A noninvasive method for measuring complex permittivity of biological tissues is proposed. The noninvasive method is based on an inverse scattering technique which employs an iterative procedure. The iterative procedure consists of solving an electric field integral equation using the method of moments and minimizing the square difference between calculated and measured scattered fields. Implementation of the noninvasive method requires the knowledge of the target shape, the incident and measured scattered fields. Based on the noninvasive method, a measuring system of complex permittivity is developed and its reliability is verified.

Let S(n) be a space constructible function such that S(n) log n. In this paper, we show that AuxSpTu (S(n),T(n)) NSPACE (S(n)log T(n)), where AuxSpTu (S(n),T(n)) is the class of languages accepted by nondeterministic auxiliary pushdown automata operating simultaneously in O(S(n)) space and O(T(n)) turns of the auxiliary tape head.

We propose a beam tracing frame which shifts together with either the guiding structure or the beam propagation in optical circuits. This frame is adaptive to the beam propagation analysis based on the finite-element method and can reduce the computational window size.

It is an important problem whether or not we can reject the disturbances from distributed parameter circuit. In order to analyze this problem structurally, it is necessary to investigate the basic equation of distributed parameter circuit in the framework of state space. Since the basic equation has two parameters for time and space, the state value belongs to an infinite-dimensional space. In this paper, the disturbance-rejection problems with incomplete state feedback and/or incomplete state feedback and feedforward for infinite-dimensional systems are studied in the framework of geometric approach. And under certain assumptions, necessary and/or sufficient conditions for these problems to be solvable are proved.

To investigate the effect of alkyl chain length and adsorption time on the charge-transfer complex formation, ultraviolet-visible absorption and inelastic electron tunneling (IET) spectroscopy measurements were carried out for the tetramethylphenylenediamine (TMPD; donor molecule) adsorbed dodecyl-, pentadecyl- and octadecyl-tetracyanoquinodimethane (TCNQ) Langmuir-Blodgett (LB) films. In the optical absorption spectra, the main peak of LB films shows a red-shift depending on alkyl chain length and adsorption time. Furthermore, the dependence on alkyl chain length and adsorption time are also shown in the IET spectra. These results demonstrate that adsorption LB methods enable to control the adsorption ratio of functional molecules and the CT complex formation.

Truncated sum (TSUM for short) is useful for MV-PLA's realization. This paper introduces a new class of multiple-valued logic functions that are expressed by truncated sum, differential product (DPRODUCT for short), NOT and variables, where TSUM (x, y)min (xy, p1) and DPRODUCT (x, y)max (xy(p1), 0) is newly defined as the product that is derived by applying De Morgan's laws to TSUM. We call the functions T-functios. First, this paper clarifies that a set of T-functions is not a lattice. It clarifies that Lukasiewicz implication can be expressed by TSUM and NOT. It guarantees that a set of p-valued T-functios is not complete but complete with constants. Next, the speculations of the number of T-functions for less than ten radixes are derived. For eleven or more radix p, a speculation of the number of p-valued T-functions is shown. Moreover, it compares the T-functions with B-functions. The B-functions have been defined as the functions expressed by MAX, MIN, NOT and variables. As a result, it shows that a set of T-functions includes a set of B-functions. Finally, an inclusion relation among these functional sets and normality condition is shown.

J. Bresnan and R. M. Kaplan introduced lexical-functional grammars (LFGs, for short) as a new formalism for human language syntax. It is important to show formal properties of this kind of grammars in order to characterize the formal complexity of human languages. In this paper, we will show that the emptiness problem for LFGs is undecidable.

A new regularization cost function for generalization in real-valued function learning is proposed. This cost function is derived from the maximum likelihood method using a modified sample distribution, and consists of a sum of square errors and a stabilizer which is a function of integrated square derivatives. Each of the regularization parameters which gives the minimum estimation error can be obtained uniquely and non-empirically. The parameters are not constants and change in value during learning. Numerical simulation shows that this cost function predicts the true error accurately and is effective in neural network learning.

This document describes a proposal for the implementation of a new VLSI neural network technique called Parallel Propagated Targets (PPT). This technique differs from existing techniques because all layer, within a given network, can learn simultaneously and not sequentially as with the Back Propagation algorithm. the Parallel Propagated Target algorithm uses only information local to each layer and therefore there is no backward flow of information within the network. This allows a simplification in the system design and a reduction in the complexity of implementation, as well as acheiving greater efficiency in terms of computation. Since all synapses can be calculated simultaneously it is possible using the PPT neural algorithm, to parallelly compute all layers of a multi-layered network for the first time.

This paper proposes a new method to determine the shape of a surface by learning the mapping between three image irradiances observed under illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function to describe specular reflectance. Lambertian reflectance is included as a special case. A neural network is constructed to estimate the values of reflectance parameters and the object surface gradient distribution under the assumption that the values of reflectance parameters are not known in advance. The method reconstructs the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consists of one to extract two gradient parameters from three image irradiances and its inverse one. The effectiveness of this proposed neural network is confirmed by computer simulations and by experiment with a real object.

We propose a fast computation method of stochastic relaxation for the continuous-valued Markov random field (MRF) whose energy function is represented in the quadratic form. In the case of regularization in visual information processing, the probability density function of a state transition can be transformed to a Gaussian function, therefore, the probablistic state transition is realized with Gaussian random numbers whose mean value and variance are calculated based on the condition of the input data and the neighborhood. Early visual information processing can be represented with a coupled MRF model which consists of continuity and discontinuity processes. Each of the continuity or discontinuity processes represents a visual property, which is like an intensity pattern, or a discontinuity of the continuity process. Since most of the energy function for early visual information processing can be represented by the quadratic form in the continuity process, the probability density of local computation variables in the continuity process is equivalent to the Gaussian function. If we use this characteristic, it is not necessary for the discrimination function computation to calculate the summation of the probabilities corresponding to all possible states, therefore, the computation load for the state transition is drastically decreased. Furthermore, if the continuous-valued discontinuity process is introduced, the MRF model can directly represent the strength of discontinuity. Moreover, the discrimination function of this energy function in the discontinuity process, which is linear, can also be calculated without probability summation. In this paper, a fast method for calculating the state transition probability for the continuous-valued MRF on the visual informtion processing is theoretically explained. Next, initial condition dependency, computation time and dependency on the statistical estimation of the condition are investigated in comparison with conventional methods using the examples of the data restoration for a corrupted square wave and a corrupted one-dimensional slice of a natural image.

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.

A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.

An improved reflection wave method was described for measurement of complex permittivity of low-loss materials over 100MHz-1GHz range. The residual impedance Zr and stray admittance Ys surrounding the test sample, which terminated the transmission line, were evaluated using sapphire as a reference material. The correction by the obtained Zr and Ys gave accurate values of complex permittivities of alumina and mullite ceramics as 100MHz-1GHz.

The Alopex algorithm is presented as a universal learning algorithm for neural networks. Alopex is a stochastic parallel process which has been previously applied in the theory of perception. It has also been applied to several nonlinear optimization problems such as the Travelling Salesman Problem. It estimates the weight changes by using only a scalar cost function which is measure of global performance. In this paper we describe the use of Alopex algorithm for solving nonlinear learning tasks by multilayer feed-forward networks. Alopex has several advantages such as, ability to escape from local minima, rapid algorithmic computation based on a scalar cost function and synchronous updation of weights. We present the results of computer simulations for several tasks, such as learning of parity, encoder problems and the MONK's problems. The learning performance as well as the generalization capacity of the Alopex algorithm are compared with those of the backpropagation procedure, and it is shown that the Alopex has specific advantages over backpropagation. An important advantage of the Alopex algorithm is its ability to extract information from noisy data. We investigate the efficacy of the algorithm for faster convergence by considering different error functions. We show that an information theoretic error measure shows better convergence characteristics. The algorithm has also been applied to more complex practical problems such as undersea target recognition from sonar returns and adaptive control of dynamical systems and the results are discussed.

In this paper, it is proven that the following three decision problems on validation of protocols with bounded capacity channels are NP-complete. (1) Given a protocol with the channel capacity being 1, determine whether or not there exist deadlocks in the protocol. (2) Given a protocol with the channel capacity being 1, determine whether or not there exist unspecified receptions in the protocol. (3) Given a protocol with the channel capacity being 2, determine whether or not there exist overflows such that the channel capacity is not bounded by 1 in the protocol. These results suggest that, even when all channeles in a protocol are bounded by 1 or 2, protocol validation should be in general interactable. It also clarifies the boundary of computational complexity of protocol validation problems because the channel capacity 0 does not allow protocols to transmit and recieve messages.

We consider a class of unknown scenes Sk(n) with rectangular obstacles aligned with the axes such that Euclidean distance between the start point and the target is n, and any side length of each obstacle is at most k. We propose a strategy called the adaptive-bias heuristic for navigating a robot in such a scene, and analyze its efficiency. We show that a ratio of the total distance walked by a robot using the strategy to the shortest path distance between the start point and the target is at most 1+(3/5) k, if k=o(n) and if the start point and the target are at the same horizontal level. This ratio is better than a ratio obtained by any strategy previously known in the class of scenes, Sk(n), such that k=o(n).

An extension of the notion of cryptographically strong pseudo-random generator to a distributed setting is proposed in this paper. Instead of a deterministic function to generate a pseudo-random bit string from a truly random shorter string, we have a deterministic secure protocol for a group of separate entities to compute a secretly shared pseudo-random string from a secretly shared and truly random shorter string. We propose a precise definition of this notion in terms of Yao's computational entropy and describe a concrete construction using Shamir's pseudo-random number generator. Several practical applications are also discussed.

This paper investigates the accepting powers of one-way alternating multi-stack-counter automata (lamsca's) and one-way alternating multi-counter automata (lamsca's) which operate in realtime. For each k1, let 1ASCA (k, real) (1ACA(k, real)) denote the class of sets accepted by realtime one-way alternating k-stach-counter (k-counter) automata, and let 1USCA(k, real)(1UCA(k, real)) denote the class of sets accepted by realtime one-way alternating k-stack-counter (k-counter) automata with only universal states. We first investigate a relationship between the accepting powers of realtime lamsca's (lamca's) with only universal states, with only existential states, and with full alternation. We then investigate hierarchical properties based on the numbers of counters and stackcounters, and show, for example, that for each k1, 1USCA(k+1, real)-1ASCA(k, real)φ and 1UCA(k+1, real)-1ACA(k, real)φ. We finally investigate a relationship between the accepting powers of realtime lamsca's and lamca's, and show, for example, that there are no i and j such that 1UCA(i, real)=1USCA(j, real), and 1USCA(k, real)-1ACA(k, real)φ for each k1.

Recent developments and case studies regarding VLSI device chip failure analysis are reviewed. The key failure analysis techniques reviewed include EMMS (emission microscopy), OBIC (optical beam induced current), LCM (liquid crystal method), EBP (electron beam probing), and FIB (focused ion beam method). Further, future possibilities in failure analysis, and some promising new tools are introduced.