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Analysis of Optical signal Transmission Characteristics

Analysis of Optical Signal Transmission Characteristics

2011 Fourth International Conference on intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

作者： Zhu Jun Xiang Bingbing Sun Yufa Liu Zhichao key Laboratory of Intelligent Computing & Singnal Processing Ministry of Education Anhui University Hefei 230039 P.R. China

This papa- reports the optical signal transmission characteristic in the G.652 fiber based on the software *** simulation results are in agreement with theoretical analysis. Without taking into account the non-linear effect and in the same condition, the longer the transmission distance, the signal distortion caused by the group velocity dispersion is clearer while the bit rate increasing,. Through reducing the initial chirp of optical signal, the distortion can be reduced at fiber-optic transmission, and the transmission quality is improved.

关键词： Chirp signal transmission Group velocity dispersion Eye diagram

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Zhou, Xionghui Liu, Juan Liu, Changning Rayner, Simon Liang, Fengji Ju, Jingfang Li, Yinghui Chen, Shanguang Xiong, Jianghui School of Computer Science Wuhan University Wuhan China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences Wuhan China Department of Pathology Stony Brook University Medical Center NY United States

ISBN: (纸本)9781457716669

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context). The microarray gene expression profile of brain tumors from 356 patients (The Cancer Genome Atlas dataset) was converted into a CoMi activity pattern, and showed significant positive correlation with the corresponding miRNA expression pattern. In a breast cancer cohort, the differential CoMi activity between good prognosis (longer survival) vs. bad prognosis patients forms a scale-free network, which highlighted a group of important cancer-related microRNAs and GO terms, e.g. hsa-miR-34a and 'cell adhesion'. Then two breast cancer cohorts were used in outcome prediction in an independent test. Using a popular T-test feature selection method and a support vector machine (SVM) classifier with 10-fold cross-validation, the CoMi activity feature achieves an area under curve (AUC) of 0.7155, better than the AUC value of 0.6339 for feature selection based on mRNA expression. In an independent test, CoMi feature selection achieved an AUC of 0.6874. Survival analysis also shows signatures defined by CoMi activity was predictive of survival and superior to mRNAs signatures. In short, we have demonstrated the first interrogation of dynamic remodeling of context specific miRNAs regulation networks in cancer. The altered microRNAs regulation on specific contexts could be used to predi

关键词： Diseases

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Attention driven face recognition: A combination of spatial variant fixations and glance

Attention driven face recognition: A combination of spatial ...

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IEEE International Conference on Automatic Face and Gesture Recognition

作者： Wang, Chongxiu Qing, Laiyun Miao, Jun Fang, Fang Chen, Xilin School of Information Science and Engineering Graduate University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9781424491407

This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face images and find that the observers are highly consistent in the regions fixated. Inspired by the fact that fovea of HVS has a much higher spatial acuity than the periphery, a face recognition algorithm based on spatial variant sampling is proposed to simulate such foveated imaging phenomenon, where more information is reserved for the fixated regions. Moreover, information extracted from glance which adopts the low spatial frequency components of the image is integrated into the face recognition system to elicit a percept that occurs before any fixations. The experimental results on FERET database [2] demonstrate that the proposed method not only reduces the computational cost, but also achieves comparable performance, which shows that the characteristics of the HVS provide valuable insights into face recognition. © 2011 IEEE.

关键词： Face recognition

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Local Regression Model for Automatic Face Sketch Generation

Local Regression Model for Automatic Face Sketch Generation

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International Conference on Image and Graphics (ICIG)

作者： Naye Ji Xiujuan Chai Shiguang Shan Xilin Chen Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo, (2) our synthesized sketches presents more pencil sketch texture.

关键词： Face Training Databases Hair Rendering (computer graphics) Measurement Law enforcement

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An Effective Method to Solve the Induced Surface Current for Arbitrary Conducting Objects at Resonant Frequencies

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电子学报(英文版) 2010年第1期19卷 66-68页

作者： SONG Kaihong WU Xianliang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A novel scheme is developed to compute correctly the induced current based on the Electric field integral equation (EFIE) by the Method of moments (MOM) at resonant frequencies. As a First step, the inaccurate induced current is obtained by solving the EFIE. Then, the scattered magnetic field due to the inaccurate induced current is calculated at any given point on the surface of the object. Finally, the accurate induced current at any given point is determined through the total magnetic field. The proposed approach is applied to the case of the infinitely Perfectly electric conducting (PEC) cylinder and PEC sphere to check its accuracy and efficiency. It is found that the numerical results match the analytical solution or the Combined field integral equation (CFIE) solution.

关键词：感应电流共振频率导体目标表面诱导电场积分方程数值结果总磁场

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Particle swarm optimization for automatic parameters determination of pulse coupled neural network

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Journal of Computers 2011年第8期6卷 1546-1553页

作者： Xinzheng, Xu Shifei, Ding Zhongzhi, Shi Zuopeng, Zhao Hong, Zhu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Pulse coupled neural network (PCNN), a wellknown class of neural networks, has original advantage when applied to image processing because of its biological background. However, when PCNN is used, the main problem is that its parameters aren't self-adapting according to different image which limits the application range of PCNN. Considering that, this paper proposed a new method based on particle swarm optimization (PSO) to determine automatically the parameters of PCNN. In this method, the algorithm of PSO is applied to search automatically optimum in the solution space of PCNN's parameters until finding global optimal solution. Experimental results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is better than the methods of Otsu, manual adjustment of parameters when mutual information is adopted as evaluation criteria. © 2011 ACADEMY PUBLISHER.

关键词： Image segmentation

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Study on the dynamic monitoring of urban expansion of Nanning City using Remote Sensing

Study on the dynamic monitoring of urban expansion of Nannin...

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International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE)

作者： Chao-kui Wu Chang-an Yuan Li-qiong Zhang Juan Lao Guangxi University Nanning China Key Laboratory of Scientific Computing & Intelligent Information processing in Universities of Guangxi Guangxi Teachers Education University Nanning China

Take multi-time-phase Landstat TM/ETM+ Remote Sensing images (1990, 2002, 2007) of Nanning City as data source and use RS and GIS intergration technology to extract the information of city. The paper analyses the prop... 详细信息

ISBN: (纸本)9781424491728

关键词： Cities and towns Remote sensing Monitoring Data mining Geographic Information Systems Economics Satellites

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A rough RBF neural networks optimized by the genetic algorithm

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Advances in Information Sciences and Service Sciences 2011年第7期3卷 332-339页

作者： Ding, Shifei Ma, Gang Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China

The large-scale data parallelism processing is an inherent characteristic of artificial neural networks, but the networks bring the efficiency problems of data processing. As one of the artificial neural networks, Radial Basis Function (RBF) neural networks have the same problem. Therefore, how to reduce the scale of data to improve the efficiency of data processing has been a hot issue among the artificial intelligence scholars. Based on the traditional RBF neural networks, this paper puts forward a method which determines the important degree of the sample attributes based on knowledge entropy of Rough set by analyzing the relationship between the knowledge entropy and the weight of the sample attributes, and assesses the importance of the sample attributes between the input layer and the hidden layer, namely the attribution reduction, so as to achieve reduce the scale of data processing. The ultimate aim of training RBF neural networks is to seek a set of suitable networks parameters which makes the sample output error achieve the minimum or required accuracy, while Genetic Algorithm (GA) has the properties of finding out the optimal solution through multiplepoint random search in the solution space, so Genetic Algorithm is used to optimize the centers, the widths and the weights between the hidden layer and the output layer of RBF neural networks in training the networks. Finally, a model about A Rough RBF Neural Networks Optimized by the Genetic Algorithm (GA-RS-RBF) is proposed in this paper. The simulation results show that the rough RBF neural network optimized by the Genetic Algorithm is better than the traditional RBF neural networks in classification about Iris datasets.

关键词： Genetic algorithms

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DH-TRIE frequent pattern mining on Hadoop using JPA

DH-TRIE frequent pattern mining on Hadoop using JPA

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2011 IEEE International Conference on Granular computing, GrC 2011

作者： Yang, Lai Shi, Zhongzhi Xu, Li D. Liang, Fan Kirsh, Ilan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Dept. of ITDS Old Dominion University Norfolk VA 23529 United States Academic College of Tel-Aviv Yaffo Israel

ISBN: (纸本)9781457703713

The FPgrowth is a famous frequent pattern's algorithm in data mining when working with high-dimensional, large-scale data sets. It is also known as great complexity on memory for the recursively processing. In general, FPgrowth cannot handle large-scale data set unless dividing a whole data set into small blocks. Based on Hadoop, the open cloud computing model, a distributed DH-TRIE frequent pattern algorithm using JPA is proposed, which solved the three problems (globalization, random-write and duration). The algorithm is shown good flexibility and scalability by comparisons to mahout project. By applied to a virtualization platform Vega Cloud, the algorithm will be used in far-ranging situations. © 2011 IEEE.

关键词： Cloud computing

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Study of millimetre-wave generation based on optical frequency multiplication

Study of millimetre-wave generation based on optical frequen...

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IET International Communication Conference on Wireless Mobile and computing (CCWMC 2011)

作者： Yan Ding Jun Zhu Wei-Dong Fang Xiao-Guang Ge Xiang Chen Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui China Shanghai Research Centre for Wireless Communications China School of Communication and Information Engineering Key Laboratory of Specialty Fiber Optics and Optical Access Net works Shanghai University Shanghai China

ISBN: (纸本)9781618399571

To meet the increasing capacity and mobility as well as decrease the costs in next-generation optical access networks, RoF technology is a promising technique in the emerging optical and wireless convergence network, mm-wave generation is a key technique to realize the convergence network. In this paper, existing optical mm-wave generation technologies are introduced, including direct modulation, optical heterodyning and external modulation. Associated with Shanghai University, a scheme based on Optical Frequency Multiplication employing a dual drive Mach-Zehnder Modulator (DD-MZM) is presented. The novel efficient technique does not require expensive high-frequency electrical equipment. Moreover, no optical filtering is used, which significantly reduces the cost.

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