scholarly journals Independent Component Analysis and Graph Theoretical Analysis in Patients with Narcolepsy

2018 ◽  
Vol 35 (4) ◽  
pp. 743-755 ◽  
Author(s):  
Fulong Xiao ◽  
Chao Lu ◽  
Dianjiang Zhao ◽  
Qihong Zou ◽  
Liyue Xu ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Graph Theoretical Analysis

A Detection Technology for Weak Vibration Signal Based on Independent Component Analysis

2012 ◽  
Vol 226-228 ◽  
pp. 312-315
Author(s):  
Hai Dong Guo ◽  
Shun Ming Li ◽  
Yuan Yuan Zhang ◽  
Xing Xing Wang ◽  
Sai Ma
Keyword(s):  
Theoretical Analysis ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Detection Method ◽  
Vibration Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Independent Source ◽  
Detection Technology ◽  
Source Signals

For weak vibration signal with strong noise, a new kind of weak vibration signal detection method was proposed in this paper. Based on the redundancy reducing capability and the uncertain amplitude of independent component analysis, virtual noise was introduced to extend the dimension of original observed signal after we analyzed the prior features of noises in observed signal. Then extended signals were processed to get the independent source signals by applying to blind source separation (BSS). Thus, the noise embedded in observed signal was removed and characteristics of weak vibration signal were obtained successfully. Through the theoretical analysis and the simulation, the introduced method of this paper was checked to be available and then it was applied to faults analysis of rotor misalignment successfully. Finally, we made a conclusion that this method had great application value for the extraction of weak vibration signal.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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