In this letter, we propose a blind adaptation method for the decision feedback equalizer (DFE). In the proposed scheme, a DFE is divided into two parts: a front-end linear equalizer (LE), and a prediction error filter (PEF) followed by a feedback part. The coefficients of the filters in each part are updated using constant modulus algorithm and decision feedback prediction algorithm, respectively. The front-end LE removes intersymbol interference ISI, and the PEF with feedback part whitens the noise to reduce noise power enhanced by the LE. Pre-processing by the LE enables the DFE to equalize nonminimum phase channels. Simulation results show that the proposed scheme provides reliable convergence, and the resulting symbol error rate is much less than that of the conventional LE and very close to that of the DFE using a training sequence.

A direct-mapped cache takes less time for accessing data than a set-associative cache because the time needed for selecting a cache line among the set is not necessary. The hit ratio of a direct-mapped cache, however, is lower due to the conflict misses caused by mapping multiple addresses to the same cache line. Addressing cache memory by virtual addresses reduces the cache access time by eliminating the time needed for address translation. The synonym problem in virtual cache necessitates an additional field in the cache tag to denote the process to which cache line belongs. In this paper, we propose a new virtual cache architecture whose average access time is almost the same as the direct-mapped caches while the hit ratio is the same as the set-associative cashes. A victim for cache replacement is selected from those that belong to a process which is most remote from being scheduled. The entire cache memory is divided into n banks, and each process is assigned to a bank. Then, each process runs on the assigned bank, and the cache behaves like a direct-mapped cache. Trace-driven simulations confirm that the new scheme removes almost as many conflict misses as does the set-associative cache, while cache access time is similar to a direct-mapped cache.

We present an extended quarter-plane lattice model for generating two-dimensional (2-D) autoregressive fields. This work is a generalization of the extended lattice filter of diagonal form (ELDF) developed by Ertuzun et al. The proposed model represents a wider class of 2-D AR fields than conventional lattice models. Several examples are presented to demonstrate the applicability of the proposed model. Furthermore, the proposed structure is compared with other conventional lattice filters based on the computation of their entropy values.

A new prediction method for the effective address is presented. This method works with the buffer named the address prediction buffer, and allows the data cache to be accessed speculatively. As a consequence of the trend toward increasing clock frequency, the internal cache is no longer able to fill the speed gap between the processor and the external memory, and the data cache latency degrades the processor performance. In order to hide this latency, the prediction method is proposed. By this method, the load address is predicted, and the data is fetched earlier than the memory access stage. In the case that the prediction is correct, the latency is hidden. Even if the prediction is incorrect, the performance is not degraded by any miss penalties. We have found that the prediction accuracy is 81.9% on average, and thus the performance is improved by 6.6% on average and a maximum of 12.1% for the integer programs.

In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.

For very low bit-rate video coding such as under 64 kbps, it is unreasonable to encode and transmit all the information. Thus, it is very important to choose the "important" information and encode it efficiently. In this paper, we first propose an image separation-composition method to solve this problem. At the encoder, an image is separated into a low-frequency part and two (horizontal and vertical) edge parts, which are considered as "important" information for human visualization. The low-frequency part is encoded by using block DCT and linear quantization. And the edges are selected by their values and encoded by using Chain coding to remain the most of the important parts for human visualization. At the decoder, the image is reconstructed by first generating the high-frequency parts from the horizontal and vertical edge parts, respectively, and then applying the inverse wavelet transform to the low frequency part and high frequency parts. This composition algorithm has less computational complexity than the conventional analytic/synthetic algorithms because it is not based on iterating approach. Moreover, to reduce the temporal redundancy efficiently, we propose a hierarchical motion detection and a motion interpolation /extrapolation algorithm. We detect motion vectors and motion regions between two reconstructed images and then predict the motion vectors of the current image from the previous detected motion vectors and motion regions by using the interpolation/extrapolation both at the encoder and at the decoder. Therefore, it is unnecessary to transmit the motion vectors and motion regions. This algorithm reduces not only the temporal redundancy but also bit-rates for coding side information . Furthermore, because the motion detection is completely syntax independent, any type of motion detection can be used. We show some simulation results of the proposed video coding algorithm with the coding bit-rate down to 24 kbps and 10 kbps.

This paper proposes linear predictive transmission diversity for TDMA/TDD personal communication systems and evaluates the effects of fading correlation and unequal average signal power Rayleigh fading on these system. The average bit error rate (BER) performance is calculated by computer simulation and the BER of zero order prediction is theoretically analyzed. The performance degradation caused by the error from prediction, fading correlation, and unequal average signal power is found to be almost independent of each other.

Hybrid image coding method is one of the most promising methods for efficient coding of moving images. The method makes use of jointly motion-compensated prediction and orthogonal transform like DCT. This type of coding scheme was adopted in several world standards such as H.261 and MPEG in ITU-T and ISO as a basic framework [1], [2]. Most of the work done in motion-compensated prediction has been based on a block matching method. However, when input moving images include complicated motion like rotation or enlargement, it often causes block distortion in decoded images, especially in the case of very low bit-rate image coding. Recently, as one way of solving this problem, some motion-compensated prediction methods based on an affine transform or bilinear transform were developed [3]-[8]. These methods, however, cannot always express the appearance of the motion in the image plane, which is projected plane form 3-D space to a 2-D plane, since the perspective transform is usually assumed. Also, a motion-compensation method using a perspective transform was discussed in Ref, [6]. Since the motion detection method is defined as an extension of the block matching method, it can not always detect motion parameters accurately when compared to gradient-based motion detection. In this paper, we propose a new motion-compensated prediction method for coding of moving images, especially for very low bit-rate image coding such as less than 64 kbit/s. The proposed method is based on a perspective transform and the constraint principle for the temporal and spatial gradients of pixel value, and complicated motion in the image plane including rotation and enlargement based on camera zooming can also be detected theoretically in addition to translational motion. A computer simulation was performed using moving test images, and the resulting predicted images were compared with conventional methods such as the block matching method using the criteria of SNR and entropy. The results showed that SNR and entropy of the proposed method are better than those of conventional methods. Also, the proposed method was applied to very low bit-rate image coding at 16 kbit/s, and was compared with a conventional method, H.261. The resulting SNR and decoded images in the proposed method were better than those of H.261. We conclude that the proposed method is effective as a motion-compensated prediction method.

Deterministic nonlinear prediction is applied to both artificial and real time series data in order to investigate orbital-instabilities, short-term predictabilities and long-term unpredictabilities, which are important characteristics of deterministic chaos. As an example of artificial data, bimodal maps of chaotic neuron models are approximated by radial basis function networks, and the approximation abilities are evaluated by applying deterministic nonlinear prediction, estimating Lyapunov exponents and reconstructing bifurcation diagrams of chaotic neuron models. The functional approximation is also applied to squid giant axon response as an example of real data. Two metnods, the standard and smoothing interpolation, are adopted to construct radial basis function networks; while the former is the conventional method that reproduces data points strictly, the latter considers both faithfulness and smoothness of interpolation which is suitable under existence of noise. In order to take a balance between faithfulness and smoothness of interpolation, cross validation is applied to obtain an optimal one. As a result, it is confirmed that by the smoothing interpolation prediction performances are very high and estimated Lyapunov exponents are very similar to actual ones, even though in the case of periodic responses. Moreover, it is confirmed that reconstructed bifurcation diagrams are very similar to the original ones.

Bifurcation Phenomena observed in a circuit containing two Josephson junctions coupled by a resistor are investigated. This circuit model has a mechanical analogue: Two damped pendula linked by a clutch exchanging kinetic energy of each pendulum. In this paper, firstly we study equilibria of the system. Bifurcations and topological properties of the equilibria are clarified. Secondly we analyze periodic solutions in the system by using suitable Poincare mapping and obtain a bifurcation diagram. There are two types of limit cycles distinguished by whether the motion is in S1R3 or T2R2, since at most two cyclic coordinates are included in the state space. There ia a typical structure of tangent bifurcation for 2-periodic solutions with a cusp point. We found chaotic orbits via the period-doubling cascade, and a long-period stepwise orbit.

A method is presented for detecting impulsive noises in chaotic time series, based on a new nonlinear prediction algorithm. A multi-dimensional trajectory is reconstructed from a time series using delay coordinates. The future value of a point on the trajectory is predicted using a local approximation technique revised by adding the Biweight estimation method and then the prediction error is calculated. Impulsive noises are detected by examining the prediction errors for all points on the trajectory. The proposed method is applied to the time series of the pupil area and the refractive power of the lens in the human eye. The Lyapunov exponent analysis for thses time series is conducted. As a result, it is shown that the proposed method is effective in detecting impulsive noises caused by blinking in these time series.

For indoor wireless communication systems, transceivers need to be placed strategically to achieve optimum communication coverage area at the lowest cost. Unfortunately the coverage region for a transceiver depends heavily on the type of building and on the placement of walls within the building. This paper proposed a slab model to simulate the wave transmission in the wall and employed this simple path loss model to predict the coverage region. This method prevents the complicated computation of wave propagation, so it could predict the coverage area real time. Numerical results show predicted path loss date are well agreed with the measurement ones.

Several two-dimensional largest common subpatterns (LCP) between pictures are defined and their computing methods are proposed. The time and space complexities of the computing methods are O(IJMN) to obtain the size of LCPs between a picture with IJ pixels and a picture with MN pixels. These LCPs can be used as similarity measures between pictures and can be applied to texture recognition and classification.

The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.

This paper describes a trial of evaluating the proper characteristics of multiple sound insulatain systems from their output responses contaminated by unknown background noises. The unknown parameters of sound insulation systems are first estimated on the basis of hte linear time series on an intensity scale, describing functionally the input-output relation of the systems. Then, their output probability distributions are predicted when an arbitrary input noise passes through these insulation systems.

An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.

A deterministic pushdown automaton (dpda) having just one stack symbol is called a deterministic restricted one-counter automaton (droca). A deterministic one-counter automaton (doca) is a dpda having only one stack symbol, with the exception of a bottom-of-stack market. The class of languages accepted by droca's is a proper subclass of the class of languages accepted by doca's. Valiant has shown that the regularity problem for doca's is decidable in a single exponential worst-case time complexity. In this paper, we prove that the class of languages accepted by droca's which accept by final state is incomparable with the class of languages accepted by droca's which accept by empty stack (strict droca's), and that the intersection of them is equal to the class of strict regular languages. In addition, we present a new direct branching algorithm for checking the regularity for not only a strict droca but also a real-time droca which accepts by final state. Then we show that the worst-case time complexity of our algorithm is polynomial in the size of each droca.

One of the important properties of multihop network is the mean internodal distance to evaluate the transmission delay, and the connective semi-random network achieves smaller mean internodal distance than other networks. However, the results are shown only by computer simulation and no theoretical analysis is investigated. Moreover, the network connective probability of the connective semi-random network is relatively small. In this paper, we propose the restricted connective semi-random network whose network connective probability is larger than that of the conventional connective semi-random network. And we theoretically analyze the mean internodal distance and the network connective probability of these two networks. It is shown that if the restriction is loose, the mean internodal distance of our model is almost the same as that of the conventional model, whereas the network connective probability of our model is larger than that of the conventional model. Moreover, the theoretical analyzed results of the mean internodal distance agree well with the simulated results in the conventional model and our model with small restriction.

For the purpose of automatic speech recognition, language models (LMs) are used to predict possible succeeding words for a given partial word sequence and thereby to reduce the search space. In this paper several kinds of stochastic language models (SLMs) are evaluated-bigram, trigram, hidden Markov model (HMM), bigram-HMM, stochastic context-free grammar (SCFG) and hand-written Bunsetsu Grammar. To compare the predictive power of these SLMs, the evaluation was conducted from two points of views: (1) relationship between the number of model parameters and entropy, (2) predictive rate of succeeding part of speech (POS) and succeeding word. We propose a new type of bigram-HMM and compare it with the other models. Two kinds of approximations are tried and examined through experiments. Results based on both of English Brown-Corpus and Japanese ATR dialog database showed that the extended bigram-HMM had better performance than the others and was more suitable to be a language model.

PID-type controllers have been well-known and widely used in many industries. Their regulation property of those was more improved through the addition of Bang-Bang-action. In spite of the potentials of these PID-plus Bang-Bang controllers, their regulation property is still limited by the fixed window limit value that determines the control action, i. e., PID or Bang-Bang. Thus, this paper presents an approach for improving the regulation property by dynamically changing the window limit value according to the plant dynamics with Neural Network predictive model. The improved regulation property is illustrated through simulation studies for position control of DC servo-motor system in the sense of classical figures of merit such as overshoot and rise time.