One approach to accommodating IP traffic on a wavelength division multiplexing (WDM) network is to construct a logical topology, establishing a set of lightpaths between nodes. The lightpaths carry IP traffic but do not require any electronic packet processing at intermediate nodes, thereby reducing the load on those nodes. When the IP and WDM networks have independent routing functions, however, the lightpaths in the WDM network may not be fully utilized by the IP router. It is therefore necessary to integrate the two routing mechanisms in order to utilize resources efficiently and adapt to changes in traffic. In this paper, we propose an integrated routing mechanism for IP over WDM networks. The key idea is to first prepare a set of virtual-links representing the lightpaths that can be established by the WDM network, then calculate the minimum cost route on an IP network including those links. Our simulation results show that when traffic patterns do not change, the throughput of our method is almost the same as that of a logical topology optimally designed for a given traffic demand. When traffic patterns change, the throughput of our method is about 50% higher than that of the logical topology.

A total coloring of a graph G is a coloring of all elements of G, i.e. vertices and edges, such that no two adjacent or incident elements receive the same color. Let L(x) be a set of colors assigned to each element x of G. Then a list total coloring of G is a total coloring such that each element x receives a color contained in L(x). The list total coloring problem asks whether G has a list total coloring for given L. In this paper, we give a sufficient condition for a series-parallel graph to have a list total coloring, and we present a linear-time algorithm to find a list total coloring of a given series-parallel graph G if G and L satisfy the sufficient condition.

We study quantum entanglement by Schmidt decomposition for some typical quantum algorithms. In the Shor's exponentially fast algorithm the quantum entanglement holds almost maximal, which is a major factor that a classical computer is not adequate to simulate quantum efficient algorithms.

We consider the capacitated multi-source multicast tree routing problem (CMMTR) in an undirected graph G=(V,E) with a vertex set V, an edge set E and an edge weight w(e) ≥ 0, e ∈ E. We are given a source set S ⊆ V with a weight g(e) ≥ 0, e ∈ S, a terminal set M ⊆ V-S with a demand function q : M → R+, and a real number κ > 0, where g(s) means the cost for opening a vertex s ∈ S as a source in a multicast tree. Then the CMMTR asks to find a subset S′⊆ S, a partition {Z1,Z2,...,Zl} of M, and a set of subtrees T1,T2,...,Tl of G such that, for each i, ∑t∈Ziq(t) ≤ κ and Ti spans Zi∪{s} for some s ∈ S′. The objective is to minimize the sum of the opening cost of S′and the constructing cost of {Ti}, i.e., ∑s∈S′g(s)+w(Ti), where w(Ti) denotes the sum of weights of all edges in Ti. In this paper, we propose a (2ρUFL+ρST)-approximation algorithm to the CMMTR, where ρUFL and ρST are any approximation ratios achievable for the uncapacitated facility location and the Steiner tree problems, respectively. When all terminals have unit demands, we give a ((3/2)ρUFL+(4/3)ρST)-approximation algorithm.

An adaptive array code acquisition for direct-sequence/code-division multiple access (DS/CDMA) systems was recently proposed to enhance the performance of the conventional correlator-based method. The scheme consists of an adaptive spatial and an adaptive temporal filter, and can simultaneously perform beamforming and code-delay estimation. Unfortunately, the scheme uses a least-mean-square (LMS) adaptive algorithm, and its convergence is slow. Although the recursive-least-squares (RLS) algorithm can be applied, the computational complexity will greatly increase. In this paper, we solve the dilemma with a low-complexity conjugate gradient (LCG) algorithm, which can be considered as a special case of a modified conjugate gradient (MCG) algorithm. Unlike the original conjugate gradient (CG) algorithm developed for adaptive applications, the proposed method, exploiting the special structure inherent in the input correlation matrix, requires a low computational-complexity. It can be shown that the computational complexity of the proposed method is on the same order of the LMS algorithm. However, the convergence rate is improved significantly. Simulation results show that the performance of adaptive array code acquisition with the proposed CG algorithm is comparable to that with the original CG algorithm.

Heterogeneous distributed computing environments are well suited to meet the fast increasing computational demands. Task scheduling is very important for a heterogeneous distributed system to satisfy the large computational demands of applications. The performance of a scheduler in a heterogeneous distributed system normally has something to do with the dynamic task flow, that is, the scheduler always suffers from the heterogeneity of task sizes and the variety of task arrivals. From the long-term viewpoint it is necessary and possible to improve the performance of the scheduler serving the dynamic task flow. In this paper we propose a task scheduling method including a scheduling strategy which adapts to the dynamic task flow and a genetic algorithm which can achieve the short completion time of a batch of tasks. The strategy and the genetic algorithm work with each other to enhance the scheduler's efficiency and performance. We simulated a task flow with enough tasks, the scheduler with our strategy and algorithm, and the schedulers with other strategies and algorithms. We also simulated a complex scenario including the variant arrival rate of tasks and the heterogeneous computational nodes. The simulation results show that our scheduler achieves much better scheduling results than the others, in terms of the average waiting time, the average response time, and the finish time of all tasks.

Manually collecting contexts of a target word and grouping them based on their meanings yields a set of word senses but the task is quite tedious. Towards automated lexicography, this paper proposes a word-sense discrimination method based on two modern techniques; EM algorithm and principal component analysis (PCA). The spherical Gaussian EM algorithm enhanced with PCA for robust initialization is proposed to cluster word senses of a target word automatically. Three variants of the algorithm, namely PCA, sGEM, and PCA-sGEM, are investigated using a gold standard dataset of two polysemous words. The clustering result is evaluated using the measures of purity and entropy as well as a more recent measure called normalized mutual information (NMI). The experimental result indicates that the proposed algorithms gain promising performance with regard to discriminate word senses and the PCA-sGEM outperforms the other two methods to some extent.

This paper presents a simple and effective method to further reduce the search points in multilevel successive elimination algorithm (MSEA). Because the calculated sea values of those best matching search points are much smaller than the current minimum SAD, we can simply increase the calculated sea values to increase the elimination ratio without much affecting the coding quality. Compared with the original MSEA algorithm, the proposed strict MSEA algorithm (SMSEA) can provide average 6.52 times speedup. Compared with other lossy fast ME algorithms such as TSS and DS, the proposed SMSEA can maintain more stable image quality. In practice, the proposed technique can also be used in the fine granularity SEA (FGSEA) algorithm and the calculation process is almost the same.

An edge-weighted directed graph is referred to as a network in this paper, and an edge operation is an operation that increases or decreases an edge weight. Decreasing an edge weight from the infinite to a finite value or increasing any edge weight from a finite one to the infinite corresponds to addition or deletion of this edge, respectively. The dynamic shortest path problem (DSPP for short) is defined by "Given any network with a specified vertex (denoted as s), and any sequence of edge operations, construct a shortest path tree of each network obtained by executing those edge operations one by one in the order of the sequence." As an application, fast routing for an interior network using link state protocols, such as OSPF and IS-IS, requires solving DSPP efficiently. In this paper, among as many existing algorithms as possible, including those which execute several edge operations simultaneously, fundamental and/or important algorithms are implemented and their capability is evaluated based on the results of computational experiments.

MC-CDMA is an attractive multi-access method for the next generation high-speed mobile communication systems. The uplink transmission performance is limited by the multi-access interference (MAI) from other users since all users share the same bandwidth. Adaptive antenna array can be used to suppress the MAI and to improve the uplink transmission performance. In this paper, we propose a frequency-domain adaptive antenna array for multi-code MC-CDMA. The proposed frequency-domain adaptive antenna array uses a simple normalized LMS (NLMS) algorithm. Although the NLMS algorithm is used, very fast weight convergence within one MC-CDMA symbol duration is achieved since the weight updating is possible as many times as the number of subcarriers within one MC-CDMA symbol duration.

For more efficient data transmissions, a new MLP/BP-based channel equalizer is proposed to compensate for multi-path fading in wireless applications. In this work, for better system performance, we apply the soft output and the soft feedback structure as well as the soft decision channel decoding. Moreover, to improve packet error rate (PER) and bit error rate (BER), we search for the optimal scaling factor of the transfer function in the output layer of the MLP/BP neural networks and add small random disturbances to the training data. As compared with the conventional MLP/BP-based DFEs and the soft output MLP/BP-based DFEs, the proposed MLP/BP-based soft DFEs under multi-path fading channels can improve over 3-0.6 dB at PER=10-1 and over 3.3-0.8 dB at BER=10-3.

Recently, research on parallel processing systems is very active, and many complex topologies have been proposed. A burnt pancake graph is one such topology. In this paper, we prove that a faulty burnt pancake graph with degree n has a fault-free Hamiltonian cycle if the number of the faulty elements is n-2 or less, and it has a fault-free Hamiltonian path between any pair of nonfaulty nodes if the number of the faulty elements is n-3 or less.

We propose a gradient-limited affine projection algorithm (GL-APA), which can achieve fast and double-talk-robust convergence in acoustic echo cancellation. GL-APA is derived from the M-estimation-based nonlinear cost function extended for evaluating multiple error signals dealt with in the affine projection algorithm (APA). By considering the nonlinearity of the gradient, we carefully formulate an update equation consistent with multiple input-output relationships, which the conventional APA inherently satisfies to achieve fast convergence. We also newly introduce a scaling rule for the nonlinearity, so we can easily implement GL-APA by using a predetermined primary function as a basis of scaling with any projection order. This guarantees a linkage between GL-APA and the gradient-limited normalized least-mean-squares algorithm (GL-NLMS), which is a conventional algorithm that corresponds to the GL-APA of the first order. The performance of GL-APA is demonstrated with simulation results.

Low-density parity-check (LDPC) codes are one of the most powerful error correcting codes and are attracting much attention these days. LDPC codes are promising for communications and broadcasting as well where the use of error correcting codes are essential. LDPC codes have been standardized in some communication standards, such as, IEEE802.16e, DVB-S2, IEEE802.3an (10BASE-T), and so on. The performance of LDPC codes largely depend on their code structure and decoding algorithm. In this paper, we present the basics of LDPC codes and their decoding algorithms. We also present some LDPC codes that have good performance and are receiving much attention particularly in communication systems. We also overview some standardized LDPC codes, the LDPC codes standardized in DVB-S2 and the IEEE802.16e standard LDPC codes. Moreover, we present some research on LDPC coded MIMO systems and HARQ using LDPC codes.

An optimization model with maximum ratio combining (MRC) diversity soft handover is proposed for WCDMA base station location planning with heuristic algorithm, which can calculate the influence of MRC diversity soft handover directly in the process of base station location planning. Experimental results show that the proposed model can get better capacity and coverage performance in the planning results than the traditional optimization model without MRC diversity.

Privacy is increasingly viewed as a key concern in multi-agent based algorithms for Distributed Constraint Satisfaction Problems (DCSP) such as the Meeting Scheduling (MS) problem. Many algorithms aim for a global objective function and as a result, incur performance penalties in computational complexity and personal privacy. This paper describes a mobile agent-based scheduling scheme called Efficient and Privacy-aware Meeting Scheduling (EPMS), which results in a tradeoff among complexity, privacy, and global utility. It also introduces a privacy loss model for collaborative computation, multiple criteria for evaluating privacy in the MS problem, and a privacy measurement metric called the Min privacy metric. We have utilized a common computational space in EPMS for reducing the complexity and the privacy loss in the MS problem. The analytical results show that EPMS has a polynomial time computational complexity. In addition, simulation results show that the obtained global utility for scheduling multiple meetings with EPMS is close to the optimal level and the resulting privacy loss is less than for those in existing algorithms.

Localization of mobile terminals has received considerable attention in wireless communications. In this letter, we present a covariance shaping least squares (CSLS) estimator using time-of-arrival measurements of the signal from the mobile station received at three or more base stations. It is shown that the CSLS estimator yields better performance than the other LS estimators at low signal-to-noise ratio conditions.

Ultra-wideband pulse radars are promising candidates for 3-dimensional environment measurements by autonomous robots. Estimating 3-dimensional target shapes by scanning with an omni-directional antenna is an ill-posed inverse problem. Conventional algorithms such as the synthetic aperture method or parametric algorithms have a problem in terms of their calculation times. We have clarified the existence of a reversible transform between received data and target shapes for 3-dimensional systems. Calculation times are remarkably reduced by applying this transform because it directly estimates target shapes without iterations. We propose a new algorithm based on the transform and present an application example using numerical simulations. We confirm that the proposed algorithm has sufficient accuracy and a short calculation time.

An iterative reconstruction algorithm of accelerating the estimation of the complex relative permittivity of a cylindrical dielectric object based on the multigrid optimization method (MGOM) is presented. A cost functional is defined by the norm of a difference between the scattered electric fields measured and calculated for an estimated contrast function, which is expressed as a function of the complex relative permittivity of the object. Then the electromagnetic inverse scattering problem can be treated as an optimization problem where the contrast function is determined by minimizing the cost functional. We apply the conjugate gradient method (CGM) and the frequency-hopping technique (FHT) to the minimization of the cost functional, and also employ the multigrid method (MGM) with a V-cycle to accelerate the rate of convergence for getting the reconstructed profile. The reconstruction scheme is called the multigrid optimization method. Computer simulations are performed for lossy and inhomogeneous dielectric circular cylinders by using single-frequency or multifrequency scattering data. The numerical results demonstrate that the rate of convergence of the proposed metod is much faster than that of the conventional CGM for both noise-free and noisy cases.

We present theoretical foundations about error estimations of the global Krylov subspace techniques for multiple-inputs multiple-outputs (MIMO) Interconnect reductions. Analytical relationships between Lyapunov functions of the original interconnect network and those of the reduced system generated by the global Arnoldi algorithm will be developed. Under this framework, a new moment matching reduced network is proposed. Also, we will show that the reduced system can be expressed as the original network with some additive perturbations.