A Mur type analytical absorbing boundary condition (A-ABC), which is based on the one-dimensional one-way wave equation, is proposed for multidimensional wave analysis by introducing the directional splitting technique. This new absorbing boundary condition is expansion of the first-order Mur. The absorbing ability, required memory, and calculation speed of the Mur type A-ABC are evaluated by comparison with those of conventional ABCs. The result indicated that absorbing ability of the proposed ABC is higher than the first-order Mur and lower than the second-order Mur at large incident angle. While, our proposed ABC has advantage in both required memory and calculation speed by comparison with the second-order Mur. Thus, effectivity of the proposed Mur type A-ABC is shown.

A total-field/scattered-field (TF/SF) boundary for the constrained interpolation profile (CIP) method is proposed for multi-dimensional electromagnetic problems. Incident fields are added to or subtracted from update equations in order to satisfy advection equations into which Maxwell's equations are reduced by means of the directional splitting. Modified incident fields are introduced to take into account electromagnetic fields after advection. The developed TF/SF boundary is examined numerically, and the results show that it operates with good performance. Finally, we apply the proposed TF/SF boundary to a scattering problem, and it can be solved successfully.

The Kobayashi potential in electromagnetic theory is reviewed. As an illustration we consider two problems, diffraction of plane wave by disk and rectangular plate of perfect conductor. Some numerical results are compared with approximated and experimental results when they are available to verify the validity of the present method. We think the present method can be used as reference solutions of the related problems.

This paper presents the iterative progressive numerical methods (IPNMs) based on the induced dimension reduction (IDR) theorem. The IDR theorem is mainly utilized for the development of new nonstationary linear iterative solvers. On the other hand, the use of the IDR theorem enables to revise the classical linear iterative solvers like the Jacobi, the Gauss-Seidel (GS), the relaxed Jacobi, the successive overrelaxation (SOR), and the symmetric SOR (SSOR) methods. The new IPNMs are based on the revised solvers because the original one is similar to the Jacobi method. In the new IPNMs, namely the IDR-based IPNMs, we repeatedly solve linear systems of equations by using a nonstationary linear iterative solver. An initial guess and a stopping criterion are discussed in order to realize a fast computation. We treat electromagnetic wave scattering from 27 perfectly electric conducting spheres and reports comparatively the performance of the IDR-based IPNMs. However, the IDR-based SOR- and the IDR-based SSOR-type IPNMs are not subject to the above numerical test in this paper because of the problem with an optimal relaxation parameter. The performance evaluation reveals that the IDR-based IPNMs are better than the conventional ones in terms of the net computation time and the application range for the distance between objects. The IDR-based GS-type IPNM is the best among the conventional and the IDR-based IPNMs and converges 5 times faster than a standard computation by way of the boundary element method.

Physical channels of the intra-body communications, in which communications are performed by exciting electric field around the human body, have been treated as a capacitive circuit from the beginning of the development. Although the circuit-like understanding of the channels are helpful to design devices and systems, there is a problem that the results may be invalid if the circuit parameters are incorrectly estimated. In the present study, the values of the circuit parameters are properly derived by solving a boundary value problem of electric potentials of the conductors. Furthermore, approximate models which are appropriate for cases that some of the conductors are grounded are investigated.

This letter proposes a slot-based opportunistic spectrum access for cognitive radio networks. To reduce the slot-boundary impact, control frames are used to achieve channel reservation. The saturation throughput of our scheme is estimated by an analytical model. The accuracy of the model is validated by extensive simulation.

This paper presents various types of iterative progressive numerical methods (IPNMs) for the computation of electromagnetic (EM) wave scattering from many objects and reports comparatively the performance of these methods. The original IPNM is similar to the Jacobi method which is one of the classical linear iterative solvers. Then the modified IPNMs are based on other classical solvers like the Gauss-Seidel (GS), the relaxed Jacobi, the successive overrelaxation (SOR), and the symmetric SOR (SSOR) methods. In the original and modified IPNMs, we repeatedly solve linear systems of equations by using a nonstationary iterative solver. An initial guess and a stopping criterion are discussed in order to realize a fast computation. We treat EM wave scattering from 27 perfectly electric conducting (PEC) spheres and evaluate the performance of the IPNMs. However, the SOR- and SSOR-type IPNMs are not subject to the above numerical test in this paper because an optimal relaxation parameter is not possible to determine in advance. The evaluation reveals that the IPNMs converge much faster than a standard BEM computation. The relaxed Jacobi-type IPNM is better than the other types in terms of the net computation time and the application range for the distance between objects.

This paper presents a formulation of two-dimensional photonic crystal waveguide devices formed by circular cylinders. The device structures are considered as cascade connections of straight waveguides. Decomposing the structure into layers of the cylinder arrays, the input/output properties of the devices are obtained using an analysis method of multilayer structure. We introduce periodic boundary conditions in the direction perpendicular to the wave propagation, and the Floquet-modes of each layer are calculated by the Fourier series expansion method with the help of the recursive transition-matrix algorithm. Then, the input/output properties of the devices are obtained by recursive calculation of scattering matrix with each layer. The presented formulation is validated by numerical experiments by comparing with the previous works.

A dependency structure interprets modification relationships between words or phrases and is recognized as an important element in semantic information analysis. With the conventional approaches for extracting this dependency structure, it is assumed that the complete sentence is known before the analysis starts. For spontaneous speech data, however, this assumption is not necessarily correct since sentence boundaries are not marked in the data. Although sentence boundaries can be detected before dependency analysis, this cascaded implementation is not suitable for online processing since it delays the responses of the application. To solve these problems, we proposed a sequential dependency analysis (SDA) method for online spontaneous speech processing, which enabled us to analyze incomplete sentences sequentially and detect sentence boundaries simultaneously. In this paper, we propose an improved SDA integrating a labeling-based sentence boundary detection (SntBD) technique based on Conditional Random Fields (CRFs). In the new method, we use CRF for soft decision of sentence boundaries and combine it with SDA to retain its online framework. Since CRF-based SntBD yields better estimates of sentence boundaries, SDA can provide better results in which the dependency structure and sentence boundaries are consistent. Experimental results using spontaneous lecture speech from the Corpus of Spontaneous Japanese show that our improved SDA outperforms the original SDA with SntBD accuracy providing better dependency analysis results.

We try to use a computer algebra system Mathematica as a test case generation system. In test case generation, we generally need to solve equations and inequalities. The main reason why we take Mathematica is because it has a built-in function to solve equations and inequalities. In this paper, we deal with both black-box testing and white-box testing. First, we show two black-box test case generation procedures described in Mathematica. The first one is based on equivalence partitioning. Mathematica explicitly shows a case that test cases do no exist. This is an advantage in using Mathematica. The second procedure is a modification of the first one adopting boundary value analysis. For implementation of boundary value analysis, we give a formalization for it. Next, we show a white-box test case generation procedure. For this purpose, we also give a model for source programs. It is like a control flow graph model. The proposed procedure analyzes a model description of a program.

This paper presents unconditionally stable and conformal FDTD schemes which are based on the alternating-direction implicit finite difference time domain (ADI-FDTD) method for accurate modeling of perfectly electric conducting (PEC) objects. The proposed schemes are formulated within the framework of the matrix-vector notation of the finite integration technique (FIT), which allows a systematic and consistent extension of finite difference solution of Maxwell's equations on dual grids. As possible choices of second-order convergent conformal method, we apply the partially filled cell (PFC) and the uniformly stable conformal (USC) schemes for the ADI-FDTD method. The unconditional stability and the rates of convergence of the proposed conformal ADI-FDTD (CADI-FDTD) schemes are verified by means of numerical examples of waveguide problems.

In this paper, we propose a method for extracting an object boundary from a low-quality image such as an infrared one. To take full advantage of a training set, the overall shape is modeled by incorporating statistical characteristics of moments into the point distribution model (PDM). Furthermore, a differential equation for the moment of overall shape is derived for shape refinement, which leads to accurate and rapid deformation of a boundary template toward real object boundary. The simulation results show that the proposed method has better performance than conventional boundary extraction methods.

The purpose of the paper is to show that boundary implication results hold for complex-valued uncertain linear time-delay systems. The results are derived by the Lambert W function and yield tractable robust stability criteria for simultaneously triangularizable linear time-delay systems. The setting is similar to a recently reported extreme-point result, but the assumed uncertainty sets can be much more free in shape.

While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naive Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.930.50% when the first rank is selected while it gains 97.260.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naive Bayes classifier and the vanilla version. Another result on applying only best features show 93.930.22% and up to 98.85 0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

This Letter proposes a new kind of features for color image retrieval based on Distance-weighted Boundary Predictive Vector Quantization (DWBPVQ) Index Histograms. For each color image in the database, 6 histograms (2 for each color component) are calculated from the six corresponding DWBPVQ index sequences. The retrieval simulation results show that, compared with the traditional Spatial-domain Color-Histogram-based (SCH) features and the DCTVQ index histogram-based (DCTVQIH) features, the proposed DWBPVQIH features can greatly improve the recall and precision performance.

In modern VLSI physical design, huge integration scale necessitates hierarchical design and IP reuse to cope with design complexity. Besides, interconnect delay becomes dominant to overall circuit performance. These critical factors require some modules to be placed along designated boundaries to effectively facilitate hierarchical design and interconnection optimization related problems. In this paper, boundary constraints of general floorplan are solved smoothly based on the novel representation Single-Sequence (SS). Necessary and sufficient conditions of rooms along specified boundaries of a floorplan are proposed and proved. By assigning constrained modules to proper boundary rooms, our proposed algorithm always guarantees a feasible SS code with appropriate boundary constraints in each perturbation. Time complexity of the proposed algorithm is O(n). Experimental results on MCNC benchmarks show effectiveness and efficiency of the proposed method.

Salient edge detection which is mentioned less frequently than salient point detection is another important cue for subsequent processing in computer vision. How to find the salient edges in natural images is not an easy work. This paper proposes a simple method for salient edge detection which preserves the edges with more salient points on the boundaries and cancels the less salient ones or noise edges in natural images. According to the Gestalt Principles of past experience and entirety, we should not detect the whole edges in natural images. Only salient ones can be an advantageous tool for the following step just like object tracking, image segmentation or contour detection. Salient edges can also enhance the efficiency of computing and save the space of storage. The experiments show the promising results.

A new hierarchical isosurface reconstruction scheme from a set of tomographic cross sectional images is presented. From the input data, we construct a hierarchy of volume, called the volume pyramid, based on a 3D dilation filter. After extracting the base mesh from the volume at the coarsest level by the cell-boundary method, we iteratively fit the mesh to the isopoints representing the actual isosurface of the volume. The SWIS (Shrink-wrapped isosurface) algorithm is adopted in this process, and a mesh subdivision scheme is utilized to reconstruct fine detail of the isosurface. According to experiments, our method is proved to produce a hierarchical isosurface which can be utilized by various multiresolution algorithms such as interactive visualization and progressive transmission.

Boundary detection is one of the most studied problems in computer vision. It is the foundation of contour grouping, and initially affects the performance of grouping algorithms. In this paper we propose a novel boundary detection algorithm for contour grouping, which is a selective attention guided coarse-to-fine scale pyramid model. Our algorithm evaluates each edge instead of each pixel location, which is different from others and suitable for contour grouping. Selective attention focuses on the whole saliency objects instead of local details, and gives global spatial prior for boundary existence of objects. The evolving process of edges through the coarsest scale to the finest scale reflects the importance and energy of edges. The combination of these two cues produces the most saliency boundaries. We show applications for boundary detection on natural images. We also test our approach on the Berkeley dataset and use it for contour grouping. The results obtained are pretty good.

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.