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2014 International Conference on Multimedia Computing and Systems, ICMCS 2014

作者： Kalache, Mohamed Aissa Fergani, Lamya Speech Communication and Signal Processing Laboratory Faculty of Electronic and Computer Science University of Science and Technology Houari Boumediene Algiers Algeria

ISBN: (纸本)9781479938254

In RFID systems, one of the problems that we must solve is the collision between tags which lowers their efficiency. Two main approaches to tag collision are generally adopted: ALOHA and Tree algorithms. This paper reviews several existing anti-collision algorithms and compares their performances through different simulation experiments. © 2014 IEEE.

关键词： Collision avoidance

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Zernike moments and histogram of oriented gradient descriptors for face recognition from video sequence 2

Zernike moments and histogram of oriented gradient descripto...

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2nd World Conference on Complex Systems, WCCS 2014

作者： Chelali, Fatma Zohra Djeradi, Amar Speech communication and signal processing Laboratory Faculty of electronics and computer science University of science and technology Houari Boumedienne USTHB Box No 32 Algiers16111 Algeria

ISBN: (纸本)9781479946471

The aim of our work is to investigate the use of two powerful feature descriptor known as Zernike moments and histogram of oriented gradient (HOG) for facial images extracted from a video sequence uttered ten times by four speakers where MLP neural network classifier is used to identify the correct speaker. We've demonstrated good recognition rate obtained for both Zernike moments and HOG descriptors. © 2014 IEEE.

关键词： Face recognition

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Robust Multimodal Biometric Identification System Using Finger-Knuckle-Print features

Robust Multimodal Biometric Identification System Using Fing...

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International Conference on Control, Engineering & Information Technology

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Univ Ouargla Fac. des nouvelles technologies de l'information et de la communication Lab. de Genie Electrique Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB School of Computing Engineering and Information Sciences Northumbria University

ISBN: (纸本)9781479982134

Automatic personal identification is becoming an increasingly important requirement in a variety of applications like access control, surveillance systems and physical buildings. In recent years, biometric systems are used in these fields, offering greater convenience and several advantages over traditional security. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) images. In this study, the finger image is characterized by the two dimensional Bloc based Discrete Cosine Transform (2D-BDCT) coefficients. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same finger is transformed by the two dimensional Discrete Fourier Transform (2D-DFT). The response of this transformation is directly used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. The results on a medium-size database, 150 users, show good identification performance based on individual modalities as well as after fusing multiple finger types.

关键词： Biometrics Identification FKP 2D-BDCT 2D-DFT HMM Data fusion

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A new structure for acoustic echo cancellation in double-talk scenario using auxiliary filter

A new structure for acoustic echo cancellation in double-tal...

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International Workshop on Acoustic signal Enhancement (IWAENC)

作者： Mahfoud Hamidia Abderrahmane Amrouche Faculty of Electronics and Computer Science USTHB Speech Communication and Signal Processing Laboratory (LCPTS) Algeria

In this paper, a new structure for acoustic echo cancellation is presented. The role of acoustic echo canceller (AEC) is to remove undesirable acoustic echoes in communication systems. However, in double-talk case the performance of the AEC is degraded, thus, a double-talk detector (DTD) must be used for controlling the AEC. A new structure for AEC using an auxiliary adaptive filter is proposed in this paper. Experimental results using speech signals obtained from the TIMIT database show an improvement in acoustic echo cancellation by the proposed structure compared to the standard structure of adaptive filter controlling by the DTD.

关键词： Adaptive filters speech Echo cancellers Detectors Vectors Conferences

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A New Multi-Class WSVM Classification to Imbalanced Human Activity Dataset

A New Multi-Class WSVM Classification to Imbalanced Human Ac...

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The 6th International Conference on computer Research and Development

作者： M'hamed B.Abidine Belkacem Fergani Speech Communication & Signal Processing Laboratory Faculty of Electronics and Computer Sciences USTHB

This paper is concerned with the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classification algorithms. To deal this problem, we propose a new version of the multi-class Weighted Support Vector Machines(WSVM) method to perform automatic recognition of activities in a smart home environment. Then, we compare this approach with CRF, k-NN and SVM considered as the reference methods. Our experimental results carried out on various real world imbalanced datasets show that the new WSVM is capable of solving the class imbalance problem by improving the class accuracy of activity classification compared to other methods.

关键词： Imbalanced data support vectors machines activity recognition machine learning

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Acoustic analysis of lip-velarization of consonants with the locus equations

Acoustic analysis of lip-velarization of consonants with the...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Leila Falek Hocine Teffahi Amar Djeradi Electronics and Computer Science Faculty USTHB Speech communication and signal processing laboratory El alia Algiers

We used locus equations for characterizing the two berber consonants “lip-vélarized” /gw/ and /kw/. The aim is to show that these two phonemes are consonants distinct from their homologous velar /g/ and /k/. Th... 详细信息

ISBN: (纸本)9781479938254

关键词： Equations Protocols

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Investigation of DCT/PCA combined with Kohonen classifier for human identification

Investigation of DCT/PCA combined with Kohonen classifier fo...

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International Conference on Electrical Engineering

作者： Fatma zohra CHELALI A. DJERADI N. CHERABIT Speech communication and signal processing laboratory LCPTS Faculty of Electronicas and Computing University of Science and Technology Houari Boumediene USTHB National School of Technology ENST

ISBN: (纸本)9781467366748

In this work, we use DCT transform and eigenfaces method to describe our facial dataset and to build a face recognition system using Kohonen self organizing map neural network classifier. Ascendant hierarchical classification is used to describe the efficiency of DCT transform. A Kohonen SOM neural network classifier is used to classify an individual from three face databases: The ORL, computer vision and Caltech databases. These data sets are used to illustrate the effect of variation of illumination, background and facial rotation. Our study demonstrates the efficiency of DCT without PCA reduction especially in controlled environment.

关键词： DCT SOM PCA Face recognition

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Double-talk detector based on speech feature extraction for acoustic echo cancellation

Double-talk detector based on speech feature extraction for ...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Mahfoud Hamidia Abderrahmane Amrouche USTHB Faculty of Electronics and Computer Science Speech Communication and Signal Processing Laboratory (LCPTS) Bab Ezzouar Algiers Algeria

This paper presents a new method of the Double Talk Detection (DTD) for acoustic echo cancellation. The main goal is to remove the undesirable acoustic echoes produced by the coupling between the loudspeaker and the microphone of the mobile station. Acoustic Echo Canceller (AEC) based on adaptive filtering is an attractive solution. In this work, DTD using discriminative speech feature extraction from the near-end and the microphone speech signals was performed. The main purpose is to discriminate between these signals for sensing Double Talk (DT) periods. To evaluate the performance we use the NLMS algorithm to update the filter coefficients. Results obtained from the TIMIT database show that the performances of the proposed method are significantly improved, compared to the Normalized Cross Correlation (NCC) and Geigel methods.

关键词： speech Adaptive filters Microphones Echo cancellers Detectors Feature extraction

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Writer-independent Handwritten Signature Verification based on One-Class SVM classifier

Writer-independent Handwritten Signature Verification based ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Y. Guerbai Y. Chibani B. Hadjadji Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene (USTHB) Bab Ezzouar Algiers Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology HouariBoumediene (USTHB) 32 El Alia Bab Ezzouar 16111 Algiers Algeria

The limited number of writers and the lack of forgeries as counterexample to construct the systems is the main difficulty task for designing a robust off-line Handwritten Signature Verification System (HSVS). In this paper, we propose to study the influence of writer's number using conjointly the curvelet transform and the One-Class Support Vector Machine (OC-SVM), which takes in consideration only genuine signatures. The design of the HSVS is based on the writer-independent approach. Experimental results conducted on the standard CEDAR and GPDS datasets demonstrate that the proposed method allows achieving the lowest Average Error Rate with a limited number of writers.

关键词： Support vector machines Transforms Error analysis Hidden Markov models Forgery

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