ElectroMyoGraphy (EMG) signal decomposition is the inverse process of the generation of EMG signals. The complete EMG decomposition was completed based on the superposition waveforms resolution in order to obtain information about Motor Unit (MU) template waveform and firing pattern. Firstly, noise was removed from the original EMG signals based on wavelet filtering and wavelet threshold estimation; then all the Motor Unit Action Potential (MUAP) waveforms were detected using amplitude-slope double threshold filtering, and all the detected MUAPs were classified into their constituent Motor Unit Action Potential Trains (MUAPT) through fuzzy K-means clustering and minimum distance classifier. Finally the superposition waveforms resolution procedure was finished using pseudo-correlation technique and peeling-off technique. This decomposition system has been evaluated using synthetic and real EMG signals. The average accuracy of the EMG decomposition system was above 87%.

Outsourcing data owner’s data to Database Services Provider (DSP) securely provides XML database service for companies and organizations, which is an important data service form in cloud computing. This paper proposed an improved polyphonic splitting scheme for XML database service(IPSS-XML). IPSS-XML overcame the drawback of low verifying efficiency in other existing schemes by adding an Assistant Verifying Data (AVD) to each non-leaf node at low cost. The improvement enhances query executing efficiency without breaking the confidentiality constraints.

When the tracked targets get seriously obscured, temporarily leave the tracking screen or have significant displacement variation, adjoining interval updating algorithm based on on-line Boosting will lead to the error accumulation thus producing the drift or even tracking failure. Therefore, a reformative target tracking method based on on-line Boosting was proposed. The classifier feature library was updated by using on-line Boosting algorithm, and the threshold was dynamically renewed by using Kalman filter, hence the system could automatically capture the local features and apply corresponding adjustment to the value of threshold according to the tracking confidence of the object. When the confidence of the moving target was less than the lower threshold value, Blob tracking methodology would be applied. It processed as follows: the target was segmented into many regions according to the similarity of both color and space, and each single region contained the information of region number, location and size. One of the regions would be randomly selected into an on-line Boosting tracking module for testing, and the switch to the adjacent region by applying update algorithm for tracking would not happen unless the captured confidence level reached the upper threshold. Results of tests on different video sequences show that the proposed algorithm is capable of speedily and accurately capturing the target object real-time and holding a better robustness in comparison of the traditional on-line Boosting algorithm and other tracking algorithms.

Ray tracing algorithm needs a great deal of computation time. To improve its efficiency, a parallel ray tracing model, based on the image theory and the framework of .NET Remoting, was proposed in this paper. It scheduled and started the remote computing object, and assigned masks dynamically through computing the completion description with the sub-tree of secondary visual source. The comparison among numeric results indicates that the parallel model considerably reduces the computation time without losing precision. It can be applied to urban microcellular environments with arbitrary building layouts and heights.