The authors examined the effect of feedbacking information on learners of their test results obtained through computerized tests. The learner's acceptability of computerized tests was revealed to be improved by distribution and explanation of newly devised feedback charts including data on one's response history and response latency during computerized testing that was carried out in formative evaluation. The feedback chart composed of graphic representation of relationship between degree of difficulty of each question and its response latency got a particularly high evaluation among learners. It was revealed that types of feedback chart that stood highest in learner's estimate varied with the learner traits. This observation will serve to develop educational systems that incorporate computerized tests into school lessons.

Three dimensional (3-D) optics offers potential advantages to the massively-parallel systems over electronics from the view point of information transfer. The purpose of this paper is to survey some aspects of the 3-D optical interconnection technology for the future massively-parallel computing systems. At first, the state-of-art of the current optoelectronic array devices to build the interconnection networks are described, with emphasis on those based on the semiconductor technology. Next, the principles, basic architectures, several examples of the 3-D optical interconnection systems in neural networks and multiprocessor systems are described. Finally, the issues that are needed to be solved for putting such technology into practical use are summarized.

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's main objective is to analyze several procedures which select the model g among a set G of stochastic models to minimize the value of an information criterion in the form of L(g)H[g](zn)+(k(g)/2)c(n), where zn is the n observed data emitted by an information source θ which consists of the model gθ∈G and k(gθ) mutually independent stochastic parameters in the model gθ∈G, H[g](zn) is (-1) (the maximum log likelihood value of the data zn with respect to a model g∈G), and c(n) is a predetermined function (penalty function) of n which controls the amount of penalty for increasing the model size. The result is focused on specific performances when the information criteria are applied to the framework of so-called state decomposition. Especially, upper bounds are derived of the following two performance measures for each penalty function c(n): the error probability of the model selection, and the average Kullback-Leibler information between the true information source and the estimated information source.

In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.

Based on distributed artificial intelligence technology, the paper proposes a distributed expert system for distribution system planning. The developed expert system is made up of a set of problem-solving agents that autonomously process local tasks and cooperatively interoperate with each other by a shared database in order to reach a proper distribution plan. In addition, a two-level control mechanism composed of local-control and meta-control is also proposed to achieve a high degree of goodness in distribution system planning. To demonstrate its effect, the distributed expert system is implemented on basis of NASA's CLIPS and SUN's RPC and applied to the planning of distribution system in Taiwan. Test results indicate that the distributed expert system assists system planners in making an appropriate plan.

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.

It is known that the resolution conversion based on orthogonal transform has a problem that is difference of luminance between the converted image and the original. In this paper, the scale factor of the system employing various orthogonal transforms is generally formulated by considering the DC gain, and the condition of alias free for DC component is indicated. If the condition is satisfied, then the scale factor is determined by only the basis functions.

An automatic tap assignment method in sub-band adaptive filter is proposed in this letter. The number of taps of the adaptive filter in each band is controlled by the mean-squared error. The numbers of taps increase in the bands which have large errors, while they decrease in the bands having small errors, until residual errors in all the bands become the same. In this way, the number of taps in a band is roughly proportional to the length of the impulse response of the unknown system in this band. The convergence rate and the residual error are improved, in comparison with existing uniform tap assignment. Effectiveness of the proposed method has been confirmed through computer simulation.

This paper describes the learning performance of the deterministic Boltzmann machine (DBM), which is a promising neural network model suitable for analog LSI implementation. (i) A new learning procedure suitable for LSI implementation is proposed. This is fully-on-line learning in which different sample patterns are presented in consecutive clamped and free phases and the weights are modified in each phase. This procedure is implemented without extra memories for learning operation, and reduces the chip area and power consumption for learning by 50 percent. (ii) Learning in a layer-type DBM with one output unit has characteristic local minima which reduce the effective number of available hidden units. Effective methods to avoid reaching these local minima are proposed. (iii) Although DBM learning is not suitable for mapping problems with analog target values, it is useful for analog data discrimination problems.

This system called COKIS automatically extracts knowledge about C functions from the UNIX on-line manual by using its description paragraph and the user can interactively inquire to the system in order to know about UNIX C functions. The idea is motivated on the one side to free users from being involved in an exhaustive knowledge acquisition in the past, and to examine problems in understanding knowledge itself on the other. We propose Memory Processor which is implemented to realize extracting knowledges from corpus and processing dialogues in the inquiry system at the same modules.

We propose a fully digital architecture for Kohonen network suitable for VLSI implementation. The proposed architecture adopts a functional memory type parallel processor (FMPP) architecture which has a structure similar to a content addressable memory (CAM). One word of CAM is regarded as a processing element and a group of elements forms a neuron. All processing elements execute the same operation in bit-serial but in processor-parallel. Thus the number of instructions for realizing the network algorithm is independent of the number of neurons in the network. With reference to a previously reported CAM, we estimate a network with 96 neurons for speech recognition could be integrated on three chips using a 1.2 µm process, and it operates 50 times faster than a sequential hardware. Owing to its highly regular structure of memories, the proposed hardware architecture is well compatible with current VLSI technology.

A novel external clocking magnetic disk recording channel is proposed and examined. Timing not only for data recovery but for recording is given by a bit clock which is synchronized with dedicated clock marks on patterned discrete track media. Jitter of the bit clock is 2.5 ns (rms), which is good enough for data rates up to about 20 Mbit/s. Using an MR/Inductive head and PRML (Partial Response Maximum Likelihood) signal processing, an error rate of 110-6 is obtained at linear density 3146 bit/mm.

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.

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.

Sea clutter was measured using an X-band radar at very high grazing angles between 8.2 and 17.5. The sea state was 7 with the wave height of 6 to 9m. The wind velocity was 25m/s. It is shown that sea clutter amplitudes obey the log-normal and K distributions using the Akaike Information Criterion (AIC) , which is more rigorous fit to the distribution to the data than the least squares method.

In order to describe the flow passing through the glottis, we constructed a dynamic three-dimensional finite element model of the human larynx. The transient flow fields in the laryngeal model were calculated to examine the dynamic effects generated by the vocal fold vibration. A phase difference between the upper and lower edges of the vocal folds was included in the model to investigate the effect of the glottal shapes on pressure-flow relationships in the larynx during the vocal fold vibration. Using STAR-CD thermofluids analysis system, which is capable of treating the transient flow in moving-boundary situations with finite volume method, we solved the viscous incompressible Navier-Stokes equations to investigate the glottal flows and transglottal pressures as a function of the vocal fold vibration. The results were compared to the uniform glottis model and the theoretical model proposed by Ishizaka and Matsudaira, respectively. The effects of dynamic factors on the pressure distributions and flow patterns in the larynx resulting from the vocal-fold vibration were also discussed.

A new type jacket cutter for optical fibers is designed, and it is confirmed experimentally that its performance is superior to those of the conventional cutters. Using this new cutter which is mainly consisted of a rotatable fiber holder and a pair of blades separated by a distance of 0.3-0.4mm, only the tight jacket is cut and removed while the primary coating and the fiber are kept intact. As the result, the probability of damage to the fiber surface during jacket removal is reduced to about 0% compared to 10% found in the case of a conventional cutter. This result is useful to increase the reliability of optical fibers during assembling efforts.

This paper deals with study results on the effect of nonlinear amplification in the CDMA system using offset-QPSK signals bandlimited with a square-root cosine roll-off filter. As a result of the study, it is shown that the nonlinear amplification does not affect bit error rate performance with reasonable out-of-band emission characteristics when the roll-off factor of the transmit filter is one.

The advantages of a neuro-chip architecture based on a DRAM are demonstrated through a discussion of the general issuse regarding a memory based neuro-chip architecture and a comparison with a chip based on an SRAM. The performance of both chips is compared assuming digital operation, a 1.5-V supply voltage, a 106-synapse neural network capability, and a 0.5-µm CMOS design rule. The use of a one-transistor DRAM cell array for the storage of synapse weights results in a chip 55% smaller than an SRAM based chip with the same 8-Mbit memory capacity and the same number of processing elements. No additional operations for refreshing the DRAM cell array are necessary during the processing of the neural networks. This is because all the synapse weights in the array are transferred to the processing elements during the processing and the DRAM cells in the array are automatically refreshed when they are selected. The precharge operation of the DRAM cell array degrades the processing speed, however a processing speed of 1.37 GCPS is expected for the DRAM based chip. That speed is comparable to the 1.71 GCPS for the SRAM based chip with the same 256 parallel-processing elements. A DRAM cell array has the additional advantage of lower power dissipation in this specific usage for the neuro-chip. The dynamic operation of the DRAM cell array results in a 10% lower operating power dissipation than a chip using an SRAM cell array at the same processing speed of 1.37 GCPS. That lower operating power dissipation enables a DRAM based chip to run on a 1.5-V dry cell for longer under intermittent daily use even though the SRAM cell array has little power dissipation in data-holding mode.