A Generalized Method of Decreasing Data Redundancy

Abstract

In this paper, a method of decreasing the redundancy of information flow by using recurrent properties of Galois code sequences is proposed. For this purpose, the service information is compiled and the priority compression is identified. The method is based on applying one of the adaptive algorithms (prediction first-order, interpolation zero-order, interpolation first-order) by comparing the efficiency of its use when applied to the selected fragments of a signal. It is shown that the developed method is effective for the quick-change signals when the structure and behavior of a signal change drastically. The efficiency of redundancy decreasing at the different sampling rate and the number of the significant samples is evaluated. This makes it possible to establish the limits of the positive effect for redundancy of information flows for the existing and developed methods. Experimental research is carried out for various permissible deviations with obtaining the number of the significant readings. A comparison of the obtained data with results of applying the existing methods in deep pumping installations proved that the proposed method is in 1.3 times more effective than existing ones.