Recently， the leading human pose estimation algorithms are heatmap-based algorithms. Heatmap decoding （i.e. transforming heatmaps to coordinates of human joint points） is a basic step of these algorithms. The existing heatmap decoding algorithms neglect the effect of systematic errors. Therefore， an error compensation based heatmap decoding algorithm was proposed. Firstly， an error compensation factor of the system was estimated during training. Then， the error compensation factor was used to compensate the prediction errors including both systematic error and random error of human joint points in the inference stage. Extensive experiments were carried out on different network architectures， input resolutions， evaluation metrics and datasets. The results show that compared with the existing optimal algorithm， the proposed algorithm achieves significant accuracy gain. Specifically， by using the proposed algorithm， the Average Precision （AP） of the HRNet-W48-256×192 model is improved by 2.86 percentage points on Common Objects in COntext （COCO）dataset， and the Percentage of Correct Keypoints with respect to head （PCKh） of the ResNet-152-256×256 model is improved by 7.8 percentage points on Max Planck Institute for Informatics （MPII）dataset. Besides， unlike the existing algorithms， the proposed algorithm did not need Gaussian smoothing preprocessing and derivation operation， so that it is 2 times faster than the existing optimal algorithm. It can be seen that the proposed algorithm has applicable values to performing fast and accurate human pose estimation.

Aiming at the problems of low detection efficiency and accuracy in the health management process of industrial robot axis， a new Health Index （HI） construction method based on action cycle degradation similarity measurement under the background of mechanical axis operation monitoring big data was proposed， and the robot Remaining Useful Life （RUL） prediction was carried out by combining Long Short-Term Memory （LSTM） network. Firstly， MPdist was used to focus on the similarity features of sub-cycle sequences between different action cycles of mechanical axis， and the deviation distance between normal cycle data and degradation cycle data was calculated， so that the HI was constructed. Then， the LSTM network model was trained by HI set， and the mapping relationship between HI and RUL was established. Finally， the MPdist-LSTM hybrid model was used to automatically calculate the RUL and give early warning in time. The six-axis industrial robot of a company was used to carry the experiments， and about 15 million pieces of data were collected. The monotonicity， robustness and trend of HI and Mean Absolute Error （MAE）， Root Mean Square Error （RMSE）， R-Square （ R^{\mathrm{2}} ）， Error Range （ER）， Early Prediction （EP） and Late Prediction （LP） of RUL were tested. The proposed method were compared with the methods such as Dynamic Time Warping （DTW）， Euclidean Distance （ED）， Time Domain Eigenvalue （TDE） combined with LSTM， MPdist combined with RNN and LSTM. The experimental results show that， compared with other comparison methods， the proposed method has the HI monotonicity and trend higher by at least 0.07 and 0.13 respectively， the higher RUL prediction accuracy， and the smaller ER， which verifies the effectiveness of the proposed method.

Micro-Expression (ME) occurrence is only related to local region of face, with very short time and subtle movement intensity. There are also some unrelated muscle movements in the face during the occurrence of micro-expressions. By using existing global method of micro-expression recognition, the spatio-temporal patterns of these unrelated changes were extracted, thereby reducing the representation capability of feature vectors, and thus affecting the recognition performance. To solve this problem, the local region method was proposed to recognize micro-expression. Firstly, according to the region with the Action Units (AU) related to the micro-expression, seven local regions related to the micro-expression were partitioned by facial key coordinates. Then, the spatio-temporal patterns of these local regions were extracted and connected in series to form feature vectors for micro-expression recognition. The experimental results of leave-one-subject-out cross validation show that the micro-expression recognition accuracy of local region method is 9.878% higher than that of global region method. The analysis of the confusion matrix of each region's recognition result shows that the proposed method makes full use of the structural information of each local region of face, effectively eliminating the influence of unrelated regions of the micro-expression on the recognition performance, and its performance of micro-expression recognition can be significantly improved compared with the global region method.

In view of the characteristics of flicker and jitter in the moving target imaging of the through-wall-radar, the moving target tracking algorithm based on improved Camshift for through-wall-radar imaging was proposed. With respect to the continuous multi-frame images formed by through-wall-radar and the corresponding distribution maps of color probability, the target prediction process was firstly introduced to determine the wave gate for target searching in the image, removing clutter interference outside the wave gate. Then in order to effectively extract target location, by utilizing the distribution map of color probability, the iterative adjusting strategy for the scale of target searching window was designed to adaptively match the target image with changing shape and size. Finally, a contiguous and smoothing trajectory of target moving was obtained by performing α-β filtering on the extracted multi-frame target locations. Experimental results of Multi-Input Multi-Output (MIMO) through-wall-radar show that compared with the traditional Camshift and Meanshift algorithms, the tracking error of the improved algorithm is reduced by 40.99% and 43.09% respectively, achieving a more accurate and smooth moving target trajectory.

Since it is necessary to evaluate and analyze the service performance of cloud computing center to guarantee Quality of Service (QoS) and avoid violation of Service Layer Agreement (SLA), a approximated analysis model based on M/M/n/n+r queue theory was proposed for cloud computing center. By solving this model the probability distribution function of response time and other QoS indicators were acquired, meanwhile the relationship among the number of servers, size of queue buffers, response time, blocking probability and instance service probability were revealed and verified by simulation.The experimental results indicate that improving server service rate is better than increasing the number of servers for improving service performance.

Designated Verifier Signature (DVS) eliminates the publicly verifiable feature of traditional digital signature to prevent a verifier from proving to a third party the fact that a particular digital signature held by him is issued by a signer. Traditionally, security of digital signature schemes relies on the ideal assumption that an adversary has no access to the information of a secret key. However, side-channel attacks show that the physical implementation of a cryptographic algorithm will leak partial secret information to an adversary. Hence it is necessary to design a leakage-resilient designated verifier signature. Okamoto authentication scheme was converted into a designated verifier signature scheme by using the OR proof technique. Given the leakage bound, the security of the presented scheme was proved under the relative leakage model.

With regard to the requirement of Internet of Things (IoT) application development and the data unification-modeling requirement of Measuring and Control System (MCS), the paper designed a MCS structure based on IEEE1451 standard, which had sensors plug-and-play and IPv6 communication functions. The paper realized dynamic description of network data flow from underlying sensor, intelligent measuring and control node to remote monitor and control application by UML modeling, and finished a general XML data-exchanging interface design. The designed MCS was applied in the process industry of ethanol production. The application results show that it realizes accurate and efficient transmission by using XML data exchanging technology based on IEEE1451 standard. Its data exchanging delay is about 0.51 ms, which can satisfy the requirements of openness, crossing platform and network real-time monitoring application.

Concerning the overburdened third party problem of the present digital copyright protection protocol, this paper put forward a simple, efficient, secure copyright protection protocol and model to protect the buyer and seller's interests. Through two mechanisms of content server generating digital watermarking pool and mobile Agent dynamic distributing license, the protocol solved the security problems such as tolerance of conspiracy, middle attacks, hard disk cloning attacks and user treason. And it ensured the data security and integrity in the protocol entities exchange through encryption, authentication, digital signature and one-way permutation function. In addition, it still adopted the mechanism of buyer from arbitration, which enabled protocol more perfect and feasible.

TI TMS320DM642 is a powerful multimedia DSP of TI.Methods transferring large quantities of data between DM642 and PC throught PCI data bus in multithread application were introduced,and a fast and stable way was presented in detail.