A weakly supervised action localization method， which integrated snippet contrastive learning， was proposed to address the issue of misclassification of snippets at action boundaries in existing attention-based methods. First， an attention mechanism with three branches was introduced to measure the possibility of each video frame being an action instance， context， or background. Second， the Class Activation Sequences （CAS） corresponding to each branch were constructed based on the obtained attention values. Then， positive and negative sample pairs were generated using a snippet mining algorithm. Finally， the network was guided through snippet contrastive learning to correctly classify hard snippets. Experimental results indicated that at an Intersection over Union （IoU） of 0.5， the mean Average Precisions （mAP） of the proposed method on THUMOS14 and ActivityNet1.3 datasets are 33.9% and 40.1% respectively， with improvements of 1.1 and 2.9 percentage points compared to the DGCNN （Dynamic Graph modeling for weakly-supervised temporal action localization Convolutional Neural Network） weakly supervised action localization model， validating the effectiveness of the proposed method.

Both the S-box （multiple outputs） in block ciphers and the feedback function in stream ciphers require special Boolean functions to ensure the security of the cipher algorithm. To solve the problems of excessive resource consumption of reconfigurable hardware operation units and low clock frequency caused by Non-Linear Boolean Function （NLBF） in the existing algorithms of stream cipher， a high-efficiency AIC（And-Inverter Cone）-based design scheme for NLBF reconfigurable operation units was proposed， namely RA-NLBF. Based on the theories of cryptography， after analyzing the NLBF characteristics of various stream cipher algorithms and extracting the function features of NLBF including the times of AND terms， the number of AND terms， and the number of input ports， an NLBF simplification method based on the dual-logic hybrid form of “Mixed Polarity Reed-Muller （MPRM）” and “Traditional Boolean function （TB）” was proposed， which reduced the number of NLBF AND terms by 29% and formed an NLBF expression suitable for the AIC. Based on the simplified expression characteristics， such as the distribution of the number of AND terms and the times of AND terms， reconfigurable AIC units and interconnection networks were designed to form the reconfigurable units that can satisfy the NLBF operation in the existing public stream cipher algorithms. The proposed RA-NLBF was verified by logic synthesis based on CMOS 180 nm technology， and the results show that the area of RA-NLBF is 12 949.67 μm2， and the clock frequency reaches 505 MHz， which is a 59.7% reduction in area and a 37.3% increase in clock frequency compared with Reconfigurable Logic Unit for Sequence Cryptographic （RSCLU）， an existing method with the same function.

Concerning the sensor data based activity recognition problem， deep Convolutional Neural Network （CNN） was used to perform activity recognition on public OPPORTUNITY sensor dataset， and an improved Progressive Neural Architecture Search （PNAS） algorithm was proposed. Firstly， in the process of neural network model design， without manual selection of suitable topology， PNAS algorithm was used to design the optimal topology in order to maximize the F1 score. Secondly， a Sequential Model-Based Optimization （SMBO） strategy was used， in which the structure space was searched in the order of low complexity to high complexity， while a surrogate function was learned to guide the search of the structure space. Finally， the top 20 models with the best performance in the search process were fully trained on OPPORTUNIT dataset， and the best performing model was selected as the optimal architecture searched. The F1 score of the optimal architecture searched in this way reaches 93.08% on OPPORTUNITY dataset， which is increased by 1.34% and 1.73% respectively compared with those of the optimal architecture searched by evolutionary algorithm and DeepConvlSTM， which indicates that the proposed method can improve previously manually-designed architectures and is feasible and effective.

The high communication complexity of Practical Byzantine Fault Tolerance （PBFT） consensus protocol will lead to low consensus efficiency， the failure or the existing of Byzantine behavior of the single primary node will lead to the stop of consensus process. In order to solve these problems， an Improved Multi-primary-node Practical Byzantine Fault Tolerance （IMPBFT） consensus mechanism was proposed. Firstly， the number of effective consensus rounds of nodes was calculated by the number of consensus rounds of nodes， the number of consensus rounds with Byzantine behavior and the priority values assigned to the nodes， and several primary nodes were selected according to the size of effective consensus rounds. Then， the original consensus mechanism was improved to make all nodes use the improved consensus mechanism for consensus. Finally， pipeline was introduced to implement the concurrent execution of IMPBFT consensus. In the pipeline operation， multi-stage messages of different rounds’ consensus were signed together， and no fixed cycle was used to control the pipeline. Theoretical research and experimental results show that， the multi-primary-node structure of IMPBFT is more secure and stable than the consensus structure of single primary node. Compared with PBFT and Credit-Delegated Byzantine Fault Tolerance （CDBFT） consensus with square level traffic， the proposed IMPBFT reduces the traffic to linear level. The IMPBFT has better performance than PBFT and CDBFT in terms of transaction throughput， scalability and transaction delay. The IMPBFT using the “multi-stage messages signed together with no fixed cycle” pipeline has improved the transaction throughput by 75.2% compared with the IMPBFT without pipeline.

To reduce the computation of Gauss mixture model effectively and improve the accuracy of shadow elimination in moving object detection, an algorithm which updated the model selectively and eliminated the shadow by the change of brightness was proposed. Firstly, the weight of the Gauss distribution and the rate of those that did not belong to the background were compared before updating the Gauss distribution, if the former was larger, then did not update it, otherwise, updated it; Secondly, the range of brightness change was chosen to be a threshold factor of shadow detection, so that the threshold could be adjusted adaptively according to the change of brightness. Finally, compared this algorithm with the traditional ones through experiments on indoor and outdoor videos, the experimental results show that the time consumption of the algorithm is about one-third of the traditional ones, the accuracy of shadow eliminating is improved and the efficiency of the algorithm is confirmed.

Wireless channel of monitoring the outdoor environment is very complex, and gets impacted by many factors such as multipath fading and noise, which deteriorates awfully the quality of signal reception. Further research on fading channel is helpful for receiving better signal and improving system performance. The factors of impacting fading channel were analyzed in detail, and the transmitting performance with fading channel was researched. The impact of fading channel on the probability of system detection was simulated, and the multi-bit local decisions in decision fusion with fading channel were researched. The simulation results suggest that the probability of system detection is lower compared to non-fading channel, and the sensor transmitting a bit decision is optimal.

Since the current contour correspondence algorithms are apt to cause erroneous correspondence relationship and their calculation efficiency is low, a method based on the circumcircles of triangles was proposed in this paper. Each contour in every cross-section was triangulated first, and then the circumcircles of triangles that had been legalized were extracted. By investigating the correspondence relationship among circumcircles, the correspondence relationship among contours, which lay on adjacent cross-sections, can be determined. The experimental results show that the method proposed in this paper can robustly and rapidly process objects of complicated shapes.

In the case that any prior knowledge was unknown, a new fusion algorithm was proposed in order to deal with the defect of the existing algorithm. In the algorithm, for better fusion accuracy, the mean and entropy based on multi-sensor support degree were used to excavate the redundant information sufficiently, and then the weight coefficient of sensor could be determined. In addition, a limited memory fusion was used to avoid data saturation when the old measurement information was increasing, and ensured delicacy degree when the measurement was changing. To verify the effectiveness of this algorithm, three algorithms were used to detect the sample data. The simulation result shows the weight distribution gained through the new algorithm is more effective, and the accuracy of fusion can be further improved.

Based on Shao et al’s ID-based proxy re-signature, an ID-based bidirectional threshold proxy re-signature scheme in the standard model is presented in this paper. Our scheme eliminates the cost of restoring and managing certificates, and solves the difficult problem of excessive rights of the proxy in the proxy re-signature shceme. The scheme can tolerate t

Information of optical flow and epipolar constraint were used to get the initial segmentation region of motion object. The value of motion speed and the angle value of optical flow were extracted and feature vector was constructed using these two features. K-means cluster algorithm was used to get the region of motion object and level set was used to get information of image spatial segmentation. Minimization of the energy function led to the optimal segmentation of moving object by curve evolution. Evolution curve stopped at the position that spatial gradient was great and the close curve of moving object was produced. The experimental results show that the method can detect the whole motion object from image sequence.

One query expansion method, which was based on concept graph, was proposed to solve the low recall and precision rates in the traditional information retrieval methods based on matching keywords. On one hand, words and phrases, which are retrieved by users, can be expanded based on HowNet .Meanwhile, user queries and documents will be transformed into concept graphs. On the other hand, partial matching and semantic similarity based on concept graphs is adopted to acquire similarity between concept graphs, which will optimize the whole retrieval process. This method is proved to be more effective by experiment.

The Certificateless Signature (CLS) scheme simplifies the need of certificates in the Public Key Infrastructure (PKI), thus the CLS scheme is vulnerable to key replacement attacks. The schemes proposed by Ming-Wang, Li-Cheng-Sun and Cao-Paterson-Kou were analyzed. It is shown that these schemes are insecure because of an adversary who replaces the public key of a signer can forge valid signatures. Then the schemes were improved by binding technology, which satisfied a definition of Rafael-Ricardo’s certificateless general mode. Finally, it was shown that the identity-based cryptosystem signature scheme cannot be applied to CLS directly when a CLS scheme is designed.

With the development of the Epaper technology, mobilereader becomes a necessity, and the most important thing of it is format-parser. There are so many kinds of format, that have respective characteristics and many versions, so that designing a system contained multiformat parsers is the pivotal technology. A system scheme containing multiformat parsers was proposed. It could solve the pivotal problems: how to make multi-parsers compatible, realize the expansibility and the platform independence, read big pages on small screens, and improve the speed of display. This system that has been validated on the platform of a company was proved feasible.

To assist the signature specialists, the authentication system for offline Chinese signatures was developed based on a new Integrated Neural Network (INN), which was composed of the modules, namely Feature Distributed Network (FDN), Neutral Networks Classifier (NNC) and Decision Fusional Network (DFN). In the method, a specific classifier was created for each of the signatures，and both training sets were constructed to solve the verification and recognition problems. The proposed method yields satisfactory results on signature verification experiments, and can be used to support human experts to authenticate handwriting signature.