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.

We observed a ship as a radar target embedded in sea clutter using a millimeter wave radar. The shape of the ship and sea clutter were discriminated by using texture analysis in image processing. As a discriminator, a nonlinear transformation of a local pattern was defined to deal with high order statistics.

Since semiconductor memory chip has been growing rapidly in its capacity, memory testing has become a crucial problem in RAMs. This paper proposes a new RAM test algorithm, called generalized marching test (GMT), which detects static and dynamic pattern sensitive faults (PSF) in RAM chips. The memory array with N cells is partitioned into B sets in which every two cells has a cell-distance of at least d. The proposed GMT performs the ordinary marching test in each set and finally detects PSF having cell-distance d. By changing the number of partitions B, the GMT includes the ordinary marching test for B1 and the walking test for BN. This paper demonstrates the practical GMT with B2, capable of detecting PSF, as well as other faults, such as cell stuck-at faults, coupling faults, and decoder faults with a short testing time.

Design of high-speed digital circuits such as adders and multipliers is one of the most important issues to implement high performance VLSI systems. This paper proposes a new multiple-valued code assignment algorithm to implement locally computable combinational circuits for k-ary operations. By the decomposition of a given k-ary operation into unary operations, a code assignment algorithm for k-ary operations is developed. Partition theory usually used in the design of sequential circuits is effectively employed for optimal code assignment. Some examples are shown to demonstrate the usefulness of the proposed algorithm.

This paper proposes new architecture LSIs based on wave-parallel computing to provide an essential solution to the interconnection problems in massively parallel processing. The basic concept is ferquency multiplexing of digital information, which enables us to utilize the parallelism of electrical (or optical) waves for parallel processing. This wave-parallel computing concept is capable of performing several independent binary funtions in parallel with a single module. In this paper, we discuss the design of wave-parallel image processing LSI to demonstrate the feasibility of reducing the number of interconnections among modules.

In a two-layer self-diplexing antenna fed at two ports, theoretical analysis has already shown that the isolation characteristics can be improved by adjusting the angle between the feed locations of the transmitting and receiving antennas. In this letter, we experimentally investigate the isolation characteristics of the self-diplexing array antenna. First, calculated and experimental results for each feed location of the element antenna are compared and good agreement is found. Second, experimental results with a 19-element planar array indicate that a self-diplexing antenna with suitably chosen feed configuration is effective in improving the isolation in a phased array antenna.

We propose a new architecture of Functional Memory type Parallel Processor (FMPP) architectures called bit-parallel block-parallel (BPBP) FMPP. Design details of a prototype BPBP FMPP chip are also shown. FMPP is a massively parallel processor architecture that has a memory-based simple two-dimensional regular array structure suitable for memory VLSI technology. Computation space increases as integration density of memory increases. Computation time does not depend on the number of processors. So far, a bit-serial word-parallel (BSWP) implementation based on a content addressable memory (CAM) is mainly investigated as one of promising architectures of FMPP. In a BSWP FMPP, each word of a CAM works as a processor, and the amount of hardware is minimized by abopting a bit-serial operation, thus maximizing integration scale. The BSWP FMPP, however, does not allow operations between two words, which restriction limits the applicability of the BSWP FMPP. On the other hand, the proposed BPBP FMPP is designed to execute logical and arithmetic operations on two words. These operations are performed simultaneously on every group of words called a block. BPBP FMPP hereby achieves a high performance while maintaining high integration density of the BSWP, and is suitable for various applications.

A new neural network system for object recognition is proposed which is invariant to translation, scaling and rotation. The system consists of two parts. The first is a preprocessor which obtains projection from the input image plane such that the projection features are translation and scale invariant, and then adopts the Rapid Transform which makes the transformed outputs rotation invariant. The second part is a neural net classifier which receives the outputs of preprocessing part as the input signals. The most attractive feature of this system is that, by using only a simple shift invariant transformation (Rapid transformation) in conjunction with the projection of the input image plane, invariancy is achieved and the system is of reasonably small size. Experiments with six geometrical objects with different degrees of scaling and rotation shows that the proposed system performs excellent when the neural net classifier is trained by the Cascade-correlation learning algorithm proposed by Fahlman and Lebiere.

The matrix whose eigenvectors are the basis vectors of the DCT is introduced. This matrix leads to a convolution-product property using the DCT. Based on the property, the parameter of uniform blur, such as motion blur or out-of-focus blur, is estimated from the local minima of the DCT energy spectrum of a blurred image. Computer experiments confirmed that the DCT is superior to the DFT for estimating the parameter.

To investigate necessary conditions for the object recognition by simulations using neural network models is one of ways to acquire suggestions for understanding the neuronal representation of objects in the brain. In the present study, we trained a three layered neural network to form a geometrical feature representation in its output layer using back-propagation algorithm. After training using 73 learning examples, 65 testing patterns made by various combinations of above features could be recognized with the network at a rate of 95.3% appropriate response. We could classify four types of hidden layer units on the basis of effects on the output layer.

This paper proposes a multi-layer cellular network in which a self-organizing method is implemented. The network is developed for the purpose of data clustering and recognition. A multi-layer structure is presented to realize the sophisticated combination of several sub-spaces which are spanned by given input characteristic data. A self-organizing method is useful for evaluating the set of clusters for input data without a supervisor. Thus, using these techniques this network can provide good clustering ability as an example for image/pattern data which have complicated and structured characteristics. In addition to the development of this algorithm, this paper also presents a parallel VLSI architecture for realizing the mechanism with high efficiency. Since the locality can be kept among all processing elements on every layer, the system is easily designed without large global data communication.

An estimation method of region is presented, in which a solution path of the so-called Newton type homotopy equation in guaranteed to exist, it is applied to a certain class of uniquely solvable nonlinear equations. The region can be estimated a posteriori, and its upper bound also can be estimated a priori.

The locally exhaustive testing of multiple output combinational circuits is the test which provides exhaustive test patterns for each set of inputs on which each output depends. First, this paper presents a sufficient condition under which a minimum test set (MLTS) for the locally exhaustive testing has 2w test patterns, where w is the maximum number of inputs on which any output depends. Next, we clarify that any CUT with up to four outputs satisfies the condition, independently of w and n, where n is the number of inputs of the CUT. Finally, we clarify that any CUT with five outputs also satisfies the condition for 1w2 or n2wn.

The main result of this paper is an almost-everywhere hierarchy theorem for nondeterministic space that is as tight as the well-known infinitely-often hierarchy theorems for deterministic and nondeterministic space. In addition, we show that the complexity-theoretic notion of almost-everywhere complex functions is identical to the recursion-theoretic notion of bi-immune sets in the nondeterministic space domain. Finally, we investigate bi-immunity in nondeterministic and alternating time complexity classes and derive a similar hierarchy result for alternating time.

The most commonly used activation function in Backpropagation learning is sigmoidal while linear function is also sometimes used at the output layer with the view that choice between these activation functions does not make considerable differences in network's performance. In this letter, we show distinct performance between a network with linear output units and a similar network with sigmoid output units in terms of convergence behavior and generalization ability. We experimented with two types of cost functions, namely, sum-squared error used in standard Backpropagation and log-likelihood recently reported. We find that, with sum-squared error cost function and hidden units with nonsteep sigmoid function, use of linear units at the output layer instead of sigmoidal ones accelerates the convergence speed considerably while generalization ability is slightly degraded. Network with sigmoid output units trained by log-likelihood cost function yields even faster convergence and better generalization but does not converge at all with linear output units. It is also shown that a network with linear output units needs more hidden units for convergence.

It has been reported that generalization performance of multilayer feedformard networks strongly depends on the attainment of saturated hidden outputs in response to the training set. Usually standard Backpropagation (BP) network mostly uses intermediate values of hidden units as the internal representation of the training patterns. In this letter, we propose construction of a 3-layer cascaded network in which two 2-layer networks are first trained independently by delta rule and then cascaded. After cascading, the intermediate layer can be viewed as hidden layer which is trained to attain preassigned saturated outputs in response to the training set. This network is particularly easier to construct for linearly separable training set, and can also be constructed for nonlinearly separable tasks by using higher order inputs at the input layer or by assigning proper codes at the intermediate layer which can be obtained from a trained Fahlman and Lebiere's network. Simulation results show that, at least, when the training set is linearly separable, use of the proposed cascaded network significantly enhances the generalization performance compared to BP network, and also maintains high generalization ability for nonlinearly separable training set. Performance of cascaded network depending on the preassigned codes at the intermediate layer is discussed and a suggestion about the preassigned coding is presented.

A wideband high power amplifier design using a novel band-pass filter with FET's parasitic reactances has been developed. The feature of this design is in that it can provide wide bandwidth and high gain of high power amplifiers. Furthermore, the lower cutoff frequency and bandwidth can be varied independently. With the use of this design, a Ku-band two-stage high power amplifier having a bandwidth of 18% has achieved a linear gain of 9.751.75 dB, a saturated output power of greater than 37 dBm, and a power-added efficiency of greater than 10.4%.

A dielectric rod waveguide applicator for microwave heating such as microwave hyperthermia is described. The applicator consists of the acrylic cylinder filled with deionized water. By circulating the deionized water, the dielectric rod waveguide applicator acts as a surface cooling device, so that it doesn't need any bolus. This surface cooling device enables the dielectric rod waveguide applicator to control the site of effective heating region along the depth axis. Useful pattern of the circular or spheroidal shape and axially symmetric effective heating region were obtained. Furthermore metal strips provided on the aperture of applicator control the shape of the heating pattern.

This paper describes bandpass filters using linear tapered transmission line resonators (LTLR's). Bandpass filters are designed on the basis of the approximate description of LTLR's with cascaded multi-sections of uniform transmission lines whose widths are slightly different. By this design method, the fundamental characteristics of LTLR's and filter design parameters can be easily obtained using a general-purpose microwave circuit simulator. Trial LTLR bandpass filters showed excellent performance such as low insertion losses and the ability to control spurious responses, then their measured responses indicated close correspondence with the design results.

A superresolution technique is considered for use in antenna gain measurements. A modification of the MUSIC algorithm is employed to resolve incident signals separately in the time domain. The modification involves preprocessing the received data using a spatial scheme prior to applying the MUSIC algorithm. Interference rejection in the antenna measurements using the fast Fourier transform (FFT) based techniques have been realized by a recently developed vector network analyzer, and its availability has been reported in the literature. However, response resolution in the time domain of these conventional techniques is limited by the antenna bandwidth. The MUSIC algorithm has the advantage of being able to eliminate unwanted responses when performing antenna measurements in situations where the antenna band-width is too narrow to support FFT based techniques. In this paper, experimental results of antenna gain measurements in a multipath environment show the accuracy and resolving power of this technique.