Compressive Sensing and Its Applications

Dear Colleagues,

The advancement of compressive sensing and sparse signal processing has received a lot of attention in academia and industry over the last two decades. This technique allows efficient sub-Nyquist sampling with reasonable accuracy by smartly collecting the important information of the underlying sparse/compressible signal and reconstruct the signal using sparse recovery algorithms. There have been many works related to the theory of compressive sensing, including sampling methods, and the associated reconstruction algorithms via convex optimization, sparse Bayesian learning, and greedy algorithms. However, due to the wide applicability of compressive sensing, there is still room for advancements on the theoretical aspects of sensing methods and the reconstruction algorithms to make the overall processing more efficient and with higher accuracy.  There are also opportunities for developments which exploit the particular properties of underlying signal models.  With the wide applications of compressive sensing in areas such as imaging, biomedical engineering, mining engineering, civil engineering, and many more, various scientists and engineering have been attracted to the compressive sensing technique.

This special issue of Signals aims to be a forum for presentation of new, improved, and developing techniques in the general area of compressive sensing.

This Special Issue will accept unpublished original papers and comprehensive reviews with topics related to the following areas:

- The theory and advancement of compressive sensing

- Detection and estimation of signals using compressive sensing

- Sparse recovery using variational Bayes inference, MCMC method, etc.

- Compressive sensing using machine learning methods (supervised and unsupervised)

- Recent applications of compressive sensing

- Implementation of compressive sensing in real-world problems such as communications, Radar, camera, video, biomedical engineering, etc.

- Application of compressive sensing in big-data

- Statistical modeling techniques for compressive sensing and sparse signal recovery

- Review on the compressive sensing theory and applications

- Hardware implementation of compressive sensing

- Deep compressive sensing

Dr. Mohammad Shekaramiz
Prof. Dr. Todd K. Moon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.