R-regular Mth band filters are an important class of digital filters and are used in constructing Mth-band wavelet filter banks, where the regularity is essential. But this kind of filter has larger stopband peak errors compared with a minimax filter of the same length. In this paper, peak errors in stopband of R-regular 4th-band filters are reduced by means of superimposing two filters with successive regularities. Then the stopband peak errors in the resulting filters are compared with the original ones. The results show that the stopband peak errors are reduced significantly in the synthesized filter that has the same length as the longer one of the two original filters, at the cost of regularity.

Irregular Low-Density Parity-Check (LDPC) codes generally achieve better performance than regular LDPC codes at low Eb/N0 values. They have, however, higher error floors than regular LDPC codes. With respect to the construction of the irregular LDPC code, it can achieve the trade-off between the performance degradation of low Eb/N0 region and lowering error floor. It is known that a decoding algorithm can achieve very good performance if it combines the Ordered Statistic Decoding (OSD) algorithm and the Log Likelihood Ratio-Belief Propagation (LLR-BP) decoding algorithm. Unfortunately, all the codewords obtained by the OSD algorithm satisfy the parity check equation of the LDPC code. While we can not use the parity check equation of the LDPC code to stop the decoding process, the wrong codeword that satisfies the parity check equation raises the error floor. Once a codeword that satisfies the parity check equation is generated by the LLR-BP decoding algorithm, we regard that codeword as the final estimate and halt decoding; the OSD algorithm is not performed. In this paper, we propose a new encoding/decoding scheme to lower the error floor created by irregular LDPC codes. The proposed encoding scheme encodes information bits by Cyclic Redundancy Check (CRC) and LDPC code. The proposed decoding scheme, which consists of the LLR-BP decoding, CRC check, and OSD decoding, detects errors in the codewords obtained by the LLR-BP decoding algorithm and the OSD decoding algorithm using the parity check equations of LDPC codes and CRC. Computer simulations show that the proposed encoding/decoding scheme can lower the error floor of irregular LDPC codes.

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, we propose a technique for detection and real-time tracking of moving targets. This uses a color segmentation algorithm robust to irregular illumination variation and a line-based tracker. The former is based on statistical representation of a color. And, we can obtain a real-time property for detection and tracking of moving targets from the latter.

This paper presents a multi-resolution optical flow estimation method that is robust against large variation in the estimation parameter. For each level solution of the multi-grid estimation, a nonlinear iteration is proposed differently from the existing method, where the incremental displacement from the coarser level optical flow is calculated by linear iteration. The experimental results show that the proposed scheme has better error-performance in a much wider range of regularization parameters.

In this paper, we provide explicit representations of average weight and stopping set distributions and asymptotic expressions of their exponent for detailedly represented irregular LDPC code ensembles. Further we present numerical examples which compare a detailedly represented irregular LDPC code ensemble with a conventional one with respect to both of weight and stopping set distributions.

In this paper, we propose a method for constructing quasi-cyclic low-density parity-check codes randomly using cyclic shift submatrices on the basis of the girth of the Tanner graphs of these codes. We consider (3, K)-regular codes and first derive the necessary and sufficient conditions for weight-4 and weight-6 codewords to exist. On the basis of these conditions, it is possible to estimate the probability that a random method will generate a (3, K)-regular code with a minimum distance less than or equal to 6, and the proposed method is shown to offer a lower probability than does conventional random construction. Simulation results also show that it is capable of generating good codes both regular and irregular.

The proposed DOA (Direction Of Arrival) estimation method by integrating the frequency array data generated from microphone pairs in an equilateral-triangular microphone array is extended here. The method uses four microphones located at the apices of regular tetrahedron to enable to estimate the elevation angle from the array plane as well. Furthermore, we introduce an idea for separate estimation of azimuth and elevation to reduce the computational loads.

In this paper we propose a parallel and distributed computation of genetic local search with irregular topology in distributed environments. The scheme we propose in this paper is implemented with a tree topology established on an irregular network where each computing element carries out genetic local search on its own chromosome set and communicates with its parent when the best solution of each generation is updated. We evaluate the proposed algorithm by a simulation system implemented on a PC-cluster. We test our algorithm on four types topologies: star, line, balanced binary tree and sided binary tree, and investigate the influence of communication topology and delay on the evolution process.

In this paper, we investigate how to initialise cellular automata implemented in the hyperbolic plane. We generalise a technique which was indicated in to the case of any rectangular regular grid of the hyperbolic plane. This allows us to construct the initial configuration of any cellular automaton belonging to a rather large class of problems.

Richardson and Urbanke developed a powerful method density evolution which determines, for various channels, the capacity of irregular low-density parity-check code ensembles. We develop generalized density evolution for minutely represented ensembles and show it includes conventional representation as a special case. Furthermore, we present an example of code ensembles used over binary erasure channel and binary input additive white Gaussian noise channel which have better thresholds than highly optimized ensembles with conventional representation.

In most cases of distributed memory computations, node programs are executed on processors according to the owner computes rule. However, owner computes rule is not best suited for irregular application codes. In irregular application codes, use of indirection in accessing left hand side array makes it difficult to partition the loop iterations, and because of use of indirection in accessing right hand side elements, we may reduce total communication by using heuristics other than owner computes rule. In this paper, we propose a communication cost reduction computes rule for irregular loop partitioning, called least communication computes rule. We partition a loop iteration to a processor on which the minimal communication cost is ensured when executing that iteration. Then, after all iterations are partitioned into various processors, we give global vs. local data transformation rule, indirection arrays remapping and communication optimization methods. The experimental results show that, in most cases, our approaches achieved better performance than other loop partitioning rules.

The current paper presents an effective deblocking algorithm for block-based coded images using singularity detection in a wavelet transform. Blocking artifacts appear periodically at block boundaries in block-based coded images. The local maxima of a wavelet transform modulus detect all singularities, including blocking artifacts, from multiscale edges. Accordingly, the current study discriminates between a blocking artifact and an edge by estimating the Lipschitz regularity of the local maxima and removing the wavelet transform modulus of a blocking artifact that has a negative Lipschitz regularity exponent. Experimental results showed that the performance of the proposed algorithm was objectively and subjectively superior.

We propose a scheme for the design of irregular low-density parity-check (LDPC) codes based on Euclidian Geometry using Latin square matrices of random sequence. Our scheme is a deterministic method that allows the easy design of good irregular LDPC codes for any code rate and degree distribution. We optimize the LDPC codes using the Gaussian approximation method. A Euclidean Geometry LDPC code (EG-LDPC) is used as the basis for the construction of an irregular LDPC code. The base EG-LDPC code is extended by splitting rows and columns using a table of Latin square matrices of random sequence to determine the edges along which to split. We provide simulation results for codes constructed in this manner evaluated in terms of bit error rate (BER) performance in AWGN channels. We believe that our scheme is superior in terms of computational requirements and resulting BER performance in comparison to creation of irregular LDPC codes by means of random construction using a search algorithm to exclude cycles of length four.

Due to the rapid development of computer and information technology, 3D modeling and rendering capabilities are becoming increasingly important in many applications, including industrial design, architecture, CAD/CAM, video games, and medical imaging. Since 3D mesh models often have huge amounts of the data, it is time-consuming to retrieve from a storage device or to download from the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display the model, even when the user is interested only in a low-resolution version of the model. Therefore, progressive coding that enables multiresolution transmission of 3D models is desired. In this paper, we propose the progressive coding scheme of 3D meshes with texture, in which we convert irregular meshes to semi-regular using texture coordinates, map them on planes, and apply 2D image coding algorithm to mesh compression. As our method uses the wavelet transform, the encoded bitstream has a progressive nature. We gain high compression rate with the same visual quality as original models.

Wavelength division multiplexed (WDM) routed optical networks represent the direction towards future high-capacity wide-area network applications. A serious issue in WDM-routed networks, though, is light-path allocation which requires a combination of optical routing and wavelength assignment. While near-optimal-routing and wavelength-assignment algorithms aimed at minimizing network wavelength requirements have been reported, the practicability of wavelength-routed optical networks depends on the number of wavelengths required to satisfy a given traffic demand. In this paper, we proposed two symmetrical routing and wavelength-assignment methods for optical networks with a Grid or ShuffleNet physical topology. Here, we consider the case of non-adaptive wavelength routing systems, where the operations performed in nodes are independent of the network traffic load. In this case, the routing differs somewhat from that in adaptive routing networks where the routing function may produce different results at different times. The path followed by a wavelength never changes in non-adaptive wavelength-routing networks. When all N(N-1) node-pairs are to be connected, our methods lower the wavelength requirement to (or close to) its calculated minimum. Symmetry is a basic feature of both these regular topologies, but there are differences in the features within the topologies. Our goal has been to try to make use of the symmetry, and the differences in the native symmetry features, of these regular topologies to yield a lower wavelength requirement.

The class NQP was proposed as the class of problems that are solvable by non-deterministic quantum Turing machines in polynomial time. In this paper, we introduce non-deterministic quantum finite automata in which the same non-determinism as in non-deterministic quantum Turing machines is applied. We compare non-deterministic quantum finite automata with the classical counterparts, and show that (unlike the case of classical finite automata) the class of languages recognizable by non-deterministic quantum finite automata properly contains the class of all regular languages.

We examine diagnosis of processor array systems formed as two-dimensional grids, with boundaries, and either four or eight neighbors for each interior processor. We employ a parallel test schedule. Neighboring processors test each other and report the results. Our diagnostic objective is to find a fault-free processor or set of processors. The system may then be sequentially diagnosed by repairing those processors tested faulty according to the identified fault-free set. We establish an upper bound on the maximum number of faults that can be sustained without invalidating the test results under worst case conditions. We give test schedules and diagnostic algorithms that meet the upper bound as far as the highest order term. We compare these near optimal diagnostic algorithms to alternative algorithms--both new and already in the literature.

This paper presents a fast inversion method for electromagnetic imaging of cylindrical dielectric objects with the optimal regularization parameter used in the Levenberg-Marquardt method. A novel procedure for choosing the optimal regularization parameter is proposed. The method of moments with pulse-basis functions and point matching is applied to discretize the equations for the scattered electric field and the total electric field inside the object. Then the inverse scattering problem is reduced to solving the matrix equation for the unknown expansion coefficients of a contrast function, which is represented as a function of the relative permittivity of the object. The matrix equation may be solved in the least-squares sense with the Levenberg-Marquardt method. Thus the contrast function can be reconstructed by the minimization of a functional, which is expressed as the sum of a standard error term on the scattered electric field and an additional regularization term. While a regularization parameter is usually chosen according to the generalized cross-validation (GCV) method, the optimal one is now determined by minimizing the absolute value of the radius of curvature of the GCV function. This scheme is quite different from the GCV method. Numerical results are presented for a circular cylinder and a stratified circular cylinder consisting of two concentric homogeneous layers. The convergence behaviors of the proposed method and the GCV method are compared with each other. It is confirmed from the numerical results that the proposed method provides successful reconstructions with the property of much faster convergence than the conventional GCV method.

This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.