期刊文献+

基于上下文感知的内容适应算法及其在UPnP AV中的应用 被引量:3

Contest-based content adaptation algorithm and its application in UPnP AV
下载PDF
导出
摘要 针对UPnP AV如何根据AV播放环境进行内容适应的问题,提出一种基于上下文感知的内容适应算法CBCAA。首先获取AV播放环境上下文信息并进行分类;然后根据不同类型的上下文信息构建约束模型,利用约束满足求解方法得到相应的媒体内容描述决策集MDDS;最后将媒体源码按MDDS的描述转码,得到适应上下文要求的媒体内容目标码。CBCAA算法能够实现UPnP AV对AV播放环境的内容适应,从而提供智能多媒体服务。仿真实验表明了该算法的有效性。 The content adaptation based on context in UPnP AV is investigated. A Context-based Content Adaptation Algorithm (CBCAA) is proposed. First, the algorithm obtains and classifies the context information. Then it builds the constraint model according to the different types of information, and the constraint satisfaction method is used to acquire Media Description Decision Set (MDDS). Finally, a bit-stream adaptation engine transcodes the media from source media to object media based on MDDS. CBCAA enables UPnP AV adapt the content to environment and provides intelligent services. Simulation results demonstrate the effectiveness of proposed algorithm.
出处 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2014年第5期1441-1446,共6页 Journal of Jilin University:Engineering and Technology Edition
基金 国家科技重大专项项目(2013ZX03005011)
关键词 计算机应用 上下文感知 内容适应 约束满足问题 computer application context awareness content adaptation constraint satisfaction problem
  • 相关文献

参考文献11

  • 1RitchieJ, Kuhnel T, KangJ, et al. UPnP AVAr chitecture: 1 [EB/OL]. [ 2008-09-30]. http://upnp org,/sdcps and-certi-ficat on/-standards/device ar chitecture-documents/.
  • 2Sung Jongwoo, Kim Daeyoung, Song Hyungjoo, et al. UPnP based intelliget multimedia service archi- tecture for digital home network[J]. Software Tech- nologies for Future Embedded and Ubiquitous Sys- tems, 2006, 3(4): 521-526.
  • 3Kang Dong-Oh, Kang Kyuchang, Choi Sunggi, et al. UPnP AV Architectural multimedia system with a home gateway powered by the OSGi platform[J]. IEEE Transactions on Consumer Electronics, 2005, 51(1): 87-93.
  • 4Mets K, Nelis J, Verslype D, et al. Design of acontext aware multimedia management system for home environments [J]. Computation World: Fu- ture Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009, 111 ( 1 ) : 49- 54.
  • 5Pereira F, Vetro A, Sikora T. Multimedia retrieval and delivery., essential metadata challenges and standards[J]. Proceedings of the IEEE, 2008, 96 (4) : 721- 744.
  • 6Feng Yu-qing, Tang Rui-chun, Zhai Yi-li, et al. Personalized media recommendation algorithm based on smart home[C] // The Second International Con ference on e-Technologies and Networks for Devel- opment(ICeND), Malaysia, 2013: 63-67.
  • 7孙吉贵,高健,张永刚.一个基于最小冲突修补的动态约束满足求解算法[J].计算机研究与发展,2007,44(12):2078-2084. 被引量:12
  • 8Jannaeh D, Leopold K, Timmerer C, et al. A knowledge-based framework for multimedia adapta- tion[J]. Applied Intelligence, 2006, 24 (2): 109- 125.
  • 9Kofler l, Seidl J, Timmerer C, et al. Using MPEO- 21 for cross-layer multimedia content adaptation[J]. Journal on Signal, Image and Video Processing, 2008, 2(4): 355-370.
  • 10Sofokleous A A, Angelides M C. DCAF: an MPEG-21 dynamic content adaptation framework [J]. Multimedia Tools and Applications, 2008, 40 (2):151-182.

二级参考文献16

  • 1杨轻云,孙吉贵,张居阳.最大度二元约束满足问题粒子群算法[J].计算机研究与发展,2006,43(3):436-441. 被引量:19
  • 2J Amilhastre, H Fargier, P Marguis. Consistency restoration and explanations in dynamic csps-Application to configuration [J]. Artificial Intelligence, 2002, 135(1-2): 199-234.
  • 3Gerard Verfaillie, Narendra Jussien. Constraint solving in uncertain and dynamic environments: A survey [J]. Constraints, 2005, 10(3): 253-281.
  • 4Ian Miguel, Qiang Shen. Fuzzy rrDFCSP and planning [ J ]. Artificial Intelligence, 2003, 148(1-2): 11-52.
  • 5T Schiex, G Verfaillie. Nogood recording for static and dynamic constraint satisfaction problems [J]. International Journal of Artificial Intelligence Tools, 1994, 3(2):187--207.
  • 6Roman Bartdk, Pavel Surynek. An improved algorithm for maintaining arc consistency in dynamic constraint satisfaction problems [C]. In: Proc of Int'l Florida Artificial Intelligence Research Society Conference 2005. Washington: AAAI Press, 2005.
  • 7P Surynek, R Bartak. A new algorithm for maintaining arc consistency after constraint retraction [C]. In: Mark Wallace ed. Principles and Practice of Constraint Programming, LNCS 3258. Berlin: Springer, 2004. 767-771.
  • 8G Verfaillie, T Schiex. Solution reuse in dynamic constraint satisfaction problems [C]. AAAI-94, Seattle, WA, 1994.
  • 9I Miguel, Q Shen, P Jarvis. Efficient flexible planning via dynamic flexible constraint satisfaction [J]. Engineering Applications of Artificial Intelligence, 2001, 14(3) : 301-327.
  • 10I Miguel, Q Shen. Dynamic flexible constraint satisfaction [J]. Applied Intelligence, 2000, 13(3): 231-245.

共引文献11

同被引文献57

  • 1JI ShiMing1,2, XIAO FengQing1,2 & TAN DaPeng1,2 1Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology, (Zhejiang University of Technology), Ministry of Education, Hangzhou 310014, China,2Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology of Zhejiang Province, Hangzhou 310014, China.Analytical method for softness abrasive flow field based on discrete phase model[J].Science China(Technological Sciences),2010,53(10):2867-2877. 被引量:29
  • 2TAN DaPeng1,2,JI ShiMing1,LI PeiYu2 & PAN XiaoHong2 1 Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education,Zhejiang University of Technology,Hangzhou 310014,China,2 Department of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China.Development of vibration style ladle slag detection methods and the key technologies[J].Science China(Technological Sciences),2010,53(9):2378-2387. 被引量:7
  • 3Huu T T,Tham C K. An auction-based resource al location model for green cloud computing[C]//2013 IEEE International Conference on Cloud Engineer- ing, Redwood City, CA, 2013 : 269-278.
  • 4Wang L J,Meng M Q H. A game theoretical band- width allocation mechanism for cloud robotics[C]//2012 10th World Congress on Intelligent Control and Automation, Beijing, 2012 : 3828-3833.
  • 5Ye D S, Chen J H. Non-cooperative games on multi- dimensional resource allocation[J]. Future Genera- tion Computer Systems,2013,29(6) :1345-1352.
  • 6Copil G, Moldovan D, Salomie I. Cloud SLA negotia- tion for energy saving-A particle swarm optimization approach[C]//2012 IEEE 8th International Confer- ence on Intelligent Computer Communication and Processing, Cluj-Napoca, 2012 : 289-296.
  • 7Son S,Jung G,Jun S C. An SLA-based cloud com- puting that facilitates resource allocation in the dis- tributed data centers of a cloud provider[J]. Journal of Supercomputing, 2013,64(2) : 606-637.
  • 8Minarolli D, Freisleben B. Utility-based resource al- location for virtual machines in Cloud computing[C] //2011 IEEE Symposium on Computers and Com- munications, Kerkyra, 2011 : 410-417.
  • 9Nan X, He Y, Guan L. Optimal resource allocation for multimedia cloud based on queuing modell-C] ff 13th International Workshop on Multimedia Signal Processing, Hangzhou, 2011 : 1-6.
  • 10Hong Bo-hai,Tang Rui-ehun,Zhai Yi-li,et al. A re- sources allocation algorithm based on media task QoS in cloud computing[C] //The 4th IEEE Interna- tional Conference on Software Engineering and Serv- ice Sciences, Beijing, 2013 : 841-844.

引证文献3

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部