This paper introduces the hardware platform of the structured light processing based on depth imaging to perform a 3D modeling of cluttered workspace for home service robots. We have discovered that the degradation of precision and robustness comes mainly from the overlapping of multiple codes in the signal received at a camera pixel. Considering the criticality of separating the overlapped codes to precision and robustness, we proposed a novel signal separation code, referred to here as "Hierarchically Orthogonal Code (HOC)," for depth imaging. The proposed HOC algorithm was implemented by using hardware platform which applies the Xilinx XC2V6000 FPGA to perform a real time 3D modeling and the invisible IR (Infrared) pattern lights to eliminate any inconveniences for the home environment. The experimental results have shown that the proposed HOC algorithm significantly enhances the robustness and precision in depth imaging, compared to the best known conventional approaches. Furthermore, after we processed the HOC algorithm implemented on our hardware platform, the results showed that it required 34 ms of time to generate one 3D image. This processing time is about 24 times faster than the same implementation of HOC algorithm using software, and the real-time processing is realized.

In unidentifiable models, the Bayes estimation has the advantage of generalization performance over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach. A subspace Bayes approach is an empirical Bayes approach where a part of the parameters are regarded as hyperparameters. Consequently, in some three-layer models, this approach requires much less computational costs than Markov chain Monte Carlo methods. We show that, in three-layer linear neural networks, a subspace Bayes approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation, and theoretically clarify its generalization error and training error. We also discuss the domination over the maximum likelihood estimation and the relation to the variational Bayes approach.

We propose a novel simplified Viterbi equalizer for high symbol rate FWA (Fixed Wireless Access) systems carrying 64QAM signals. Reduced complexity and improved performance are achieved adopting two approaches. The first one is reducing the number of survival paths, taking advantage of the large D/U common in LOS (line of sight) communications. The second one is using a multi-stage process to generate desired signal replicas based on their likelihoods. Computer simulations confirm that the proposed replica generation method offers a performance improvement of about 1 dB and the proposed Viterbi equalizer offers reduced complexity with no performance penalty compared to full Viterbi equalizer.

We present a training algorithm to create a neural network (NN) ensemble that performs classification tasks. It employs a competitive decay of hidden nodes in the component NNs as well as a selective deletion of NNs in ensemble, thus named a pruning algorithm for NN ensembles (PNNE). A node cooperation function of hidden nodes in each NN is introduced in order to support the decaying process. The training is based on the negative correlation learning that ensures diversity among the component NNs in ensemble. The less important networks are deleted by a criterion that indicates over-fitting. The PNNE has been tested extensively on a number of standard benchmark problems in machine learning, including the Australian credit card assessment, breast cancer, circle-in-the-square, diabetes, glass identification, ionosphere, iris identification, and soybean identification problems. The results show that classification performances of NN ensemble produced by the PNNE are better than or competitive to those by the conventional constructive and fixed architecture algorithms. Furthermore, in comparison to the constructive algorithm, NN ensemble produced by the PNNE consists of a smaller number of component NNs, and they are more diverse owing to the uniform training for all component NNs.

A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.

This paper presents method that offers the fast and accurate analysis of large-scale periodic array antennas by conjugate-gradient fast Fourier transform (CG-FFT) combined with an equivalent sub-array preconditioner. Method of moments (MoM) is used to discretize the electric field integral equation (EFIE) and form the impedance matrix equation. By properly dividing a large array into equivalent sub-blocks level by level, the impedance matrix becomes a structure of Three-level Block Toeplitz Matrices. The Three-level Block Toeplitz Matrices are further transformed to Circulant Matrix, whose multiplication with a vector can be rapidly implemented by one-dimension (1-D) fast Fourier transform (FFT). Thus, the conjugate-gradient fast Fourier transform (CG-FFT) is successfully applied to the analysis of a large-scale periodic dipole array by speeding up the matrix-vector multiplication in the iterative solver. Furthermore, an equivalent sub-array preconditioner is proposed to combine with the CG-FFT analysis to reduce iterative steps and the whole CPU-time of the iteration. Some numerical results are given to illustrate the high efficiency and accuracy of the present method.

This work proposes a new method for RLCG interconnect model-order reductions in consideration with the adjoint network. Relationships between an original MNA network and its corresponding adjoint MNA network will be explored first. It will be shown that the congruence transformation matrix used in the one-sided projection can be constructed by using the bi-orthogonal bases developed from the Lanczos-type algorithms. In particular, if the multi-port driving-point impedance of RLCG interconnect circuits is the main concern, the transfer functions and system moments of the adjoint network can be directly calculated from those of the original RLCG interconnect network by exploring symmetric properties of the MNA formulation. Therefore, the cost of constructing the congruence transformation matrix can be simplified by up to 50% of the previous methods. Comparative studies among various standard methods and the proposed methods are also investigated. Experimental results on large-scale RLCG interconnect circuits will demonstrate the accuracy and the efficiency of the proposed method.

We proposed and evaluated a speech packet loss concealment method which predicts lost segments from speech included in packets either before, or both before and after the lost packet. The lost segments are predicted recursively by using linear prediction both in the forward direction from the packet preceding the loss, and in the backward direction from the packet succeeding the lost segment. Predicted samples in each direction are smoothed by averaging using linear weights to obtain the final interpolated signal. The adjacent segments are also smoothed extensively to significantly reduce the speech quality discontinuity between the interpolated signal and the received speech signal. Subjective quality comparisons between the proposed method and the the packet loss concealment algorithm described in the ITU standard G.711 Appendix I showed similar scores up to about 10% packet loss. However, the proposed method showed higher scores above this loss rate, with Mean Opinion Score rating exceeding 2.4, even at an extremely high packet loss rate of 30%. Packet loss concealment of speech degraded with G.729 coding, and babble noise mixed speech showed similar trends, with the proposed method showing higher qualities at high loss rates. We plan to further improve the performance by using adaptive LPC prediction order depending on the estimated pitch, and adaptive LPC bandwidth expansion depending on the consecutive number of repetitive prediction, among many other improvements. We also plan to investigate complexity reduction using gradient LPC coefficient updates, and processing delay reduction using adaptive forward/bidirectional prediction modes depending on the measured packet loss ratio.

We propose the concept of an opinion model for interactive multimodal services and apply it to an audiovisual communication service. First, psychological factors of an audiovisual communication service were extracted by using the semantic differential (SD) technique and factor analysis. Forty subjects participated in subjective tests and performed point-to-point conversational tasks on a PC-based video phone that exhibited various network qualities. The subjects assessed those qualities on the basis of 25 pairs of adjectives. Two psychological factors, i.e., an aesthetic feeling and a feeling of activity, were extracted from the results. Then, quality impairment factors affecting these two psychological factors were analyzed. We found that the aesthetic feeling was affected by IP packet loss and video coding bit rate, and the feeling of activity depended on delay time, video packet loss, video coding bit rate, and video frame rate. Using this result, we formulated an opinion model derived from the relationships among quality impairment factors, psychological factors, and overall quality. The validation test results indicated that the estimation error of our model was almost equivalent to the statistical reliability of the subjective score.

The computational grid provides a promising platform for the deployment of various high-performance computing applications. Problem in implementing computational grid environments is how to effectively use various resources in the system, such as CPU cycle, memory, communication network, and data storage. There are many effective heuristic algorithms for scheduling in the computational grid, however most scheduling strategies have no theoretical guarantee at all. In this paper, a cost-based online scheduling algorithm is presented for job assignment in the grid environment with theoretical guarantee. Firstly, a scheduling framework is described, where the grid environment is characterized, and the online job model is defined. Secondly, the cost-based online scheduling algorithm is presented where costs of resources are exponential functions of their loads, and the performance of this algorithm is theoretically analyzed against the performance of the optimal offline algorithm. Finally, we implement the algorithm in the grid simulation environment, and compare the performance of the presented algorithm with the other three algorithms, and experimental results indicate that the cost-based online scheduling algorithm can outperform the other three online algorithms.

In this paper, we examine the efficiency of tunneling techniques since they will accelerate multicast deployment. Our motivation is that, despite the many proposals focused on tunneling techniques, their impact on multicast efficiency has yet to be assessed sufficiently. First, the structure of multicast delivery trees is examined based on the seminal work of Phillips et al. [26]. We then quantitatively assess the impact of tunneling, such as loads imposed on the tunnel endpoints and redundant traffic. We also formulate a critical size of multicast island, above which the loads are suddenly diminished. Finally, a unique delivery tree model is introduced, which is so simple yet practical, to better understand the performance of the multicast-related protocols. This paper is the first to formulate the impact of tunneling.

Cross-document personal name resolution is the process of identifying whether or not a common personal name mentioned in different documents refers to the same individual. Most previous approaches usually rely on lexical matching such as the occurrence of common words surrounding the entity name to measure the similarity between documents, and then clusters the documents according to their referents. In spite of certain successes, measuring similarity based on lexical comparison sometimes ignores important linguistic phenomena at the semantic level such as synonym or paraphrase. This paper presents a semantics-based approach to the resolution of personal name crossover documents that can make the most of both lexical evidences and semantic clues. In our method, the similarity values between documents are determined by estimating the semantic relatedness between words. Further, the semantic labels attached to sentences allow us to highlight the common personal facts that are potentially available among documents. An evaluation on three web datasets demonstrates that our method achieves the better performance than the previous work.

The paper presents an adaptive algorithm named adaptive threshold nonlinear algorithm for use in adaptive filters in the complex-number domain (c-ATNA) in applications to digital QAM systems. Although the c-ATNA is very simple to implement, it makes adaptive filters highly robust against impulse noise and at the same time it ensures filter convergence as fast as that of the well-known LMS algorithm. Analysis is developed to derive a set of difference equations for calculating transient behavior as well as steady-state performance. Experiment with simulations and theoretical calculations for some examples of filter convergence in the presence of Contaminated Gaussian Noise demonstrates that the c-ATNA is effective in combating impulse noise. Good agreement between simulated and theoretical convergence proves the validity of the analysis.

As an alternative solution to provide the quality of services (QoS) for broadband access over Ethernet Passive Optical Network (EPON), we present the usage of MAC control message for plural class queues and a traffic-class burst-polling based delta dynamic bandwidth allocation (DBA), referred to as TCBP-DDBA, scheme. For better QoS support, the TCBP-DDBA minimizes packet delays and delay variations for expedited forwarding packet and maximizes throughput for assured forwarding and best effort packets. The network resources are efficiently utilized and adaptively allocated to the three traffic classes for the given unbalanced traffic conditions by guaranteeing the requested QoS. Simulation results using OPNET show that the TCBP-DDBA scheme performs well in comparison to the conventional unit-based allocation scheme over the measurement parameters such as: packet delay, packet delay variation, and channel utilization.

Let G = (V,E) be an edge weighted graph with n vertices and m edges. For a given integer p with 1 < p < n, we call a set X V of p vertices a p-maximizer if X has a property that the edge-connectivity of the graph obtained by contracting X into a single vertex is no less than that of the graph obtained by contracting any other subset of p vertices. In this paper, we first show that there always exists an ordering v1,v2,...,vn of vertices in V such that, for each i = 2,3,...,n - 1, set {v1,v2,...,vi} is an i-maximizer. We give an O(mn + n2log n) time algorithm for finding such an ordering and then show an application to the source location problem.

In this letter, we propose a butterfly structure for rate 2/n convolutional codes to reduce the computational complexity of Viterbi decoders. By using the butterfly structure, the branch metric computation complexity of some best known codes can be reduced by a factor of 2 or 4.

Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that it can lead to significant runtime savings on PC clusters and computational grids.

The Visual Secret Sharing (VSS) scheme proposed by Naor and Shamir is a perfectly secure scheme to share a secret image. By using m sub pixels to represent one pixel, we encrypt the secret image into several noise-like shadow images. The value of m is known as the pixel expansion. More pixel expansion increases the shadow size and makes VSS schemes impractical for real application. In this paper, we propose new size-reduced VSS schemes and dramatically decrease the pixel expansion by a half.

A chordal graph is a graph which contains no chordless cycle of at least four edges as an induced subgraph. The class of chordal graphs contains many famous graph classes such as trees, interval graphs, and split graphs, and is also a subclass of perfect graphs. In this paper, we address the problem of enumerating all labeled chordal graphs included in a given graph. We think of some variations of this problem. First we introduce an algorithm to enumerate all connected labeled chordal graphs in a complete graph of n vertices. Next, we extend the algorithm to an algorithm to enumerate all labeled chordal graphs in a n-vertices complete graph. Then, we show that we can use, with small changes, these algorithms to generate all (connected or not necessarily connected) labeled chordal graphs in arbitrary graph. All our algorithms are based on reverse search method, and time complexities to generate a chordal graph are O(1), and also O(1) delay. Additionally, we present an algorithm to generate every clique of a given chordal graph in constant time. Using these algorithms we obtain combinatorial Gray code like sequences for these graph structures in which the differences between two consecutive graphs are bounded by a constant size.

This study is devoted to a half-wave dipole with a conductor plane at a distance much smaller than a quarter wavelength which we designate as an ultra low profile dipole (ULPD) antenna in this paper. The concerns of ULPD antenna are the feeding method and the impedance matching, because the input impedance usually tends to be lowered by the existence of a metallic structure in its proximity. In this paper, we propose a ULPD antenna with an excellent impedance matching and a coaxial feed built within the antenna structure so that the external matching and a balun are not required. A coaxial cable is used as a feed line and extended to be a half of a half wavelength dipole. The other half is made up of a parasitic element, which is connected to the outer conductor of the coaxial radiator. To make a matching, the outer conductor of the coaxial radiator is stripped off at a suitable length, and the total length of a dipole is considered for its resonance at a desired frequency of 2 GHz. The experiment has been conducted. The results show the return loss of -27 dB and the maximum gain of 9 dBi in the normal direction to the conductor plane. The computational results are also obtained, which agree well with the experimental results.