Fast and simple algorithm of a parity checker for a large residue numbers is presented. A new set of RNS moduli with 2r-(2l1) form for fast modular multiplication is proposed. The proposed RNS moduli has a large dynamic range for a large RNS number. The parity of a residue number can be checked by the Chinese remainder theorem (CRT). A CRT-based parity checker is simply organized by the Montgomery reduction method (MRM), implemented by using multipliers and the carry-save adder array. We present a fast parity checker with minimal hardware processed in three clock cycles for 32-bit RNS modulus set.

Particles several tens of nanometers in size were aligned in the desired positions in a controlled manner by using capillary force interaction and suspension flow. Latex beads 40-nm in diameter were aligned linearly around a 10-µm-hole template fabricated by lithography. Further control of their position and separation was realized using colloidal gold nanoparticles by controlling the particle-substrate and particle-particle interactions using an optical near field generated on the edge of a Si wedge, in which the separation of the colloidal gold nanoparticles was controlled by the direction of polarization.

Direction-of-arrival (DOA) estimation based on subspace methods has collected much interest over a few decades, and adaptive DOA estimation with rapidly changing parameters will be necessary for wireless communications. This paper is concerned with a new subspace tracking scheme by using an accelerated LMS and RLS algorithms for time-varying parameters. The proposed accelerated adaptive algorithms are based on the internal model principle by approximately expressing the changing parameters by an expansion of polynomial time functions. Thus its application to DOA estimation based on the MUSIC and MODE schemes is presented and the effectiveness is validated in numerical simulations.

This paper presents a new algorithm for efficiently detecting silhouette voxels in volume objects. The high performance of the algorithm is partly due to its ability to exclude all the gradient vectors not associated with silhouettes from further consideration. A judicious re-arrangement of the voxels enhances its efficiency. We have studied its performance through computer simulations. The results indicate a manifold improvement over conventional algorithms. A parallel version of the algorithm has also been described in the paper. Its performance is quite understandably impressive.

In this paper we focus on the decoding error of the Log-Likelihood Ratio Belief Propagation (LLR-BP) decoding algorithm caused by oscillation. The decoding error caused by the oscillation is dominant in high Eb/N0 region. Oscillation of the LLR of the extrinsic value in the bit node process (ex-LLR) is propagated to the other bits and affects the whole decoding. The Ordered Statistic Decoding (OSD) algorithm is known to improve the error rate performance of the LLR-BP decoding algorithm. The OSD algorithm is performed by deciding the reliability of each bit based on a posteriori probability. In this paper we propose two decoding algorithms based on two types of oscillations of LLR for LDPC codes. One is the oscillation-based OSD algorithm with deciding the reliability of each bit based on oscillation. The other is the oscillation-based LLR-BP decoding algorithm that modifies ex-LLR based on oscillation. In the oscillation-based LLR-BP decoding algorithm, when ex-LLR oscillates, then we reduce the magnitude of this ex-LLR to reduce the effects on the other bits. Both algorithms improve the decoding errors caused by oscillation. From the computer simulations, we show that paying attention to the oscillation, we can improve the error rate performance of the LLR-BP decoding algorithm.

A method for constructing low-density convolutional (LDC) codes with the degree distribution optimized for block low-density parity-check (LDPC) codes is presented. If the degree distribution is irregular, the constructed LDC codes are also irregular. In this letter we give the encoding and decoding method for LDC codes, and study how to avoid the short cycles of LDC codes. Some simulation results are also presented.

In this paper, dosimetry of an in vitro exposure apparatus based on a cylindrical waveguide is performed. The SAR distributions are first obtained numerically by using FDTD method. The thermal fields in the medium are then estimated by numerical calculations of the equation of heat conduction. The maximum temperature rise for 17.9 W/kg average SAR during 3000 s exposure is about 2 on the bottom of the medium where cells are located. The thermal distribution is relatively uniform near the center of the dish and the temperature in this region is around 38.7. The results of the numerical calculation are experimentally supported. The results provide the electromagnetic and thermal characteristics of the exposure apparatus, which will define the exposure conditions of the planned experiments using this apparatus.

A distributed network-oriented Intrusion Detection System (IDS) is a mechanism which detects misuse accesses to an intra-network by distributed IDSs on the network with decomposed attack scenarios. However, there are only ad hoc algorithms for determining a deployment of distributed IDSs and a partition of the attack scenarios. In this paper, we formally define this problem as the IDS partition deployment problem and design an efficient algorithm for a simplified version of the problem by graph theoretical techniques.

Recently, DOAS (differential optical absorption spectroscopy) has been used for nondestructive air monitoring, in which the LS (least squares) method is used to calculate trace gas concentrations due to its computational simplicity. This paper applies the ICA (independent component analysis) method to the DOAS system of air monitoring, since the LS method is insufficient to recover the desired spectra perfectly due to sparsity characteristic. If the sparsity of reference spectra in the DOAS system imposes the assumption of independence, the ICA algorithm can be used. The proposed method is used to regress the observed spectrum on the estimates of the reference spectra. The ICA algorithm can be seen as a preprocessing method where the ICs of the references are used as the input in the regression. The performance of the proposed method is evaluated in simulation studies using synthetic data.

In this paper, we propose a new error feedback (EF) structure for 2-D separable-denominator digital filters described by a rational transfer function. In implementing two-dimensional separable-denominator digital filters, the minimum delay elements structures are common. In the proposed structure, the filter feedback-loop corresponding to denominator polynomial is placed at a different location compared to the commonly used structures. The proposed structure can minimize the roundoff noise more than the previous structure though the number of multipliers is less than that of previous one. Finally, we present a numerical example by designing the EF on the proposed structure and demonstrate the effectiveness of the proposed method.

In this paper, we address the blind separation problem of binaural mixed signals, and we propose a novel blind separation method, in which a self-generator for initial filters of Single-Input-Multiple-Output-model-based independent component analysis (SIMO-ICA) is implemented. The original SIMO-ICA which has been proposed by the authors can separate mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Although this attractive feature of SIMO-ICA is beneficial to the binaural sound separation, the current SIMO-ICA has a serious drawback in its high sensitivity to the initial settings of the separation filter. In the proposed method, the self-generator for the initial filter functions as the preprocessor of SIMO-ICA, and thus it can provide a valid initial filter for SIMO-ICA. The self-generator is still a blind process because it mainly consists of a frequency-domain ICA (FDICA) part and the direction of arrival estimation part which is driven by the separated outputs of the FDICA. To evaluate its effectiveness, binaural sound separation experiments are carried out under a reverberant condition. The experimental results reveal that the separation performance of the proposed method is superior to those of conventional methods.

This paper presents a study on the blind separation of a convoluted mixture of speech signals using Frequency Domain Independent Component Analysis (FDICA) algorithm based on the negentropy maximization of Time Frequency Series of Speech (TFSS). The comparative studies on the negentropy approximation of TFSS using generalized Higher Order Statistics (HOS) of different nonquadratic, nonlinear functions are presented. A new nonlinear function based on the statistical modeling of TFSS by exponential power functions has also been proposed. The estimation of standard error and bias, obtained using the sequential delete-one jackknifing method, in the approximation of negentropy of TFSS by different nonlinear functions along with their signal separation performance indicate the superlative power of the exponential-power-based nonlinear function. The proposed nonlinear function has been found to speed-up convergence with slight improvement in the separation quality under reverberant conditions.

When a dependency parser analyzes long sentences with fewer subjects than predicates, it is difficult for it to recognize which predicate governs which subject. To handle such syntactic ambiguity between subjects and predicates, we define an "a subject clause (s-clause)" as a group of words containing several predicates and their common subject. This paper proposes a two-phase method for S-clause segmentation. The first phase reduces the number of candidates of S-clause boundaries, and the second performs S-clause segmentation using decision trees. In experimental evaluation, the S-clause information turned out to be effective for determining the governor of a subject and that of a predicate in dependency parsing. Further syntactic analysis using S-clauses achieved an improvement in precision of 5 percent.

In an Orthogonal Frequency Division Multiplexing (OFDM) systems, the Peak to Average power Ratio (PAR) is high. The clipping signal scheme is a useful and simple method to reduce the PAR. However, it introduces additional noise that degrades the systems performance. We propose an oversampling scheme to deal with the received signal in order to reduce the clipping noise by using finite impulse response (FIR) filter. Coefficients of the filter are obtained by correlation function of the received signal and the oversampling information at receiver. The performance of the proposed technique is evaluated for frequency selective channel. Results show that the proposed scheme can mitigate the clipping noise significantly for OFDM systems and in order to maintain the system's capacity, the clipping ratio should be larger than 2.5.

This paper overviews a total solution for frequency-domain blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circularity, and complex activation function solutions. Experimental results of 22, 33, 44, 68, and 22 (moving sources), (#sources#microphones) in a room are promising.

A rotator graph was proposed as a topology for interconnection networks of parallel computers, and it is promising because of its small diameter and small degree. However, a rotator graph is a directed graph that sometimes behaves harmfully when it is applied to actual problems. A bi-rotator graph is obtained by making each edge of a rotator graph bi-directional. In a bi-rotator graph, average distance is improved against a rotator graph with the same number of nodes. In this paper, we give an algorithm for the container problem in bi-rotator graphs with its evaluation results. The solution achieves some fault tolerance such as file distribution based information dispersal technique. The algorithm is of polynomial order of n for an n-bi-rotator graph. It is based on recursion and divided into two cases according to the position of the destination node. The time complexity of the algorithm and the maximum length of paths obtained are estimated to be O(n3) and 4n-5, respectively. Average performance of the algorithm is also evaluated by computer experiments.

This paper presents a highly parallel architecture for deblocking filter in H.264/AVC. We adopt various parallel schemes in memory sub-system and datapath. A 2-dimensional parallel memory scheme is employed to support efficient parallel access in both horizontal and vertical directions in order to speed up the whole filtering process. This parallel memory also eliminates the need for a transpose circuit. In the datapath, an algorithm optimization is performed to implement parallel filtering with hardware reuse. Pipeline techniques are also adopted to improve the throughput of filtering operations. Our design is implemented under TSMC 0.18 µm technology. Results show that the core size is 0.821.13 mm2 when the maximum frequency is 230 MHz. Compared to other existing architectures, our design has advantages in both speed and area.

In this paper, the 1-D real-valued discrete Gabor transform (RDGT) proposed in our previous work and its relationship with the complex-valued discrete Gabor transform (CDGT) are briefly reviewed. Block time-recursive RDGT algorithms for the efficient and fast computation of the 1-D RDGT coefficients and for the fast reconstruction of the original signal from the coefficients are then developed in both the critical sampling case and the oversampling case. Unified parallel lattice structures for the implementation of the algorithms are studied. And the computational complexity analysis and comparison show that the proposed algorithms provide a more efficient and faster approach for the computation of the discrete Gabor transforms.

This paper focuses on the underdetermined blind source separation (BSS) of three speech signals mixed in a real environment from measurements provided by two sensors. To date, solutions to the underdetermined BSS problem have mainly been based on the assumption that the speech signals are sufficiently sparse. They involve designing binary masks that extract signals at time-frequency points where only one signal was assumed to exist. The major issue encountered in previous work relates to the occurrence of distortion, which affects a separated signal with loud musical noise. To overcome this problem, we propose combining sparseness with the use of an estimated mixing matrix. First, we use a geometrical approach to detect when only one source is active and to perform a preliminary separation with a time-frequency mask. This information is then used to estimate the mixing matrix, which allows us to improve our separation. Experimental results show that this combination of time-frequency mask and mixing matrix estimation provides separated signals of better quality (less distortion, less musical noise) than those extracted without using the estimated mixing matrix in reverberant conditions where the reverberant time (TR) was 130 ms and 200 ms. Furthermore, informal listening tests clearly show that musical noise is deeply lowered by the proposed method comparatively to the classical approaches.

In this letter, a new computation method for the noise parameters of a linear noisy two-port network is introduced. A new error function, which considers noise figure and source admittance error simultaneously, is proposed to estimate the four noise parameters. The global optimization of the error function is searched directly by using a genetic algorithm.