scholarly journals Direction-of-arrival estimation of multipath signals using independent component analysis and compressive sensing

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181838 ◽  
Author(s):  
Lin Zhao ◽  
Jian Xu ◽  
Jicheng Ding ◽  
Aimeng Liu ◽  
Liang Li
Keyword(s):  
Independent Component Analysis ◽  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Component Analysis ◽  
Independent Component ◽  
Direction Of Arrival Estimation ◽  
Multipath Signals

scholarly journals Underdetermined DOA Estimation via Independent Component Analysis and Time-Frequency Masking

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Peter Jančovič ◽  
Xin Zou ◽  
Münevver Köküer
Keyword(s):  
Independent Component Analysis ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Component Analysis ◽  
Independent Component ◽  
Speech Signals ◽  
Dominant Source ◽  
Time Frequency ◽  
Real Environment

This paper presents an algorithm for the estimation of the direction of arrival (DOA) in underdetermined situations, that is, there is more sources than sensors. The algorithm performs the estimation in an iterative manner, each iteration consists of two-steps: first estimation of the DOA of a dominant source via the Independent Component Analysis and then removal of the detected source from the mixture via time-frequency masking. Experiments, performed using speech signals mixed in real environment when only two microphones are used but three and four sources are present, demonstrate that the proposed algorithm can estimate the DOAs more accurately than two previously used underdetermined DOA algorithms.

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.

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