Results 131 to 140 of about 191,485 (185)

Blind signal separation using QMF

2015 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2015
Blind signal separation method is the method that the source signals are obtained by separating only the mixed signals. Generally, separation precision is lower when blind signal separation method is used for the sound signals. It is the cause that the power of the sound signals is concentrated in the low-frequency band.
Kazuaki Matsushima, Tomomi Ogawa, Hiroki Matsumoto   +2 more
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Blind signal separation using ICA

2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479), 2002
In this paper, a steepest descent algorithm for independent component analysis (ICA) is proposed. In contrast to most blind source separation algorithms, the method does not employ higher order statistics. A pre-whitening procedure is performed to de-correlate the sensor (mixed) signals before extracting the vector. The proposed method is verified with
null Weidong Zhou, null Lei Jia
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Blind signal separation for convolved nonstationary signals

Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 2000
International ...
Kawamoto, Mitsuru, Alan Kardec, Barros, Mansour, Ali, Matsuoka, K., Noboru, Ohnishi   +4 more
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Blind Source Separation and Blind Equalization Algorithms for Mechanical Signal Separation and Identification

Journal of Vibration and Control, 2006
Many advanced techniques have been developed for diagnosis of machine faults caused by vibration. They are effective if the inspected vibration is well isolated from interference caused by vibrations from adjacent components. However, the components of manufacturing machines are numerous, small, and packed closely together.
Tse, Peter W., Zhang, J. Y., Wang, X. J.
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Fetal Magnetocardiographic Signals Extracted by ‘Signal Subspace’ Blind Source Separation

IEEE Transactions on Biomedical Engineering, 2005
In this paper, we apply independent component analysis to fetal magnetocardiographic data. In particular, we propose an extension of the "cumulant-based iterative inversion" algorithm to include a two-step "signal subspace" subdivision, which allows the user to control the number of components to be estimated by analyzing the eigenvalues distribution ...
C. Salustri, BARBATI, GIULIA, C. Porcaro
openaire   +5 more sources

Application of Blind-Signal-Processing Algorithm in Image Separation - Blind-Signal-Processing in Image Separation

2019 International SoC Design Conference (ISOCC), 2019
We take advantage of the relative gradient method and the bound component analysis algorithm to propose the relativegradient bound component analysis algorithm in this paper. This algorithm does not need to compute the inverse matrix and the covariance matrix. It can succesfully separate the mixed pictures without whitening. The time complexity and the
Chuen-Yau Chen, Cheng-Yuan Lin, Wei-Ching Liu, Yen-Ting Chen   +3 more
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Multichannel blind signal separation and reconstruction

IEEE Transactions on Speech and Audio Processing, 1997
The separation of multiple signals from their superposition recorded at several sensors is addressed. The methods employ polyspectra of the sensor data in order to extract the unknown signals and estimate the finite impulse response (FIR) coupling systems via a linear equation based algorithm.
S. Shamsunder, G.B. Giannakis
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Blind Signal Separation and Blind Deconvolution

2001
This chapter introduces basic concepts, criteria, and algorithms for Blind signal separation (BSS) and blind deconvolution and explores relationships between the BSS and blind deconvolution tasks. The chapter considers open issues and challenges within these related fields. BSS is sometimes used interchangeably with independent component analysis (ICA),
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Blind signal separation: statistical principles

Proceedings of the IEEE, 1998
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or "sources" from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals.
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Blind Separation of Cyclostationary Signals

2009
In this paper, we propose a new method for the blind source separation with assuming that the source signals are cyclostationarity. The proposed method exploits the characteristics of cyclostationary signals in the Fraction-of-Time probability framework in order to simultaneously separate all sources without restricting the distribution or the number ...
Nhat Anh Cheviet, Mohamed El Badaoui, Adel Belouchrani, Francois Guillet   +3 more
openaire   +1 more source