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MSVQ design for packet networks with application to LSF quantization

MSVQ design for packet networks with application to LSF quan...

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International Conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： H. Khalil K. Rose Signal Compression Laboratory Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

The design of a multi-stage vector quantizer (MSVQ) based source-channel coding system is optimized for packet networks. Resilience to packet loss is further enhanced by a proposed interleaving approach that ensures that a single lost packet only eliminates a subset of the vector stages. The design is optimized while taking into account compression efficiency, packet loss rate, and the interleaving technique in use. The new source-channel-optimized MSVQ is tested on memoryless line spectral frequency (LSF) parameter quantization in speech coders. A source-channel-optimization MSVQ is shown to yield a gain of up to 2.0 dB in SNR, for coding the LSFs, over traditional MSVQ and to substantially enhance the robustness of packetized speech transmission. Although the formulation is given in the context of packet networks, the work is directly extendible to the broader category of erasure channels.

关键词： Quantization Robustness Decoding Interleaved codes Design optimization Resilience Laboratories signal design Speech coding Application software

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Fractal modelling of residues in linear predictive coding of speech

Fractal modelling of residues in linear predictive coding of...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Epiphany Vera 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 a novel approach of fractal modelling and coding of residuals for excitation in the linear predictive coding of speech. This work was motivated by reducing the bit rate to 1200 bps, while maintaining a good quality of speech. Linear prediction based speech coders differ primarily in the modelling of the residual. The design trade-off in the modelling of the residual is between quality and bit-rate. In this paper fractal modelling is used to model the residual. We show that fractal modelling reduces the bit-rate while maintaining quality. A 6 kbps speech coder was implemented using the piecewise self-affine fractal model. The new coder has a signal-to-noise ratio of 10.9 dB. An informal subjective measure found the perceptual quality to be comparable to that of the 13 kbps GSM coder.

关键词： Fractals Predictive models Linear predictive coding Speech coding Interpolation Magnetic materials Speech recognition signal to noise ratio Speech codecs GSM

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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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Challenges in the Design of Adaptive, Intelligent and Cognitive Systems

Challenges in the Design of Adaptive, Intelligent and Cognit...

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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

Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive manner. Such cognitive machines ought to be aware of their environments which include not only other machines, but also human beings. Such machines ought to understand the meaning of information in more human-like ways by grounding knowledge in the physical world and in the machines' own goals. The motivation for developing such machines range from self-evidenced practical reasons such as the expense of computer maintenance, to wearable computing in health care, and gaining a better understanding of the cognitive capabilities of the human brain. To achieve such an ambitious goal requires solutions to many problems, ranging from human perception, attention, concept creation, cognition, consciousness, executive processes guided by emotions and value, and symbiotic conversational human-machine interactions. This paper discusses some of the challenges emerging from this new design paradigm, including systemic problems, design issues, teaching the subjects to undergraduate students in electrical and computer engineering programs, research related to design.

关键词： Intelligent systems Humans Machine intelligence Wearable computers Grounding Medical services Cognition Symbiosis Man machine systems Education

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Comparison of entropy-based characterization of lightning strike maps using planar and spherical coordinates

Comparison of entropy-based characterization of lightning st...

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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 Insulation Engineering and Testing Department Manitoba Hydro Winnipeg MAN Canada

In this paper, effects of including the curvature of the Earth in the characterization of the lightning strike maps (LSMs) are studied. Lightning strikes can adversely affect the power generation and distribution systems, and behaviour prediction of such LSMs is of interest for protection and planning in the power industry. Characterization is one of the important stages of the behaviour prediction. We have shown that multifractal measures such as the Renyi fractal dimension spectrum (RS) are appropriate for characterization of such LSMs which are self-affine. The computation of the RS is based on estimating the probability density function (pdf) of the lightning strikes distribution. Since the LSMs range from very large to low densities over a large geographical area such as Manitoba, computing this pdf in planar coordinates is subject to a nonlinear error due to the curvature of the Earth. Thus, modelling and calculating the maps in spherical coordinates yields a better estimation of the pdf. The data of the LSMs have been collected by the Canadian Lightning Detection Network (CLDN) during the year 2002. The LSMs of Manitoba are characterized through regular (using planar coordinates) and modified (using spherical coordinates) RS techniques. The results indicate that regular RS technique alters the characteristics of the feature space, especially in latitudinal direction, and causes sparser features in the feature space, while using the modified RS technique alleviates all these problems, and provides a more accurate characterization which is necessary for a reliable classification of the maps

关键词： Lightning Earth Fractals Power generation planning Power system protection Power system planning Power industry Distributed computing Probability density function Yield estimation

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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

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 and fuzzy classification of lightning strike maps

Multifractal characterization and fuzzy classification of li...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

This paper investigates optimal classification of lightning strike maps (LSMs), using regular and fuzzy K-means classifiers. It also describes a novel approach for measurements of the physical LSMs. Lightning strikes are physical phenomena, which have adverse effects on power transmission. Modelling and classification of the LSMs can lead to prediction of their time-space behaviour, and enhance the protection of the existing and new power systems and transmission lines. Prediction requires the classification of the simulated results and physical data, and comparing them together using characterization. Since the LSMs are highly nonlinear, nonstationary, and stochastic, ordinary models and analyses are incapable of simulating and characterizing these phenomena. Since self-affinity of such maps is an indication of multifractality, percolation models and complexity measures such as the Renyi fractal dimension spectrum (RS) and Mandelbrot singularity spectrum (MS) are utilized. The data of the LSMs have been obtained through the Canadian Lightning Detection Network (CLDN) for Manitoba in the year 2002. The data have been characterized by both the RS and MS. Features have been extracted using the RS. Classification employed the regular and fuzzy K-means methods. For each classification method, 50 tests have been utilized, and classification performance for 2 to 15 classes has been investigated through Davis-Bouldin criterion. The fuzzy K-means classifier identified 6 optimal classes with a confidence level of 0.78, while the regular K-means algorithm could not distinguish the classes.

关键词： Fractals Lightning Power system modeling Power system simulation Predictive models Power system protection Transmission line measurements Power transmission Power system analysis computing Power system measurements

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Image denoising for reduced-search fractal block coding

Image denoising for reduced-search fractal block coding

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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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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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