It is well known that turbo decoding always begins from the first component decoder and supposes that the apriori information is '0' at the first iterative decoding. To alternatively start decoding at two comp...It is well known that turbo decoding always begins from the first component decoder and supposes that the apriori information is '0' at the first iterative decoding. To alternatively start decoding at two component decoders, we can gain two soft output values for the received observation of an input bit. It is obvious that two soft output values comprise more sufficient extrinsic information than only one output value obtained in the conventional scheme since different start points of decoding result in different combinations of the a priori information and the input codewords with different symbol orders due to the permutation of an interleaver. Summarizing two soft output values for erery bit before making hard decisions, we can correct more errors due to their complement. Consequently, turbo codes can achieve better error correcting performance than before in this way. Simulation results show that the performance of turbo codes using the novel proposed decoding scheme can get a growing improvement with the increment of SNR in general compared to the conventional scheme. When the bit error probability is 10-5 , the proposed scheme can achieve 0.5 dB asymptotic coding gain or so under the given simulation conditions.展开更多
Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated t...Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated that this feature itself is a very good mechanism for image filtering when the image is damaged with pep and salt (PAS) type noise. An adaptive filtering method, in which the noisy pixels are first located and then filtered based on the output of the PCNN, is presented. The threshold function of a neuron in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise contaminated image respectively. The filtered image has no distortion for noisy pixels and only less mistiness for non-noisy pixels, compared with the conventional window-based filtering method. Excellent experimental results show great effectiveness and efficiency of the proposed method, especially for heavy-noise contaminated images.展开更多
文摘It is well known that turbo decoding always begins from the first component decoder and supposes that the apriori information is '0' at the first iterative decoding. To alternatively start decoding at two component decoders, we can gain two soft output values for the received observation of an input bit. It is obvious that two soft output values comprise more sufficient extrinsic information than only one output value obtained in the conventional scheme since different start points of decoding result in different combinations of the a priori information and the input codewords with different symbol orders due to the permutation of an interleaver. Summarizing two soft output values for erery bit before making hard decisions, we can correct more errors due to their complement. Consequently, turbo codes can achieve better error correcting performance than before in this way. Simulation results show that the performance of turbo codes using the novel proposed decoding scheme can get a growing improvement with the increment of SNR in general compared to the conventional scheme. When the bit error probability is 10-5 , the proposed scheme can achieve 0.5 dB asymptotic coding gain or so under the given simulation conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.60371044,60071026)the National Visiting Scholar Fund
文摘Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated that this feature itself is a very good mechanism for image filtering when the image is damaged with pep and salt (PAS) type noise. An adaptive filtering method, in which the noisy pixels are first located and then filtered based on the output of the PCNN, is presented. The threshold function of a neuron in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise contaminated image respectively. The filtered image has no distortion for noisy pixels and only less mistiness for non-noisy pixels, compared with the conventional window-based filtering method. Excellent experimental results show great effectiveness and efficiency of the proposed method, especially for heavy-noise contaminated images.
