Gou Hosoya

The performance of a combination of the irregular low-density parity-check (LDPC) codes and the belief-propagation (BP) decoding algorithm strongly depends on a structure of their parity-check matrixes, and some of the codes exhibit bad performance. In this paper, we propose methods for constructing structured irregular LDPC codes which effectively facilitate for the BP decoding algorithm. We also develop methods for effective updating rules according to the bit node positions for the Shuffled BP decoding algorithm. We also show by simulation results that the decoding performances of the proposed irregular LDPC codes are better than those of the conventional ones.

In this paper, a new pre-trained detection scheme for near-duplicated images is proposed. By using the assumption that the difference vectors between the original image and its near-duplicated image form altered or non-altered spaces independent of the original images. With pre-trained results, we regard features with large values in the difference vectors as the affected features by alteration. To avoid mis-detection, the proposed method codenses similar features together. The proposed method is valid for the original images without pre-training. We show by simulation results that the precision of the proposed method is larger than that of the conventional ones.

We study a modification method for constructing low-density parity-cheek (LDPC) codes for solid burst erasures. our proposed modification method is based on a column permutation technique for a parity-check matrix of the original LDPC codes. It can change the burst erasure correction capabilities without degradation in the performance over random erasure channels. We show by simulation results that the performances of codes permuted by our method are better than that of the original codes, especially with two or more solid burst erasures.

Decoding algorithms based on belief propagation, which iteratively compute a posteriori probability of received symbols, are well-known as decoding methods for Low-Density Parity-Check (LDPC) codes. It is known that decoding algorithms based on belief propargation, such as sum-product algorithm, does not work well when there exist loops of short length in the parity-check matrix. For this problem, several researchers have proposed construction methods of LDPC codes whose parity-check matrices have no loops of length 4. In this paper, we devise a shortening method for cyclic LDPC codes. We show by computer simulations that shortened codes obtained by the devised method have good performances.

Sum-product algorithm, a well-known decoding algorithm of LDPC codes, is based on APP decoding to decode each received symbol. We usually assume that the parameters of channels are known, since a likelihood of channel noise is needed. When we consider applying it to practical channels, it is rarely that the parameters of channels are known. In this paper we propose a decoding algorithm combined with estimating unknown parameters of channels and we show that the bit error probability is as close as that of a decoding algorithm in which the parameters are known.