An adaptive array has been proposed as a canceller for both inter-symbol interference (ISI) and co-channel interference (CCI). However, it has no path-diversity gain since it selects just one signal correlated to the reference signal. In this paper, a novel interference canceller having sufficient path-diversity gain is proposed. The canceller is characterized by the combined configuration of an adaptive array and an equalizer. In the proposed system, a pre-selecting adaptive array is installed first. By employing a specific training sequence and sampling timing at the receiver during the training period, the perfect correlation between the "desired signal" and "short delayed" is achieved. Therefore, the pre-selecting adaptive array can extract the desired and ISI signals simultaneously, and the cascaded adaptive equalizer can provide the path-diversity gain without degradation by interference. The proposed system achieves a simple configuration and robustness against both ISI and CCI with a sufficient path diversity gain. In computer simulations, average BER characteristics of the proposed system were evaluated in a quasi-static Rayleigh fading channel. The simulation results showed that the system can reduce both long-delayed ISI and CCI efficiently, and that the expected path diversity gain is obtained even with strong CCI. They also showed that the degradation is not so serious when the number of antenna elements is less than that of incoming signals.

We implement a graphical interface that automatically transforms a figure input by a mouse into a regular figure which the system infers is the closest to the input. The difficulty lies in the fact that the classes into which the input is to be classified have inclusion relations, which prohibit us from using a simple distance criterion. In this letter, we show that this problem can be resolved by introducing the geometric AIC.

We show that moving objects can be detected from optical flow without using any knowledge about the magnitude of the noise in the flow or any thresholds to be adjusted empirically. The underlying principle is viewing a particular interpretation about the flow as a geometric model and comparing the relative "goodness" of candidate models measured by the geometric AIC.

We previously proposed a robust hybrid edge detector which relaxes the trade off between robustess against noise and accurate localization of the edges. This hybrid detector separates the tasks of localization and noise suppresion between two sub-detectors. In this paper, we present an extension to this hybrid detector to determine its optimal parameters, independently of the scene. This extension defines a probabilistic cost function using for criteria the probability of missing an edge buried in noise and the probability of detecting false edges. The optimization of this cost function allows the automatic selection of the parameters of the hybrid edge detector given the height of the minimum edge to be detected and the variance of the noise, σ2n. The results were applied to the 2D case and the performance of the adaptive hybrid detector was compared to other detectors.

Introducing a mathematical model of noise in stereo images, we propose a new criterion for intelligent statistical inference about the scene we are viewing by using the geometric information criterion (geometric AIC). Using synthetic and real-image experiments, we demonstrate that a robot can test whether or not the object is located very far away or the object is a planar surface without using any knowledge about the noise magnitude or any empirically adjustable thresholds.

Within the framework of a previously proposed vision system, a new part-segmentation algorithm, that breaks an object defined by its contour into its constituent parts, is presented. The contour is assumed to be obtained using an edge detector. This decomposition is achieved in two stages. The first stage is a preprocessing step which consists of extracting the convex dominant points (CDPs) of the contour. For this aim, we present a new technique which relaxes the compromise that exists in most classical methods for the selection of the width of the Gaussian filter. In the subsequent stage, the extracted CDPs are used to break the object into convex parts. This is performed as follows: among all the points of the contour only the CDPs are moved along their normals nutil they touch another moving CDP or a point on the contour. The results show that this part-segmentation algorithm is invariant to transformations such as rotation, scaling and shift in position of the object, which is very important for object recognition. The algorithm has been tested on many object contours, with and without noise and the advantages of the algorithm are listed in this paper. Our results are visually similar to a human intuitive decomposition of objects into their parts.

We propose a new QOS routing algorithm for finding a path that guarantees several quality of service (QOS) parameters requested by users, for ATM networks. It is known that a routing problem is NP-complete, if the number of additive QOS parameters, such as delay and cost, are more than or equal to two. Although a number of heuristic algorithms have been proposed recently to solve this problem, the appropriate choice of routing algorithms is still an open issue. In this paper, we propose a new heuristic routing algorithm, while being compliant with PNNI routing and signaling specification in the ATM Forum. The performance of algorithms is evaluated by simulation with a various network topologies and loading scenarios. This simulation results demonstrate that the proposed scheme improves the performance while reducing computational complexity.

The Michelson-interferometer (MI) optical resonator has been applied, together with physical interests, to a low pressure and slow-flow type CO2 laser for specifying the system to a probe laser source. The fundamental characteristics online-selection, oscillation power and transverse mode are also investigated in comparison to the CO2 laser obtained for various resonators such as an open-ended reflective-multiple interferometer (RMI), an open-sided MI, a Fox-Smith interferometer and soon. Consequently, it is confirmedthat the MI type laser proposed can be one of the promising scheme, without losing oscillation power much and transverse mode quality as a probe laser towards lineshape (or laser) parameter analysis. Translating one of the MI mirror by a slight distance on the order of a micron meter along the gain axis, we can not only switch either a single rotational-vibrational line or combination of multiple lines, but also obtain different combination of lines by translating a large amount of the translation distance of the order of 100 µm. Moreover, elimination of one of the side-mirrors in the MI resonator enables us to switch the oscillation lines at the expense of some output power.

This paper consists of two parts. The first part is devoted to comparative study on handwritten ZIP code numeral recognition using seventeen typical feature vectors and seven statistical classifiers. This part is the counterpart of the sister paper Handwritten Postal Code Recognition by Neural Network - A Comparative Study" in this special issue. In the second part, a procedure for feature synthesis from the original feature vectors is studied. In order to reduce the dimensionality of the synthesized feature vector, the effect of the dimension reduction on classification accuracy is examined. The best synthesized feature vector of size 400 achieves remarkably higher recognition accuracy than any of the original feature vectors in recognition experiment using a large number of numeral samples collected from real postal ZIP codes.

This paper presents a technique for disparity selection in the context of binocular pursuit. For vergence control in binocular pursuit, it is a crucial problem to find the disparity which corresponds to the target among multiple disparities generally observed in a scene. To solve the problem of the selection, we propose an approach based on histogramming the disparities obtained in the scene. Here we use an extended phase-based disparity estimation algorithm. The idea is to slice the scene using the disparity histogram so that only the target remains. The slice is chosen around a peak in the histogram using prediction of the target disparity and target location obtained by back projection. The tracking of the peak enables robustness against other, possibly dominant, objects in the scene. The approach is investigated through experiments and shown to work appropriately.

This paper proposes and investigates a tap selectable Viterbi equalizer for mobile radio communications. When the multipath channel is modeled by a tapped delay line only, the taps which may seriously affect the data sequence estimation are selected and used to calculate the trellis metric in the Viterbi algorithm. The proposed equalization algorithm can reduce the number of path metric calculations and the number of path selections in the Viterbi algorithm. Moreover, we propose an extended equalizer which has antenna diversity. This equalizer calculates the path metric using the antenna outputs and results of channel estimators. Computer simulation is used to evaluate the BER performance of the proposed equalizer in a multipath radio channel.

The selection of an appropriate key search algorithm for a specific application field is an important issue in application systems development. This is because data retrieval is the most time-consuming part of many application programs. An automatic selection method for key search algorithms is presented in this paper. The methodology has been implemented in a system called KESE2 (KEy-SEarch ALgorithm SElection). Key search algorithms are selected according to the user's requirements through interaction with KESE2 which bases its inferences on an evaluation table. This evaluation table contains values rating the performance of each key search algorithm for the different searching properties, or characteristics. The selection algorithm presented is based on step by step reduction of unsuitable key search algorithms and searching properties. The paper also proposes assistance facilities that consist of both a support function and a program synthesis function. Experimental results show that the appropriate key search algorithms are effectively selected, and that the necessary number of questions asked, to select the appropriate algorithm, is reduced to less than half of the total number of possible questions. The support function is useful for the user during the selection process and the program synthesis function fully translates a selected key search algorithm into high level language in an average of less than 1 hour.

This paper deals with an efficient radix-2 divider design theory that uses carry-propagation-free adders based on redundant binary{1, 0, 1} representation. In order to compute the division fast, we look ahead to the next step quotient-digit selection embedded in the current partial remainder calculation. The solution is a function of the four most significant digits of the current partial remainder, when scaling the divisor in the range [1, 9/8). In gate depth, this result is better than the higher radix-4 case without the look-ahead quotient-digit selection and the design is simple.

In order to shorten the time-to-market, Application-Specific Integrated Circuits (ASIC's) are designed from a library of pre-defined layout implementations for register-transfer modules such as multipliers, adders, RAM, ROM, etc. Current approaches to selecting the implementations from the library usually deal with their timing-area estimates and do not consider delay of the intermodule wiring. However, as sub-micron design rules are utilized for IC fabrication, wiring delay becomes comparable to the functional unit delay and can not longer be ignored even in register-transfer synthesis. In this paper we propose an algorithm that combines module selection with Performance-Driven module placement and reduces an impact of wiring on sub-micron ASIC performance. The algorithm not only efficiently exploits multiple module realizations in the design library, but also finds the module placement which minimizes wiring delay. Experimental results on several benchmarks show that considering both module and wiring issues, more than 30% reduction of the total circuit delay can be achieved.

The efficiency of a guided-probe fault location process is affected by the number of the probed lines. This number depends on the size of the target area and the method by which a line is selected for probing. This paper presents a method for reducing the size of the target area in a sequential circuit by introducing the concepts of Type- and Type- faults. This paper also presents a method of selecting lines for probing in a more efficient way. The efficiency of the proposed methods is demonstrated by experimental results.

The performance of parallel combinatory spread spectrum (PC/SS) communication systems in the frequency-nonselective, slowly Rayleigh fading channel is studied. Performance is evaluated by symbol error rate using numerical computation. To overcome the performance degradation caused by fading, we also studied the effects of selection diversity and Reed-Solomon coding applied to the PC/SS system. As a result, a remarkable improvement in error rate performance is achieved with Reed-Solomon coding and diversity technique. The coding rate for the maximum coding gain is almost a half of that in the additive white gaussian noise channel.

In this paper, an ARMA order selection method is proposed with a fuzzy reasoning method. In order to identify the reference model with the ARMA model, we need to determine its ARMA order. A less or more ARMA order, other than a suitable order causes problems such as; lack of spectral information, increasing calculation cost, etc. Therefore, ARMA order selection is significant for a high accurate ARMA model identification. The proposed method attempts to select an ARMA order of a time-varying model with the following procedures: (1) Suppose the parameters of the reference model change slowly, by introducing recursive fuzzy reasoning method, the estimated order is selected. (2) By introducing a fuzzy c-mean clustering methed, the period of the time during which the reference model is changing is detected and the forgetting factor of the recursive fuzzy reasoning method is set. Further, membership functions used in our algorithm are original, which are realized by experiments. In this paper, experiments are documented in order to validate the performance of the proposed method.

The performance of selection diversity combined with decision feedback equalizer for reception of TDMA carriers is investigated in this paper. The second generation digital land mobile communication systems standardized in the U.S., Japan, and Europe employ TDMA carriers at transmission bit rates up to several hundreds kbit/s. In order to provide higher quality of mobile communications services to the user with employing TDMA carriers, the systems would require both diversity and equalization techniques to combat attenuation of received signal power level due to Rayleigh fading and intersymbol interference resulting from time-variant multipath fading, respectively. This paper proposes a novel integration method of selection diversity and decision feedback equalization techniques which provides the better bit error rate performance than that for the conventional selection diversity method with decision feedback equalizer. The feature of proposed method is that selection diversity and decision feedback equalization techniques are integrated so as to interwork each other. We call the proposed method by the Decision Feedback Diversity with Decision Feedback Equalizer. The detailed algorithm of the proposed method is first presented, and then the system parameters for the method are evaluated based on the computer simulation results. Finally the computer simulation results for the performance of the proposed method are presented and compared to those for the conventional Selection Diversity with Decision Feedback Equalizer and the conventional Dual Diversity Combining and Equalization method under the typical mobile radio environments, in order to demonstrate the validity of the proposed method.

Antenna selection diversity is an effective method to achieve both better transmission performance and compact circuit implementation in TDMA portable radio communications. However, diversity performance in fast fading environments is insufficient. This paper proposes a novel predictive antenna selection diversity scheme, PASD, which improves the diversity performance for higher fading rates. In PASD, received signal power for the assigned data slot is predicted from previously measured data. Thus, selection errors due to the receiving power changes caused by fast Rayleigh fading can be effectively avoided. An experimental result for a 3-ch TDMA system with a frame duration of 20ms shows that the diversity gain was increased by 1.3dB over the conventional method for a fading rate of 40Hz. PASD is also shown to have improved diversity performance against cochannel interference.

This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).