Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31093
Compression and Filtering of Random Signals under Constraint of Variable Memory

Authors: Anatoli Torokhti, Stan Miklavcic


We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that some information about compressed data can be obtained from a solution of the associated problem without constraints of causality and memory. This allows us to consider two separate problem related to compression and decompression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided.

Keywords: stochastic signals, optimization problems in signal processing

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1053


[1] I.T. Jolliffe, Principal Component Analysis, Springer Verlag, New York, 1986.
[2] Y. Hua and M. Nikpour, Computing the reduced rank Wiener filter by IQMD, IEEE Signal Processing Letters, No. 9, Vol. 6, pp. 240-242, 1999.
[3] L. L. Scharf, The SVD and reduced rank signal processing, Signal Processing, vol. 25, 113 - 133, 1991.
[4] Y. Hua and W. Q. Liu, Generalized Karhunen-Lo`eve transform, IEEE Signal Processing Letters, vol. 5, pp. 141-143, 1998.
[5] A. Torokhti and P. Howlett, Computational Methods for Modelling of Nonlinear Systems, Elsevier, 2007.
[6] A. Torokhti, P. Howlett, IEEE Trans. Circuits & Syst., II, Analog & Digit. Signal Processing, 48, 2001.
[7] S. Friedland, A. Niknejad, M. Kaveh, H. Zare, Fast Monte-Carlo low rank approximations for matrices, 10 pp. submited.
[8] T. Zhang, G. Golub, Rank-One Approximation to High Order Tensors, SIAM J. Matrix Anal. Appl., 23, 2001.
[9] P. Common, G.H. Golub, Tracking a few extreme singular values and vectors in signal processing, Proc. IEEE, 78, 1990.
[10] E. D. Sontag, Lecture Notes in Control and Information Sciences, 13 1979.
[11] R. M. De Santis, Causality Theory in Systems Analysis, Proc. of IEEE, 64, pp. 36-44, 1976.
[12] G. H. Golub and C. F. Van Loan, Matrix Computations, Baltimore, MD: Johns Hopkins University Press, 1996.
[13] A. Ben-Israel and T. N. E. Greville, Generalized Inverses: Theory and Applications, John Wiley & Sons, New York, 1974.
[14] V. Gimeno, Obtaining the EEG envelope in real time: a practical method based on homomorphic filtering, Neuropsychobiology, 18, (1987) pp 110- 112.
[15] H. Kim, G.H. Golub, H. Park, Missing value estimation for DNA microarray gene expression data: local least squares imputation, Bioinformatics, 21, 2005.