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Bayesian blind source separation for data with network structure. [PDF]
J Comput Biol, 2014 Illner K, Fuchs C, Theis FJ.
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Blind Source Separation-Based Motion Detector for Imaging Super-Paramagnetic Iron Oxide (SPIO) Particles in Magnetomotive Ultrasound Imaging. [PDF]
IEEE Trans Med Imaging, 2018 Hossain MM +4 more
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Blind Source Separation - Based Motion Detector for Sub-Micrometer, Periodic Displacement in Ultrasonic Imaging. [PDF]
IEEE Int Ultrason Symp, 2016 Hossain MM +4 more
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PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG. [PDF]
Comput Intell Neurosci, 2016 Ball K +3 more
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Compressive blind source separation
2010 IEEE International Conference on Image Processing, 2010The central goal of compressive sensing is to reconstruct a signal that is sparse or compressible in some basis using very few measurements. However reconstruction is often not the ultimate goal and it is of considerable interest to be able to deduce attributes of the signal from the measurements without explicitly reconstructing the full signal.
Yiyue Wu +2 more
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Blind source separation for BLAST
2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628), 2003We present a new blind source separation (BSS) approach for Bell Labs layered space-time (BLAST) communication system. The proposed algorithm is derived from the multimodulus algorithm (MMA) and employs the Gram-Schmidt orthogonalization procedure. The basic idea is to adjust the real and imaginary parts of the equalizer matrix separately and then ...
P. Sansrimahachai +2 more
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Blind Source Separation of Graph Signals
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information.
Vorobyov, Sergiy A. +3 more
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Blind Separation of Nonstationary Sources
IEEE Signal Processing Letters, 2004We propose a blind separation technique for nonstationary sources that exploits both auto-terms and cross-terms of the time-frequency distributions. The technique is based on the simultaneous joint diagonalization and off-diagonalization of spatial time-frequency distributions.
Adel Belouchrani +3 more
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The Journal of the Acoustical Society of America, 1999
In signal processing the received data can be modeled as a superposition of a finite number of elementary source signals and an additive noise. Generally, in a multi-sensor environment application, such as underwater acoustics, the principal objective is estimating the number and directions of radiating sources. In the last decade, eigenstructure-based
Miloud Frikel +2 more
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In signal processing the received data can be modeled as a superposition of a finite number of elementary source signals and an additive noise. Generally, in a multi-sensor environment application, such as underwater acoustics, the principal objective is estimating the number and directions of radiating sources. In the last decade, eigenstructure-based
Miloud Frikel +2 more
openaire +2 more sources