Mathematical Problems in Engineering

Algorithms for Compressive Sensing Signal Reconstruction with Applications


Publishing date
19 Aug 2016
Status
Published
Submission deadline
01 Apr 2016

1University of Montenegro, Podgorica, Montenegro

2Grenoble INP/ENSE3, Grenoble, France

3Hangzhou Normal University, Hangzhou, China

4University of Split, Split, Croatia


Algorithms for Compressive Sensing Signal Reconstruction with Applications

Description

Compressive sensing appeared recently as a new sensing framework and very quickly became a rapidly growing area of great interest in many research communities and applications. As an alternative to the traditional sampling theory, this modern approach allows acquiring much smaller amount of data, still achieving the same quality (or almost the same) of the final representation. Compressive sensing opens the possibility to simplify acquisition devices and apparatus for data and reduce the number of sensors, acquisition time, and storage capacities. Also, compressive sensing principles have been used for denoising by considering the randomly corrupted data as unavailable. The signal reconstruction from small set of measurements is possible if these are incoherent and the signal itself is sparse in a certain transform domain. The main challenges that arise here are related to the design of measurements process/matrix, exploring signal sparsity over certain transform basis, and design of suitable reconstruction algorithms. Generally, the signal reconstruction problem in CS is formulated as an underdetermined system of linear equations that needs to be solved using sparse priors. Depending on the application of interest, the challenge in signal reconstruction is focused toward the development of fast reconstruction algorithm with high accuracy.

Nowadays, compressive sensing has found the potential applications in many real-world systems showing ability to provide substantial gain over traditional approach. Particularly, the applications range from the radar, sonar and remote sensing systems, biomedical imaging, multimedia systems, communications, and sparse channel estimation.

The objective of this special issue is to bring together novel theoretical developments and modern applications dealing with compressive sensing strategy and thus to promote the state of the art in this attractive research area and to bring new advanced results. Therefore, this special issue welcomes novel contributions, modifications, and extensions in theory, analysis, and algorithms, with the special emphasis on applications.

Potential topics include, but are not limited to:

  • CS reconstruction of 1D and 2D signals
  • CS based denoising methods
  • Exploring signal sparsity in transformation domain
  • CS and time-frequency analysis
  • Biomedical applications
  • Applications in radars
  • Applications in multimedia systems
  • Compressive sensing techniques for instantaneous frequency estimation
  • Applications of compressive sensing and robust signal analysis
  • Real-time systems for CS signal acquisition and reconstruction

Articles

  • Special Issue
  • - Volume 2016
  • - Article ID 8376531
  • - Editorial

Algorithms for Compressive Sensing Signal Reconstruction with Applications

Srdjan Stanković | Cornel Ioana | ... | Vladan Papic
  • Special Issue
  • - Volume 2016
  • - Article ID 6827414
  • - Research Article

Gradient Compressive Sensing for Image Data Reduction in UAV Based Search and Rescue in the Wild

Josip Musić | Irena Orović | ... | Srdjan Stanković
  • Special Issue
  • - Volume 2016
  • - Article ID 7616393
  • - Review Article

Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

Irena Orović | Vladan Papić | ... | Srdjan Stanković
  • Special Issue
  • - Volume 2016
  • - Article ID 3982360
  • - Research Article

Underdetermined Separation of Speech Mixture Based on Sparse Bayesian Learning

Zhe Wang | Luyun Wang | ... | Guoan Bi
  • Special Issue
  • - Volume 2016
  • - Article ID 9641608
  • - Research Article

Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

Ming Yin | Kai Yu | Zhi Wang
  • Special Issue
  • - Volume 2016
  • - Article ID 3986903
  • - Research Article

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

Hanwei Liu | Yongshun Zhang | ... | Yifeng Wu
  • Special Issue
  • - Volume 2016
  • - Article ID 9407503
  • - Research Article

Incoherent Dictionary Learning Method Based on Unit Norm Tight Frame and Manifold Optimization for Sparse Representation

HongZhong Tang | Xiaogang Zhang | ... | Xiao Li
  • Special Issue
  • - Volume 2016
  • - Article ID 8397201
  • - Research Article

A Novel Approach to Wideband Spectrum Compressive Sensing Based on DST for Frequency Availability in LEO Mobile Satellite Systems

Feilong Li | Guangxia Li | ... | Gengxin Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 2594752
  • - Research Article

Approximately Normalized Iterative Hard Thresholding for Nonlinear Compressive Sensing

Xunzhi Zhu
  • Special Issue
  • - Volume 2016
  • - Article ID 5737381
  • - Research Article

An Efficient Algorithm for Learning Dictionary under Coherence Constraint

Huang Bai | Sheng Li | Qianru Jiang
Mathematical Problems in Engineering
 Journal metrics
Acceptance rate29%
Submission to final decision66 days
Acceptance to publication35 days
CiteScore1.800
Impact Factor1.009
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