This paper proposes a novel wavelet de-noising scheme regarding the existing burst noises that consist of background and impulsive noises in power-line communications. The proposed de-noising scheme employs multi-level threshold functions to efficiently and adaptively reduce the given burst noises. The experiment results show that the proposed de-noising scheme significantly outperformed the conventional schemes.

In this paper, performance of chaos and burst noises injected to the Hopfield Neural Network for quadratic assignment problems is investigated. For the evaluation of the noises, two methods to appreciate finding a lot of nearly optimal solutions are proposed. By computer simulations, it is confirmed that the burst noise generated by the Gilbert model with a laminar part and a burst part achieved the good performance as the intermittency chaos noise near the three-periodic window.

In this study, a modeling method of the intermittency chaos using the Markov chain is proposed. The performances of the intermittency chaos and the Markov chain model are investigated when they are injected to the Hopfield Neural Network for a quadratic assignment problem or an associative memory. Computer simulated results show that the proposed modeling is good enough to gain similar performance of the intermittency chaos.

This paper presents improvement of data error rate against burst noise by using both chip interleaving and hard limiter in direct sequence spread spectrum (DS/SS) communication systems. Chip interleaving, which is a unique method of DS/SS systems, is effective when burst noise power is small. However, when the burst noise power is large, date error rate is degraded. While, though hard limiter suppresses burst noise power, it gives little effectiveness when the burst noise length is long. Using chip interleaving and hard limiter together, as they work complementary, stable and considerable improvement of data error rate is achieved.