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检索条件"机构=Signal and Image Processing Laboratory Department of Electrical and Electronics Engineering"

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Sequential nonnegative tucker decomposition on multi-way array of time-frequency transformed event-related potentials

Sequential nonnegative tucker decomposition on multi-way arr...

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IEEE Workshop on Machine Learning for signal processing

作者： Fengyu Cong Guoxu Zhou Qibin Zhao Qiang Wu Asoke K. Nandi Tapani Ristaniemi Andrzej Cichocki Department of Mathematical Information Technology University of Jyväskylä Finland Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Japan School of Information Science and Engineering Shandong University Jinan Shandong China Department of Electrical Engineering and Electronics University of Liverpool UK

Tensor factorization has exciting advantages to analyze EEG for simultaneously exploiting its information in the time, frequency and spatial domains as well as for sufficiently visualizing data in different domains concurrently. Event-related potentials (ERPs) are usually investigated by the group-level analysis, for which tensor factorization can be used. However, sizes of a tensor including time-frequency representation of ERPs of multiple channels of multiple participants can be immense. It is time-consuming to decompose such a tensor. The low-rank approximation based sequential nonnegative Tucker decomposition (LraSNTD) has been recently developed and shown to be computationally efficient with respect to some benchmark datasets. Here, LraSNTD is applied to decompose a fourth-order tensor representation of ERPs. We find that the decomposed results from LraSNTD and a benchmark nonnegative Tucker decomposition algorithm are very similar. So, LraSNTD is promising for ERP studies.

关键词： Tensile stress Feature extraction Electroencephalography Time frequency analysis Approximation methods Algorithm design and analysis signal processing algorithms

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Multimodal biometric person recognition system based on fingerprint & Finger-Knuckle-Print using correlation filter classifier

Multimodal biometric person recognition system based on fing...

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IEEE International Conference on Communications (ICC)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Algiers Algeria Department of Computer Science King Saud University Riyadh Saudi Arabia School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

ISBN: (纸本)9781457720529

Biometrics is an effective technology for personnel identity recognition, but uni-modal biometric systems which use a single trait for recognition will suffer from problems like noisy sensor data, non-universality, lack of distinctiveness of the biometric trait, and spoof attacks. These problems can be tackled by using multi-biometrics in the system. Hand-based person recognition provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. As one of the most popular biometric traits, fingerprints (FP) are widely used in personal recognition. However, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. In this paper, FP and FKP are integrated in order to construct an efficient multi-biometric recognition system based on matching score level and image level fusion. In this study we use the minimum average correlation energy (MACE) and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane performance measures, max peak value and peak-to-sidelobe ratio, to determine the effectiveness of this method. The experimental results showed that the designed system achieves an excellent recognition rate on the Hong Kong polytechnic university (PolyU) FKP and high resolution fingerprint database.

关键词： Correlation Databases Training Discrete wavelet transforms Biomedical imaging Biometrics (access control) Fingers

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ANALYSIS OF ONGOING EEG ELICITED BY NATURAL MUSIC STIMULI USING NONNEGATIVE TENSOR FACTORIZATION

ANALYSIS OF ONGOING EEG ELICITED BY NATURAL MUSIC STIMULI US...

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European signal processing Conference

作者： Fengyu Cong Anh Huy Phan Qibin Zhao Asoke K. Nandi Vinoo Alluri Petri Toiviainen Hanna Poikonen Minna Huotilainen Andrzej Cichocki Tapani Ristaniemi Department of Mathematical Information Technology University of Jyvaskyla Laboratory of Advanced Brain Signal Processing RIKEN Brain Science Institute Department of Electrical Engineering and Electronics The University of Liverpool Finnish Centre of Excellence in Interdisciplinary Music Research University of Jyvaskyla Cognitive Brain Research Unit Institute of Behavioral Sciences University of Helsinki

ISBN: (纸本)9781467310680

This study proposes a six-step approach to analyze ongoing EEG elicited by 512-second long natural music (modern tango). The spectrogram of the ongoing EEG was first produced, and then a fourth-order tensor including the spectrograms of multiple channels of multiple participants was decomposed via nonnegative tensor factorization (NTF) into four factors, including temporal, spectral and spatial components, and multi-domain features of all participants. We found one extracted temporal component by NTF significantly (p ＜ 0.01) correlated with the temporal course of a long-term music feature, 'fluctuation centroid';moreover, the power of posterior alpha activity was found to be associated with this temporal component. Hence, it looks promising to apply the proposed method for analyzing other ongoing EEG elicited by other natural stimuli.

关键词： EEG Ongoing Music Nonnegative tensor factorization Spectrogram

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Quadrature cosine-basis beamforming in wireless networks using dual-polarized circular multimode antenna arrays

Quadrature cosine-basis beamforming in wireless networks usi...

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17th Asia Pacific Conference on Communications, APCC 2011

作者： Ertuǧ, Özgür Gazi University Electrical and Electronics Engineering Department Telecommunications and Signal Processing Laboratory 06570 Ankara Turkey

ISBN: (纸本)9781457703881

Co-channel interference in wireless networks has a spatial nature due to geographically distributed users in the coverage area and has to be suppressed using beamfoming techniques to achieve higher spectral and power efficiencies. Beamforming with conventional center-fed dipole arrays has several drawbacks such as low array gain, low spatial selectivity due to wide main beamwidths and possibility of occurence for grating lobes due to spatial aliasing. Motivated with these drawbacks of DPA-BMF, a conventional beamforming method using dual-polarized circular multimode microstrip antennas, denoted as quadrature cosine-basis beamforming (QCB-BMF), is proposed in this work that achieves much higher beamforming performance than DPA-BMF at comparable compactness and improved ergonomy with respect to DPA-BMF as well as higher spectral and power efficiency when used in interference-limited wireless communication networks. © 2011 IEEE.

关键词： Beamforming

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Improving palmprint identification by combining multiple classifiers and using gabor filter

Improving palmprint identification by combining multiple cla...

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IEEE International Conference on electronics, Circuits and Systems (ICECS)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Laboratoire de Génie Électrique. Faculté des Sciences et de la Technologie et des Sciences de la Matière Université Kasdi Merbah Ouargla Ouargla Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty Algiers Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle upon Tyne UK Department of Computer Science King Saud University Riyadh Saudi Arabia

Biometrics technology has been attracting extensive attention from researchers of personal authentication due to the ever growing demand on access control, public security, forensics and e-banking. Diverse biometric systems have been now widely used in various applications. The palmprint is most popular modality that has the largest shares in the biometrics market. In this paper, an efficient online personal identification system based on palmprint is proposed. Firstly, the palmprint is filtered by the Gabor filter. The real and imaginary responses of the filtering are used to create three different feature vectors. Subsequently, the Hidden Markov Model (HMM), the Gaussian Mixture Model (GMM) and the Radial Basis Function (RBF) are used for modeling and so for classifying the feature vectors. The results of the three classifiers are combined using matching score level fusion strategy. The proposed system is tested and evaluated using PolyU 2D and 3D palmprint database of 250 persons. The obtained experimental results show that the system yields the best performance for identifying palmprint and it is able to provide the highest degree of biometrics-based system security.

关键词： Hidden Markov models Vectors Feature extraction Databases Computational modeling Authentication

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The texture classification using the fusion of decisions from different texture classifiers

The texture classification using the fusion of decisions fro...

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International Conference on electrical engineering/electronics, Computer, Telecommunications and Information Technology

作者： Pharsook, S. Kasetkasem, T. Larmsrichan, P. Siddhichai, S. Chanwimaluang, T. Isshiki, T. Department of Electrical Engineering Kasetsart University Thailand Image Processing Laboratory National Electronics and Computer Technology Center Thailand Department of Communication and Integrated Systems Tokyo Institue of Technology Japan

ISBN: (纸本)9781457704246

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%. © 2011 IEEE.

关键词： Support vector machines

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Hidden conditional random fields for classification of imaginary motor tasks from EEG data

Hidden conditional random fields for classification of imagi...

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19th European signal processing Conference, EUSIPCO 2011

作者： Delgado Saa, Jaime F. Çetin, Müjdat Signal Processing and Information Systems Laboratory Sabanci University Orhanli Tuzla 34956 Istanbul Turkey Department of Electrical and Electronics Engineering Universidad del Norte Barranquilla Colombia

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem;they do not suffer from some of the limitations of generative models;and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy. © 2011 EURASIP.

关键词： Brain computer interface

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Fingerprint Core Point Detection Using Ridge Curvature Analysis

Fingerprint Core Point Detection Using Ridge Curvature Analy...

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2011 3rd IEEE International Conference on Information Management and engineering(ICIME 2011)

作者： Sarut Rungrattana-Ubon Vutipong Areekul Kasetsart Signal&Image Processing Laboratory(KSIP Lab) Department of Electrical EngineeringFaculty of EngineeringKasetsart University

ISBN: (纸本)9781612842387

This paper presents a new and efficient approach to detect fingerprint core *** curvature of fingerprint is analyzed to find the highest curvature *** location of the core point is computed by using the focal point localization *** proposed algorithm outperforms most core point detection algorithms in literature in term of accuracy.

关键词： core point detection focal point detection fingerprint singular point detection

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MMRO: A Feature Selection Criterion for MR images Based on Alpha Stable Filter Responses

MMRO: A Feature Selection Criterion for MR Images Based on A...

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Iranian Machine Vision and image processing (MVIP)

作者： Reza Abbasi-Asl Emad Fatemizadeh Biomedical Image and Signal Processing Laboratory (BiSIPL) Department of Electrical Engineering Sharif University of Technology Tehran Iran

In feature-based image registration, feature selection and reduction methods play an important role in decreasing computational burden of these operations. In this paper, a new approach is introduced to reduce the dimension of extracted feature vectors of MR images. This approach is based on the selection of the maximum and minimum responses of the alpha stable filter for the MR images over the extracted features with different orientation in frequency domain. This algorithm selects the rotation invariant features which are suitable for image registration purposes. It has been shown that these features could efficiently describe the image elements. The discriminating ability of the features selected by the proposed method is compared with MRO method and an average improvement of 175 % was obtained based on the defined discriminating value.

关键词： Feature extraction Vectors image registration Frequency domain analysis image segmentation Robustness Equations

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Long term geo-electric potential signal analysis using the Teager Huang Transform

Long term geo-electric potential signal analysis using the T...

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8th IASTED International Conference on signal processing, Pattern Recognition, and Applications, SPPRA 2011

作者： Barkoula, Konstantina Ifantis, Apostolus Economou, George Department of Electrical Engineering Control System and Signal Processing Laboratory Technological Educational Institute of Patras Greece Department of Physics Electronics Laboratory University of Patras M. Alexandrou 1 Koukouli Patras Greece Campus of University of Patras Rion 26500 Greece

ISBN: (纸本)9780889868656

The long term geo-electric potential (LTGP) signals measured in a variety of experiment contexts have shown strong indications on their precursor nature to major earthquake events. Measurements of LTGP signals in Western Greece have been collected for more than twenty years. The measured signals are processed in several ways to extract features with a direct correlation to major earthquake events. In the field of spectral analysis, new methods that do not imply linearity and stationarity of the analysed signal have been introduced. Such a novel promising method is the Teager Huang Transform (THT) that combines Empirical Mode Decomposition (EMD) of a signal to a number of components on which the application of Teager Kaiser Operator (TKO) can lead to a sufficient estimation of the signal's spectral content. In this paper, we apply the TKO, EMD and THT on LTGP signals, aiming to have an accurate view of LTGP signals content precursor to major earthquake events.

关键词： Electric potential

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