Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is...Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.展开更多
Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α ...Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.展开更多
It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） re...It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.展开更多
基金Project 60575046 supported by the National Natural Science Foundation of China
文摘Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.
基金supported by the National Natural Science Foundation of China under Grants 61372092"863" Program under Grants 2014AA01A701
文摘Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.
基金the National Natural Science Foundation of China under grants，NUAA research funding
文摘It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.
