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Multimodal Synchronization for Human-Machine Interaction in Virtual Environment
(2002-12-01)
Wongwirat, O.
;
Chotikakamthorn, N.
;
Ohara, S.
Human-machine interfacing technologies lead the human to communicate with virtual environment in various effective format and more interactive manner. With the combination of keyboard, mouse, haptic, gesture, speech, and vision modes, the interfaces allow human to be involved with the virtual environment in multimodal form, as if they are embedded as a part of simulation. However, with different mechanism and computing method in multimode interface, they lack of synchronization to support human interaction in the virtual environment. This paper explained human-machine interfacing techniques and proposed multimodal synchronization mechanism to control the tempo and to solve the problems of multimodal interaction. The experimental set to verify human perception with multimode human-machine interface in distributed virtual environment was described at the end. The result gave some figure of how human could adjust their perception to be synchronized with the interactive event in the virtual environment through multimode interface.
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A learning mechanism for adaptive fitness function in auto 3D graphics layout using genetic algorithm
(2001-01-01)
Walairacht, A.
;
Thanapandi, C.
;
Ohara, S.
;
Wongwirat, O.
;
Burintramart, I.
In computer-aided drafting and designing area, interactive graphics are used for designing components, systems, layouts, and structures. There are several approaches using for automated graphical layout tools in designing the graphic currently. Our research work aims to reduce design-working time, to learn user preferences, and to generate 3D graphical layout design automatically. An attempt to enable computers to generate 3D graphical layout design and presentation automatically by using Genetic Algorithm (GA) has been discussed for a decade. An effective use of GA in automated graphical layout design relies on how close to define a fitness function that can be reflected the user preferences. In this paper, we introduce the methods to define chromosome structures and fitness functions and of the selected objects. A learning mechanism is employed to adjust the fitness values of the objects in the selected layout choosing by the users. By this approach, the fitness functions can be changed adaptively reflecting the selection and user preferences.
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GTCS: A Greedy Task Clustering and Scheduling algorithm for distributed memory processor architecture
(2000-01-01)
Piyatamrong, Banjong
;
Ohara, S.
;
Kantakajorn, S.
Presents a task-clustering algorithm named GTCS (Greedy Task Clustering and Scheduling). The algorithm represents the tasks of a parallel program as nodes and schedules them with a directed acyclic task graph (DAG). The parallel hardware architecture focused on in this paper is the distributed memory architecture. GTCS implements the task-clustering method by partitioning the nodes on a DAG. Each group or cluster within the DAG is placed on only one processor. The algorithm partitions the DAG into several clusters to reduce the communication overhead between the processors, because the network has a lot of communication overhead, which increases the total execution time or makespan of the tasks, especially in parallel programs that perform fine-grained tasks on a network. The GTCS algorithm produces a schedule whose makespan is at most twice that of the optimal cluster. GTCS has a time complexity equal to O[m(|V|·lg|V|+|E|)] where m is the number of clusters and 1≤m≤V.

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