Using the sequential regression (SER) algorithm for long-term signal processing. [Intrusion detection]

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The use of the sequential regression (SER) algorithm (Electron. Lett., 14, 118(1978); 13, 446(1977)) for long-term processing applications is limited by two problems that can occur when an SER predictor has more weights than required to predict the input signal. First, computational difficulties related to updating the autocorrelation matrix inverse could arise, since no unique least-squares solution exists. Second, the predictor strives to remove very low-level components in the input, and hence could implement a gain function that is essentially zero over the entire passband. The predictor would then tend to become a no-pass filter which is undesirable in certain ... continued below

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Pages: 4

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Soldan, D. L.; Ahmed, N. & Stearns, S. D. January 1, 1980.

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The use of the sequential regression (SER) algorithm (Electron. Lett., 14, 118(1978); 13, 446(1977)) for long-term processing applications is limited by two problems that can occur when an SER predictor has more weights than required to predict the input signal. First, computational difficulties related to updating the autocorrelation matrix inverse could arise, since no unique least-squares solution exists. Second, the predictor strives to remove very low-level components in the input, and hence could implement a gain function that is essentially zero over the entire passband. The predictor would then tend to become a no-pass filter which is undesirable in certain applications, e.g., intrusion detection (SAND--78-1032). Modifications to the SER algorithm that overcome the above problems are presented, which enable its use for long-term signal processing applications. 3 figures.

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Pages: 4

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Dep. NTIS, PC A02/MF A01.

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  • IEEE international conference on acoustics, speech, and signal processing, Denver, CO, USA, Apr 1980

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  • Report No.: SAND-80-0430C
  • Report No.: CONF-800404-2
  • Grant Number: EY-76-C-04-0789
  • Office of Scientific & Technical Information Report Number: 5677399
  • Archival Resource Key: ark:/67531/metadc1093073

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  • January 1, 1980

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  • Feb. 10, 2018, 10:06 p.m.

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  • March 26, 2018, 1:59 p.m.

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Soldan, D. L.; Ahmed, N. & Stearns, S. D. Using the sequential regression (SER) algorithm for long-term signal processing. [Intrusion detection], article, January 1, 1980; United States. (digital.library.unt.edu/ark:/67531/metadc1093073/: accessed November 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.