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Power system transient modelling and classification

Power system transient modelling and classification

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Canadian Conference on electrical and computer engineering (CCECE)

作者： J. Chen W. Kinsner B. Huang Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents a method of modelling of power transients and their classification. A discrete wavelet transform and multifractal analysis based on a variance fractal dimension trajectory technique are used as tools to analyze the transients for feature extraction. A probabilistic neural network is used as a classifier for classification of transients associated with power system faults and switching. Experiments show that the classification system can achieve classification rate of 99% for power transients, and is robust in noisy environments.

关键词： Power system transients Power system modeling Discrete wavelet transforms Fractals Analysis of variance Transient analysis Wavelet analysis Feature extraction Neural networks Power system faults

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Adaptive wavelet subband coding for music compression

Adaptive wavelet subband coding for music compression

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data compression Conference (DCC)

作者： K. Ferens W. Kinsner Department of Electrical & Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper describes modelling of the coefficient domain in wavelet subbands of wideband audio signals for low-bit rate and high-quality compression. The purpose is to develop models of the perception of wideband audio signals in the wavelet domain. The coefficients in the wavelet subbands are quantized using a scheme that adapts to the subband signal by setting the quantization step size for a particular subband to a size that is inversely proportional to the subband energy, and then, within a subband, by modifying the energy determined step size as inversely proportional to the amplitude probability density of the coefficient. The amplitude probability density of the coefficients in each subband is modelled using learned vector/scalar quantization employing frequency sensitive competitive learning. The source data consists of 1-channel, 16-bit linear data sampled at 44.1 kHz from a CD containing major classical and pop music. Preliminary results show a bit-rate of 150 kbps, rather than 705.6 kbps, with no perceptual loss in quality. The wavelet transform provides better results for representing multifractal signals, such as wide band audio, than do other standard transforms, such as the Fourier transform.

关键词： Multiple signal classification Wavelet transforms Psychoacoustic models Wideband Frequency Fourier transforms signal resolution data compression Wavelet domain Fractals

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A study of microscopic images of human breast disease using competitive neural networks

A study of microscopic images of human breast disease using ...

引用

Canadian Conference on electrical and computer engineering (CCECE)

作者： R. Allan W. Kinsner Department of Electrical and Computer Engineering Signal & Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

ISBN: (纸本)0780367154

Competitive neural networks offer a unique opportunity to extract features from medical images objectively. An advantage of this approach is that medical image analysis could be automated or semi-automated. This automation could lead to improved precision and accuracy of diagnostic interpretation, while semi-automation could achieve much the same goal and would serve as a natural stepping-stone to full automation. This paper shows that all types of competitive neural networks can extract general features from images obtained through a microscope of four types of human breast disease, two benign and two malignant. Assessed qualitatively, the features broadly encompass thresholding and edge detection. These features are extracted regardless of supervision or lack of supervision. To visual inspection, there are no obvious sharp distinctions between benign and malignant diagnoses, the most important distinction in tissue diagnosis.

关键词： Microscopy Humans Breast Diseases Feature extraction Neural networks Biomedical imaging Medical diagnostic imaging Image edge detection Automation

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Modelling of multifractal object boundaries

Modelling of multifractal object boundaries

引用

Canadian Conference on electrical and computer engineering (CCECE)

作者： S. Siddiqui W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper presents a new technique that combines fractal and wavelet analyses to model rough (nonsmooth) but crisp (one-pixel wide) object boundaries that have fractal or multifractal characteristics. The boundary is represented compactly by two sets of descriptors and control points. The first set contains information about complexities present on the boundary and is calculated using a fractal dimension analysis. The second set contains information about the shape of the boundary and is calculated by using wavelet analysis. We apply the midpoint displacement algorithm on the two sets of control points in order to reconstruct boundaries with the required fractal or multifractal dimension. The quality of reconstruction is measured using the Renyi fractal dimension singularity measure. Experimental results produced compression ratios in the range of 300:1 to 450:1, while preserving the complexities of the original boundary, as measured by the above multifractal metrics.

关键词： Fractals Image coding Wavelet analysis Image segmentation Image reconstruction Shape Information analysis Video compression Curve fitting Image analysis

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Complexity and its measures in cognitive and other complex systems

Complexity and its measures in cognitive and other complex s...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

ISBN: (纸本)9781424425389

The last few decades of physics, chemistry, biology, computer science, engineering, and social sciences have been marked by major developments of views on cognitive systems, dynamical systems, complex systems, complexity, self-organization, and emergent phenomena that originate from the interactions among the constituent components (agents) and with the environment, without any central authority. How can measures of complexity capture the intuitive sense of pattern, order, structure, regularity, evolution of features, memory, and correlation? This paper describes several key ideas, including dynamical systems, complex systems, complexity, and quantification of complexity. As there is no single definition of a complex system, its complexity and complexity measures too have many definitions. This papers also addresses some practical aspects of acquiring the observables.

关键词： Dynamical systems Complex systems Cognitive systems Complexity Complexity measures Monoscale Multiscale measures Complex systems cognitive function Complexity DYNAMICAL SYSTEMS emergent phenomena

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ECG compression with block encoding

ECG compression with block encoding

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Canadian Conference on electrical and computer engineering (CCECE)

作者： B. Huang W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents an electrocardiogram (ECG) frame (beat) compression scheme using block encoding and windowed-variance techniques, where the ECG frame has already been classified. compression of the complicated ECG frame is converted into a linear time registration problem of segments in this scheme. The segment in the frame is detected and partitioned by the windowed-variance technique. It is computationally inexpensive. A high compression ratio of nearly 50:1 is achieved and not related to the reconstruction error. The normalized percent root-mean-square difference is about 2.53% for a 10-minute ECG recording.

关键词： Electrocardiography Encoding data compression signal sampling Laboratories signal resolution Sampling methods Hospitals databases Telephony

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Transient analysis and genetic algorithms for classification

Transient analysis and genetic algorithms for classification

引用

Communications, Power and Computing Conference: WESCANEX

作者： J. Toonstra W. Kinsner Department of Electrical & Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents a method of generating unique fingerprints of radio transmitter turn-on transients. The fingerprinting system consists of the application of multiresolution wavelet analysis used to characterize the features contained in the transient followed by the use of a genetic algorithm to extract the wavelet coefficients that represent critical features of the transient. To measure the ability of the system to generate efficient and unique fingerprints, a neural network is used to classify the transients by their fingerprints. To test the noise sensitivity of the system, noisy transients were applied to a trained neural network, the network was able to positively classify noisy transients with 20 dB signal to noise ratios (SNR) and up. Experiments with real radio transients show that the system is able generate uniqiue fingerprints for absolute classification by a neural network for radios of differing model type as well as radios of the same model type.

关键词： Transient analysis Genetic algorithms Fingerprint recognition signal to noise ratio Neural networks Radio transmitters signal resolution Wavelet analysis Algorithm design and analysis Wavelet coefficients

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Multifractal characterization of synthetic ECG in the presence of coloured noise

Multifractal characterization of synthetic ECG in the presen...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The electrocardiogram (ECG) is a signal resulting from a complicated dynamical process. The paper describes an extension to a previous method of reconstruction and characterization of real ECG to a new "realistic" dynamical model of an ECG. A noise-free ECG is generated at 360 samples per second from the dynamical model, and contaminated with 10 dB of white, pink, or brown noise. The dynamical phase-space is then reconstructed. It is observed that the noise-free ECG has a Renyi fractal dimension spectrum (RS) bounded between 1 and 2. It is also observed that noise introduces a positive bias in the RS estimate. This bias is greater for uncorrelated noise, but more asymmetric with increasing correlation. These are the first characterizations of a realistic estimate of the noise-free ECG attractor. It is also the first analysis of the effect of noise correlation on the RS of ECGs in isolation of sampling effects. This demonstrates that a disagreement with the RS of a real ECG signal is not due to noise.

关键词： Fractals Electrocardiography Colored noise 1f noise signal processing Biomedical monitoring Noise generators Condition monitoring Equations signal analysis

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A study of outliers for robust independent component analysis

A study of outliers for robust independent component analysi...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： N. Gadhok W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The impact of outliers on the signal separation performance of an independent component analysis (ICA) algorithm is an important characteristic in assessing the algorithm's utility in real-world applications. If an ICA estimator has the property of B-robustness, the influence of an extreme point is bounded, leading to good separation performance in the presence of outliers. In recent work, major ICA estimators, such as FastICA, have been proven not to be B-robust. We seek to enhance the non-B-robust FastICA estimator by the introduction of K-means clustering for outlier mitigation. We compare our algorithm with the B-robust /spl beta/-divergence algorithm by conducting a simulation to reproduce published results. The paper demonstrates the utility of the K-means clustering algorithm to mitigate a class of outliers such that our ICA separation performance is at least equal to that of published results for the B-robust /spl beta/-divergence estimator.

关键词： Robustness Independent component analysis signal processing algorithms Clustering algorithms Higher order statistics Statistical analysis Blind source separation Vectors data compression Laboratories

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Separation performance of ICA on simulated EEG and ECG signals contaminated by noise

Separation performance of ICA on simulated EEG and ECG signa...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： M. Potter N. Gadhok W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Evaluates the performance of the extended-infomax independent component analysis (ICA) algorithm in a simulated biomedical blind source separation problem. Independent signals representing an alphawave and a heartbeat are generated and then mixed linearly in the presence of white or pink noise to simulate a one-minute recording of an electroencephalogram and electrocardiogram. The selected ICA algorithm separates the white and pink noises equally well. The maximum estimation signal-to-noise ratio of the source estimates is equivalent to the added noise level, so the separation is optimum to second-order. The higher-order demixing performance, as measured by the Amari index, indicates that when the noise contamination exceeds the mixing contamination the ICA separation is reduced. These results represent a lower bound to the performance of extended-infomax ICA in noisy, time-correlated electrophysiological conditions.

关键词： Independent component analysis Brain modeling Electroencephalography Electrocardiography 1f noise Contamination Analytical models Blind source separation Heart beat signal generators

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