A group key exchange (GKE) protocol allows a group of parties communicating over a public network to establish a common secret key. As group-oriented applications gain popularity over the Internet, a number of GKE protocols have been suggested to provide those applications with a secure multicast channel. In this work, we investigate the security of Wu and Zhu's password-authenticated GKE protocol presented recently in FC'08. Wu and Zhu's protocol is efficient, supports dynamic groups, and can be constructed generically from any password-authenticated 2-party key exchange protocol. However, despite its attractive features, the Wu-Zhu protocol should not be adopted in its present form. Due to a flaw in its design, the Wu-Zhu protocol fails to achieve authenticated key exchange. We here report this security problem with the Wu-Zhu protocol and show how to solve it.

In a rooted triangulated planar graph, an outer vertex and two outer edges incident to it are designated as its root, respectively. Two plane embeddings of rooted triangulated planar graphs are defined to be equivalent if they admit an isomorphism such that the designated roots correspond to each other. Given a positive integer n, we give an O(n)-space and O(1)-time delay algorithm that generates all biconnected rooted triangulated planar graphs with at most n vertices without delivering two reflectively symmetric copies.

In this paper, the performance of the induced dimension reduction (IDR) method implemented along with the method of moments (MoM) is described. The MoM is based on a combined field integral equation for solving large-scale electromagnetic scattering problems involving conducting objects. The IDR method is one of Krylov subspace methods. This method was initially developed by Peter Sonneveld in 1979; it was subsequently generalized to the IDR(s) method. The method has recently attracted considerable attention in the field of computational physics. However, the performance of the IDR(s) has hardly been studied or practiced for electromagnetic wave problems. In this study, the performance of the IDR(s) is investigated and clarified by comparing the convergence property and memory requirement of the IDR(s) with those of other representative Krylov solvers such as biconjugate gradient (BiCG) methods and generalized minimal residual algorithm (GMRES). Numerical experiments reveal that the characteristics of the IDR(s) against the parameter s strongly depend on the geometry of the problem; in a problem with a complex geometry, s should be set to an adequately small value in order to avoid the "spurious convergence" which is a problem that the IDR(s) inherently holds. As for the convergence behavior, we observe that the IDR(s) has a better convergence ability than GPBiCG and GMRES(m) in a variety of problems with different complexities. Furthermore, we also confirm the IDR(s)'s inherent advantage in terms of the memory requirements over GMRES(m).

In a rooted graph, a vertex is designated as its root. An outerplanar graph is represented by a plane embedding such that all vertices appear along its outer boundary. Two different plane embeddings of a rooted outerplanar graphs are called symmetric copies. Given integers n ≥ 3 and g ≥ 3, we give an O(n)-space and O(1)-time delay algorithm that generates all biconnected rooted outerplanar graphs with exactly n vertices such that the size of each inner face is at most g without delivering two symmetric copies of the same graph.

In this letter, we propose a low-complexity coarse frequency offset estimation scheme in an orthogonal frequency division multiplexing (OFDM) system using non-uniform phased pilot symbols. In our approach, the pilot symbol used for frequency estimation is grouped into a number of pilot subsets so that the phase of pilots in each subset is unique. We show via simulations that such a design achieves not only a low computational load but also comparable performance, when compared to the conventional estimator.

This study focuses on speaker adaptation techniques for Computer-Assisted Language Learning (CALL). We first investigate the effects and problems of Maximum Likelihood Linear Regression (MLLR) speaker adaptation when used in pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners' English pronunciation. As we expected, over-adaptation causes misjudgment of pronunciation accuracy. Following the analysis, we propose a novel method, Regularized Maximum Likelihood Regression (Regularized-MLLR) adaptation, to solve the problem of the adverse effects of MLLR adaptation. This method uses a group of teachers' data to regularize learners' transformation matrices so that erroneous pronunciations will not be erroneously transformed as correct ones. We implement this idea in two ways: one is using the average of the teachers' transformation matrices as a constraint to MLLR, and the other is using linear combinations of the teachers' matrices to represent learners' transformations. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation.

The firefighter problem is used to model the spread of fire, infectious diseases, and computer viruses. This paper deals with firefighter problem on rooted trees. It is known that the firefighter problem is NP-hard even for rooted trees of maximum degree 3. We propose techniques to improve a given approximation algorithm. First, we introduce an implicit enumeration technique. By applying the technique to existing ()-approximation algorithm, we obtain -approximation algorithm when a root has k children. In case of ternary trees, k=3 and thus the approximation ratio satisfies ≥ 0.6892, which improves the existing result ≥ 0.6321. Second technique is based on backward induction and improves an approximation algorithm for firefighter problem on ternary trees. If we apply the technique to existing () -approximation algorithm, we obtain 0.6976-approximation algorithm. Lastly, we combine the above two techniques and obtain 0.7144-approximation algorithm for firefighter problem on ternary trees.

We study problems in computational geometry from the viewpoint of adaptive algorithms. Adaptive algorithms have been extensively studied for the sorting problem, and in this paper we generalize the framework to geometric problems. To this end, we think of geometric problems as permutation (or rearrangement) problems of arrays, and define the "presortedness" as a distance from the input array to the desired output array. We call an algorithm adaptive if it runs faster when a given input array is closer to the desired output, and furthermore it does not make use of any information of the presortedness. As a case study, we look into the planar convex hull problem for which we discover two natural formulations as permutation problems. An interesting phenomenon that we prove is that for one formulation the problem can be solved adaptively, but for the other formulation no adaptive algorithm can be better than an optimal output-sensitive algorithm for the planar convex hull problem. To further pursue the possibility of adaptive computational geometry, we also consider constructing a kd-tree.

The Towers of Hanoi problem is a classical problem in puzzles, games, mathematics, data structures, and algorithms. In this letter, a least memory used algorithm is proposed by combining the source array and target array for comparing the sizes of disk and labeling the disks in the towers of Hanoi problem. As a result, the proposed algorithm reduces the space needed from 2n+2 to n+5, where n represents the disks number.

This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).

This paper presents a novel time-domain design procedure for fast-settling three-stage nested-Miller compensated (NMC) amplifiers. In the proposed design methodology, the amplifier is designed to settle within a definite time period with a given settling accuracy by optimizing both the power consumption and silicon die area. Detailed design equations are presented and the circuit level simulation results are provided to verify the usefulness of the proposed design procedure with respect to the previously reported design schemes.

A transmitting and receiving modulation MIMO radar system is effective to obtaining 3D resolution without a 2D array and to simplification of the electronic circuits in Tx and Rx array. But the dynamic range of the conventional system is limited by the interchannel interference of the used preferred pair M-sequence codes for Tx and Rx modulation. This paper presents a TRM-MIMO radar system based on orthogonal coded theory. We derive a condition which the Tx and Rx codes doubly modulated at the Tx and Rx arrays should satisfy. The acquisition time and code length is theoretically discussed. The experiments are carried out in order to demonstrate the effectiveness of this method by using a developed TRM-MIMO radar system with Hadamard codes. As the result, it is found that the proposed orthogonal code modulation method achieves more than 20 dB improvement of the dynamic range which is limited due to the interchannel interference of a moving clutter in a conventional system with M-sequence codes. Moreover, 5 times faster acquisition time is achieved.

This letter introduces a new fairness concept, namely proportional quasi-fairness and proves that the optimal end-to-end rate of a network utility maximization can be proportionally quasi-fair with a properly chosen network utility function for an arbitrary compact feasible set.

Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.

This letter investigates the effects of spatial correlation on several multiple antenna schemes in multiuser environments. Using an order statistics upper bound on achievable capacity, we quantify the interaction among spatial correlation, spatial diversity, spatial multiplexing and multiuser diversity. Also, it is verified that the upper bound is tighter than asymptotic capacity when the number of users is relatively small.

Thin films of a divinyl derivative of tetraphenyldiaminobiphenyl DvTPD were prepared by vapor deposition followed by annealing. After annealing at 200°C for 1 h, the film became practically insoluble to organic solvents due to polymerization. Electrical characteristics of the films were measured by current-voltage measurement, time-of-flight measurement, and dielectric measurement. It was found that the hole mobility of DvTPD decreases when the film is polymerized. As a consequence of the decrease of hole mobility, carrier balance in the emissive layer of an organic light emitting diode (OLED) was improved, leading to a higher quantum efficiency and a pure emission spectrum. The dielectric measurement also confirmed the high thermal stability of the polymerized film.

Frequency estimation of a complex single-tone in additive white Gaussian noise from irregularly-spaced samples is addressed. In this Letter, we study the periodogram and weighted phase averager, which are standard solutions in the uniform sampling scenarios, for tackling the problem. It is shown that the estimation performance of both approaches can attain the optimum benchmark of the Cramér-Rao lower bound, although the former technique has a smaller threshold signal-to-noise ratio.

DCT encoding of images leads to block artifact and mosquito noise degradations in the decoded pictures. We propose an estimation to determine the mosquito noise block and level; however, this technique lacks sufficient linearity. To improve its performance, we use the sub-divided block for edge effect suppression. The subsequent results are mostly linear with the quantization.

Current Web authentication frameworks have well-known weaknesses. HTTP provides an access authentication framework, but it is rarely used because it lacks presentational control. Forms and cookies, which are most commonly used, have the long-standing privacy issue raised by tracking. URI sessions, which are used in some mobile services like i-mode 1.0, disclose session identifiers unintentionally. This paper proposes iAuth, which integrates better parts of the existing frameworks and fixes their problems; iAuth allows servers to provide log-in forms, and introduces a session header to avoid servers' tracking and unintentional disclosure. Since iAuth has backward compatibility with the major legacy browsers, developers can freely introduce iAuth into their Web sites or browsers as needed. Experiments confirm its correct operation; an iAuth server is shown to support not only an iAuth client but major legacy browsers. We believe that iAuth will resolve the long-standing issues in Web authentication.

Bounded model checking is a widely used formal technique in both hardware and software verification. However, it cannot be applied if the bounds (number of time frames to be analyzed) become large, and deep bugs which are observed only through very long counter-examples cannot be detected. This paper presents a method concatenating multiple bounded model checking results efficiently with symbolic simulation. A bounded model checking with a large bound is recursively decomposed into multiple ones with smaller bounds, and symbolic simulation on each counterexample supports smooth connections to the others. A strong heuristic for the proposed method that targets deep bugs is also presented, and can be applied together with other efficient bounded model checking methods since it does not touch the basic bounded model checking algorithm.