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international conference on Digital Content, Multimedia Technology and its Applications (IDC)

作者： Somayeh Cheshomi Rahati-Q Saeed Mohammah-R Akbarzadeh-T Islamic Azad university Mashhad Iran Ferdowsi University Mashhad Iran

HMM has high power to describe complex phenomena. the Baum-Welch (BW) algorithm is very popular estimation method that use for estimating HMM model parameters but it start with an initial guess and finally converge to a local optimum in practice. Chaos often exists in nonlinear systems. It has many good properties such as ergodicity, stochastic properties, regularity and high sensitivity to initial states. In this paper by use of these properties of chaos, an effective hybrid CHAOS-BW optimization method is proposed that uses the Chaos Optimization algorithm to optimize the initial values of Baum Welch algorithm. this algorithm not only overcomes the shortcoming of becoming trapped in local optimum of the BW algorithm, but is also fast and requires less storage than other hybrid optimization algorithms such as GABW, PSOBW and GAPSOBW. Experimental results on Persian digit dataset show that the propose method has both qualities of global search as well as rapid convergence. Comparison with several other more conventional approaches also reveals superior performance of the proposed model.

关键词： Hidden Markov models Chaos

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Membrane protein prediction using wavelet decomposition and pseudo amino acid based feature extraction

Membrane protein prediction using wavelet decomposition and ...

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international conference on Emerging Technologies, ICET

作者： Maqsood Hayat Asifullah Khan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan

Membrane proteins play an important role in many biological processes and are attractive drug targets. In this study, membrane proteins are classified using two feature extraction and several classification strategies. the first feature extraction strategy is pseudo amino acid (PseAA) composition; utilizing hydrophobicity and hydrophilicity for reflecting the sequence order effects, while the second method is discrete wavelet analysis (DWT); analyzing the different components of a signal localized both in space and scale domains. the nearest neighbor, probabilistic neural network, support vector machine, random forest, and Adaboost are used as basic learning mechanisms. the predicted results of the base learners are combined using majority voting to form an ensemble classifier. the best accuracy obtained for the Jackknife and independent dataset test is 85.4% and 95.3%, respectively. Using performance measures such as MCC, Sensitivity, Specificity, and F-measure, it has been observed that PseAA based prediction is significantly higher than that of the DWT, and is also the best reported, so far.

关键词： Proteins discrete wavelet transforms Biomembranes Support vector machines Amino acids Accuracy Feature extraction

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EEG different frequency sound response identification using neural network and fuzzy techniques

EEG different frequency sound response identification using ...

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international Colloquium on Signal Processing & Its Applications, CSPA

作者： R. Sudirman A. C. Koh N. M. Safri W. B. Daud N. H. Mahmood Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

ISBN: (纸本)9781424471218;9781424471225

Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. this research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system.

关键词： Electroencephalography Frequency Neural networks Fuzzy neural networks Signal processing algorithms Humans Brain discrete wavelet transforms Fast Fourier transforms Signal processing

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K-means based clustering on mobile usage for social network analysis purpose

K-means based clustering on mobile usage for social network ...

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international conference on Advanced Information Management and Service

作者： Xu Yang Yapeng Wang Dan Wu Athen Ma MPI-QMUL Information Systems Research Centre Macao Polytechnic Institute Macao China International School Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering and Computer Science Queen''s Mary University of London London UK

the development of mobile network technology provides a great potential for social networking services. this paper studied data mining for social network analysis purpose, which aims at find people's social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT's Reality Mining project is employed. the classification model presented in this project provides a new approach to find the proximity between users - based on their registration frequencies to specific cellular towers associated their working places. K-means Algorithm is applied for clustering, and we find the result could achieve the highest accuracy 0.823 at the number groups k = 6. the clustering result successfully reflects the higher proximity (at work) for the intra-class subjects.

关键词： Poles and towers Data mining Social network services Mobile communication Mobile handsets Classification algorithms Context

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Wavelet use for reduction of watershed transform over-segmentation in biomedical images processing

Wavelet use for reduction of watershed transform over-segmen...

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IEEE EMBS international conference on Information Technology Applications in Biomedicine (ITAB)

作者： Aleš Procházka Oldrich Vysata Eva Jerhotova Department of Computing and Control Engineering Department of Computing and Control Engineering Prague 6 Czech Republic Neurocenter Caregroup Rychnov nad Kněžnou Czech Republic

Problems of multi-dimensional signal enhancement, segmentation, feature extraction and components classification is essential in many engineering and biomedical applications. the paper is devoted to the use of watershed transform and wavelet transform for MR image components detection and discussion of over-segmentation problems. the goal of the paper is in (i) analysis of image de-noising, (ii) discussion of image enhancement, and (iii) multi-resolutional approach application for reduction of over-segmentation problems. Proposed algorithms include the use of wavelet transform and gradient methods in the preprocessing stage and application of the watershed transform for enhanced images. Resulting algorithms are verified for simulated images and applied for a selected MR biomedical images containing different structures.

关键词： Image segmentation Biomedical imaging Image edge detection Robustness discrete wavelet transforms Time frequency analysis

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A joint initiative to support the semantic interoperability within the giida project 6

A joint initiative to support the semantic interoperability ...

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6th international Symposium on Geo-Information for Disaster Management, Gi4DM 2010

作者： Plini, P. Di Franco, S. De Santis, V. Uricchio, V.F. De Carlo, D. D'Arpa, S. De Martino, M. Albertoni, R. 300 Italy CNR Water Research Institute Viale Francesco De Blasio 5 Bari70132 Italy CNR Institute of Applied Mathematics and Information Technology Via de Marini 6 Genova16149 Italy

the GIIDA project aims to develop a digital infrastructure for the spatial information within CNR. It is foreseen to use semantic-oriented technologies to ease information modeling and connecting, according to international standards like the ISO/IEC 11179. Complex information management systems, like GIIDA, will take benefit from the use of terminological tools like thesauri that make available a reference lexicon for the indexing and retrieval of information. Within GIIDA the goal is to make available the EARth thesaurus (Environmental Applications Reference thesaurus), developed by the CNR-IIA-EKOLab. A web-based software, developed by the CNR-Water Research Institute (IRSA) was implemented to allow consultation and utilization of thesaurus through the web. this service is a useful tool to ensure interoperability between thesaurus and other systems of the indexing, with, the idea of cooperating to develop a comprehensive system of knowledge organization, that could be defined integrated, open, multi-functional and multilingual. Currently the system is available in multiple languages mode (Italian - English) and navigation can be done in the following ways: Alphabetical, Hierarchical and for themes. A full search allows to find any term by searching for the whole term or a part of it and as well as allows to filter the results by themes. Within a collaborative initiative with the CNR-Institute of applied mathematics and Information Technology (IMATI) a SKOS (Simple Knowledge Organization System) version of EARth was developed. this will ensure the possibility to support the use of the thesaurus within the framework of the Semantic Web in order to be used in decentralized metadata applications. © 2010 Copernicus GmbH. All rights reserved.

关键词： thesauri

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Minimal arithmetic thickness connecting discrete planes

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discrete applied mathematics 2009年第3期157卷 500-509页

作者： Jamet, Damien Toutant, Jean-Luc Loria Univ Nancy 1 F-54506 Vandoeuvre Les Nancy France Univ Montpellier 2 LIRMM CNRS F-34392 Montpellier France

While connected arithmetic discrete lines are entirely characterized, only partial results exist for the more general case of arithmetic discrete hyperplanes. In the present paper, we focus on the three-dimensional case. that is on arithmetic discrete planes. thanks to arithmetic reductions on a vector n, we provide algorithms either to determine whether a given arithmetic discrete plane with n as normal vector is connected, or to compute the minimal thickness for which an arithmetic discrete plane with normal vector n is connected. (c) 2008 Published by Elsevier B.V.

关键词： discrete geometry Arithmetic discrete planes Connectedness

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SUBOPTIMAL DUAL CONTROL algorithms FOR discrete-TIME STOCHASTIC SYSTEMS UNDER INPUT CONSTRAINT

SUBOPTIMAL DUAL CONTROL ALGORITHMS FOR DISCRETE-TIME STOCHAS...

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6th international conference on Informatics in Control, Automation and Robotics

作者： Krolikowski, Andrzej Horla, Dariusz Poznan Tech Univ Inst Control & Informat Engn Poznan Poland

ISBN: (纸本)9789896740016

the paper considers a suboptimal solution to the dual control problem for discrete-time stochastic systems under the amplitude-constrained control signal. the objective of the control is to minimize the two-step quadratic cost function for the problem of tracking the given reference sequence. the presented approach is based on the MIDC (Modified Innovation Dual Controller) derived from an IDC (Innovation Dual Controller) and the TSDSC (Two-stage Dual Suboptimal Control. As a result, a new algorithm, i.e. the two-stage innovation dual control (TSIDC) algorithm is proposed. the standard Kalman filter equations are applied for estimation of the unknown system parameters. Example of second order system is simulated in order to compare the performance of proposed control algorithms. Conclusions yielded from simulation study are given.

关键词： Input constraint Suboptimal dual control

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Generic iterative subset algorithms for discrete tomography

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discrete applied mathematics 2009年第3期157卷 438-451页

作者： Batenburg, K. J. Sijbers, J. Univ Antwerp Vis Lab B-2610 Antwerp Belgium

discrete tomography deals with the reconstruction of images from their projections where the images are assumed to contain only a small number of grey values. In particular, there is a strong focus on the reconstruction of binary images (binary tomography). A variety of binary tomography problems have been considered in the literature, each using different projection models or additional constraints. in this paper, we propose a generic iterative reconstruction algorithm that can be used for many different binary reconstruction problems. In every iteration, a subproblem is solved based on at most two of the available projections. Each of the subproblems can be solved efficiently using network flow methods. We report experimental results for various reconstruction problems. Our results demonstrate that the algorithm is capable of reconstructing complex objects from a small number of projections. (c) 2008 Elsevier B.V. All rights reserved.

关键词： discrete tomography Network flows

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Gall bladder ultrasonic image analysis by using discrete wavelet transform

Gall bladder ultrasonic image analysis by using discrete wav...

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6th international conference on Electrical and Electronics Engineering, ELECO 2009

作者： Karahoda, Bertan Köktürk, Gülden Dokuz Eylül University Mechanical Engineering Department Izmir Turkey Dokuz Eylül University Electrical-Electronics Engineering Department Izmir Turkey

ISBN: (纸本)9789944898188

the edge detection algorithms are important in biomedical image analysis. In this work, the histogram equalization and the discrete wavelet transform techniques were used to improve the quality of the gall bladder ultrasonic images for edge detection. Also the median filtering algorithm was used after applying the both techniques. then the performances of the two algorithms were compared by several performance measures such as image entropy, paired t-test, and CPU time.

关键词： discrete wavelet transforms

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