In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.

It has been shown that virtual output queuing (VOQ) and a sophisticated scheduling algorithm enable an input-queued switch to achieve 100% throughput for independent arrival process. Several of the scheduling algorithms that have been proposed can be classified as either iterative scheduling algorithms or symmetric crossbar arbitration algorithms. i-OCF (oldest-cell-first) and TSA (two step arbiter) are well-known examples of iterative scheduling algorithms and symmetric crossbar arbitration algorithms, respectively. However, there are drawbacks in using these algorithms. i-OCF takes long time to find completely a conflict-free match between input ports and output ports because it requires multiple iterations. If i-OCF cannot find a conflict-free match completely, the switch throughput falls. TSA has the possibility that it finds a conflict-free match faster than i-OCF because it does not need any iterations. However, TSA suffers from the starvation problem. In this paper, we propose a new scheduling algorithm. It uses two schedulers, which we call scheduler 1 and scheduler 2, in parallel. After cells were transmitted, the information that input port i granted the offer from output port j in scheduler 2 is mapped to scheduler 1 if and only if input port i has at least one cell destined for output port j. If the information is moved, input port i and output port j are matched in scheduler 1 at the beginning of the next time slot. Our proposed algorithm uses one scheduler based on TSA and the other scheduler based on i-OCF. Numerical results show that the proposed scheduling algorithm does not require multiple iterations to find a conflict-free match completely and suffer from the starvation problem for both uniform and bursty traffic.

A Wireless Sensor Network (WSN, for short) is a distributed system consisting of n sensor nodes and a base station. In this paper, we propose an energy-efficient protocol to initialize the sensor nodes in a WSN, that is, to assign a unique ID to each sensor node. We show that if an upper bound u on the number n of sensor nodes is known beforehand, for any f 1 and any small µ (0<µ<1), a WSN without collision detection capability can be initialized in O((log (1/µ) + log f)u1+µ) time slots, with probability exceeding 1-(1/f), with no sensor node being awake for more than O(log (1/µ)+ log f) time slots.

Y. S. Han et al. have proposed an efficient maximum likelihood decoding (MLD) algorithm using A* algorithm which is the graph search method. In this paper, we propose a new MLD algorithm for linear block codes. The MLD algorithm proposed in this paper improves that given by Han et al. utilizing codewords of dual codes. This scheme reduces the number of generated codewords in the MLD algorithm. We show that the complexity of the proposed decoding algorithm is reduced compared to that given by Han et al. without increasing the probability of decoding error.

A novel rough neural network (RNN) structure and its application are proposed in this paper. We principally introduce its architecture and training algorithms: the genetic training algorithm (GA) and the tabu search training algorithm (TSA). We first compare RNN with the conventional NN trained by the BP algorithm in two-dimensional data classification. Then we compare RNN with NN by the same training algorithm (TSA) in functional approximation. Experiment results show that the proposed RNN is more effective than NN, not only in computation time but also in performance.

Several implementation methods for a MAP decoder are proposed in this paper. Using a novel pipeline structured time-shared process, the authors are able to efficiently overcome the restrictions imposed by the recursion process on state metrics, and the complexity of the MAP decoder can be reduced to a level on the order of a SOVA (Soft Output Viterbi Algorithm) decoder. In addition, the authors propose an efficient controller structure that can be used for variable frame-size systems such as cdma-2000. The MAP decoder using a block-wise algorithm designed here was implemented in only one 20,000 gate circuit. It was validated by VHDL, which was compared with the results of the initial simulation (C programs). The decoder demonstrated a 300 kbps decoding processing ability with 8 iterations on a FPGA circuit, with a deviation only about 0.1-0.2 dB greater than that for an ideal MAP decoder, even when all hardware environments are considered.

We consider to design approximation algorithms for the survivable network design problem in hypergraphs (SNDPHG) based on algorithms developed for the survivable network design problem in graphs (SNDP) or the element connectivity problem in graphs (ECP). Given an instance of the SNDPHG, by replacing each hyperedge e={v1,,vk} with a new vertex we and k edges {we, v1},, {we, vk}, we define an SNDP or ECP in the resulting graph. We show that by approximately solving the SNDP or ECP defined in this way, several approximation algorithms for the SNDPHG can be obtained. One of our results is a dmax+-approximation algorithm for the SNDPHG with dmax 3, where dmax (resp. dmax+) is the maximum degree of hyperedges (resp. hyperedges with positive cost). Another is a dmax+(rmax)-approximation algorithm for the SNDPHG, where (i)=j=1i(1/j) is the harmonic function and rmax is the maximum connectivity requirement.

The mobile computing system is a set of functions on a distributed environment organized to support mobile hosts. In this environment, mobile hosts should be able to move without any constraints and should remain connected to the network even while moving. Also, they should be able to get necessary information regardless of their current location and time. Distributed mutual exclusion methods for supporting distributed algorithms have hitherto been designed for networks only with static hosts. However, with the emergence of mobile computing environments, a new distributed mutual exclusion method needs to be developed for integrating mobile hosts with underlying distributed systems. In the sense, many issues that should be considered stem from three essential properties of mobile computing system such as wireless communication, portability, and mobility. Thus far, distributed mutual exclusion methods for mobile computing environments were designed based on a token ring structure, which has the drawback of requiring high costs in order to locate mobile hosts. In this paper, we propose not only a distributed mutual exclusion method that can reduce such costs by structuring the entire system as a tree-based logical structure but also recovery schemes that can be applied when a node failure occurs. Finally, we evaluate the operation costs for the mutual exclusion scheme and the recovery scheme.

When a randomized algorithm elects a leader on anonymous networks, initial information (which is called in general initial condition in this paper) of some sort is always needed. In this paper, we study common properties of initial conditions that enable a randomized algorithm to elect a leader. In the previous papers, the author introduced the notion of transformation between initial conditions using distributed algorithms. By using this notion of transformation, we investigate the property of initial conditions for the leader election. We define that an initial condition C is p(N)-complete if there exists some randomized algorithm that elects a leader with probability p(N) on any size N network satisfying C. We show that we can divide p(N)-completeness into four types as follows. 1. p(N)=1: For any 1-complete initial conditions, there exists a deterministic distributed algorithm that can compute the size of the network for any initial information satisfying the initial condition. 2. inf p(N) >0: For any p(N)-complete initial conditions with inf p(N) >0, there exists a deterministic distributed algorithm that can compute an upper-bound for the size of the network for any initial information satisfying the initial condition. 3. inf p(N) converges to 0: The set of p(N)-complete initial conditions varies depending on the decrease rate of p(N). 4. p(N) decreases exponentially: Any initial condition is regarded as p(N)-complete.

In functional brain images obtained by analyzing higher human brain functions using functional magnetic resonance imaging (fMRI), one serious problem is that these images depict false activation areas (artifacts) resulting from image-to-image physiological movements of subject during fMRI data acquisition. In order to truly detect functional activation areas, it is necessary to eliminate the effects of physiological movements of subject (i.e., gross head motion, pulsatile blood and cerebrospinal fluid (CSF) flow) from fMRI time series data. In this paper, we propose a method for eliminating artifacts due to not only rigid-body motion such as gross head motion, but also non-rigid-body motion like the deformation caused by the pulsatile blood and CSF flow. The proposed method estimates subject movements by using gradient methods which can detect subpixel optical flow. Our method estimates the subject movements on a "pixel-by-pixel" basis, and achieves the accurate estimation of both rigid-body and non-rigid-body motion. The artifacts are reduced by correction based on the estimated movements. Therefore, brain activation areas are accurately detected in functional brain images. We demonstrate that our method is valid by applying it to real fMRI data and that it can improve the detection of brain activation areas.

We propose new lossless medical image compression method based on hierarchical sorting technique. Hierarchical sorting is a technique to achieve high compression ratio by detecting the regions where image pattern varies abruptly and sorting pixel order by its value to increase predictability. In this method, we can control sorting accuracy along with size and complexity. As the result, we can reduce the sizes of the permutation-tables and reuse the tables to other image regions. Comparison using experimental implementation of this method shows better performance for medical image set measured by X-ray CT and MRI instruments where similar sub-block patterns appear frequently. This technique applies quad-tree division method to divide an image to blocks in order to support progressive decoding and fast preview of large images.

An algorithm based on the wavelet transform (WT) was developed to analyze the QRS complex in a three-dimensional magnetocardiogram (3-D MCG) recorded from 3 normal subjects and 1 patient with anterior myocardial infarction (MI). By using a wavelet equivalent filter constructed with the WT algorithm, the high frequency components of the QRS complex related to the late fields (LF) were detected for the patient with anterior MI at different scale. We quantified the high frequency components of the QRS complex by calculating root-mean-square (RMS) value at different scale. The LF mainly existed in the frequency band of about 35.5 to 110.5 Hz with the amplitude of about 0.1 to 0.4 pT for Bx, By, and Bz components. In order to discuss the activities of the heart between the normal subject and the patient with anterior MI, we have also evaluated the spatial energy distribution (SED) of the QRS complex by displaying isoenergy contour maps at different scale. Being different from the normal subject, the patient with anterior MI represented different the pattern of the SED in various frequency band for the ST segment of the QRS complex of Bx, By, and Bz components. It is efficient to use the WT algorithm for analyzing the QRS complex in the 3-D MCG.

We propose a new and fast full search (FS) motion estimation algorithm for video coding. The computational reduction comes from sequential rejection of impossible candidates with derived formula and subblock norms. Our algorithm reduces more the computations than the recent fast full search (FS) motion estimation algorithms.

In this paper, a hardware-oriented Genetic Algorithm (GA) was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuous generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1 MHz), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.

This paper studies effect of packet scheduling algorithms at routers on media synchronization quality in live audio and video transmission by experiment. In the experiment, we deal with four packet scheduling algorithms: First-In First-Out, Priority Queueing, Class-Based Queueing and Weighted Fair Queueing. We assess the synchronization quality of both intra-stream and inter-stream with and without media synchronization control. The paper clarifies the features of each algorithm from a media synchronization point of view. A comparison of the experimental results shows that Weighted Fair Queueing is the most efficient packet scheduling algorithm for continuous media among the four.

In this paper we addresses the problem of finding feasible solutions for the Group Multicast Routing Problem (GMRP). This problem is a generalization of the multicast routing problem whereby every member of the group is allowed to multicast messages to other members from the same group. The routing problem involves the construction of a set of low cost multicast trees with bandwidth requirements for all the group members in the network. We first prove that the problem of finding feasible solutions to GMRP is NP-complete. Following that we propose a new heuristic algorithm for constructing feasible solutions for GMRP. Simulation results show that our proposed algorithm is able to achieve good performance in terms of its ability of finding feasible solutions whenever one exist.

The explosive growth of World Wide Web usage is causing a number of performance problems, including slow response times, network congestion, and denial of service. Web site that has a huge number of accesses and requires high quality of services, such as a site offering hosting services, or content delivery services, usually uses a cache server to reduce the load on the original server offering the original content. To increase the throughput of the caching process and to improve service availability, multiple cache servers are often positioned in front of the original server. This requires a switch to direct incoming requests to one of the multiple cache servers. In this paper, we propose a routing algorithm for such a switch in front of clustered multiple cache servers and evaluate its performance by simulation. The results show that our routing algorithm is effective when content has request locality and a short period of validity, for example, news, map data, road traffic data, or weather information. We also identify points to consider when the proposed algorithm is applied to a real system.

An improved algorithm is developed for correcting the topographic impact on satellite imagery. First, we analyze the topography induced distortion on satellite image. It is shown that the variation of aspect can cause the obvious different distortions in the remotely sensed image, and also effect the image illumination significantly. Because the illumination is the basis for topographic correction algorithms, we consider its variation in different sun-facing aspects in calculation a correction parameter and take it as a key element in the modified correction algorithm. Then, we apply the modified correction method on the actual Landsat Thematic Mapper satellite image. The topographic correction was done in different image data with different season and different solar angle. The corrected results show the effectiveness and accuracy using this approach.

In order to maintain the diversity of structures in the population and prevent premature convergence, I have developed a new genetic algorithm called DCGA. In the experiments on many standard benchmark problems, DCGA showed good performances, whereas with harder problems, in some cases, the phenomena were observed that the search was stagnated at a local optimum despite that the diversity of the population is maintained. In this paper, I propose methods for escaping such phenomena and improving the performance by reinitializing the population, that is, a method called each-structure-based reinitializing method with a deterministic structure diverging procedure as a method for producing new structures and an adaptive improvement probability bound as a search termination criterion. The results of experiments demonstrate that DCGA becomes robust in harder problems by employing these proposed methods.

In this paper, we propose a frequency domain active noise control (ANC) system without a secondary path model. The proposed system is based on the frequency domain simultaneous perturbation (FDSP) method we have proposed. In this system, the coefficients of the adaptive filter are updated only by error signals. The conventional ANC system using the filtered-x algorithm becomes unstable due to the error between the secondary path, from secondary source to error sensor, and its model. In contrast, the proposed ANC system has the advantage not to use the model. In this paper, we show the principle of the proposed ANC system, and examine its efficiency through computer simulations.