By Saehwa Kim (auth.), Tai-hoon Kim, Hojjat Adeli, William I. Grosky, Niki Pissinou, Timothy K. Shih, Edward J. Rothwell, Byeong-Ho Kang, Seung-Jung Shin (eds.)
The quantity set, CCIS 262 and 263, constitutes the refereed complaints of the foreign convention, MulGraB 2011, held as a part of the long run iteration details expertise convention, FGIT 2011, along side GDC 2011, Jeju Island, Korea, in December 2011. The papers offered have been conscientiously reviewed and chosen from quite a few submissions and focuse at the a variety of facets of multimedia, special effects and broadcasting.
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Additional info for Multimedia, Computer Graphics and Broadcasting: International Conference, MulGraB 2011, Held as Part of the Future Generation Information Technology Conference, FGIT 2011, in Conjunction with GDC 2011, Jeju Island, Korea, December 8-10, 2011. Proceedings,
Sample text
Slotted FAMA [9] is a MAC protocol for underwater acoustic communication, which is a variant to existing FAMA to suitable for underwater environment. Slotted FAMA is to design FAMA combines MACA (Multiple Access Collision Avoidance) method and CS (Carrier Sensing) technique. However, it has some problem such as data collision with long length of RTS/CTS required in FAMA and low transmission rate in underwater environment. Thus, FAMA isn’t suitable for underwater environment. To solve this problem, Slotted FAMA for underwater acoustic network is proposed.
MulGraB 2011, Part II, CCIS 263, pp. 29–37, 2011. -Y. Lee et al. However, it has several challenges to design underwater acoustic sensor networks, for instance, limited bandwidth, multi-path, padding, long propagation delay, high bit error, temporary losses of connectivity and limited battery power. Nowadays many studies are being conducted to overcome abovementioned problems [1]. In recent researches, there were not comparative analyses of MAC protocols for underwater acoustic communication according to network topology.
Football Video Segmentation Based on Video Production Strategy. M. ) ECIR 2005. LNCS, vol. 3408, pp. 433–446. M. Zawbaa et al. 13. : Semantic analysis of soccer video using dynamic Bayesian network. IEEE Transactions on Multimedia 8(4) (2006) 14. : Highlight summarization in soccer video based on goalmouth detection. In: Asia-Pacific Workshop on Visual Information Processing (2006) 15. : Real-time Goal-Mouth Detection in MPEG Soccer Video. In: Proceedings of ACM MM 2003, Berkeley, USA, pp. 311–314 (2003) 16.