In image processing, most of the anisotropic diffusion models based on partial differential equation use gradient information to detect image edge. If the image edge is seriously polluted by noise, these methods would not be able to detect image edge, so the edge features cannot be retained. Pulse coupled neural network (PCNN) has the property that similar input neurons can generate pulse at the same time; this property is used to process the noisy image, and we can get an image entropy sequence. The image entropy sequence which will be used as an edge detecting operator is introduced into the diffusion equation, and this will not only reduce the defects produced when the gradient is used as an edge detecting operator so it is easily affected by the noise, but the area image information can also retain more completely. Then, we will use the rule of minimum cross entropy to search for a minimum threshold, which would satisfy the condition that the information difference between noisy image and denoised image is the minimum. The optimal threshold designed will control diffusion intensity reasonably, and the anisotropic diffusion model based on pulse coupled neural network and image entropy (PCNN-IEAD) can be established. Analysis and simulation results show that the proposed model preserves more image information than the classical ones. It removes the image noise and at the same time protects the edge texture details of the image; the proposed model retains the area image information more completely, the performance indexes can also confirm the superiority of the new model. In addition, the operating time of the proposed model is shorter than that of the classical models, therefore, the proposed model may be the ideal one.

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In recent years, the low-order modulation signal cannot meet current demands due to the band tension and high demand of data transfer rate. In this paper, a higher-order modulation signal generator based on software-defined radio idea was designed. The design implements an efficient plan based on DDS technology by combining the TMS320C5509 chip with CPLD, which can send and receive signals and do floating-point data operation by the method of a 16-bit fixed-point DSP calibration. The validity of the proposed method has been verified by CCS simulation of the 64 QAM, 128 QAM and 256 QAM signal constellation graph mapping.

In order to solve the problem of robustness of beamforming algorithm with microphone array channel mismatch, an adaptive dynamic-weighted constrained least square algorithm-based microphone array robustness frequency invariant beamforming algorithm is proposed. In the proposed algorithm, by analyzing the microphone array model, with or without channel mismatch, the disadvantages of the constrained least square frequency invariant beamforming algorithm with channel mismatch are studied. After the probability density functions of the microphones are defined as the robustness factors and added to the constraint least square frequency invariant beamforming algorithm, the robustness is improved to a certain extent, but it is still poor. In order to further improve the robustness of the algorithm, dynamic-weighted coefficients for controlling frequency invariance in the cost function are used to regulate the sidelobe spectrum energy. The fluctuation error is defined as the ratio of the maximum to minimum value of array response formed by the same angle of arrival at different frequencies, within the frequency range of frequency invariant, to compare the proposed algorithm with the constrained least square robustness frequency invariant and minmax robustness broadband beamforming algorithm. Experimental results of the algorithms show that the fluctuation errors of the proposed algorithm are the smallest and its robustness is the best; it can effectively overcome the poor robustness of the beamforming algorithm caused by microphone array channel mismatch, and can be applied to any arbitrary array structure.

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