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Synthetic aperture radar automatic target recognition based on curvelet transform

Synthetic aperture radar automatic target recognition based ...

MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Wang, Shuang Liu, Zhuo Jiao, Licheng He, Jun Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

A novel synthetic aperture radar (SAR) automatic target recognition (ATR) approach based on Curvelet Transform is proposed. However, the existing approaches can not extract the more effective feature. In this paper, our method is concentrated on a new effective representation of the moving and stationary target acquisition and recognition (MSTAR) database to obtain a more accurate target region and reduce feature dimension. Firstly, MSTAR database can be extracted feature through the optimal sparse representation by curvelets to obtain a clear target region. However, considering the loss of part of edges of image. We extract coarse feature, which is to compensate fine feature error brought by segmentation. The final features consisting of fine and coarse feature are classified by SVM with Gaussian radial basis function (RBF) kernel. The experiments show that our proposed algorithm can achieve a better correct classification rate. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Automatic target recognition

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Gene transposon based clonal selection algorithm for clustering

Gene transposon based clonal selection algorithm for cluster...

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11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

作者： Liu, Ruochen Sheng, Zhengchun Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781605583259

Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for clustering multi-class data sets named as Gene Transposition based Clone Selection Algorithm (GTCSA) is proposed in this paper, The proposed algorithm does not require a prior knowledge of the numbers of clustering;an improved variant of the clonal selection algorithm has been used to determine the number of clusters as well as to refine the cluster center. a novel operator called antibody transposon is introduced to the framework of clonal selection algorithm which can realize to find the optimal number of cluster automatically. The proposed method has been extensively compared with Variable-string-length Genetic Algorithm(VGA)based clustering techniques over a test suit of several real life data sets and synthetic data sets. The results of experiments indicate the superiority of the GTCSA over VGA on stability and convergence rate, when clustering multi-class data sets. Copyright 2009 ACM.

关键词： Genes

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Quantum-inspired evolutionary clustering algorithm based on manifold distance 09

Quantum-inspired evolutionary clustering algorithm based on ...

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2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

作者： Yangyang, Li Hongzhu, Shi Maoguo, Gong Ronghua, Shang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781605583266

Based on the concepts and principles of quantum computing, a quantum-inspired evolutionary algorithm for data clustering (QECA) is proposed in this paper. And a novel distance measurement index called manifold distance is introduced. These attribute data are the main source of clustering problem, due to its complex distribution, most clustering algorithms available are only suitable for these types of characteristic data. In this study, a new algorithm which can deal with these data with manifold distribution is more effective. The main motives of using QECA consist in searching for appropriate cluster center so that a similarity metric of clusters are optimized more quickly and effectively. The superiority of QECA over fuzzy c-means (FCM) algorithm and immune evolutionary clustering algorithm (IECA) is extensively demonstrated in our experiments. Copyright 2009 ACM.

关键词： Clustering algorithms

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Algorithm of partition based network boosting for imbalanced data classification

Algorithm of partition based network boosting for imbalanced...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Shuiping, Gou Hui, Yang Licheng, Jiao Xiong, Zhuang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424469178

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine. © 2010 IEEE.

关键词： Adaptive boosting

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Multi-agent containment control with input saturation on switching topologies

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IET control Theory and Applications 2015年第3期9卷 399-409页

作者： Su, Housheng Chen, Michael A.Q. School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong

This study aims to investigate containment control of linear multi-agent systems with input saturation on switching topologies. For such a multi-agent system, both state feedback and output feedback containment control protocols are proposed via a novel low-gain feedback approach. Consequently, the authors show that, under the assumption that the switching networks are jointly connected, semi-global state feedback and output feedback containment control of the multi-agent system can be attained. The effectiveness of the proposed algorithms is illustrated via numerical examples. © 2015 The Institution of Engineering and Technology.

关键词： Multi agent systems

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An online ascent phase trajectory reconstruction algorithm using Gauss Pseudospectral Method

An online ascent phase trajectory reconstruction algorithm u...

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2012 International Conference on Modelling, Identification and control, ICMIC 2012

作者： Zhang, Da Lu, Xuefang Liu, Lei Wang, Yong Ji Key Labo-ratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780956715715

In this paper, we present an online ascent phase trajectory reconstruction algorithm using Gauss Pseudospectral Method. Gauss Pseudospectral Method (GPM) was used to transform the ascent phase trajectory optimization problem into Nonlinear Program Problem (NLP), we solve the problem using a optimization software named GPOPS in matlab. Using this method we can calculate the optimal trajectory control volume and feedback to update the guidance commands online. Credible analysis of the optimized results has been done to prove its accuracy. At last, we compare the trajectory recon- struction algorithm to a law without reconstruction algorithm via simulation with aerodynamic and thrust disturbance. The simulation results indicate that the method proposed above offers improved performance and has ability for real-time calculating. © 2012 Huazhong Univ of Sci & Tec.

关键词： Nonlinear programming

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SAR target recognition based on improved sparse LSSVM

SAR target recognition based on improved sparse LSSVM

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Xiangrong Zhang, Yifan Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

An Improved Fast Sparse Least Squares Support Vector Machine (IFSLSSVM) is proposed for Synthetic Aperture Radar (SAR) target recognition. Least Squares Support Vector Machine (LSSVM) is a least square version of Support Vector Machine (SVM), but it lacks the sparseness compared with SVM. IFSLSSVM, which combines the incremental learning and decremental learning, selects those important samples as the support vectors, and implements pruning by a certain condition, can solve the non-sparse problem of LSSVM effectively. Benchmarking UCI datasets are firstly used for testing the performance of our algorithm, followed by SAR target recognition. Experimental results on MSTAR SAR dataset show that IFSLSSVM is an effective SAR target recognition approach (SAR-ATR), which not only reduces the number of support vectors but also enhances the recognition rate. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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Semi-global containment control of multi-agent systems with input saturation

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IET control Theory and Applications 2014年第18期8卷 2229-2237页

作者： Su, Housheng Jia, Gui Chen, Michael Z.Q. School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong

This study investigates containment control of multi-agent systems with input saturation and multiple leaders on directed networks. Both state feedback and output feedback containment control protocols are designed via low-gain feedback approach. Under the assumption that the system is asymptotically null controllable with bounded controls and there exists at least one leader that has a directed path to each follower, semi-global state feedback and output feedback containment control of the multi-agent system can be attained. Numerical examples are provided to illustrate the effectiveness of the theoretical results. © The Institution of Engineering and Technology 2014.

关键词： Multi agent systems

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High performance word level sequential and parallel coding methods and architectures for bit plane coding

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Science in China(Series F) 2008年第4期51卷 337-351页

作者： XIONG ChengYi TIAN JinWen LIU Jian College of Electronic Information Engineering South-Central University for Nationalities Wuhan 430074 China Institute of Pattern Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China

This paper introduced a novel high performance algorithm and VLSI architectures for achieving bit plane coding （BPC） in word level sequential and parallel mode. The proposed BPC algorithm adopts the techniques of coding pass prediction and parallel ＆ pipeline to reduce the number of accessing memory and to increase the ability of concurrently processing of the system, where all the coefficient bits of a code block could be coded by only one scan. A new parallel bit plane architecture （PA） was proposed to achieve word-level sequential coding. Moreover, an efficient high-speed architecture （HA） was presented to achieve multi-word parallel coding. Compared to the state of the art, the proposed PA could reduce the hardware cost more efficiently, though the throughput retains one coefficient coded per clock. While the proposed HA could perform coding for 4 coefficients belonging to a stripe column at one intra-clock cycle, so that coding for an NxN code-block could be completed in approximate N2/4 intra-clock cycles. Theoretical analysis and experimental results demonstrate that the proposed designs have high throughput rate with good performance in terms of speedup to cost, which can be good alternatives for low power applications.

关键词： bit plane coding high performance word-level sequential multi-word parallel

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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