A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

Image coding using vector quantization: a review

The paper describes the method of determining direction and frequency content of the brain activity flow. The method was formulated in the framework of the AR model. The transfer function matrix was found for multichannel EEG process. Elements of this matrix, properly normalized, appeared to be good estimators of the propagation direction and spectral properties of the investigated signals. Simulation experiments have shown that the estimator proposed by us unequivocally reveals the direction of the signal flow and is able to distinguish between direct and indirect transfer of information. The method was applied to the signals recorded in the brain structures of the experimental animals and also to the human normal and epileptic EEG. The sensitivity of the method and its usefulness in the neurological and clinical applications was demonstrated.

A new method of the description of the information flow in the brain structures

This paper discusses an asymmetric cryptosystem C* which consists of public transformations of compIerity O(m2n3) and secret transformations of complexity O((mn)2(m + logn)), where each complexity is measured in the total number of bit-operations for processing an mn-bit message block. Each public key of C* is an n-tuple of quadratic n-variate polynomials over GF(2m) and can be used for both verifying signatures and encrypting plaintexts. This paper also shows that for C* it is practically infeasible to extract the n-tuple of n-variate polynomials representing the inverse of the corresponding public key.

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Public quadratic polynomial-tuples for efficient signature-verification and message-encryption

With the advent of `multimedia', digital signal processing (DSP) of sound has emerged from the shadow of bandwidth limited speech processing to become a research field of its own. To date, most research in DSP applied to sound has been concentrated on speech, which is bandwidth limited to about 4 kilohertz. Speech processing is also limited by the low fidelity typically expected in the telephone network. Today, the main applications of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including perceptual measurement techniques and analysis/synthesis methods. Additional important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application-oriented research. The frequency range of wideband audio has an upper limit of 20 kilohertz and the resulting difference in frequency range and Signal to Noise Ratio (SNR) due to sample size must be taken into account when designing DSP algorithms. There are whole classes of algorithms that the speech community is not interested in pursuing or using. These algorithms and techniques are revealed in this book. This book is suitable for advanced level courses and serves as a valuable reference for researchers in the field. Interested and informed engineers will also find the book useful in their work.

Applications of digital signal processing to audio and acoustics

A PC-based system has been developed to automatically detect epileptiform activity in 16-channel bipolar EEGs. The system consists of 3 stages: data collection, feature extraction, and event detection. The feature extractor employs a mimetic approach to detect candidate epileptiform transients on individual channels, while an expert system is used to detect focal and nonfocal multichannel epileptiform events. Considerable use of spatial and temporal contextual information present in the EEG aids both in the detection of epileptiform events and in the rejection of artifacts and background activity as events. Classification of events as definite or probable overcomes, to some extent, the problem of maintaining high detection rates while eliminating false detections. So far, the system has only been evaluated on development data but, although this does not provide a true measure of performance, the results are nevertheless impressive. Data from 11 patients, totaling 180 minutes of 16-channel bipolar EEGs, have been analyzed. A total of 45-71% (average 58%) of epileptiform events reported by the human expert in any EEG were detected as definite with no false detections (i.e., 100% selectivity) and 60-100% (average 80%) as either definite or probable but at the expense of up to 9 false detections per hour. Importantly, the highest detection rates were achieved on EEGs containing little epileptiform activity and no false detections were made on normal EEGs. >

A multistage system to detect epileptiform activity in the EEG

This paper proposes an ϵ-separating nonlinear digital filter (called an ϵ-filter). This filter is intended for effective filtering of low-amplitude noise superposed on the signal with sharp discontinuities and can be realized by combining a simple nonlinear element with a conventional linear filter. In this paper, the basic ϵ-filter and its modifications to a trend-adaptive filter and a two-dimensional filter are described. The effectiveness of the new filter is demonstrated by computer simulation. Some of its application to EEG analysis, image processing and coding are also presented.

ϵ‐separating nonlinear digital filter and its applications

In a previous paper we presented procedures of analyzing causality between a time series based on information theory and information flow between time series based on a concept of directed transinformation, which is an information quantity with a direction. In principle, the directed transinformation is defined based on joint probability density between time series. As the length of the time series becomes larger, however, the volume of computation needed also increases exponentially. In this paper, we will discuss efficient computation algorithms. First, we assume Gaussian properties with the time series to present the “correlation function method” as a means of computing of the directed transinformation from correlation function. Second, we present a “linear production model method” in which the information quantity is computed by means of impulse response of a linear production model of the time series. Also, we proved that the linear production model can easily be composed by auto-regression if the time series is steady. Finally, we discussed the validity of time-series analysis based on the directed transinformation through analysis of an artificially produced simulated series. We have concluded that the proposed computation method can provide correct analysis even if the causality of the time series can not be analyzed correctly based on the correlation function.

A time‐series analysis method based on the directed transinformation

A new nonlinear digital filter which separates nonstationary waves such as spikes from stationary background waves of the EEG is proposed. This filter is composed of a prediction filter and a simple nonlinear function. Some examples showing the separation of spikes from EEG data of epileptic patients are given.

Separation of a Nonstationary Component from the EEG by a Nonlinear Digital Filter

Maximum likelihood decoding of convolutional codes by Viterbi decoding method is an effective error correcting technique. However, little study on Viterbi decoding of high rate codes has been done since the number of paths entering each trellis state increases as the code rate increases. This paper presents a new maximum likelihood decoding of convolutional codes of rate (n - 1)/n using a trellis. the trellis is drawn according to the state transitions of the syndrome former of the code, and has only one or two paths entering each state rather than 2n-1 paths as in the case of conventional trellises. Thus, we can replace the 2n-1-ary comparisons at each state needed in the conventional Viterbi decoding for the code of rate (n - 1)/n by binary comparisons. the implementation of maximum likelihood decoders for high rate convolutional codes is simplified greatly by this approach. Moreover, this decoding method shows the same performance as that of conventional Viterbi decoding method from the viewpoint of selecting the maximum likelihood code sequence.

A new maximum likelihood decoding of high rate convolutional codes using a trellis

An efficient discrete cosine transform image coding system using the gain/shape vector quantizers (DCT-G/S VQ) is presented. In the coding system, AC transform coefficients in a subblock are partitoned into several bands according to the Schaming's method, and the normalized AC transform coefficients of each band are quantized with the gain/shape vector quantizer designed on a spherically symmetric probability model. In addition, an adaptive DCT-G/S VQ (A-DCT-G/S VQ) is presented by incorporating a modification of the recursive quantization technique in the DCT-G/S VQ. The coding systems are simulated on color images, and their performance is compared to that of previously reported discrete cosine transform coding systems using the Max-type scalor quantizers.

Hiroshi Miyakawa

Papers

ϵ‐separating nonlinear digital filter and its applications

A time‐series analysis method based on the directed transinformation

Separation of a Nonstationary Component from the EEG by a Nonlinear Digital Filter

A new maximum likelihood decoding of high rate convolutional codes using a trellis

Adaptive discrete cosine transform image coding using gain/Shape vector quantizers