In our previous work, the hyper H∞ filter is developed for tracking of unknown time-varying systems. Additionally, a fast algorithm, called the fast H∞ filter, of the hyper H∞ filter is derived on condition that the observation matrix has a shifting property. This algorithm has a computational complexity of O(N) where N is the dimension of the state vector. However, there still remains a possibility of deriving alternative forms of the hyper H∞ filter. In this work, a fast J-unitary form of the hyper H∞ filter is derived, providing a new H∞ fast algorithm, called the J-fast H∞ filter. The J-fast H∞ filter possesses a computational complexity of O(N), and the resulting algorithm is very amenable to parallel processing. The validity and performance of the derived algorithm are confirmed by computer simulations.

We define discretized Markov transformations and find an algorithm to give the number of maximal-period sequences based on discretized Markov transformations. In this report, we focus on the discretized dyadic transformations and the discretized golden mean transformations. Then we find an algorithm to give the number of maximal-period sequences based on these discretized transformations. Moreover, we define a number-theoretic function related to the numbers of maximal-period sequences based on these discretized transformations. We also introduce the entropy of the maximal-period sequences based on these discretized transformations.

This paper proposes the application of tree-structured clustering to the processing of noisy speech collected under various SNR conditions in the framework of piecewise-linear transformation (PLT)-based HMM adaptation for noisy speech. Three kinds of clustering methods are described: a one-step clustering method that integrates noise and SNR conditions and two two-step clustering methods that construct trees for each SNR condition. According to the clustering results, a noisy speech HMM is made for each node of the tree structure. Based on the likelihood maximization criterion, the HMM that best matches the input speech is selected by tracing the tree from top to bottom, and the selected HMM is further adapted by linear transformation. The proposed methods are evaluated by applying them to a Japanese dialogue recognition system. The results confirm that the proposed methods are effective in recognizing digitally noise-added speech and actual noisy speech issued by a wide range of speakers under various noise conditions. The results also indicate that the one-step clustering method gives better performance than the two-step clustering methods.

Embedded systems are used in broad fields. They are one of the indispensable and fundamental technologies in a highly informative society in recent years. As embedded systems are large-scale and complicated, it is prosperous to design and develop a system LSI (Large Scale Integration). The structure of the system LSI has been increasing complexity every year. The degree of improvement of its design productivity has not caught up with the degree of its complexity by conventional methods or techniques. Hence, an idea for the design of a system LSI which has the flow of describing specifications of a system in UML (Unified Modeling Language) and then designing the system in a system level language has already proposed. It is important to establish how to convert from UML to a system level language in specification description or design with the idea. This paper proposes, extracts and verifies transformation rules from UML to SpecC which is one of system level languages. SpecC code has been generated actually from elements in diagrams in UML based on the rules. As an example to verify the rules, "headlights control system of a car" is adopted. SpecC code has been generated actually from elements in diagrams in UML based on the rules. It has been confirmed that the example is executed correctly in simulations. By using the transformation rules proposed in this paper, specification and implementation of a system can be connected seamlessly. Hence, it can improve the design productivity of a system LSI and the productivity of embedded systems.

Recursive programs are often easier to read and write than iterative ones, but their execution frequently requires large numbers of procedure calls and stack operations. This causes problems in program optimization related to inline coding and the locality of data references. In addition to these problems, defining programs recursively sometimes leads to repetitive execution of similar computations, causing programs to have exponential time complexity. As a result, recursion removal methods, which transform a given recursive program to an iterative one without using the stack and increasing the amount of computation time, have been studied since the 1970s. In 1998, our group proposed a recursion removal method for a linear recursive program. In this paper, we extend the method to deal with non-linear recursive programs with one descent function (RPODs), which are programs of the form f(x) = if p(x) then b(x) else a(c(x),f(d(x)),f(d2(x)),...,f(dn(x))). First, we define the cumulative function for an RPOD. Next, based on the new cumulative function, several transformation techniques for RPODs are shown. These include a unified method of deriving logarithmic-order iterative programs or loop-free programs. Finally, the relationships between our method and various tupling strategies are discussed.

In this paper, we propose a transformation function for a user's raw iris data, an "iris code" in iris scanning verification on the server, since the iris code requires to be hidden from even a server administrator. We then show that the user can be properly authenticated on the server, even though the iris code is transformed by the proposed function. The reason is that the function has a characteristic, "The (normalized) Hamming distances between the enrolled iris codes and the verified iris codes are conserved before and after the computation of the function," that is, the normalized Hamming distance in this scheme is equal to that in the existing scheme. We also show that the transformed iris code is sufficiently secure to hide the original iris code, even if a stronger attack model is supposed than the previously described model. That can be explained from the following two reasons. One reason is that nonlinear function, which consists of the three-dimensional rotation about the x-axis and the y-axis with the iris code lengthened bit by bit, and the cyclic shift, does not enable an attacker to conjecture the iris code. The other reason is that the success probabilities for the exhaustive search attack concerning the iris code in the supposed attack models are lower than those of the previously proposed methods and are negligible.

This paper presents a new template matching method based on marker-controlled watershed segmentation (TMCWS). It is applied to recognize numbers on special metal plates in production lines where traditional image recognition methods do not work well. TMCWS is a shape based matching method that uses different pattern images and their corresponding marker images as probes to explore a gradient space of an unknown image to determine which pattern best matches a target object in it. Different from other matching algorithms, TMCWS firstly creates a marker image for each pattern, and then takes both the pattern image and its corresponding marker image as a template window and shifts this window across a gradient space pixel by pixel to do a search. At each position, the marker image is used to try to extract the contour of the target object with the help of marker-controlled watershed segmentation, and the pattern image is employed to evaluate the extracted shape in each trial. All of the pattern images and their corresponding marker images are tried and the pattern that best matches the target object is the recognition result. TMCWS contains shape extraction procedures and it is a high-level template matching method. Experiments are performed with this method on nearly 400 images of metal plates and the test results show its effectiveness in recognizing numbers in noisy images.

Both the Hopfield network and the genetic algorithm are powerful tools for object recognition tasks, e.g., subgraph matching problems. Unfortunately, they both have serious drawbacks. The Hopfield network is very sensitive to its initial state, and it stops at a local minimum if the initial state is not properly given. The genetic algorithm, on the other hand, usually only finds a near-global solution, and it is time-consuming for large-scale problems. In this paper, we propose an integrated scheme of these two methods, while eliminating their drawbacks and keeping their advantages, to solve object recognition problems under affine transformations. Some arrangements and programming strategies are required. First, we use some specialized 2-D genetic algorithm operators to accelerate the convergence. Second, we extract the "seeds" of the solution of the genetic algorithm to serve as the initial state of the Hopfield network. This procedure further improves the efficiency of the system. In addition, we also include several pertinent post matching algorithms for refining the accuracy and robustness. In the examples, the proposed scheme is used to solve some subgraph matching problems with occlusions under affine transformations. As shown by the results, this integrated scheme does outperform many counterpart algorithms in accuracy, efficiency, and stability.

In this paper, we propose a new method which makes transient simulation faster for the circuit including both nonlinear and linear elements. First, the method for generating the projection matrix with Krylov-subspace technique is described. The order of the circuit equation is reduced by congruence transformation with the projection matrix. Next, we suggest a method which can calculate the reduced Jacobian matrix directly in each Newton-Raphson iteration. Since this technique does not need to calculate the original size of Jacobian matrix, the calculation cost is reduced drastically. Therefore, efficient circuit simulation can be achieved. Finally, our method is applied to some example circuits and the validity of the nonlinear circuit reduction technique is verified.

Based on Radon transform, a novel method for registering a periodic (self-referencing) watermark is presented. Although the periodic watermark is widely used as a countermeasure for affine transformation, there is no known efficient method to register it. Experimental results show that the proposed method is effective for registering the watermark from an image that had undergone both affine transformations and severe lossy compression.

Integral equation method is used to compute three-dimension-structure capacitance in this paper. Since some multi-conductor structures present regular periodic property, the periodic cell is used to reduce the computational domain with adding appropriate magnetic and electric walls. The periodic Green's function in the integral equation method is represented in the form of infinite series with slow convergence. In this paper, Shanks transformation is used to accelerate the convergence. Numerical examples show that the proposed method is accurate with a much higher efficiency in capacitance extraction for 3-D periodic structures.

In this work, we study on a data encryption scheme based on a shape-variable truncated Baker transformation. First we show statistical properties of the shape-variable truncated Baker transformation. Then we propose a data encryption scheme with this map and verify its performance whether it satisfies fundamental conditions on confusion and diffusion required for data encryption schemes.

In this work, a novel Multiple Valued Exclusive-Or Sum Of Products (MVESOP) minimization formulation is analyzed and an algorithm is presented that detects minimum MVESOP expressions when the weight of the function is less than eight. A heuristic MVESOP algorithm based on a novel cube transformation operation is then presented. Experimental results on MCNC benchmarks and randomly generated functions indicate that the algorithm matches or outperforms the quality of the state of the art in ESOP minimizers.

Refactoring is one of the promising techniques for improving software design by means of behavior-preserving structural transformation, and is widely taken into practice. In particular, it is frequently applied to design models represented with UML such as class diagrams. However, since UML design models includes multiple diagrams which are closely related from various views, to get behavior-preserving property, we should get the other types of design information and should handle with the propagation of the change on a diagram to the other diagrams. For example, to refactor a class diagram, we need behavioral information of methods included in the class and should also refactor diagrams which represent the behavior, such as state diagrams, activity diagrams. In this paper, we introduce refactoring on design models as transformations of a graph described by UML class diagram and action semantics. First, we define basic transformations of design models that preserve the behavior of designed software, and compose them into refactoring operations. We use Object Constraint Language (OCL) to specify when we can apply a refactoring operation. Furthermore we implement our technique on a graph transformation system AGG to support the automation of refactoring, together with evaluation mechanism of OCL expressions. Some illustrations are presented to show its effectiveness. The work is the first step to handle with refactoring on UML design models in integrated way.

To realize the hardware object which facilitates the application development in the reconfigurable computing system, a hardware module (HwModule) is proposed and implemented. To access the circuit in the HwModule from the standard PC without detailed knowledge of the hardware, an object manager (ObjectManager) is also implemented. With the help of the ObjectManager, the programmers can use the hardware objects like the usual software objects. The HwModule is applied to the image matching, and the easiness of the application development for the HwModule is confirmed.

This paper describes a novel method of shape matching by means of unification and expansion of local correspondences on the feature points. The method has the ability to simultaneously locate plural similar parts of two-dimensional objects under affine transformation. Furthermore, the method is applicable to the objects partially occluded. Experimental results show that the method yields results that are satisfactory, even for the cases with additions, deletions and local deviations of some feature points.

Usually, paintings are more appealing than photographic images. This is because paintings can incorporate styles based on the artist's subjective view of motif. This style can be distinguished by looking at elements such as motif, color, shape deformation and brush texture. In our work, we focus on the effect of "color" element and devise a method for transforming the color of an input photograph according to a reference painting. To do this, we consider basic color category concepts in the color transformation process. We assume that color transformations from one basic color category to another may cause peculiar feelings. Therefore, we restrict each color transformation within the same basic color category. For this, our algorithm first categorizes each pixel color of a photograph into one of eleven basic color categories. Next, for every pixel color of the photograph, the algorithm finds its corresponding color in the same category of a reference painting. Finally, the algorithm substitutes the pixel color with its corresponding color. In this way, we achieve large but natural color transformations of an image.

This paper proposes a new approach to fuzzy inference system for modeling nonlinear systems based on measured input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the extended kernel method. The extended kernel method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. Especially, the process of linear transformation is needed in order to solve difficulty determining the type of kernel function which presents the nonlinear mapping in according to nonlinear system. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated results of the proposed technique are illustrated by examples involving benchmark nonlinear systems.

This paper presents the design consideration of a polarization-transformation transmission filter, which is composed of a multilayered chiral slab. The optimal material parameters and thickness of each layer of the slab can be determined by using a genetic algorithm (GA). Substituting the constitutive relations for a chiral medium into Maxwell's equations, the electromagnetic field in the medium is obtained. A chain-matrix formulation is used to derive the relationship between the components of the incident, the reflected, and the transmitted electric fields. The cross- and co-polarized powers carried by the transmitted and reflected waves are represented in terms of their electric field components. The procedure proposed for the design of a polarization-transformation filter is divided into two stages. An ordinary filter without polarization-transformation and a polarization-transformation filter for the transmitted wave are designed with a multilayered non-chiral slab and a multilayered chiral slab at the first and the second stages, respectively. According to the specifications of the filters, two functionals are defined with the transmitted and reflected powers. Thus the optimal design of a polarization-transformation filter with the multilayered chiral slab is reduced to an optimization problem where the material parameters and thickness of each chiral layer are found by maximizing the functionals. Applying the GA to the maximization of the functionals, one can obtain the optimal material parameters and thicknesses of the multilayered chiral slab. Numerical results are presented to confirm the effectiveness of the two-stage design procedure. For three types of multilayered chiral slabs, optimal values of refractive indices, thicknesses, and chiral admittances are obtained. It is seen from the numerical results that the proposed procedure is very effective in the optimal design of polarization-transformation filters for the transmitted wave.

A wavelet-based vector quantization scheme for image compression is introduced here. The proposed scheme obtains a better compression efficiency by the following three methods. (1) Utilizing the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different levels. (3) Preserving the most important information of the image. In our experiments, simulation results show that this technique outperforms the recent SMVQ-ABC [1] and WTC-NIVQ [2] techniques.