Handwriting identification is a technique of automatic person identification based on the personal handwriting. It is a hot research topic in the field of pattern recognition due to its indispensible role in the biometric individual identification. Although many approaches have emerged, recent research has shown that off-line Chinese handwriting identification remains a challenge problem. In this paper, we propose a novel method for off-line Chinese handwriting identification based on stroke shapes and structures. To extract the features embedded in Chinese handwriting characters, two special structures have been explored according to the trait of Chinese handwriting characters. These two structures are the bounding rectangle and the TBLR quadrilateral. Sixteen features are extracted from the two structures, which are used to compute the unadjusted similarity, and the other four commonly used features are also computed to adjust the similarity adaptively. The final identification is performed on the similarity. Experimental results on the SYSU and HanjaDB1 databases have validated the effectiveness of the proposed method.

To effectively solve the problem of rapid measurement and recognition about large underwater sound source, continuous scanning is applied to measure the large underwater sound source. The theory of sound source recognition based on mobile framework technology (FAH)nd Helmholtz equation least squares method (HELS)s investigated. Combination of acoustic holography method based on MFAH and HELS is created and verified through simulation and basin test. The study shows that combination algorithm can accurately identify all kinds of underwater source and obtain a high positioning accuracy of the noise source, and can be used for a wide frequency range; when there are multiple coherent sound sources in the complex sound field, noise source identification and location only requires that an array holographic measurement surface is 1.3 times for the reconstruction surface. Using a small measuring surface to quickly identify large underwater sound source is achieved. The shortcomings of workload and time-consuming in the traditional measurement are resolved. And it provides convenience for engineering applications.

Plants is an important component of natural scene. Unfortunately, due to high level complexity of the structure of plant, simulating plant becomes extremely a difficult task. When the fractal theory is imported, it provides a broader development space for the plant modeling. With the development of the fractal research, virtual plant has become a hot and interesting research topic in computer graphics area. The virtual plants technology is very important in guiding the crop production, implementing the agriculture informationization and constructing the virtual environment. At present a single virtual plant modeling technology is quite mature, the method to generate a body of plants often uses the even algorithm or the normal algorithm, but a body of plants in the real world is not even, and is not normal also, the cloud model relaxes the precise determination membership function to expectation function with normal distributed membership degree, combines ambiguity and randomness organically to fit the real world objectively. So it has general applicability, producing a body of plants based on the cloud model can simulate plant's condition and the distribution well.

With global non-renewable resources and environmental issues becoming more apparent, the development of new energy vehicles have become the trend of auto industry. Hybrid vehicle becomes the key development of new energy vehicles with its long distance, low pollution, low fuel consumption characteristics and so on. The battery performances directly influence the quality of the whole vehicle performance. Considering the importance of the battery state of charge (SOC) estimation and the nonlinear relationship between the battery SOC and the external characteristic, genetic algorithm (GA) and back propagation (BP) neural network are proposed. Because of the strong global search capability of the genetic algorithm and the generalization ability of BP neural network, the hybrid vehicle Ni-MH power battery GA-BP charging model is designed. In this approach, the network training speed is superior to the traditional BP network. According to the real-time data of the batteries, the optimal solution can be concluded in a short time and with high estimation precision.

In this paper, the passivity analysis of Takagi-Sugeno (T-S) fuzzy neutral system with interval time-varying delay and linear fractional parametric uncertainty is investigated. Based on the Lyapunov-Krasovskii functional and the free weighting matrix method, delay-dependent sufficient conditions for solvability of the passive problem are obtained in terms of Linear matrix inequalities (LMIs). Finally, a simulation example is provided to demonstrate effectiveness and applicability of the theoretical results.

The virtual point-mass method has been widely used in dealing with the approximation of the local gravity field which is a difficult problem in internal currently. In this paper, the approximation theory of point-mass model is briefly introduced, and the characteristics of the elements in the coefficient matrix for the model construction are analyzed by numerical calculation. The observations of gravity anomaly is simulated from EGM2008 with degree and order 720 and the approximated region is 32～34Nand 103～105E. A four-tier point-mass model which is on the base of the geopotential model with degree and order 36 from low frequency to high frequency is applied to approximate the local earth’s gravity field. The results of the experiments show that the truncation error of gravity disturbance created by using the point-mass model is less than 2 mGal on the radial direction and there is an optimal truncation error for some certain spectrum gravity field in the space.

For the problem of tracking multiple targets, the Joint Probabilistic Data Association approach has shown to be very effective in handling clutter and missed detections. However, it tends to coalesce neighboring tracks and ignores the coupling between those tracks. To avoid track coalescence，a K Nearest Neighbor Joint Probabilistic Data Association algorithm is proposed in this paper. Like the Joint Probabilistic Data Association algorithm, the association possibilities of target with every measurement will be computed in the new algorithm, but only the first K measurements whose association probabilities with the target are larger than others’ are used to estimate target’s state. Finally, through Monte Carlo simulations, it is shown that the new algorithm is able to avoid track coalescence and keeps good tracking performance in heavy clutter and missed detections.

There has been large amounts of research on image authentication method. Many of the schemes perform well in verification results; however, most of them are time-consuming in traditional serial manners. And improving the efficiency of authentication process has become one of the challenges in image authentication field today. In the future, it’s a trend that authentication system with the properties of high performance, real-time, flexible and ease for development. In this paper, we present a CUDA-based implementation of an image authentication algorithm with NVIDIA’s Tesla C1060 GPU devices. Comparing with the original implementation on CPU, our CUDA-based implementation works 20x-50x faster with single GPU device. And experiment shows that, by using two GPUs, the performance gains can be further improved around 1.2 times in contras to single GPU.