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Found 20 papers, 1 papers with code
Markovian Block Sparse Signal Detection Using One Bit Measurements
This paper presents a novel sparse signal detection scheme designed for a correlated Markovian Bernoulli-Gaussian sparse signal model, which can equivalently be viewed as a block sparse signal model.
Second-Order Nonlinearity Estimated and Compensated Diffusion LMS Algorithm: Theoretical Upper Bound, Cramer-Rao Lower bound, and Convergence Analysis
In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed.
Double-Private Distributed Estimation Algorithm Using Differential Privacy and a Key-Like Proportionate Matrix with Its Performance Analysis
In this brief, we present an enhanced privacy-preserving distributed estimation algorithm, referred to as the ``Double-Private Algorithm," which combines the principles of both differential privacy (DP) and cryptography.
Proportionate Adaptive Graph Signal Recovery
The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm.
Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning
In addition, some synthetic and real-world experiments show the advantage of the proposed algorithm in comparison to some other adaptive algorithms in the literature of adaptive graph signal recovery.
Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure
Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember.
Statistical Graph Signal Recovery Using Variational Bayes
In this paper, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF).
Model-based Decentralized Bayesian Algorithm for Distributed Compressed Sensing
Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations.
Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data
In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery.
Sparsity Constrained Distributed Unmixing of Hyperspectral Data
Each pixel in the hyperspectral images is considered as a node in this network.
Hyperspectral Unmixing Based on Clustered Multitask Networks
In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing.
Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm
In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of $\ell_p$-norm.
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint
Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.
Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure
In this paper, we study the missing sample recovery problem using methods based on sparse approximation.
A Convex Similarity Index for Sparse Recovery of Missing Image Samples
The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal.
Double-detector for Sparse Signal Detection from One Bit Compressed Sensing Measurements
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection.
Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks
A diffusion strategy is suggested for distributive learning of the sparse vector.
Dictionary Learning for Blind One Bit Compressed Sensing
In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain.
Bayesian Hypothesis Testing for Block Sparse Signal Recovery
The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal.
Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals
Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identically distributed (i. i. d.
