This paper addresses the problem of estimating the parameters of multivariate ARMA processes by using higher-order statistics called cumulants. The main objective in this paper is to extend the idea of the q-slice algorithm in univariate ARMA processes to multivariate ARMA processes. It is shown for a multivariate ARMA process that the MA coefficient matrices can be estimated up to postmultiplication of a permutation matrix by using the third-order cumulants and of an extended permutation matrix by using the fourth-order cumulants. Simulation examples are included to demonstrate the effectiveness of the proposed method.

This paper proposes using an adaptive array in a base station for signal reception and transmission in order to increase the spectral efficiency without decreasing the cell radius. The adaptive array controls the directivity pattern of the base station to reduce co-channel interference during reception; the same array pattern is applied during transmission to prevent unnecessary illumination. Computer simulation results show that the cluster size can be reduced to one with time division duplexing (TDD), indicating that we can reuse the same frequency group at all cells. Thus, the improvement in spectral efficiency is as much as 16 fold that of an omni-antenna. Moreover, load sharing, which is expected to improve the channel utilization for unbalanced load situations, is available by cell overlapping. Frequency division duplexing (FDD) requires a weight adjust function to be applied for transmission since the difference in frequency between signal reception and transmission causes null positioning error. However, simple LMS-adjusting can provide a cluster size of one as well as cell overlapping when the frequency deference is 5%.

Reduction of the complexity of the NLMS algorithm has received attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to the average of the absolute values of the input samples, has been proposed. The convergence analysis of the processing cost reduction method has been derived from a low-pass filter expression. However, in this analysis the effect of the weignt vector components whose adaptations are skipped is not considered in terms of the direction of the gradient estimation vector. In this paper, we use an arbitrary value instead of the average of the absolute values of the input samples as a threshold level, and we derive the convergence characteristics of the processing cost reduction method with arbitrary threshold level for zero-mean white Gaussian samples. From the analytical results, it is shown that the range of the gain constant to insure convergence and the misadjustment are independent of the threshold level. Moreover, it is shown that the convergence rate is a function of the threshold level as well as the gain constant. When the gain constant is small, the processing cost is reduced by using a large threshold level without a large degradation of the convergence rate.

An adaptive array antenna can be considered as a useful tool of combating with fading in mobile communications. We can directly obtain the optimal weight coefficients without updating in temporal sampling, if the arrival angles and signal-to-noise ratio (SNR) of the desired and the undesired signals can be accurately estimated. The Maximum Entropy Method (MEM) can estimate the arrival angles, and the SNR from spatially sampled signals by an array antenna more precisely than the Discrete Fourier Transform (DFT). Therefore, this paper proposes and investigates an adaptive array antenna based on spatial spectral estimation using MEM. We call it MEM array. In order to reduce complexity for implementation, we also propose a modified algorithm using temporal updating as well. Furthermore, we propose a method of both improving estimation accuracy and reducing the number of antenna elements. In the method, the arrival angles can be approximately estimated by using temporal sampling instead of spatial sampling. Computer simulations evaluate MEM array in comparison with DFT array and LMS array, and show improvement owing to its modified algorithm and performance of the improved method.

We develop a convergence theory of the simple genetic algorithm (SGA) for two-bit problems (Type I TBP and Type II TBP). SGA consists of two operations, reproduction and crossover. These are imitations of selection and recombination in biological systems. TBP is the simplest optimization problem that is devised with an intention to deceive SGA into deviating from the maximum point. It has been believed that, empirically, SGA can deviate from the maximum point for Type II while it always converges to the maximum point for Type I. Our convergence theory is a first mathematical achievement to ensure that the belief is true. Specifically, we demonstrate the following. (a) SGA always converges to the maximum point for Type I, starting from any initial point. (b) SGA converges either to the maximum or second maximum point for Type II, depending upon its initial points. Regarding Type II, we furthermore elucidate a typical sufficient initial condition under which SGA converges either to the maximum or second maximum point. Consequently, our convergence theory establishes a solid foundation for more general GA convergence theory that is in its initial stage of research. Moreover, it can bring powerful analytical techniques back to the research of original biological systems.

When measuring the ejection fraction for the evaluation of the ventricular pumping function by means of the thermodilution technique, the slow response a conventional thermistor has caused it to be considered unsuitable, and fast thermistors have been proposed as an alternative. However, in this paper we propose improving the time-domain response of a conventional thermistor using a signal processing technique composed of a series of first-order high-pass filters which is known as the natural observation system. We considered the rise time of the thermistor in response to a step temperature change to effect correction for the measurement of the ejection fraction. The coefficients of the natural observation system were calculated by minimizing the square error between the step-response signal of the thermistor and the band-limited reference signal. In an experiment using a model ventricle, the thermodilution curve obtained from a conventional thermistor was improved using the proposed technique, thus enabling successful measurement of the ejection fraction of the ventricles.

In this paper, the relationship between the recursive least square (RLS) method with a U-D decomposition algorithm and ARMA lattice filter realization algorithm is presented. Both the RLS method and the lattice filter realization algorithm are used for the same applications, such as model identification, etc., therefore, it is expected that the lattice filter algorithm is in some ways related to the RLS. Though some of the proposed lattice filter algorithms have been derived by the RLS method, they do not express the relationship between RLS snd ARMA lattice filter realization algorithm. In order to describe the relation clearly, a new structure of ARMA lattice filter is proposed. Further, based on the relationship, a method of model identification with frequency weighting (MIFW), which is different from a previous method, is derived. The new MIFW method modifies the lattice parameters which are acquired without a frequency weighting and obtain the parameters of an ARMA model, which is identified with frequency weighting. The proposed MIFW method has the following restrictions: (1) The used frequency weighting is FIR filter with a low order. (2) By using the parameters of the ARMA lattice filter with ARMA (N,M) order and the frequency weighting with L order, the new ARMA parameter with the frequency weignting is with ARMA(N-L,M-L) order. By using the proposed MIFW method, the ARMA parameters estimated with the frequency weighting can be obtained without starting the computation again.

In many applications involving the processing of noisy signals, it is desired to know the noise variance. This paper proposes a new method for estimating the noise variance from the signals of autoregressive (AR) and autoregressive moving-average (ARMA) systems corrupted by additive white noise. The method proposed here uses the low-order Yule-Walker (LOYW) equations and the lattice filter (LF) algorithm for the estimation of noise variance from the noisy output measurements of AR and ARMA systems, respectively. Two techniques are proposed here: iterative technique and recursive one. The accuracy of the methods depends on SNR levels, more specifically on the inherent accuracy of the Yule-Walker and lattice filter methods for signal plus noise system. The estimated noise variance is used for the blind indentification of AR and ARMA systems. Finally, to demonstrate the effectiveness of the method proposed here many numerical results are presented.

The optical waveguides containing phthalocyanine as an optically active material were fabricated and transmission properties were investigated experimentally and numerically. The positive refractive index change was observed in the glass waveguide with a vanadyl phthalocyanine thin film as a top layer. The thermal influence on refractive index change was estimated by surface plasmon measurements.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

When intermetal oxide film which contains much water deposited on MOSFET, degradation of hot carrier characteristics is enhanced. This mechanism is considered to be as follows. During the annealing process water is desorbed from the intermetal oxide. The desorbed water reaches the MOSFET and eventually hydrogens terminate silicon dangling bonds in the gate oxide. This paper describes a new approach which uses ESR to analyze this mechanism. The ESR measurement of number of the silicon dangling bonds in undoped polysilicon lying under the intermetal oxide shows that water diffuses from intermetal oxide to MOSFET during the annealing process. The water diffusion is blocked by introduction between the polysilicon and the intermetal oxides of P-SiN layer or CVD SiO2 damaged by implantation.

An analysis of the circuit for dead angle compensation in the dc-to-dc converter controlled by a magnetic amplifier is presented. This circuit suppresses the dead angle so that the core loss may be reduced without spoiling the current surge suppression characteristics of the magnetic amplifier. The analysis is given by modeling the magnetization characteristics of the core containing the saturation inductance and the reverse recovery of the diode. As a result, the control characteristics of the converter with the compensation circuit are expressed analytically and a limit of compensation is derived theoretically.

Precise matching of the SEM (secondary electron microscope) image of the DUT (device under test) interconnection pattern with the CAD layout is required in the CAD-linked electron beam test system. We propose the point pattern matching method that utilizes a corner pattern in the CAD layout. In the method, a corner pattern which consists of a small number of pixels is derived by taking into account the design rules of VLSIs. By using the corner pattern as a template, the matching points of the template are sought in both the SEM image and CAD layout. Then, the point image obtained from the SEM image of DUT is matched with that from the CAD layout. Even if the number of points obtained in the DUT pattern is different from that in the CAD layout due to the influence of noise present in the SEM image of the DUT pattern, the point matching method would be successful. The method is applied to nonpassivated and passivated LSIs. Even for the passivated LSI where the contrast in the SEM image is mainly determined by voltage contrast, matching is successful. The computing time of the proposed method is found to be shortened by a factor of 4 to 10 compared with that in a conventional correlation coefficient method.

This letter extends the Yule-Walker method to the estimation of ARMA parameters from output measurements corrupted by noise. In the proposed method it is assumed that the noise variance and the input are unknown. An algorithm for the estimation of noise variance is, therefore, given. The use of the variance estimation method proposed here together with the Yule-Walker equations allow the estimation of the parameters of a minimum phase ARMA model based only on noisy measurements of its output. Moreover, using this method it is not necessary to slove a set of nonlinear equations for MA parameter estimation as required in the conventional correlation based methods.

This paper advocates the use of linear objective function in analytic analog placement. The role of linear and quadratic objctive functions in the behavior and results of an analog placement algorithm based on the force directed method is discussed. Experimental results for a MCNC benchmark circuit and another one from text books are shown to demonstrate the effect of a linear and a quadratic objective function on the analog constraint satisfaction and CPU time. By introducing linear objective function to the algorithm, we obtain better placements in terms of analog constraint satisfaction and computation cost than in case of conventional quadratic objective function.

In this paper, we show that the neural network can approximate the chaotic behavior in nonlinear dynamical system by experimental study. Chaotic neural activities have been reported in many respects including neural network field. On the contrary, can the neural network learn the chaotic behavior? There have been explored the neural network architecture for predicting successive elements of a sequence. Also there have been several studies related to learning algorithms for general recurrent neural networks. But they often require complicated procedure in time calculation. We use simple standard backpropagation for a kind of simple recurrent neural network. Two types of chaotic system, differential equation and difference equation, are examined to compare characteristics. In the experiments, Lorenz equation is used as an example of differential equation. One-dimensional logistic equation and Henon equation are used as examples of difference equation. As a result, we show the approximation ability of chaotic dynamics in difference equation, which is logistic equation and Henon equation, by neural network. To indicate the chaotic state, we use Lyapunov exponent which represents chaotic activity.

Results of a DC motor driving test with a power sent by a microwave and extracted with a rectenna array are reported. No significant difference has been observed in the output DC power from the rectenna array between a motor load and a resistive load. Mechanical output could be extracted from the received microwave power with an efficiency of 26%.

Resonant slots are widely used for conventional slotted waveguide array. Reflection from each slot causes a standing wave in the waveguide and beam tilting technique is essential to suppress the reflection at the antenna input port. But the slot reflection narrows the overall frequency bandwidth and the design taking it into account is complicated. This paper proposes a reflection cancelling slot pair as an array element, which consists of two slots spaced by 1/4λg. Round trip path-length difference between them is 1/2λg and reflection waves from a pair disappear and traveling-wave excitation in the waveguide is realized. The full wave analysis reveals that mutual coupling between paired slots is large and seriously reduces the radiation from a pair. Offset arrangement of slots in a pair is recommended to decrease the mutual coupling and to realize strong coupling. In practical array design, the mutual couplings from other pairs were simulated by imposing periodic boundary conditions above the aperture. To clarify the advantages of the slot pair over a conventional resonant slot, the predicted characteristics are compared. Reflection characteristics of the array using the slot pair is excellent and a boresite beam array can be realized. In addition, a slot pair can realize stronger coupling than the conventional resonant slot, while the bandwidth of the former in terms of the aperture field phase illumination is narrower than that of the latter. These suggests that the slot pair array is much more suitable for a small array than conventional one. Finally, the predicted characteristics are confirmed by experiments.

This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of leaning completion. The conclusions is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.

This paper presents an efficient algorithm for constructing at-most-k levels of an arrangement of n lines in the plane in time O(nk+n log n), which is optimal since Ω(nk) line segments are included there. The algorithm can sweep the at-most-k levels of the arrangement using O(n) space. Although Everett et al. recently gave an algorithm for constructing the at-most-k levels with the same time complexity independently, our algorithm is superior with respect to the space complexity as a sweep algorithm. Then, we apply the algorithm to a bipartitioning problem of a bichromatic point set： For r red points and b blue points in the plane and a directed line L, the figure of demerit fd(L) associated with L is defined to be the sum of the number of blue points below L and that of red ones above L. The problem we are going to consider is to find an optimal partitioning line to minimize the figure of demerit. Given a number k, our algorithm first determines whether there is a line whose figure of demerit is at most k, and further finds an optimal bipartitioning line if there is one. It runs in O(kn+n log n) time (n=r+b), which is subquadratic if k is sublinear.