• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1025-1033
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• 2004

This paper covers the detection of covert direct sequence spread spectrum signal without the PN(Pseudo Noise) code information. Due to its low probability of interception, the difficulty of spectrum surveillance increases. Detection parameters are the signal existence of given bandwidth, the length of spreading sequence used by transmitter, and the identification of spreading code for detected chip length. The triple correlation function(TCF) value which is one of the higher order statistical signal processing techniques can be used to detect spread spectrum signal without a prior knowledge, but, it has weakness that TCF results depend on the spread data sequence in actual application. This paper proposes the new scheme that not only overcomes the weakness but also presents better performance than the traditional TCF scheme. The performance comparison of conventional TCF with proposed technique shows that the triple correlation estimator(TCE) has better detection capability.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.873-876
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• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.