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MediaHub: An Intelligent Multimedia Distributed Hub Student: Glenn Campbell Supervisors: Dr. Tom Lunney Prof. Paul Mc Kevitt School of Computing and Intelligent.

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Presentation on theme: "MediaHub: An Intelligent Multimedia Distributed Hub Student: Glenn Campbell Supervisors: Dr. Tom Lunney Prof. Paul Mc Kevitt School of Computing and Intelligent."— Presentation transcript:

1 MediaHub: An Intelligent Multimedia Distributed Hub Student: Glenn Campbell Supervisors: Dr. Tom Lunney Prof. Paul Mc Kevitt School of Computing and Intelligent Systems Faculty of Engineering University of Ulster, Magee

2 Project goals The primary objectives of this research are to:  Interpret/generate semantic representations of multimodal input/output  Perform decision-making (fusion and synchronisation) over multimodal data  Implement MediaHub, a multimodal platform hub

3 Project objectives Focus on following research questions:  Will MediaHub use frames for semantic representation or XML or one of its derivatives?  How will MediaHub communicate with various elements of a platform?  Will MediaHub constitute a blackboard or non- blackboard model?  What mechanism will be implemented for decision-making within MediaHub?

4 Key research problems  Semantic Representation Represent language and vision Frames or XML?  Semantic Storage Blackboard model? Non-blackboard model?  Decision-making Fusion and synchronisation AI technique

5  Frames (CHAMELEON) [ MODULE INPUT: input INTENTION: intention- type TIME: timestamp] [SPEECH- RECOGNISER UTTERANCE:(Point to Hanne’s office) INTENTION: instruction! TIME: timestamp] [ GESTURE GESTURE: coordinates (3, 2) INTENTION: pointing TIME: timestamp]  XML (M3L, SmartKom) list epg_browse now T19:42: T22:00: T19:50: T19:55:00 Today’s Stock News ARD …….. Semantic representation

6 Semantic storage  Blackboard or Non-blackboard? High coupling – Blackboard? Low coupling - distributed architecture?  Communication Via central blackboard? Message passing between modules?

7 Decision-making (fusion & synchronisation)  Rule-based  Potential for Other AI techniques Fuzzy Logic Neural Networks Genetic Algorithms Bayesian Networks (CPNs)

8 Distributed processing  PVM (Parallel Virtual Machine) (Sunderam 1990, Fink et al. 1995)  ICE (Amtrup 1995)  DACS (Fink et al. 1995, 1996)  Open Agent Architecture (OAA) (Cheyer et al. 1998, OAA 2004)  JATLite (Kristensen 2001, Jeon et al. 2000)  JavaSpaces (Freeman 2004)  CORBA (Vinoski 1993)

9 Intelligent Multimedia Distributed Platforms  Blackboard Model: Ymir (Thórisson 1999) CHAMELEON (Brøndsted et al. 1998, 2001) Smartkom (Bühler et al. 2002, Wahlster et al. 2001, SmartKom 2004) DARBS (Nolle et al. 2001) DARPA Galaxy Communicator (Bayer et al. 2001) Psyclone (Psyclone 2004) Spoken Image/SONAS (Ó Nualláin et al. 1994, Ó Nualláin & Smith 1994, Kelleher et al. 2000)

10 Intelligent Multimedia Distributed Platforms  Non-blackboard Model: WAXHOLM (Carlson et al. 1996) AESOPWORLD (Okada 1996) COLLAGEN (Rich et al. 1997) INTERACT (Waibel et al. 1996) Oxygen (Oxygen 2004) EMBASSI (Kirste 2001, EMBASSI 2004) MIAMM (MIAMM 2004)

11 CHAMELEON  Language & vision integration system consists of ten modules, mostly programmed in C and C++ DACS communication system used for communication Blackboard stores semantic representations produced by other modules Communication between modules achieved by exchanging semantic representations between themselves or blackboard Semantic representation in form of input, output and integration frames

12 Architecture of CHAMELEON

13 SmartKom  User adaptive interface for human-computer interaction  Mobile  Public  Home/Office  Facilitates speech, gestures and facial expression input  XML-based mark-up language, M3L, used for semantic representation  Distributed multiple blackboard model

14 Architecture of SmartKom

15  Dialogue Manager Acts as a blackboard module Facilitates communication between other modules Synchronisation  Semantic Representation Database Provides semantic representation of language and vision data  Decision Making Module AI technique for a unique form of decision-making  Bayesian Networks (CPNs)  Neural Networks, Genetic Algorithms, Fuzzy Logic Project proposal

16 Architecture of MediaHub

17 Comparison of Intelligent MultiMedia Platforms

18 Software Analysis  Main Programming Language Java C++  Semantic Representation XML XHTML + Voice SMIL RDF Schema MPEG-7  Decision Making HUGIN (Bayesian Networks) (Hugin 2004) FuzzyJ Toolkit (Fuzzy Logic) (NRC 2004)

19 Project Schedule

20 Conclusion  An intelligent multimodal distributed platform hub called MediaHub will be developed  MediaHub will interpret and generate semantic representations of multimodal input and output  MediaHub will perform fusion and synchronisation of language and vision data  Unique contribution of MediaHub is to provide a new method of decision making  MediaHub will be tested within an existing multimodal platform (e.g. CONFUCIUS)


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