%0 Journal Article %A Anatoli Torokhti and Stan Miklavcic %D 2008 %J International Journal of Mathematical and Computational Sciences %B World Academy of Science, Engineering and Technology %I Open Science Index 23, 2008 %T Compression and Filtering of Random Signals under Constraint of Variable Memory %U https://publications.waset.org/pdf/2811 %V 23 %X 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. %P 771 - 776