[PDF] Top 20 Robust compressive sensing of sparse signals: a review
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Robust compressive sensing of sparse signals: a review
... models. Robust statistics, more specifically, the stability theory of statistical procedures, systematically investigates the effects of deviation from modeling assumptions ...of robust statistics are of ... See full document
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Sparse Array Antenna Signal Reconstruction using Compressive Sensing for Direction of Arrival Estimation
... Hence signals may be received coming from the sidelobes of the transmit ...These signals can influence targets in the main beam, and when the grid is limited the existence of these signals is not ... See full document
89
Robust group compressive sensing for DOA estimation with partially distorted observations
... group sparse signal model utilizing the received multi- frequency ...we review the complex muti- task Bayesian compressive sensing (CMT-BCS) technique to solve the complex value group ... See full document
10
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
... Data-driven Module: Inspired by the deep methods which can effectively simulate the data distribution by training nu- merous paired input and output. A learning based module with deep architecture is taken into account ... See full document
8
Identification of Sparse Audio Tampering Using Distributed Source Coding and Compressive Sensing Techniques
... between robust and fragile ...be robust with respect to legitimate, perceptually irrelevant modifications ...exhaustive review of audio watermarking for authentication and tampering identification ... See full document
12
COMPRESSIVE SENSING
... Compressed sensing (CS) has emerged as a new framework for signal acquisition and sensor ...for sensing signals that have a sparse or compressible ...is sparse in a known basis we can ... See full document
11
Performance of compressive sensing algorithms over time varying frequency selective channel
... training signals since the system has huge number of channel ...called sparse multi-path ...channels, Compressive Sensing (CS) based channel estimation method provides better estimation of ... See full document
6
A probabilistic compressive sensing framework with applications to ultrasound signal processing
... Whilst the Fourier and wavelet transforms are not the topic of this paper, they do play hidden roles in the background, and so it is worth starting with a brief discussion of sparsity and signals, with how these ... See full document
21
Robust Object Tracking using compressive sparse vectors Pranathi Shirga & Mrs Udayini Chandana
... for robust object tracking in recent ...The compressive sensing (CS) theory, shows that if the dimension of the feature space is sufficiently high, these features can be projected to a randomly ... See full document
6
MRI Reconstruction using Compressive Sensing : A Review Paper
... reconstructing signals or images from processed data follows Shannon’s celebrated theory "which states that the sampling rate must be at least twice the highest ...results. Compressive sensing is ... See full document
5
A Novel Robust Digital Watermarking Technique Using Compressive Sensing for Biometric Data Protection
... Then we are start to analyzed proposed watermarking technique by generating biometric watermarked image and recover secure fingerprint image from sparse linear measurement at detector where compressive ... See full document
7
Title: A Review on Compressive Sensing in Synthetic Aperture Radar (SAR)
... ABSTRACT: The system parameters have been specified in view of all the constraints and practical limitations. The performance metrics of the system such as range resolution and cross-range resolution, etc. have been ... See full document
7
A probabilistic compressive sensing framework with applications to ultrasound signal processing
... Whilst the Fourier and wavelet transforms are not the topic of this paper, they do play hidden roles in the background, and so it is worth starting with a brief discussion of sparsity and signals, with how these ... See full document
20
Sparse Representation for Wireless Communications:A Compressive Sensing Approach
... proper sparse domain is quite essential in many appli- cations that invoke ...identified sparse domains mainly include frequency domain, spatial domain, wavelet do- main, DCT domain, ...spectrum ... See full document
25
RZA NLMF algorithm based adaptive sparse sensing for realizing compressive sensing
... Compressive sensing (CS) [1,2] has been attracting high attention in compressive radar/sonar sensing [3,4] due to its many applications such as civilian, military, and ...(1) sparse ... See full document
10
Compressive sensing for sparse approximations: constructions, algorithms, and analysis
... high-dimensional signals exactly or accurately, by using a much smaller number of non-adaptive linear samplings or ...general, signals in this context are represented by vectors from linear spaces, many of ... See full document
248
Compressive Sensing Based Approach to the Design of Linear Robust Sparse Antenna Arrays with Physical Size Constraint
... This is a repository copy of Compressive Sensing Based Approach to the Design of Linear Robust Sparse Antenna Arrays with Physical Size Constraint.. White Rose Research Online URL for th[r] ... See full document
24
Automatic facial expression recognition based on MRELBP and compressive sensing
... a robust hybrid technique for facial expression recognition based on compressive sensing theory and using MRELBP and HOG ...system compressive sensing is used to reduce the ... See full document
7
Four-Dimensional SAR Imaging Scheme Based on Compressive Sensing
... is sparse and non-uniform in the baseline- time ...favorable sparse reconstruction technique, CS reconstructs spectral lines instead of sinclike point response functions and shows a lower sidelobes ... See full document
15
A novel sensing noise and gaussian noise removal methods via sparse representation using SVD and compressive sensing methods
... imagery. Denoising of HSI with the help of SVD is accomplished by discovering a low rank approximation of a matrix representation of the hyperspectral image cube. This approach of denoising can competently eliminate a ... See full document
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