Since multiview video coding (MVC) based on H.264/AVC uses a prediction scheme exploiting inter-view correlation among multiview video, MVC encoder compresses multiple views more efficiently than simulcast H.264/AVC encoder. However, in case that the number of views to be encoded increases in MVC, the total encoding time will be greatly increased. To reduce computational complexity in MVC, a fast mode decision using both Macroblock-based region segmentation information and global disparity vector among views is proposed to reduce the encoding time. The proposed method achieves on the average 1.5 2.9 reduction of the total encoding time with the PSNR (Peak Signal-to-Noise Ratio) degradation of about 0.05 dB.

In this paper, we propose a Modified nested sparse grid based Adaptive Stochastic Collocation Method (MASCM) for block-based Statistical Static Timing Analysis (SSTA). The proposed MASCM employs an improved adaptive strategy derived from the existing Adaptive Stochastic Collocation Method (ASCM) to approximate the key operator MAX during timing analysis. In contrast to ASCM which uses non-nested sparse grid and tensor product quadratures to approximate the MAX operator for weakly and strongly nonlinear conditions respectively, MASCM proposes a modified nested sparse grid quadrature to approximate the MAX operator for both weakly and strongly nonlinear conditions. In the modified nested sparse grid quadrature, we firstly construct the second order quadrature points based on extended Gauss-Hermite quadrature and nested sparse grid technique, and then discard those quadrature points that do not contribute significantly to the computation accuracy to enhance the efficiency of the MAX approximation. Compared with the non-nested sparse grid quadrature, the proposed modified nested sparse grid quadrature not only employs much fewer collocation points, but also offers much higher accuracy. Compared with the tensor product quadrature, the modified nested sparse grid quadrature greatly reduced the computational cost, while still maintains sufficient accuracy for the MAX operator approximation. As a result, the proposed MASCM provides comparable accuracy while remarkably reduces the computational cost compared with ASCM. The numerical results show that with comparable accuracy MASCM has 50% reduction in run time compared with ASCM.

We have fabricated the transparent bottom gate and top gate TFTs using new oxide material of Al-Zn-Sn-O (AZTO) as an active layer. The AZTO active layer was deposited by RF magnetron sputtering at room temperature. Our novel TFT showed good TFT performance without post-annealing. The field effect mobility and the sub-threshold swing were improved by the post-annealing, and the mobility increased with SnO2 content. The AZTO TFT (about 4 mol% AlOx, 66 mol% ZnO, and 30 mol% SnO2) exhibited a mobility of 10.3 cm2/Vs, a turn-on voltage of 0.4 V, a sub-threshold swing of 0.6 V/dec, and an on/off ratio of 109. Though the bottom gate AZTO TFT showed good electrical performance, the bias stability was relatively poor. The bias stability was significantly improved in the top gate AZTO TFT. We have successfully fabricated the transparent AMOLED panel using the back-plane composed with top gate AZTO TFT array.

In this study, we introduce shift-invariant sparse image representations using tree-structured dictionaries. Sparse coding is a generative signal model that approximates signals by the linear combinations of atoms in a dictionary. Since a sparsity penalty is introduced during signal approximation and dictionary learning, the dictionary represents the primal structures of the signals. Under the shift-invariance constraint, the dictionary comprises translated structuring elements (SEs). The computational cost and number of atoms in the dictionary increase along with the increasing number of SEs. In this paper, we propose an algorithm for shift-invariant sparse image representation, in which SEs are learnt with a tree-structured approach. By using a tree-structured dictionary, we can reduce the computational cost of the image decomposition to the logarithmic order of the number of SEs. We also present the results of our experiments on the SE learning and the use of our algorithm in image recovery applications.

Networking technologies have recently been evolving and network applications are now expected to support flexible composition of upper-layer network services, such as security, QoS, or personal firewall. We propose a multi-platform framework called FreeNA* that extends existing applications by incorporating the services based on user definitions. This extension does not require users to modify their systems at all. Therefore, FreeNA is valuable for experimental system usage. We implemented FreeNA on both Linux and Microsoft Windows operating systems, and evaluated their functionality and performance. In this paper, we describe the design and implementation of FreeNA including details on how to insert network services into existing applications and how to create services in a multi-platform environment. We also give an example implementation of a service with SSL, a functionality comparison with relevant systems, and our performance evaluation results. The results show that FreeNA offers finer configurability, composability, and usability than other similar systems. We also show that the throughput degradation of transparent service insertion is 2% at most compared with a method of directly inserting such services into applications.

A channel code is constructed using sparse matrices for stationary memoryless channels that do not necessarily have a symmetric property like a binary symmetric channel. It is also shown that the constructed code has the following remarkable properties. 1. Joint source-channel coding: Combining channel code with lossy source code, which is also constructed by sparse matrices, a simpler joint source-channel code can be constructed than that constructed by the ordinary block code. 2. Universal coding: The constructed channel code has a universal property under a specified condition.

The electromagnetic fields emitted from an electrostatic discharge (ESD) event occurring between charged metals cause seriously damage high-tech equipment. In order to clarify the generation mechanism of such ESD fields and also to reduce them, we previously proposed a finite-difference time-domain (FDTD) algorithm based on a delta-gap feeding method and a frequency dispersion characteristic formula (Naito's formula) of ferrite material for simulating the ESD fields due to a spark between the charged metals with ferrite core attachment. In the present study, by integrating the above FDTD algorithm and a spark-resistance formula, we simulated both of the ESD itself and the resultant fields for the metal bars with ferrite core attachment, and demonstrated that the core attachment close to the spark gap suppresses the magnetic field level. This finding was also validated via 6-GHz wide-band measurement of the magnetic near-field.

Given a sparse linear system, A x = b, we can solve the equivalent system P A PT y = P b, x = PT y, where P is a permutation matrix. It has been known that, for example, when P is the RCMK (Reverse Cuthill-Mckee) ordering permutation, the convergence rate of the Krylov subspace method combined with the ILU-type preconditioner is often enhanced, especially if the matrix A is highly nonsymmetric. In this paper we offer a reordering heuristic for accelerating the solution of large sparse linear systems by the Krylov subspace methods with the ILUT preconditioner. It is the LRB (Line Red/Black) ordering based on the well-known 2-point Red-Black ordering. We show that for some model-like PDE (partial differential equation)s the LRB ordered FDM (Finite Difference Method)/FEM (Finite Element Method) discretization matrices require much less fill-ins in the ILUT factorizations than those of the Natural ordering and the RCMK ordering and hence, produces a more accurate preconditioner, if a high level of fill-in is used. It implies that the LRB ordering could outperform the other two orderings combined with the ILUT preconditioned Krylov subspace method if the level of fill-in is high enough. We compare the performance of our heuristic with that of the RCMK (Reverse Cuthill-McKee) ordering. Our test matrices are obtained from various standard discretizations of two-dimensional and three-dimensional model-like PDEs on structured grids by the FDM or the FEM. We claim that for the resulting matrices the performance of our reordering strategy for the Krylov subspace method combined with the ILUT preconditioner is superior to that of RCMK ordering, when the proper number of fill-in was used for the ILUT. Also, while the RCMK ordering is known to have little advantage over the Natural ordering in the case of symmetric matrices, the LRB ordering still can improve the convergence rate, even if the matrices are symmetric.

In this paper, a new local matching algorithm, to estimate dense disparity map in stereo vision, consisting of two stages is presented. At the first stage, the reduction of search space is carried out with a high efficiency, i.e. remarkable decrease in the average number of candidates per pixel, with low computational cost and high assurance of retaining the correct answer. This outcome being due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner, retains those candidates which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows; i.e., the disparity consistency of the window pixels. Such an output from the first stage, while speeding up the final selection of disparity in the second stage due to search space reduction, is also promising a more accurate result due to having more reliable candidates. In the second stage, the weighted window, although not necessarily being the exclusive choice, is employed and examined. The experimental results on the standard stereo benchmarks for the developed algorithm are presented, confirming that the massive computations to obtain more precise matching costs in weighted window is reduced to about 1/11 and the final disparity map is also improved.

Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.

Ga-doped ZnO thin films were prepared by RF magnetron sputtering. The effects of adding H2 to pure Ar sputtering gas were investigated. In the case of pure Ar at 2 Pa, the resistivity is 7.4510-3 Ωcm, whereas for Ar+1%H2 at 0.3 Pa, it markedly decreases to 2.5210-4 Ωcm. In this case, the carrier density and Hall mobility are 1.121021 cm-3 and 23.4 cm2/Vs, respectively. This conductive film also exhibits a transmittance of 90% within the visible-wavelength range. The addition of H2 and the decrease in the pressure results in the fabrication of a significantly more transparent and conductive film.

Tin-doped indium oxide (ITO) thin films were prepared on a polyethylene terephthalate (PET) foil by bias sputtering. In the absence of a substrate bias, films having a high resistivity of 210-2 Ωcm were formed. On the other hand, by the application of an rf substrate bias, films having a low resistivity of 2.610-4 Ωcm were formed. The energy of ions that bombarded the substrate during bias sputtering was estimated by a simulation of the ion acceleration. The optimum ion-energy required for the reduction of resistivity was found to be approximately 50 eV.

In this paper we compare various parallel preconditioners such as Point-SSOR (Symmetric Successive OverRelaxation), ILU(0) (Incomplete LU) in the Wavefront ordering, ILU(0) in the Multi-color ordering, Multi-Color Block SOR (Successive OverRelaxation), SPAI (SParse Approximate Inverse) and pARMS (Parallel Algebraic Recursive Multilevel Solver) for solving large sparse linear systems arising from two-dimensional PDE (Partial Differential Equation)s on structured grids. Point-SSOR is well-known, and ILU(0) is one of the most popular preconditioner, but it is inherently serial. ILU(0) in the Wavefront ordering maximizes the parallelism in the natural order, but the lengths of the wavefronts are often nonuniform. ILU(0) in the Multi-color ordering is a simple way of achieving a parallelism of the order N, where N is the order of the matrix, but its convergence rate often deteriorates as compared to that of natural ordering. We have chosen the Multi-Color Block SOR preconditioner combined with direct sparse matrix solver, since for the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with the Multi-Color ordering. By using block version we expect to minimize the interprocessor communications. SPAI computes the sparse approximate inverse directly by least squares method. Finally, ARMS is a preconditioner recursively exploiting the concept of independent sets and pARMS is the parallel version of ARMS. Experiments were conducted for the Finite Difference and Finite Element discretizations of five two-dimensional PDEs with large meshsizes up to a million on an IBM p595 machine with distributed memory. Our matrices are real positive, i.e., their real parts of the eigenvalues are positive. We have used GMRES(m) as our outer iterative method, so that the convergence of GMRES(m) for our test matrices are mathematically guaranteed. Interprocessor communications were done using MPI (Message Passing Interface) primitives. The results show that in general ILU(0) in the Multi-Color ordering and ILU(0) in the Wavefront ordering outperform the other methods but for symmetric and nearly symmetric 5-point matrices Multi-Color Block SOR gives the best performance, except for a few cases with a small number of processors.

This study proposes a method to decompose a signal into a set of periodic signals. The proposed decomposition method imposes a penalty on the resultant periodic subsignals in order to improve the sparsity of decomposition and avoid the overestimation of periods. This penalty is defined as the weighted sum of the l2 norms of the resultant periodic subsignals. This decomposition is approximated by an unconstrained minimization problem. In order to solve this problem, a relaxation algorithm is applied. In the experiments, decomposition results are presented to demonstrate the simultaneous detection of periods and waveforms hidden in signal mixtures.

We address pilot-aided channel estimation for a flat-fading spatially-correlated MIMO system, which employing Uniform Linear Arrays (ULA) on dual side and working in sparse scattering (multipath) environment. In case of sparse scattering, channel matrix and spatial correlation of flat-fading MIMO systems are parameterized by structure of multipaths, which is represented as Direction of Arrivals (DOAs), Direction of Departures (DODs) and complex path fading of each path. Based on this and block-fading property of channel, we design a channel estimation method via estimating multipath parameters using ESPRIT-like DOA-Matrix method which exploits shift-invariance property of ULA. The proposed method is able to obtain improved Mean-Square-Error performance than Least-Square method without prior information of spatial correlation.

In this paper, we propose a new code set which has very low spectral peak to average ratio (SPAR) and good correlation properties for DS-UWB. The codes which have low SPAR are suitable for DS-UWB system which operates in UWB (3.110.4 GHz) because they can utilize more power than high SPAR codes can do. And, in order to reduce inter symbol interference (ISI) and inter piconet interferences, the codes which have good auto- and cross-correlation properties must be used. In this paper, we propose three items; (1) a new code generation method which can generate good SPAR and auto-correlation codes, (2) code selection criteria, and (3) a code set, which has been selected according to the proposed selection criteria. The proposed code set has SPAR reduced about 0.22 dB and GMF improved by 30% compared to the previous code set while it is maintaining almost same cross-correlation properties. Each code of the proposed code set, therefore, has gained 1.43 dB SIR on an average compared to that of the previous code set.

We demonstrate a novel display structure for color electronic paper for the first time. Fully transparent amorphous oxide TFT array is directly deposited onto color filter array and combined with E Ink Imaging Film. Taking advantage of the transparent property of the oxide TFT, the color filter and TFT array are positioned at the viewing side of the display. This novel "Front Drive" display structure facilitates the alignment of the color filter and TFT dramatically.

The radiation characteristics of a monopole antenna that consists of one-half of a bow-tie dipole antenna, made of optically transparent conductive thin film and mounted above a ground plane, are investigated. The antenna's performance is measured for several films with different sheet resistivities. It is found that the gain lowering of the antenna caused by material resistance decreases from 4.4 dB to 0.2 dB at 2.4 GHz and the efficiency of the antenna increases from 46% to 93% at the same frequency, as the sheet resistivity decreases from 19.8 Ω/ to 1.3 Ω/. The antenna is analyzed by the moment method. A wire-grid model with resistance loading on every discretized wire is applied. The analyzed results agree with the experimental values very well.

In this paper, a sparse multipath channel estimation algorithm is proposed for multiple-input multiple-output (MIMO) single-carrier systems with frequency-domain decision feedback equalizer (FD-DFE). This algorithm exploits the orthogonality of an optimal MIMO preamble based on repeated, phase-rotated, Chu (RPC) sequences, leading to a dramatic reduction in computation. Furthermore, the proposed algorithm employs an improved non-iterative procedure utilizing the Generalized AIC criterion (GAIC), resulting in further computational saving and performance improvement. The proposed scheme is simulated for 802.16d SCa-PHY and SUI-5 sparse channel model under a 22 spatial multiplexing scenario, with the MIMO FD-DFE as the receiver. The result shows that the channel estimation accuracy is significantly improved, and the performance loss compared to the known channel case is only 0.7 dB at the BER of 10-3.

The most time consuming operation to verify a signature with the Elliptic Curve Digital Signature Algorithm is a multi-scalar multiplication with two scalars. Efficient methods for its computation are the Shamir method and the Interleave method, whereas the performance of those methods can be improved by using general base-2 representations of the scalars. In exchange for the speed-up, those representations require the precomputation of several points that must be stored. In the case of two precomputed points, the Interleave method and the Shamir method provide the same, optimal efficiency. In the case of more precomputed points, only the Interleave method can be sped-up in an optimal way and is currently more efficient than the Shamir method. This paper proposes a new general base-2 representation of the scalars that can be used to speed up the Shamir method. It requires the precomputation of ten points and is more efficient than any other representation that also requires ten precomputed points. Therefore, the proposed method is the first to improve the Shamir method such that it is faster than the Interleave method.