Detection of facial emotions are mainly addressed by computer vision researchers based on facial display. Also detection of vocal expressions of emotions is found in research work done by acoustic researchers. Most of these research paradigms are devoted purely to visual or purely to auditory human emotion detection. However we found that it is very interesting to consider both of these auditory and visual informations together, for processing, since we hope this kind of multimodal information processing will become a datum of information processing in future multimedia era. By several intensive subjective evaluation studies we found that human beings recognize Anger, happiness, Surprise and Dislike by their visual appearance, compared to voice only detection. When the audio track of each emotion clip is dubbed with a different type of auditory emotional expression, still Anger, Happiness and Surprise were video dominant. However Dislike emotion gave mixed responses to different speakers. In both studies we found that Sadness and Fear emotions were audio dominant. As a conclusion to the paper we propose a method of facial emotion detection by using a hybrid approach, which uses multimodal informations for facial emotion recognition.

Considering the pattern classification/recognition tasks, the influence of the activation function on fault tolerance property of feedforward neural networks is empirically investigated. The simulation results show that the activation function largely influences the fault tolerance and the generalization property of neural networks. It is found that, neural networks with symmetric sigmoid activation function are largely fault tolerant than the networks with asymmetric sigmoid function. However the close relation between the fault tolerance and the generalization property was not observed and the networks with asymmetric activation function slightly generalize better than the networks with the symmetric activation function. First, the influence of the activation function on fault tolerance property of neural networks is investigated on the XOR problem, then the results are generalized by evaluating the fault tolerance property of different NNs implementing different benchmark problems.

A deterministic pushdown automaton (dpda) having just one stack symbol is called a deterministic restricted one-counter automaton (droca). A deterministic one-counter automaton (doca) is a dpda having only one stack symbol, with the exception of a bottom-of-stack marker. The class of languages accepted by droca's which accept by final state is a proper subclass of the class of languages accepted by doca's. Valiant has proved the decidability of the equivalence problem for doca's and the undecidability of the inclusion problem for doca's. Thus the decidability of the equivalence problem for droca's is obvious. In this paper, we evaluate the upper bound of the length of the shortest input string (shortest witness) that disproves the inclusion for a pair of real-time droca's which accept by accept mode, and present a direct branching algorithm for checking the inclusion for a pair of languages accepted by these droca's. Then we show that the worst-case time complexity of our algorithm is polynomial in the size of these droca's.

Though causal order of message delivery simplifies the design and development of distributed applications, the overhead of enforcing it is not negligible. We claim that a causal order algorithm which does not send any redundant information is efficient in the sense of communication overhead. We characterize and classify the redundant information into four categories: information regarding just delivered, already delivered, just replaced, and already replaced messages. We propose an efficient causal multicast algorithm which prevents propagation of these redundant information. Our algorithm sends less amount of control information needed to ensure causal order than other existing algorithms and can also be applied to systems whose communication channels are not FIFO. Since our algorithm's communication overhead increases relatively slowly as the number of processes increases, it shows good scalability feature. The potential of our algorithm is shown by simulation study.

Mobile databases will play an important role in mobile computing environment, to provide data storing and data retrieval functionalities which are needed by most applications. In mobile computing environment, the wireless communication poses some problems, which require us to minimize its use. Replication is a database technique that is commonly used to fulfill the requirement in minimizing network usage. In this paper, we propose two replica allocation strategies, called primary-copy tracking replica allocation (PTRA) and user majority replica allocation (UMRA), which are better suited to the mobile computing environment. Their proposals are intended to cope with cost performance issues in data replication due to user mobility in mobile computing environment. To investigate their effectiveness, we provide access cost analysis and comparison on these strategies and the static replica allocation (SRA) strategy. We show that our proposed strategies outperform the SRA strategy when user mobility (inter-cell movement) is relatively low as compared with data access rate.

This paper proposes a time-interval data model in which all temporal representation and operations can be expressed with time intervals. The model expresses not only real time intervals, in which an event exists, but also null time intervals, in which an event is suspended. We model the history of a real-world event as a composite time interval, which is defined in this paper. Operations on the composite time intervals are also defined, and it is shown how these operations can be used to express temporal constraints with time intervals.

This paper proposes a design method of feature spaces in a two-stage image recognition method that improves the recognition accuracy and efficiency in statistical image recognition. The two stages are (1) image screening and (2) image recognition. Statistical image recognition methods require a lot of calculations for spatially matching between subimages and reference patterns of the specified objects to be detected in input images. Our image screening method is effective in lowering the calculation load and improving recognition accuracy. This method selects a candidate set of subimages similar to those in the object class by using a lower dimensional feature vector, while rejecting the rest. Since a set of selected subimages is recognized by using a higher dimensional feature vector, overall recognition efficiency is improved. The classifier for recognition is designed from the selected subimages and also improves recognition accuracy, since the selected subimages are less contaminated than the originals. Even when conventional recognition methods based on linear transformation algorithms, i. e. principal component analysis (PCA) and projection pursuit (PP), are applied to the recognition stage in our method, recognition accuracy and efficiency may be improved. A new criterion, called a screening criterion, for measuring overall efficiency and accuracy of image recognition is introduced to efficiently design the feature spaces of image screening and recognition. The feature space for image screening are empirically designed subject to taking the lower number of dimensions for the feature space referred to as LS and the larger value of the screening criterion. Then, the recognition feature space which number of dimensions is referred to as LR is designed under the condition LSLR. The two detection tasks were conducted in order to examine the performance of image screening. One task is to detect the eye- and-mouth-areas in a face image and the other is to detect the text-area in a document image. The experimental results demonstrate that image screening for these two tasks improves both recognition accuracy and throughput when compared to the conventional one-stage recognition method.

This paper presents a new self-organization classification algorithm for remote-sensing images. Kohonen and other scholars have proposed self-organization algorithms. Kohonen's model easily converges to the local minimum by tuning the elaborate parameters. In addition to others, S. C. Amatur and Y. Takefuji have also proposed self-organization algorithm model. In their algorithm, the maximum neuron model (winner-take-all neuron model) is used where the parameter-tuning is not needed. The algorithm is able to shorten the computation time without a burden on the parameter-tuning. However, their model has a tendency to converge to the local minimum easily. To remove these obstacles produced by the two algorithms, we have proposed a new self-organization algorithm where these two algorithms are fused such that the advantages of the two algorithms are combined. The number of required neurons is the number of pixels multiplied by the number of clusters. The algorithm is composed of two stages: in the first stage we use the maximum self-organization algorithm until the state of the system converges to the local-minimum, then, the Kohonen self-organization algorithm is used in the last stage in order to improve the solution quality by escaping from the local minimum of the first stage. We have simulated a LANDSAT-TM image data with 500 pixel 100 pixel image and 8-bit gray scaled. The results justifies all our claims to the proposed algorithm.

The act of finding or constructing a model for a portion of a given polynomial or Bezier parametric surface from the whole original one is an encountered problem in surface modeling. A new method is described for constructing polynomial or Bezier piecewise model from an original one. It is based on the "Parametric Piecewise Model," abbreviated to PPM, of curve representation. The PPM representation is given by explicit expressions in terms of only control points or polynomial coefficients. The generated piecewise model behaves completely as a normal, polynomial or Bezier model in the same way as the original one for the piece of region considered. Also it has all characteristics, i. e, order and number of control points as the original one, and satisfies at the boundaries all order continuities. The PPM representation permits normalization, piecewise modeling, PPM reduction and systematic processes.

In this paper, a dynamic constructive algorithm for fault tolerant feedforward neural network, called DCFTA, is proposed. The algorithm starts with a network with single hidden neuron, and a new hidden unit is added dynamically to the network whenever it fails to converge. Before inserting the new hidden neuron into the network, only the weights connecting the new hidden neuron to the other neurons are trained (i. e. , updated) until there is no significant reduction of the output error. To generate a fault tolerant network, the relevance of each synaptic weight is estimated in each cycle, and only the weights which have their relevance less than a specified threshold are updated in that cycle. The loss of a connections between neurons (which are equivalent to stuck-at-0 faults) are assumed. The simulation results indicate that the network constructed by DCFTA has a significant fault tolerance ability.

We present a new scheduling policy named Value-based Processor Allocation (VPA-k) for scheduling value-based transactions in a multiprocessor real-time database system. The value of a transaction represents the profit the transaction contributes to the system if it is completed before its deadline. Using VPA-k policy, the transactions with higher values are given higher priorities to execute first, while at most k percentage of the total processors are allocated to the urgent transactions dynamically. Through simulation experiments, VPA-k policy is shown to outperform other scheduling policies substantially in both maximizing the totally obtained values and minimizing the number of missed transactions.

The basic operation characteristics of an asymmetric turnstile which transfers each electron one by one in one direction is described. A novel single electron counter circuit consisting of the asymmetric turnstiles, a load capacitor and an inverter which counts the number of high inputs is proposed. Monte Carlo circuit simulations reveal that the gate clock time of the counter circuit should be long enough to achieve allowable minimum error rate. The counter circuit implementing asymmetric single electron turnstiles is demonstrated to be applicable to a noise reduction system, a Winner-Take-All circuit and an artificial neuron circuit.

An econominal implementation of a chaos circuit onto the hardware is an important subject. In this letter, a two-dimensional digital chaos circuit realizing a Henon map is designed. Concerning the attractor and the bifurcation diagram of the proposed circuit, numerical simulations are performed to confirm the validity of the circuit algorithm. Furthermore, the proposed digital chaos circuit is designed by Verilog-HDL (Hardware Description Language). The proposed digital chaos circuit can be implemented into the form of the FPGA (Field Programmable Gate Array).

An adaptive stability-growth (ASG) learning algorithm is proposed for improving, as much as possible, the stability of a Hopfield-type associative memory. While the ASG algorithm can be used to determine the optimal stability instead of the well-known minimum-overlap (MO) learning algorithm with sufficiently large lower bound for MO value, it converges much more quickly than the MO algorithm in real implementation. Therefore, the proposed ASG algorithm is more suitable than the MO algorithm for real-world design of an optimal Hopfield-type associative memory.

This paper proposes a new method for estimating route outage probability in non-regenerative repeater digital microwave radio systems. In this method, the route outage probability is estimated by various means, including path correlation of fading occurrence and C/N degradation corresponding to the number of non-regenerative repeater stations with and without demodulator devices. In the conventional method, the path correlation is treated as 0 and the C/N degradation is taken as a constant value on each path. To confirm the proposed method's effectiveness, a field test is carried out in which 16QAM signals pass through two non-regenerative repeater stations. The results obtained are in good agreement with the estimated outage probability.

We analyze M,MMPP/G/1 finite queues with queue-length-threshold (QLT) scheduling policy and Bernoulli schedule where the arrival of type-1 customers (nonreal-time traffic) is Poisson and the arrival of type-2 customers (real-time traffic) is a Markov-modulated Poisson process (MMPP). The next customer to be served is determined by the queue length in the buffer of type-1 customers. We obtain the joint queue length distribution for customers of both types at departure epochs by using the embedded Markov chain method, and then obtain the queue length distribution at an arbitrary time by using the supplementary variable method. From these results, we obtain the loss probabilities and the mean waiting times for customers of each type. The numerical examples show the effects of the QLT scheduling policy on performance measures of the nonreal-time traffic and the bursty real-time traffic in ATM networks.

An adaptive infinite impulse response (IIR) filter implemented using an allpass and a minimum phase system has an advantage of its poles converging to the poles of the unknown system when the input is a white signal. However, when the input signal is colored, convergence speed deteriorates considerably, even to the point of lack of convergence for certain colored signals. Furthermore with a colored input signal, there is no guarantee that the poles of the adaptive digital filter (ADF) will converge to the poles of the unknown system. In this paper we propose a method which uses a linear predictor filter to whiten the input signal so as to improve the convergence characteristic. Computer simulation results confirm the increase in convergence speed and the convergence of the poles of the ADF to the poles of the unknown system even when the input is a colored signal.

We propose concepts of Paley-Wiener multiresolution analysis and Paley-Wiener wavelet frame based on general, not limited to dyadic, dilations of functions. Such a wavelet frame is an extension both of the Shannon wavelet basis and the Journe-Meyer wavelet basis. A concept of "natural" Paley-Wiener wavelet frame is also proposed to clarify whether a Paley-Wiener wavelet frame can naturally express functions from the point of view of the multiresolution analysis. A method of constructing a natural Paley-Wiener wavelet frame is given. By using this method, illustrative examples of Paley-Wiener wavelet frames with general scales are provided. Finally, we show that functions can be more efficiently expressed by using a Paley-Wiener wavelet frame with general scales.

The efficiency and reliability of an ultrasonic motor, operating in longitudinal-torsional degenerate-mode, are investigated. It is essential to miniaturize both longitudinal and torsional mode piezoelectric ceramic elements, in order to produce low-cost ultrasonic motors, and to realize a motor with low battery power consumption. The ultrasonic motor is designed with an accurate mechanical equivalent circuit, which can produce high design precision notwithstanding low computation cost. It is important in this design that the resonant frequencies of longitudinal mode and torsional mode coincide with each other under the pertinent rotor pressing force and longitudinal and torsional mode piezoelectric ceramic elements are located in the vibration nodes for the longitudinal mode and the torsional mode, respectively. As a result, the fabricated motor, whose rotor diameter was 12 mm, produced 480 r.p.m. no-load revolution speed, 0.55 kgfcm maximum torque, 50% maximum efficiency, 2.5 W consumed power and a lifetime over 1000 hours with continuous rotation.

This letter proposes and investigates a method of estimating the direction of arrival (DOA) of wideband signals such as spread spectrum signals, in a multipath channel. The DOA estimation method can reduce the effect of signal distortion due to bandwidth of signals by creating a spatial spectrum wihch satisfies the sampling theory in the time domain. The DOA estimate calculated from this spatial spectrum is robust against signal distortion due to multipath. Computer simulations numerically evaluate the proposed method. In comparison with conventional MUSIC algorithm, the proposed method achieves superior performance in a multipath channel.