A fast decoding algorithm for low-density parity-check codes is presented based on graph decomposition and two-way message passing schedule. Simulations show that the convergence speed of the proposed algorithm is about twice that of the conventional belief propagation algorithm.

This letter describes a permutation attack (PA) to the subsampling-based watermarking scheme where the high correlations between subimages obtained by subsampling the original image are used for watermark embedding. We show that the correlations can also be easily used to attack the watermarking scheme through a simple permutation procedure, while the quality degradation of attacked watermarked image is visually acceptable. Experimental results show the efficiency of the proposed attack algorithm.

A novel microstrip dual-mode bandpass filter with ultra-broad stopband is proposed using the aperture-backed stepped-impedance ring resonator (SIRR). This SIRR consists of low-impedance strips in the four bended corners and high-impedance strips in the four straight sides. With the cross-shaped aperture placed on the ground underneath the SIRR, the upper stopband is significantly broadened. In particular, the 2nd resonant frequency of this proposed SIRR is confirmed to exceed the four times of its 1st counterpart. The dual-mode filter with the passband of 7.5% at 1.59 GHz is then designed and implemented, demonstrating the measured stopband of 1.70-5.80 GHz and size reduction of 56.0%.

Eye contact is an effective means of controlling human communication, such as in starting communication. It seems that we can make eye contact if we simply look at each other. However, this alone does not establish eye contact. Both parties also need to be aware of being watched by the other. We propose a method of bidirectional eye contact satisfying these conditions for human-robot communication. When a human wants to start communication with a robot, he/she watches the robot. If it finds a human looking at it, the robot turns to him/her, changing its facial expressions to let him/her know its awareness of his/her gaze. When the robot wants to initiate communication with a particular person, it moves its body and face toward him/her and changes its facial expressions to make the person notice its gaze. We show several experimental results to prove the effectiveness of this method. Moreover, we present a robot that can recognize hand gestures after making eye contact with the human to show the usefulness of eye contact as a means of controlling communication.

A range top-k query returns the topmost k records in the order set by a measure attribute within a specified region of multi-dimensional data. The range top-k query is a powerful tool for analysis in spatial databases and data warehouse environments. In this paper, we propose an algorithm to answer the query by selectively traversing an aggregate R-tree having MAX as the aggregate values. The algorithm can execute the query by accessing only a small part of the leaf nodes within a query region. Therefore, it shows good query performance regardless of the size of the query region. We suggest an efficient pruning technique for the priority queue, which reduces the cost of handling the priority queue, and also propose an efficient technique for leaf node organization to reduce the number of node accesses to execute the range top-k queries.

This work proposes a technique to automatically obtain timing constraints for a given timed circuit to operate correctly. A designated set of delay parameters of a circuit are first set to sufficiently large bounds, and verification runs followed by failure analysis are repeated. Each verification run performs timed state space enumeration under the given delay bounds, and produces a failure trace if it exists. The failure trace is analyzed, and sufficient timing constraints to prevent the failure are obtained. Then, the delay bounds are tightened according to the timing constraints by using an ILP (Integer Linear Programming) solver. This process terminates when either some delay bounds under which no failure is detected are found or no new delay bounds to prevent the failures can be obtained. The experimental results using a naive implementation show that the proposed method can efficiently handle asynchronous benchmark circuits and nontrivial GasP circuits.

A rack-mounted DC power-supply system utilizing Li-ion batteries, which have higher energy density than conventional VRLA batteries, was developed. The system was designed to have the management functions of Li-ion batteries, such as overcharge protection, over-discharge protection, and cell-voltage equalization, by taking operational requirements into consideration. The volume and weight of the entire system were decreased to one-fourth and three-fifths, respectively, of the volume and weight of a conventional system, making the proposed system ideal as a high-energy-density backup power supply. The functions, system configuration, and characteristics of this rack-mounted DC power supply system utilizing Li-ion batteries are described.

An accurate audio-visual speech corpus is inevitable for talking-heads research. This paper presents our audio-visual speech corpus collection and proposes a head-movement normalization method and a facial motion generation method. The audio-visual corpus contains speech data, movie data on faces, and positions and movements of facial organs. The corpus consists of Japanese phoneme-balanced sentences uttered by a female native speaker. An accurate facial capture is realized by using an optical motion-capture system. We captured high-resolution 3D data by arranging many markers on the speaker's face. In addition, we propose a method of acquiring the facial movements and removing head movements by using affine transformation for computing displacements of pure facial organs. Finally, in order to easily create facial animation from this motion data, we propose a technique assigning the captured data to the facial polygon model. Evaluation results demonstrate the effectiveness of the proposed facial motion generation method and show the relationship between the number of markers and errors.

This letter deals with our investigations into improving the performance of a wireless uplink system when an orthogonal frequency-division multiple access (OFDMA) is used as an access scheme. To do this, the OFDMA-based uplink system adopts a frequency diversity coupled with a cyclic time shift (CTS) at the transmitter, which is named as the FD-OFDMA system with CTS. It is found that the multi-user FD-OFDMA system equipping with CTS can decrease the probability of destroying the orthogonality among the users and provide the MAI-robust reception without decreasing the bandwidth efficiency of the system.

To cut down the sidelobe level of radiation pattern, a novel adaptive algorithm is proposed for electronic steering parasitic antenna. The composite objective function in this algorithm takes both directivity and sidelobe level of pattern into account, and the steepest gradient algorithm is selected to search the optimum value of reactive load. Simulations are carried out to validate the algorithm, simulated results show that the levels of sidelobe are both below -4 dB in different beamforming cases, and the front to back ratios are better than 10 dB.

Petri nets are a well-known graphical and modeling tool for concurrent and distributed systems, and there have been many results on the theory, and also on practical applications. In the last decade, various Object-Oriented Petri nets (OO-nets) are proposed. As object orientation was adopted for programming languages, extension to OO-nets inspired from object-oriented programming is a natural flow. This article presents state-of-the-art on OO-nets.

To develop human interfaces such as home information equipment, highly capable word learning ability is required. In particular, in order to realize user-customized and situation-dependent interaction using language, a function is needed that can build new categories online in response to presented objects for an advanced human interface. However, at present, there are few basic studies focusing on the purpose of language acquisition with category formation. In this study, taking hints from an analogy between machine learning and infant developmental word acquisition, we propose a taxonomy-based word-learning model using a neural network. Through computer simulations, we show that our model can build categories and find the name of an object based on categorization.

This paper deals with the use of neural network rule extraction techniques based on the Genetic Programming (GP) to build intelligent and explanatory evaluation systems. Recent development in algorithms that extract rules from trained neural networks enable us to generate classification rules in spite of their intrinsically black-box nature. However, in the original decompositional method looking at the internal structure of the networks, the comprehensive methods combining the output to the inputs using parameters are complicated. Then, in our paper, we utilized the GP to automatize the rule extraction process in the trained neural networks where the statements changed into a binary classification. Even though the production (classification) rule generation based on the GP alone are applicable straightforward to the underlying problems for decision making, but in the original GP method production rules include many statements described by arithmetic expressions as well as basic logical expressions, and it makes the rule generation process very complicated. Therefore, we utilize the neural network and binary classification to obtain simple and relevant classification rules in real applications by avoiding straightforward applications of the GP procedure to the arithmetic expressions. At first, the pruning process of weight among neurons is applied to obtain simple but substantial binary expressions which are used as statements is classification rules. Then, the GP is applied to generate ultimate rules. As applications, we generate rules to prediction of bankruptcy and creditworthiness for binary classifications, and the apply the method to multi-level classification of corporate bonds (rating) by using the financial indicators.

In reconstructing 3-D from images based on feature points, one usually defines a triangular mesh that has these feature points as vertices and displays the scene as a polyhedron. If the scene itself is a polyhedron, however, some of the displayed edges may be inconsistent with the true shape. This paper presents a new technique for automatically eliminating such inconsistencies by using a special template. We also present a technique for removing spurious occluding edges. All the procedures do not require any thresholds to be adjusted. Using real images, we demonstrate that our method has high capability to correct inconsistencies.

We analyze the noise sensitivity of the focal length computation, the principal point estimation, and the orthogonality enforcement for single-view 3-D reconstruction based on vanishing points and orthogonality. We point out that due to the nonlinearity of the problem the standard statistical optimization is not very effective. We present a practical compromise for avoiding the computational failure and preserving high accuracy, allowing a consistent 3-D shape in the presence of however large noise.

This paper describes a sophisticated method to estimate visual direction using iris contours. This method requires only one monocular image taken by a camera with unknown focal length. In order to estimate the visual direction, we assume the visual directions of both eyes are parallel and iris boundaries are circles in 3D space. In this case, the two planes where the iris boundaries reside are also parallel. We estimate the normal vector of the two planes from the iris contours extracted from an input image by using an extended "two-circle" algorithm. Unlike most existing gaze estimation algorithms that require information about eye corners and heuristic knowledge about 3D structure of the eye in addition to the iris contours, our method uses two iris contours only. Another contribution of our method is the ability of estimating the focal length of the camera. It allows one to use a zoom lens to take images and the focal length can be adjusted at any time. The extensive experiments over simulated images and real images demonstrate the robustness and the effectiveness of our method.

A novel digital redesign methodology based on evolutionary programming (EP) is introduced to find the 'best' digital controller for optimal tracking design of hybrid uncertain multi-input/ multi-output (MIMO) input-delay systems with constraints on states and controls. To deal with these multivariable concurrent specifications and system restrictions, instead of conventional interval methods, the proposed global optimization scheme is able to practically implement optimal digital controller for constrained uncertain hybrid systems with input time delay. Further, an illustrative example is included to demonstrate the efficiency of the proposed method.

In order to solve large Multidimensional Knapsack problems we examine a technique which decomposes a problem instance into two parts. The first part is solved using a traditional technique, such as Dynamic Programming, to reduce the number of variables in the problem by creating a single variable with many non-dominated states. In the second part the remaining variables are determined by an algorithm that repeatedly enumerates them with different constraint and objective requirements. The constraint and objective requirements are imposed by the various non-dominated states of the variable created in the first part of this technique. The main advantage of this approach is that when memory requirements prevent traditional techniques solving a problem instance, the enumeration provides a much less memory-intensive method, enabling a solution to be found. Two approaches are proposed for repeatedly enumerating a 0/1 Multidimensional Knapsack problem. It is demonstrated how these enumeration methods, in conjunction with the Modular Approach, were used to find the optimal solutions to a number of 500-variable, 5-constraint Multidimensional Knapsack problem instances proposed in the literature. The exact solutions to these instances were previously unknown.

This paper presents properties of role-based access control which were obtained through a development of a prototype of a teaching management system. These properties are related to assignment of temporal constraints and access control procedure in terms of the corresponding flow of user's view and considered to be suitable to other information systems.

This paper presents a novel method of structure selection and identification for Hammerstein type nonlinear systems. An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The connection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. The adjusting parameters for the ACF model structure, i.e. the number and widths of the subdomains and the shape of the ACF are properly selected by using a genetic algorithm, in which the Akaike information criterion is utilized as the fitness value function. The effectiveness of the proposed method is confirmed through numerical experiments.