Results 261 to 270 of about 451,315 (310)

Blind Source Separation of Graph Signals

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
With 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., Ollila, Esa, Miettinen, Jari   +3 more
openaire   +1 more source

Compressive blind source separation

2010 IEEE International Conference on Image Processing, 2010
The 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, Yuejie Chi, Robert Calderbank
openaire   +1 more source

Complex Blind Source Separation

Circuits, Systems, and Signal Processing, 2017
Blind source separation (BSS) techniques aim at recovering the original source signals from observed mixtures without a priori information. The bivariate empirical mode decomposition (BEMD) algorithm combined with complex independent component analysis by entropy bound minimization (ICA-EBM) technique is proposed as an alternative to separate ...
Mina Kemiha, Abdellah Kacha
openaire   +1 more source

UXO Discrimination Using Blind Source Separation

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2005, 2005
Statistical signal processing techniques have shown progress in discriminating UXO from clutter when the objects occur in isolation. Under this condition, only a single object contributes to the sensor measurement. For multiple closely-spaced subsurface objects, however, the unprocessed sensor measurement is a mixture of the responses from several ...
Yingyi Tan, Stacy L. Tantum, Leslie M. Collins   +2 more
openaire   +1 more source

Blind source separation

2019
This paper presents a mathematical approach to methods of blind sources separation(BSS) such as principal component analysis(PCA) and independent component analysis(ICA).
openaire   +2 more sources

Blind source separation: a unified approach

Neurocomputing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Singh, Yogesh, Rai, C. S.
openaire   +1 more source

Blind source separation of composite bearing vibration signals with low-rank and sparse decomposition

Measurement, 2019
Fault diagnosis is pivotal for health monitoring of rotating machinery. On practical engineering occasions, collected signals are usually from multi-sources. Moreover, the complex transmission path between multi-source and sensors further complicates the
Guozheng Li, Gang Tang, Huaqing Wang, Yanan Wang   +3 more
semanticscholar   +1 more source

Nonlinear blind source separation using kernels

IEEE Transactions on Neural Networks, 2003
We derive a new method for solving nonlinear blind source separation (BSS) problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the nonlinear domain using the kernel trick originally applied in support vector machines (SVMs).
Martinez, Dominique, Bray, Alistair
openaire   +3 more sources

A novel underdetermined blind source separation method with noise and unknown source number

Journal of Sound and Vibration, 2019
It has been challenging to correctly separate sources from their mixtures with large noise and unknown source number in underdetermined cases. To address this problem, a novel underdetermined blind source separation (UBSS) method is proposed using ...
Jiantao Lu, Wei Cheng, Dong He, Y. Zi
semanticscholar   +1 more source

Kernel-Based Nonlinear Blind Source Separation

Neural Computation, 2003
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity.
Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.   +3 more
openaire   +3 more sources