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 7473041
  • - Research Article

An Efficient Method for Convex Constrained Rank Minimization Problems Based on DC Programming

Wanping Yang | Jinkai Zhao | Fengmin Xu
  • Special Issue
  • - Volume 2016
  • - Article ID 6329740
  • - Research Article

An Efficient Algorithm for Overcomplete Sparsifying Transform Learning with Signal Denoising

Beiping Hou | Zhihui Zhu | ... | Aihua Yu
  • Special Issue
  • - Volume 2016
  • - Article ID 9853714
  • - Research Article

Norm-Minimized Scattering Data from Intensity Spectra

Alexander Seel | Arman Davtyan | ... | Otmar Loffeld
  • Special Issue
  • - Volume 2016
  • - Article ID 4571359
  • - Research Article

Sliding-MOMP Based Channel Estimation Scheme for ISDB-T Systems

Ziji Ma | Shuaifeng Guo | ... | Minoru Okada
  • Special Issue
  • - Volume 2016
  • - Article ID 9410873
  • - Research Article

A Sparse Signal Reconstruction Algorithm in Wireless Sensor Networks

Zhi Zhao | Jiuchao Feng
  • Special Issue
  • - Volume 2016
  • - Article ID 6212674
  • - Research Article

On a Gradient-Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain

Ljubiša Stanković | Miloš Daković
  • Special Issue
  • - Volume 2016
  • - Article ID 6587309
  • - Review Article

An Overview of Moving Object Trajectory Compression Algorithms

Penghui Sun | Shixiong Xia | ... | Daxing Li
Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
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Impact Factor1.305
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