JACIII Vol.11 No.10 pp. 1197-1203
doi: 10.20965/jaciii.2007.p1197


Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

Tuan A. Duong, Margaret A. Ryan, and Vu A. Duong

Jet Propulsion Laboratory/California Institute of Technology, 4800 Oak Grove Dr. Pasadena, CA 91109

October 27, 2006
September 5, 2007
December 20, 2007
space invariant independent component analysis, ENose, chemical detection

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

Cite this article as:
T. Duong, M. Ryan, and V. Duong, “Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.10, pp. 1197-1203, 2007.
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