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Tumor gene expressive data classification based on locally linear representation Fisher criterion

9th International Conference on Intelligent Computing, ICIC 2013

作者： Li, Bo Tian, Bei-Bei Liu, Jin School of Computer Science and Technology Wuhan University of Science and Technology 430081 Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China State Key Lab. of Software Engineering Wuhan China

ISBN: (纸本)9783642394812

In this paper, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene expressive data. In the proposed LLRFC, an inter-class graph and intra-class graph is constructed based on the class information of tumor gene expressive data, where the weights between nodes in both graph are optimized using locally linear representation trick. Moreover, a Fisher criterion is modeled to maximize the inter-class scatter and minimize the intra-class scatter simultaneously. Experiments on some benchmark tumor gene expressive data validate its efficiency. © 2013 Springer-Verlag.

关键词： Genes

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Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization for Block-sparse Compressive Sensing

Simultaneous Sensing Matrix and Sparsifying Dictionary Optim...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Shuang Li Qiuwei Li Gang Li Xiongxiong He Liping Chang Zhejiang Provincial Key Lab. for Signal Processing Zhejiang University of Technology Hangzhou Zhejiang P.R. China

In this paper, we propose a new method to optimize the sensing matrix and the overcomplete dictionary simultaneously in a block-sparse system. This method mainly includes two parts: the optimization of the sensing matrix for a given dictionary and the optimization of the overcomplete dictionary with a block structure for a predefined sensing matrix. Simulation results show that our novel method can significantly improve the dictionary recovery ability and lower the representation error compared with other dictionary learning methods in block-sparse systems.

关键词： Dictionaries Sensors Sparse matrices Image reconstruction Optimization Vectors Coherence

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Hierarchy identification for automatically generating table-of-contents

Hierarchy identification for automatically generating table-...

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9th International Conference on Recent Advances in Natural Language processing, RANLP 2013

作者： Erbs, Nicolai Gurevych, Iryna Zesch, Torsten Ubiquitous Knowledge Processing Lab. Department of Computer Science Technische Universität Darmstadt Germany Information Center for Education German Institute for Educational Research and Educational Information Germany Language Technology University of Duisburg-Essen Germany

A table-of-contents (TOC) provides a quick reference to a document's content and structure. We present the first study on identifying the hierarchical structure for automatically generating a TOC using only textual features instead of structural hints e.g. from HTML-tags. We create two new datasets to evaluate our approaches for hierarchy identification. We find that our algorithm performs on a level that is sufficient for a fully automated system. For documents without given segment titles, we extend our work by automatically generating segment titles. We make the datasets and our experimental framework publicly availab.e in order to foster future research in TOC generation.

关键词： Automation

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Real-Time Digital Modulation Classification Based on Support Vector Machines

Real-Time Digital Modulation Classification Based on Support...

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Symposium on Integrated Circuits and Systems Design

作者： Edson Sorato Eduardo P. Fronza Paulo R. F. M. M. Barbosa Jose Luis Guntzel Adalbery R. Castro Aldebaro Klautau Embedded Computing Lab. (ECL) Department of Informatics and Statistics - PPGCC Federal University of Santa Catarina Signal Processing Lab. (LaPs) Computer Engineering Department Federal University of Para

ISBN: (纸本)9781479911301

In this paper we investigate the use of the Support Vector Machine (SVM) approach to develop simple and efficient VLSI architectures for real-time digital modulation classification. Such simplicity and efficiency arise from the adoption of a front end block that is based on histograms. Particularly, we compare two decision schemes to solve the multiclass classification problem with linear SVMs, Pairwise and One Against the Rest (OAR), and propose an enhanced OAR scheme to improve the hit rate for low SNR values. Dedicated VLSI architectures for the three schemes were developed and logically synthesized with an industrial standard-cell flow for a 90 nm library. Functional simulation results show that the Enhanced-OAR verifier achieves up to 76% of hit rate in the 0 to 5 dB range, which corresponds to accuracy improvements of up to 162% over the OAR classifier. Synthesis results indicate a 21.8% of area overhead and 2% of power and energy increases. The results also pointed out that the Enhanced-OAR classifier is 14.1% smaller, consumes 30.1% less power and is 30.2% more energy-efficient than the Pairwise classifier, while providing up to 58.3% of accuracy improvements.

关键词： hit rate Support Vector Network Digital modulation front-ends Energy sources Classifiers Very large scale integration

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Monopulse angle estimation using sum-difference multi-beamforming based on the phased array at the sub-array level

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Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2013年第1期40卷 19-25+43页

作者： Zeng, Cao Chen, Hao He, Xuehui Yang, Yang National Key Lab. of Radar Signal Processing Xidian Univ. Xi'an 710071 China The 27th Research Institute of China Electronics Technology Group Corporation ZhengZhou 450000 China

For the problem of monopulse angle estimations of multiple targets based on the large phased array, a method for estimating bias angles using sum-difference multi-beamforming at the sub-array level is proposed. Firstly, system complexity is reduced by combining the outputs of sensors;secondly, sum-difference multiple beamformings are achieved by phased-alone sub-array steering vectors with the anti-symmetric mode;finally, the 2-dimensional bias angles of targets in arbitrary steering beams are estimated based on the two slopes along azimuth and elevation axes pointing to the normal. Compared with the method of four steering sum-difference beamforming, the proposed method has the advantages of higher accuracy and lower complexity. The measured and simulated data show the effectiveness and superiority of the proposed method.

关键词： Array processing

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VLSI Architectures for Digital Modulation Classification using Support Vector Machines

VLSI Architectures for Digital Modulation Classification usi...

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IEEE Latin American Symposium on Circuits and Systems

作者： Edson Sorato Renan Netto Pedro Michel José Luís Güntzel Adalbery R. Castro Aldebaro Klautau Embedded Computing Lab. (ECL) Department of Informatics and Statistics - PPGCC Federal University of Santa Catarina - Florianopolis Brazil Signal Processing Lab. (LaPs) Computer Engineering Department Federal University of Para - Belem Brazil

ISBN: (纸本)9781467348973

This paper presents VLSI architectures to perform Digital Modulation Classification based on Support Vector Machines. In order to obtain suitably small circuitry, the designed architectures use a recently proposed front end that is based on histograms. Four versions of classifier architectures were modeled in Verilog and synthesized for a 90 nm commercial standard cells library, two of them using the pairwise and two with the one against rest (OAR) multiclass classification schemes. Synthesis results showed that the OAR are 32.7% smaller, consume 32% less power and are 32% more energy-efficient than the pairwise classifiers, while achieving the same accuracy.

关键词： VLSI OAR Vector Machines

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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Design of lattice-based digital IIR filters with discrete coefficients

Design of lattice-based digital IIR filters with discrete co...

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International Conference on Natural Computation (ICNC)

作者： Hong Xu Chaogeng Huang Feng Ye Zhejiang Provincial Key Lab. for Signal Processing Zhejiang University of Technology Hangzhou Zhejiang China Hangzhou Nationalchip Science&Technology Co. Ltd Hangzhou Zhejiang China

In this paper, the problem of designing discrete coefficient infinite impulse response (IIR) digital filters is discussed. The digital filters are characterized with a lattice-based filter structure that has three sets of parameters. The filters, obtained in our proposed design procedure, are determined with all the sets of parameters optimized over a discrete power-of-two coefficient space such that the signal power ratio, an FWL-related measure, is minimized subject to the frequency response specification satisfied. A genetic algorithm (GA) is derived to solve the corresponding optimization. Two design examples are given, which show that the filters designed by the proposed method not only have a low implementation complexity but also possess much improved finite word length properties such as very low parameter sensitivity and very uniform signal powers across signal nodes.

关键词： Optimization Finite impulse response filters Transfer functions Complexity theory Genetic algorithms Algorithm design and analysis Passband

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A Hessenberg-based input balanced realization for all-pass systems

A Hessenberg-based input balanced realization for all-pass s...

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International Conference on Information, Communications and signal processing

作者： Tao Hong Chaogeng Huang Gang Li Yong Ching Lim Zhejiang Key Lab. for Signal Processing College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang P.R. China Zhejiang Key Lab. for Signal Processing College of Information Zhejiang University of Finance & Economics Hangzhou Zhejiang P.R. China School of EEE Nanyang Technological University Singapore Republic of Singapore

ISBN: (纸本)9781479904327

In this paper, a novel Hessenberg-based input balanced realization for all-pass systems is derived. The expression of roundoff noise gain for this structure is obtained. An Nth order system implemented with a coupled all-pass sub-filters proposed in [18], in which each all-pass filter is realized using our proposed structure, yields a roundoff noise gain of N + 1, smaller than that of the classical optimal roundoff noise statespace realizations. A design example is presented to illustrate the behavior of the proposed structure and to compare it with a class of existing input balanced structures and the classical optimal roundoff noise realizations. It is shown that for the all-pass filters, the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.

关键词： Noise Complexity theory Educational institutions Sensitivity Gain Speech

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013年 8105 LNAI卷 VII-VIII页

作者： Gurevych, Iryna Biemann, Chris Zesch, Torsten Ubiquitous Knowledge Processing Lab. Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Frankfurt am Main Germany FG Language Technology Department of Computer Science Technische Universität Darmstadt Darmstadt Germany

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