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.

The finite difference time domain (FDTD) method has been developed in tandem with the progress of computer technology since K. S. Yee applied it to the analysis of an electromagnetic problem in 1966. The FDTD method is widely recognized as a powerful computational tool for analyzing electromagnetic problems involving complex geometries, such as antennas, microwave and optical waveguides and interaction between antennas and the human body. The commercial electromagnetic simulators based on the FDTD are also being developed very actively because users are able to trace temporal electromagnetic behaviors and to easily obtain a practical level of accuracy. However, the user must understand the principle of the method in order to use the simulator efficiently. In this paper, the basic concept and the principle of the FDTD method are reviewed for beginners, including graduate course students, rather than specialists in this discipline. Several recent topics concerning electromagnetic and antenna problems are also introduced.

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.

A new interacting multiple model (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown target acceleration by a fuzzy system using the relation between the residuals of the maneuvering filter and the non-maneuvering filter. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of target acceleration. Then, multiple models are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of target acceleration. In computer simulation for an incoming anti-ship missile, it is shown that the proposed method has better tracking performance compared with the adaptive interacting multiple model (AIMM) algorithm.

In this paper we give an algorithm to generates all series-parallel graphs with at most m edges. This algorithm generate each series-parallel graph in constant time on average.

Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.

"Can one walk to infinity on Gaussian primes taking steps of bounded length?" We adopted computational techniques to probe into this open problem. We propose an efficient method to search for the farthest point reachable from the origin, which can be parallelized easily, and have confirmed the existence of a moat of width k =, whereas the best previous result was k = due to Gethner et al. The amount of computation needed for k = is about 5000 times larger than that for k =. A refinement of Vardi's estimate for the farthest distance reachable from the origin is proposed. The proposed estimate incorporates discreteness into Vardi's that is based on percolation theory.

A new edge-coloring algorithm for bipartite graphs is presented. This algorithm, based on the framework of the O(m log d + (m/d) log (m/d) log d) algorithm by Makino-Takabatake-Fujishige and the O(m log m) one by Alon, finds an optimal edge-coloring of a bipartite graph with m edges and maximum degree d in O(m log d + (m/d) log (m/d)) time. This algorithm does not require elaborate data structures, which the best known O(m log d) algorithm due to Cole-Ost-Schirra depends on.

Cyberworlds are being formed in cyberspaces as computational spaces. Now cyberspaces are rapidly expanding on the Web either intentionally or spontaneously, with or without design. Widespread and intensive local activities are melting each other on the web globally to create cyberworlds. The major key players of cyberworlds include e-finance that trades a GDP-equivalent a day and e-manufacturing that is transforming industrial production into Web shopping of product components and assembly factories. Lacking proper theory and design, cyberworlds have continued to grow chaotic and are now out of human understanding and control. This research first presents a generic theoretical framework and design based on algebraic topology, and also provides an axiomatic approach to theorize the potentials of cyberworlds.

Network survivability is one of the most pivotal issues in optical WDM networks. In particular, if a conduit is cut, approximately 16 terabits per millisecond can be lost in recent technology. A huge loss even by a single conduit failure fatally damages the performance and operation of the whole network. In this paper, we propose a new heuristic algorithm, called the Generalized Minimum-Cost (GMC) selection algorithm, to choose a pair of working and backup path which firstly minimizes total number of required wavelengths of working and backup path and secondly distributes lightpath request traffic into whole network links, if there are several pairs to require the same number of minimum wavelengths, in order to achieve load-balancing effect. GMC selection algorithm contains several formulas to get Working and Backup path Reservation Cost (WBRC) which can be obtained through heuristic GMC function. By using WBRC, our GMC selection algorithm achieves superior performance compared to the current Combined Min-Cost (CMC) selection algorithm and random selection algorithm in terms of the amount of wavelength consumption and blocked lightpath requests, especially on the relatively less-connected New Jersey LATA and 28-node US networks. Furthermore, we suggest a maximum number of non-blocked lightpath requests against single link failure in simulated networks for network operators to consider acceptable maximum traffic on their networks, so that they can provide 100% restoration capability in a single link failure without lightpath request blocking. We also analyze the complexity of the GMC selection algorithm and verify that the complexity of the GMC selection algorithm is lower than that of the CMC selection algorithm if the number of lightpath requests is sufficiently large.

In this paper, we focus on the problem that in an ad hoc network, how to send a message securely between two users using the certificate dispersal system. In this system, special data called certificate is issued between two users and these issued certificates are stored among the network. Our final purpose on this certificate dispersal problem is to construct certificate graphs with lower dispersability cost which indicates the average number of certificates stored in each node in an ad hoc network. As our first step, when a certificate graph is given, we construct two efficient certificate dispersal algorithms for strongly connected graphs and directed graphs in this paper. We can show that for a strongly connected graph G =(V, E) and a directed graph H =(V ′, E ′), new upper bounds on dispersability cost on the average number of certificates stored in one node are O(DG +|E|/|V|) and O(pG dmax +|E ′|/|V ′|) respectively, where DG is the diameter of G, dmax is the maximum diameter of strongly connected components of H and pG is the number of strongly connected components of H. Furthermore, we give some new lower bounds for the problem and we also show that our algorithms are optimal for several graph classes.

The (k + δ)-edge-connectivity augmentation problem for a specified set of vertices ((k + δ)ECA-SV) is defined as follows: "Given an undirected graph G =(V,E), a specified set of vertices Γ V, a subgraph G ′=(V,E ′) with λ(Γ;G ′) = k of G and a cost function c: E Z+ (nonnegative integers), find a set E* E - E ′of edges, each connecting distinct vertices of V, of minimum total cost such that λ(Γ;G″) k + δ for G"=(V,E ′∪E*)," where λ(Γ;G″) is the minimum value of the maximum number of edge disjoint paths between any pair of vertices in Γ of G". The paper proposes an O(Δ+|V||E|) time 2-approximation algorithm FSAR for (k + 1)ECA-SV with a restriction λ(V;G ′) = λ(Γ;G ′), where Δ is the time complexity of constructing a structural graph of a given graph G ′.

Just-in-time scheduling problem is the problem of finding an optimal schedule such that each job finishes exactly at its due date. We study the problem under a realistic assumption called periodic time slots. In this paper, we prove that this problem cannot be approximated, assuming P≠NP. Next, we present a heuristic algorithm, assuming that the number of machines is one. The key idea is a reduction of the problem to a network flow problem. The heuristic algorithm is fast because its main part consists of computation of the minimum cost flow that dominates the total time. Our algorithm is O(n3) in the worst case, where n is the number of jobs. Next, we show some simulation results. Finally, we show cases in which our algorithm returns an optimal schedule and is a factor 1.5 approximation algorithm, respectively, and also give an approximation ratio depending on the upper bound of set-up times.

In this paper, the false-peaks problem of the conventional correlation-based video tracking is investigated using a simple mathematical analysis. To reduce the false detection problem, a selective-attention correlation measure is proposed. The problem with the conventional correlation measures is that all pixels in the reference block image are equally treated in the computation of the correlation measures irrespective of target or background pixels. Therefore, the more the reference block image includes background pixels, the higher probability of false-peaks is introduced due to the correlation between the background pixels of the reference block and those of the input search image. The proposed selective-attention correlation measure has different consideration according to target and background pixels in the matching process, which conform with the selective-attention property of human visual system. Various computer simulations validated these analyses and confirmed that the proposed selective-attention measure is effective to reduce considerably the probability of the false-peaks.

The wavelength-division multiplexing (WDM) technology has been popular in communication societies for providing very large communication bands by multiple lightpaths with different wavelengths on a single optical fiber. Particularly, a double-ring optical network architecture based on the packet-over-WDM technology such as the HORNET architecture, has been extensively studied as a next generation platform for metropolitan area networks (MANs). Each node in this architecture is equipped with a wavelength-fixed optical-drop and a fast tunable transmitter so that a lightpath can be established between any pair of nodes without wavelength conversions. In this paper, we formulate the optical-drop wavelength assignment problem (ODWAP) for efficient wavelength reuse under heterogeneous traffic in this network, and prove the NP-completeness of its decision problem. Then, we propose a simple heuristic algorithm for the basic case of ODWAP. Through extensive simulations, we demonstrate the effectiveness of our approach in reducing waiting times for packet transmissions when a small number of wavelengths are available to retain the network cost for MANs.

Let G = (V,E) be a connected graph such that each edge e ∈ E and each vertex v ∈ V are weighted by nonnegative reals w(e) and h(v), respectively. Let r be a vertex designated as a root, and p be a positive integer. The minmax rooted-subtree cover problem (MRSC) asks to find a partition X = {X1,X2,...,Xp of V and a set of p subtrees T1,T2,...,Tp such that each Ti contains Xi∪{r} so as to minimize the maximum cost of the subtrees, where the cost of Ti is defined to be the sum of the weights of edges in Ti and the weights of vertices in Xi. Similarly, the minmax rooted-cycle cover problem (MRCC) asks to find a partition X = {X1,X2,...,Xp} of V and a set of p cycles C1,C2,...,Cp such that Ci contains Xi∪{r} so as to minimize the maximum cost of the cycles, where the cost of Ci is defined analogously with the MRSC. In this paper, we first propose a (3-2/(p+1))-approximation algorithm to the MRSC with a general graph G, and we then give a (6-4/(p+1))-approximation algorithm to the MRCC with a metric (G,w).

Self-stabilization is a theoretical framework of non-masking fault-tolerant distributed algorithms. In this paper, we investigate a self-stabilizing distributed approximation for the minimum k-dominating set (KDS) problem in general networks. The minimum KDS problem is a generalization of the well-known dominating set problem in graph theory. For a graph G = (V,E), a set Dk V is a KDS of G if and only if each vertex not in Dk is adjacent to at least k vertices in Dk. The approximation ratio of our algorithm is , where Δ is the maximum degree of G, in the networks of which the minimum degree is more than or equal to k.

This paper investigates the application of the computational model of Cellular Automata (CA) for pattern classification of real valued data. A special class of CA referred to as Fuzzy CA (FCA) is employed to design the pattern classifier. It is a natural extension of conventional CA, which operates on binary string employing boolean logic as next state function of a cell. By contrast, FCA employs fuzzy logic suitable for modeling real valued functions. A matrix algebraic formulation has been proposed for analysis and synthesis of FCA. An efficient formulation of Genetic Algorithm (GA) is reported for evolution of desired FCA to be employed as a classifier of datasets having attributes expressed as real numbers. Extensive experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples, and attributes. Excellent classification accuracy has established the FCA based pattern classifier as an efficient and cost-effective solutions for the classification problem.

This paper newly proposes a method to automatically decompose real scene images into multiple object-oriented component regions. First, histogram patterns of a specific image feature, such as intensity or hue value, are estimated from image sequence and stored up. Next, Gaussian distribution parameters which correspond to object components involved in the scene are estimated by applying the EM algorithm to the accumulated histogram. The number of the components is simultaneously estimated by evaluating the minimum value of Bayesian Information Criterion (BIC). This method can be applied to a variety of computer vision issues, for example, the color image segmentation and the recognition of scene situation transition. Experimental results applied for indoor and outdoor scenes showed the effectiveness of the proposed method.

The 2-vertex-connectivity augmentation problem of a graph with degree constraints, 2VCA-DC, is defined as follows: "Given an undirected graph G = (V,E) and an upper bound a(v;G) Z+{} on vertex-degree increase for each v V, find a smallest set E′ of edges such that (V,E E′) has at least two internally-disjoint paths between any pair of vertices in V and such that vertex-degree increase of each v V by the addition of E′ to G is at most a(v;G), where Z+ is the set of nonnegative integers." In this paper we show that checking the existence of a feasible solution and finding an optimum solution to 2VCA-DC can be done in O(|V|+|E|) time.