In this paper, a new design method of IIR Hilbert transformers is proposed by using a nonlinear optimization algorithm. The initial conditions for nonlinear optimization are given by controlling the tap values of IIR Hilbert transformer in the time domain. A parameter optimization technique is used to obtain an IIR Hilbert transformer whose output characteristics in the time domain are close to those of the reference FIR Hilbert transformer. Then, the tap values are optimized by the Fletcher-Powell method in the frequency domain. In the proposed method, the performance criterion for optimization consists of two functions. One represents the error of magnitude response and the other represents that of phase responses. The error ratio of magnitude and phase responses is controlled by different weights to each function. Finally, some examples of IIR Hilbert transformers using this method are shown.
A Bayesian time varying coefficient regression model with smoothness priors is introduced for inferring the dynamic relationship between two time series. Smoothness prior in the form of a Gaussian stochastic difference equation is imposed on the regression coefficient. The estimates of hyperparameters and the order of the difference equation are determined by maximizing marginal likelihood of the hyperparameters and using the minimum ABIC procedure. The estimate of the time varying regression coefficient is obtained by maximizing a posterior density of the coefficient. A numerical example and two simulation studies on the accuracy of the procedure are given. The model is applied to the analyses of the dynamic dependences of steel consumption on GNP for four countries.
In this paper we present two estimation algorithms of static relative positioning by GPS, where the vector between two antenna stations is determined. There are two important types of GPS observable quantities ; pseudo ranges and carrier phases. The first algorithm here is to utilize the double difference of the carrier phases. And the second algorithm for relative positioning is shown based upon the difference of the estimates for point positioning by using the pseudo ranges, specially, applying Dilution of Precision (DOP). Finally, the experimental results for estimation of the baseline length are shown by using real receiver data obtained at two static points.
This study offers a three-dimensional motion compensation system that is effective when the moving body is a polyhedron. Features of this system are the introduction of a borderline extraction method which extracts only the contour of a pattern-bearing solid, as well as the extraction of the information on surface disappearance and appearance which uses frame-to-frame changes in the vertex position. Detected three-dimensional motion information has also been used to achieve accurate synthesis of a moving-body image.
In this paper, we revise a previous axiom of dominance for constructing a measurable value function under uncertainty based on Dempster-Shafer theory of probability. The previous axiom of dominance has dealt with only the best and the worst results in the set element. In this paper we propose a new axiom of dominance after defining the value of the set element taking into account the average of the value of all the results included in the set element. We could construct a measurable value function under uncertainty for an ordinary, pessimistic or optimistic person, based on this new axiom of dominance. An example of evaluating the alternatives to regulate CO2emission for avoiding global warming is included.