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Group Sparse Representation Enhances Brain Network Classification of Major Depressive Disorder in Two Chinese Cohorts. [PDF]

Alpha Psychiatry
Zhang D, Lin C, Zhang A, Wang X, Xia W, Wang Y, Du Y, DIRECT Consortium, Yu H, Ji S.   +9 more
europepmc   +1 more source

Hierarchical linguistic predictions and cross-level information updating during narrative comprehension. [PDF]

Commun Biol
Zhou F, Zhou S, Long Y, Flinker A, Lu C.
europepmc   +1 more source

Sparse Transform and Compressed Sensing Methods to Improve Efficiency and Quality in Magnetic Resonance Medical Imaging. [PDF]

Sensors (Basel)
Villota S, Inga E.
europepmc   +1 more source

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Applications of sparse signal processing

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and ...
Masoumeh Azghani, Farokh Marvasti
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Sparse Array Signal Processing

2023
This dissertation details three approaches for direction-of-arrival (DOA) estimation or beamforming in array signal processing from the perspective of sparsity. In the first part of this dissertation, we consider sparse array beamformer design based on the alternating direction method of multipliers (ADMM); in the second part of this dissertation, the ...
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Sparse representation in speech signal processing

SPIE Proceedings, 2003
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals.
Te-Won Lee, Gil-Jin Jang, Oh-Wook Kwon
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Sparse Signal Processing

2014
Conventional sampling techniques are based on Shannon-Nyquist theory which states that the required sampling rate for perfect recovery of a band-limited signal is at least twice its bandwidth. The band-limitedness property of the signal plays a significant role in the design of conventional sampling and reconstruction systems.
Masoumeh Azghani, Farokh Marvasti
openaire   +1 more source