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Constructing Green System of Engineering Experimental Teaching Based on Virtual Instrument technology

Constructing Green System of Engineering Experimental Teachi...

The 5th International Conference on computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE'10)

作者： Baosheng Yang Hong Li Laboratory of Intelligent Information Processing Suzhou University Suzhou 234000 China Department of computer science and technology Suzhou University Suzhou 234000 China

Constructing green innovation system is the science and technology development strategy which the nation is trying to advocate. Scientific research, teaching research and industrial development must impose green-oriented in university, to meet the national green technology development strategy. To form green teaching atmosphere, constructing the green campus culture and infiltrating it into the teaching activities are the main ways, it is necessary to explore new ideas for the construction of green laboratories. According to the requirements of green engineering experiment, we proposed to create a green laboratory used virtual instrument (VI) technology, and optimized the experimental teaching. VI with high technical content, ease of configuration and maintenance easy to save, effective experimental, can provide a comfortable experiment environment, better for the research and teaching.

关键词： laboratory construction:green experiment virtual instrument:filter

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Effective camera motion analysis approach

Effective camera motion analysis approach

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IEEE International Conference on Networking, Sensing and Control

作者： Shugao Ma Weiqiang Wang Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing the Institute of Computer Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424464500;9781424464531

Camera motion analysis (CMA) is very useful for many video content analysis tasks, but few works competently handle the videos with significant camera or object motion. In this paper, we present an effective CMA approach for such challenging cases. The effectiveness of our approach comes from two aspects: (1) reliably matching keypoints on consecutively sampled frames, where both the appearance similarity and motion smoothness of keypoints are considered; (2) effectively distinguishing background keypoints from foreground ones by a novel and advanced voting process, where the voting weights of keypoints are dynamically adjusted to guarantee that the influence of foreground motion can be largely reduced in CMA. Thus, a parametric camera motion model can be naturally derived by the accurate estimation of the motion of background keypoints. The experimental results on the TRECV2005 dataset and 500 shots from seven classic action movies demonstrate the effectiveness of our approach.

关键词： Cameras Motion analysis Motion estimation Image motion analysis Pattern analysis Voting information analysis Event detection Optical filters Vectors

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Image segmentation based on fuzzy entropy and Bee Colony Algorithm

Image segmentation based on fuzzy entropy and Bee Colony Alg...

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International Conference on Natural Computation (ICNC)

作者： Yonghao Xiao Weiyu Yu Jing Tian Foshan University Foshan China School of Electronic and Information Engineering South China University of Technology Guangzhou China Provincial Key Laboratory of Computer Information Processing Technology Soochow University China Singapore

Image segmentation based on Bee Colony Algorithm (BCA) and fuzzy entropy is presented in this paper. The fuzzy entropy function is simplified with single parameter. The BCA is applied to search the minimum value of fuzzy entropy function. According to the minimum function value, the optimal image threshold is obtained. Experimental results are provided to demonstrate the superior performance of the proposed approach.

关键词： Entropy Image segmentation Lead Noise Optimization Mathematical model Equations

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Leakage-resilient identity-based encryption scheme

Leakage-resilient identity-based encryption scheme

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International Conference on Networked Computing and Advanced information Management (NCM)

作者： Xizhao Luo Peide Qian Yanqin Zhu Shangping Wang School of Computer Science and Technology Soochow University Suzhou China Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou China School of Science Xi'an University of Technology Xi'an Shaanxi China

ISBN: (纸本)9781424476718

We present an identity-based encryption in the relative-leakage model. In our construction, we firstly make use of a notion of new cryptographic primitive, identity-based hash proof system(IB-HPS), to construct a leakage-resilient IB-HPS based on a simple assumption and also prove its security by the variant of dual system encryption. Then, based on our IB-HPS scheme, we construct directly an efficient leakage-resilient identity-based encryption(IBE) scheme in the model.

关键词： Zinc Erbium Cryptography

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A NEW APPROACH FOR ROBUST REALTIME VOICE ACTIVITY DETECTION USING SPECTRAL PATTERN

A NEW APPROACH FOR ROBUST REALTIME VOICE ACTIVITY DETECTION ...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： M. H. Moattar M. M. Homayounpour Nima Khademi Kalantari Laboratory for Intelligent Signal and speech Processing Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran Electrical Engineering Department Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781424442959

In this paper a Voice Activity Detection approach is proposed which applies a voting algorithm to decide on the existence of speech in audio signal. For this purpose, the proposed approach uses three different short time features along with the pattern of spectral peaks of every frame. Spectral peaks pattern is appropriate for determining vowel sounds in speech signal even in the presence of noise. Therefore this measure can be applicable in voice activity detection in which the vowels characterize the speech signal. Experiments show that incorporating this measure along with our recently proposed approach for VAD, will improve the results of the algorithm considerably while imposing little computational overhead. The proposed approach is evaluated on different datasets with various noises and SNR levels and satisfying results are achieved.

关键词： Voice Activity Detection Spectral Peaks Pattern Spectral Flatness Activity Detection Voice Signal Voice Noise audio signals voice activity

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IPv6-based dynamic quality of service control strategy for wireless network

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Journal of Networks 2010年第9期5卷 1098-1105页

作者： Chen, Zhifeng Zhu, Qiaoming Qian, Peide Chen, Fengyan Key Laboratory of Computer Information Processing Technology of Jiangsu Province School of Computer Science and Technology Soochow University Suzhou 215006 China Suzhou Institute of Trade and Commerce Suzhou 215009 China School of Foreign Language Soochow University Suzhou 215006 China

On the basis of the architecture for fast handover of hierarchical IPv6, a coordinated call admission control mechanism was presented, providing an end-to-end RSVP context transfer for real-time applications to overcome the weakness of the context transfer protocol which cannot meet the need of end-to-end QoS mechanisms because the contexts were only transferred between the previous and new access routers. The mechanism decreased the delay caused by re-initiate QoS signaling from scratch to reestablish QoS forwarding process, and minimized the handover service disruption. The signaling overhead and handover latencies could be reduced by adopting t he fast handover for hierarchical IPv6 architecture. The approach was compared with the re-initiating RSVP signaling to reestablish QoS states using network simulator ns2. The numerical results showed that the scheme has the less latency and packet loss than that of the re-initiating approach. © 2010 ACADEMY PUBLISHER.

关键词： Quality of service

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On using Tabu Search for RNA secondary structure prediction

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2010年第9期4卷 252-262页

作者： Liu, Yongguo Hao, Jianrui School of Computer Science Engineering University of Electronic Science and Technology of China Chengdu 611731 China Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China

In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification search and diversification search, are developed to exploit the local regions around the current solution and explore the unvisited solution space, respectively. Simulation experiments are conducted on eight RNA sequences to show that the proposed method is feasible and effective.

关键词： Tabu search

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A Sample Pre-mapping Method Enhancing Boosting for Object Detection

A Sample Pre-mapping Method Enhancing Boosting for Object De...

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International Conference on Pattern Recognition

作者： Haoyu Ren Xiaopeng Hong Cher-Keng Heng Luhong Liang Xilin Chen Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China School of Computer Science and Technology Harbin Institute of Technology China Panasonic Singapore Laboratories Private Limited Singapore

ISBN: (纸本)9781424475421

We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common used methods on public datasets in both pedestrian detection and car detection.

关键词： Classification algorithms Training Boosting Kernel Support vector machine classification Accuracy Object detection

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Boosted Sigma Set for Pedestrian Detection

Boosted Sigma Set for Pedestrian Detection

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International Conference on Pattern Recognition

作者： Xiaopeng Hong Hong Chang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Institute of Digital Media Peking University China

ISBN: (纸本)9781424475421

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.

关键词： Classification algorithms Manifolds Detectors Feature extraction Training Covariance matrix Support vector machine classification

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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