Concerning the problems that basic Ant Colony Optimization (ACO) is easy to fall into the local optimum, and has too long path and excessive turning angles during path search, an improved ACO algorithm based on turning angle constraint was proposed. Firstly, the initial pheromone concentration of the area between the starting point and the target point was enhanced to avoid the initial blind search. Then, the A ^* algorithm's evaluation function and the turning angle constraint factor were added to the heuristic function. In this way, the node with the shortest path length and least number of turns was able to be selected at the next step. Finally, the distribution principle of wolf pack algorithm was introduced in the pheromone updating part to enhance the influence of high-quality population. At the same time, the Max and Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid the algorithm being trapped into the local optimum. Matlab simulation showed that compared with the traditional ACO, the improved algorithm was able to shorten the planned path length by 13.7%, reduce the number of turns by 64.3% and decrease the accumulated turning angle by 76.7%. Experimental results show that the improved ACO algorithm can effectively solve the global path planning problem and avoid the excessive energy loss of mobile robots.

Gait recognition has the advantages of requiring no contact, non-invasion and easy detection. However, for the cross-view gait recognition, the contour of pedestrians varies with the change of people's viewpoints, thus affecting the performance of gait recognition. Therefore, a capsule network with sharing transformation matrix and its improved dynamic routing algorithm were proposed, which reduce the network training parameters. On this basis, by fusing the view features and using Triplet loss and Margin loss, a cross-view gait recognition model fused with view features was proposed. Experimental result on CASIA-B dataset show that it is feasible to extract gait features using the capsule network with sharing transformation matrix. Under the conditions of normal walking, carrying a bag, and wearing a coat, the proposed model fusing view features has the recognition accuracy improved by 4.13% compared to the cross-view gait recognition method based on convolutional neural network, and has better performance for gait recognition across large views.

For the differences between Virtex-5 RocketIOTM GTP and Virtex-6 RocketIOTM GTX adjustments should be made on pre-emphasis/de-emphasis values and reception equalization values,the receiving terminal voltage and differential voltage transmission values, so as to adapt to the data communication between GTP and GTX. By using ChipsCope Pro_IBERT to measure the actual communication parameters to set GTP/GTX transceiver side, this paper defined the data frame structure through custom communication protocol, and designed a new RocketIO data transceiver interface controller. By analyzing the phenomenon of byte misalignment caused by some uncertainty such as high frequency instability, and clock drift, this paper added a data misalignment correction module in custom communication protocol, thus greatly reducing the error rate of data transmission. The experimental results show that high speed serial communication between Virtex-5 RocketIOTM GTP and Virtex-6 RocketIOTM GTX can be implemented, and the data transceiver interface controller is of data transmission stability, low bit error rata and good generality.

Aiming at the problem that the traditional nonlinear robust filtering will be severely degraded when the distribution of measurement noise deviates from the assumed Gaussian distribution, a new robust nonlinear Kalman filter based on M-estimation and detection method was proposed. The proposed robust filtering algorithm set a threshold using Chi-square test to delete mutation outliers, and modified the measurement update using M-estimation. Several conventional nonlinear filtering methods were evaluated under different measurement noises in terms of accuracy and stability. Under non-Gaussian noise and strong interference, the proposed algorithm outperforms the traditional robust algorithm with higher estimation accuracy by 25.5% and lower estimation covariance by 18.3%. The experimental results show that the proposed filtering algorithm can suppress the influence of non-Gaussian noise and strong interference, and increase the estimation accuracy and stability.

To solve the problems that the digital watermark information are vulnerable to geometric attacks and the balance between invisibility and robustness of watermarking algorithm, a digital watermarking algorithm was proposed based on Scale Invariant Feature Transform (SIFT) for image local feature points. Based on SIFT algorithm local feature points, circular Local Feature Area (LFA) that met certain conditions were found. After the LFA regularization, the watermarking was embedded in intermediate frequency coefficient of LFA Discrete Cosine Transform (DCT) domain. Among them, considering the influence of the watermark information on the image quality, the embedding strength was dynamically adjusted according to the Watson human visual model. The experimental results show that the Peak Signal-to-Noise Ratio (PSNR) and the similarity of watermark get higher. The algorithm guarantees better watermark invisibility and robustness performance under certain geometric attacks.

A new fusion method of multi-focus images based on the four-channel non-separable wavelet was proposed, which aimed to solve the problem which exists in the separable wavelet-based fusion methods. First, a 4×4 non-separable wavelet 4-channel filter bank with linear phase using the theory of non-separable wavelets was constructed. Then images involving the fusion were decomposed by using the filter bank, for low-frequency part, the average value was selected, for the three high-frequency parts of each level, the value of the area window whose energy was bigger was selected. Finally, the new fused image was reconstructed. The performance of the method was evaluated using entropy, average gradient, etc. The experimental results show that it has good effect on the fusion of multi-focus images. The performance is better than that of the separable wavelet fusion method by using the same fusion algorithm. According to this method, the fused images are clearer and the detailed edge information of low-frequency domain is better obtained.