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A dual population parallel ant colony optimization algorithm for solving the traveling salesman problem

Journal of Convergence information Technology 2012年第5期7卷 66-74页

作者： Liu, Fang Software Institute Dalian Jiaotong University Dalian 116028 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

In allusion to the phenomenon of stagnation and precocity during evolution in ant colony optimization (ACO) algorithm, this paper proposed a dual population parallel ant colony optimization (DPPACO) algorithm, which was applied to the traveling salesman problem. The DPPACO algorithm separated the ants into soldier ant population and worker ant population which evolve separately by parallel method and exchanges information timely. The dynamic equilibrium between solution diversity and convergence speed is achieved by using the effect of the soldier ant's distribution to worker ants' movement choice. The DPPACO algorithm can enlarge searching range and avoid local minimum, prevent local convergence caused by misbalance of the pheromone and can improve the searching performance of the algorithm effectively. The proposed algorithm is applied in the traveling salesman problem by using the 17 data sets obtained from the TSPLIB. We compare the experimental results of the proposed DPPACO method with the traditional methods. The experimental results demonstrate that the proposed algorithm has a better global searching ability, higher convergence speed and solution diversity.

关键词： Ant colony optimization

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A novel approach based on domain ontology for web information extraction

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International Journal of Advancements in Computing Technology 2012年第7期4卷 185-194页

The effect of Web information extraction depends on the quality of extraction rules. But for most approaches for Web information extraction, independence of extraction rules is their common shortages. In this paper, we propose a novel approach based on ontology for Web information extraction. We sum up four features for information items and induce these features to a group of extraction rules. Then according to a group of mapping rules between elements of ontology and extraction rules, extraction rules are well organized in ontology. According to properties of concept in ontology, the initial result of information extraction is got and then the final result is obtained by simplifying the initial results. Experiments show that our approach has higher precision.

关键词： Ontology

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A survey on video-based vehicle behavior analysis algorithms

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Journal of Multimedia 2012年第3期7卷 223-230页

作者： Wu, Jian Cui, Zhi-Ming Chen, Jian-Ming Zhang, Guang-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China Jiangsu Yihe Traffic Engineering Co. Ltd Suzhou 215002 China

Analysis of the Vehicle Behavior is mainly to analyze and identify the vehicles' motion pattern, and describe it by the use of natural language. It is a considerable challenge to analyze and describe the vehicles' behavior in a complex scene. This paper first hackles the development history of the intelligent transportation system and analysis of vehicles' behavior, and then conducts an indepth analysis of current situation of vehicle behavior analysis from the video processing, video analysis and video understanding, summarizes the achieved results and the key technical problems, and prospects the future development of vehicle behavior analysis. © 2012 ACADEMY PUBLISHER.

关键词： Video signal processing

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Cooperative Synthetic Aperture radar Image Segmentation Using Learning Sparse Representation Based Clustering Scheme

Cooperative Synthetic Aperture Radar Image Segmentation Usin...

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Shuyuan Yang Junlin Zhu Zailin Hu Min Wang Licheng Jiao Institute of Intelligent Information Processing National Key Laboratory of Radar Signal Processing Department of Electrical Engineering Xidian University Xi'an China

Based on a recent proposed and popular sparse representation based classifier (SRC), in this paper we presented a novel Learning Sparse Representation based Clustering (LSRC) scheme for Synthetic Aperture radar (SAR) segmentation. LSRC introduces the examples-based dictionary learning technology in SRC to find a dictionary that is adaptable to sparsely representing samples, which is liable to provide more accurate approximation of samples and subsequently achieve higher classification accuracy rate. Moreover, for the intrinsic supervised nature of LSRC, we adopt an unsupervised-clustering cooperative approach to provide training samples for LSRC, in which some "good" samples with higher membership degrees are selected from the clustering result of K-means algorithm. Some experiments are taken on segmentation of both the texture images and SAR images to investigate the performance of our proposed method, and the results prove its superiority to its counterparts.

关键词： Dictionaries Image segmentation Classification algorithms Clustering algorithms Training Pixel Synthetic aperture radar

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Novel Super Resolution Restoration of Remote Sensing Images Based on Compressive Sensing and Example Patches-Aided Dictionary Learning

Novel Super Resolution Restoration of Remote Sensing Images ...

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Shuyuan Yang Fenghua Sun Min Wang Zhizhou Liu Licheng Jiao Institute of Intelligent Information Processing National Key Laboratory of Radar Signal Processing Department of Electrical Engineering Xidian University Xi'an China

A novel machine learning and compressive sensing (CS) based super-resolution (SR) algorithm for the restoration of remote sensing images is proposed in this paper. This new algorithm relies on the idea that high-resolution (HR) image patches can be correctly recovered from the downsampled low-resolution (LR) image patches under two mild conditions, i.e., the sparsity of image patches, and the incoherence between the sensing and projection matrix. Consequently if most of HR image patches can be represented as a sparse linear combination of elements from a dictionary that is incoherent with sensing matrix, the HR image patches can be recovered accurately from its LR version. To find a dictionary which can sparsely represent HR image patches to guarantee the reconstruction error over a set of patches be minimal, an example patches-aided dictionary learning algorithm named KSVD algorithm is adopted. Moreover, the incoherence between the learned dictionary and sensing matrix is experimentally investigated. The new proposed method is tested on the restoration of remote sensing images came from USC-SIPI Image Database, and the results show that the proposed algorithm can provide substantial improvement in resolution of remote sensing images, and the restored images are superior in quality to that of other related methods.

关键词： Dictionaries Remote sensing Image restoration Image reconstruction PSNR Spatial resolution

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High Resolution radar Imaging Based on Compressed Sensing and Fast Bayesian Matching Pursuit

High Resolution Radar Imaging Based on Compressed Sensing an...

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Min Wang Shuyuan Yang Yanyan Wan Jing Wang Institute of Intelligent Information Processing National Key Laboratory of Radar Signal Processing Department of Electrical Engineering Xidian University Xi'an China

Recently the rapid imaging based on the compressive sensing (CS) theory have attracted increasing interests, which simultaneously sampling and compressing signals or images. radar imaging based CS is a potential way to obtain the high-resolution radar images without the constraint of Nyquist sampling rate. In this paper, we proposed a radar remote-sensing imaging approach based on compressive sensing and fast Bayesian matching pursuit (FBMP) recovery algorithm. Some experiments are taken and the results indicate that an accurate reconstruction of high-resolution radar images are obtained, with fewer measurements than most its counterparts(e.g., MP, OMP, StOMP, GPSR),but resulting in lower normalized MSE(NMSE). Although BCS obtains lower NMSE than FBMP,simultaneously with higher time complexity and sparsity.

关键词： radar imaging Matching pursuit algorithms Compressed sensing Imaging Image reconstruction Bayesian methods

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Multitask Learning and Sparse Representation Based Super-Resolution Reconstruction of Synthetic Aperture radar Images

Multitask Learning and Sparse Representation Based Super-Res...

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Shuyuan Yang Zhizhou Liu Min Wang Fenghua Sun Licheng Jiao Institute of Intelligent Information Processing National Key Laboratory of Radar Signal Processing Department of Electrical Engineering Xidian University Xi'an China

In earth observing remote sensing fields, to recognize objects whose size approaches the limiting spatial resolution scale especially in Synthetic Aperture radar (SAR) images, spatial resolution enhancement is usually required. In this paper, we proposed a multi-task learning and K-SVD based Superresolution image restoration method where K-SVD algorithm is employed to learn a redundant dictionary from some example image patches. In order to learn more accurate dictionary and reduce the complexity of dictionary learning, multitask learning concept is adopted to learn multiple dictionaries from the samples classified by K-means clustering. Some experiments are taken to investigate the performance of our proposed method, and the visual result and numerical guidelines both prove its superiority to some start-of-art SRIR methods.

关键词： Dictionaries Image reconstruction Training signal resolution Optimization Spatial resolution

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InSAR interferometric phase estimation based on correlation weight subspace projection

InSAR interferometric phase estimation based on correlation ...

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6th International Conference on radar, radar 2011

作者： Li, Hai Wu, Renbiao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin 300300 China Intelligent Information Institute of ATR Lab Shenzhen University Shenzhen 518060 China

ISBN: (纸本)9781424484416

In this paper we propose a method to estimate the InSAR interferometric phase using the correlation weight subspace projection technique. In the method the correlation weight data vector is constructed, thus the noise subspace dimension of the corresponding covariance matrix will not be affected by the coregistration error, then avoiding the trouble of calculating the noise subspace dimension before estimating the InSAR interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. The effectiveness of the method is verified via simulated data. © 2011 IEEE.

关键词： Covariance matrix

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Crashed plane detection based on the fusion of CFAR detection and polarimetric decomposition

Crashed plane detection based on the fusion of CFAR detectio...

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6th International Conference on radar, radar 2011

作者： Han, Ping Ge, Peng Wu, Renbiao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin 300300 China Intelligent Information Institute of ATR Lab Shenzhen University Shenzhen 518060 China

ISBN: (纸本)9781424484416

Aiming at the problem of high false alarm rate and missing rate with single detection method, an improved target detection algorithm for crashed plane detection is proposed in this paper. The method firstly detects the possible existence of the crashed plane with Yamaguchi decomposition and Constant False Alarm Rate (CFAR). Then, the detection results of the two methods are processed by pre-determination, and the final result is achieved by fusion and complementary detection. Simulation experimental results with real polarimetric data from NASA/JPL UAVSAR system demonstrate that the proposed method decreases the false alarm rate and missing rate effectively. © 2011 IEEE.

关键词： Synthetic aperture radar

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Rebar echo detection and suppression in runway using GPR

Rebar echo detection and suppression in runway using GPR

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6th International Conference on radar, radar 2011

作者： Wu, Rebiao Zhong, Yuzhong Liu, Jiaxue Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin 300300 China Intelligent Information Institute of ATR Lab Shenzhen University Shenzhen 518060 China

ISBN: (纸本)9781424484416

Strong rebar echo which can't be eliminated in tx domain directly will disturb the detection and discrimination of the interested targets like cryptic disasters. This paper presents the application of Hyp-curvelet transform in detecting and suppressing rebar echo. Echoes from different targets are concentrated in their own apex coordinate but separated with each other in Hyp-curvelet space after transformed. The peak value in Hyp-curvelet space indicates the location of target. With some discrimination algorithm rebar's location could be obtained, and then its components in Hyp-curvelet space can be eliminated. In this way the reconstructed data will not contain rebar echo. Simulation results show that targets can be detected in low SNR condition, and rebar echo is rarely left over and diseases echoes are hardly any damaged in reconstructed data. © 2011 IEEE.

关键词： Echo suppression

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