The minimum number of comparators in a (6,6)-merging network is shown to be 17. The number has been known to be either 16 or 17 [See Knuth, The Art of Computer Programming Vol. 3: Sorting and Searching, p. 230]. Minimum numbers for (n,n)-merging netwerks, 1 n 9, n 6, were already known. The problem had been open for more than two decades.

This paper describes embeddings of chordal rings and pyramids into mesh-connected computers with multiple buses which have a bus on each row and each column, called MCCMBs. MCCMBs have two types of communication. The one is local communication, provided by local links, and the other is global communication, provided by buses. By efficiently combining the two types of communication, optimal or efficient embeddings are achieved. For a large set of chordal rings, optimal embeddings, whose expansion, load, dilation and congestion are 1, are given. For pyramids, an efficient embedding based on a two phase strategy is presented. The embedding balances dilation and congestion.

In this paper, we present two parallel algorithms for computing the all nearest neighbors of an n n binary image on the Bulk-Synchronous Parallel(BSP) model. The BSP model is an asynchronous parallel computing model, where its communication features are abstracted by two parameters L and g: L denotes synchronization periodicity and g denotes a reciprocal of communication bandwidth. We propose two parallel algorithms for the all nearest neighbor problems based on two distance metrics. The first algorithm is for L_p distance, and the second algorithm is for weighted distance. Both two algorithms run in O(n²/p + L) computation time and in O(g(n/p) + L) communication time using p (1 p n) processors and in O(n²/p + (d+L)(log(p/n)/log(d+1))) computation time and in O(g(n/p) + (gd+L)(log(p/n)/log(d+1))) communication time using p (n< p n²) processors on the BSP model, for any integer d(1 dp/n).

In a Cache Coherent Non-Uniform Memory Access (CC-NUMA) system, memory transactions can be classified into two types: inter-node transactions and intra-node transactions. Because the latency of inter-node transactions is usually hundreds times larger than that of intra-node transactions, it is important to reduce the latency of inter-node transactions. Even though the remote cache in the CC-NUMA systems improves the latency of inter-node transactions through caching the remote memory lines, the remote and processor caches of snoop-based CC-NUMA systems have to retain the multi-level cache inclusion property for the simplification of snooping. The inclusion property degrades the cache performance by following factors. First, all the remote memory lines in a processor cache should be preserved in the remote cache of the same node. Second, a line replacement at the remote cache replaces the same address line in the processor caches, which does not comply with the replacement policy of the processor caches. In this paper, we propose Access-list which renders the inclusion property unnecessary, and evaluate the performance of the proposed system by program-driven simulation. From the simulation results, it is shown that the miss rates of caches are reduced and the efficiency of the snoop filtering is similar to the system with the inclusion property. It turns out that the performance of the proposed system is improved up to 1.28 times.

Prefetching is a promising approach to tackle the memory latency problem. Two basic variants of hardware data prefetching methods are sequential prefetching and stride prefetching. The latter based on stride calculation of future references has the potential to out-perform the former which is based on the data locality. In this paper, a typical stride prefetching and its improved version, adaptive stride prefetching, are compared in quantitative way using simulation for some parallel benchmark programs in the context of uniform memory access and non-uniform memory access architectures. The simulation results show that adaptability of stride is essential since the proposed adaptive scheme can reduce pending stall time which is large in the typical scheme.

This paper presents a robust and nonblocking group membership protocol for large-scale distributed systems. This protocol uses the causal relation between membership-updating messages (i. e. , those specifying the adding and deleting of members) and allows the messages to be executed in a nonblocking manner. It differs from conventional group membership protocols in the following points: (1) neither global locking nor global synchronization is required; (2) membership-updating messages can be issued without being synchronized with each other, and they can be executed immediately after their arrival. The proposed protocol therefore is highly scalable, and is more tolerant to node and network failures and to network partitions than are the conventional protocols. This paper proves that the proposed protocol works properly as long as messages can eventually be received by their destinations. This paper also discusses some design issues, such as multicast communication of the regular messages, fault tolerance and application to reliable communication protocols (e. g. , TCP/IP).

The use of Internet for various business applications and resource sharing has grown tremendously over the last few years. Internet security has become an important issue for both academic and industrial sectors. Much related network security research has been conducted such as user authentication, data confidentiality, and data integrity. In some applications, a critical document can be divided into pieces and allocated in different locations over the Internet for security access concern. To access such an important document, one must reconstruct the divided pieces from different locations under the given Internet environment. In this paper, a probability model for reconstructing secret sharing and algorithms to perform share assignment are presented. Also, an evaluation algorithm to measure the probability of secret sharing reconstruction is proposed. Illustrative examples and simulation results are provided to demonstrate the applicability of our method.

Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.

We introduce a new wavelet image coding framework using context-based zerotree quantization, where an unique and efficient method for optimization of zerotree quantization is proposed. Because of the localization properties of wavelets, when a wavelet coefficient is to be quantized, the best quantizer is expected to be designed to match the statistics of the wavelet coefficients in its neighborhood, that is, the quantizer should be adaptive both in space and frequency domain. Previous image coders tended to design quantizers in a band or a class level, which limited their performances as it is difficult for the localization properties of wavelets to be exploited. Contrasting with previous coders, we propose to trace the localization properties with the combination of the tree-structured wavelet representations and adaptive models which are spatial-varying according to the local statistics. In the paper, we describe the proposed coding algorithm, where the spatial-varying models are estimated from the quantized causal neighborhoods and the zerotree pruning is based on the Lagrangian cost that can be evaluated from the statistics nearby the tree. In this way, optimization of zerotree quantization is no longer a joint optimization problem as in SFQ. Simulation results demonstrate that the coding performance is competitive, and sometimes is superior to the best results of zerotree-based coding reported in SFQ.

The purpose of this paper is to deal with the problem of recovering a signal from its noisy version. One example is to restore old images degraded by noise. The recovery solution is given within the framework of series expansion and we shall show that for the general case the recovery functions have to be elements of an extended pseudo biorthogonal basis (EPBOB) in order to suppress efficiently the corruption noise. After we discuss the different situations of noise, we provide some methods to construct the optimal EPBOB in order to deal with these situations.

This paper proposes an Iterated Function System (IFS) which can reduce effects of quantization errors of the IFS parameters. The proposed method skips conventional analog-parameter search and directly selects optimum IFS parameters from pools of discrete IFS parameters. In conventional IFS-based image coding the IFS parameters are quantized after their analog optimum values are determined. The image reconstructed from the quantized parameters is degraded with errors that are traced back to quantization errors amplified in the iterated mappings. The effectiveness of this new realistic approach is demonstrated by simulation results over the conventional method.

A large number of techniques have been proposed for acceleration of the Hough Transform, because the transformation is computationally very expensive in general. It is known that the sampling interval in parameter space is strongly related to the computation cost. The precision of the transformation and the processing speed are in a trade-off relationship. No fair comparison of the processing speed between various methods was performed in all previous works, because no criterion had been given for the sampling interval of parameter, and because the precision of parameter was not equal between methods. At the beginning of our research, we derive the relationship between the sampling interval and the precision of parameter. Then we derive a framework for comparing computation cost under equal condition for precision of parameter, regarding the total number of sampling points of a parameter as the computation cost. We define the transformation error in the Hough Transform, and the error is regarded as transformation noise. In this paper we also propose a design method called "Noise-level Shaping," by which we can set the transformation noise to an arbitrarily level. The level of the noise is varied according to the value of a parameter. Noise-level Shaping makes it possible for us to find the efficient parameterization and to find the efficient sampling interval in a specific application of the Hough Transform.

Muscle based face image synthesis is one of the most realistic approaches to the realization of a life-like agent in computers. A facial muscle model is composed of facial tissue elements and simulated muscles. In this model, forces are calculated effecting a facial tissue element by contraction of each muscle string, so the combination of each muscle contracting force decides a specific facial expression. This muscle parameter is determined on a trial and error basis by comparing the sample photograph and a generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D markers'movements located on a face using a neural network. This corresponds to the non-realtime 3D facial motion capturing from 2D camera image under the physics based condition.

Visualization of 3-D ultrasound images is a challenging task due to the noisy and fuzzy nature of ultrasound imaging. This paper presents an efficient volume rendering technique for 3-D ultrasound image. A preprocessing technique of 2-D truncated-median filtering is proposed to reduce speckle noise of the ultrasound image. This paper also introduces an adaptive boundary detection method to reduce the computation time for volume rendering of ultrasound image. The proposed technique is compared to the conventional volume rendering methods with respect to the computation time and the subjective image quality. According to the comparison study, the proposed volume rendering method shows good performance for visualization of 3-D ultrasound image.

We propose a new sampling method for 2D and 3D implicit surfaces. The method is based on a stochastic process defined by the Langevin equation with a Gaussian random-force term. Our Langevin equation describes a stochastic-dynamical particle, which develops in time confined around the sampled implicit surface with small width. Its numerically generated solutions can be easily moved onto the surface strictly with very few iteration of the Newton correction. The method is robust in a sense that an arbitrary number of sample points can be obtained starting from one simple initial condition. It is because (1) the time development of the stochastic-dynamical particle does not terminate even when it reaches the sampled implicit surface, and (2) there is non-zero transition probability from one disconnected component to another. The method works very well for implicit surfaces which are complicated topologically, mathematically, and/or in shape. It also has some advantageous features in rendering 3D implicit surfaces. Many examples of applying our sampling method to real 2D and 3D implicit surfaces are presented.

We have developed a new disparity mapping technique for image morphing which prevents synthesized images from blurring and a fast rendering technique which realizes interactive morphing animation. In the image morphing rendering process, all pixels are moved according to their disparity maps and then distorted images are mixed with each other. Calculation costs of this process tend to be high because pixel per pixel moving and mixing are included. And if the accuracy of the disparity maps is low, synthesized images become blurred. This paper describes new two techniques for overcoming these problems. One is a disparity mapping technique by which the edges in each input image are accurately mapped to each other. This technique reduces blurring in synthesized images. The other is a data transformation technique by which the morphing rendering process is replaced with texture mapping, orthographic camera, α-brending and z-buffering. This transformation enables the morphing rendering process to be accelerated by 3D accelerators, thus enabling interactive morphing animations to be achieved on ordinary PCs.

Two different examples have been respectively given by Aggarwal and Viswanathan to establish the necessity of (n + 2)/5 edge guards for spiral polygons. However, the former example is incorrect. To show why it is wrong, we give an alternate proof of sufficiency of (n + 2)/5 edge guards for spiral polygons. Our proof is simpler than the sufficiency proof given by Viswanathan.

This paper investigates some fundamental properties of 2-way alternating multi-counter automata (2amca's) with only existential (universal) states which have sublinear space and 1 inkdot. It is shown that for any function s(n) log n such that log s(n)=o(log n), s(n) space-bounded 1-inkdot 2amca's with only existential states are incomparable with the ones with only universal states, and the ones with only existential (universal) states are not closed under complementation.

Causal message logging has many benefits such as nonblocking message logging and no rollback propagation. In this paper, we consider the problem of the recovery in causally-logged distributed system and give a condition for consistent recovery. We then show that, based on the impossibility of the consensus, the consistent causal recovery cannot be solved in asynchronous systems.

An ATMS (Assumption-based Truth Maintenance System) has been widely used for maintaining the truth of an information by detecting and solving the contradictions in rule-based systems. However, the ATMS cannot correctly maintain the truth of the information in case that the generated information is satisfied within a time interval or includes data about temporal relations of events in time varying situations, because it has no mechanism manipulating temporal data. In this paper, we propose the extended ATMS that can maintain the truth of the information in the knowledge-based system using information changing over time or temporal relations of events. To maintain the contexts generated by relations of events, we modify the label representation method, the disjunction and conjunction simplification method in the label-propagation procedure and the nogood handling method of the conventional ATMS.

A new phasor model of neural networks is proposed in which the state of each neuron possibly takes the value at the origin as well as on the unit circle. A stability property of equilibria is studied in association with the energy landscape. It is shown that a simple condition guarantees an equilibrium to be asymptotically stable.

Heartbeat interval time series is an example of natural signals with 1/f characteristics. The exponent α of the 1/f^α spectrum has some clinical significance. But sometimes the 1/f components is superimposed by some sinusoid components in the signal. To estimate the slope accurately, the 1/f component must be extracted from the signal. The singular spectrum analysis (SSA) method is recruited here to perform the task. Experimental results on data from real patients are satisfactory.