A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.

This letter describes several techniques for optimizing software implementations of E2 on various platforms. We propose optimization techniques for each part of E2; a new inversion algorithm, efficient byte splitting and merging for BP-Function, and an efficient SPN (Substitution-Permutation Network) implementation for 32- or 64-bit processors. As a result, E2 achieves the encryption speeds of 100.5 kb/s, 68.3 Mb/s, 162.3 Mb/s, and 130.8 Mb/s for H8/300 (5 MHz), Pentium Pro (200 MHz), Pentium II (450 MHz), and 21164A (600 MHz).

This paper proposes a fast encoding algorithm for iterated function system (IFS) coding of gray-level homogeneous fractal images. In order to realize IFS coding of high order fractal images, it is necessary to solve a set of simultaneous equations with many unknowns. Solving the simultaneous equations using a multi-dimensional, numerical root-finding method is however very time consuming. As preprocessing of numerical computation, the proposed algorithm employs univariate polynomial manipulation, which requires less computation time than multivariate polynomial manipulation. Moreover, the symmetry of the simultaneous equations with respect to the displacement coefficients enables us to derive an equation with a single unknown from the simultaneous equations using univariate polynomial manipulation. An experimental result is presented to illustrate that the encoding time of the proposed algorithm is about 5 seconds on a personal computer with a 400 MHz Pentium II processor.

We propose a new inverse modeling method to extract 2D channel dopant profile in an MOSFET. The profile is extracted from threshold voltage (Vth) of MOSFETs with a series of gate lengths. The uniqueness of the extracted channel and drain profile is confirmed through test simulations. The extracted profile of actual 0.1 µm nMOSFETs explains reverse short channel effects (RSCE) of threshold voltage dependent on gate length including substrate bias dependence.

This paper reports the evaluation results of the channel boron distribution in the deep sub-0.1 [µm] n-MOSFETs for the first time. It has been found that the boron depletion effect becomes dominant and the reverse short channel effect becomes less significant in the deep sub-0.1 [µm] n-MOSFETs. It has been also found that the sheet charge distribution responsible for the reverse short channel effect is localized within a distance of 100 [nm] from the source/drain-extension junction.

The pseudo-inverse model for the associative memory has an iterative algorithm converging to its weight matrix. The present letter shows that the same algorithm except for the lack of self couplings can be derived by simple consideration of the energy of the network state.

In this paper, we discuss the problem of active training data selection for improving the generalization capability of a neural network. We look at the learning problem from a function approximation perspective and formalize it as an inverse problem. Based on this framework, we analytically derive a method of choosing a training data set optimized with respect to the Wiener optimization criterion. The final result uses the apriori correlation information on the original function ensemble to devise an efficient sampling scheme which, when used in conjunction with the learning scheme described here, is shown to result in optimal generalization. This result is substantiated through a simulated example and a learning problem in high dimensional function space.

In this paper, we analyze the inverse scattering problem by a new deterministic method called "Source and Radiation Field Solution," which has the merit that both the source and the radiation field can be treated at the same time, the effect of which has already shown in ordinary scattering problems.

Retrieving the unknown parameters of scattering objects from measured field data is the subject of microwave imaging. This is naturally and usually posed as an optimization problem. In this paper, micro genetic algorithm coupled with deterministic method is applied to the shape reconstruction of perfectly conducting cylinders. The combined approach, with a very small population like the micro genetic algorithm, performs much better than the conventional large population genetic algorithms (GA's) in reaching the optimal region. In addition, we propose a criterion for switching the micro GA to the deterministic optimizer. The micro GA is utilized to effectively locate the vicinity of the global optimum, while the deterministic optimizer is employed to efficiently reach the optimum after inside this region. Therefore, the combined approach converges to the optimum much faster than the micro GA. The proposed approach is first tested by a function optimization problem, then applied to reconstruct perfectly conducting cylinders from both synthetic data and real data. Impressive and satisfactory results are obtained for both cases, which demonstrate the validity and effectiveness of the proposed approach.

This paper addresses the important issue of estimating realistic grasping postures, and presents a methodology and algorithm to automate the generation of hand and body postures during the grasp of arbitrary shaped objects. Predefined body postures stored in a database are generalized to adapt to a specific grasp using inverse kinematics. The reachable space is represented discretely dividing into small subvolumes, which enables to construct the database. The paper also addresses some common problems of articulated figure animation. A new approach for body positioning with kinematic constraints on both hands is described. An efficient and accurate manipulation of joint constraints is presented. Obtained results are quite satisfactory, and some of them are shown in the paper. The proposed algorithms can find application in the motion of virtual actors, all kinds of animation systems including human motion, robotics and some other fields such as medicine, for instance, to move the artificial limbs of handicapped people in a natural way.

It is shown from the Hilberts theory that if the real function Π(θ) has no zeros over the interval [0, 2π], it can be factorized into a product of the factor π+(θ) and its complex conjugate π-(θ)(=). This factorization is tested to decompose a real far-zone field pattern having zeros. To this end, the factorized factors are described in terms of bicomplex mathematics. In our bicomplex mathematics, the temporal imaginary unit "j" is newly defined to distinguish from the spatial imaginary unit i, both of which satisfy i2=-1 and j2=-1.

A bicomplex representation for time-harmonic electromagnetic fields appearing in scattering and diffraction problems is given using two imaginary units i and j. Fieldsolution integral-expressions obtained in the high-frequency and low-frequency limits are shown to provide the new relation between high-frequency diffraction and low-frequency scattering. Simple examples for direct scattering problems are illustrated. It may also be possible to characterize electric or magnetic currents induced on the obstacle in terms of geometrical optics far-fields. This paper outlines some algebraic rules of bicomplex mathematics for diffraction or scattering fields and describes mathematical evidence of the solutions. Major discussions on the relationship between high-frequency and low-frequency fields are relegated to the companion paper which will be published in another journal.

This paper deals with two different quantitative inversion algorithms for reconstructing the complex permittivity profile of bounded inhomogeneous objects from measured scattered field data. The first algorithm involves an imaging method with single frequency excitation and multiincidence illumination and the second algorithm involves a method with synthetic pulse (multifrequency mode) excitation for objects surrounded by freespace or buried in stratified half-space media. Transmission or reflection imaging protocols are considered depending on aimed applications: microwave imaging in free-space from far-field data for target identification, microwave imaging from near-field data for nondestructive testing (NDT), microwave tomography of buried objects for mine detection and localization, civil engineering and geophysical applications. And Edge-Preserving regularization scheme leading to a significant enhancement in the image reconstructions is also proposed. The methods are illustrated with synthetic and experimental data.

The Multiple input-output INverse/filtering Theorem (MINT) proves that N + 1 inverse filters are necessary to precisely control sound at N points in a space, and gives the minimum orders of such filters. In this paper, we propose the Indefinite MINT Filters (IMFs) for adding one or more control points to the above framework without increasing the number of inverse filters. Although the controllability of the new point is not sufficient, that of the other points is still maintained high enough by the principle of the MINT. In a two point sound control (using two inverse filters), the IMFs could reduce the squared error to the desired sound up to - 10 dB at the second point which is not controlled by the MINT.

We present a very fast method for calculating an inverse filter for audio reproduction system. The proposed method of FFT-based inverse filter design, which combines the well-known principles of least squares optimization and regularization, can be used for inverting systems comprising any number of inputs and outputs. The method was developed for the purpose of designing digital filters for multi-channel sound reproduction. It is typically several hundred times faster than a conventional steepest descent algorithm implemented in the time domain. A matrix of causal inverse FIR (finite impulse response) filters is calculated by optimizing the performance of the filters at a large number of discrete frequencies. Consequently, this deconvolution method is useful only when it is feasible in practice to use relatively long inverse filters. The circular convolution effect in the time domain is controlled by zeroth-order regularization of the inversion problem. It is necessary to set the regularization parameter β to an appropriate value, but the exact value of β is usually not critical. For single-channel systems, a reliable numerical method for determining β without the need for subjective assessment is given. The deconvolution method is based on the analysis of a matrix of exact least squares inverse filters. The positions of the poles of those filters are shown to be particularly important.

In the measurement of actual random phenomenon, the observed data often contain the fuzziness due to the existence of confidence limitation in measuring instruments, permissible error in experimental data, some practical simplification of evaluation procedure and a quantized error in digitized observation. In this study, by introducing the well-known fuzzy theory, a state estimation method based on the above fuzzy observations is theoretically proposed through an establishment of wide sense digital filter under the actual situation of existence of the background noise in close connection of the inverse problem. The validity and effectiveness of the proposed method are experimentally confirmed by applying it to the actual fuzzy data observed in an acoustic environment.

A new method is proposed for recovering an unknown source signal ,which is observed through two unknown channels characterized by non-minimum phase FIR filters. Conventional methods cannot estimate the non-minimum phase parts and recover the source signal. Our method is based on computing the eigenvector corresponding to the smallest eigenvalue of the input correlation matrix and using the criterion with the multi-channnel inverse filtering theory. The impulse responses are estimated by computing the eigenvector for all modeling orders. The optimum order is searched for using the criterion and the most appropriate impulse responses are estimated. Multi-channel inverse filtering with the estimated impulse responses is used to recover the unknown source signal. Computer simulation shows that our method can estimate nonminimum phase impulse responses from two reverberant signals and recover the source signal.

The applicability of a boundary matching technique is presented for reconstructing the refractive-index profile of a circularly symmetric cylinder from the measurement of the scattered wave when the cylinder is illuminated by an H-polarized plane wave. The algorithm of reconstruction is based on an iterative procedure of matching the scattered wave calculated from a certain refractive-index distribution with the measured scattered-wave. The limits of reconstruction for strongly inhomogeneous lossless and lossy cylinders are numerically shown through computer simulations under noisy environment, and are compared with those in the E-wave case.

This paper presents a new method for the selftuning control of nonminimum phase discrete-time stochastic systems using approximate inverse systems obtained from the leastsquares approximation. Using this approximate inverse system the gain response of the system can be made approximately unit and phase response exactly zero. We show how unstable polezero cancellations can be avoided. This approximate inverse system can be used in the same manner for both minimum and nonminimum phase systems. Moreover, the degrees of the controller polynomials do not depend on the approximate inverse system. We just need an extra FIR filter in the feedforward path.

This paper is devoted to a new design method for infinite impulse response approximate inverse system of a nonminimum phase system. The design is carried out such that the convolution of the nonminimum phase polynomial and its approximate inverse system can be represented by an approximately linear phase all-pass filter. A method for estimating the time delay and order of an approximate inverse system is also presented. Using infinite impulse response approximate inverse systems better accuracy is achieved with reduced computational complexity. Numerical examples are included to show the effectiveness of the proposed method.