This letter proposes a multiple symbol differential detection (MSDD) with majority decision method for differentially coded quadrature phase-shift keying (DQPSK) in Rician fading channels. The proposed method shows better BER performance than the conventional MSDD. Simulation results show that the proposed MSDD with a majority decision method improves the system's BER performance for DQPSK signals under the AWGN channel and it approaches asymptotically the theoretical BER performance of coherent detection. Furthermore, the proposed method shows better BER performance under the Rician fading channel with large frequency offsets especially for the range of C/M > 12 dB in comparison with the conventional MSDD.

We have a job with N tandem tasks each of which is executed successively until it is completed. A double modular system of error detection for the processing of each task is adopted. Either type of checkpoints such as compare-checkpoint or compare-and-store-checkpoint can be placed at the end of tasks. Three schemes for the above process of a job are considered and the mean execution time of each scheme is obtained. Three schemes are compared and the best scheme is determined numerically. As an example, a job with 4 tasks is given and 6 types of schemes are compared numerically. Finally, we consider a majority decision system as an error masking system and compute the mean execution time for three schemes.

We have developed Multi-modal Neural Networks (MNN) to improve the accuracy of symbolic sequence pattern classification. The basic structure of the MNN is composed of several sub-classifiers using neural networks and a decision unit. Two types of the MNN are proposed: a primary MNN and a twofold MNN. In the primary MNN, the sub-classifier is composed of a conventional three-layer neural network. The decision unit uses the majority decision to produce the final decisions from the outputs of the sub-classifiers. In the twofold MNN, the sub-classifier is composed of the primary MNN for partial classification. The decision unit uses a three-layer neural network to produce the final decisions. In the latter type of the MNN, since the structure of the primary MNN is folded into the sub-classifier, the basic structure of the MNN is used twice, which is the reason why we call the method twofold MNN. The MNN is validated with two benchmark tests: EPR (English Pronunciation Reasoning) and prediction of protein secondary structure. The reasoning accuracy of EPR is improved from 85.4% by using a three-layer neural network to 87.7% by using the primary MNN. In the prediction of protein secondary structure, the average accuracy is improved from 69.1% of a three-layer neural network to 74.6% by the primary MNN and 75.6% by the twofold MNN. The prediction test is based on a database of 126 non-homologous protein sequences.