Advanced Materials Research Vols. 532-533

Abstract: This paper summarized some major methods in image progressing and existent problems. Also we analyzed and summarized the support vector machine pattern recognition which is described as a new direction in pattern recognition in recent years.

Abstract: The advantage and potential applications of cloud storage technology in video surveillance, on the basis of the concept and technology character of the cloud storage, have been discussed in the paper. Some important problems of the cloud storage technology applications in video surveillance have also been analyzed.

Abstract: According to the problem that the mass remote sensing image cannot be stored rapidly in the traditional storage system, the new SSD-based storage technology for mass remote sensing image is proposed and analyzed. The technology consists of image blocking technology, parallel bus storage technology and pipeline storage technology between flash channels, which makes the real-time storage of mass remote sensing image possible. The results of theoretic analysis show that the proposed technology in this paper can improve the storage speed for remote sensing image remarkably.

Abstract: To capture the genomic profiles for histone modification, chromatin immunoprecipitation (ChIP) is combined with next generation sequencing, which is called ChIP-seq. However, enriched regions generated from the ChIP-seq data are only evaluated on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework, which integrates multiple knowledge sources such as biological literature, Gene Ontology, and microarray data. In order to precisely analyze ChIP-seq data for histone modification, knowledge integration is based on a unified probabilistic model. The model is employed to re-rank the enriched regions generated from peak finding algorithms. Through filtering the reranked enriched regions using some predefined threshold, more reliable and precise results could be generated. The combination of the multiple knowledge sources with the peaking finding algorithm produces a new paradigm for ChIP-seq data analysis.

Abstract: The program for vibration data processing is developed based on MATLAB software. Different methods are used to extract the important information from the random, sinusoidal vibration and shock test. Some main codes in the program are listed in the paper, and the examples for the random and sinusoidal vibration data processing are also shown. Results indicated that the program can help the analyzer to exactly catch the test information and automatically generate the report document.

Abstract: This article applies the BP neural network theory into the measuring of harmonics, through the model automatically learn training capacity to create real-time monitoring model of harmonic. And that I am using matlab simulation, to obtained harmonic simulation data. Through this simulation technology to monitor the harmonic and obtain real-time data, the data can provide reference for harmonic suppression.

Abstract: This paper proposes a novel method for image de-noising, the algorithm is improved the data-adaptive kernel regression method. The process of each pixel is: first determine whether the pixel is on boundary, for the pixels on the edge to establish the kernel which shape is adaptive with the boundary, and then use iterative process for de-noising. For non-boundary pixels, use the data-adaptive iterative kernel regression method. Experiments have shown promising results in image de-noising; the algorithm is able to filter out the high-frequency noise of image while it retains the details of the image characteristics.

Abstract: Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum particle swarm optimization algorithm can improve the efficiency of the distributed database query, and is an effective way to solve the optimization of distributed database query.

Abstract: In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

Abstract: Independent component analysis is an efficient way to solve blind source separation, which has been broadly used in many fields, such as speech recognition, image processing, wireless communication system, biomedical signal processing etc. Independent component analysis for the traditional ways to solve the blind source separation problem only considers the non-Gaussian signal, without taking into account the time structure of the signal information. Proposed based on generalized self-related and non-Gaussian source separation method, the full account of the non-Gaussian signal and time structure information, to solve the blind source separation problem in the time structure of the signal. Finally, this simulation method is validated, the simulation results show that the method is effective and worthy of promotion.