We propose a novel propagation measurement scheme for terrestrial TV signal indoor reception based on a virtual array technique. The system proposed in this paper carries out two-branch recording of target signals and the reference signal. By using the signal phase reference in the reference signal, we clarify the spatial propagation characteristics obtained from the two-dimensional virtual array outputs. Outdoor measurements were performed first to investigate the validity of the proposed measurement system. The results confirm its effectiveness in accurately determining the direction-of-arrival (DOA). We then investigated the propagation characteristics in an indoor environment. The angular spectrum obtained showed clear wave propagation structure. Thus, our proposed system is promising as a very accurate measurement tool for indoor propagation analysis.

With the increased deployment of wireless sensor networks (WSNs) in location-based services, the need for accurate localization of sensor nodes is gaining importance. Sensor nodes in a WSN localize themselves with the help of anchors that know their own positions. Some anchors may be malicious and provide incorrect information to the sensor nodes. In this case, accurate localization of a sensor node may be severely affected. In this study, we propose a secure and lightweight localization method. In the proposed method, uncertainties in the estimated distance between the anchors and a sensor node are taken into account to improve localization accuracy. That is, we minimize the weighted summation of the residual squares. Simulation results show that our method is very effective for accurate localization of sensor nodes. The proposed method can accurately localize a sensor node in the presence of malicious anchors and it is computationally efficient.

A novel shadow removal approach is proposed by using block-wise transform domain shadow detection. The approach is based on the fact that the spatial frequency distributions on normal background areas and those under casted shadows from foreground objects are the same. The proposed approach is especially useful for silhouette extraction by using the Gaussian Mixture background Model (GMM) foreground segmentation in the transform domain, because the frequency distribution has already been calculated in the foreground segmentation. The stable shadow removal is realized, due to the transform domain implementation.

Given a graph G = (V,E) together with a nonnegative integer requirement on vertices r:V Z+, the annotated edge dominating set problem is to find a minimum set M ⊆ E such that, each edge in E - M is adjacent to some edge in M, and M contains at least r(v) edges incident on each vertex v ∈ V. The annotated edge dominating set problem is a natural extension of the classical edge dominating set problem, in which the requirement on vertices is zero. The edge dominating set problem is an important graph problem and has been extensively studied. It is well known that the problem is NP-hard, even when the graph is restricted to a planar or bipartite graph with maximum degree 3. In this paper, we show that the annotated edge dominating set problem in graphs with maximum degree 3 can be solved in O*(1.2721n) time and polynomial space, where n is the number of vertices in the graph. We also show that there is an O*(2.2306k)-time polynomial-space algorithm to decide whether a graph with maximum degree 3 has an annotated edge dominating set of size k or not.

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method.

In this paper, an energy- and traffic-balance-aware mapping algorithm from IP cores to nodes in a network is proposed for application-specific Network-on-Chip(NoC). The multi-objective optimization model is set up by considering the NoC architecture, and addressed by the proposed mapping algorithm that decomposes mapping optimization into a number of scalar subproblems simultaneously. In order to show performance of the proposed algorithm, the application specific benchmark is applied in the simulation. The experimental results demonstrate that the algorithm has advantages in energy consumption and traffic balance over other algorithms.

Let Gs=(Vs, Es) be a simple connected graph. A vertex v ∈ Vs is an articulation vertex if deletion of v and its incident edges from Gs disconnects the graph into at least two connected components. Finding all articulation vertices of a given graph is called the articulation vertex problem. A vertex u ∈ Vs is called a hinge vertex if there exist any two vertices x and y in Gs whose distance increase when u is removed. Finding all hinge vertices of a given graph is called the hinge vertex problem. These problems can be applied to improve the stability and robustness of communication network systems. In this paper, we propose linear time algorithms for the articulation vertex problem and the hinge vertex problem of circular permutation graphs.

PREIMAGE CONSTRUCTION problem by Kratsch and Hemaspaandra naturally arose from the famous graph reconstruction conjecture. It deals with the algorithmic aspects of the conjecture. We present an O(n8) time algorithm for PREIMAGE CONSTRUCTION on permutation graphs and an O(n4(n+m)) time algorithm for PREIMAGE CONSTRUCTION on distance-hereditary graphs, where n is the number of graphs in the input, and m is the number of edges in a preimage. Since each graph of the input has n-1 vertices and O(n2) edges, the input size is O(n3) (, or O(nm)). There are polynomial time isomorphism algorithms for permutation graphs and distance-hereditary graphs. However the number of permutation (distance-hereditary) graphs obtained by adding a vertex to a permutation (distance-hereditary) graph is generally exponentially large. Thus exhaustive checking of these graphs does not achieve any polynomial time algorithm. Therefore reducing the number of preimage candidates is the key point.

This paper considers online vertex exploration problems in a simple polygon where starting from a point in the inside of a simple polygon, a searcher is required to explore a simple polygon to visit all its vertices and finally return to the initial position as quickly as possible. The information of the polygon is given online. As the exploration proceeds, the searcher gains more information of the polygon. We give a 1.219-competitive algorithm for this problem. We also study the case of a rectilinear simple polygon, and give a 1.167-competitive algorithm.

In this paper, we consider a problem of simultaneously optimizing a sequence of graphs and a route which exhaustively visits the vertices from each pair of successive graphs in the sequence. This type of problem arises from repetitive routing of grasp-and-delivery robots used in the production of printed circuit boards. The problem is formulated as follows. We are given a metric graph G*=(V*,E*), a set of m+1 disjoint subsets Ci ⊆ V* of vertices with |Ci|=n, i=0,1,...,m, and a starting vertex s ∈ C0. We seek to find a sequence π=(Ci1, Ci2, ..., Cim) of the subsets of vertices and a shortest walk P which visits all (m+1)n vertices in G* in such a way that after starting from s, the walk alternately visits the vertices in Cik-1 and Cik, for k=1,2,...,m (i0=0). Thus, P is a walk with m(2n-1) edges obtained by concatenating m alternating Hamiltonian paths between Cik-1 and Cik, k=1,2,...,m. In this paper, we show that an approximate sequence of subsets of vertices and an approximate walk with at most three times the optimal route length can be found in polynomial time.

A timed Petri net, an extended model of an ordinary Petri net with introduction of discrete time delay in firing activity, is practically useful in performance evaluation of real-time systems and so on. Unfortunately though, it is often too difficult to solve (efficiently) even most basic problems in timed Petri net theory. This motivates us to do research on analyzing complexity of Petri net problems and on designing efficient and/or heuristic algorithms. The minimum initial marking problem of timed Petri nets (TPMIM) is defined as follows: “Given a timed Petri net, a firing count vector X and a nonnegative integer π, find a minimum initial marking (an initial marking with the minimum total token number) among those initial ones M each of which satisfies that there is a firing scheduling which is legal on M with respect to X and whose completion time is no more than π, and, if any, find such a firing scheduling.” In a production system like factory automation, economical distribution of initial resources, from which a schedule of job-processings is executable, can be formulated as TPMIM. The subject of the paper is to propose two pseudo-polynomial time algorithms TPM and TMDLO for TPMIM, and to evaluate them by means of computer experiment. Each of the two algorithms finds an initial marking and a firing sequence by means of algorithms for MIM (the initial marking problem for non-timed Petri nets), and then converts it to a firing scheduling of a given timed Petri net. It is shown through our computer experiments that TPM has highest capability among our implemented algorithms including TPM and TMDLO.

The authors propose a method to make a movement plan for relocation of the railway cars in preparation for the final assembly. It obtains solution through three steps. The first step is to extract the order constraints between the movements of the railway cars based on their locations before and after relocation. The second step is to introduce the movement which puts a railway car into another location temporarily, in order to avoid a deadlock in the movements. And the final step is to obtain the movement order for carrying out the relocation in the shortest time in accordance with the calculated order constraints by using the genetic algorithm (GA). The order constraints are resolved in advance and therefore the movement order can easily be decided by GA. As the result, the developed system takes time shorter than an expert for planning the relocation.

The security of biometric authentication systems against impersonation attack is usually evaluated by the false accept rate, FAR. The false accept rate FAR is a metric for zero-effort impersonation attack assuming that the attacker attempts to impersonate a user by presenting his own biometric sample to the system. However, when the attacker has some information about algorithms in the biometric authentication system, he might be able to find a “strange” sample (called a wolf) which shows high similarity to many templates and attempt to impersonate a user by presenting a wolf. Une, Otsuka, Imai [22],[23] formulated such a stronger impersonation attack (called it wolf attack), defined a new security metric (called wolf attack probability, WAP), and showed that WAP is extremely higher than FAR in a fingerprint-minutiae matching algorithm proposed by Ratha et al. [19] and in a finger-vein-patterns matching algorithm proposed by Miura et al. [15]. Previously, we constructed secure matching algorithms based on a feature-dependent threshold approach [8] and showed that if the score distribution is perfectly estimated for each input feature data, then the proposed algorithms can lower WAP to a small value almost the same as FAR. In this paper, in addition to reintroducing the results of our previous work [8], we show that the proposed matching algorithm can keep the false reject rate (FRR) low enough without degrading security, if the score distribution is normal for each feature data.

In this paper, we give some results towards the conjecture that σ2t+1l-1,2t are the only nonlinear balanced elementary symmetric Boolean functions where t and l are positive integers. At first, a unified and simple proof of some earlier results is shown. Then a property of balanced elementary symmetric Boolean functions is presented. With this property, we prove that the conjecture is true for n=2m+2t-1 where m,t (m>t) are two non-negative integers, which verified the conjecture for a large infinite class of integer n.

Sequence diagrams are often used in the modular design of softwares. In this paper, we propose a method to verify correctness of sequence diagrams. With this method, using the process algebra CSP, concurrent systems can be synthesized from a number of sequence diagrams. We define new CSP operators for the synthesis of sequence diagrams. We also report on a tool implementing our synthesis method and demonstrate how the tool analyzes sequence diagrams.

This paper presents a new span-based dependency chart parsing algorithm that models the relations between the left and right dependents of a head. Such relations cannot be modeled in existing span-based algorithms, despite their popularity in dependency corpora. We address this problem through ternary-span combination during the subtree derivation. By modeling the relations between the left and right dependents of a head, our proposed algorithm provides a better capability of coordination disambiguation when the conjunction is annotated as the head of the left and right conjuncts. This eventually leads to state-of-the-art performance of dependency parsing on the Chinese data of the CoNLL shared task.

Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.

This paper proposes a dynamic bandwidth allocation algorithm that improves the network performance and bandwidth sharing efficiency in the upstream channels of a hybrid passive optical network (PON) that combines a fiber-to-the-home (FTTH) access network and wireless sensor networks (WSNs). The algorithm is called the adaptive limited dynamic bandwidth allocation (ALDBA) algorithm. Unlike existing algorithms, the ALDBA algorithm is not limited to controlling just FTTH access networks, it also supports WSNs. For the proposed algorithm, we investigate the difference in the lengths of generated data packets between the FTTH terminals and sensor nodes of WSN to effectively evaluate the end-to-end average packet delay, bandwidth utilization, time jitter, and upstream efficiency. Two variants of the proposed algorithm and a limited service (LS) scheme, which is an existing well-known algorithm, are compared under non-uniform traffic conditions without taking into consideration priority scheduling. We demonstrate the proposed scheme through simulation by generating a realistic network traffic model, called self-similar network traffic. We conducted a detailed simulation using several performance parameters to validate the effectiveness of the proposed scheme. The results of the simulation showed that both ALDBA variants outperformed the existing LS scheme in terms of average packet delay, bandwidth utilization, jitter, and upstream efficiency for both low and high traffic loads.

In OFDM systems, residual inter-block interference can be suppressed by a time-domain equalizer that blindly shortens the effective length of a channel impulse response. To further improve the performance of blind equalizers, we propose a channel shortening method that attempts to maximize the minimum FFT output power over data subcarriers. Simulation results indicate that the max-min strategy has performance improvement over a conventional channel shortening method.

The broadcast scheduling problem (BSP) in wireless ad-hoc networks is a well-known NP-complete combinatorial optimization problem. The BSP aims at finding a transmission schedule whose time slots are collision free in a wireless ad-hoc network with time-division multiple access (TDMA). The transmission schedule is optimized for minimizing the frame length of the node transmissions and maximizing the utilization of the shared channel. Recently, many metaheuristics can optimally solve smaller problem instances of the BSP. However, for complex problem instances, the computation of metaheuristics can be quite time and memory consuming. In this work, we propose a greedy genetic algorithm for solving the BSP with a large number of nodes. We present three heuristic genetic operators, including a greedy crossover and two greedy mutation operators, to optimize both objectives of the BSP. These heuristic genetic operators can generate good solutions. Our experiments use both benchmark data sets and randomly generated problem instances. The experimental results show that our genetic algorithm is effective in solving the BSP problem instances of large-scale networks with 2,500 nodes.