The IEEE 1394 is a standard for the high performance serial bus interface. This standard has the isochronous transfer mode that is suitable for real-time applications and the asynchronous transfer mode for delay-insensitive applications. It can be used to construct a small-size local area network. We propose a queueing model for a network with this standard under some assumptions, and calculate the average waiting time of an asynchronous packet in the buffer in the steady state. We give some numerical results, along with validation by simulation, in order to evaluate its performance.

As Information Technology progresses, our daily lives are getting "connected" more and more. At the same time, however, problems are appearing. The center of these problems can be captured as the "Communication Overflow. " To cope with such problems, we propose an approach that tries to provide a communication environment that assists users in managing their communication activities. The key notion of this approach is to enhance the "Awareness of Connectedness. " Here, agents which are suggestive of awareness of connectedness play an important role. In this paper, we describe the key notion and introduce a brief road-map towards the environment for the awareness of connectedness. Two candidate tools for the environment are described. The first one is a visualization tool for communication media that provides feedback of users' communication activities. Its purpose is to enhance the awareness for communication. The second tool is a simple, intuitive interactive media that exchanges the statuses of users. It is an alternative network communication media that might be suitable for very light-weight, almost-acknowledge-only communication mode. Some results on an experiment of these two tools are also reported.

In this paper, we explore the possibility of applying associative memories for locating frontal views of human faces in complex scenes. An appealing property of the associative-memory-based face detection system is that learning of the associative memory may be achieved by using a simple Hebbian learning rule. In addition, a simple heuristic rule is used to quickly filter a certain amount of nonface images at the very beginning of the whole detection procedure. By using the rule, we won't waste unnecessary computational resources on those nonface images. A database consisting of 74 images was used to test the performance of our associative-memory-based human face detection system.

This paper presents a new multi-target data association method for automotive radar which we call the order statistics joint probabilistic data association (OSJPDA). The method is formulated using the association probabilities of the joint probabilistic data association (JPDA) filter and an optimal target-to-measurement data association is accomplished using the decision logic algorithm. Simulation results for heavily cluttered conditions show that the tracking performance of the OSJPDA filter is better than that of the JPDA filter in terms of tracking accuracy by about 18%.

This paper presents an application of Complex-Valued Associative Memory Model(CAMM) for image processing. An image association system applying CAMM, combined with a 2-dimensional discrete Fourier transform (2-D DFT) process is proposed. Discussed are how a gray level image can be expressed using CAMM, and the image association that can be performed by CAMM. In the proposed system, input images are transformed to phase matrices and the image association can be performed by making use of the phase information. Practical examples are also presented.

Tracking many targets simultaneously using a search radar has been one of the major research areas in radar signal processing. The primary difficulty in this problem arises from the noise characteristics of the incoming data. Hence it is crucial to obtain an accurate association between targets and noisy measurements in multi-target tracking. We introduce a new scheme for optimal data association, based on a MAP approach, and thereby derive an efficient energy function. Unlike the previous approaches, the new constraints between targets and measurements can manage the cases of target missing and false alarm. Presently, most algorithms need heuristic adjustments of the parameters. Instead, this paper suggests a mechanism that determines the parameters in an automated manner. Experimental results, including PDA and NNF, show that the proposed method reduces position errors in crossing trajectories by 32.8% on the average compared to NNF.

This paper proposes an electronic soccer lottery protocol suitable for the Internet environment. Recently, protocols based on public-key schemes such as digital signature have been proposed for electronic voting systems or other similar systems. For a soccer lottery system in particular, it is important to reduce the computational complexity and the amount of communication data required, because we must expect that a large number of tickets will be purchased simultaneously. These problems can be solved by introducing hash functions as the core of protocol. This paper shows a practical soccer lottery system based on bit commitment and hash functions, in which the privacy of prize-winners is protected and illegal acts by the lottery promoter or lottery ticket shops can be revealed.

In this paper we shall put forward a novel circularly connected synergetic neural network extending the previously studied auto-correlation or cross-correlation dynamics so as to realise a group memory retrieval. The present model is substantially based on a top-down approach of the dynamic rule of an analog neural network in the similar manner to the conventional synergetic dynamics early proposed by Haken. It will be proved that a complete association can be assured up to the same number of the embedded patterns as the minimal number of neurons of the linked synergetic neural networks. In addition, one finds that a searching process of a couple of embedded patterns can be also realised by means of controlling attraction parameters as was previously reported in the autoassociative synergetic models.

A new phasor model of neural networks is proposed in which the state of each neuron possibly takes the value at the origin as well as on the unit circle. A stability property of equilibria is studied in association with the energy landscape. It is shown that a simple condition guarantees an equilibrium to be asymptotically stable.

This paper proposes an associative memory neural network whose limiting state is the nearest point in a polyhedron from a given input. Two implementations of the proposed associative memory network are presented based on Dykstra's algorithm and a fixed point theorem for nonexpansive mappings. By these implementations, the set of all correctable errors by the network is characterized as a dual cone of the polyhedron at each pattern to be memorized, which leads to a simple amplifying technique to improve the error correction capability. It is shown by numerical examples that the proposed associative memory realizes much better error correction performance than the conventional one based on POCS at the expense of the increase of necessary number of iterations in the recalling stage.

Recent developments in computer technology allow us to analyze all the data in a huge database. Data mining is to analyze all the data in such a database and to obtain useful information for database users. One of the well-studied problems in data mining is the search for meaningful association rules in a market basket database which contains massive amounts of transactions. One way to find meaningful association rules is to find all the large itemsets first, and then to find meaningful association rules from the large itemsets. Although a number of algorithms for computing all the large itemsets have been proposed, the computational complexity of them is scarcely disscussed. In this paper, we show that it is NP-complete to decide whether there exists a large itemset that has a given cardinality. Also, we propose subclasses of databases in which all the meaningful association rules can be computed in time polynomial of the size of a database.

A new vector-matching circuit technology has been developed aiming at compact implementation of maximum likelihood search engine for neuron-MOS associative processor. The new matching cell developed in this work possessed the template information in the form of an analog mask ROM and calculates the absolute value of difference between the template vector and the input vector components. The analog-mask ROM merged matching cell is composed of only five transistors to be compared with our earlier-version memory separated matching cell of 13 transistors. In addition, the undesirable cell-to-cell data interference through the common floating node ("boot-strap effect") has been eliminated without using power-consuming current source loads in source followers. As a result, dc-current-free matching cell operation has been established, making it possible to build a low-power, high-density search engine. Test circuits were fabricated by a 0.8-µm double-polysilicon double-metal n-well CMOS process, and the circuit operation has been experimentally verified.

We propose a new associative memory named Multi-Winner Associative Memory (MWAM) and study its bidirectional association properties in this paper. The proposed MWAM has two processes for pattern pairs storage: storage process and recall process. For the storage process, the proposed MWAM can represent a half of pattern pair in the distributed representation layer and can store the correspondence of pattern and its representation using the upward weights. In addition, the MWAM can store the correspondence of the distributed representation and the other half of pattern pair in the downward weights. For the recall process, the MWAM can recall information bidirectionally: a half of the stored pattern pair can be recalled by receiving the other half in the input-output layer for any stored pattern pairs.

The conventional synthesis procedure of discrete time sparsely interconnected neural networks (DTSINNs) for associative memories may generate the cells with only self-feedback due to the sparsely interconnected structure. Although this problem is solved by increasing the number of interconnections, hardware implementation becomes very difficult. In this letter, we propose the DTSINN system which stores the 2-dimensional discrete Walsh transforms (DWTs) of memory patterns. As each element of DWT involves the information of whole sample data, our system can associate the desired memory patterns, which the conventional DTSINN fails to do.

This paper describes low-power architecture-methodologies for programmable multimedia processors, which will become major functional units in System-On-a-Chip. After brief review on multimedia processing and low-power considerations, recent programmable chips, including MPUs and DSPs, are investigated in terms of low-power implementation. In order to show the difference of the low-power approaches between programmable processors and ASIC processors, a single-chip MPEG-2 encoder is also included as an example of ASIC design.

The pseudo-inverse model for the associative memory has an iterative algorithm converging to its weight matrix. The present letter shows that the same algorithm except for the lack of self couplings can be derived by simple consideration of the energy of the network state.

We consider an algorithm for finding all solutions in order to clarify all the stable equilibrium points of a hysteresis neural network. The algorithm includes sign test, linear programming test and a novel subroutine that divides the solution domain efficiently. Using the hysteresis network, we synthesize an associative memory whose cross connection parameters are trinalized. Applying the algorithm to the case where 10 desired memories are stored into 77 cells network, we have clarified all the solutions. Especially, we have confirmed that no spurious memory exists as the trinalization is suitable.

We apply some variants of evolutionary computations to the fully-connected neural network model of associative memory. Among others, when we regard it as a parameter optimization problem, we notice that the model has some favorable properties as a test function of evolutionary computations. So far, many functions have been proposed for comparative study. However, as Whitley and his colleagues suggested, many of the existing common test functions have some problems in comparing and evaluating evolutionary computations. In this paper, we focus on the possibilities of using the fully-connected neural network model as a test function of evolutionary computations.

This paper proposed the method as an estimation on the size of discharge spots through observation on traces after the discharge arose in circumstances gases mixed hydrocarbon gas. Namely, the circular carbonaceous deposit and the carbonaceous heap are observed on cathode and anode surface, respectively, after the short gap discharge arises in N2+NO+CH4 gases. The current density, which is the normal conversion current density, is calculated from the size of the trace of discharge and its value is about 1.010-9 A/(cm2 Pa2) in case that the concentration of CH4 is 0.6%. The value is about 1/5 of values that are reported in the former articles and is reasonable one.

A double-layer Associative Memory System (AMS) based on the Cerebella Model Articulation Controller (CMAC) (CMAC-AMS), owing to its advantages of simple structures, fast searching procedures and strong mapping capability between multidimensional input/output vectors, has been successfully used in such applications as real-time intelligent control, signal processing and pattern recognition. However, it is still suffering from its requirement for a large memory size and relatively low precision. Furthermore, the hash code used in its addressing mechanism for memory size reduction can cause a data-collision problem. In this paper, a new high-order Associative Memory System based on the Newton's forward interpolation formula (NFI-AMS) is proposed. The NFI-AMS is capable of implementing high-precision approximation to multivariable functions with arbitrarily given sampling data. A learning algorithm and a convergence theorem of the NFI-AMS are proposed. The network structure and the scheme of its learning algorithm reveal that the NFI-AMS has advantages over the conventional CMAC-type AMS in terms of high precision of learning, much less required memory size without the data-collision problem, and also has advantages over the multilayer Back Propagation (BP) neural networks in terms of much less computational effort for learning and fast convergence rate. Numerical simulations verify these advantages. The proposed NFI-AMS, therefore, has potential in many application areas as a new kind of associative memory system.