A new circuit design of Josephson ternary δ-gate composed of Josephson junction devices is presented. Mathematical theory for synthesizing, analyzing, and realizing any given function in ternary system using Josephson ternary δ-gate is introduced. The Josephson ternary δ-gate is realized using SQUID technique. Circuit simulation results using J-SPICE demonstrated the feasibility and the reliability operations of Josephson ternary δ-gate with very high performances for both speed and power consumption (max. propagation delay time44 ps and max. power consumption2.6µW). The Josephson ternary δ-gate forms a complete set (completeness) with the ternary constants (1, 0, 1). The number of SQUIDs that are needed to perform the operation of δ-gate is 6. Different design with less than 6 SQUIDs is not possible because it can not perform the operation of δ-gate. The advantages of Josephson ternary δ-gate compared with different Josephson logic circuits are as follows: The δ-gate has the property that a simple realization to any given ternary logic function as the building blocks can be achieved. The δ-gate has simple construction with small number of SQUIDs. The δ-gate can realize a large number of ternary functions with small number of input/output pins. The performances of δ-gate is very high, very low power consumption and ultra high speed switching operation.

A hardware algorithm for modular inversion is proposed. It is based on the extended Euclidean algorithm. All intermediate results are represented in a redundant binary representation with a digit set {0, 1,1}. All addition/subtractions are performed without carry propagation. A modular inversion is carried out in O (n) clock cycles where n is the word length of the modulus. The length of each clock cycle is constant independent of n. A modular inverter based on the algorithm has a regular cellular array structure with a bit slice feature and is very suitable for VLSI implementation. Its amount of hardware is proportional to n.

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.

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.

This paper introduces the concept of an extended pseudo-biorthogonal basis" (EPBOB), which is a generalization of the concepts of an orthonormal (OB), a biorthonormal (BOB), a pseudo-orthogonal (POB), and a pseudo-biorthogonal (PBOB) bases. Let H_N be a subspace of a Hilbert space H. The concept of EPBOB says that we can always construct a set of 2M (MN) elements of H but not necessarily all in H_N such that like BOB any element f in H_N can be expressed by f^MΣ_m=1(f,φ^*_m)φ_m. For a better understanding and a wide application of EPBOB, this paper provides their characterization and shows how they preserve the formalism of BOB. It also shows how to construct them.

This paper presents a new idea of photometric stereo method which uses 3 point light sources as illumination source. Its intention is to extract the 3-D information of gastric surface. The merit of this method is that it is applicable to the textured and/or specular surfaces, moreover whose environment is too narrow, like gastric surface. The verification of the proposed method was achieved by the theoretical proof and experiment.

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.

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.

In this paper, we define an attributed random graph, which can be considered as a generalization of conventional ones, to include multiple attributes as well as numeric attribute instead of a single nominal attribute in random vertices and edges. Then we derive the probability equations for an attributed graph to be an outcome graph of the attributed random graph, and the equations for the entropy calculation of the attributed random graph. Finally, we propose the application areas to computer vision and machine learning using these concepts.

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.

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.

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.

We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.

To clarify the stereopsis disturbance in patients with Alzheimer's disease (AD), we analyzed binocular eye movement when subjects shifted their gaze between targets at different depths. Subjects are patients with Alzheimer's disease, Mluti-infarct dementia (MID), or Olivopontocerebellar atrophy (OPCA), and healthy controls. Targets are arranged in two ways: along the median plane and asymmetrically crossing the median plane, at distances from the eyes of 1000 mm and 300 mm. When the targets are switched at the onset of a beep, the subjects shifted their gaze to the lit target. The experiment is conducted in a dimly lit room whose structure is capable of providing good binocular cues for depth. In AD subjects, especially in the subjects whose symptoms are moderate (advanced stage), vergence is limited and the change in the convergence angle is small, unstable, and non-uniform. These results are different from those of other patients (MID) and OPCA) or healthy controls and suggest a disturbance of stereopsis in the parietal lobe where AD patients typically have dysfunctions.