A lexicon is an important linguistic resource needed for both shallow and deep language processing. Currently, there are few machine-readable Thai dictionaries available, and most of them do not satisfy the computational requirements. This paper presents the design of a Thai lexicon named the TCL's Computational Lexicon (TCLLEX) and proposes a method to construct a large-scale Thai lexicon by re-using two existing dictionaries and a large number of texts on the Internet. In addition to morphological, syntactic, semantic case role and logical information in the existing dictionaries, a sort of semantic constraint called selectional preference is automatically acquired by analyzing Thai texts on the web and then added into the lexicon. In the acquisition process of the selectional preferences, the so-called Bayesian Information Criterion (BIC) is applied as the measure in a tree cut model. The experiments are done to verify the feasibility and effectiveness of obtained selection preferences.

Recent work has shown that the usage of multiple antennas at the transmitter and receiver in a flat fading environment results in a linear increase in channel capacity. But increasing the number of antennas induces the higher hardware costs and computational burden. To overcome those problems, it is effective to select antennas appropriately among all available ones. In this paper, a new antenna selection method is proposed. The transmit antennas are selected so as to maximize the channel capacity using the genetic algorithm (GA) which is the one of the general random search algorithm. The results show that the proposed GA achieves almost the same performance as the optimal selection method with less computational amount.

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the overall translation accuracy of machine translation systems. In this paper, we present a new approach to Korean target word selection for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean Local Context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. To evaluate our approach, we applied the method to Tellus-EK system, English-Korean automatic translation system currently developed at ETRI [1],[2]. The experiment showed promising results for diverse sentences from web documents.

In closed-loop multiple-input and multiple-output space-division multiplexing (MIMO-SDM) systems, allocating power among multiple transmit data streams improves the channel capacity. However, the optimum power allocation values are not always available in closed-form. For instance, when we use transmission and reception schemes that do not transfer the MIMO channel into parallel orthogonal channels (e.g., eigen-mode SDM), the signal to interference plus noise ratio (SINR) of each data stream at the output of the receiver is not proportional to its corresponding transmit power. This feature makes it difficult to obtain the optimal closed-form power allocation value for each data stream. Thus, in this paper, we propose an iterative power allocation scheme for MIMO-SDM systems where the SINR is not proportional to the transmit power. Furthermore, we incorporate a transmit antenna selection scheme into the proposed power allocation scheme in order to attain further capacity enhancement. Computer simulation results are provided to show the effectiveness of the proposed power allocation schemes.

Efficient task scheduling is critical for achieving high performance in grid computing systems. Existing task scheduling algorithms for grid environments usually assume that the performance prediction for both tasks and resources is perfectly accurate. In practice, however, it is very difficult to achieve such an accurate prediction in a heterogeneous and dynamic grid environment. Therefore, the performance of a task scheduling algorithm may be significantly influenced by prediction inaccuracy. In this paper, we study the influence of inaccurate predictions on task scheduling in the contexts of task selection and processor selection, which are two critical phases in task scheduling algorithms. We develop formulas for the misprediction degree, which is defined as the probability that the predicted values for the performances of tasks and processors reveal different orders from their real values. Based on these formulas, we also investigate the effect of several key parameters on the misprediction degree. Finally, we conduct extensive simulation for the sensitivities of some existing task scheduling algorithms to the prediction errors.

Our group is exploring interactive multi- and hypermedia, especially applied to virtual and mixed reality multimodal groupware systems. We are researching user interfaces to control source→sink transmissions in synchronous groupware (like teleconferences, chatspaces, virtual concerts, etc.). We have developed two interfaces for privacy visualization of narrowcasting (selection) functions in collaborative virtual environments (CVES): for a workstation WIMP (windows/icon/menu/pointer) GUI (graphical user interface), and for networked mobile devices, 2.5- and 3rd-generation mobile phones. The interfaces are integrated with other CVE clients, interoperating with a heterogeneous multimodal groupware suite, including stereographic panoramic browsers and spatial audio backends & speaker arrays. The narrowcasting operations comprise an idiom for selective attention, presence, and privacy-- an infrastructure for rich conferencing capability.

To achieve profits derived from multihoming, policy routing is supposed to be necessary. For routers' policy routing, some systems and implementations already exist. In addition to routers' operating systems with policy routing functions, IP Filter and iproute2 are useful for policy routing on routers. But they don't suit for policy routing on multihomed end-hosts because of differences on network environments between routers and end-hosts. In contrast with routers' network environments, there are some dynamic changes of network environments with some types of end-hosts, for example, laptop computers. Therefore, another policy routing system is needed for end-hosts' policy routing, which adapts to dynamic changes of network environment. PMPATH (Policy based MultiPATH routing system) is a policy routing system and designed especially for end-hosts uses. PMPATH uses source address selection as network selection, PMPATH can adapt to dynamic changes of network environment. PMPATH's policies for source address selection are defined in form of address block. PMPATH also provides outgoing interface selection. We implemented PMPATH on NetBSD 2.99.15 and tested its performance on multihomed end-hosts. PMPATH implementation works well even if there are changes of network environment. In this result, it is shown that PMPATH is useful for multihomed end-hosts' policy routing.

In optical burst switching (OBS) networks, the contention of optical bursts is the most serious problem due to the lack of buffers within the networks. Various deflection routing schemes and a routing scheme based on pre-calculated multiple paths have been proposed to resolve the contention. The latter routing scheme can successfully maintain a relatively limited transfer delay of optical bursts. This paper proposes a new decentralized routing scheme based on multiple paths to effectively resolve the contention of optical bursts. In this scheme, each source node splits the traffic load into pre-calculated multiple paths adaptively according to the measured loss rate of the optical bursts transferred through each path. This scheme does not require frequent notification of the measured loss rate because each source node selects one of the multiple paths probabilistically. In the OBS networks, the average transfer delay in the multi-path routing always exceeds that in a single-path routing because alternative paths with a larger transfer delay are also utilized in the multi-path routing. Thus, this paper proposes an adaptive load splitting method in which load splitting ratios for the multiple paths are autonomously adjusted to minimize the average transfer delay based on the condition that the required loss rate of optical bursts is satisfied. The performance of the proposed scheme was evaluated by computer simulation and based on the evaluation results; the ability of the proposed scheme to adjust the load splitting ratios for the multiple paths autonomously and avoid the contention of optical bursts adaptively is clarified even if the traffic load applied to the OBS network changes.

Access selection in future multiple radio access environments is considered in this paper from a new perspective, that of the consumer. A model is proposed for the automatic acquisition of user preferences to assist in access selection decision making. The proposed approach uses a two-level Bayesian C-Metanetwork that models individual user preferences in terms of affordable cost, acceptable level of quality of service and reputation of the access networks. User preferences under different contexts, such as leisure and business, are also considered. The model also adapts to the change of user preferences over time. A simulator has been developed to evaluate the proposed model and the simulation results are promising in terms of the proportion of correct preference predictions after a small number of training samples.

This paper proposes a novel speech synthesis method to generate human-like natural speech. The conventional unit-selection-based synthesis method selects speech units from a large database, and concatenates them with or without modifying the prosody to generate synthetic speech. This method features highly human-like voice quality. The method, however, has a problem that a suitable speech unit is not necessarily selected. Since the unsuitable speech unit selection causes discontinuity between the consecutive speech units, the synthesized speech quality deteriorates. It might be considered that the conventional method can attain higher speech quality if the database size increases. However, preparation of a larger database requires a longer recording time. The narrator's voice quality does not remain constant throughout the recording period. This fact deteriorates the database quality, and still leaves the problem of unsuitable selection. We propose the plural unit selection and fusion method which avoids this problem. This method integrates the unit fusion used in the unit-training-based method with the conventional unit-selection-based method. The proposed method selects plural speech units for each segment, fuses the selected speech units for each segment, modifies the prosody of the fused speech units, and concatenates them to generate synthetic speech. This unit fusion creates speech units which are connected to one another with much less voice discontinuity, and realizes high quality speech. A subjective evaluation test showed that the proposed method greatly improves the speech quality compared with the conventional method. Also, it showed that the speech quality of the proposed method is kept high regardless of the database size, from small (10 minutes) to large (40 minutes). The proposed method is a new framework in the sense that it is a hybrid method between the unit-selection-based method and the unit-training-based method. In the framework, the algorithms of the unit selection and the unit fusion are exchangeable for more efficient techniques. Thus, the framework is expected to lead to new synthesis methods.

In this study, we present a way to choose route selection metric while discovering a new route in ad hoc mobile networks. We have used link expiration time and busy rate to calculate the route cost. The route cost is compared to a threshold value to decide whether the traffic of the route is high or low. If it is high then the system chooses busy rate as a route selection metric to avoid traffic congestion and if it is low the link expiration time is used to select the longlasting route. We have examined the characteristics of the routing protocol by computer simulation and found that it over performs the conventional protocols.

In the future network, optical technology will play a stronger role not only for transmission but also for switching. Optical burst switching (OBS) emerged as a promising switching paradigm. It brings together the complementary strengths of optics and electronics. This paper presents the design and implementation of an overlay mode burst-switched photonic network testbed, including its architecture, protocols, algorithms and experiments. We propose a flexible "transceiver + forwarding" OBS node architecture to perform both electronic burst assembly/disassembly and optical burst forwarding. It has been designed to provide class of service (CoS), wavelength selection for local bursts, and transparency to cut-through bursts. The functional modules of OBS control plane and its key design issues are presented, including signaling, routing, and a novel scheduling mechanism with combined contention resolution in space and wavelength domains. Finally, we report the experimental results on functional verification, performance analysis and service demonstration.

Deflection routing is one of the promising approaches to resolve contention in the optical burst switching networks. In the conventional deflection routing scheme, optical bursts may be unable to traverse the route evaluated to select an outgoing link because of the contention at succeeding downstream transit nodes. As a result, the optical bursts may traverse a different route resulting in a long distance or decreased performance. This paper proposes a deflection routing scheme that considers the possibility of the contention at downstream nodes. This scheme utilizes the "expected route distance" instead of the static route distance toward a destination node. The expected route distance considers the possibility of contention at each downstream transit node and is calculated using measured link blocking probabilities at each downstream transit node. The selection priority of each outgoing link is given dynamically based on its expected route distance toward a destination node. By considering the possibility of contention at downstream nodes, a routing scheme with high performance can be realized. The loss rate of optical bursts is improved when an imbalanced load is applied to the network, and the loss rate of optical bursts is also improved when the network includes links with extremely different distances.

This paper proposes a "structuring search space" (SSS) method aimed to accelerate recognition of large character sets. We divide the feature space of character categories into smaller clusters and derive the centroid of each cluster as a pivot. Given an input pattern, it is compared with all the pivots and only a limited number of clusters whose pivots have higher similarity (or smaller distance) to the input pattern are searched in, thus accelerating the recognition speed. This is based on the assumption that the search space is a distance space. We also consider two ways of candidate selection and finally combine them the method has been applied to a practical off-line Japanese character recognizer with the result that the coarse classification time is reduced to 56% and the whole recognition time is reduced to 52% while keeping its recognition rate as the original.

Future wireless/mobile system is expected to have heterogeneous wireless overlay networks for ubiquitous multimedia communication. In a such network environment, mobile users are likely to try to get attached to higher bandwidth network as bandwidth-hungry multimedia applications are increasing. However, the users have to perform vertical handoff to lower bandwidth network, as high bandwidth network become unavailable due to various reasons (such as its limited coverage, network congestion, etc.). In this paper, we discuss the problem of vertical handoff from a user's perspective. For this purpose, we formulate user satisfaction as a function of bandwidth utility and handoff latency. Then, we investigate the effect of call holding time, user movement probability, etc. on the satisfaction that a user derives from the use of network service for multimedia applications. In addition, based on the evaluation, we present an algorithm for selecting a wireless network, which maximizes the effective user satisfaction.

When video data are transmitted via the network, the quality of video data must be carefully chosen to be best under the condition that the transmission is not influenced by other internet services. They often use the simulcast type, which uses independent streams that are stored and transmitted for the quality, considering implementation, when they select the video quality. On the other hand, we had already proposed the scalable structure, which consists of base and enhancement data, but when they require the high quality video, these data are combined using the transcoding methods. In this paper, we propose the video contents delivery methods with scalable transcoding, in which users can update the quality of video data even after the transmission by base data and differential data. In order to reduce the total time of not only users' access time, but also watching time, we compare simulcast method with proposed methods in the total content utilization time using a video contents access model, and evaluate required transcoding time to reduce the waiting time of users.

Mobile devices with the ability to access IEEE 802.3, 802.11, 802.16 and cellular networks in various combinations are driving the need for seamless session continuity and IP address continuity when transitioning between these networks. An approach to facilitating service transition based on triggering and providing network detection and selection assistance is being followed in the IEEE 802.21 working group. This paper presents some of the issues and proposed approaches.

One of the most challenging tasks in computer graphics and cyberworlds is the realistic animation of the behavior of virtual agents emulating human beings and evolving within virtual 3D worlds. In a previous paper, the authors presented a new, sophisticated behavioral system that allows the agents to take intelligent decisions by themselves. A central issue of this process is the adequate choice of appropriate mechanisms for goal selection. This is actually the aim of the present contribution. In this paper a new scheme for goal selection is described. According to it, the goal's priority is calculated as a combination of different agent's internal states (given by mathematical functions also described in this paper) and external factors (which will determine the goal's feasibility). The architecture of the goal selection module as well as its simulation flow are also analyzed in this paper. Finally, the excellent performance of this new scheme is enlightened by means of a simple yet illustrative example.

Recent studies show that several FFT-based high-accuracy frequency estimation methods achieve very good performance. In this letter, we select three methods, which are the zero-padding, weighted multipoint interpolated DFT, and phase difference approximation respectively, and discuss the window selection for each method. Experiments show that the window selection primarily depends on the signal-to-noise ratio (SNR). As a result, the optimal window selection for each method and, reversely, the optimal selection of the estimation method for a specific window are discussed as a function of SNR. Consideration on the computational load and the resolution problem is also briefly discussed.

Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.