• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.691-701
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• 2010

We propose a haptic rendering technology for touchable video. Our touchable video technique allows users for feeling the sense of touch while probing directly on 2D objects in video scenes or manipulating 3D objects brought out from video scenes using haptic devices. In our technique, a server sends video and haptic data as well as the information of 3D model objects. The clients receive video and haptic data from the server and render 3D models. A video scene is divided into small grids, and each cell has its tactile information which corresponds to a specific combination of four attributes: stiffness, damping, static friction, and dynamic friction. Users can feel the sense of touch when they touch directly cells of a scene using a haptic device. Users can also examine objects by touching or manipulating them after bringing out the corresponding 3D objects from the screen. Our touchable video technique proposed in this paper can lead us to feel maximum satisfaction the haptic-audio-vidual effects directly on the video scenes of movies or home-shopping video contents.

• Food Science and Preservation
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• v.10 no.1
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• pp.47-53
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• 2003

This study was carried out to investigate the quality characteristics and its distribution of Fuji Apple produced in Korea. The appels were collected 1mm the major producing areas of the apples, Cheongsong, Yechun, Bongwha, Singju and Yesan. Quality characteristics of the apples produced in these areas for 3 years were measured in terms of redness, size, weight, uniformity, defects, sweetness, firmness, pH and total acidity, The average redness for 3 years in the apples was 88.94% and the apples in the third year had the highest value, 93.88%, in redness. The average size toy 3 years in the apples was 87.17mm and the average weight was 276.74g. In external appearance, defects on the surface of the apples caused by physical factors were more serious than those by insects. Sweetness was 14.81$^{\circ}$Brix and its variation was 1.78$^{\circ}$Brix. The average firmness for 3 years in the apples was 0.83kgf, The pH value and total acidity for 3 years in Fuji apples were 3.83 and 0.32 ％, respectively, and the values had no significant difference between production years. According to the producing areas, the apples of area A in redness showed the highest value, 91.10% and these of area D showed the lowest value, 86.15%. The highest value for size was 88.12mm for area C and the lowest value was 86.40mm for area D. The apples of area B had the heaviest weight, 280.49g and these of area D in weight had the lowest value, 273.89g. No great difference showed in external defects and total acidity of the apples among the areas. In sweetness by districts, the apples of area E had the highest value, 15.32$^{\circ}$Brix, while area B was for the lowest value, 14.58$^{\circ}$Brix. The firmness of Fuji apples produced in area D and E was the highest value, 0.83kgf, and the apples of area C were the lowest value, 0.76kgf far firmness.

• Journal of KIISE:Information Networking
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• v.30 no.5
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• pp.631-640
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• 2003

Because of multi-hop wireless communication, P2P applications in ad hoc networks suffer poor performance. We Propose neighbor caching strategy to overcome this shortcoming and show it is more efficient than self caching that nodes store data in theirs own cache individually. A node can extend its caching storage instantaneously with neighbor caching by borrowing the storage from idle neighbors, so overcome multi-hop wireless communications with data source long distance away from itself. We also present the ranking based prediction that selects the most appropriate neighbor which data can be stored in. The node that uses the ranking based prediction can select the neighbor that has high possibility to keep data for a long time and avoid caching the low ranked data. Therefore the ranking based prediction improves the throughput of neighbor caching. In the simulation results, we observe that neighbor caching has better performance, as large as network size, as long as idle time, and as small as cache size. We also show the ranking based prediction is an adaptive algorithm that adjusts times of data movement into the neighbor, so makes neighbor caching flexible according to the idleness of nodes