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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S. A. Fattah W.-P. Zhu M. O. Ahmad Centre of Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

ISBN: (纸本)9781424438273

Formant frequency is a one of the most important speech feature, which has widespread applications in speech recognition, synthesis, and compression. In this paper, a new time-frequency domain scheme for the estimation of formant frequencies from noise-corrupted speech signals is presented. In order to overcome the adverse effect of noise, instead of conventional autocorrelation function (ACF), a repeated ACF (RACF) of the noisy speech is employed. Exploiting the characteristics of the zero lag, a set of equations containing the lower lags of the RACF of the noisy speech is used to estimate the formant frequencies. In order to avoid estimation errors that may occur in the case of weak formants, a frequency-domain algorithm is introduced utilizing the RACF of the observed speech. Formant frequency estimation accuracy is measured for different natural and synthetic vowels in noisy environments and even at low levels of signal-to-noise ratio, a better performance is obtained by the proposed scheme in comparison to some of the existing methods.

关键词： Time frequency analysis Frequency estimation Speech synthesis Working environment noise signal to noise ratio Frequency synthesizers Speech recognition signal synthesis Autocorrelation Speech enhancement

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A new improved model-based seizure detection using statistically optimal null filter

A new improved model-based seizure detection using statistic...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rajeev Yadav R. Agarwal M. N.S. Swamy Center of Signal Processing and Communications (CENSIPCOM) Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

A patient-specific model-based seizure detection method using statistically optimal null filters (SONF) has been recently proposed to aid the review of long-term EEG [1, 2]. The method relies on the model of a priori known seizure (template pattern) for subsequent detection of similar seizures. Artifacts, non-epileptic EEG rhythms, and at times modeling errors lead to increased false or missed detections. In this paper, we present a new improved model-based seizure detection that introduces a pre-processing block for artifact rejection, an adaptive technique of modeling the template patterns, and a new evolution-based classifier. The proposed classifier tracks the temporal evolution of seizure to improve the classification accuracy. With the help of simulated EEG, we illustrate the significance and need for these modifications. Further, performance of the complete algorithm is tested on single channel depth EEG of seven patients, and compared with the previous approaches. In terms of sensitivity and specificity, the proposed method resulted in 84% and 100%, method of 65% and 84%, and method of, 84% and 90% respectively. An overall performance improvement is seen as enhanced detection sensitivity and reduced false positives. This is preliminary result on seven patient data.

关键词： Electroencephalography Brain modeling Rhythm Filters Monitoring Epilepsy Sampling methods Frequency USA Councils Testing

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Spectral derivative feature coding for hyperspectral signature analysis

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Pattern Recognition 2009年第3期42卷 395-408页

作者： Chang, Chein-I Chakravarty, Sumit Chen, Hsian-Min Ouyang, Yen-Chieh Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Environmental Restoration and Disaster Reduction Research Center National Chung Hsing University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Radiology China Medical University Hospital Taichung Taiwan

This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC. © 2008 Elsevier Ltd. All rights reserved.

关键词： Spectrum analysis

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Composite Kernels for Fusing Colour Information in Face Verification Systems

Composite Kernels for Fusing Colour Information in Face Veri...

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International Conference on Digital Image processing, ICDIP

作者： Fahimeh Salimi Mohammad T. Sadeghi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

In this work, a novel method of fusing colour information in feature level is proposed considering a face verification system. For this purpose, composite kernels which have been already used in support vector machine classifier is applied within the framework of the generalised discriminant analysis (GDA) algorithm. The performance of the resulting system is evaluated using the XM2VTS face database and its associated experimental protocols. Our experiments show that by combining colour information using the proposed approach the good classification performance demonstrated by the kernel based methods can be further improved.

关键词： Kernel Principal component analysis Feature extraction Support vector machines Support vector machine classification signal processing algorithms Face recognition Linear discriminant analysis Color Digital signal processing

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Fast face recognition using a combination of image pyramid and hierarchical clustering algorithms

Fast face recognition using a combination of image pyramid a...

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International Conference on Wireless communications and signal processing (WCSP)

作者： Hajar Momeni Mohammad T Sadeghi Hamid R Abutalebi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogram using a Hierarchical Agglomerative Clustering (HAC) algorithm and searching the database images from coarse to fine resolutions using image pyramids are two important groups of techniques widely used for dealing with this problem. In this paper, a novel algorithm is proposed by combining these methods within the framework of a face recognition system. The search process is performed in a coarse-to-fine manner using image pyramids. On the bottom level of the pyramid (the finest resolution), a set of dendrograms is formed using the HAC algorithm. Our experimental studies show that the recognition process can be speeded up by a factor of around 65 compare to the basic nearest neighbour classifier. In such a condition, however, the recognition rate is slightly reduced.

关键词： Face recognition Clustering algorithms Prototypes Image resolution signal processing algorithms Image databases Computational complexity Fingerprint recognition Face detection Laboratories

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Iteratively detected MIMO-OFDM twisted-pair transmission schemes

Iteratively detected MIMO-OFDM twisted-pair transmission sch...

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作者： Ahrens, Andreas Lange, Christoph Department of Electrical Engineering Computer Science Communications Signal Processing Group University of Technology Philipp-Müller-Straße 14 23966 Wismar Germany T-Systems Enterprise Services GmbH Goslarer Ufer 35 Berlin 10589 Germany

ISBN: (纸本)9783642051968

Crosstalk between neighbouring wire pairs in multi-pair copper cables is an important disturbance, which essentially limits the transmission quality and the throughput of such cables. For high-rate transmission, often the strong near-end crosstalk (NEXT) disturbance is avoided or suppressed and only the far-end crosstalk (FEXT) remains as crosstalk influence. In this contribution the FEXT impact in iteratively detected MIMO-OFDM transmission schemes is studied. Extrinsic information transfer (EXIT) charts are used for analyzing and optimizing the convergence behaviour of the iterative demapping and decoding. Our results show that FEXT is not necessarily a limiting factor if appropriate signal processing strategies are used. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Orthogonal frequency division multiplexing

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A theoretical model for spontaneous seizure generation based on Markov chain process

A theoretical model for spontaneous seizure generation based...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Farzaneh Shayegh Saeed Sadri Rasoul AmirFattahi Digital signal Processing Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Here a theoretical model for seizure generation is presented. This is done according to a simple physiological model of epileptic activities in which seizure occurrence is mainly attributed to imbalance of excitatory/inhibitory ratio. Although factors that play a role in ictogenesis are not exactly known, this process can be assumed as a stochastic process. Indeed, we claim that in neural synapses both excitatory and inhibitory gains change randomly. We propose that their values theoretically change as a tri-variant 2 nd -order Markov chain process. States of Markov chain must be selected such that any sudden change is avoided. Important result of this modeling is that epileptic behavior of a cortical area may appear in various manners not in a unique one. Thus different observations in seizure onset all are validated and it is proved that they do not contradict each other.

关键词： Brain modeling Epilepsy Electroencephalography Neurons signal generators Stochastic processes Neural engineering Digital signal processing Electronic mail Synchronous generators

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Performance analysis of the Neyman-Pearson fusion center for spectrum sensing in a Cognitive Radio network

Performance analysis of the Neyman-Pearson fusion center for...

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Eurocon

作者： Somayeh Mosleh Ali Akbar Tadaion Mostafa Derakhtian Signal Processing Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran Department of Electrical Engineering Shiraz University Shiraz Iran

A key technology in cognitive radio (CR) is spectrum sensing that senses the spectrum and reports the available vacant channels. However, due to some effects such as fading or shadowing, an individual sensor may not be able to reliably detect the existence of a primary user (PU). Cooperative spectrum sensing that is proposed to solve such problem, uses a distributed detection system to overcome the severe decadent of received signal strength at some locations in the network. This paper considers the performance of a distributed Neyman-Pearson (N-P) detection system consisting of N sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are mutually independent conditioned on both hypotheses. Theoretical analysis on the performance of this fusion center is carried out. We obtain the conditions for the fusion center to achieve an overall probability of detection that is greater than the local probability of detection of each sensor. Numerical results show that the AND, OR and majority decision fusion rules are the special cases of the N-P fusion rule.

关键词： Performance analysis Cognitive radio Chromium Frequency Sensor fusion Detectors Sensor systems Fading Intelligent networks Shadow mapping

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Decision Level Fusion of Colour Histogram Based Classifiers for Clustering of Mouth Area Images

Decision Level Fusion of Colour Histogram Based Classifiers ...

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International Conference on Digital Image processing, ICDIP

作者： Fahimeh Salimi Mohammad T. Sadeghi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Engineering Research Centre Yazd University Yazd Iran

It is well known that in many situations combining diverse classifiers can improve the performance of a classification system. In this paper, a new histogram based lip segmentation technique is proposed considering local kernel histograms in different illumination invariant colour spaces. The histogram is computed in local areas using two Gaussian kernels; one in the colour space and the other in the spatial domain. Using the estimated histogram, the posterior probability associated to non-lip class is then computed for each pixel. This process is performed considering different colour spaces. A weighted averaging method is then used for fusing the posterior probability values. As the result a new score is obtained which is used for labeling the pixels as lip or non-lip. The advantage of the proposed method is that the segmentation process is totally unsupervised. So, the method is robust against different variations such as variation in lip shape, skin colour, facial hair, illumination, etc. Moreover, an improved performance is achieved by fusing colour information.

关键词： Histograms Mouth Kernel Lighting Diversity reception Image segmentation Color Labeling Robustness Shape

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Incremental learning in nonstationary environments with controlled forgetting

Incremental learning in nonstationary environments with cont...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ryan Elwell Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Department Rowan University Glassboro NJ USA

We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.

关键词： Neutron spin echo Algorithm design and analysis Voting

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