In order to improve the accuracy of crowd detection in crowded scenes， a dense crowd detection algorithm based on improved Faster Region-based Convolutional Neural Network （Faster R-CNN） was proposed. Firstly， the spatial and channel attention mechanisms were added to feature extraction stage and Strong-Bidirectional Feature Pyramid Network（S-BiFPN） was used to replace the multi-scale Feature Pyramid Network （FPN） in the original network， so that the network was able to autonomously learn important features and the extraction of deep image features was strengthened. Secondly， Multi-Instance Prediction （MIP） algorithm was introduced to predict instances， thus avoiding the model’s missed detection of targets in crowded scenes. Finally， Non-Maximum Suppression （NMS） in the model was optimized， and an additional Intersection over Union （IoU） threshold was added to accurately suppress the interference items of the detection results. Experimental results on the open source dense crowd detection dataset show that compared with the original Faster R-CNN algorithm， the proposed algorithm has the Average Precision （AP） increased by 5.6%， and Jaccard index value increased by 3.2%. The proposed algorithm has high detection precision and stability， which can meet the needs of crowd detection in dense scenes.

Instant messaging is fundamental to various mobile Internet applications; however, it is still an open problem to implement secure instant messaging in untrusted Internet environment. An approach for secure instant messaging of mobile intelligent terminal was presented, and a protocol for Trusted Session Key Agreement (TSKA) was designed and implemented. Theoretical analysis shows that the proposed TSKA can ensure the authenticity, freshness and confidentiality of the negotiated session key, even in the condition that both of the instant messaging server and the communication channel are not trusted. After TSKA, instant audio/video messages can be sent to the other side in a confidential and complete way. Experimental results in real Internet environment show that the proposed approach is efficient and secure, the session key can be negotiated within 1-2 seconds, and attackers cannot obtain any plaintext of instant messages.

To increase the difficulty of software reverse analysis and improve software security, a software protection method based on monitoring attack threats was proposed. By deploying the threat-monitoring net, a variety of threats in software execution process could be real-time detected and resolved, so that the software is in a relatively safe environment and difficult to be reversely analyzed. There are three main research aspects in this protection scheme:1) Attack threat description. The potential attack threats were analyzed and then they were described with a triple . 2) Deployment of threat-monitoring net. The node base was constructed after analyzing the feature of each threat and designing the corresponding detection methods. The reasonable deployment scheme based on characteristics of nodes was selected, and these nodes were deployed effectively into different places of software. 3) Prototype system implementation and experimental design. According to the idea of this protection scheme, a prototype system was implemented, and a group of test cases was protected with the system to collect the experimental data. The evaluation on the aspects of performance consumption and security was made. The final result shows the proposed method is feasible and effective to protect software.

Ray tracing is a technique for image synthesis: creating a photorealistic picture of a 3-D world. But the technique tends to be a time-consuming process, and how to decrease the expensive computational cost has become a hotspot. Considering the factors of task partition and load balance, with the two levels of task partition method, a new dynamically self-adaptive distributed ray tracing algorithm was developed to enhance the efficiency of ray tracing based on Physically Based Ray Tracing (PBRT), which is a famous ray tracing framework from Stanford University. When using CPU cores to 80 in experiment, the corresponding speedup of the proposed algorithm versus the original PRBT algorithm reached to a perfect value, which is close to theoretic linear speedup. It shows that the proposed algorithm is effective and highly scalable with respect to PBRT. The proposed method can be applied to ray tracing process effectively to accelerate the efficiency of rendering.

Concerning the problems of low recognition efficiency and high misreading rate on the occasions of many tags which move fast, an anti-collision algorithm based on packet-first then handling-second was proposed. The algorithm reduced the misreading rate by to grouping the tags according to the order of arriving, it could adaptively adjust frame length based on slot situation to improve the search efficiency, and use jumping dynamic searching algorithm to deal with conflict slots, which could reduce the number of search for readers and the transmission of system. Matlab simulation results show that the algorithm’s communication complexity is lower than other commonly used algorithms, and throughput can achieve 0.59~0.6. The larger the number of tags is the more obvious superiority of the algorithm is.

High performance analysis is a major topic in science computation and large-scale software system.Using a better data sampling method will gain better performance analysis.Traditional performance data are sampled by operation system based on rough granularity source code level,such as process time,memory usage percentage,etc.These data cannot show up hypostasis of software and provide a better effective proposition for optimization.This paper introduced a much more precise sampling method using CPU hardware performance counters(CHPC) to provide CPU data like instruction cycles,cache misses,branch prediction,and so on,and given detail scene of software status.With CHPC data,more accuracy software performance analysis and compiler optimization can be reached.