The aim of this research is the efficient cryptanalysis of the Shrinking Generator through its characterization by means of Linear Hybrid Cellular Automata. This paper describes a new known-plaintext attack based on the computation of the characteristic polynomials of sub-automata and on the generation of the Galois field associated to one of the Linear Feedback Shift Registers components of the generator. The proposed algorithm allows predicting with absolute certainty, many unseen bits of the keystream sequence, thanks to the knowledge of both registers lengths, the characteristic polynomial of one of the registers, and the interception of a variable number of keystream bits.

Fingerprints are useful for biometric purposes because of their well known properties of distinctiveness and persistence over time. However, owing to skin conditions or incorrect finger pressure, original fingerprint images always contain noise. Especially, some of them contain useless components, which are often mistaken for the terminations that are an essential minutia of a fingerprint. Mathematical Morphology (MM) is a powerful tool in image processing. In this paper, we propose a linear time algorithm to eliminate impulsive noise and useless components, which employs generalized and ordinary morphological operators based on Euclidean distance transform. There are two contributions. The first is the simple and efficient MM method to eliminate impulsive noise, which can be restricted to a minimum number of pixels. We know the performance of MM is heavily dependent on structuring elements (SEs), but finding an optimal SE is a difficult and nontrivial task. So the second contribution is providing an automatic approach without any experiential parameter for choosing appropriate SEs to eliminate useless components. We have developed a novel algorithm for the binarization of fingerprint images [1]. The information of distance transform values can be obtained directly from the binarization phase. The results show that using this method on fingerprint images with impulsive noise and useless components is faster than existing denoising methods and achieves better quality than earlier methods.

This paper describes an approach to extending the learning experience using a Web-CAI system incorporated with MATHEMATICA, which is an advanced calculating software widely used in science and engineering fields. This approach provides the possibility of extending access to the courses that students have learned at school. We can use variables in mathematical formulas so that different problems can be shown to students. At the same time, we can also use algebraic formulas. In addition, applying MATHEMATICA to the given process for the answer automatically makes the answer of the problem. And two types of the answer expressions are acceptable which are filling in text by keyboard and selecting by click. This paper presents the design for the system and its specific implementation, and the technical solving scheme. At the end of this paper, the learning evaluations and the problem-editing interface design are discussed.

The aim of this work is to investigate the possibility of designing zero-knowledge identification schemes based on hard-on-average problems. It includes a new two-party identification protocol whose security relies on a discrete mathematics problem classified as DistNP-Complete under the average-case analysis, the so-called Distributional Matrix Representability Problem. Thanks to the use of the search version of the mentioned problem, the zero-knowledge property is formally proved by black-box simulation, and consequently the security of the proposed scheme is actually guaranteed. Furthermore, with the proposal of a new zero-knowledge proof based on a problem never used before for this purpose, the set of tools for designing cryptographic applications is enlarged.

An efficient computational Zero-Knowledge Proof of Knowledge whose security relies on the NP-completeness of the Independent Set Problem is presented here. The proposed algorithm is constructed from a bit commitment scheme based on the hardness of the Discrete Logarithm Problem, which guarantees the fulfillment of soundness, completeness and computational zero-knowledge properties, and allows avoiding the use of the Graph Isomorphism Problem, which is present in every known Zero-Knowledge Proofs for the Independent Set Problem.

Cohesion is an important software attribute, and it is one of significant criteria for assessing object-oriented software quality. Although several metrics for measuring cohesion have been proposed, there is an aspect which has not been supported by those existing metrics, that is "cohesive-part size." This paper proposes a new metric focusing on "cohesive-part size," and evaluates it in both of qualitative and quantitative ways, with a mathematical framework and an experiment measuring some Java classes, respectively. Through those evaluations, the proposed metric is showed to be a reasonable metric, and not redundant one. It can collaborate with other existing metrics in measuring class cohesion, and will contribute to more accurate measurement.

Edge detection has been an essential step in image processing, and there has been much work undertaken to date. This paper inspects a fuzzy mathematical morphology in order to reach a higher-level of edge-image processing. The proposed scheme uses a fuzzy morphological gradient to detect object boundaries, when the boundaries are roughly defined as a curve or a surface separating homogeneous regions. The automatic edge detection algorithm consists of two major steps. First, a new version of anisotropic diffusion is proposed for edge detection and image restoration. All improvements of the new version use fuzzy mathematical morphology to preserve the edge accuracy and to restore the images to homogeneity. Second, the fuzzy morphological gradient operation detects the step edges between the homogeneous regions as object boundaries. This operation uses geometrical characteristics contained in the structuring element in order to extract the edge features in the set of edgeness, a set consisting of the quality values of the edge pixels. This set is prepared with fuzzy logic for decision and selection of authentic edge pixels. For experimental results, the proposed method has been tested successfully with both synthetic and real pictures.

Integration of the IP/MPLS network and the WDM optical mesh network is a promising approach to realizing an efficient backbone network. Because of the great volumes of traffic carried, the social cost incurred by a failure will be extremely high, so survivability is very important in the backbone network. In survivable IP/MPLS over WDM backbone networks, cooperation of the optical level fault recovery and the IP/MPLS level fault recovery is essential. This paper analyzes cost characteristics of the optical level fault recovery and the IP/MPLS level fault recovery. A mathematical programming method is proposed to minimize the initial network cost when the IP/MPLS level fault recovery is utilized in the survivable IP/MPLS over WDM networks. Using this method, the initial network cost needed for the IP/MPLS level fault recovery is compared with that for the optical level fault recovery. The initial network cost for the LSP (Label Switched Path) protection scheme is smaller than that for the shared light-path protection scheme and larger than that for the pre-plan type light-path restoration scheme. The LSP protection scheme is suitable for the best-effort type traffic while the shared light-path protection scheme may be suitable for the bandwidth guaranteed type traffic.

A novel method for the primitive description of the multiprimitive texture is proposed. This method segments a texture by the watershed algorithm into fragments each of which contains one grain. The similar fragments are grouped by the cluster analysis in the feature space whose basis is the morphological size density. Each primitive is extracted as the grain of the central fragment in each cluster.

In this paper, a novel approach is proposed to identify the detailed typeface of Gothic characters in document images. The identification is performed by evaluating two types of typeface models, named the Gs-pattern and the Gd-pattern according to the principle of MDL. The typeface models are generated from the observed character image by using morphology and are viewed as approximating expressions of the observed character. Consequently, this method is unique in that it is free from both character recognition and dictionary lookup.

It is proposed a novel method that optimizes nonlinear filters by unsupervised learning using a novel definition of morphological pattern spectrum, called "morphological opening/closing spectrum (MOCS)." The MOCS can separate smaller portions of image objects from approximate shapes even if the shapes are degraded by noisy pixels. Our optimization method analogizes the linear low-pass filtering and Fourier spectrum: filter parameters are adjusted to reduce the portions of smaller sizes in MOCS, since they are regarded as the contributions of noises like high-frequency components. This method has an advantage that it uses only target noisy images and requires no example of ideal outputs. Experimental results of applications of this method to optimization of morphological open-closing filter for binary images are presented.

This paper describes a theoretical foundation of fuzzy morphological operations and architectural extension of the shared-weight neural network (SWNN). The network performs shift-invariant filtering using fuzzy-morphological operations for feature extraction. The nodes in the feature extraction stage employ the generalized-mean operator to implement fuzzy-morphological operations. The parameters of the SWNN, weights, morphological structuring element and fuzziness, are optimized by the error back-propagation (EBP) training method. The parameter values of the trained SWNN are then implanted into the extended SWNN (ESWNN) which is a simple convolution neural network. The ESWNN architecture dramatically reduces the amount of computation by avoiding segmentation process. The neural network is applied to automatic recognition of a vehicle in visible images. The network is tested with several sequences of images that include targets ranging from no occlusion to almost full occlusion. The results demonstrate an ability to detect occluded targets, while trained with non-occluded ones. In comparison, the proposed network was superior to the Minimum-Average Correlation filter systems and produced better results than the ordinary SWNN.

The pattern spectrum has been proposed to represent morphological size distribution of an image. However, the conventional pattern spectrum cannot extract approximate shape information from image objects spotted by noisy pixels since this is based only on opening. In this paper, a novel definition of the pattern spectrum, morphological multiresolution pattern spectrum (MPS), involving both opening and closing is proposed. MPS is capable of distinguishing details from approximate information of the image.

In this paper, we discuss the principle of the clumsy painter method proposed for extracting interested regions from image signals automatically. We theoretically clarify the reason why the clumsy painter method is effective so well. We compare its algorithm with the opening operation in mathematical morphology, and prove that the clumsy painter method has the advantage over the opening operation in mathematical morphology on removing uninterested regions from image signals. Simulating these two methods on two simple geometrical models, we show that the extracted redults by the opening operation are included in those by the clumsy painter method.

This paper describes the results obtained of a prototype system, VeriProc/1, based on an algorithm we first presented in [13] which can prove the correctness of pipelined processors automatically without pipeline invariant, human interaction, or additional information. No timing relations such as an abstract function or β-relation is required. The only information required is to specify the location of the selectors in the design. The performance is independent of not only data width but also memory size. Detailed analysis of CPU time is presented. Further, don't-care forcing using additional data easily prepared by the user can improve performance.

We thought that multiple skeletons were inherent in an ordinary three-dimensional object. A thinning method is developed to extract multiple skeletons using 333 templates for boundary deletion based on the hit or miss transformation and 222 templates for checking one voxel thickness. We prepared twelve sets of deleting templates consisting of total 194 templates and 72 one voxel checking templates. One repetitive iteration using one sequential use of the template sets extracts one skeleton. Some of the skeletons thus obtained are identical; however, multiple independent skeletons are extracted by this method. These skeletons fulfill the well-recognized three conditions for a skeleton. We extracted three skeletons from the cube, two from the space shuttle model and four from the L-shaped figure by Tsao and Fu. The digital medial skeleton, which is not otherwise extracted, is extracted by comparing the multiple skeletons with the digital medial-axis-like-figure. One of our skeletons for the cude agreed with the ideal medial axis. The locations of the gravity center of the multiple skeletons are compared with that of the original shape to evaluate how uniform or non-biased skeletons are extracted. For the L-shaped figure, one of our skeletons is found to be most desirable from the medial and uniform points of view.

The character binarization method MTC is developed for enhancing the recognition of characters in general outdoor images. Such recognition is traditionally difficult because of the influence of illumination changes, especially strong shadow, and also changes in character, such as apparent character sizes. One way to overcome such difficulties is to restrict objects to be processed by using strong hypotheses, such as type of object, object orientation and distance. Several systems for automatic license plate reading are being developed using such strong hypotheses. However. their strong assumptions limit their applications and complicate the extension of the systems. The MTC method assumes the most reasonable hypotheses possible for characters: they occupy plane areas, consist of narrow lines, and external shadow is considerably larger than character lines. The first step is to eliminate the effect of local brightness changes by enhancing feature including characters. This is achieved by applying mathematical morphology by using a logarithmic function. The enhanced gray-scale image is then binarized. Accurate binarization is achieved because local thresholds are determined from the edges detected in the image. The MTC method yields stable binary results under illumination changes, and, consequently, ensures high character reading rates. This is confirmed with a large number of images collected under a wide variety of weather conditions. It is also shown experimentally that MTC permits stable recognition rate even if the characters vary in size.

Mathematical morphology clarified geometrical properties of shape analysis algorithms for binary pictures. Results of labelling, distance transform, and adjacent numbering are, however, coded pictures. For full descriptions of shape analysis algorithms in the framework of mathematical morphology, it is necessary to extend morphological operations to code-labelled pictorial data. Nevertheless, extensions of morphology to code-labelled pictures have never discussed though the theory of gray morphology is well studied by several authors. Hence, this paper proposes a theory of the coded morphology which is based on the binary scaling of labels of pixels. The method uses n-layered binary sub-pictures for the processing of a picture with 2n labels. By introducing morphological operations for the coded point sets, we express some coding functions in the manner of the mathematical morphology. We also derive multidimensional array registers and gates which store and process coded pictures and morphological operations to them by proposing basic gates which compute parallelly logical operations for elements of Boolean layered arrays. These gates and registers are suitable for the implementation of the shape analysis processors on the three-dimensional VLSI and ULSI.

This paper focuses on methodology underlying the application to fault tolerant computer systems with "no down communication" capability of stochastic Petri nets with general firing times. Based on a formal specification of the stochastic Petri net, we provide criteria for the marking process to be a regenerative process in continuous time with finite cycle-length moments. These results lead to strongly consistent point estimates and asymptotic confidence intervals for limiting system availability indices. We also show how the building blocks of stochastic Petri nets with general firing times facilitate the modeling of non-deterministic transition firing and illustrate the use of "interrupter input places" for graphical representation of transition interruptions.