In order to achieve the stable and precise control of industrial processes with non-linearity， hysteresis， and strong coupling， a new control method based on Local Policy Interaction Exploration-based Deep Deterministic Policy Gradient （LPIE-DDPG） was proposed for the continuous control of deep reinforcement learning. Firstly， the Deep Deterministic Policy Gradient （DDPG） algorithm was used as the control strategy to greatly reduce the phenomena of overshoot and oscillation in the control process. At the same time， the control strategy of original controller was used as the local strategy for searching， and interactive exploration was used as the rule for learning， thereby improving the learning efficiency and stability. Finally， a penicillin fermentation process simulation platform was built under the framework of Gym and the experiments were carried out. Simulation results show that， compared with DDPG， the proposed LPIE-DDPG improves the convergence efficiency by 27.3%； compared with Proportion-Integration-Differentiation （PID）， the proposed LPIE-DDPG has fewer overshoot and oscillation phenomena on temperature control effect， and has the penicillin concentration increased by 3.8% in yield. In conclusion， the proposed method can effectively improve the training efficiency and improve the stability of industrial process control.

In massive Multiple-Input Multiple-Output （MIMO） systems， Minimum Mean Square Error （MMSE） detection algorithm has the problems of poor adaptability， high computational complexity and low efficiency on the reconfigurable array structure. Based on the reconfigurable array processor developed by the project team， a parallel mapping method based on MMSE algorithm was proposed. Firstly， a pipeline acceleration scheme which could be highly parallel in time and space was designed based on the relatively simple data dependency of Gram matrix calculation. Secondly， according to the relatively independent characteristic of Gram matrix calculation and matched filter calculation module in MMSE algorithm， a modular parallel mapping scheme was designed. Finally， the mapping scheme was implemented based on Xilinx Virtex-6 development board， and the statistics of its performance were performed. Experimental results show that， the proposed method achieves the acceleration ratio of 2.80， 4.04 and 5.57 in Quadrature Phase Shift Keying （QPSK） uplink with the MIMO scale of \mathrm{128} \times \mathrm{4} ， \mathrm{128} \times \mathrm{8} and \mathrm{128} \times \mathrm{16} ， respectively， and the reconfigurable array processor reduces the resource consumption by 42.6% compared with the dedicated hardware in the \mathrm{128} \times \mathrm{16} massive MIMO system.

Rate-Distortion （R-D） optimization is a crucial technique in video encoders. However， the widely used independent R-D optimization is far from being global optimal. In order to further improve the compression performance of High Efficiency Video Coding （HEVC）， a two-pass encoding algorithm combined with both R-D dependency and R-D characteristic was proposed. Firstly， the current frame was encoded with the original method in HEVC， and the number of bits consumed by the current frame and the R-D model parameters of each Coding Tree Unit （CTU） were obtained. Then， combined with time domain dependent rate distortion optimization， the optimal Lagrange multiplier and quantization parameter for each CTU were determined according to the information including current frame bit budget and R-D model parameters. Finally， the current frame was re-encoded， where each CTU had different optimization goal according to its Lagrange multiplier. Experimental results show that the proposed algorithm achieves significant rate-distortion performance improvement. Specifically， the proposed algorithm saves 3.5% and 3.8% bitrate at the same coding quality， compared with the original HEVC encoder， under the coding configurations of low-delay B and P frames.

Aiming at improving the robustness in pre-processing and extracting features sufficiently for Synthetic Aperture Radar (SAR) images, an automatic target recognition algorithm for SAR images based on Deep Belief Network (DBN) was proposed. Firstly, a non-local means image despeckling algorithm was proposed based on Dual-Tree Complex Wavelet Transformation (DT-CWT); then combined with the estimation of the object azimuth, a robust process on original data was achieved; finally a multi-layer DBN was applied to extract the deeply abstract visual information as features to complete target recognition. The experiments were conducted on three Moving and Stationary Target Acquisition and Recognition (MSTAR) databases. The results show that the algorithm performs efficiently with high accuracy and robustness.

In order to solve the problems that exist in 3D segmentation reconstruction given a series of images from calibrated cameras, a variational method based on probabilistic formulation was proposed. First, through computing most probable surface that gave rise to the images, a 3D surface consistent with these segmentations was built. Then, through fusing joint probabilities, the mean intensity and variance of the extracted object and background were reconstructed. At last, by using a level set framework, the numerical implementation of surface energy equation was carried out. The proposed method can reconstruct complex topologies and cope with noisy data. Compared to carving techniques and stereoscopic segmentation, the experimental results show the effectiveness and robustness of the method with segmentation and reconstruction of arbitrary 3D objects.

Combined feature extraction in Reverse Engineering (RE) is beneficial to improving the whole quality of reconstruction model and reflecting the original design intention, but currently reletated research is not intensive. In order to extract bump feature which belongs to a simple combined feature from point cloud data in reverse engineering, a method based on Attribute Adjacency Graph (AAG) for extraction bump feature was proposed. Firstly, the algorithm based on AAG decomposition was used to recognize stock feature. Then, the parameters of stock feature were extracted and the type of stock feature was distinguished. The experimental results show that the method is effective and direct to extract different types of stock features.

In this paper, a new efficient algorithm for ellipse detection was proposed, which was based on edge grouping, different from standard Hough transform. Firstly, It separated edge boundary into different arcs at the intersections, divided those arcs into two categories: the long and the short and sorted the two categories at non-increasing sequence, then estimated the parameters of the ellipses using least square fitting method with arcs which may belong to the same ellipse; at last testified whether ellipses coming from the front steps are real ones. The method has been tested on synthetic and real-world images containing both complete and incomplete ellipses. The outcome demonstrates that the algorithm is robust, accurate and effective.

Topic crawler is a new and practical application in the field of information retrieval. The main idea is to selectively collect Web pages on a predefined topic and avoid downloading irrelative Web pages in order to find more accurate and useful information for the user. Several key issues of topic crawler were discussed and corresponding new approaches were proposed. Then a topic crawler system was designed and implemented, employing topic sensitive Hyperlink-Induced Topic Search (HITS) to predict the priority of fetched Web pages. The experiments show our system performs well.