This paper describes methods of tracking of moving objects in a cluttered background by integrating optical flow, depth data, and/or uniform brightness regions. First, a basic method is introduced which extracts a region with uniform optical flow as the target region. Then an extended method is described in which optical flow and depth are fused. A target region is extracted by Baysian inference in term of optical flow, depth and the predicted target location. This method works only for textured objects because optical flow or depth are extracted for textured objects. In order to solve this problem, uniform regions in addition to the optical flow are used for tracking. Realtime human tracking is realized for real image sequences by using a real time processor with multiple DSPs.

The second generation of mobile communications is growing rapidly to the third generation due to various communication techniques and the increasing number of users. PCS, the communication method of the third generation, should be able to provide users with various services, independently of the current location. To PCS, the mobility management of users is essential. The mobility management method which has been used has a structural drawback: as the number of users increase, HLR becomes the bottleneck. Everyone is expected to have one terminal in the third generation mobile communications age. Therefore, an enhanced mobility management scheme to reduce the bottleneck of the HLR, should be used in the third generation mobile communications. In this paper, we propose a new mobility management method where the trace of terminals is left in the VLRs, so that a call can be connected by querying only to the VLRs rather than to the HLR when the terminal-terminated-call occurs. The proposed method distributes messages to VLRs and effectively reduce mobility management cost. To estimate overall mobility management cost, we simulated the new method of PCS network. The simulation model is based on the Jackson's network, and makes it possible to estimate mobility management cost of PCS networks. IS-41 and proposed scheme are compared based on the computer simulation. Considering the delay times both in HLR and VLR, and considering both location registration cost and call delivery cost, the proposed modeling method shows the improvement.

A numerical method is proposed for efficiently locating fold bifurcation points of periodic orbits of high-dimensional differential-equation systems. This method is an extension of the subspace shooting method (or the Newton-Picard shooting method) that locates periodic orbits by combining the conventional shooting method and the brute-force method. Fold bifurcation points are located by combining a variant of the subspace shooting method with a fixed parameter value and the secant method for searching the parameter value of the bifurcation point. The target in the subspace-shooting part is an (not necessarily periodic) orbit represented by a Poincare mapping point which is close to the center manifold and satisfies the eigenvalue condition for the bifurcation. The secant-search part finds the parameter value where this orbit becomes periodic. Avoiding the need for differentiating the Poincare map with respect to the bifurcation parameter and exploiting several properties of the center manifold, the proposed method is both robust and easy to implement.

In order to demodulate a Differential Chaos Shift Keying (DCSK) signal, the energy carried by the received chaotic signal must be determined. Since a chaotic signal is not periodic, the energy per bit carried by the chaotic signal can only be estimated, even in the noise-free case. This estimation has a non-zero variance that limits the attainable data rate. In this paper the DCSK technique is combined with frequency modulation in order to overcome the estimation problem and to improve the data rate of DCSK modulation.

The naturalness of normal sustained vowels is considered to be attributable to the fluctuations observed in the steady part where speech signal is seemingly almost periodic. There always exist two kinds of involuntary fluctuations in the steady part of sustained vowels, even if the sustained vowels are phonated as steadily as possible. One is pitch period fluctuation and the other is waveform fluctuation. In this study, frequency analyses on these fluctuations were conducted in order to investigate their general characteristics. The results of the analyses suggested that the frequency characteristics of the fluctuations were possible to be approximated as 1/fβ-like, which is regarded as the specific feature of random fractal. Therefore, a procedure based on random fractal generation methods was proposed in order to produce these fluctuations for the improvement of the voice quality of synthesized sustained vowels. A series of psychoacoustic experiments was also conducted to evaluate the proposed technique. Experimental results indicated that the proposed technique was effective for synthesized sustained vowels to be perceived as human-like. Unlike the sustained vowels which were synthesized without pitch period fluctuation nor waveform fluctuation, the synthesized sustained vowels which contained the fluctuations were not perceived as buzzer-like, which is the major problem of the voice quality of synthesized sustained vowels. However, it was also found that both of the fluctuations were not always the acoustic cues for the naturalness of normal sustained vowels. The synthesized sustained vowels which contained the fluctuations whose frequency characteristics were the same as that of white noise were perceived as noise-like, which is not at all the voice quality of normal sustained vowels. The results of psychoacoustic experiments indicated that the frequency characteristics of the fluctuations, which are possible to be modeled as 1/fβ-like, were the significant factors for the naturalness of normal sustained vowels.

This paper describes an efficient simulation algorithm for the spatiotemporal pattern analysis of the continuous-time neural networks with the multivalued logic (multivalued continuous-time neural networks). The multivalued transfer function of neuron is approximated to the stepwise constant function which is constructed by the sum of the step functions with the different thresholds. By this approximation, the dynamics of the network can be formulated as a stepwise constant linear differential equation at each timestep and the optimal timestep for the numerical integration can be obtained analytically. Finally, it is shown that the proposed method is much faster than a variety of conventional simulators.

This paper gives two kinds of functions for which Uesaka's Conjecture, stating that the globally optimum (not a local minimum) of a quadratic function F(x)=-(1/2)xtAx in the n-dimensional hypercube may be obtained by solving a differential equation, holds true, where n denotes the dimension of the vector x. Uesaka stated in his paper that he proved the conjecture only for n=2. This corresponds to a very special case of this paper. The results of this paper suggest that the conjecture really holds for a wide class of quadratic functions and therefore support the conjecture partially.

This letter presents a new algorithm for improving the Signal to Noise Ratio (SNR) of complex sinusoidal signals contaminated by additive Gaussian noises using sum of Higher-Order Statistics (HOS). We conduct some computer simulations to show that the proposed algorithm can improve the SNR more than 7 dB compared with the conventional coherent integration when the SNR of the input signal is -10 dB.

We present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.

Modeling error is the major concerning issue in the trajectory estimation. This paper formulates the dynamic model of a reentry vehicle in reentry phase for identification with an unmodeled acceleration input covering possible model errors. Moreover, this work presents a novel on-line estimation approach, adaptive filter, to identify the trajectory of a reentry vehicle from a single radar measured data. This proposed approach combines the extended Kalman filter and the recursive least-squares estimator of input with the hypothetical testing scheme. The recursive least-squares estimator is provided not only to extract the magnitude of the unmodeled input but to offer a testing criterion to detect the onset and presence of the input. Numerical simulation demonstrates the superior capabilities in accuracy and robustness of the proposed method. In real flight analysis, the adaptive filter also performs an excellent estimation and prediction performances. The recommended trajectory estimation method can support defense and tactical operations for anti-tactical ballistic missile warfare.

The hippocampus is thought to play an important role in the transformation from short-term memory into long-term memory, which is called consolidation. The physiological phenomenon of synaptic change called LTP or LTD has been studied as a basic mechanism for learning and memory. The neural network mechanism of the consolidation, however, is not clarified yet. The authors' approach is to construct information processing theory in learning and memory, which can explain the physiological data and behavioral data. This paper proposes a dynamical hippocampal model which can store and recall spatial input patterns. The authors assume that the primary functions of hippocampus are to store episodic information of sensory signals and to keep them for a while until the neocortex stores them as a long-term memory. On the basis of the hippocampal architecture and hypothetical synaptic dynamics of LTP/LTD, the authors construct a hippocampal model. This model considers: (1) divergent connections, (2) the synaptic dynamics of LTP and LTD based on pre- and postsynaptic coincidence, and (3) propagation of LTD. Computer simulations show that this model can store and recall its input spatial pattern by self-organizing closed activating pathways. By the backward propagation of LTD, the synaptic pathway for a specific spatial input pattern can be selected among the divergent closed connections. In addition, the output pattern also suggests that this model is sensitive to the temporal timing of input signals. This timing sensitivity suggests the applicability to spatio-temporal input patterns of this model. Future extensions of this model are also discussed.

Related with accuracy, computational complexity and so on, quality of computing for the so-called homotopy method has been discussed recently. In this paper, we shall propose an estimation method with interval analysis of region in which unique solution path of the homotopy equation is guaranteed to exist, when it is applied to a certain class of uniquely solvable nonlinear equations. By the estimation, we can estimate the region a posteriori, and estimate a priori an upper bound of the region.

In order to improve microprocessor performance, we propose to utilize histories of dynamic instruction sequences. A lot of special purpose memories integrated in a processor chip hold the histories. In this paper, we describe the usefulness of using two special purpose memories: Non-Consecutive basic block Buffer (NCB) and Reference Prediction Table (RPT). The NCB improves instruction fetching efficiency in order to relieve control dependences. The RPT predicts data addresses in order to speculate data dependences. From the simulation study, it has been found that the proposed mechanisms improve processor performance by up to 49. 2%.

Though response of neurons is mainly decided by synaptic events, the length of a time window for the neuronal response has still not been clarified. In this paper, we analyse the time window within which a neuron processes synaptic events, on the basis of the Hodgkin-Huxley equations. Our simulation shows that an active membrane property makes neurons' behavior complex, and that a few milliseconds is plausible as the time window. A neuron seems to detect coincidence synaptic events in such a time window.

Conventional approaches to neural network training do not consider possibility of selecting training samples dynamically during the learning phase. Neural network is simply presented with the complete training set at each iteration of the learning. The learning can then become very costly for large data sets. Huge redundancy of data samples may lead to the ill-conditioned training problem. Ill-conditioning during the training causes rank-deficiencies of error and Jacobean matrices, which results in slower convergence speed, or in the worst case, the failure of the algorithm to progress. Rank-deficiencies of essential matrices can be avoided by an appropriate selection of training exemplars at each iteration of training. This article presents underlying theoretical grounds for dynamic sample selection (DSS), that is mechanism enabling to select a subset of training set at each iteration. Theoretical material is first presented for general objective functions, and then for the objective functions satisfying the Lipschitz continuity condition. Furthermore, implementation specifics of DSS to first order line search techniques are theoretically described.

Recently, a trellis-based recursive maximum likelihood decoding (RMLD) algorithm has been proposed for decoding binary linear block codes. This RMLD algorithm is computationally more efficient than the Viterbi decoding algorithm. However, the computational complexity of the RMLD algorithm depends on the sectionalization of a code trellis. In general, minimization of the computational complexity results in non-uniform sectionalization of a code trellis. From implementation point of view, uniform sectionalization of a code trellis and regularity among the trellis sections are desirable. In this paper, we apply the RMLD algorithm to a class of codes which are transitive invariant. This class includes Reed-Muller (RM) codes, the extended and permuted BCH (EBCH) codes and their subcodes. For this class of codes, the binary uniform sectionalization of a code trellis results in the following regular structure. At each step of decoding recursion, the metric table construction procedure is applied uniformly to all the sections and the size and structure of each metric table are the same. This simplifies the implementation of the RMLD algorithm. Furthermore, for all RM codes of lengths 64 and 128 and EBCH codes of lengths 64 and 128 with relatively low rate, the computational complexity of the RMLD algorithm based on the binary uniform sectionalization of a code trellis is almost the same as that based on an optimum sectionalization of a code trellis.

We demonstrate a very simple and compact optical transceiver diode module using a passive alignment on a silicon bench with a V-groove. The excess loss caused by the passive alignment of an optical transceiver diode and a flat-end optical fiber is only 0. 6 dB. A high coupling efficiency of -4. 3 dB is obtained. This results in a high responsivity with a wavelength- and polarization-independence of 0. 5 dB over a 70 nm wavelength range and in good laser performance.

This paper proposes Emergent Behavior Based Architecture (EBBA) that fusions heterogeneous sensor information at the level of behavior modules. The characteristics of EBBA are as follows. i) sensor based architecture, ii) constructed by a set of concurrently executable behavior modules, iii) to have multiple methods to achieve given tasks by utilizing behavior modules, iv) a planner can control emergent behaviors. We also have developed mobile robot navigation system based on EBBA and confirmed the efficiency by experiments in the various situations.

This paper presents both new analytical and new numerical solutions to the problem of generating waveforms exhibiting a low peak-to-peak factor. One important application of these results is in the generation of pseudo-white noise signals that are commonly uses in multi-frequency measurements. These measurements often require maximum signal-to-noise ratio while maintaining the lowest peak-to-peak excursion. The new synthesis scheme introduced in this paper uses the Discrete Fourier Transform (DFT) to generate pseudo-white noise sequence that theoretically has a minimized peak-to-peak factor, Fp-p. Unlike theoretical works in the literature, the method presented here is based in purely discrete mathematics, and hence is directly applicable to the digital synthesis of signals. With this method the shape of the signal can be controlled with about N parameters given N harmonic components. A different permutation of the same set of offset phases of the "source harmonics" creates an entirely different sequence.

An elastic-Vt CMOS circuit is proposed which facilitates both high speed and low power consumption at low supply voltages. This circuit permits fine-grain power control on each multiple circuit block composing a chip, and it is not sensitive to design factors as device-parameter deviations or operating-environment variations. It also does not require any such additional fabrication technology as triple-well structure or multi-threshold voltage. The effectiveness of the circuits design was confirmed in applying it to specially fabricated 16-bit adders and 4-kb SRAMs based on 1. 5-V, 0. 35- µm CMOS technology.