Recently, in wired networks, a hierarchical structure has been introduced to improve management and routing. In ad hoc networks, we introduce a hierarchical structure to achieve the same goal. However, it is difficult to introduce the hierarchical structure because all mobile hosts are always moving around the network. So, we proposed the clustering scheme to construct the hierarchical structure before. In this paper, we propose a new hierarchical routing protocol called Hi-TORA based on the clustering scheme. And we show the experimental evaluation of Hi-TORA with respect to the number of control packets, accuracy of packet delivery and hop counts in comparison with TORA.

This paper proposes the conditional LZ complexity and analyzes its property. Especially, we show an inequality corresponding to Ziv's inequality concerning a distinct parsing of a pair of sequences. Further, as a byproduct of the result, we show a simple proof of the asymptotical optimality of Ziv's universal source coding algorithm with side information.

Broadcast disks are suited for disseminating information to a large number of clients in mobile computing environments. In broadcast disks, the server continuously and repeatedly broadcasts all data items in the database to clients without specific requests. The clients monitor the broadcast channel and read data items as they arrive on the broadcast channel. The broadcast channel then becomes a disk from which clients can read data items. In this paper, we propose a cache conscious concurrency control (C4) algorithm to preserve the consistency of read-only client transactions, when the values of broadcast data items are updated at the server. The C4 algorithm is novel in the sense that it can reduce the response time of client transactions with minimal control information to be broadcast from the server. This is achieved by the judicious caching strategy of the client and by adjusting the currency of data items read by the client.

This paper assesses the media synchronization quality of recovery control schemes from asynchrony, which are referred to as reactive control schemes here, in terms of objective and subjective measures. We deal with four reactive control techniques: skipping, discarding, shortening and extension of output duration, and virtual-time contraction and expansion. We have carried out subjective and objective assessment of the media synchronization quality of nine schemes which consist of combinations of the four techniques. The paper makes a comparison of media synchronization quality among the schemes. It also clarifies the relations between the two kinds of quality measures.

The Earth Simulator (ES), developed by the Japanese government's initiative "Earth Simulator project," is a highly parallel vector supercomputer system. In May 2002, the ES was proven to be the most powerful computer in the world by achieving 35.86 teraflops on the LINPACK benchmark and 26.58 teraflops for a global atmospheric circulation model with the spectral method. Three architectural features enabled these great achievements; vector processor, shared-memory and high-bandwidth non-blocking interconnection crossbar network. In this paper, an overview of the ES, the three architectural features and the result of performance evaluation are described particularly with its hardware realization of the interconnection among 640 processor nodes.

Ranking fuzzy numbers is one of very important research topics in fuzzy set theory because it is a base of decision-making in applications. However, fuzzy numbers may not be easily ordered into one sequence according to their magnitudes because they represent uncertain values. When two fuzzy numbers overlap with each other, a fuzzy number may not be considered absolutely larger than the other. That is, even when a fuzzy number may be considered larger than the other, it may also be considered smaller than the other. It means that for a given set of fuzzy numbers, several ranking sequences possibly exist. However, most of the existing ranking methods produce only one ranking sequence. They ignore other possible sequences due to the overlap between fuzzy numbers. We propose a ranking method which generates possible ranking sequences of given fuzzy numbers. Our method takes a viewpoint from users, and uses it for evaluation of fuzzy numbers. Fuzzy numbers will be ranked based on the evaluations and a fuzzy set of sequences of fuzzy numbers will be produced as a ranking results. Numeric examples and comparisons with other methods are also presented.

RHiNET-3/SW is the third-generation switch used in the RHiNET-3 system. It provides both low-latency processing and flexible connection due to its use of a credit-based flow-control mechanism, topology-free routing, and deadlock-free routing. The aggregate throughput of RHiNET-3/SW is 80 Gbps, and the latency is 140 ns. RHiNET-3/SW also provides a hop-by-hop retransmission mechanism. Simulation demonstrated that the effective throughput at a node in a 64-node torus RHiNET-3 system is equivalent to the effective throughput of a 64-bit 33-MHz PCI bus and that the performance of RHiNET-3/SW almost equals or exceeds the best performance of RHiNET-2/SW, the second-generation switch. Although credit-based flow control requires 26% more gates than rate-based flow control to manage the virtual channels (VCs), it requires less VC memory than rate-based flow control. Moreover, its use in a network system reduces latency and increases the maximum throughput compared to rate-based flow control.

We have constructed a parallelizing compiler for Standard ML (SML) based upon algorithmic skeletons. We present an implementation of a Parallel Molecular Dynamics (PMD) simulation in order to compare our functional approach with a traditional imperative approach. Although we present performance data, the principal benefits from our approach are in the modularity of the code and the ease of programming. Extant FORTRAN90 code for an O(N 2) algorithm is translated, firstly into imperative SML and then into purely functional SML which is then parallelized. The ease of programming and the performance of the FORTRAN90 and SML code are compared. Modest parallel performance is obtained from the parallel SML but with a much slower sequential execution time compared to the FORTRAN90. We then improve the implementation with a ring topology implementation which gives much closer performance to the FORTRAN90 implementation.

Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.

In this paper color image compression using a fuzzy Hopfield-model net based on rough-set reasoning is created to generate optimal codebook based on Vector Quantization (VQ) in Discrete Wavelet Transform (DWT). The main purpose is to embed rough-set learning scheme into the fuzzy Hopfield network to construct a compression system named Rough Fuzzy Hopfield Net (RFHN). First a color image is decomposed into 3-D pyramid structure with various frequency bands. Then the RFHN is used to create different codebooks for various bands. The energy function of RFHN is defined as the upper- and lower-bound fuzzy membership grades between training samples and codevectors. Finally, near global-minimum codebooks in frequency domain can be obtained when the energy function converges to a stable state. Therefore, only 32/N pixels are selected as the training samples if a 3N-dimensional color image was used. In the simulation results, the proposed network not only reduces the consuming time but also preserves the compression performance.

In this paper, we present our investigation of Latent Semantic Indexing (LSI) on the local query regions for solving the computation restrictions of the LSI on the global information space. Through the experiments with different SVD dimensionality on the local query regions, the results show that low-dimensional LSI can achieve much better precision than VSM and similar precision to global LSI. Such small SVD factors indicate that there is an almost linear surface in the local query regions. The largest or the two largest singular vectors have the ability to capture such a linear surface and benefit the particular query. In spite of the fact that Local LSI analysis needs to perform the Singular Value Decomposition (SVD) computation for each query, the surprisingly small requirements of the SVD dimension resolve the computation restrictions of LSI for large scale IR tasks. Moreover, on the condition that several relevant sample documents are available, application of low dimensional LSI for these documents can obtain comparable precision with the Local RF in a different manner.

Digital watermarking systems are required to embed as much information as possible in a digital media without the perceptual distortion as well as to extract it correctly with high probabilities, even though the media is subjected to many kinds of operations. To this end, guided scrambling (GS) techniques, usually used for a recording channel, are applied to digital watermarking systems. A simple GS scheme can make the power of a watermark signal larger against the power of media noise under the condition of preserving the perceptual fidelity, resulting in smaller error probabilities of the retrieved watermark bits. In addition, watermarking systems based on the GS can have more robustness to some specified operations if the prior information on the operations is given to the embedder. JPEG compression is a good example of such an operation when still images are transmitted over the Internet. In order for watermark signals to be more tolerable to the known JPEG attack, the GS-based watermark embedder is informed of advance knowledge of the JPEG compression. Further, a configuration of the GS concatenated with turbo coding is introduced to lower the bit error rate more.

Affine arithmetic is a kind of interval arithmetic defined by Stolfi et al. In affine arithmetic, it is difficult to realize the efficient nonlinear binomial operations. The purpose of this letter is to propose a new dividing method which is able to supply more suitable evaluation than the old dividing method. And this letter also shows the efficiency of the new dividing method by numerical examples.

In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.

A brain-computer interface using an electroencephalogram as input into an artificial neural network is investigated as a potentially general control system applicable to all subjects and time frames. Using the intent and imagination of bending the left or right elbow, the left and right desired movements are successfully distinguished using event-related desynchronization resolved by fast Fourier transformation of the electroencephalogram and analysis of the power spectrum using the artificial neural network. The influence of age was identified and eliminated through the use of a frequency distribution in the α band, and the recognition rate was further improved by confirmation based on forced excitement of the β band in the case of an error. The proposed system was effectively trained for general use by using the combined data of a cross-section of subjects.

Complete complementary codes have the property that the sum of correlation functions of several sequences satisfies both ideal autocorrelation and cross-correlation values. Modulatable complete complementary codes (MCC codes), which is a type of periodic complete complementary codes, are suitable for spreading sequences of M-ary CDMA systems. In the present paper, we propose a new method for constructing MCC codes. Using this method, we can easily generate various MCC codes.

Neural networks for Vector Quantization (VQ) such as K-means, Neural-Gas (NG) network and Kohonen's Self-Organizing Map (SOM) have been proposed. K-means, which is a "hard-max" approach, converges very fast. The method, however, devotes itself to local search, and it easily falls into local minima. On the other hand, the NG and SOM methods, which are "soft-max" approaches, are good at the global search ability. Though NG and SOM exhibit better performance in coming close to the optimum than that of K-means, the methods converge slower than K-means. In order to the disadvantages that exist when K-means, NG and SOM are used individually, this paper proposes hybrid methods such as NG-K, SOM-K and SOM-NG. NG-K performs NG adaptation during short period of time early in the learning process, and then the method performs K-means adaptation in the rest of the process. SOM-K and SOM-NG are similar as NG-K. From numerical simulations including an image compression problem, NG-K and SOM-K exhibit better performance than other methods.

This paper introduces a 1-inkdot two-way alternating pushdown automaton which is a two-way alternating pushdown automaton (2apda) with the additional power of marking at most 1 tape-cell on the input (with an inkdot) once. We first investigate a relationship between the accepting powers of sublogarithmically space-bounded 2apda's with and without 1 inkdot, and show, for example, that sublogarithmically space-bounded 2apda's with 1 inkdot are more powerful than those which have no inkdots. We next investigate an alternation hierarchy for sublogarithmically space-bounded 1-inkdot 2apda's, and show that the alternation hierarchy on the first level for 1-inkdot 2apda's holds, and we also show that 1-inkdot two-way nondeterministic pushdown automata using sublogarithmic space are incomparable with 1-inkdot two-way alternating pushdown automata with only universal states using the same space.

We show that the necessary and sufficient condition for the existence of a balanced bowtie decomposition of the complete multigraph λKn is n 5 and λ(n-1) 0 (mod 12). Decomposition algorithms are also given.

In wireless mobile environments, data requests based on the location of mobile clients (MCs) have increased. The requested data, called location-dependent data (LDD), may be uncertain if MCs use terms of general distance like "near". Fuzzy theory allows us to represent uncertainty without sharp boundaries. In this paper we quantify the fuzziness and propose a method for constructing the data region of LDD by the degree of the distance between LDDs' and MCs' locations. In simulation studies, we evaluate the LDD regions (LDRs) in various situations: MCs' extensive and intensive queried pattern in data regions of two "near" senses and civilized regions with regional features. Our performance evaluation shows that the number of database accesses in proposed LDRs can be reduced in each case.