In this paper, we propose a realtime concatenation technique between basic skeletal motions obtained by the motion capture technique and etc. to generate a lifelike behavior for a humanoid character (avatar). We execute several experiments to show the advantage and the property of our technique and also report the results. Finally, we describe our applied system called WonderSpace which leads participants to the exciting and attractive virtual worlds with humanoid characters in cyberspace. Our concatenation technique has the following features: (1) based on a blending method between a preceding motion and a succeeding motion by a transition function, (2) realizing "smooth transition," "monotone transition," and "equivalent transition" by the transition function called paste function, (3) generating a connecting interval by making the backward and forward predictions for the preceding and succeeding motions, (4) executing the prediction under the hypothesis of "the smooth stopping state" or "the state of connecting motion", (5) controlling the prediction intervals by the parameter indicating the importance of the motion, and (6) realizing realtime calculation.

This paper presents a method of thread composition in a hardware compiler Bach. Bach synthesizes RT level circuits from a system description written in Bach-C language, where a system is modeled as communicating processes running in parallel. The system description is decomposed into threads, i.e., strings of sequential processes, by grouping processes which are not executed in parallel. The set of threads are then converted into behavioral VHDL models and passed to a behavioral synthesizer. The proposed method attempts to find a thread configuration that maximize resource sharing among processes in the threads. Experiments on two real designs show that the circuit sizes were reduced by 3.7% and 14.7%. We also show the detailed statistics and analysis of the size of the resulting gate level circuits.

This paper introduces a flexible, stream-oriented dataflow processing model based on the "Communicating Logic (CL)" framework. As the target architecture, we adopt the dual layered "Plastic Cell Architecture (PCA). " Datapath processing functionality is encapsulated in asynchronous hardware objects with variable graining and implemented using look-up tables. Communication (i.e. connectivity and control) between the distributed processing objects is achieved by means of inter-object message passing. The key point of the CL approach is that it offers the merits of scalable performance, low power hardware implementation with the user friendly compilation and linking capabilities unique to software.

Timing verification of digital synchronous designs is a complex process that is traditionally carried out deep in the design cycle, at the gate level. A method, embodied in a C++ based design system, is presented that allows modeling and verification of clock regions at a higher level. By combining event-driven, clock-cycle true and behavioral simulation, we are able to perform static and dynamic timing analysis of the clock regions.

We propose an adaptive complex-amplitude texture classifier that takes into consideration height as well as reflection statistics of interferometric synthetic aperture radar (SAR) images. The classifier utilizes the phase information to segment the images. The system consists of a two-stage preprocessor and a complex-valued SOFM. The preprocessor extracts a complex-valued feature vectors corresponding to height and reflectance statistics of blocks in the image. The following SOFM generates a set of templates (references) adaptively and classifies a block into one of the classes represented by the templates. Experiment demonstrates that the system segments an interferometric SAR image successfully into a lake, a mountain, and so on. The performance is better than that of a conventional system dealing only with the amplitude information.

A novel SC (Switched-Capacitor) offset- and gain-compensated sample/hold circuit is presented. It is implemented by a new topology which reduces the effects due to the imperfections of op-amp. Simulation results indicate that the circuit achieves high accuracy without requiring high-quality components.

In this paper, we propose fast bitmap search algorithms to reduce the computational complexity of transform-based vector quantization (VQ) techniques, which achieve better quality in reconstructed images than the ordinary VQ. By removing the unlikely codewords in each step, the bitmap search method, which starts from the most significant bitmap then the successive significant ones, can save more than 90% computation of the ordinary transformed VQ. By applying to the singular value decomposition (SVD) VQ as an example, theoretical analyses and simulation results show that the proposed bitmap search methods dramatically reduce the computation and achieve invisible distortion in the reconstructed images.

Causal message ordering in the context of group communication ensures that all the message receivers observe consistent ordering of events affecting a group as a whole. This paper presents a scalable causal multicast protocol for mobile distributed computing systems. In our protocol, only a part of the mobility agents in the system is involved in group computations and the resulting size of control information in messages can be kept small. Our protocol can outperform qualitatively the counterparts in terms of communication overhead and handoff complexity. An analytical model is also developed to evaluate our proposal. The performance results show that the proposed protocol is promising.

Data mining is to analyze all the data in a huge database and to obtain useful information for database users. One of the well-studied problems in data mining is the search for meaningful association rules in a market basket database which contains massive amounts of sales transactions. The problem of mining meaningful association rules is to find all the large itemsets first, and then to construct meaningful association rules from the large itemsets. In our previous work, we have shown that it is NP-complete to decide whether there exists a large itemset with a given size. Also, we have proposed a subclass of databases, called k-sparse databases, for which we can efficiently find all the large itemsets. Intuitively, k-sparsity of a database means that the supports of itemsets of size k or more are sufficiently low in the database. In this paper, we introduce the notion of (k,c)-sparsity, which is strictly weaker than the k-sparsity in our previous work. The value of c represents a degree of sparsity. Using (k,c)-sparsity, we propose a larger subclass of databases for which we can still efficiently find all the large itemsets. Next, we propose alternative measures to the support. For each measure, an itemset is called highly co-occurrent if the value indicating the correlation among the items exceeds a given threshold. In this paper, we define the highly co-occurrent itemset problem formally as deciding whether there exists a highly co-occurrent itemset with a given size, and show that the problem is NP-complete under whichever measure. Furthermore, based on the notion of (k,c)-sparsity, we propose subclasses of databases for which we can efficiently find all the highly co-occurrent itemsets.

This paper describes two competitive learning algorithms from the viewpoint of deleting mechanisms of weight (reference) vectors. The techniques are termed the adaptivity and sensitivity deletions participated in the criteria of partition error and distortion error, respectively. Experimental results show the effectiveness of the proposed approaches in the average distortion.

This Letter proposes a new three dimensional (3D) visual communication approach based on the image-based rendering. We first compactly represent a reference view set by exploiting its geometric correlation and then efficiently compress the representation with appropriate coding schemes. Experimental results demonstrate that our proposed method significantly reduces the required bitrate.

An algorithm for finding the optimal sectionalization for sectionalized trellises with respect to distinct optimality criterions was presented by Lafourcade and Vardy. In this paper, for linear block codes, we give a direct method for finding the optimal sectionalization when the optimality criterion is chosen as the total number |E| of the edges, the expansion index |E|-|V|+1, or the quantity 2|E|-|V|+1, only using the dimensions of the past and future sub-codes. A more concrete method for determining the optimal sectionalization is given for the Reed-Muller codes with the natural lexicographic coordinate ordering.

This paper presents a new neural network structure, called Temporal-CombNET (T-CombNET), dedicated to the time series analysis and classification. It has been developed from a large scale Neural Network structure, CombNET-II, which is designed to deal with a very large vocabulary, such as Japanese character recognition. Our specific modifications of the original CombNET-II model allow it to do temporal analysis, and to be used in large set of human movements recognition system. In T-CombNET structure one of most important parameter to be set is the space division criterion. In this paper we analyze some practical approaches and present an Interclass Distance Measurement based criterion. The T-CombNET performance is analyzed applying to in a practical problem, Japanese Kana finger spelling recognition. The obtained results show a superior recognition rate when compared to different neural network structures, such as Multi-Layer Perceptron, Learning Vector Quantization, Elman and Jordan Partially Recurrent Neural Networks, CombNET-II, k-NN, and the proposed T-CombNET structure.

Based on the partially-depleted, thin-film SOI/CMOS technology, the influence of reduced junction capacitance on the performance of the elementary gates and large scale gate array chip is reviewed. To further reduce the power consumption, SOI-specific device configurations, in which the body-bias is individually controlled, are effective in lowering the supply voltage and hence the power consumption while keeping the circuit speed. Two attempts are introduced: (1) DTMOS (Dynamic-Threshold MOS)/SOI to achieve ultra low-voltage and yet high-speed operation, and (2) ABB (Active-Body-Bias) MOS to enhance the current drive under the low supply voltage.

In this paper, we propose path accommodation methods for unidirectional rings based on an optical compression time-division multiplexing (OCTDM) technology. We first derive a theoretical lower bound on the numbers of slots and frames, in order to allocate all paths among nodes. Three path accommodation algorithms for the all-optical access are next proposed to achieve the lower bound as closely as possible. Path splitting is next considered to improve the traffic accommodation. Finally, we analyze the packet delay time for given numbers of slots/frames, which are decided by our proposed algorithms. Numerical examples are also shown to examine the effectiveness of our proposed algorithms including path accommodation and path splitting methods.

We propose a variation of the Context Tree Weighting algorithm for tree source modified such that the growth of the context resembles Lempel-Ziv parsing. We analyze this algorithm, give a concise upper bound to the individual redundancy for any tree source, and prove the asymptotic optimality of the data compression rate for any stationary and ergodic source.

The expected lengths of the parsed segments obtained by applying Lempel-Ziv incremental parsing algorithm for i.i.d. source satisfy simple recurrence relations. By extracting a combinatorial essence from the previous proof, we obtain a simpler derivation.

The transmission S-parameter between two dipole-elements is a measure to evaluate sites for measuring complex antenna factors (CAF). In this paper, the S-parameter between two dipole-elements on a ground plane is measured using a network analyzer with its TRL (Thru-Reflect-Line) calibration. The S-parameter is also calculated by the method of moment (MoM) and compared to the measurement results. The comparison shows that the calculated S-parameter is usable as a reference value in the evaluation of CAF measurement sites. As an example of the evaluation and selection of measurement sites, the transmission S-parameter on a finite ground plane is calculated using the hybrid method combined the geometrical theory of diffraction (GTD) and MoM. As a result, a preferable antenna setting on the finite ground plane is recommended.

The use of a chirp signal is one of the methods to expand the detection range in subsurface radar. However, the presence of time-sidelobes after a conventional pulse-compression makes the detection range degraded because weak signals from underground objects are covered with a large time-sidelobe due to a ground surface reflection. In this paper, we propose a new pulse compression subsurface radar using a short chirp signal in which the echoes from the ground surface and the object are not overlapped. We show that the short chirp signal can improve the detection ability compared with a conventional chirp signal and examine the influence that the decreases of the signal duration and the compression ratio exert on the detection range. By the new pulse compression subsurface radar, the steel pipes buried down to 5 m in depth can be detected.

A new method of classification of sub-image blocks for digital image compression purposes using neural network is proposed. Two different classification algorithms are used to show their greater effectiveness than the conventional classification techniques. Simulation results are presented which demonstrate the effectiveness of the new technique.