بخشی از متن مقاله An approach to fault detection and correction in design of systems using of Turbo codes :
سال انتشار : 2016
تعداد صفحات :26
We present an approach to design of fault tolerant computing systems. In this paper, a technique is employed that enable the combination of several codes, in order to obtain flexibility in the design of error correcting codes. Code combining techniques are very effective, which one of these codes are turbo codes. The Algorithm-based fault tolerance techniques that to detect errors rely on the comparison of parity values computed in two ways, the parallel processing of input parity values produce output parity values comparable with parity values regenerated from the original processed outputs, can apply turbo codes for the redundancy. The goal is to describe new protection techniques that are easily combined with normal data processing methods, leading to more effective fault tolerance. The error detection structures are developed and they not only detected subsystem errors but also corrected errors introduced in the data processing system. Concurrent parity values techniques are very useful in detecting numerical error in the data processing operations, where a single error can propagate to many output errors. This method is a new approach to concurrent error correction in fault-tolerant computing systems. In this paper we present methods for employ turbo codes into systematic forms and evaluation them with class of Convolutional codes, which is based on burst-correcting codes, and bounds on the fault tolerance redundant computations are given. The methods and analysis of the fault tolerance for the data processing systems are presented. A new technique is presented for protecting against both hard and soft errors at the data sample level using the error-detecting properties of turbo codes. The data processing system is surrounded with parallel parity defined by a turbo code. Erroneous behavior is detected by comparing externally the calculated and regenerated parity values.