EURASIP Journal on Advances in Signal Processing welcomes submissions to this special issue on 'Advances in Sparse Recovery: From Vectors, Matrices to Tensors'.
The problem of sparse signal recovery has received much attention with the development of compressed sensing and the results providing insights into a wide-spread range of fields including signal processing, applied mathematics, statistics, computer science and more. The key idea behind sparse signal recovery is that any high-dimensional sparse signal can be successfully recovered from its significantly fewer suitable linear observations. In the past decade, this problem has been largely investigated in both theory and algorithm aspects, and has also bear fruitful applications including data compression, dictionary learning, image and video processing, machine learning and high-dimensional statistical inference. Recently, the research focus of this problem has been largely extended to deal with several new and different sparse recovery tasks, such as the sparse signal recovery corrupted with the non-Gaussian noise, neural network based methods for sparse signal recovery and the recovery of low-rank matrix and tensor recovery, to name a few.
The goal of this special section is to gather the current state-of-the-art advances in theory, algorithms and applications of the sparse recovery of signals, low-rank matrices and low-rank tensors, with the goals to highlight new achievements and developments and promising new directions and extensions. Both survey papers and the papers of original contributions that enhance the existing body of sparse signal recovery are also highly encouraged.
Topics of interest include but are not limited to:
- Sparse signal recovery in the presence of the non-Gaussian noise
- Sparse modeling/representation in deep learning
- Multiple prior information inspired methods for sparse signal recovery
- Trade-off between sparse signal recovery effectiveness and efficiency
- Sparse signal recovery from quantized measurements
- Signal recovery under non-linear sparse representation
- Theory/algorithm/applications of sparse signal recovery
- Theory/algorithm/applications of low-rank matrix recovery
- Theory /algorithm/applications of low-rank tensor recovery
- Sparse signal processing for wireless communications
Submission deadline: 15th July 2019
Lead Guest Editor:
Jianjun Wang, Southwest University, China
Jinming Wen, Jinan University, China
Jiankang Zhang, University of Southampton, UK
Wengu Chen, Beijing Institute of Applied Physics and Computational Mathematics, China
Lisimachos P. Kondi, University of Ioannina, Greece
Arun Kumar Sangaiah, VIT University, Vellore, India
Eva Lagunas, University of Luxembourg, Luxembourg