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signal classification through multifractal analysis and complex domain neural networks

Signal classification through multifractal analysis and comp...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： V. Cheung K. Cannons W. Kinsner J. Pear Department of Electrical and Computer Engineering Signal and Data Compression Laboratory Department of Psychology University of Manitoba Winnipeg MAN Canada

This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals is important because they are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals with an unknown number of classes.

关键词： Pattern classification Fractals signal analysis Neural networks signal processing Marine animals Stochastic systems Nonlinear systems Feature extraction data compression

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Progressive transmission of images using wavelets: evaluation using the Renyi generalized entropy

Progressive transmission of images using wavelets: evaluatio...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： R. Dansereau W. Kinsner TR Laboratories (Telecommunications Research Laboratories) Winnipeg MAN Canada Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper explores the quality of successive steps in a wavelet based progressive image transmission, using the Renyi generalized dimension D/sub q/. The Renyi generalized dimension is used to measure the multifractal complexity within an image giving us a tool for evaluating image quality other than the traditional PSNR. We apply this to a wavelet based progressive image transmission scheme through denoising, zerotree coding, and adaptive arithmetic coding to see how D/sub q/ changes as the image is reconstructed.

关键词： Entropy Image communication Image coding PSNR Fractals Noise reduction Arithmetic Image quality Energy measurement Image recognition

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A Relative Fractal Dimension Spectrum as a Complexity Measure

A Relative Fractal Dimension Spectrum as a Complexity Measur...

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Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

作者： W. Kinsner R. Dansereau Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University the Institute of Industrial Mathematical Sciences and Telecommunications Research Laboratories TRLa

This paper presents a derivation of a new relative fractal dimension spectrum, DRq. to measure the dissimilarity between two finite probability distributions originating from various signals. This measure is an extension of the Kullback-Leibler (KL) distance and the Renyi fractal dimension spectrum, Dq. Like the KL distance, DRq determines the dissimilarity between two probability distibutions X and Y of the same size, but does it at different scales, while the scalar KL distance is a single-scale measure. Like the Renyi fractal dimension spectrum, the DRq is also a bounded vectorial measure obtained at different scales and for different moment orders, q. However, unlike the Dq, all the elements of the new DRq become zero when X and Y are the same. Experimental results show that this objective measure is consistent with the subjective mean-opinion-score (MOS) when evaluating the perceptual quality of images reconstructed after their compression. Thus, it could also be used in other areas of cognitive informatics.

关键词： Monofractals and multifractals scalar and vector fractal dimensions Rényi generalized entropy, Rényi relative fractal dimension spectrum

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Consonant characterization using correlation fractal dimension for speech recognition

Consonant characterization using correlation fractal dimensi...

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Communications, Power and Computing Conference: WESCANEX

作者： A. Langi W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory and TRLabs (Telecommunications Research Laboratories) University of Manitoba Winnipeg MAN Canada

This paper presents a new and promising approach in characterizing speech consonants using a fractal model for speech recognition systems. Characterization of consonants has been a difficult problem because consonant waveforms may be indistinguishable in time or frequency domain. The approach views consonant waveforms as coming from a turbulent constriction (excitation) in a human speech production system, and thus exhibiting turbulent and noise like time domain appearance. However, it departs from the usual approach by modeling consonant excitation using chaotic dynamical systems capable of generating turbulent and noise-like excitations. The scheme employs correlation fractal dimension and Takens embedding theorem to measure fractal dimension from time-series observation of the dynamical systems. It uses linear predictive coding (LPC) excitation of twenty-two consonant waveforms as the time series. Furthermore, the correlation fractal dimension is calculated using a fast Grassberger algorithm. A preliminary observation shows encouraging results because every consonant results in a unique trend of fractal dimensions for different embedding dimensions and scales.

关键词： Fractals Speech recognition Chaos Noise generators Frequency domain analysis Humans Production systems Time domain analysis Linear predictive coding data compression

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Segmentation using multifractals for object-oriented video coding

Segmentation using multifractals for object-oriented video c...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： H. Chen P. Yahampath W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada Telecommunications Research Laboratory Winnipeg MAN Canada

This paper presents a study of the application of multifractal measures in the segmentation of a video sequence image for object-oriented video coding through the generalized Renyi entropy. Grey-level sequence images are analyzed from the point of view of strange attractors. Multifractal feature maps are used to extract the features from the video sequence image. The nonuniform property of the image is reflected in the singularity spectrum and the Mandelbrot spectrum. Each video-object plane (VOP) specifies a particular image sequence content and the textures in the different contents can be separated because similar textures generally will have an homogeneous property which can be characterized by the singularity and Mandelbrot dimension of the fractal sets. Consequently, we can achieve not only image segmentation, but also a separation of the video-object layers (VOLs).

关键词： Fractals Video coding Image segmentation Histograms Layout Object oriented modeling Video sequences Video compression Image processing MPEG 4 Standard

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Amplification of signal features using variance fractal dimension trajectory

Amplification of signal features using variance fractal dime...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Warren Grieder Laboratory Mathematical Sciences Research Institute USA Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory (VFDT) algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. These observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.

关键词： Fractals Speech analysis Pattern matching signal processing Magnetic materials Automatic speech recognition Natural languages Feature extraction Speech recognition Linear predictive coding

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Multifractal analysis and feature extraction of DNA sequences

Multifractal analysis and feature extraction of DNA sequence...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Hong Zhang Laboratory Mathematical Sciences Research Institute USA Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper presents feature extraction and estimations of multifractal measures for deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Levy walk, (ii) transforms the Levy walk into a signal spectrum, and (iii) breaks the spectrum into subspectra and treats each of these as an attractor from which the multifractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multifractal measures. Experimental results show that DNA is a multifractal, and that the multifractality changes depending upon the location (coding or noncoding region) in the sequence.

关键词： Fractals Feature extraction DNA Sequences

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A quantitative comparison of different mother wavelets for characterizing transients in power systems

A quantitative comparison of different mother wavelets for c...

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Canadian Conference on electrical and computer engineering 2005

作者： Safavian, L.S. Kinsner, W. Turanli, H. Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg Man. R3T 5V6 Canada Manitoba Hydro Winnipeg Man. R3C 2P4 Canada

This paper presents a study of wavelet analysis of transients in power systems. Short-term and nonstationary nature of transients necessitates the use of advanced signal processing techniques in order to detect, characterize and classify them. The paper examines the fundamental properties of a number of mother wavelets, and provides guidelines for the selection of suitable wavelets for the purpose of characterization and extraction of features of transients in power systems. As an indispensable part of such a study, the paper also presents the results of wavelet analysis of typical types of transients obtained using simulation. © 2005 IEEE.

关键词： Wavelet transforms

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Aquantitative comparison of different mother wavelets for characterizing transients in power systems

Aquantitative comparison of different mother wavelets for ch...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： L.S. Safavian W. Kinsner H. Turanli Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada Manitoba Hydro Winnipeg MAN Canada

This paper presents a study of wavelet analysis of transients in power systems. Short-term and non-stationary nature of transients necessitates the use of advanced signal processing techniques in order to detect, characterize and classify them. The paper examines the fundamental properties of a number of mother wavelets, and provides guidelines for the selection of suitable wavelets for the purpose of characterization and extraction of features of transients in power systems. As an indispensable part of such a study, the paper also presents the results of wavelet analysis of typical types of transients obtained using simulation

关键词： Power system transients Transient analysis Wavelet analysis signal analysis Power system simulation Power system analysis computing Wavelet transforms signal processing Guidelines Feature extraction

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Multifractal spectra of lightning strike maps using entropy and wavelet analyses

Multifractal spectra of lightning strike maps using entropy ...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： A. Faghfouri W. Kinsner D. Swatek Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada Manitoba Hydro Winnipeg MAN Canada

This paper describes a multifractal analysis of physical lightning strike maps obtained for Manitoba through the Canadian Lightning Detection Network. Cloud-to-ground discharges or lightning strikes are physical phenomena which may have adverse effects on power transmission and delivery. Predicting the geographical and temporal distribution of lightning strike maps through modelling can help power system designers to improve the protection of existing and new power systems and transmission lines. In order to compare the model to physical data, their characterization is required. Since lightning strike maps are highly nonlinear, nonstationary and stochastic, ordinary analyses and measures are insufficient to characterize these phenomena. Since self affinity of the lightning strike maps is the indication of multifractality, complexity measures such as the Renyi fractal dimension spectrum (RS) and Mandelbrot singularity spectrum (MS) are appropriate for such fractal analysis. In this paper, we investigate the multifractality of lightning strike maps in Manitoba through the RS and MS. Both methods generate similar results and show that the maps are multifractal. Comparing the range of results with a similar phenomenon, diffusion limited aggregates (DLA), supports the accuracy of calculations as well.

关键词： Fractals Lightning Entropy Wavelet analysis Power system modeling Power system protection Power system analysis computing Power system measurements Transmission line measurements Power transmission

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