies, compared to their small and medium-sized counterparts, and tend to suffer from decreasing returns to scale. Finally, in both samples, input-specific technical inefficiency under constant returns to scale increased during the period of the recent financial crisis, mainly due to the augmentation in scale inefficiency.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5c8c929b4895d9cbc60fb18c

•A novel nonlinear model is extended to deal with improving the discriminating power of DEA models.•The nonlinear model is linearized and it is shown that the proposed model identifies the most efficient unit.•The discriminative proposed model can fully rank all units.•A comparison between the new model and some recent models is drawn.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5cc704e66558b90bfa0342aa

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5bdc319c17c44a1f58a09cd0

Bounded additive models in data envelopment analysis (DEA) under the assumption of constant returns to scale (CRS) were recently introduced in the literature (Cooper et al. in J Product Anal 35(2):85–94, 2011; Pastor et al. in J Product Anal 40:285–292, 2013; Pastor et al. in Omega 56:16–24, 2015). In this paper, we propose to extend the so far generated knowledge about bounded additive models to the family of directional distance function (DDF) models in DEA, giving rise to a completely new subfamily of bounded or partially-bounded CRS-DDF models. We finally check the new approach on a real agricultural panel data set estimating efficiency and productivity change over time, resorting to the Luenberger indicator in a context where at least one variable is naturally bounded.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5bdc319c17c44a1f58a09cd8

In data envelopment analysis for environmental performance measurement the undesirable outputs are taken into account. Ones of the standard approaches for dealing with the undesirable outputs are the hyperbolic and the directional distance measures. They both allow a simultaneous expansion of desirable outputs and a contraction of undesirable outputs by means of a single parameter. To meet environmental requirements, a technology with no disposability of undesirable outputs is often considered and the outputs are assumed to be only weakly disposable. We show that the combination of this type of technology with the hyperbolic measure, (or with its linearization, which is a special type of the directional distance model) may lead to a misleading efficiency score of the unit under evaluation. We derive the dual of the hyperbolic model under the environmental technology and describe some of its properties. Then, we use the hyperbolic and directional distance dual models for developing a second-phase method. This enables to detect the misleading scores of the decision making units. We illustrate the results on a real world data set.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5c7575b0f56def9798a1c7cb

This paper seeks to propose a network data envelopment analysis (DEA) framework for analysis of heterogeneous systems. The paper introduces the dummy connector so that every network structure can be transformed into the sun network structure. In his case, the dummy connector allows for heterogeneity of the decision making units (DMUs) in terms of their inner structure. Based on the sun network structure, the static and dynamic network DEA models are established. Thus, DMUs with different structures can be evaluated according to the static and dynamic network DEA models. The efficiency of each sub-unit, each period and each sub-unit in each period can also be obtained. Two simulated examples are presented using the static and dynamic DEA models.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5c8f55084895d9cbc63fd1e3

•A new method for target setting in mergers is proposed.•The method combines goal programming and inverse data envelopment analysis.•The method allows decision makers to save desired resources.•An application in banking sector is proposed.

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5c8f9ef44895d9cbc6565d2b

5bdc319c17c44a1f58a09ccf

Information System (IS) project selection is a critical decision making task that can significantly impact operational excellence and competitive advantage of modern enterprises and also can involve them in a long-term commitment. This decision making is complicated due to availability of numerous IS projects, their increasing complexities, importance of timely decisions in a dynamic environment, as well as existence of multiple qualitative and quantitative criteria. This paper proposes a Data Envelopment Analysis approach to find most efficient IS projects while considering subjective opinions and intuitive senses of decision makers. The proposed approach is validated by a real world case study involving 41 IS projects at a large financial institution as well as 18 artificial projects which are defined by the decision makers.

5cc704e66558b90bfa0342af

In this paper, we proposed a new DEA approach to allocate the resource in branch network system which is not covered by the existing resource allocation works under a centralized decision-making environment. The branch network system is typically appears in multi-national or multi-regional corporations, which has many branches across multiple locations. Given the spatial distribution of the production, we imposed additional restrictions on resource allocation and divided the resource inputs into three groups: fixed inputs, regional inputs that allocated to the branches in the same area and common resource that an additional resource allocated to all the branches. Then, we generalize the model further to accommodate technological heterogeneity due to the difference in the geographical locations of the branches. And the objective of the proposed models is to maximize the gross profits of the entire organization, which is a natural assumption for a for-profit organization. Finally, an example was presented to illustrate the proposed approach with heterogeneous technology is more practically feasible and superior than the prior approach with homogeneous technology.

5c860e7a4895d9cbc60f36a6

This article contributes to the efficiency literature by defining, in the context of the data envelopment analysis framework, the directional distance function approach for measuring both technical and scale inefficiencies with regard to the use of individual inputs. The input-specific technical and scale inefficiencies are then aggregated in order to calculate the overall inefficiency measures. Empirical application focuses on a large dataset of Spanish and Portuguese construction companies between 2002 and 2010 and accounts for three inputs: materials, labor and fixed assets. The results show, first, that for both Spanish and Portuguese construction companies, fixed assets are the most technically inefficient input. Second, the most inefficient scale concerns the utilization of material input in both samples; the reason for this inefficiency is that firms tend to operate in the increasing returns to scale portion of technology set. Third, in both samples, large firms have the lowest input-specific technical inefficiencies, but the highest input-specific scale inefficiencies, compared to their small and medium-sized counterparts, and tend to suffer from decreasing returns to scale. Finally, in both samples, input-specific technical inefficiency under constant returns to scale increased during the period of the recent financial crisis, mainly due to the augmentation in scale inefficiency.

5c7575b0f56def9798a1c7cb

This paper seeks to propose a network data envelopment analysis (DEA) framework for analysis of heterogeneous systems. The paper introduces the dummy connector so that every network structure can be transformed into the sun network structure. In his case, the dummy connector allows for heterogeneity of the decision making units (DMUs) in terms of their inner structure. Based on the sun network structure, the static and dynamic network DEA models are established. Thus, DMUs with different structures can be evaluated according to the static and dynamic network DEA models. The efficiency of each sub-unit, each period and each sub-unit in each period can also be obtained. Two simulated examples are presented using the static and dynamic DEA models.

5c8c929b4895d9cbc60fb18c

•A novel nonlinear model is extended to deal with improving the discriminating power of DEA models.•The nonlinear model is linearized and it is shown that the proposed model identifies the most efficient unit.•The discriminative proposed model can fully rank all units.•A comparison between the new model and some recent models is drawn.

5c04968417c44a2c74709d0e

•To propose a resilient-sustainable supplier selection indicators’ framework.•To apply fuzzy set theory to handle with the uncertainty in supplier selection.•To develop an intelligent integrated FIS-DEA model for supplier selection.•To design a modular FIS system to have a comprehensive decision making model.•To propose a robust model with any number of indicators and alternatives.

5cc704e66558b90bfa0342aa

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

5bdc319c17c44a1f58a09cd0

Bounded additive models in data envelopment analysis (DEA) under the assumption of constant returns to scale (CRS) were recently introduced in the literature (Cooper et al. in J Product Anal 35(2):85–94, 2011; Pastor et al. in J Product Anal 40:285–292, 2013; Pastor et al. in Omega 56:16–24, 2015). In this paper, we propose to extend the so far generated knowledge about bounded additive models to the family of directional distance function (DDF) models in DEA, giving rise to a completely new subfamily of bounded or partially-bounded CRS-DDF models. We finally check the new approach on a real agricultural panel data set estimating efficiency and productivity change over time, resorting to the Luenberger indicator in a context where at least one variable is naturally bounded.

5cc704e66558b90bfa0342aa

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

5c8f9ef44895d9cbc6565d2b

5cc704e66558b90bfa0342aa

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

5cc704e66558b90bfa0342af

In this paper, we proposed a new DEA approach to allocate the resource in branch network system which is not covered by the existing resource allocation works under a centralized decision-making environment. The branch network system is typically appears in multi-national or multi-regional corporations, which has many branches across multiple locations. Given the spatial distribution of the production, we imposed additional restrictions on resource allocation and divided the resource inputs into three groups: fixed inputs, regional inputs that allocated to the branches in the same area and common resource that an additional resource allocated to all the branches. Then, we generalize the model further to accommodate technological heterogeneity due to the difference in the geographical locations of the branches. And the objective of the proposed models is to maximize the gross profits of the entire organization, which is a natural assumption for a for-profit organization. Finally, an example was presented to illustrate the proposed approach with heterogeneous technology is more practically feasible and superior than the prior approach with homogeneous technology.

5bdc319c17c44a1f58a09cd0

Bounded additive models in data envelopment analysis (DEA) under the assumption of constant returns to scale (CRS) were recently introduced in the literature (Cooper et al. in J Product Anal 35(2):85–94, 2011; Pastor et al. in J Product Anal 40:285–292, 2013; Pastor et al. in Omega 56:16–24, 2015). In this paper, we propose to extend the so far generated knowledge about bounded additive models to the family of directional distance function (DDF) models in DEA, giving rise to a completely new subfamily of bounded or partially-bounded CRS-DDF models. We finally check the new approach on a real agricultural panel data set estimating efficiency and productivity change over time, resorting to the Luenberger indicator in a context where at least one variable is naturally bounded.

5c8f9ef44895d9cbc6565d2b

5bdc319c17c44a1f58a09cd3

Research and development (R&D) of countries play a major role in a long-term development of the economy. We measure the R&D efficiency of all 28 member countries of the European Union in the years 2008–2014. Super-efficient data envelopment analysis (DEA) based on robustness of classification into efficient and inefficient units is adopted. We use the number of citations as output of basic research, the number of patents as output of applied research and R&D expenditures with manpower as inputs. To meet DEA assumptions and to capture R&D characteristics, we analyze a homogeneous sample of countries, adjust prices using purchasing power parity and consider time lag between inputs and outputs. We find that the efficiency of general R&D is higher for countries with higher GDP per capita. This relation also holds for specialized efficiencies of basic and applied research. However, it is much stronger for applied research suggesting its outputs are more easily distinguished and captured. Our findings are important in the evaluation of research and policy making.

5c8f9ef44895d9cbc6565d2b

5bdc319c17c44a1f58a09cd3

Research and development (R&D) of countries play a major role in a long-term development of the economy. We measure the R&D efficiency of all 28 member countries of the European Union in the years 2008–2014. Super-efficient data envelopment analysis (DEA) based on robustness of classification into efficient and inefficient units is adopted. We use the number of citations as output of basic research, the number of patents as output of applied research and R&D expenditures with manpower as inputs. To meet DEA assumptions and to capture R&D characteristics, we analyze a homogeneous sample of countries, adjust prices using purchasing power parity and consider time lag between inputs and outputs. We find that the efficiency of general R&D is higher for countries with higher GDP per capita. This relation also holds for specialized efficiencies of basic and applied research. However, it is much stronger for applied research suggesting its outputs are more easily distinguished and captured. Our findings are important in the evaluation of research and policy making.

602551ce91e0118ffcd4a29a

What role does culture play in determining institutions in a country? This paper argues that the establishment of institutions is a process originating predominantly in a nation’s culture and tries to discern the role of a cultural background in the governance of countries. We use the six Hofstede’s Cultural Dimensions and the six Worldwide Governance Indicators to test the strength of the relationship on 94 countries between 1996 and 2019. We find that the strongest cultural characteristics are Power Distance with negative effect on governance and Long-Term Orientation with positive effect. We also determine how well countries transform their cultural characteristics into institutions using stochastic frontier analysis.

5bdc319c17c44a1f58a09cd4

Air pollution continues to pose a major threat to human health in China and its neighbor countries including Japan. Although the general clean air trend in China is to replace older coal-fired generation with clean energy, \\(60\\%\\) coal consumption in the overall energy use still makes it the most coal-dependent country. Improvement of the coal-fired generation efficiency is fully expected to play a vital role for the foreseeable future. Focusing on the main air pollutants, this paper presents a comprehensive study from the target region selection in China to the benefit allocation analysis for a potential collaboration between China and Japan in Integrated coal Gasification Combined Cycle (IGCC). Both radial and non-radial two-stage data envelopment analysis models with undesirable intermediate measures are applied first to evaluate the regional air quality improvement efficiencies in China (2005–2014). Due to the extremely high initial and dynamic investment for IGCC, target regions with the highest priorities to be installed with this technology are selected by k-means clustering method based on the results of two-stage data transformation model under variable returns to scale. Finally, a benefit analysis by multi-criteria allocation game with the principal performance indices from two representative IGCC power plants in China and Japan provides further insights for the mutual motivations of this kind of collaboration and uncovers some international policy implications.

5c8e41e24895d9cbc6d61f50

Two-stage data envelopment analysis (TsDEA) models evaluate the performance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management. Contrary to conventional DEA models as a black-box structure, TsDEA provides further insight into sources of inefficiencies and a more informative basis for performance evaluation. In addition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system efficiency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technical efficiencies of a system and sub-processes when some data are fuzzy. To this end, we propose a possibilistic programming problem and then convert it into a deterministic interval programming problem using the α-level based method. The proposed method preserves the link between two stages in the sense that the total efficiency of the system is equal to the product of the efficiencies derived from two stages. In addition to the study of technical efficiency, this research includes two further contributions to the ancillary literature; firstly, we minutely discuss the efficiency decompositions to indicate the sources of inefficiency and secondly, we present a method for ranking the efficient units in a fuzzy environment. An empirical illustration is also utilised to show the applicability of the proposed technique.

5bdc319c17c44a1f58a09cd4

Air pollution continues to pose a major threat to human health in China and its neighbor countries including Japan. Although the general clean air trend in China is to replace older coal-fired generation with clean energy, \\(60\\%\\) coal consumption in the overall energy use still makes it the most coal-dependent country. Improvement of the coal-fired generation efficiency is fully expected to play a vital role for the foreseeable future. Focusing on the main air pollutants, this paper presents a comprehensive study from the target region selection in China to the benefit allocation analysis for a potential collaboration between China and Japan in Integrated coal Gasification Combined Cycle (IGCC). Both radial and non-radial two-stage data envelopment analysis models with undesirable intermediate measures are applied first to evaluate the regional air quality improvement efficiencies in China (2005–2014). Due to the extremely high initial and dynamic investment for IGCC, target regions with the highest priorities to be installed with this technology are selected by k-means clustering method based on the results of two-stage data transformation model under variable returns to scale. Finally, a benefit analysis by multi-criteria allocation game with the principal performance indices from two representative IGCC power plants in China and Japan provides further insights for the mutual motivations of this kind of collaboration and uncovers some international policy implications.

5c8f9ef44895d9cbc6565d2b

5c8e41e24895d9cbc6d61f50

Two-stage data envelopment analysis (TsDEA) models evaluate the performance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management. Contrary to conventional DEA models as a black-box structure, TsDEA provides further insight into sources of inefficiencies and a more informative basis for performance evaluation. In addition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system efficiency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technical efficiencies of a system and sub-processes when some data are fuzzy. To this end, we propose a possibilistic programming problem and then convert it into a deterministic interval programming problem using the α-level based method. The proposed method preserves the link between two stages in the sense that the total efficiency of the system is equal to the product of the efficiencies derived from two stages. In addition to the study of technical efficiency, this research includes two further contributions to the ancillary literature; firstly, we minutely discuss the efficiency decompositions to indicate the sources of inefficiency and secondly, we present a method for ranking the efficient units in a fuzzy environment. An empirical illustration is also utilised to show the applicability of the proposed technique.

5c04963517c44a2c74706b86

•We look into internal structures of a production system to assess its performance.•We present a common-weights DEA method for two-stage structures with fuzzy data.•We assess the efficiency of the system and component processes.•The new approach is illustrated through a numerical example.

5c8e41e24895d9cbc6d61f50

Two-stage data envelopment analysis (TsDEA) models evaluate the performance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management. Contrary to conventional DEA models as a black-box structure, TsDEA provides further insight into sources of inefficiencies and a more informative basis for performance evaluation. In addition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system efficiency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technical efficiencies of a system and sub-processes when some data are fuzzy. To this end, we propose a possibilistic programming problem and then convert it into a deterministic interval programming problem using the α-level based method. The proposed method preserves the link between two stages in the sense that the total efficiency of the system is equal to the product of the efficiencies derived from two stages. In addition to the study of technical efficiency, this research includes two further contributions to the ancillary literature; firstly, we minutely discuss the efficiency decompositions to indicate the sources of inefficiency and secondly, we present a method for ranking the efficient units in a fuzzy environment. An empirical illustration is also utilised to show the applicability of the proposed technique.

5c8f9ef44895d9cbc6565d2b