[PDF] Top 20 Sparse Representation for Wireless Communications:A Compressive Sensing Approach
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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
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A novel sensing noise and gaussian noise removal methods via sparse representation using SVD and compressive sensing methods
... modelling approach, sparse representation has been productively exploited in image denoising ...a sparse representation structure, a dictionary can be learned by exploiting the RAC in ... See full document
8
Efficient Data Gathering with Compressive Sensing in Wireless Sensor Networks
... walk approach for data gathering in ...this compressive sensing will applicable or lead to non-uniform selection of measurement which is different from uniform sampling in the traditional ... See full document
9
Compressive sensing image fusion algorithm in wireless sensor networks based on blended basis functions
... Blended basis functions are the combination of two multi-resolution analysis tools. The two functions, NSCT and wavelet, are cascaded. The image has already been decomposed into multi-scales by NSCT before the ... See full document
6
Efficient compressive sampling of spatially sparse fields in wireless sensor networks
... autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse ...bandwidth/energy-efficient compressive sampling (CS) ... See full document
19
Compressive sensing in distributed radar sensor networks using pulse compression waveforms
... (ATR) approach for both nonfluctuating and fluctuating targets in a network of multiple radar ...This representation motivates the appli- cability of the recently proposed compressive sensing ... See full document
10
Compressive sensing based random access for machine type communications considering tradeoff between link performance and latency
... alternative approach to Shannon/Nyquist sampling, where a sparse or compressible signal can be sampled at a rate much less than the Nyquist rate [10, ... See full document
11
Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction
... Tensor-based HSI-CSR approaches can improve remarkably the HSI recovery quality, since the existing methods jointly take into account the spatial-spectral information, and reduce the losses and distortions caused by HSI ... See full document
24
An advanced scheme of compressed sensing of acceleration data for telemonintoring of human gait
... Next, we evaluate the reconstruction performance of BSBL algorithm. In this experi- ment, acceleration data length is fixed to 500 (i.e. N = 500), and it is divided into 25 blocks, each block containing 20 points. Our ... See full document
15
A Compressive Sensing Based Approach to Sparse Wideband Array Design
... However, the unpredictable sidelobe behaviour associated with sparse arrays means some optimisation of sensor loca- tions is required to reach an acceptable performance level. Various nonlinear methods have been ... See full document
5
Compressive sensing image fusion algorithm based on directionlets
... the sparse matrix can be ob- tained after the directionlet coefficients are processed with the sparse treatment, then the fusion rules for the sparse matrix integration are determined, ... See full document
6
A probabilistic compressive sensing framework with applications to ultrasound signal processing
... TOF estimation has therefore attracted significant attention from the NDT community. The methods developed over the years can be split into two categories: 1) those methods that use thresholds and changes in signal phase ... See full document
20
A probabilistic compressive sensing framework with applications to ultrasound signal processing
... TOF estimation has therefore attracted significant attention from the NDT community. The methods developed over the years can be split into two categories: 1) those methods that use thresholds and changes in signal phase ... See full document
21
Efficient Sparse Algorithm for Solving Multi-Objects Scattering Based on Compressive Sensing
... form sparse transform matrices for separable subdomains without sacrificing additional memory, which can fast construct accurate undetermined equations model to further improve computing efficiency for solving ... See full document
9
EM based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system
... current wireless networks are mainly dominated by the frequency division duplex (FDD) ...Compressed sensing (CS), which can reconstruct the sparse channel through few pilots, is viewed as a promising ... See full document
7
Compressive sensing based joint frequency offset and channel estimation for OFDM
... We introduced a novel CS based framework for the joint estimation of CFO and CIR in OFDM systems. It is shown that the CIR can be represented as a 1-block sparse sig- nal if a dictionary is built by concatenating ... See full document
13
Wireless Body Area Sensor System for Monitoring Physical Activities Using GUI
... costs. The initial test results for this WBAN prototype, as well as expected technological advances, indicate the tremendous potential of WBAN technology for ambulatory monitoring. Several emerging technologies, such as ... See full document
9
Dimension-deficient channel estimation of hybrid beamforming based on compressive sensing
... Thanks to its wide-ranging applications in sparse signal pro- cessing, compressive sensing is widely used in sparse chan- nel estimation [9], [16], [18], [19]. While CS algorithms may exhibit ... See full document
8
A novel reduced power compressive sensing technique for wideband cognitive radio
... The vacant subbands used by SU are found using spec- trum sensing. The first step in spectrum sensing is power spectral density (PSD) estimation. In non-parametric PSD estimation techniques, the PSD is ... See full document
12
Compressive Sensing Based Design of Sparse Tripole Arrays
... minimisation by adding a reweighting term that penalises small weight coefficients more heavily [22]. Previous work involving CS and sparse array design has tended to focus on the case of isotropic array elements. ... See full document
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