Earticle

Context-awareness has become a distinguishing feature of Ubiquitous systems. Contrary to desktop and web applications, Ubiquitous applications gather environmental context and provide services without user intervention. In this paper, we propose a message-oriented application model that is capable of modeling both user and system initiative interaction paradigms. Systems designed based on this model are inherently loosely-coupled and scalable. Results of this research include a systematic development procedure and its supporting middleware. We show the feasibility of this model by constructing several Ubiquitous applications for two dissimilar demo sites, and discuss experiences when adopting this model.

The future wireless mobile communication systems will be required to support high-speed transmission rate. Direct sequence code-division multiple access (DS-CDMA) and multicarrier (MC-) CDMA are two important CDMA schemes for high rate wireless communication. A novel linear per user minimum mean square error (PU-MMSE) detection technique for DS-CDMA with frequency domain equalization (FDE) based on the MMSE criterion applied per user is presented. It is shown by computer simulation that PU-MMSE outperforms conventional per carrier (PC-) MMSE for DS-CDMA with FDE in frequency selective block fading channel. Especially, in case of uplink PU-MMSE gains better performance than conventional MMSE not only for non-full-load DS-CDMA with FDE but also for full-load DS-CDMA with FDE. The performance of DS-CDMA with PU-MMSE FDE can be further promoted by successive interference cancellation (SIC). Comparing to MCCDMA with PU-MMSE FDE, DS-CDMA with PU-MMSE FDE has no performance loss and has much lower peak-to-average power ratio (PAPR).

In this paper, we introduce a novel method for extracting “topics” as interesting events in a video. Here, we define the interestingness of an event by the anomaly of a target character’s appearance and disappearance pattern. As examples of abnormal patterns, shot durations in thrilling events are very short while shot durations in romantic events are very long. In contrast, as an example of non-abnormal pattern, conversation events are presented by the pattern, where the target character repeatedly appears in one shot and then another character appears in the next shot. From the above point of view, our topic extraction method aims to detect the following two types of abnormal patterns, called “bursts”. The first type of burst is a pattern where the target character appears in shots with very short durations, while the second is a pattern where he/she appears in shots with very long durations. To detect such bursts, we firstly divide the video into events characterized by specific patterns of the target character’s ppearance and disappearance. We locate these patterns in the video by using time series segmentation technique. Then, we extract topics by examining whether the pattern in each event can be regarded as a burst or not. Experiments on different videos validate that a character's ppearance and disappearance patterns are effective for obtaining semantically meaningful events. And, bursts are useful for extracting many interesting topics.

In this work, we propose a hybrid medium access scheme that can provide a virtually collision-free environment for an infrastructure high-speed WLAN. The proposed hybrid scheme may be viewed as the merger of the standard PCF and DCF schemes. It can efficiently provide the quality-of-service support like the PCF scheme, while utilizing the flexible on-demand CSMA mechanism similar to the DCF scheme. In the proposed scheme, every station employs the standard CSMA mechanism and the master station schedules backoff timers for every client station. The master station sends the backoff timer information together with the data or control frames destined to each given client station. A piggybacking frame exchange mechanism is provided to enhance throughput of the proposed system and a prioritized backoff-timer cheduling scheme is given to provide QoS support. Numerical results show that the proposed scheme outperforms the standard IEEE 802.11 DCF and PCF schemes, and can effectively support prioritized traffic services.

Data Grid environment seek to harness geographically distributed resources that deal with data-intensive problems such as those encountered in high energy physics, bio-informatics, and other disciplines. In general, grids enable the efficient sharing and management of computing resources for the purpose of performing large complex tasks. To be able to sharing data, it is recommended to use the replication technique. This technique provides an improvement in performance, fault tolerance and load balancing. The replication management and its implementation are not simple tasks and produce other problems, like consistency management of replicas. One of the concerns major in the consistency management approaches called optimistic, it is the conflicts resolution among replicas. In this paper we present negotiation mechanisms based on the various negotiation forms between virtual consistency agents to be able to critical situation for sites and to reduce the number of conflicts among replicas to converge them more quickly in data grids.

We have already proposed a new feature extraction method based on higher-order local auto-correlation and Fisher weight map (FWM) at Interspeech2006. This paper shows effectiveness of the proposed FWM in speaker dependent and speaker independent phoneme recognition. Widely used MFCC features lack temporal dynamics. To solve this problem, local auto-correlation features are computed and accumulated by weighting high scores on the discriminative areas. This score map is called Fisher weight map. From the speaker dependent phoneme recognition, the proposed FWM showed 79.5% recognition rate, by 5.0 points higher than the result by MFCC. Furhermore by combing FWM with MFCC and ¢MFCC, the recognition rate improved to 88.3%. In the speaker independent phoneme recognition, it showed 84.2% recognition rate, by 11.0 points higher than the result by MFCC. By combining FWM with MFCC and ¢MFCC, the reecognition rate improved to 89.0%.

Audio has a key index in digital videos that can provide useful information for video editing, such as capturing conversations only, clipping only talking people, and so on. In this paper, we are studying about video editing based on audio with a two-channel (stereo) microphone that is standard equipment on video cameras, where the video content is automatically recorded without a cameraman. In order to capture only a talking person on video, a novel voice/non-voice detection algorithm using AdaBoost, which can achieve extremely high detection rates in noisy environments, is used. In addition, the sound source direction is estimated by the CSP (Crosspower-Spectrum Phase) method in order to zoom in on the talking person by clipping frames from videos, where a two-channel (stereo) microphone is used to obtain information about time differences between the microphones.

For a mobile robot to serve people in actual environments, such as a living room or a party room, it must be easy to control because some users might not even be capable of operating a computer keyboard. For non-expert users, speech recognition is one of the most effective communication tools when it comes to a hands-free (human-robot) interface. This paper describes a new mobile robot with hands-free speech recognition. For a hands-free speech interface, it is important to detect commands for a robot in spontaneous utterances. Our system can understand whether user’s utterances are commands for the robot or not, where commands are discriminated from humanhuman conversations by acoustic features. Then the robot can move according to the user’s voice (command). In order to capture the user’s voice only, a robust voice detection system with AdaBoost is also described.

As an optimization technique, particle swarm optimization (PSO) has obtained much attention during the past decade. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to realize. To enhance the performance of PSO, an improved hybrid particle swarm optimization (IPSO) is proposed to solve complex optimization problems more efficiently, accurately and reliably. It provides a new way of producing new individuals through organically merges the harmony search (HS) method into particle swarm optimization (PSO). During the course of evolvement, harmony search is used to generate new solutions and this makes IPSO algorithm have more powerful exploitation capabilities. Simulation results and comparisons with the standard PSO based on several well-studied benchmarks demonstrate that the IPSO can effectively enhance the searching efficiency and greatly improve the search quality.

This paper discusses the application of Petrinet as the workflow tool to model urban Traffic system. The paper is divided into phase : the first phase deals with orthogonal extension into phase : the first phase deals with orthogonal extension of Petrinet to enhance the permutation of control and traffic flow simulation. The second phase discusses application of continuous pertinent with intelligent agents to the model the UTS in continuous format with single central control agent.