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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： S. Bal W. Kinsner Data Networking and Software Telecommunications Research Laboratories Winnipeg MAN Canada Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper examines the process of image denoising to improve the efficiency of the reduced-search fractal block coding (FBC) of greyscale images by reducing the first-order entropy of the image. The reduced-search FBC is a lossy compression technique that exploits the block-wise self-affinity of an image where portions of the image are represented by scaled and isometrically transformed copies of other portions of the image. The efficiency of this process increases with increased redundancy which is the result of lowering the entropy. Image denoising is concerned with separating noise from an image and then suppressing the noise as much as possible without altering the image itself. In this paper spatial smoothing and wavelet denoising are compared. It is shown that denoising increases the efficiency of reduced-search FBC. Spatial smoothing, however, causes a loss of signal that wavelet denoising does not. In either case, the reconstruction qualities of the peak-signal-to-noise ratio at approximately 34 dB and compression ratios of 18.9:1 and higher have been achieved. This is an improvement over the 31 dB and 18.1:1 for non-denoised images.

关键词： Image denoising Fractals Block codes Image coding Entropy Noise reduction Smoothing methods Pixel Additive noise Quantization

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Single-Scale Measures for Randomness and Complexity

Single-Scale Measures for Randomness and Complexity

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IEEE International Conference on Cognitive Informatics

作者： W. Kinsner Institute of Industrial Mathematical Sciences Telecommunications Research Laboratory Canada Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper describes the use of single-scale measures to determine the level of randomness and complexity of a sequence. Such sequences originate from either various pseudorandom number generators or natural sources of white and coloured broadband noise. The paper provides a study of seven classes of sequences using the algorithmic complexity measures (the Kolmogorov-Chaitin complexity) and the probabilistic entropy-based measures (Shannon entropy). The study shows the fundamental differences between the two measures. The single-scale measures are adequate to determine the relative randomness and complexity of a sequence. However, they are not capable of revealing the hidden patterns in scale-invariant (self-affine) sequences. This paper identifies the need for new measures for such self-affine stochastic and chaotic sequences, and investigates if the existing techniques could be modified for the multiscale measures.

关键词： Random sequences Spread spectrum communication Noise generators Automatic testing Cryptography Random number generation Electric variables measurement Entropy Frequency measurement Position measurement

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Multifractal characterization for classification of network traffic

Multifractal characterization for classification of network ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

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

In this paper, a novel multifractal approach to the classification of self-affine network traffic is presented. The fundamental advantages of using multifractal measures include their boundedness and a very high compression ratio of a signature of the traffic, thereby leading to faster implementations, and the ability to add new traffic classes without redesigning the traffic classifier. The variance fractal dimension trajectory is used to provide a multifractal "signature" for each type of traffic over its duration, and the modelling of its statistical histograms provides further compression and generalization. Principal component analysis is used to reduce the dimensionality of the data, and the K-means clustering algorithm is used to assign classes to the data. A probabilistic neural network (PNN) with a locally optimal spread parameter is trained with these signatures, and a plot of the PNN percentage correct classification accuracy as the number of assigned classes increases reveals that there are most likely three classes in the traffic recording. Finally, an optimized PNN is trained with 50 % of the multifractal signatures sampled at regular intervals from the trajectory, and achieves a representative classification accuracy of 94.8 % when classifying previously unobserved self-affine network traffic.

关键词： Fractals Telecommunication traffic Communication system traffic control Neural networks Clustering algorithms Statistical analysis Local area networks Wide area networks data compression Laboratories

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Characterization and feature extraction of transient signals using multifractal measures

Characterization and feature extraction of transient signals...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： L. Sun W. Kinsner N. Serinken Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada Communications Rcscarch Centre Ottawa ONT Canada

This paper focuses on multifractal characterization and feature extraction of nonstationary temporal signals, particularly the turn-on transients for radio transmitter classification purposes. A transient signal contaminated by noise can be considered as an output from a chaotic dynamical system. The trajectory of such a system in phase space is often attracted to a bounded fractal object called strange attractor. Transient signals are preprocessed to obtain the corresponding strange attractor in phase space. Multifractal measures based on a generalized entropy are then applied to the strange attractor and features are extracted in terms of a fractal dimension spectrum. Experimental results show that there exists a strange attractor for the transient signal and the fractal dimension spectrum can characterize a transient signal uniquely, even with a relatively short time duration.

关键词： Feature extraction Fractals Transient analysis Radio transmitters Pollution measurement Chaotic communication signal analysis Sun data compression Laboratories

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compression and its metrics for multimedia

Compression and its metrics for multimedia

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IEEE International Conference on Cognitive Informatics

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

ISBN: (纸本)0769517242

Multimedia involves a myriad of data and multidimensional signals, including not only plain and formatted text, but also mathematical and other symbols, tables, vector and bitmap graphics, images, sound, animation, video, and interactive virtual reality objects. compression of such signals is usually necessary to fit them into the available communications channels and digital storage, or for data mining. This paper provides an overview of important compression methods and techniques, including lossless entropy coding techniques designed to reduce the redundancy in the critical multimedia material, as well as lossy coding techniques designed to preserve the relevancy of the noncritical multimedia material. Modern lossy techniques often employ wavelets, wavelet packets, fractals, and neural networks. Progressive image transmission is also employed to deliver the material quickly. The paper also addresses several approaches to blind separation of signal from noise (denoising) to improve the compression, and to the difficult question of objective and subjective image quality assessment through complexity metrics.

关键词： Image coding Wavelet packets Multidimensional systems Graphics Animation Video compression Virtual reality Communication channels data mining Entropy coding

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

Canadian Journal of Electrical and Computer Engineering

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Canadian Journal of Electrical and Computer Engineering 2002年第3期27卷 123-127页

作者： Potter, M. Gadhok, N. Kinsner, W. Dept. of Elec./Computer Engineering Signal/Data Compression Laboratory University of Manitoba Winnipeg Man. R3T 5V6 Canada

This paper evaluates the performance of the extended-infomax independent component analysis (ICA) algorithm in a simulated biomedical blind source separation problem. Independent signals representing an alpha-wave 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 an electrocardiogram. The selected ICA algorithm separates the white and pink noise 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

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Multifractal characterization for classification of telecommunications traffic

Multifractal characterization for classification of telecomm...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

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

A novel multifractal approach to telecommunications traffic classification is presented as an improvement over traditional traffic classifiers. The fundamental advantages of using multifractal measures include normalization and a very high compression ratio of a signature of the traffic, thus leading to more reliable and faster implementations, and the ability to add new traffic classes without redesigning the traffic classifier. The variance fractal dimension trajectory is used to provide a multifractal "signature" for each type of traffic over its duration. As a multifractal, the Renyi dimension spectrum is constructed to show the unique characteristics of each type of traffic. A probabilistic neural network is trained with the variance fractal dimension trajectory of the traffic, and has demonstrated 90.7% classification accuracy with a 585:1 compression ratio, and 90.4% classification accuracy with a 1024:1 compression ratio.

关键词： Fractals Telecommunication traffic Communication system traffic control Traffic control Neural networks Microwave integrated circuits Telecommunication network reliability Telecommunication control Local area networks Wide area networks

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A Biologically Motivated Phase-Offset to the Piecewise-Linear Model of Instantaneous Heart-Rate Interpolation

A Biologically Motivated Phase-Offset to the Piecewise-Linea...

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

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg Manitoba Canada R3T 5V6

This paper formalizes the general theory of instantaneous heart-rate (IHR) interpolation from electrocardiogram RR-intervals. This paper also introduces a novel extension to the current basic piecewise-linear interpolation method which allows the discontinuity of the IHR interpolation to be offset from the R-wave. Thus an entire heart cycle can be modelled with a constant IHR. This phase-offset piecewise-linear IHR interpolation model is derivedfrom the general theory of IHR interpolation and results are demonstrated on some true recorded RR-sequences. Recursive and batch matrix forms are derived. Stable solutions can be found for any offset not equal to it radians, though the IHR calculated with a nonzero offset has a significant change to the HRV statistics. Applications of this method to the synthesis of ECG with natural heart-rate variability is also discussed.

关键词： instantaneous heart-rate IHR heart-rate vari-ability HRV electrocardiogram ECG RR-interval.

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Some advances in cognitive informatics

Some advances in cognitive informatics

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IEEE International Conference on Cognitive Informatics

作者： W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering and the Institute of Industrial Mathematical Sciences and Telecommunications Research Laboratories TRLaboratories University of Manitoba Winnipeg MAN Canada

This paper summarizes a few recent developments in cognitive informatics, with special emphasis on signal processing for autonomic computing and its metrics.

关键词： Cognitive informatics signal processing Biomedical measurements Pollution measurement Fractals Acoustic noise Biological materials Biomedical materials Cognition Humans

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Progressive transmission of images through fractal surface interpolation

Progressive transmission of images through fractal surface i...

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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

This paper presents a progressive image transmission technique based on fractal surface interpolation (FSI). This technique is an extension of our previous work, and relies on the extraction and reconstruction of self-affine fractal surfaces with specified Hurst exponents, H/sup */. Fractional Brownian motion (fBm) through midpoint displacement (MPD) provides the basis for generating these surfaces between interpolation points. Progressive transmission is achieved by ordering increasing levels of detail within the FSI representation. This allows for quicker recognition of the transmitted or reconstructed image within the first 5 percent of the transmission process.

关键词： Fractals Interpolation Surface reconstruction Image reconstruction Brownian motion Image coding data compression Laboratories Image communication Image recognition

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