In order to identify photovoltaic model parameters accurately and reliably, a Modified Backtracking Search Algorithm(MBSA) was proposed. In the algorithm, firstly, some individuals were selected to learn the current population and historical population information at the same time, and the others were made to learn from the best individual in the current population and stay away from the worst solution, so as to maintain the population diversity and improve the convergence speed. Then, the performances of individuals in the population were quantified by the probability. On this basis, the individuals were able to adaptively select different evolution strategies to balance the exploration and exploitation capabilities. Finally, an elite strategy based on chaotic local search was used to further improve the quality of the population. The proposed algorithm was tested on different photovoltaic models such as single diode, double diode, and photovoltaic module. Experimental results show that the convergence speed and parameter identification accuracy of Backtracking Search Algorithm(BSA) are significantly improved by the proposed strategies. Eight advanced algorithms such as Logistic Chaotic JAYA(LCJAYA) algorithm and Multiple Learning BSA(MLBSA) were compared with the proposed algorithm. Experimental results show that the robustness of the proposed algorithm is the best among these algorithms, and the identification accuracy of the proposed algorithm is better than those of JAYA, LCJAYA, Improved JAYA(IJAYA) and Teaching-Learning-Based Optimization(TLBO) algorithms on both single diode and double diode models, and the proposed algorithm outperforms JAYA, LCJAYA, IJAYA and TLBO algorithms on photovoltaic module model in identification accuracy. Under different illumination conditions and at different temperatures, the manufacturer real data on three photovoltaic modules:thin-film, mono-crystalline and multi-crystalline were used for the actual measurement test, and the results predicted by the proposed algorithm were consistent with the actual situations in the test. Simulation results show that the proposed algorithm is accurate and stable on photovoltaic model parameter identification.

To solve the problem of detecting human hand in complex background based on traditional camera, a fast, automatic method was proposed which can accurately detect and track foreground human fingertips by using Kinect camera. This method firstly used a combined vision-based information to roughly extract the hand region, then, by taking advantage of depth information, a bare hand could be successfully segmented without connecting to background. Subsequently, the fingertips of that bare hand could be extracted by using minimum circle and curvature relationship on the hand boundary. Finally, to improve the detecting accuracy, the fingertips were optimized by using Kalman filter. The experimental results show that compared with existing method the algorithm can successfully track the 3D locations of fingertips under multiple hand poses and with much lower error rate.

Concerning the problem of the background interference during the salient object detection, a key salient object detection algorithm was proposed based on filtering integration in this paper. The proposed algorithm integrated the locally guided filtering with the improved DoG (Difference of Gaussia) filtering, and made the salient object more highlighted. Then, the key points set was determined by using the saliency map, and the result of saliency detection was got by adjustment factor, which was more suitable for human visual system. The experimental results show that the proposed algorithm is superior to existing significant detection methods. And it can restrain the background interference effectively, and have higher precision and better recall rate compared with other methods, such as Local Contrast (LC), Spectral Residual (SR), Histogram-based Contrast (HC), Region Contrast (RC) and Frequency-Tuned (FT).

For a kind of linear bi-level programming problems with interval coefficients in the upper level objective, a Genetic Algorithm (GA) was proposed by using a double fitness function evaluation technique, which was characterized by simultaneously obtaining the best optimal solution as well as the worst one in one run of the genetic algorithm. Firstly, individuals were encoded by using the vertices of the constraint region, and a double fitness was constructed by the upper and lower bounds of the upper level objective coefficients. Secondly, fitness functions were used to sort all individuals in populations. According to the order, the feasibility of individual was checked one by one until a feasible individual was found. Finally, the feasible individual was updated in executing algorithm. The simulation results on four computational examples show that the proposed algorithm is feasible and efficient.

This paper proposed a new best viewpoints selection approach that was capable of selecting best viewpoints for 3D models based on a feature points detection process. First, a new saliency measure was defined to compute the saliency of 3D meshes vertices, which assumed that the saliency of a given vertex on a 3D model could be described by its average difference of distances within a local space. Then, the effective feature points were promisingly able to be extracted based on vertices saliency. Finally, a simple selection strategy was adopted to determine the best viewpoints for 3D mesh models. The quality of viewpoints was a combination of the geometirc distribution and the saliency of visible feature points. The experimental results validate the effectiveness of the proposed approach, which can measure viewpoint quality objectively and obtain the best viewpoints of good visual effect.

"Unobtrusiveness" is the essential property of ubiquitous computing. We presented a Security Protocol for Ubiquitous Environments (SPUE), which provided data authentication, and data freshness besides unobtrusiveness. Using both asymmetric-key and symmetric-key algorithms made the protocol more secure and enough for severe resource constraints, limited computation, memory, and communication capacities. Furthermore, with modeling the protocol by Communicating Sequential Processes (CSP) method and checking it with Failures Divergence's Refinement (FDR), it has been verified that security properties can be guaranteed.

A 2-secure fingerprint using concatenated codes was proposed.Its tracing algorithm was described.A new code constructed in reference[4] was used as inner code,and q-ary random integers were used as outer code.This code can trace at least one traitor when users increase but the length of codes doesn’t increase.

The approach to the freeform surface self-organizing reconstruction for the dense 3D scattered data was discussed.Based on the self-organizing feature map neural network,a rectangle mesh reconstruction approach and the training algorithm were developed.The inherent topologic relations between the scattered points on the surface were learned by the self-organizing feature map neural network.The weight vectors of the neurons on the output layer of the neural network were used to approximate the scattered data points.By this approach,not only to approximate the scattered data points and the surface which is reconstructed by this method can be as base surface for further process,but also the experiment indicates that by this approach,the reconstruction of the surface and the reduce of the dense scattered data points are combined into the same process.The computer simulation result shows that this method is effective.

To overcome the shortcomings of current methods in chromatic scan map vectorization, an interactive vectorization method was proposed. It used the color distance and line width as characteristics, and adopted the strategies such as fuzzy point selection, adjustable tracking direction and changeable tracking mode. Experiment results show that it can vectorized the chromatic scan map rapidly and interactively.