In this letter, we propose a simplified step-by-step decoding algorithm for t-error-correcting binary Bose-Chaudhuri- Hocquenghem (BCH) codes based on logical analysis. Compared to the conventional step-by-step decoding algorithm, the computation complexity of this decoder is much less, since it significantly reduces the matrix calculation and the operations of multiplication.

The authors realize a 50% length reduction of short-slot couplers in a post-wall dielectric substrate by two techniques. One is to introduce hollow rectangular holes near the side walls of the coupled region. The difference of phase constant between the TE10 and TE20 propagating modes increases and the required length to realize a desired dividing ratio is reduced. Another is to remove two reflection-suppressing posts in the coupled region. The length of the coupled region is determined to cancel the reflections at both ends of the coupled region. The total length of a 4-way Butler matrix can be reduced to 48% in comparison with the conventional one and the couplers still maintain good dividing characteristics; the dividing ratio of the hybrid is less than 0.1 dB and the isolations of the couplers are more than 20 dB.

Radio resource is the bottleneck for current multimedia wireless networks. Intelligent traffic control strategies can be enforced to optimize resource allocation so as to enhance network performance. In this study, dynamic control scheme for non-real-time traffic and autonomic control schemes for multimedia traffic are proposed to guarantee the required quality of service (QoS) in the inference-dominated high-speed wireless environment. Both handoff priority and terminal mobility are also taken into consideration. The performance of the state-dependent multidimensional birth-death process is derived by the efficient matrix-analytic methods (MAMs). Compared with the previous results, this paper shows that the proposed control methods can be used for both real-time and non-real-time multimedia traffic in order to meet the required performance without degrading the quality of multimedia services. These results are also important for the design of evolving multimedia wireless systems as well as network optimization.

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 many electromagnetic field problems, matrix equations were always deduced from using the method of moment. Among these matrix equations, some of them might require a large amount of computer memory storage which made them unrealistic to be solved on a personal computer. Virtually, these matrices might be too large to be solved efficiently. A fast algorithm based on a Toeplitz matrix solution was developed for solving a bordered Toeplitz matrix equation arising in electromagnetic problems applications. The developed matrix solution method can be applied to solve some electromagnetic problems having very large-scale matrices, which are deduced from the moment method procedure. In this paper, a study of a computationally efficient order-recursive algorithm for solving the linear electromagnetic problems [Z]I = V, where [Z] is a Toeplitz matrix, was presented. Upon the described Toeplitz matrix algorithm, this paper derives an efficient recursive algorithm for solving a bordered Toeplitz matrix with the matrix's major portion in the form of a Toeplitz matrix. This algorithm has remarkable advantages in reducing both the number of arithmetic operations and memory storage.

Convergence of the iterative method based on the successive overrelaxation (SOR) method is investigated to solve the matrix equation in the moment analysis of array antennas. It is found this method can be applied to the sub domain method of moments with fast convergence if the grouping technique is applied and the over-relaxation parameter is properly selected, and the computation time for solving the matrix equation can be reduced to be almost proportional to the second power of the number of unknowns.

L-convex functions are nonlinear discrete functions on integer points that are computationally tractable in optimization. In this paper, a discrete Hessian matrix and a local quadratic expansion are defined for L-convex functions. We characterize L-convex functions in terms of the discrete Hessian matrix and the local quadratic expansion.

This paper proposes a new method of estimating real-time traffic matrices that only incurs small errors in estimation. A traffic matrix represents flows of traffic in a network. It is an essential tool for capacity planning and traffic engineering. However, the high costs involved in measurement make it difficult to assemble an accurate traffic matrix. It is therefore important to estimate a traffic matrix using limited information that only incurs small errors. Existing approaches have used IP-related information to reduce the estimation errors and computational complexity. In contrast, our method, called spike flow measurement (SFM) reduces errors and complexity by focusing on spikes. A spike is transient excessive usage of a communications link. Spikes are easily monitored through an SNMP framework. This reduces the measurement costs compared to that of other approaches. SFM identifies spike flows from traffic byte counts by detecting pairs of incoming and outgoing spikes in a network. A matrix is then constructed from collected spike flows as an approximation of the real traffic matrix. Our experimental evaluation reveals that the average error in estimation is 28%, which is sufficiently small for the method to be applied to a wide range of network nodes, including Ethernet switches and IP routers.

This paper presents the selective orthogonal matrix least-squares (SOM-LS) method for representing a multiport network characterized by sampled data with the rational matrix, improving the previous works, and providing new criteria. Recently, it is needed in a circuit design to evaluate physical effects of interconnects and package, and the evaluation is done by numerical electromagnetic analysis or measurement by network analyzer. Here, the SOM-LS method with the criteria will play an important role for generating the macromodels of interconnects and package in circuit simulation level. The accuracy of the macromodels is predictable and controllable, that is, the SOM-LS method fits the rational matrix to the sampled data, selecting the dominant poles of the rational matrix. In examples, simple PCB models are analyzed, where the rational matrices are described by Verilog-A, and some simulations are carried out on a commercial circuit simulator.

In this paper, we describe an accelerative current-programming method for active matrix OLED (AM-OLED) display. This new method uses common source configuration, "Acceleration Control" line and some mechanisms to prevent the programming current from flowing through OLED device. It would solve the basic problem of the current-programming pixel circuit: a long programming period, especially at the dark gray-level. The proposed method accelerates the current programming process at any gray levels, and it would be the solution for the problem.

In this letter, a method to construct good binary and quaternary error correcting codes, called complex Hadamard codes, based on a complex Hadamard matrix is presented. The related properties of the codes are analyzed. In addition, through the operation in Z4 domain, a new simplex soft-decision decoding algorithm for the complex Hadamard codes is also proposed.

A low-complexity step-by-step decoding algorithm for t-error-correcting binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed. Using logical analysis, we obtained a simple rule which can directly determine whether a bit in the received word is correct. The computational complexity of this decoder is less than the conventional step-by-step decoding algorithm, since it reduces at least half of the matrix computations and the most complex element in the conventional step-by-step decoder is the "matrix-computing" element.

This paper presents a fuzzy model-based approach for synchronization of time-delay chaotic system with input saturation. Time-delay chaotic drive and response system is respectively represented by Takagi-Sugeno (T-S) fuzzy model. Specially, the response system contains input saturation. Using the unidirectional linear error feedback and the parallel distributed compensation (PDC) scheme, we design fuzzy chaotic synchronization system and analyze local stability for synchronization error dynamics. Since time-delay in the transmission channel always exists, we also take it into consideration. The sufficient condition for the local stability of the fuzzy synchronization system with input saturation and channel time-delay is derived by applying Lyapunov-Krasovskii theory and solving linear matrix inequalities (LMI's) problem. Numerical examples are given to demonstrate the validity of the proposed approach.

In this study, a fourth-order cumulants based iterative algorithm for blind channel equalization is introduced, which is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple reordering and scaling. In simulation studies, both a closed-form and a stochastic version of the proposed algorithm are tested with three-ray multi-path channels, and their performances are compared with the methods based on conventional second-order statistics and higher-order statistics (HOS) as well. Relatively good results with fast convergence speed are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

In this paper, the previous definition of the Reverse Jacket matrix (RJM) is revised and generalized. In particular, it is shown that the inverse of the RJM can be obtained easily by a constructive approach similar to that used for the RJM itself. As new results, some useful properties of RJMs, such as commutativity and the Hamiltonian symmetry appearing in half the blocks of a RJM, are shown, and also 1-D fast Reverse Jacket transform (FRJT) is presented. The algorithm of the FRJT is remarkably efficient than that of the center-weighted Hadamard transform (CWHT). The FRJT is extended in terms of the Kronecker products of the Hadamard matrix. The 1-D FRJT is applied to the discrete Fourier transform (DFT) with order 4, and the N-point DFT can be expressed in terms of matrix decomposition by using 4 4 FRJT.

The scattering problem by metallic gratings has become one of fundamental problems in electromagnetics. In this paper, a thin metallic grating placed in conical mounting is treated as a lossy dielectric grating expressed by complex permittivity and thickness. The solution of the metallic grating by using the matrix eigenvalue calculations is compared with that of the plane grating by using the resistive boundary condition and the spectral Galerkin procedure, and the availability of the resistive boundary condition for thin metallic gratings in conical mounting is investigated. In order to improve the convergence of the solutions of thin metallic gratings, the spatial harmonics of flux densities which are continuous function instead of electromagnetic fields are used.

In this letter, we propose an iterative decoding with LDPC based unitary matrix modulated OFDM with splitting the diagonal components over the coherence bandwidth. The proposed system can obtain a frequency diversity gain by splitting the diagonal components of unitary matrix modulated symbols, and also obtain large coding gain by using LDPC code.

The multiplication of large spare matrices is a basic operation in many scientific and engineering applications. There exist some high-performance library routines for this operation. They are often optimized based on the target architecture. For a parallel environment, it is essential to partition the entire operation into well balanced tasks and assign them to individual processing elements. Most of the existing techniques partition the given matrices based on some kind of workload estimation. For irregular sparse matrices on PC clusters, however, the workloads may not be well estimated in advance. Any approach other than run-time dynamic partitioning may degrade performance. In this paper, we apply our super-programming approach to parallel large matrix multiplication on PC clusters. In our approach, tasks are partitioned into super-instructions that are dynamically assigned to member computer nodes. Thus, the load balancing logic is separated from the computing logic; the former is taken over by the runtime environment. Our super-programming approach facilitates ease of program development and targets high efficiency in dynamic load balancing. Workloads can be balanced effectively and the optimization overhead is small. The results prove the viability of our approach.

The objective of this research is to compare the performance of the Matrix Pencil Method (MPM) and well known root-MUSIC algorithm for high resolution DOA estimation. Performance of each technique in terms of the probability of resolution and SNR in the presence of noise is investigated. Simulation results show that the MPM has a superior resolution to the root-MUSIC algorithm.

The matrix inequality condition has been considered as the main condition for the stability of RHC. But it is difficult to apply the matrix inequality condition for guaranteeing the stability of any physical system because of the high gain problem brought about the high value of the final state weighting matrix. Therefore, in this study, a new stability condition for RHC is proposed and it extends the range of the final state weighting matrix guaranteeing the stability of RHC in comparison with the case of the matrix inequality condition. The proposed stability condition is based not only on a final state weighting matrix but also on a horizon size and guarantees the stability for other forms of model predictive control just like the matrix inequality condition.