This paper describes an adaptive sensing system with tracking and zooming a moving object in the stable environment. Both the close contour matching technique and the effective determination of zoom ratio by fuzzy control are proposed for achieving the sensing system. First, the estimation of object feature parameters, 2-dimensional velocity and size, is based on close contour matching. The correspondence problem is solved with cross-correlation in projections extracted from object contours in the specialized difference images. In the stable environment, these contours matching, capable of eliminating occluded contours or random noises as well as background, works well without heavy-cost optical flow calculation. Next, in order to zoom the tracked object in accordance with the state of its shape or movement practically, fuzzy control is approached first. Three sets of input membership function--the confidence of object shape, the variance of object velocity, and the object size--are evaluated with the simplified implementation. The optimal focal length is achieved of not only desired size but safe tracking in combination with fuzzy rule matrix constituted of membership functions. Experimental results show that the proposed system is robust and valid for numerous kind of moving object in real scene with system period 1.85 sec.

This paper presents a technique by which any linear CCD camera, be it one with lens distortions, or even one with misaligned lens and CCD, may be calibrated to obtain optimum performance characteristics. The camera-image formation model is described as a polynomial expression, which provides the line-of-sight flat-beam, including the target light-spot. The coefficients of the expression, which are referred to as camera parameters, can be estimated using the linear least-squares technique, in order to minimize the discrepancy between the reference points and the model-driven flat-beam. This technique requires, however, that a rough estimate of camera orientation, as well as a number of reference points, are provided. Experiments employing both computer simulations and actual CCD equipment certified that the model proposed can accurately describe the system, and that the parameter estimation is robust against noise.

A network-topology-independent static task allocation strategy has been designed and implemented for massively parallel computers. For mapping a task graph to a processor graph, this strategy evaluates several functions that represent some intuitively feasible properties or the graphs. They include the connectivity with the allocated nodes, distance from the median of a graph, connectivity with candidate nodes, and the number of candidate nodes within a distance. Several greedy strategies are defined to guide the mapping process, utilizing the indicated function values. An allocation system has been designed and implemented based on the allocation strategy. In experiments we have defined about 1000 nodes in task graphs with regular and irregular topologies, and the same order of processors with mesh, tree, and hypercube topologies. The results are summarized as follows. 1) The system can yield 4.0 times better total communication costs than an arbitrary allocation. 2) It is difficult to select a single strategy capable of providing the best solutions for a wide range of task-processor combinations. 3) Comparison with hypercube-topology-dependent research indicates that our topology-independent allocator produces better results than the dependent ones. 4) The order of computaion time of the allocator is experimentally proved to be O (n2) where n represents the number of tasks.

Power transmission properties are investigated for a butt-joint which contains an air gap between an isotropic planar waveguide and an anisotropic one whose optical axis is lying in the plane defined by the propagation axis and the normal of the waveguide surface. New transmission coefficients are introduced for estimating the optical-power which is launched out into the gap from the incoming waveguide. Wave propagation through the gap is analyzed on the basis of the BPM concept. And the power transmitted across the interface between the gap and the outgoing waveguide is evaluated by means of the overlap integral of the field profiles. The effects of the air gap and the refractive index of filling liquid as well as axial displacement and angular misalignment are discussed on the basis of numerical results.

In multidimensional signal sampling, the orthogonal sampling scheme is the simplest one and is employed in various applications, while a non-orthogonal sampling scheme is its alternative candidate. The latter sampling scheme is used mainly in application where the reduction of the sampling rate is important. In three-dimensional (3-D) signal processing, there are two typical sampling schemes which belong to the non-orthogonal samplings; one is face-centered cubic sampling (FCCS) and the other is body-centered cubic sampling (BCCS). This paper proposes a new design method for 3-D band-limiting FIR filters required for such non-orthogonal sampling schemes. The proposed method employs the McClellan transformation technique. Unlike the usual 3-D McClellan transformation, however, the proposed design method uses 2-D prototype filters and 2-D transformation filters to obtain 3-D FIR filters. First, 3-D general sampling theory is discussed and the two types of typical non-orthogonal sampling schemes, FCCS and BCCS, are explained. Then, the proposed design method of 3-D bandlimiting filters for these sampling schemes is explained and an effective implementation of the designed filters is discussed briefly. Finally, design examples are given and the proposed method is compared with other method to show the effectiveness of our methos.

A new trial of statistical evaluation for an output response of power linear type acoustic systems with nonstationary random input is proposed. The purpose of this study is to predict the output probability distribution function on the basis of a standard type pre-experiment in a laboratoty. The statistical properties like nonstationarity, non-Gamma distribution property and various type linear and non-linear correlations of input signal are reflected in the form of differential operation with respect to distribution parameters. More concretely, the pre-experiment is carried out for a power linear acoustic system excited only by the Gamma distribution type sandard random input. Considering the non-negative random property for the output response of a power linear system, the well-known statistical Laguerre expansion series type probability expression is first employed as the framework of basic probability distribution expression on the output power fluctuation. Then, the objective output probability distribution for a non-stationary case can be easily derived only by successively employing newly introduced differential operators to this basic probability distribution of statistical Laguerre expansion series type. As an application to the actual noise environment, the proposed method is employed for an evaluation problem on the stochastic response probability distribution for an acoustic sound insulation system excited by a nonstationary input noise.

This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.

In this paper, we propose an adaptive RAKE receiver, which does not need to send the sounding signals and can track the fluctuations caused by fading. The channel estimation can be done by using a least squares method of the first and second equations suppressing additive noise and tracking the channel fluctuations. It is confirmed by computer simulations that the result has good agreement with theory and the performance is almost same as that of the conventional RAKE with the sounding signals.

It is concluded from numerical examples for the well-known linear PN sequence families of a large range of periods that the mean-square cross-correlation value between sequences is the dominating parameter to the average signal-to-noise power ratio performance of an asynchronous direct-sequence (DS) code-division multiple-access (CDMA) system. The performance parameters derived by Pursley and Sarwate are used for numerical evaluation and the validity of conclusion is supported by reviewing the other related works. The mean-square periodic cross-correlation takes the equal value p (code period) for the known CDMA code families. The equal mean-square cross-correlation performance results from the basic results of coding theory.

In order to explain the temperature and frequency characteristics of high-Tc superconductors, a new model is proposed, which will be called the improved three-fluid model, where the momentum relaxation time τ is assumed to depend on temperature in the superconducting and normal states, respectively, although τ has been assumed to be independent of temperature for the conventional three-fluid model. According to this model, the complex conductivity σ1jσ2 and the surface impedance ZsRsjXs, where Rs is the surface resistance and Xs is the surface reactance, are expressed as a function of temperature. The temperature dependences of Zs and for a YBCO bulk estimated using this model agree very well with ones measured by the dielectric-loaded cavity method in room to cryogenic temperature. In particular, a peak of σ1 observed just below the critical temperature Tc in experiments, appeared in the calculated results based on this model. This phenomenon has been already known in the BCS theory. Thus, it is verified that this model is useful to explain the microwave characteristics of high-Tc superconductors in room to cryogenic temperature. On the other hand, the residual normal electron density nres4.2541023 m-3 and the total electron density nt7.3081024 m-3 are obtained by calculation. The ratio nres/nt0.058 can be used as figure of merit to evaluate material quality of high-Tc superconductors; thus it means that there is 5.8% nonpairing electron in this YBCO bulk.

An algorithm interpolating parallel cross-sections between CT slices is described. Contours of equiscalar or constant-density surfaces on cross-sections are directly obtained as non-intersecting loops from grayscale slice images. This algorithm is based on a general algorithm that the authors have proposed earlier, constructing triangulated orientable closed surfaces from grayscale volumes and is particularly suited for a new technique, called laser stereolithography, which creates real 3D plastic objects using UV laser to scan and harden liquid polymer. The process of laser stereolithography is executed slice by slice, and this technique really requires some interpolation of intermediate cross-sections between slices. For visualizing, surfaces are only expected to be shaded almost continuously. The local defects are invisible and not cared about if the picture resolution is rather poor. On the contrary, topological faults are fatal to construct solid models by laser stereolithography, i.e., every contour line on cross-sections must be closed with no intersection. Not a single break of a contour line is tolerated. We already have many algorithms available for equiscalar surface construction, and it seems that if we cut the surfaces, then contour lines could be obtained. However, few of them are directly applicable to solid modeling. Marching cubes algorithm, for example, does not ensure the consistency of surface topology. Our algorithm guarantee an adequate topology of contour lines.

An attempt to apply neural networks to the acoustic diagnosis for the reciprocating compressor is described. The proposed neural network, Hybrid Neural Network (HNN), is composed of two multi-layered neural networks, an Acoustic Feature Extraction Network (AFEN) and a Fault Discrimination Network (FDN). The AFEN has multi-layers and the number of units in the middle hidden layer is smaller than the others. The input patterns of the AFEN are the logarithmic power spectra. In the AFEN, the error back propagation method is applied as the learning algorithm and the target patterns for the output layer are the same as the input patterns. After the learning, the hidden layer acquires the compressed input information. The architecture of the AFEN appropriate for the acoustic diagnosis is examined. This includes the determination of the form of the activation function in the output layer, the number of hidden layers and the numbers of units in the hidden layers. The FDN is composed of three layers and the learning algorithm is the same as the AFEN. The appropriate number of units in the hidden layer of the FDN is examined. The input patterns of the FDN are fed from the output of the hidden layer in the learned AFEN. The task of the HNN is to discriminate the types of faults in the compressor's two elements, the valve plate and the valve spring. The performance of the FDN are compared between the different inputs; the output of the hidden layer in the AFEN, the conventional cepstral coefficients and the filterbank's outputs. Furthermore, the FDN itself is compared to the conventional pattern recognition technique based on the feature vector distance, the Euclid distance measure, where the input is taken from the AFEN. The obtained results show that the discrimination accuracy with the HNN is better than that with the other combination of the discrimination method and its input. The output criteria of network for practical use is also discussed. The discrimination accuracy with this criteria is 85.4% and there is no case which mistakes the fault condition for the normal condition. These results suggest that the proposed decision network is effective for the acoustic diagnosis.

We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.

The well known optimum approach to detect spread spectrum signals transmitted in bursts over frequency selective radio channels is matched filtering, which performs despreading, and subsequent Viterbi equalization (VE) to cope with intersymbol interference (ISI). With respect to complexity, VE is feasible only if data modulation schemes with a few symbol levels as e.g. 2PSK are used and if the delay spread of the channel is not too large. The paper gives a survey of suboptimum data detectors based on linear block estimation. Such data detectors are less expensive than VE especially in the case of multilevel data modulation schemes as 4PSK or 16QAM. Special emphasis is laid on data detectors based on Gauss-Markoff estimation because these detectors combine the advantages of unbiasedness and minimum variance of the estimate. In computer simulations, the Gauss-Markoff estimation algorithm is applied to spread spectrum burst transmission over radio channels specified by COST 207. It is shown that the SNR degradation which is a measure of the suboptimality of the detector does not exceed a few dB, and that even moderate spectrum spreading considerably reduces the detrimental effect of channel frequency selectivity.

In this paper we propose an architecture and an algorithm for the parallel execution of OPS5 production systems. It is known that current OPS5 production system interpreters spend almost 90% of their execution time in the match step. Thus, in this paper we focus on the speedup of the match step. The match algorithm used in OPS5 is called Rete and the algorithm uses a special kind of a date-flow network compiled from the left hand sides of rules. To achieve the maximum degree of parallelism of a given OPS5 program by as few processors as possible, the proposed parallel machine uses loosely coupled multiprocessors. Parallel machines designed for fine-grain parallelism, such as DADO, also use loosely coupled multiprocessors. However, the proposed machine differs from such machines at the following points: use of powerful processors which have large amounts of memories and small cycle times; use of a shared Rete network (parallel machines designed for fine-grain parallelism use an unshared Rete network); high hardware utilization. Basic ideas of the proposed parallel machine are as follows. (1) Use of a modified Rete network in which node sharing is used only for constant-test nodes and each memory node is lumped with the child two-input node. (2) Static allocation of the nodes of the modified Rete network onto processors. (3) Partition of the set of processors into three subsets: constant-test node processors, two-input node processors and conflict-set processors. (4) Use of a ring network for the interconnection network among two-input node processors. In addition to an architecture for parallel execution of OPS5 production systems, we propose a scheme for mapping the modified Rete network into the proposed architecture. The results of simulation experiments showed that the proposed architecture is promising for parallel execution of OPS5 production systems.

The threshold characteristics of mutually coupled SQUIDs (Superconducting Quantum Interference Devices) have been analytically and numerically investigated. The mutually coupled SQUIDs investigated is composed of an rf-SQUID and a dc-SQUID. Here, the rf-SQUID is a flux quantum generator and the dc-SQUID is a flux detector. The linearization method substituting sin-1x by (π/2)x (1x1) is found valid when it is applied to the mutually coupled SQUIDs, because it is possible to obtain the superconducting regions analytically. By computer implementation of linearization method, we found this method is very effective and very quick compared to the ordinary methods. We report the internal flux on an rf-SQUID, the threshold of a dc-SQUID, and that of mutually coupled SQUIDs obtained by Lagrange multiplier formulation and linearization. The features of the threshold characteristics of the mutually coupled SQUIDs with various parameters are also reported. The discontinuous behavior of threshold of the mutually coupled SQUIDs are attractive for digital applications. We suggest three applications of the mutually coupled SQUIDs, that is, a logic gate for high-Tc superconductors (HTSs), a neuron device, and an A/D converter.

Japan's PARTNERS Project, one of the programmes of ISY advocated by UN, has just started. This letter is a brief introduction of the trials being carried out by the partners in the University of Electro-communications under the Project. The focus is on the distance education and training via ETS-V overcoming the geographical extent and the cultural diversity of the Asia-Pacific Region.

Recently, there has been increasing interest in Code Division Multiplex (CDM) systems. We reported the CDM system using the -chip shift multiplex operation. So far the performance of this system evaluated under the optimum . In this letter, we evaluate an influence of the phase difference between the groups on BER performance in 2M-plex system.

This paper's main objective is to clearly describe the construction of a universal code for minimizing Davisson's minimax redundancy in a range where the true model and stochastic parameters are unknown. Minimax redundancy is defined as the maximum difference between the expected persymbol code length and the per-symbol source entropy in the source range. A universal coding scheme is here formulated in terms of the weight function, i.e., a method is presented for determining a weight function which minimizes the minimax redundancy even when the true model is unknown. It is subsequently shown that the minimax redundancy achieved through the presented coding method is upper-bounded by the minimax redundancy of Rissanen's semi-predictive coding method.

In this paper, we show that without any unproven assumption, there exists a "four" move blackbox simulation perfect zero-knowledge interactive proof system of computational ability for any random self-reducible relation R whose domain is in BPP, and that without any unproven assumption, there exists a "four" move blackbox simulation perfect zero-knowledge interactive proof system of knowledge on the prime factorization. These results are optimal in the light of the round complexity, because it is shown that if a relation R has a three move blackbox simulation (perfect) zero-knowledge interactive proof system of computational ability (or of knowledge), then there exists a probabilistic polynomial time algorithm that on input x ∈ {0, 1}*, outputs y such that (x, y)∈R with overwhelming probability if x ∈dom R, and outputs "⊥" with probability 1 if x dom R.