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By Sebastian Miron

ISBN-10: 9537619915

ISBN-13: 9789537619916

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In the next section we will see how sparse codes are generated and why they are efficient. 3. Sparse Coding Research on sparse coding is generally conducted almost independently by two group of researchers: signal processing engineers and biophysicists. In this chapter, we will try to make a link between these two realms. In a mathematical sense, sparse coding generally refers to a representation where a small number of components are active. In the biological realm, a sparse code generally refers to a representation where a small number of neurons are active with the majority of neurons being inactive or showing low activity (Graham & Field, 2006).

And B. Friedlander (1993a). Analysis of a signal estimation algorithm for diversely polarized arrays. IEEE Trans. Signal Process. 41(8), 2628–2638. Weiss, A. J and B. Friedlander (1993b). Direction finding for diversely polarized signals using polynomial rooting. IEEE Trans. Signal Processing 41(5), 1893–1905. Wong, K. T. and M. D. Zoltowski (1997). Uni-vector-sensor ESPRIT for multisource azimuth, elevation, and polarization estimation. IEEE Trans. Antennas Propagat. 45(10), 1467– 1474. Zhang, X.

301–304. -C. Ho and A. Nehorai (1996a). Linear independence of steering vectors of an electromagnetic vector sensor. IEEE Trans. Signal Process. 44(12), 3099–3107. -C. Ho and A. Nehorai (1996b). Uniqueness study of measurements obtainable with arrays of electromagnetic vector sensors. IEEE Trans. Signal Process. 44(4), 1036– 1039. 36 Signal Processing Weiss, A. J. and B. Friedlander (1993a). Analysis of a signal estimation algorithm for diversely polarized arrays. IEEE Trans. Signal Process. 41(8), 2628–2638.

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