Focusing on the problem of nonlinear time series prediction in the field of analog circuit fault prediction and the problem of error accumulation in traditional Support Vector Regression (SVR) multi-step prediction, a new adaptive SVR prediction algorithm based on phase space reconstruction was proposed. Firstly, the significance of SVR multi-step prediction method for time series trend prediction and the error accumulation problem caused by multi-step prediction were analyzed. Secondly, phase space reconstruction technique was introduced into SVR prediction, the phase space of the time series of the analog circuit state was reconstructed, and then the SVR prediction was carried out. Thirdly, on the basis of the two SVR prediction of the error accumulated sequence generated in the multi-step prediction process, the adaptive correction of the initial prediction error was realized. Finally, the proposed algorithm was simulated and verified. The simulation verification results and experimental results of the health degree prediction of the analog circuit show that the proposed algorithm can effectively reduce the error accumulation caused by multi-step prediction, and significantly improve the accuracy of regression estimation, and better predict the change trend of analog circuit state.

The unexpected power loss or system crash can result in data inconsistency upon updating a persistent data structure. Most existing file systems use some consistency techniques such as write-ahead logging, copy-on-write to avoid this situation. These mechanisms, however, introduce a significant overhead, and fail to adapt to the diversity of directory and heterogeneity of data reliability demands. Existing file-adaptive journaling technique is required to modify legacy applications. Therefore, a directory-adaptive journaling mode selective mechanism for Android systems was proposed to choose different journaling modes with strong or weak consistency guarantees in terms of different directories reliability demands. This mechanism is transparent to developers, and also matches the feature of Android systems, hence, it greatly reduces the consistency guarantee overhead without sacrifice of reliability. The experimental results show that modified file system can identify directories in which a file resides, meanwhile, choose reasonable pre-defined journaling mode.

Concerning the Bit Error Rate (BER) and channel capacity optimization in the data transfer process of Multi-Input Multi-Output (MIMO) system, a multi-user MIMO multi-hop relay system based on cooperative likelihood detection and sphere decoding was proposed. Firstly, it constructed a second-order cooperative MIMO relay system model to analyze the relay transmission process of channel data, as well as path loss and shadow fading. Secondly, the sphere decoding was used to derive the equivalent maximum likelihood rule for log-normal shadow fading detection. Finally, the maximum channel power harmonic mean selection policy was put forward, and an access link with smaller BER was chosen for user based on correlation link metric and maximum channel power threshold value, so as to improve the performance of multi-user MIMO system. Simulation results show that, compared with the multi-user MIMO multi-hop relay system based on mutual information maximization and the relay system based on decoding-forwarding and MIMO orthogonal Space Time Block Code (STBC), the average BER of the proposed system reduced by 27.4% and 32.6% respectively, and the average channel capacity increased by 9.5% and 12.7% respectively. The results demonstrate that the proposed system has a good effect in reducing BER and improving channel capacity.

The particle system can hardly simulate the natural features effectively and real-time at the same time. Based on the analysis of traditional simulation methods, a real-time rendering algorithm was presented. The algorithm used rectangular elementary to simulate snow particles，added texture mapping based on superposition，normalized snow lifecycle for color blending，and regulated the size of snow particle and snow density according to the size of temperature. In the snow-falling stage, this algorithm introduced Level Of Detail (LOD) technology, imitated the movement effect according to the whereabouts of the snow particles moving force characteristics, took the force comprehensive consideration and simplified to reduce the computational complexity and improve the snow simulation authenticity.In the snow-accumulating stage, this method obtained the exposed surface of the scene model and its height field. And then based on a point coordinates and the point height in the field in a plane, it got the position of snow particles steadily to simulate the height change effect. This new method used the whole surface as ground-object emitter and superimposed the corresponding particle texture to improve authenticity.

As the fabric defect type of diversity and traditional artificial detection methods inefficient ,in order to detect the fabric defect more effective, A new approach, SGE, based on saliency region feature for fabric defect detection is studied. In this approach, the original image is divided into two parts, one extracts the saliency region feature of fabric defect by improved FSR roughly, another employing the gabor filter and taking the amplitude as an output characteristics, and extracts the saliency region feature of fabric defect by PSR accurately, then by using maximum entropy to segment the saliency region respectively and fused the sub-images. The result is get got by calculating perimeter and area of the contours to removal the isolated points. The experiment selects four types of typical fabric defect images and OpenCV library is used. The experiment result shows that the algorithm, without prior learning，meet the real-time.