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With the acceleration of information and the rapid development of economy, the management of various industries in the society will tend to be intelligent, information and standardization. There are various new technologies speed up the pace of security management. In this paper, we adopted sensing layer data acquisition technology of Internet of things based on RFID to design an intelligent security patrolling system of the police robots. Meanwhile, we put forward RFID core technology and optimized cryptography technology, which are used to solve the reliability and integrity verification requirements of data acquisition by nodes of the sensing layer of Internet of things.

Detection of moving objects is a kind of segmentation techniques based on regional characteristic such as color, gray, texture, which is the key technology in analyzing and processing of video image. A real-time motion detection method based on improved Gaussian Mixture Model is presented in this paper which is optimized and structure adjusted from Gaussian Mixture Model. Gaussian Mixture Model has been widely used in complex background scene modeling, especially in some occasions with small repetitive motion, such as shaking of the leaves, a rotating fan, bushes, the sea waves, rain, snow, etc.

The processing efficiency of Intelligent Transportation System has become an issue which is attracting more and more attention in order to combat vehicle crimes. The vehicle color recognition plays an important role in identifying, searching, improving and enhancing vehicle Intelligent Transportation System. However, area identification or location and surface high light detection are needed before vehicle color recognition. Convolution neural network training algorithm is taken as the best choice to conduct vehicle color recognition for its parameters are less and computation speed is faster. Neural network algorithm is of high parallelism, and its algorithm can even be further optimized if CUDA acceleration is selected. This research results have reference value for improving the processing efficiency of Intelligent Transportation System.

Vehicle recognition is the important content of intelligent transportation system, there have been many researches on vehicle recognition, and the technology of vehicle recognition based on CPU and DSP cannot meet the needs of the present. This article is about the study of Vehicle recognition and how to realize the GPU algorithm on the CUDA transplantation, make the algorithm parallel, thus speeding up the computation efficiency of vehicle recognition. This thesis is based on the Jeston TK1 development board as the experimental object, achieving high efficiency of GPU image processing.

High Performance computing (HPC) are becoming not only more complex but also challenging in terms of speedup and scalability. As the size of compute intensive problems increases, Intel MIC architecture comes true. In this paper, PCG method based on Intel MIC architecture is employed to solve large scale linear equations. The numerical results show that PCG method based on Intel MIC architecture has a considerable speedup and scalability.