In this letter, iterative sequence estimation technique based on expectation-maximization (EM) algorithm is considered for quadrature amplitude modulation (QAM)-orthogonal frequency division multiplexing (OFDM) signals. For QAM-OFDM signaling, the optimal EM algorithm requires high computational complexity due to the inversion of complex matrix executed at each iteration. To avoid this problem, we propose a sub-optimal iterative sequence estimation algorithm with some approximations, which results in reduced computational complexity for QAM-OFDM signals. Moreover, we use two different approaches to obtain initial estimate for beginning iteration of proposed algorithm. One is for less time-dispersive but fast fading channel and the other is for highly time-dispersive but relatively slow fading channel. The bit error rate (BER) performances of the proposed algorithm are evaluated using computer simulations. The results show that the proposed algorithm performs nearly as well as the optimal EM algorithm.

A new algorithm for the LogMAP decoding of linear block codes is considered. The decoding complexity is evaluated analytically and by computer simulation. The proposed algorithm is an improvement of the recursive LogMAP algorithm proposed by the authors. The recursive LogMAP algorithm is more efficient than the BCJR algorithm for low-rate codes, but the complexity grows considerably large for high-rate codes. The aim of the proposed algorithm is to solve the complexity explosion of the recursive LogMAP algorithm for high-rate codes. The proposed algorithm is more efficient than the BCJR algorithm for well-known linear block codes.

The IEEE 802.11 WLAN standards adopt CSMA/CA protocol with a backoff algorithm as medium access control technique. When the number of stations which attempt to access a network increases, the throughput efficiency of the standard goes down. In this paper, we propose a modified backoff algorithm which adaptively selects the Contention Window (CW) size according to the variation of the number of contending stations and present the results of simulation analysis.

In this paper, we present the results of a comparative analysis of Atomic Broadcast algorithms. The analysis was done by using an analytical method to compare the performance of five different classes of Atomic Broadcast algorithms. The five classes of Atomic Broadcast algorithms are determined by the mechanisms used by the algorithms to define the delivery order. To evaluate the performance of algorithms, the analysis relies on contention-aware metrics to provide a measure for both their latency and their throughput. The results thus obtained yield interesting insight into the performance tradeoffs of different Atomic Broadcast algorithms, thus providing helpful information to algorithms and systems designers.

Because most link-state routing protocols, such as OSPF and IS-IS, calculate routes using the Dijkstra algorithm, which poses scalability problems, implementors often introduce an artificial delay to reduce the number of route calculations. Although this delay directly affects IP packet forwarding, it can be acceptable when the network topology does not change often. However, when the topology of a network changes frequently, this delay can lead to a complete loss of IP reachability for the affected network prefixes during the unstable period. In this paper, we propose the Cached Shortest-path Tree (CST) approach, which speeds up intra-domain routing convergence without extra execution of the Dijkstra algorithm, even if the routing for a network is quite unstable. The basic idea of CST is to cache shortest-path trees (SPTs) of network topologies that appear frequently, and use these SPTs to instantly generate a routing table when the topology after a change matches one in the caches. CST depends on a characteristic that we found from an investigation of routing instability conducted on the WIDE Internet in Japan. That is, under unstable routing conditions, both frequently changing Link State Advertisements (LSAs) and their instances tend to be limited. At the end of this paper, we show CST's effectiveness by a trace-driven simulation.

Given an edge-weighted graph G, the minimum maximal matching problem asks to find a minimum weight maximal matching. The problem is known to be NP-hard even if the graph is planar and unweighted. In this paper, we consider the problem in planar graphs. First, we prove a strong inapproximability for the problem in weighted planar graphs. Second, in contrast with the first result, we show that a polynomial time approximation scheme (PTAS) for the problem in unweighted planar graphs can be obtained by a divide-and-conquer method based on the planar separator theorem. For a given ε > 0, our scheme delivers in time a solution with size at most (1 + ε) times the optimal value, where n is the number of vertices in G and α is a constant number.

The High-Low Water Mark destage (HLWM) algorithm is widely used to enable cached RAID5 to flush dirty data from its write cache to disks due to the simplicity of its operations. It starts and stops a destaging process based on the two thresholds that are configured at the initialization time with the best knowledge of its underlying storage performance capability and its workload pattern which includes traffic intensity, access patterns, etc. However, each time the current workload varies from the original, the thresholds need to be re-configured with the changed workload. This paper proposes an efficient destage algorithm which automatically re-configures its initial thresholds according to the changed traffic intensity and access patterns, called adaptive thresholding. The core of adaptive thresholding is to define the two thresholds as the multiplication of the referenced increasing and decreasing rates of the write cache occupancy level and the time required to fill and empty the write cache. We implement the proposed algorithm upon an actual RAID system and then verify the ability of the auto-reconfiguration with synthetic workloads having a different level of traffic intensity and access patterns. Performance evaluations under well-known traced workloads reveal that the proposed algorithm reduces disk IO traffic by about 12% with a 6% increase in the overwrite ratio compared with the HLWM algorithm.

A burnt pancake graph is a variant of Cayley graphs and its topology is suitable for massively parallel systems. However, for a burnt pancake graph, there is much room for further research. Hence, in this study, we focus on n-burnt pancake graphs and propose an algorithm to obtain n disjoint paths from a source node to n destination nodes in polynomial order time of n, n being the degree of the graph. In addition, we estimate the time complexity of the algorithm and the sum of path lengths. We also give a proof of correctness of the algorithm. Moreover, we report the results of computer simulation to evaluate the average performance of the algorithm.

One conventional technique for source localization is to utilize the time-difference-of-arrival (TDOA) measurements of a signal received at spatially separated sensors. A simple TDOA-based location algorithm that combines the advantages of two efficient positioning methods is developed. It is demonstrated that the proposed approach can give optimum performance in geolocation via satellites at different noise conditions.

We propose an efficient algorithm for making multi-layered neural networks (MLN) fault-tolerant to all multiple weight faults in a multi-dimensional interval by injecting intentionally two extreme multi-dimensional values in the interval into the weights of the selected multiple links in a learning phase. The degree of fault-tolerance to a multiple weight fault is measured by the number of essential multiple links. First, we analytically discuss how to choose effectively the multiple links to be injected, and present a learning algorithm for making MLNs fault tolerant to all multiple (i.e., simultaneous) faults in the interval defined by two multi-dimensional extreme points. Then it is proved that after the learning algorithm successfully finishes, MLNs become fault tolerant to all multiple faults in the interval. It is also shown that the time in a weight modification cycle depends little on multiplicity of faults k for small k. These are confirmed by simulation.

In this paper, an efficient optimization algorithm for solving the inverse problem of a two-dimensional lossless homogeneous dielectric object is investigated. A lossless homogeneous dielectric cylinder of unknown permittivity scatters the incident wave in free space and the scattered fields are recorded. Based on the boundary condition and the incident field, a set of nonlinear surface integral equation is derived. The imaging problem is reformulated into optimization problem and the steady-state genetic algorithm is employed to reconstruct the shape and the dielectric constant of the object. Numerical results show that the permittivity of the cylinders can be successfully reconstructed even when the permittivity is fairly large. The effect of random noise on imaging reconstruction is also investigated.

The propagator method (PM) belongs to a class of subspace based methods for direction-of-arrival estimation which only requires linear operations but does not involve any eigendecomposition or singular value decomposition as in common subspace techniques. In this paper, we apply the PM for estimating the frequencies of multiple real sinusoids in noise and a computationally simple as well as high resolution multiple frequency estimation algorithm is developed. The estimation accuracy of the proposed method is contrasted with the conventional MUSIC and Cramer-Rao lower bound under different noise conditions.

The boundary element method (BEM), a representative method of numerical calculation of electromagnetic wave scattering, has been used for solving boundary integral equations. Using BEM, however, we finally have to solve a linear system of L equations expressed by dense coefficient matrix. The floating-point operation is O(L2) due to a matrix-vector product in iterative process. Greengard-Rokhlin's fast multipole algorithm (GRFMA) can reduce the operation to O(L). In this paper, we describe GRFMA and its floating-point operation theoretically. Moreover, we apply the fast Fourier transform to the calculation processes of GRFMA. In numerical examples, we show the experimental results for the computation time, the amount of used memory and the relative error of matrix-vector product expedited by GRFMA. We also discuss the convergence and the relative error of solution obtained by the BEM with GRFMA.

A tree 3-spanner T of a graph G is a spanning tree of G such that the distance between any two vertices in T is at most 3 times of their distance in G. Madanlal et al. have presented an O(n + m) time algorithm for finding a tree 3-spanner of a permutation graph. However, the complexity of their algorithm is not optimal, and their algorithm can not be easily parallelized. In this paper, we will propose an improved algorithm to solve the same problem in O(n) time. Moreover, our algorithm can be easily parallelized so that a tree 3-spanner of a permutation graph can be found in O(log n) time with processors on the EREW PRAM computational model.

In this letter, we propose the Low-Density Parity-Check (LDPC) coded Orthogonal Frequency Division Multiplexing (OFDM) systems to improve the error rate performance of OFDM. We also evaluate the iterative decoding performance on both an AWGN and a frequency-selective fading channels. We show that when the energy per information bit to the noise power spectral density ratio Eb/N0 is not small, the LDPC coded OFDM (LDPC-COFDM) systems have the good error rate performance with a small number of iterations. We also show that when the Eb/N0 is small, the BER of the LDPC-COFDM systems is worse than that of the Turbo coded OFDM (TCOFDM) systems, while when the Eb/N0 is not small, the BER of the LDPC-COFDM systems is better with a small number of iterations.

Finding a nonnegative integer solution x for Ax = b (A Zmn, b Zm1) in Petri nets is NP-complete. Being NP-complete, even algorithms with theoretically bad worst case and with average complexity can be useful for a special class of problems, hence deserve investigation. Then a Grobner basis approach to integer programming problems was proposed in 1991 and some symbolic computation systems became to have useful tools for ideals, varieties, and algorithms for algebraic geometry. In this letter, Grobner basis approach is applied to three typical problems with respect to state equation in P/T Petri nets. In other words, after Grobner bases are derived by the tool Maple 7, we consider how to derive the T-invariants and particular solutions of the Petri nets by using them in this letter.

OFDM is a good scheme to transmit high rate data under a multi-path environment. With a sufficiently long guard interval (GI), it is possible to totally eliminate interference between symbols or carriers with OFDM. However, long guard intervals reduce the spectrum efficiency of OFDM. Thus, shortening the guard interval as much as possible is highly desirable. As short guard intervals will usually result in interference in an OFDM system, an interference canceller would be necessary to enable the use of short guard intervals without unduly degrading system performance. This paper presents a possible adaptive interference cancellation scheme for OFDM to help attain this objective.

Genetic algorithm (GA) is a widely used numerical technique to simplify some analytical solutions in electromagnetic theory. Genetic algorithms can be combined with the geometric optics method to tackle electromagnetic scattering problems. This paper presents an extrapolation procedure, which derived, as a first step, a functional representation of the radar cross section (RCS) of three different dielectric objects that was computed via the Mie solution or the method of moments (MOM). An algorithm was employed to fit the scattering characteristics of dielectric objects at high frequencies.

Turbo coding is a powerful coding technique that can provide highly reliable data transmission at extremely low signal-to-noise ratios. Owing to the computational complexity of the employed decoding algorithm, the realization of turbo decoders usually takes a large amount of memory space and potentially long decoding delay. Therefore, an efficient memory management strategy becomes one of the key factors toward successfully implementing turbo decoders. This paper focuses on the development of general structures for efficient memory management of turbo decoders employing the sliding-window (Log-) MAP algorithm. Three different structures and the associated mathematic representations are derived to evaluate the required memory size, average decoding rate, and latency based on the speed and the number of the adopted processors. Comparative results show the dependency of the resulting performance based on a set of parameters; thus provide useful and general information on practical implementations of turbo decoders.

The adaptive cross-spectral (ACS) technique recently introduced by Okuno et al. provides an attractive solution to acoustic echo cancellation (AEC) as it does not require double-talk (DT) detection. In this paper, we first introduce a generalized ACS (GACS) technique where a step-size parameter is used to control the magnitude of the incremental correction applied to the coefficient vector of the adaptive filter. Based on the study of the effects of the step-size on the GACS convergence behaviour, a new variable step-size ACS (VSS-ACS) algorithm is proposed, where the value of the step-size is commanded dynamically by a special finite state machine. Furthermore, the proposed algorithm has a new adaptation scheme to improve the initial convergence rate when the network connection is created. Experimental results show that the new VSS-ACS algorithm outperforms the original ACS in terms of a higher acoustic echo attenuation during DT periods and faster convergence rate.