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In this article, the weighted three-point integral quadrature formula is estimated by the new bounds using the new method of calculating estimates for quadrature rules applying the weighted Hermite-Hadamard inequality for higher-order convex functions and weighted version of the integral identity expressed by w-harmonic sequences of functions. The importance of those results lies in providing new estimates of the definite integral values by using weighted three-point formula for numerical integration. The obtained results are employed in establishing new estimates for the Legendre-Gauss three-point quadrature formula with the use of specific form of the weight function w. [ABSTRACT FROM AUTHOR]

The aim of this article is to construct a (p , q) -analogue of wavelets Kantorovich–Baskakov operators and investigate some statistical approximation properties. We study weighted statistical approximation by means of a Bohman–Korovkin-type theorem, and statistical rate of convergence by means of the weighted modulus of smoothness ω ρ α associated to the space B ρ ⁢ α ⁢ (ℝ +) and Lipschitz-type maximal functions. [ABSTRACT FROM AUTHOR]

The basic assumption of the 3rd order acceleration method is that the acceleration changes in the time interval Δt is in the form of a third degree polynomial. To quantitatively compare the error rate of this method with other numerical methods, a linear one-degree-of-freedom system was considered. Then, sinusoidal harmonic loading with a specific frequency was applied to this system. Using numerical methods of second-order acceleration and third-order acceleration and other numerical methods and using the root mean square criterion, the errors of different methods were quantitatively calculated and compared with each other. Also, 3rd degree polynomials were obtained for the trends of error changes in terms of time step and damping, which had a very good correlation with its real values.. [ABSTRACT FROM AUTHOR]

Recently, the field of infrared dim small target detection is paid considerable attention and various solutions are proposed. However, the problem of detection in complex background is unsolved. Clutter in complex backgrounds is difficult to eliminate, and significant results are difficult to obtain in target detection. For this reason, an improved algorithm, high-boost weighted tri-Layer local contrast measure (HB-WTLLCM) was proposed, which enhance the target detection in complex background, and to improve the detection rate. An improved high boost filter was designed in this algorithm to preprocessed the original infrared image. Then the tri-layer window was used for enhancing the local contrast. Finally, a weighted algorithm based on complexity evaluation was introduced for further target enhancement and random noise suppression. Experimental results show that, compared with mainstream algorithms, the proposed algorithm is stronger in target enhancement and improves detection rate under complex background of multiple buildings and trees. It is suggested that the HB-WTLLCM algorithm proposed in this paper can detect infrared dim and small targets well in complex background. [ABSTRACT FROM AUTHOR]

This work utilizes the transit network, which aims to combine the genetic algorithm for analyzing the theory and traffic scheduling based on the traditional methodology. The dynamic methodology is used to schedule the model of transit system, which aims to optimize the demand in the transit network. This model illustrates the methodology of the genetic based transit network (GATN) algorithm to enhance the primary challenges in the transit network. The proposed methodology provides to be significant, with minimizing the objective model of around 27.2%. The model significantly managed to lower the total routes available in the transit network and all travelers related to the time and the transit trip from the initial stage. The significant system obtained using the optimization methodology has 180 routes, 110 less than the initial network, which has a variation by different transit network. This final transmission has been minimized to 33.6% by the proposed methodology in the transit network length and 4.1% reduction in the transfer average. The transition obtained from the multi-level objective function to unique optimization that considers the weighted function proved to be effective.

In the context of torpedo guidance systems, the performance of active sonar in channel parameter estimation and target detection and recognition is significantly degraded by the multipath effect and the time-varying characteristics of the underwater acoustic (UWA) channel. Therefore, it is urgent to propose an algorithm that can accurately estimate the channel parameters in multipath time-varying UWA channels. To solve these problems, this study developed a modified phase transform (PHAT)-weighted function and applied it to the orthogonal matching pursuit (OMP) algorithm, named M-PHAT-OMP. The proposed algorithm is more robust, improves the resolution of the time delay and further improves the estimation accuracy of the parameters in the case of motion. Furthermore, with the aim of solving the problem of the difficulty that the traditional OMP algorithm has in determining sparsity, this study proposes a joint-threshold method, where the threshold value serves as the condition for terminating the algorithm's iteration. The simulation results demonstrate that the M-PHAT-OMP algorithm proposed in this study exhibits a superior performance compared to other algorithms, as evidenced by its lower root mean square error (RMSE) for delay. Moreover, the experimental results also validate that the proposed algorithm has superior robustness and resolution of the time delay in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we first prove a new identity based on which we have established some weighted Simpson-type inequalities for functions whose first derivatives are beta-preinvex. Some applications of our finding are proposed. [ABSTRACT FROM AUTHOR]

This paper is concerned with the blow-up phenomenon for classical heat equation with a nonlocal weighted exponential boundary flux. Based on the method of super- and sub-solutions, Kaplan's argument, the Bernoulli's techniques and modified differential inequality, we analyze the influence of the weighted function and the nonlocal exponential boundary flux on the solution exists globally or blows up in finite time. Moreover, the life span bounds of blow-up solutions are derived under appropriate measure, and some examples for application are presented. [ABSTRACT FROM AUTHOR]

Abstract As early as 1910, Weyl gave a classification of the singular Sturm–Liouville equation, and divided it into the Limit Point Case and the Limit Circle Case at infinity. This led to the study of singular Sturm–Liouville spectrum theory. With the development of applications, the importance of singular Sturm–Liouville problems with a weighted function becomes more and more significant. This paper focuses on the study of singular Sturm–Liouville problems with a weighted function. Finally, an example of singular Sturm–Liouville problems with a weighted function is given.

In the context of torpedo guidance systems, the performance of active sonar in channel parameter estimation and target detection and recognition is significantly degraded by the multipath effect and the time-varying characteristics of the underwater acoustic (UWA) channel. Therefore, it is urgent to propose an algorithm that can accurately estimate the channel parameters in multipath time-varying UWA channels. To solve these problems, this study developed a modified phase transform (PHAT)-weighted function and applied it to the orthogonal matching pursuit (OMP) algorithm, named M-PHAT-OMP. The proposed algorithm is more robust, improves the resolution of the time delay and further improves the estimation accuracy of the parameters in the case of motion. Furthermore, with the aim of solving the problem of the difficulty that the traditional OMP algorithm has in determining sparsity, this study proposes a joint-threshold method, where the threshold value serves as the condition for terminating the algorithm’s iteration. The simulation results demonstrate that the M-PHAT-OMP algorithm proposed in this study exhibits a superior performance compared to other algorithms, as evidenced by its lower root mean square error (RMSE) for delay. Moreover, the experimental results also validate that the proposed algorithm has superior robustness and resolution of the time delay in practical applications.

As early as 1910, Weyl gave a classification of the singular Sturm–Liouville equation, and divided it into the Limit Point Case and the Limit Circle Case at infinity. This led to the study of singular Sturm–Liouville spectrum theory. With the development of applications, the importance of singular Sturm–Liouville problems with a weighted function becomes more and more significant. This paper focuses on the study of singular Sturm–Liouville problems with a weighted function. Finally, an example of singular Sturm–Liouville problems with a weighted function is given. [ABSTRACT FROM AUTHOR]

In this contribution we address our attention on the aggregation of partial T -indistinguishability operators (relations) and partial pseudo-metrics. A characterization of those functions that allow to merge a collection of partial T –indistinguishability operators into a new one was provided by Calvo et al. in [10] by means of (T , T M) -tuples, but here we present another characterization in terms of (+ , max ⁡) -tuples. Also, we analyze the aggregation of a collection (E i) i = 1 n of partial T i -indistinguishability operators. Moreover, we provide that a generalized inter–exchange composition functions condition is a sufficient condition to guarantee that a function merges partial T i -indistinguishability operators into a single one. In addition, we give different expressions of those aggregation functions that are object of our study, most of them are defined by means of the additive generators of the corresponding t-norms and another particular function. We see that the functions, that merge partial S -pseudo-metrics into a new one, are related to the functions that aggregate partial pseudo-metrics. Finally, we show the relation between the functions, that merge partial T -indistinguishability operators and the functions that preserve the partial T ⁎ -pseudo-metrics in the aggregation process. [ABSTRACT FROM AUTHOR]

Seismic wave velocity is one of the most important processing parameters of seismic data, which also determines the accuracy of imaging. The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity, producing the velocity spectrum by superposing energy or similarity coefficients. In this method, however, the sensitivity of the semblance spectrum to change of velocity is weak, so the resolution is poor. In this paper, to solve the above deficiencies of conventional velocity analysis, a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed. By introducing two weighting functions, the resolution of the similarity spectrum in time and velocity is improved. Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums; at the same time, the method shows good noise-resistibility. [ABSTRACT FROM AUTHOR]

Multi-hop wireless sensor networks are widely used in many location-dependent applications. Most applications assume the knowledge of geographic location of sensor nodes; however, in practical scenarios, the high accuracy on position estimates of sensor nodes is still a great challenge. In this research, we propose a hop-weighted scheme that can be useful in distance-based distributed multi-hop localization. The hop-weighted localization approach generates spatial locations around position estimates of unknown sensors and computes local functions that minimize distance errors among hop-weighted and static neighboring sensors. The iterative process of each unknown sensor to re-estimate its own location allows a significant reduction of initial position estimates. Simulations demonstrate that this weighted localization approach, when compared with other schemes, can be suitable to be used as a refinement stage to improve localization in both isotropic and anisotropic networks. Also, under rough initial position estimates, the proposed algorithm achieves root mean square error values less than the radio range of unknown sensors, in average, with only a few iterations. [ABSTRACT FROM AUTHOR]

In this paper, we present a modified weighted fuzzy time series model for forecasting based on two-factors fuzzy logical relationship groups. The proposed method define a new technique to partition the universe of discourse into different length of intervals to different factors. Also, the proposed method fuzzifies the historical data sets of the main factor and second factor to their maximum membership grades, obtained by their corresponding triangular fuzzy sets and further constructs the fuzzy logical relationship groups which is based on the two factors to increase in the forecasting accuracy rates. This study also introduces a new defuzzification technique based on the weighted function define on two-factors fuzzy logical relationship groups. The implementation of the proposed method is verified in forecasting on Bombay stock exchange Sensex historical data and compares the forecasted accuracy rate in terms of root mean square and average forecasting error which indicates that the proposed method can achieve more accurate forecasted output over the existing models on fuzzy time series. [ABSTRACT FROM AUTHOR]

In Aerial surveillance, thermal images acquired by unmanned aerial vehicle (UAV) are greatly affected due to various external interferences, which results in a low contrast image. Widely used conventional contrast enhancement methods such as histogram equalization and dynamic range partitioning techniques suffer from severe brightness changes and reduced sharpness, which in turn fail to preserve the edge details of the image. Thus for efficient target detection, it is essential to develop effective thermal infrared image contrast and edge enhancement technique. In this paper, wavelet transform (WT) and singular value decomposition (SVD)-based image enhancement technique is attempted for the target detection using thermal images captured by UAV. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and SVD are used for texture feature enhancement, edge enhancement and illumination correction, respectively. The experimental results show that the proposed technique yields higher entropy (6.7485), EMEE (2.1212), MSSIM (0.8719) and lower AMBE (21.9049) values when compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Due to the good filter performance for the non-Gaussian noise, the adaptive filters with error nonlinearities have received increasing attention recently. From the viewpoint of the weighted function, in this paper, the existing least mean square (LMS)-based adaptive algorithms with error nonlinearities are divided into three types, i.e., V-shaped, A-shaped, and M-shaped algorithms. Then, to obtain the merits of the V-shaped and A-shaped algorithms, a new family of robust M-shaped error weighted LMS algorithms is proposed. Their steady-state mean square deviation (MSD) analyses are made, which reveal the learning abilities of error nonlinearities: 1) for the V-shaped algorithm, it can achieve smaller steady state MSD for sub-Gaussian noise than that for super-Gaussian noise; 2) the A-shaped algorithm can be used more effectively for super-Gaussian noise than that for sub-Gaussian noise; and 3) the M-shaped algorithm combines the characteristics of the V-shaped and A-shaped algorithms. Furthermore, based on the proposed robust M-shaped function, a proportionate normalized robust M-shaped algorithm is presented for echo cancellation application. Finally, Monte Carlo simulations are conducted to verify the theoretical results and to demonstrate the efficiency of the proposed algorithms in different environments.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• In this work, ensemble bionic machine learning model is proposed for the classification of Parkinson's disease (PD) from healthy controls using MR images. • Adaptive median filter along with threshold computation is used for picture denoising. Image enhancement factor (IEF), peak signal-to-noise ratio (PSNR) and Mean square error (MSE) are assessed to objectively measure the denoising effect of the given model. • The first and second order statistical features are extracted in the feature extraction stage, • Adapted fused slime salp mould (AFSSM) soft computing approach is used in feature selection. • Ebola optimized ensemble SVM, XGBoost, and Random Forest machine learning model is used for PD detection. Parkinson's disease (PD) manifests as a loss of dopamine-producing cells present in the substantia nigra region of the brain's central nervous system (CNS). The proposed model influences magnetic resonance imaging (MRI) for detection. The research paper consist of four stages: pre-processing, feature extraction, feature selection and classification. The proposed model influences MR images for detection during the pre-processing stage, composed with an adaptive median filter along with threshold computation. The primarily getting a binary image is that it reduces the complexity of the data and makes the recognition and classification processes easier. To reduce the training period, reducing over fitting and enhancing the accuracy, first and second order features are extracted. In third stage, adapted fused slime salp mould (AFSSM) soft computing approach is used in feature selection. The AFSSM give a fixed threshold for the selection process and find optimum values very rapidly. The ensemble machine learning model (SVM, Random Forest and XGBoost) is introduced along with Ebola optimisation through voting method. The proposed achieves the mean square error, peak signal-to-noise ratio and image enhancement factor values of 4.15, 39.1 and 0.92 respectively when compared with the existing filtering models like mean, Kalman, gaussian and weiner filter. Along with this, the performance measures like accuracy, sensitivity, precision, F1-score and specificity are examined and attains an outcome of 98.4%, 98.4%, 98%, 98.8% and 98.2%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.

Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link of CS, the goal of sparse signal reconstruction is how to recover accurately and effectively the original signal from an underdetermined linear system of equations (ULSE). For this problem, we propose a new algorithm called the weighted regularized smoothed L 0 -norm minimization algorithm (WReSL0). Under the framework of this algorithm, we have done three things: (1) proposed a new smoothed function called the compound inverse proportional function (CIPF); (2) proposed a new weighted function; and (3) a new regularization form is derived and constructed. In this algorithm, the weighted function and the new smoothed function are combined as the sparsity-promoting object, and a new regularization form is derived and constructed to enhance de-noising performance. Performance simulation experiments on both the real signal and real images show that the proposed WReSL0 algorithm outperforms other popular approaches, such as SL0, BPDN, NSL0, and L p -RLSand achieves better performances when it is used for UBSS.

Aiming at the problem that the positioning accuracy based on received signal strength (RSSI) algorithm was easily affected by environmental disturbance, an improved weighted centroid positioning algorithm was proposed. First, an optimal length was adopted to improve localization accuracy by analyzing the relationship between the communication distance and ranging error. Then, the localization area was divided into several honeycomb sub-regions with the optimal length according to the situation of the overall environment. And these honeycomb sub-regions developed their environmental parameters by the least squares fitting method. Last, RSSI values were filtrated by Gaussian distribution model and weighted arithmetic to ensure the reliability. The simulation results showed the improved algorithm had better efficiency and positioning accuracy, compared with the traditional weighted triangular centroid positioning algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we establish a weighted Hardy-Littlewood-Sobolev (HLS) inequality on the upper half space using a weighted Hardy type inequality on the upper half space with boundary term, and discuss the existence of extremal functions based on symmetrization argument. As an application, we can show a weighted Sobolev-Hardy trace inequality with -biharmonic operator. [ABSTRACT FROM AUTHOR]

Investment portfolios are typically selected to reduce investment risk. In an economic recession or depression, investment strategies tend to be short term, subtle, and uncertain. When the economy is recovering or booming, investors should approach portfolio selection differently in response to the varying investment return and risk. Therefore, this study posits that different portfolios should be selected in different stages of the business cycle. An improved function for weighting possibilistic mean and variance is proposed, and a weighted fuzzy portfolio model for various investment conditions is then derived. Finally, a numerical example is presented to illustrate that the proposed models can obtain the optimal proportion of an investment throughout the business cycle to meet investors' expectations. [ABSTRACT FROM AUTHOR]

Compared to tests for localized clusters, the tests for global clustering only collect evidence for clustering throughout the study region without evaluating the statistical significance of the individual clusters. The weighted likelihood ratio (WLR) test based on the weighted sum of likelihood ratios represents an important class of tests for global clustering. Song and Kulldorff (Likelihood based tests for spatial randomness. Stat Med. 2006;25(5):825–839) developed a wide variety of weight functions with the WLR test for global clustering. However, these weight functions are often defined based on the cell population size or the geographic information such as area size and distance between cells. They do not make use of the information from the observed count, although the likelihood ratio of a potential cluster depends on both the observed count and its population size. In this paper, we develop a self-adjusted weight function to directly allocate weights onto the likelihood ratios according to their values. The power of the test was evaluated and compared with existing methods based on a benchmark data set. The comparison results favour the suggested test especially under global chain clustering models. [ABSTRACT FROM AUTHOR]

We develop weighted generalization of the recently obtained Ostrowski type inequalities for continuous functions with eventually one point of non-differentiability. We apply these general result for some famous weight functions. [ABSTRACT FROM AUTHOR]

Abstract: Link weights have the equally important position as links in complex networks, and they are closely associated with each other for the emergence of communities. How to assign link weights to make a clear distinction between internal links of communities and external links connecting communities is of vital importance for community detection. Edge centralities provide a powerful approach for distinguishing internal links from external ones. Here, we first use edge centralities such as betweenness, information centrality and edge clustering coefficient to weight links of networks respectively to transform unweighted networks into weighted ones, and then a weighted function that both considers links and link weights is adopted on the weighted networks for community detection. We evaluate the performance of our approach on random networks as well as real-world networks. Better results are achieved on weighted networks with stronger weights of internal links of communities, and the results on unweighted networks outperform that of weighted networks with weaker weights of internal links of communities. The availability of our findings is also well-supported by the study of Granovetter that the weak links maintain the global integrity of the network while the strong links maintain the communities. Especially in the Karate club network, all the nodes are correctly classified when we weight links by edge betweenness. The results also give us a more comprehensive understanding on the correlation between links and link weights for community detection. [Copyright &y& Elsevier]

Abstract: In this work, we analyze the new generalized Emden–Fowler equation with neutral type delays:where . By use of averaging technique and specific analytical skills, some easily-accessible oscillation and asymptotic criteria are established, which have extended the results in the cited literature. [Copyright &y& Elsevier]

This paper deals with some new generalizations of the Hardy-Hilbert type integral inequalities with some parameters. We also consider the equivalent inequalities and the reverse forms. [ABSTRACT FROM AUTHOR]

A normalized space constructed by tensor product is used in field function approach to give a special case of moving least squares (MLS) interpolation scheme. In the regular domain, the field function which meets homogenous boundary conditions is constructed by spanning base space to make the MLS interpolation scheme simpler and more efficient. Owing to expanded basis functions selection, some drawbacks in general MLS method, for example repeated inversion, low calculation efficiency, and complex criterions, can be avoided completely. Numerical examples illustrate that the proposed method is characterized by simple mathematical concept, convenient repeat calculations with high accuracy, good continuity, less computation and rapid convergence. [ABSTRACT FROM AUTHOR]

Through analyzing the uncertainty factors of multi objective optimization problems, it shows that Pareto solution should be generalized to fuzzy multi-objective optimization model, and the model is used to find the satisfactory solution through soft computing. According to the property of membership function in fuzzy sets theory, each sub objective can be dealt with satisfactory function. After analyzing the similarity between closed loop control theory in control system and the dynamic progress in soft computing of multi objective satisfactory solution optimization, the paper developed a general judgment function method of multi-objective satisfaction solution. The method is based on closed loop feedback control theory and is dealt with correcting weight function, relative optimum membership function and weight index of importance. At last, an illustrative example of multi objective optimization of mechanical component reliability was given. [ABSTRACT FROM AUTHOR]

Some oscillation criteria are established for the nonlinear damped elliptic differential equation of second order [Multiple line equation(s) cannot be represented in ASCII text] which are different from most known ones in the sense that they are based on a new weighted function H(r; s; l) defined in the sequel. Both the cases when Dibi (x) exists for all i and when it does not exist for some i are considered. [ABSTRACT FROM AUTHOR]

In this paper, the existence of the exponential attractors for the Ginzburg-Landau-BBM equations in an unbounded domain is proved by using weighted function and squeezing property. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link of CS, the goal of sparse signal reconstruction is how to recover accurately and effectively the original signal from an underdetermined linear system of equations (ULSE). For this problem, we propose a new algorithm called the weighted regularized smoothed L 0 -norm minimization algorithm (WReSL0). Under the framework of this algorithm, we have done three things: (1) proposed a new smoothed function called the compound inverse proportional function (CIPF); (2) proposed a new weighted function; and (3) a new regularization form is derived and constructed. In this algorithm, the weighted function and the new smoothed function are combined as the sparsity-promoting object, and a new regularization form is derived and constructed to enhance de-noising performance. Performance simulation experiments on both the real signal and real images show that the proposed WReSL0 algorithm outperforms other popular approaches, such as SL0, BPDN, NSL0, and L p -RLSand achieves better performances when it is used for UBSS. [ABSTRACT FROM AUTHOR]