Conference Proceedings ISBN: 978-1-912532-07-0. The proceedings will be also submitted to Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Index) and Scopus for indexation.
INAIT 2019 Conference Committee TPC Member (Kezhi Li, Imperial College London, UK) who his the Guest Editor for the Special Issue below, encourage best accepted papers to submit their extended versions to the SI of IEEE Access.
Journal: IEEE Access® is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. IEEE Access received a high impact factor of 3.244 in 2016.
SI Title: Theory, Algorithms, and Applications of Sparse Recovery
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.
Associate Editor: Jinming Wen, University of Toronto, Canada
- Jian Wang, Fudan University, China
- Bo Li, Nuance Communication, Canada
- Xin Yuan, Nokia Bell Labs, USA
- Kezhi Li, Imperial College London, UK
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