How blind are we in blind signal processing?

2006 ◽  
Vol 120 (5) ◽  
pp. 3046-3046 ◽  
Author(s):  
Walter Kellermann
Keyword(s):  
Signal Processing ◽  
Blind Signal Processing ◽  
Blind Signal

Application of Blind Signal Processing in Image Watermarking Systems

2020 ◽  
Author(s):  
Chuen-Yau Chen ◽  
Cheng-Yuan Lin ◽  
Yi-Ze Zou ◽  
Hung-Ming Hsiao ◽  
Yen-Ting Chen
Keyword(s):  
Signal Processing ◽  
Image Watermarking ◽  
Blind Signal Processing ◽  
Blind Signal

W1066 Detection of Small Bowel Slow Wave Frequencies with Noninvasive Biomagnetic Recording and Semi-Blind Signal Processing

2009 ◽  
Vol 136 (5) ◽  
pp. A-646
Author(s):  
Jonathan Erickson ◽  
Chike B. Obioha ◽  
Leonard A. Bradshaw ◽  
William O. Richards
Keyword(s):  
Signal Processing ◽  
Small Bowel ◽  
Slow Wave ◽  
Blind Signal Processing ◽  
Blind Signal

Study on the Mechanical Noise Sources Identification Without Source Signal Inputs Based on Blind Signal Processing

2007 ◽  
Author(s):  
Yong Jiang ◽  
Lin He ◽  
Lin-Ke Zhang
Keyword(s):  
Signal Processing ◽  
Blind Separation ◽  
Noise Sources ◽  
Blind Signal Processing ◽  
Sources Identification ◽  
Blind Signal ◽  
Mechanical Noise ◽  
Input Signals ◽  
Tank Experiment ◽  
Source Signal

The mechanical noise sources identification without source signal inputs was mainly studied in this paper with the theory of blind signal processing (BSP). In traditional noise sources identification methods, the preknowledge of noise source input signals and transmission paths was required in advance. In order to overcome this shortage, a blind sources separation/deconvolution model of mechanical noise sources identification was suggested, based on the analysis of the characteristics of vibration and acoustic signals’ production, transmission and mixing. And a natural gradient method of convolutive blind separation (CBS) was carried out based on minimal mutual information (MMI). Accordingly the validity of this method was confirmed by tank experiment.

scholarly journals Lie Group Methods in Blind Signal Processing

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 440 ◽  
Author(s):  
Dariusz Mika ◽  
Jerzy Jozwik
Keyword(s):  
Signal Processing ◽  
Lie Groups ◽  
Lie Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Group Methods

This paper deals with the use of Lie group methods to solve optimization problems in blind signal processing (BSP), including Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). The paper presents the theoretical fundamentals of Lie groups and Lie algebra, the geometry of problems in BSP as well as the basic ideas of optimization techniques based on Lie groups. Optimization algorithms based on the properties of Lie groups are characterized by the fact that during optimization motion, they ensure permanent bonding with a search space. This property is extremely significant in terms of the stability and dynamics of optimization algorithms. The specific geometry of problems such as ICA and ISA along with the search space homogeneity enable the use of optimization techniques based on the properties of the Lie groups O ( n ) and S O ( n ) . An interesting idea is that of optimization motion in one-parameter commutative subalgebras and toral subalgebras that ensure low computational complexity and high-speed algorithms.

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