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Performance of Gaussian waveform in OvTDM

The Journal of China Universities of Posts and Telecommunications 2009年第1期16卷 32-37页

作者： YANG Xun WANG Jian LI Dao-ben Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Waveforms play a key role in overlapping time division multiplexing （OvTDM） system. The OvTDM has been proved to be a novel transmission technique that can utilize time diversity to improve the bit error ratio （BER） performance in Rayleigh channel. According to the resulted law, the BER performance of Gaussian waveform in OvTDM also improves with the increase in the waveform parameter monotonically, and the rectangular waveform is the worst case of Gaussian waveform. In contrast, the spectrum effectiveness of Gaussian waveform OvTDM decreases with the increase in the waveform parameter. In the study, it is obtained that waveforms can achieve the best performance when the smallest correlation coefficient between neighbor symbols is e^-1 .

关键词： OvTDM inter symbol interference （ISI） Gaussian waveform rectangular waveforrn BER spectrum effectiveness

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Solving NP-complete problems by spiking neural P systems with budding rules

Solving NP-complete problems by spiking neural P systems wit...

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10th International Workshop on Membrane Computing, WMC 2009

作者： Ishdorj, Tseren-Onolt Leporati, Alberto Pan, Linqiang Wang, Jun Computational Biomodelling Laboratory Åbo Akademi University Department of Information Technologies 20520 Turku Finland Università degli Studi di Milano - Bicocca Dipartimento di Informatica Sistemistica e Comunicazione Viale Sarca 336/14 20126 Milano Italy Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of CS and AI University of Sevilla Avda Reina Mercedes s/n 41012 Sevilla Spain

ISBN: (纸本)3642114660

Inspired by the growth of dendritic trees in biological neurons, we introduce spiking neural P systems with budding rules. By applying these rules in a maximally parallel way, a spiking neural P system can exponentially increase the size of its synapse graph in a polynomial number of computation steps. Such a possibility can be exploited to efficiently solve computationally difficult problems in deterministic polynomial time, as it is shown in this paper for the NP-complete decision problem sat. © 2010 Springer-Verlag.

关键词： Computational complexity

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A Discriminative Model for Traditional Mongolian Part of Speech

A Discriminative Model for Traditional Mongolian Part of Spe...

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Chinese Conference on Pattern Recognition (CCPR)

作者： Guanhong Zhang sloglo Odbal Department of Computer Science and Technology Key Laboratory of Network and Intelligent Information Processing Hefei University Hefei Anhui China Inner Mongolia University for the Nationalities Hohhot Inner Mongolia China Hefei Institutes of Physical Science Chinese Academy and Sciences Hefei Anhui China

This paper presents a discriminative model for part of speech tagging of traditional *** use Maximum Entropy Model with Morphological features of Mongolian. First, the context feature templates are defined and extracted from the training corpus. Then, the parameters of maximum entropy probability models are calculated. Experimental results show that integration of morphological features of Maximum Entropy Model for Mongolian part of speech tagging outperform HMM since they are flexible enough to capture many correlated non-independent features.

关键词： Hidden Markov models Speech Tagging Entropy Accuracy Natural languages Computational modeling

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SAR image retrieval based on gaussian mixture model classification

SAR image retrieval based on gaussian mixture model classifi...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Hou, Biao Tang, Xu Jiao, Licheng Wang, Shuang Key Laboratory of Intelligent Perception Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

SAR image retrieval, lacking of well performance recently due to the particularity of SAR image, has drawn more and more attention with the increasing volume of SAR data and the dramatically enlarging application range of SAR image. This paper considers both the characteristic of content-based image retrieval (CBIR) and SAR image, proposing a novel SAR image retrieval method. The proposed method can be divided into two parts: image classification and matching. Firstly we use Gaussian Mixture Model (GMM) to gain a precise result of classification, and then we get the retrieval results through the integrated region matching (IRM) algorithm. Experimental results show that the proposed method can retrieve SAR images which contain all kinds of surface features effectively. ©2009 IEEE.

关键词： Synthetic aperture radar

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K-means selective cluster ensembles based on multiple feature subsets

K-means selective cluster ensembles based on multiple featur...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Zou, Haishuang Huo, Jieting Wu, Caili Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Combining multiple clusterers is emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. In this paper, k-means selective cluster ensembles based on multiple feature subsets are proposed. In the ensemble, a random subset of features is used to train an individual k-means clusterer. In the final step, the selective weighted voting scheme is used for finding the best partition. The consensus function is constructed by relabeling all partitions of clusterers and finding the best partition. Experimental results on 4 UCI data sets show that our ensemble method can improve the clustering performance. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Set theory

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Center matching scheme for K-means cluster ensembles

Center matching scheme for K-means cluster ensembles

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Wu, Caili Huo, Jieting Zou, Haishuang Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Pre-extracting boundary vectors for support vector machine u...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Chen, Guirong Zhou, Hongjie Ye, Ning Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE have the similar generalization performance to SVMs. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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Algorithm and axiomatization of rough fuzzy sets based finite dimensional fuzzy vectors

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 560-568页

作者： Mingfen WU School of Information Science Wuyi UniversityJiangmen 529020China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Ch

Rough sets,proposed by Pawlak and rough fuzzy sets proposed by Dubois and Prade were expressed with the different computing formulas that were more complex and not conducive to computer operations,In this paper,we use the composition of a fuzzy matrix and fuzzy vectors in a given non-empty finite universal,constitute an algebraic system composed of finite dimensional fuzzy vectors and discuss some properties of the algebraic system about a basis and *** give an effective calculation representation of rough fuzzy sets by the inner and outer products that unify computing of rough sets and rough fuzzy sets with a *** basis of the algebraic system play a key role in this *** give some essential properties of the lower and upper approximation operators generated by reflexive,symmetric,and transitive fuzzy *** reflexive,symmetric,and transitive fuzzy relations are charac terized by the basis of the algebraic system.A set of axioms,as the axiomatic approach,has been constructed to characterize the upper approximation of fuzzy sets on the basis of the algebraic system.

关键词： fuzzy vector fuzzy matrix fuzzy relation operation basis lower and upper approximation rough fuzzy sets unary operator

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Semi-supervised segmentation of multispectral remote sensing image based on spectral clustering

Semi-supervised segmentation of multispectral remote sensing...

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MIPPR 2009 - Multispectral Image Acquisition and processing: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Zhang, Xiangrong Wang, Ting Jiao, Licheng Yang, Chun Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478054

In this paper, a new multi-spectral remote sensing image segmentation method based on multi-parameter semi-supervised spectral clustering (STS 3C) is proposed. Two types of instance-level constraints: must-link and cannot-link are incorporated into spectral cluster to construct semi-supervised spectral clustering in which the self-tuning parameter is applied to avoid the selection of the scaling parameter. Further, when STS 3C is applied to multi-spectral remote sensing image segmentation, the uniform sampling technique combined with nearest neighbor rule is used to reduce the computation complexity. Segmentation results show that STS 3C outperforms the semi-supervised spectral clustering with fixed parameter and the well-known clustering methods including k-means and FCM in multi-spectral remote sensing image segmentation. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Image segmentation

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Adaptive shrinkage despeckling for SAR image based on nonsubsampled contourlet transform

Adaptive shrinkage despeckling for SAR image based on nonsub...

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MIPPR 2009 - Multispectral Image Acquisition and processing: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Wang, Shuang Zhao, Hong Jiao, Licheng Hou, Biao Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478054

An improved Nonsubsampled Contourlet Transform (NSCT)-based method has been proposed in this paper, using subbands mask prior models and directional information for synthetic aperture radar (SAR) image despeckling. The NSCT is a shift-invariant directional wavelet transform which satisfies the demands of SAR image despeckling. Wedding coefficient statistic character of NSCT subbands and directional information captured by the transform, this paper applies anisotropic neighborhood models to the processing of adaptive shrinkage. The resulting algorithm strongly suppresses speckle, while preserving detailed information and image edges compared with other traditional despeckling methods. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Shrinkage

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