In this paper, we present a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities. Central to our method is the extension of the scale-space theory to the three-dimensional space to allow feature analysis and classification at different scales. Then, a new surface classifier is computed and used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and incomplete line features. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. Applications include 3D shape matching and retrieval, surface reconstruction and feature preserving simplification.

Many immersive displays developed in previous researches are strongly influenced by the design concept of the CAVE, which is the origin of the immersive displays. In the view of human-scale interactive system for virtual environment (VE), the existing immersive systems are not enough to use the potential of a human sense further extent. The displays require more complicated structure for flexible extension, and are more restrictive to user's movement. Therefore we propose a novel multi-projector display for immersive VE with haptic interface for more flexible and dynamic interaction. The display part of our system named "D-vision" has a hybrid curved screen which consist of compound prototype with flat and curve screen. This renders images seamlessly in real time, and generates high-quality stereovision by PC cluster and two-pass technology. Furthermore a human-scale string-based haptic device will integrate with the D-vision for more interactive and immersive VE. In this paper, we show an overview of the D-vision and technologies used for the human-scale haptic interface.

A novel NiSi technology with bi-layer Co/TiN structure as a capping layer is proposed for the highly thermal immune Ni Silicide technology. Much better thermal immunity of Ni Silicide was certified up to 700, 30 min post silicidation furnace annealing by introducing Co/TiN bi-layer capping. The proposed structure is successfully applied to nano-scale CMOSFET with a gate length of 80 nm. The sheet resistance of nano-scale gate poly shows little degradation even after the high temperature furnace annealing of 650, 30 min. The Ni/Co/TiN structure is very promising for the nano-scale MOSFET technology which needs the ultra shallow junction and high temperature post silicidation processes

The development of information/communication technology has made it possible to access substantial amounts of data and retrieve information. However, it is often difficult to locate the desired information, and it becomes necessary to spend considerable time determining how to access specific available data. This paper describes a method to quantitatively evaluate the usability of large-scale information-oriented websites and the effects of improvements made to the site design. This is achieved by utilizing the Cognitive Walkthrough for the Web and website modeling using Markov chains. We further demonstrate that we can greatly improve usability through simple modification of the link structure by applying our approach to an actual informational database website with over 40,000 records.

Current centralized restoration schemes are unsuitable for the increase of scale and complexity of networks. A novel distributed network partition scheme is proposed in this paper. In this scheme, a large-scale network can be partitioned into some wheellike sub-networks with nuclear nodes. In wheellike sub-networks, ring links and spoke links could provide reciprocal safeguard. Based on such structure, different distributed restoration schemes can be combined for failure restoration. The proposed partition approach has been implemented through computer simulation, and it was tested on practical national-scale optical networks. The simulation result shows that this scheme is practicable and effectual.

This paper presents the definition and implementation design of a low power data bus encoding scheme dedicated to system on chip video architectures. Trends in CMOS technologies focus the attention on the energy consumption issue related to on-chip global communication; this is especially true for data dominated applications such as video processing. Taking into account scaling effects a novel coupling-aware bus power model is used to investigate the statistical properties of video data collected in the system bus of a reference hardware/software H.263/MPEG-4 video coder architecture. The results of this analysis and the low complexity requirements drive the definition of a bus encoding scheme called CDSPBI (Coupling Driven Separated Partial Bus Invert), optimized ad-hoc for video data. A VLSI implementation of the coding circuits completes the work with an area/delay/power characterization that shows the effectiveness of the proposed scheme in terms of global power saving for a small circuit area overhead.

In this paper, we introduce a new technology to extract the unique features from an iris image, which uses scale-space filtering. Resulting iris code can be used to develop a system for rapid and automatic human identification with high reliability and confidence levels. First, an iris part is separated from the whole image and the radius and center of the iris are evaluated. Next, the regions that have a high possibility of being noise are discriminated and the features presented in the highly detailed pattern are then extracted. In order to conserve the original signal while minimizing the effect of noise, scale-space filtering is applied. Experiments are performed using a set of 272 iris images taken from 18 persons. Test results show that the iris feature patterns of different persons are clearly discriminated from those of the same person.

This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.

The hardware implementation of a neural network model using stochastic logic has been able to integrate numerous neuron units on a chip. However, the limitation of applications occurred since the stochastic neurosystem could execute only discrete-time dynamics. We have contrived a neuron model with continuous-time dynamics by using stochastic calculations. In this paper, we propose the circuit design of a new neuron circuit, and show the fabricated neurochip comprising 64 neurons with experimental results. Furthermore, a new asynchronous updating method and a new activation function circuit are proposed. These improvements enhance the performance of the neurochip greatly.

The sizes of recent Java-based server-side applications, like J2EE containers, have been increasing continuously. Past techniques for improving the performance of Java applications have targeted relatively small applications. Moreover, when the methods of these small target applications are invoked, they are not usually distributed over the entire memory space. As a result, these techniques cannot be applied efficiently to improve the performance of current large applications. We propose a dynamic code repositioning approach to improve the hit rates of instruction caches and translation look-aside buffers. Profiles of method invocations are collected when the application performs with its heaviest processor load, and the code is repositioned based on these profiles. We also discuss a method-splitting technique to significantly reduce the sizes of methods. Our evaluation of a prototype implementing these techniques indicated 5% improvement in the throughput of the application.

In this paper we apply a parallel adaptive solution algorithm to simulate nanoscale double-gate metal-oxide-semiconductor field effect transistors (MOSFETs) on a personal computer (PC)-based Linux cluster with the message passing interface (MPI) libraries. Based on a posteriori error estimation, the triangular mesh generation, the adaptive finite volume method, the monotone iterative method, and the parallel domain decomposition algorithm, a set of two-dimensional quantum correction hydrodynamic (HD) equations is solved numerically on our constructed cluster system. This parallel adaptive simulation methodology with 1-irregular mesh was successfully developed and applied to deep-submicron semiconductor device simulation in our recent work. A 10 nm n-type double-gate MOSFET is simulated with the developed parallel adaptive simulator. In terms of physical quantities and refined adaptive mesh, simulation results demonstrate very good accuracy and computational efficiency. Benchmark results, such as load-balancing, speedup, and parallel efficiency are achieved and exhibit excellent parallel performance. On a 16 nodes PC-based Linux cluster, the maximum difference among CPUs is less than 6%. A 12.8 times speedup and 80% parallel efficiency are simultaneously attained with respect to different simulation cases.

Recently, the small-world network model has been popular to describe a wide range of networks such as human social relations and networks formed by biological entities. The network model achieves a small diameter with relatively few links as measured by the ratio of clustering coefficient and the number of links. It is quite natural to consider email communication similar to social network patterns. Quite surprisingly, we find from our empirical study that local email networks follow a different type of network model that falls into the category of scale-free network. We propose new network models to describe such communication structure.

The number of customers of a service for Internet access from cellular phones in Japan has been explosively increasing for some time. We analyze the relation between the number of customers and the volume of traffic, with a view to finding clues to the structure of human relations among the very large set of potential customers of the service. The traffic data reveals that this structure is a scale-free network, and we calculate the exponent that governs the distribution of node degree in this network. The data also indicates that people who have many friends tend to subscribe to the service at an earlier stage. These results are useful for investigating various fields, including marketing strategies, the propagation of rumors, the spread of computer viruses, and so on.

Autonomous Decentralized Community Information System (ADCS) is a proposition made to meet the rapidly changing users' requirements and cope with the extreme dynamism in current information services. ADCS is a decentralized architecture that forms a community of individual end-users (community members) having the same interests and demands in specified time and location. It allows those members to mutually cooperate and share information without loading up any single node excessively. In this paper, an autonomous decentralized community communication technology is proposed to assure a productive cooperation, a flexible and timely communication among the community members. The main ideas behind this communication technology are: content-code communication (service-based) for flexibility and multilateral communication for timely and productive cooperation among members. All members communicate productively for the satisfaction of all the community members. The scalability of the system's response time regardless of the number of the community members is shown through simulation. Thus, the autonomous decentralized community communication technology reveals significant results when the total number of members in the community increases sharply.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.

A mathematical theory is proposed based on the concept of functional analysis, suitable for operation of network systems extraordinarily complicated and diversified on large scales, through connected-block structures. Fundamental conditions for existence and evaluation of system behaviors of such network systems are obtained in a form of fixed point theorem for system of nonlinear mappings.

The current paper presents an effective deblocking algorithm for block-based coded images using singularity detection in a wavelet transform. Blocking artifacts appear periodically at block boundaries in block-based coded images. The local maxima of a wavelet transform modulus detect all singularities, including blocking artifacts, from multiscale edges. Accordingly, the current study discriminates between a blocking artifact and an edge by estimating the Lipschitz regularity of the local maxima and removing the wavelet transform modulus of a blocking artifact that has a negative Lipschitz regularity exponent. Experimental results showed that the performance of the proposed algorithm was objectively and subjectively superior.

A mathematical theory is proposed, based on the concept of functional analysis, suitable for operation of network systems extraordinarily complicated and diversified on large scales, through connected-block structures. Fundamental conditions for existence and evaluation of system behaviors of such network systems are obtained in a form of fixed point theorem for system of nonlinear mappings.

This paper studies the local Poisson property of aggregated IP traffic. First, it describes the scenario where IP traffic presents a Poisson-like characteristic within some limited range of time scales when packets from independent traffic streams are aggregated. Each of the independent traffic streams corresponds to a series of correlated IP packets such as those of a transport connection. Since the Poisson-like characteristic is observed only within some limited range of time scales, we call this characteristic the local Poisson property. The limited range of time scales of the local Poisson property can be estimated from a network configuration and characteristics of transport connections. Second, based on these observations, we seek the possibility to apply an ordinary Poisson process to evaluation of the packet loss probability in IP networks. The analytical investigation, where IP traffic is modeled by a superposition of independent branching Poisson processes that presents the local Poisson property, suggests that the packet loss probability can be estimated by a finite-buffer queue with a Poisson process when the buffer size is within a certain range. The investigation is verified by simulations. These findings expand the applicability of conventional Poisson-based approaches to IP network design issues.

We describe a multiresolution 3D active balloon model to trace the boundaries of moving object. This model is able to analyze a shape hierarchically using 3D scale-space. The 3D scale-space can be determined by changing the parameters of the active balloon. We extended 2D process-grammar to describe the deformation process between a shape and a sphere, based on topological scale-space analysis. The geometric invariant features were used to analyze the deformation of nonrigid shapes. We analyzed the motion of a heart by using MRI data.