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Titlebook: Computer Human Interaction; 6th Asia Pacific Con Masood Masoodian,Steve Jones,Bill Rogers Conference proceedings 2004 Springer-Verlag Berli

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21#
發(fā)表于 2025-3-25 04:17:39 | 只看該作者
22#
發(fā)表于 2025-3-25 09:11:32 | 只看該作者
Does DOF Separation on Elastic Devices Improve User 3D Steering Task Performance?and errors) and speed (time). The task consisted of steering paths that required the use of two or three DOF simultaneously. We found that users performed faster on the SpaceMouse but were less coordinated and accurate than on the DigiHaptic for the most complicated paths.
23#
發(fā)表于 2025-3-25 12:52:15 | 只看該作者
24#
發(fā)表于 2025-3-25 18:55:08 | 只看該作者
25#
發(fā)表于 2025-3-25 20:05:15 | 只看該作者
Hg-Festdosierung in Leuchtstofflampen,mistake of operation. To simplify authoring of multimedia data for the instruction, the Web browser in information presentation was adopted for using the existent resources. The result of the experiment suggested that the system improved reliability of information acquisition, obtaining information of what a user sees.
26#
發(fā)表于 2025-3-26 03:59:37 | 只看該作者
Na-Verluste bei NAV-T 70 W/E-Lampen,mbership; or concentrate on accuracy or on coverage of the data..This paper describes both the visualisation system and the machine learning component, with a focus on the interactive nature of the data mining process, and provides case studies to demonstrate the capabilities of the system.
27#
發(fā)表于 2025-3-26 06:56:18 | 只看該作者
28#
發(fā)表于 2025-3-26 10:43:47 | 只看該作者
,Grenzen der Lichterzeugung bei Glühlampen,oduced the observed sequence of dynemes. This approach enables the CHMR system to track and recognise hundreds of full-body movement skills from gait to twisting summersaults. This extends the perceptual user interface beyond frontal posing or only tracking one hand to recognise and understand full-body movement in terms of everyday activities.
29#
發(fā)表于 2025-3-26 14:49:06 | 只看該作者
30#
發(fā)表于 2025-3-26 20:23:35 | 只看該作者
Die neuen Xenon-Hochdrucklampen,alues such as difference absolute mean value are used for feature extractions and Fuzzy Min-Max Neural Network (FMMNN) is used for classifying clenching patterns. The usefulness of the proposed system is confirmed by the user test.
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