This paper describes content addressable memory (CAM) -based hardware that serves as a highly parallel, compact and real-time image-processing system. The novel concept of a highly-parallel integrated circuits and system (HiPIC), in which a large-capacity CAM tuned for parallel data processing is a key element, is introduced. Several hardware algorithms for highly-parallel image processing based on a HiPIC with a CAM are presented in order to demonstrate that the HiPIC concept is effective for compact and real-time image processing. Two kinds of HiPIC-dedicated CAM have been developed. One is embedded on a 0.5-µm CMOS gate array. An embedded CAM up to 64 kbit and logic up to 40 kgate can be integrated on a single chip. The other is a 0.5-µm CMOS full-custom CAM LSI tuned for parallel data processing. A fully-parallel 336-kbit CAM LSI has been successfully developed. The HiPIC concept and CAM-based hardware described here promises to be an important step towards the realization of a compact and real-time image-processing system.

This work presents a further development of the approach to modelling thermal (i.e. carrier-velocity-fluctuation) noise in semiconductor devices proposed in papers by the present authors. The basic idea of the approach is to apply classical theory of Ito's stochastic differential equations (SDEs) and stochastic diffusion processes to describe noise in devices and circuits. This innovative combination enables to form consistent mathematical basis of the noise research and involve a great variety of results and methods of the well-known mathematical theory in device/circuit design. The above combination also makes our approach completely different, on the one hand, from standard engineering formulae which are not associated with any consistent mathematical modelling and, on the other hand, from the treatments in theoretical physics which are not aimed at device/circuit models and design. (Both these directions are discussed in more detail in Sect. 1). The present work considers the bipolar transistor compact model derived in Ref. [2] according to theory of Ito's SDEs and stochastic diffusion processes (including celebrated Kolmogorov's equations). It is shown that the compact model is transformed into the Ito SDE system. An iterative method to determine noisy currents as entries of the stationary stochastic process corresponding to the above Ito system is proposed.

This paper provides an architectural study of optical multiple-ring trunk-transmission networks using high-speed Time Division Multiplexing (TDM), and proposes two algorithms for distributed control environments. We propose a path-setup algorithm that uses Token protocol over Section Overhead (SOH) bytes, by which network-nodes communicate with each other to reserve bandwidth. A classified path restoration algorithm is also proposed that offers 3 path classes in terms of restoration performance. Class A paths, the most reliable, never lose any bit even against unpredictable disasters. They are realized by path-duplication at the source node, route diversity,and hitless switching at the destination node. Class B paths are restored by re-routing, where the original path-setup algorithm is reused. Class C paths are the most economical because a failed path is restored by maintenance action.

This paper describes a guiding principle for designing functional single-electron tunneling (SET) circuitsthat is a way to elicit the potential functions of a given SET circuit by using as a guiding tool the SET circuit stability diagram. A stability diagram is a map that depicts the stable regions of a SET circuit based on the circuit's variable coordinates. By scrutinizing the diagram, we can infer all the potential functions that can be obtained from a circuit configuration. As an example, we take up a well-known SET-inverter circuit and uncover its latent functions by studying the circuit configuration, based on its stability diagram. We can produce various functions, e.g., step-inverter, Schmidt-trigger, memory cell, literal, and stochastic-neuron functions. The last function makes good use of the inherent stochastic nature of single-electron tunneling, and can be applied to Boltzmann-machine neural network systems.

Device design to reduce the abnormal operation due to the floating body effect was investigated for 0.2µm fully depleted SOI-MOSFETs, by use of a two-dimensional device simulator. It was found that the critical drain voltage and the critical multiplication factor for the floating body effect strongly depend on the potential profile which is related to the doping concentration. Based on simulation results, a nonuniformly doped structure is proposed for optimizing the potential profile to reduce the floating body effect. The applicable voltage of this structure was found to be 40% higher than that of the uniformly doped structure. A simple model is also derived to explain the above result.

A novel learning algorithm for a neural network LSI which has low resolution synapse weights is proposed. Following a brief discussion of the synapse weight adaptation mechanism in the gradient descent scheme, we propose a way of achieving relaxation from the influence of discretized weight. Restriction of the number of synapses to be updated in one learning iteration is effective to relax the influence. Simulation results support the effectiveness of this learning algorithm. Low resolution synapses will be practical to realize large-scale neural network LSIs.

We have developed an SIMD-type neural-network processor (NEURO4) and its software environment. With the SIMD architecture, the chip executes 24 operations in a clock cycle and achieves 1.2 GFLOPS peak performance. An accelerator board, which contains four NEURO4 chips, achieves 3.2 GFLOPS. In this paper we describe features of the neural network chip, accelerator board, software environment and performance evaluation for several neural network models (LVQ, BP and Hopfield). The 3.2 GFLOPS neural network accelerator board demonstrates 1.7 GCPS and 261 MCUPS for Hopfield networks.

The key concept of Physical Optics (PO), originally developed for a perfectly electric conductor (PEC), consists in that the high frequency fields on the scatterer surface are approximated by those which would exist on the infinite flat surface tangent to the scatterer. The scattered fields at arbitrary observation points are then calculated by integrating these fields on the scatterer. This general concept can be extended to arbitrary impedance surfaces. The asymptotic evaluation of this surface integration in terms of diffraction coefficients gives us the fields in analytical forms. In this paper, uniform PO diffraction coefficients for the impedance surfaces are presented and their high accuracy is verified numerically. These coefficients are providing us with the tool for the mechanism extraction of various high frequency methods such as aperture field integration method and Kirchhoff's method.

A self-learning analog neural network LSI with non-volatile analog memory which can be updated with more than 13-bit resolution has been designed, fabricated and tasted for the first time. The non-volatile memory is attained by a new floating-gate MOSFET device that has a charge injection part and an accumulation part separated by a high resistance. We also propose a partially-serial weight-update architecture in which the plural synapse circuits use a weight-update circuit in common to reduce the circuit area. A prototype chip fabricated using a 1.3-µm double-poly CMOS process includes 50 synapse elements and its computational power is 10 MCPS. The weights can be updated at a rate of up to 40 kHz. This chip can be used to implement backpropagation networks, deterministic Boltzmann machines, and Hopfield networks with Hebbian learning.

This paper presents a 4-valued content-addressable memory (CAM) for fully parallel template-matching operations in real-time cellular logic image processing with fixed templates. A universal literal is essential to perform a multiple-valued template-matching operation. It is decomposed of a pair of a threshold operation in a CAM cell and a logic-value conversion shared by CAM cells in the same column of a CAM cellular array, which makes a CAM cell function simple. Since a threshold operation together with a 4-valued storage element can be designed by using a single floating-gate MOS transistor, a high-density 4-valued universal-literal CAM with a single-transistor cell can be implemented by using a multi-layer interconnection technology. It is demonstrated that the performance of the proposed CAM is much superior to that of conventional CAMs under the same function.

We present a comparison of correlated failures for multiversion software using community error recovery (CER) and software breeding (SB). In CER, errors are detected and recovered at checkpoints which are inserted in all the versions of the software. SB is analogous to the breeding of plants and animals. In SB, versions consist of loadable modules, and a driver exchanges the modules between versions to detect and eliminate faulty modules. We formulate reliability models to estimate the probability of failure for software using either CER or SB. Our reliability models assume failures in the checkpoints in CER and the driver in SB. We use beta-binomial distribution for modeling correlated failures of versions, because much of the evidence suggests that the assumption that failures in versions occur independently is not always true. Our comparison indicates that multiversion software using SB is more reliable than that using CER when the probability of failure in the checkpoints in CER or the driver in SB is 10-7.

In this paper, we propose a method to construct structure models of articulated objects from multiple local observations of their motion using state transition analysis of local geometric constraints. The object model is constructed by a bottom-up approach with three levels. Each level groups sensor data with a constraint among local features observed by the sensor, and constructs the local model. If the sensor data in current model conflict, the model is reconstructed. In each level, the first level estimates a local geometric feature from the local sensor data (eg. edge, feature point) The second level estimates a rigid body from the local geometric feature. The third level estimates an object from the rigid bodies. In the third level, the constraint between rigid bodies is estimated by transition states, which are motions between rigid bodies. This approach is implemented on a blackboard system.

A suggestion for creating an excitable/oscillatory field with solid-state material is proposed. In essence, the idea is to make a spatial array of "mesoscopic particles" with the characteristics of a first-order phase transition. A theoretical computation shows that an auto-wave, or excitable wave, is generated in such an excitable field. A simple example of using this system as a diode in information flow is given.

In this paper, we propose a modified R-ISMA (reserved idle signal multiple access) protocol for a wireless local area network (WLAN) with a hybrid system construction. The protocol can support a basic service area as large as that supported by a centralized system and allows the direct transmission between neighbor stations as in a distributed system without the problem of hidden terminals. Since a polling scheme is used during transmission of information packets, an ARQ (auto repeat request) scheme is easily applied. A dynamic analysis using transient fluid approximation analysys is used for performance evaluation. In the analysis, we use Fritchman channel model to describe a burst error environment. Some numerical examples using a set of practical system parameters are given. It is shown that the system performance is improved compared with a centralized system with R-ISMA.

The performances of an SET required for integration are discussed. Conventional SETs had several problems such as large leakage current, insufficient voltage gain and so on. To overcome these problems, a new SET utilizing Schottky barriers as tunnel junctions is proposed. Its current characteristics and Coulomb-blockade conditions are calculated and the effectiveness for an integrated device is discussed.

In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully.

In client-server database management systems (DBMSs), inter-transaction caching is an effective technique for improving the performance. However, inter-transaction caching requires a cache consistency maintenance (CCM) protocol to ensure that cached copies at clients are kept mutually consistent. Such a protocol could be complex to implement and expensive to run, since several rounds of message exchange may be required. In this paper, we propose a new CCM scheme based on the primary-copy locking algorithm. In the proposed scheme, a number of lock requests and a data-shipping request are combined into a single message packet to reduce client-server interactions, which are known to be very critical to the performance of clientserver DBMSs. We examine its performance tradeoffs on the basis of a simulation model under a wide range of workloads. The performance results indicate that the proposed scheme improves the overall system throughput significantly over the caching two-phase locking and the optimistic two-phase locking scheme. Its higher performance mainly results from its lower communication overhead and lower degree of transaction blocking ratio.

We propose an interactive identification scheme based on the quadratic residue problem. Prover's identity can be proved without revealing his secret information with only one accreditation. The proposed scheme requires few computations in the verification process, and a small amount of memory to store the secret information, A digital signature based on this scheme is proposed, and its validity is then proved. Lastly, analysis about the proposed scheme is presented at the end of the paper.

The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.

This paper explores the potential of multiwave interconnectionsoptical interconnections that employ wavelength components as multiplexable information carriersfor constructing next-generation multiprocessor systems using MCM technology. A hypercube-based multiprocessor network called the multiwave hypercube (MWHC) is proposed, where multiwave interconnections provide highly-flexible dynamic communication channels among processing elements. A performance analysis shows that the use of multiwavelength optics makes possible the reduction of network complexity on an MCM substrate, while supporting low-latency message routing.