Submission Deadline: 31 October 2018
IEEE Access invites manuscript submissions in the area of Theory, Algorithms, and Applications of Sparse Recovery.
Sparse recovery is a fundamental problem in the fields of compressed sensing, signal de-noising, statistical model selection, and more. The key idea of sparse recovery lies in that a suitably high dimensional sparse signal can be inferred from very few linear observations. Recent years have witnessed a great development of the sparse recovery theory and fruitful applications in the general field of information processing, including communications channel estimation, dictionary leaning, data compression, optical imaging, machine learning etc. Extensions to the recovery of low-rank matrices and higher order tensors from incomplete linear information have also been developed, and remarkable achievements have been achieved.
This Special Section is devoted to both the current state-of-the-art advances and new theory, algorithms and applications of sparse recovery, with the goals to highlight new achievements and developments, and to feature outstanding open issues and promising new directions and extensions, on the theory, algorithms, and applications. Both survey papers and papers of original contributions that enhance the existing body of sparse recovery are also highly encouraged. The topics of interest include, but are not limited to:
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Jinming Wen, University of Toronto, Canada
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
Paper submission: Contact Associate Editor and submit manuscript to:
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