1 Advanced Communications Research Institute, Sharif University of Technology, Tehran, Iran
2 Laboratoire des Signaux et Systemes (L2S), CNRS-SUPELEC UNIV PARIS SUD, Orsay, France
3 Communications Division, School of Electronic, Electrical and Systems Engineering, Loughborough University, Leicestershire, UK
EURASIP Journal on Advances in Signal Processing 2012, 2012:90 doi:10.1186/1687-6180-2012-90Published: 26 April 2012
First paragraph (this article has no abstract)
Over the last three decades, there has been increasing interest in exploiting sparseness constraints in signal processing, that is, searching for target signals with as few non-zero entries as possible. This approach was, perhaps, first exploited in the modelling of excitation signals in speech processing or reflectivity sequences in seismic deconvolution with only a small number of non-zero values. Some 30 years later, the associated mathematical methods and application domains, both for single- and multiple-dimensional signals, have evolved to a point where sparse signal processing has become an area of study in its own right.