This paper proposes a moment based encoding algorithm for iterated function system (IFS) coding of non-homogeneous fractal images with unequal probabilities. Moment based encoding algorithms for IFS coding of non-homogeneous fractal images require a solution of simultaneous algebraic equations that are difficult to handle with numerical root-finding methods. The proposed algorithm employs a variable elimination method using Grobner bases with floating-point coefficients in order to derive a numerically solvable equation with a single unknown. The algorithm also employs a varying associated-probabilities method for the purpose of decreasing the computational complexity of calculating Grobner bases. Experimental results show that the average computation time for encoding a non-homogeneous fractal image of 256256 pixels and 256 gray levels is about 200 seconds on a PC with a 400 MHz AMD K6-III processor.

This paper presents the method for an estimation of electrostatic discharge current waveshapes by radiated electromagnetic fields. The method of current waveshape estimation described is using the one-antenna method (single field method) and two-antenna method (complex field method) with a measured electric or magnetic fields at given field point by a time domain antenna. In order to verify the availability of the estimation theory, the discharge current waveform estimation were performed by one and two antenna methods using the measured electric fields of Wilson & Ma's and compared with experiments.

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.

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.

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.

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.

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.

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.

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.

In the present communication we propose the application of unsupervised Artificial Neural Networks (ANN) to solve general ill-posed problems and particularly we apply them to the the estimation of the direction of arrival (DOA) of VLF/ELF radio waves. We use the wave distribution method which consists in the reconstruction of the energy distribution of magnetospheric VLF/ELF waves at the ionospheric base from observations of the wave's electromagnetic field on the ground. The present application is similar to a number of computerized tomography and image enhancement problems and the proposed algorithm can be straightforwardly extended to other applications in which observations are linearly related to unknowns. Then, we have proven the applicability and also we indicate the superiority of the ANN to the conventional methods to handle this kind of problems.

The objective of this paper is to find a parametric representation for the vocal-tract log-area function that is directly and simply related to basic acoustic characteristics of the human vocal-tract. The importance of this representation is associated with the solution of the articulatory-to-acoustic inverse problem, where a simple mapping from the articulatory space onto the acoustic space can be very useful. The method is as follows: Firstly, given a corpus of log-area functions, a parametric model is derived following a factor analysis technique. After that, the articulatory space, defined by the parametric model, is filled with approximately uniformly distributed points, and the corresponding first three formant frequencies are calculated. These formants define an acoustic space onto which the articulatory space maps. In the next step, an independent component analysis technique is used to determine acoustic and articulatory coordinate systems whose components are as independent as possible. Finally, using singular value decomposition, acoustic and articulatory coordinate systems are rotated so that each of the first three components of the articulatory space has major influence on one, and only one, component of the acoustic space. An example showing how the proposed model can be applied to the solution of the articulatory-to-acoustic inverse problem is given at the end of the paper.

A new neural network for locating a source by integrating data from a number of sensors is considered. The network gives a solution for inverse problems using a back-propagation algorithm with the architecture to get the solution in the inter-layer weights in a coded form Three different physical quantities are applied to the network, since the scheme has three independent ports; an input port, a tutorial port and an answer port. Our architecture is useful to estimate z" in the problem whose structure is y=f(x,z) where y is the observed data, x is the sensor position and z is the source location. The network integrates the information obtained from a number of sensors and estimates the location of the source. We apply the network to two problems of location estimation: the localization of the active nerves from their evoked potential waveforms and the localization of objects from their echoes using an active sonar system.

Microwave radiometry has been investigated for non-invasive measurement of temperature in human body. Recent trends are to explore the capability of retrieving a temperature profile or map from a set of brightness temperatures measured by a multifrequency radiometer operating in a 1-6GHz range. The retrieval of temperature from the multifrequency measurement data is formulated as an inverse problem in which the number of independent measurement or data is limited (7) and the data suffer from considerably large random fluctuations. The standard deviation of the data fluctuation is given by the brightness temperature resolution of the instrument (0.04-0.1K). Solutions are prone to instabilities and large errors unless proper solution methods are used. Solution methods developed during the last few years are reviewed: singular system analysis, bio-heat transfer solution matched with radiometric data, and model-fitting combined with Monte Carlo technique. Typical results obtained by these methods are presented to indicate a crosssection of the present-state-of-the-development in the field. This review concludes with discussions on the radiometric weighting function which connects physical temperatures in object to the brightness temperature. Three-dimensional weighting functions derived by the modal analysis and the FDTD method for a rectangular waveguide antenna coupled to a four layered lossy medium are discussed. Development of temperature retrieval procedures incorporating the 3-D weighting functions is an important and challenging task for future work in this field.

This paper describes a SQUID magnetometer and the measurement of small signals. It also describes the current state of SQUID technology developed in the SSL project.

For estimating the radiated emission from a metallic enclosure, the authors have developed a numerical computational method which applied inverse analysis. A metallic enclosure containing a loop antenna was set up to be a model source for the numerical analysis. Magnetic fields around the enclosure were measured by measurement systems fabricated in the authors' laboratory. Using the measured magnetic fields, current distributions on the enclosure surface were determined by means of an inverse analysis utilizing the least squares method. From this surface current distribution, the electromagnetic field distributions were estimated by forward analysis on a cylindrical surface 3.0m in radius. The amount of the error in the estimated fields distribution was also discussed.

The invariant imbedding method combined with the time domain wave splitting technique is applied to the inverse problem for the telegraph equation t2u=(1/r)(pu)-btu+qu in a stratified half-space. The zeroth, second and fourth moments of the 3-D fields are used to reduce the three-dimensional problem to a set of one-dimensional problems. The imbedding equations for R0, R2 and R4, respectively the zeroth, second and fourth moments of the reflection operator, are derived. Numerical results for the reconstruction are presented using the reflection data on the surface.