Results 151 to 160 of about 4,215 (185)

Underdetermined blind source separation based condition monitoring

2015 International Conference on Science in Information Technology (ICSITech), 2015
A common technique of mechanical vibration measurement requires an operator to use vibrometer by attaching the accelerometer directly to a machine. The technique, however, poses a unsafe operation and involves significant man-power. This paper proposes a non-contact vibration measurement for machines condition monitoring by using acoustic emission ...
Anindita Adikaputri Vinaya, Dhany Arifianto   +1 more
openaire   +1 more source

Underdetermined Blind Source Separation Based on Sparse Component

2009 International Conference on Electronic Computer Technology, 2009
This paper presents a new algorithm to identify matrix knowing only their multiplication . Where is sparse and . The data used for matrix identification are chosen by Least Square method, whose fitting errors are smaller than a given threshold. Then, K-means clustering method is adopted.
Ming-rong Ren, Pu Wang
openaire   +1 more source

Underdetermined Blind Source Separation Using SVM

2007
A novel sparse measure of signal is proposed and the efficient number of sources is estimated by the best confidence limit in this work. The observations are classified by SVM trained through samples which are constructed by direction angle of sources. And columns of the mixing matrix corresponding to clustering centers of each class are obtained based
Yang Zuyuan, Luo Shiguang, Chen Caiyun
openaire   +1 more source

Underdetermined blind source separation in a time-varying environment

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
The problem of estimating n source signals from m measurements that are an unknown mixture of the sources is known as blind source separation. In the underdetermined —less measurements than sources— linear case, the solution process can be conveniently divided in three stages: represent the signals in a sparse domain, find the mixing matrix, and ...
L. Vielva, D. Erdogmus, C. Pantaleon, I. Santamaria, J. Pereda, J. C. Principe   +5 more
openaire   +2 more sources

Source number estimation and separation algorithms of underdetermined blind separation

Science in China Series F: Information Sciences, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, ZuYuan, Tan, BeiHai, Zhou, Guoxu, Zhang, JinLong   +3 more
openaire   +2 more sources

Underdetermined Blind Source Separation Based on Linear Membership Function

2010 International Conference on Biomedical Engineering and Computer Science, 2010
The blind source separation (BSS) using a two-stage sparse representation approach is discussed in this paper. The first challenging task of this approach is how to estimate the unknown mixing matrix precisely, to solve this problem, the algorithm based on linear membership function is proposed. And then, we proposed the optimization algorithm based on
Wen Yang, Jiexin Pu, Fangfang Zhang, Hongyi Zhang, Yongping Lin   +4 more
openaire   +1 more source

Underdetermined Sparse Blind Source Separation by Clustering on Hyperplanes

2009 Second International Symposium on Electronic Commerce and Security, 2009
In the underdetermined blind source separation and sparse component analysis, we get sensor signals. The mixed matrix and source signals aren’t known, where the number of sensor signals less than that of source signals, but we can know source signals are sparse, so we use the information to recover source signals by estimating the mixed matrix.
Beihai Tan, Zhao Min
openaire   +1 more source

Wavelet-based underdetermined Blind Source Separation of speech mixtures

2007 International Conference on Control, Automation and Systems, 2007
Blind source separation (BSS) is an approach to estimate original source signals only by observed signals through multiple sensors without any prior knowledge about their mixture process. The BSS is currently expected to be applied to such a broad field as preprocessing of speech recognition, analysis of bio-signals, etc.
null Takehiro Hamada, null Kazushi Nakano, null Akihiro Ichijo   +2 more
openaire   +1 more source

Underdetermined joint blind source separation based on tensor decomposition

2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016
In this paper, we propose a simple and effective scheme to jointly estimate the mixing matrices from multiple datasets for the underdetermined case, where the number of sources exceeds that of observations in each dataset. Currently available blind source separation (BSS) methods, including joint blind source separation (JBSS) and underdetermined blind
Liang Zou, Z. Jane Wang, Xun Chen, Xiangyang Ji   +3 more
openaire   +1 more source

Underdetermined Blind Source Separation Based on Generalized Gaussian Distribution

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 2006
In this paper, a novel method for separating underlying sources with both sub- and super-Gaussian distributions from the underdetermined mixtures is proposed. The generalized Gaussian distribution (GGD) is used to model simultaneously both sub- and super-Gaussian distributions.
Sanggyun Kim, Chang Yoo
openaire   +1 more source