A least squares approximation method of recursive digital filters for finite interval response with zero value outside the interval is presented. According to the characteristic of the method, the modified Gauss Method is utilized in iteratively determining design parameters. Convergence, together with the stability of the resulting filter, are guaranteed.

In this paper, we discuss to the intermedia synchronization problems for high speed multimedia communication. Especially, we described how software synchronization can be operated, and estimated the skew bound in CNV when considering the network delay. And we applied CNV to the intermedia synchronization and a hybrid model (HSM) is proposed. Furthermore, we used the statistical approach to evaluate the performance of the synchronization mechanisms. The results of performance evaluation show that HSM has good performance in the probability of estimation error.

This paper describes a single-bit parallel processor specified to Boltzmann Machine. The processor has SIMD (Shingle Instruction Multiple Data stream) type parallel architecture and every processing element (PE) has a single-bit ALU and a local memory storing connected weights between neurons. Features of the processor are large scale parallel processing a number of the simple single-bit PEs and effective expansion realized by multiple chips connected simple bus lines. Moreover, it is enhanced that the processing speed can be independent of the number of the neurons. We designed the PE using 1.2 µm CMOS process standard cells and confirmed the high performance using CAD simulations.

The performance evaluation by analysis of systems employing Reduced State Viterbi decoding is addressed. This type of decoding is characterized by an inherent error propagation effect, which yields a difficulty in the error probability analysis, and has been usually neglected in the literature. By modifying the Full State trellis diagram, we derive for Reduced State schemes, new transfer function bounds with the effects of error propagation. Both the Chernoff and the tight upper bound are applied to the transfer function in order to obtain the bit error probability upper bound. Furthermore, and in order to get a tighter bound for Reduced State decoding schemes with parallel transitions, the pairwise probability of the two sequences involved in an error event is upper bounded, and then the branch metric of a sequence taken from that bound is associated with a truncated instead of complete Gaussian noise probability density function. To support the analysis, particular assessment is done for a Trellis Coded Modulation scheme.

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.

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.

In this paper, we propose a fuzzy Petri net model for a rule-based decision making system which contains uncertain conditions and vague rules. Using the transformation method introduced in the paper, one can obtain the fuzzy Petri net of the rule-based system. Since the fuzzy Petri net can be represented by some matrices, the algebraic form of a state equation of the fuzzy Petri net is systematically derived. Both forward and backward reasoning are performed by using the state equations. Since the proposed reasoning methods require only simple arithmetic operations under a parallel rule firing scheme, it is possible to perform real-time decision making with applications to control systems and diagnostic systems. The methodology presented is also applicable to classical (nonfuzzy) knowledge base systems if the nonfuzzy system is considered as a special case of a fuzzy system with truth values being equal to the extreme values only. Finally, an illustrative example of a rule-based decision making system is given for automobile engine diagnosis.

The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.

Focused Ion Beam (FIB) trimming techniques for circuit optimization for GaAs MMICs by adjusting the parameters of IC components such as resistors, capacitors, microstrip lines, and FETs have been developed. The adjustment is performed by etching of the components and depositing of metal films for micro-strip lines. This technology turned out to be in need of only half a day to optimize the circuit pattern without any further wafer processes, while a conventional method that is comprised of revising mask pattern and following several cycles of wafer process has needed 0.5-1.0 year requiring huge amount of development cost. This technology has been successfully applied to optimization of an X-band low dissipation current single stage MMIC amplifier, and has shown its great feasibility for shortening the turn around time.

This paper describes the fundamental principle of novel push-push oscillators using hairpin-shaped split-ring resonators and their application to voltage controlled and injection locked oscillators for frequency synthesizers. The experimental results make it clear that the synthesizer systems discussed here have the advantages of high frequency operation, compact size and low power consumption. Experimental work has been carried out in the L band, but these systems can be applied to much higher frequencies.

A genetic method to generate a neural network which has both structure and connection weights adequate for a given task is proposed. A neural network having arbitrary connections is regarded as a virtual living thing which has genes representing its connections among neural units. Effectiveness of the network is estimated from its time sequential input and output signals. Excellent individuals, namely appropriate neural networks, are generated through generation iterations. The basic principle of the method and its applications are described. As an example of evolution from randomly generated networks to feedforward networks, an XOR problem is dealt with, and an action control problem is used for making networks containing feedback and mutual connections. The proposed method is available for designing a neural network whose adequate structure is unknown.

This paper presents a new high-speed three-dimensional (3-D) object recognition system based on two-dimensional (2-D) chain code matching. An observed 3-D object is precisely represented by a 2-D chain code sequence from the discrete surface points of the 3-D object, so that any complex objects can be recognized precisely. Moreover, the normalization procedures such as translation, rotation of 3-D objects except scale changes can be performed systematically and regularly regardless of the complexity of the shape of 3-D objects, because almost all the normalization procedures of 3-D objects are included in the 2-D chain code matching procedure. As a result, the additional normalization procedure become only the processing time for scale changes which can be performed easily by normalizing the length of the chain code sequence. In addition, the fast fourier transformation (FFT) is applicable to 2-D chain code matching which calculates cross correlation between an input object and a reference model, so that very fast recognition is performed. In fact, it is demonstrated that the total recognition time of a 3-D ofject is estimated at 5.35 (sec) using the 28.5-MIPS SPARC workstation.

A learning procedure of a three layer neural network with limited structure, called a multi-valued neural network, is proposed. The three layer net has a single linear neuron in its output layer. All input weights of a number of hidden neurons are identical. The network takes k+1 distinct stable values, where k is the number of hidden neurons. The proposed learning procedure consists of two parts, Phase and Phase . The former is one for the learning of weights between the hidden and output layers, and the latter is one for those between the input and the hidden layers. The network is applied to classification of numerals, which shows the effectiveness of the proposed learning procedure.

The goal of visual telecommunication has been to create a sense of "being there" or "telepresence." This paper introduces a novel shared drawing medium called ClearBoard that goes beyond "being there" by providing virtual shared workspace. It realizes (1) a seamless integration of shared drawing space and partner's image, and (2) eye contact to support real-time and remote collaboration by two users. We devised the key metaphor: "talking through and drawing on a transparent glass window" to design ClearBoard. A prototype, ClearBoard-1 is implemented based on the "Drafter-Mirror" architecture. This paper first reviews previous work on shared drawing support to clarify our design goals. We then examine three metaphors that fulfill these goals. The design requirements and the two possible system architectures of ClearBoard are described. Finally, some findings gained through the experimental use of the prototype, including the feature of "gaze awareness," are discussed.

Recently, fuzzy logic which is a kind of infinite multiple-valued logic has been studied to treat certain ambiguities, and its algebraic properties have been studied by the name of fuzzy logic functions. In order to treat modality (necessity, possibility) in fuzzy logic, which is an important concept of multiple-valued logic, the intuitionistic logical negation is required in addition to operations of fuzzy logic. Infinite multiple-valued logic functions introducing the intuitionistic logical negation into fuzzy logic functions are called Kleene-Stone logic functions, and they enable us to treat modality. The domain of modality in which Kleene-Stone logic functions can handle, however, is too limited. We will define α-KS logic functions as infinite multiple-valued logic functions using a unary operation instead of the intuitionistic logical negation of Kleene-Stone logic functions. In α-KS logic functions, modality is closer to our feelings. In this paper we will show some algebraic properties of α-KS logic functions. In particular we prove that any n-variable α-KS logic function is determined uniquely by all inputs of 7 values which are 7 specific truth values of the original infinite truth values. This means that there is a bijection between the set of α-KS logic functions and the set of 7-valued α-KS logic functions which are restriction of α-KS logic functions to 7 specific truth values. Finally, we show a necessary and sufficient condition for a 7-valued logic function to be a 7-valued α-KS logic function.

This paper proposes a self frequency preset (SFP) PLL synthesizer to realize a simple frequency preset PLL synthesizer with temperature-resistant and shorter frequency settling time than the conventional temperature un-compensated phase and frequency preset (PFP) PLL synthesizer. Since the proposed synthesizer employs a simple frequency locked loop (FLL) circuit to preset the output frequency at each frequency hopping period, the synthesizer eliminates the need to store f-V characteristic of the VCO in ROM. The frequency settling time of the proposed synthesizer is theoretically and experimentally analyzed. The theoretical analysis using the realistic f-V characteristic of a IF band VCO show that the frequency settling time of the proposed synthesizer is 130µs shorter than that of the conventional PFP PLL synthesizer at 40MHz hopping in the 200MHz band for all temperatures. Furthermore, the experimental results confirm that the frequency acquisition time of a prototype FLL circuit is accordant with the calculated results. Thus, the proposed SFP PLL synthesizer can achieve faster frequency settling than the conventional PFP PLL synthesizer for all temperatures and its simple configuration allows to be easily implemented with existing CMOS ASIC devices.

The mechanism of environment-dependent self-organization of "positional information" in a coupled nonlinear oscillator system is proposed as a new principle of realtime coordinative control in biological distributed system. By modeling the pattern formation in tactic response of Physarum plasmodium, it is shown that a global phase gradient pattern self-organized by mutual entrainment encodes not only the positional relationship between subsystems and the total system but also the relative relationship between internal state of the system and the environment.

The purpose of this paper is to offer a modal logic which enables us symbolic reasoning about data, especially, fuzzy relations. For such a purpose, the present author provided some systems of modal fuzzy logic. As a continuous one of those previous works, a logic based on the graded modalities is proposed. After showing some properties of this logic, the decision procedure for this logic is given in the rectangle method.

In this paper, a synthesizing method is proposed for the design of discrete-time cellular neural networks for binary image processing. Based on the theory of digital-logical design paradigm of threshold logic, the template parameters of the discrete-time cellular neural network for a prescribed binary image processing problem are calculated. Application examples including edge detection, connected component detection, and hole filling are given to demonstrate the merits and limitations of the proposed method. For a given realization of the parameters of the cloning template, a guideline for the selection of the offset Ic for maximum error tolerance is also considered.

The method of estimating multiple sound source locations based on a neural network algorithm and its performance are described in this paper. An evaluation function is first defined to reflect both properties of sound propagation of spherical wave front and the uniqueness of solution. A neural network is then composed to satisfy the conditions for the above evaluation function. Locations of multiple sources are given as exciting neurons. The proposed method is evaluated and compared with the deterministic method based on the Hyperbolic Method for the case of 8 sources on a square plane of 200m200m. It is found that the solutions are obtained correctly without any pseudo or dropped-out solutions. The proposed method is also applied to another case in which 54 sound sources are composed of 9 sound groups, each of which contains 6 sound sources. The proposed method is found to be effective and sufficient for practical application.