Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Embedded Intelligent Systems

    A tantárgy neve magyarul / Name of the subject in Hungarian: Beágyazott intelligens rendszerek

    Last updated: 2015. február 17.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Applied Informatics Branch

     

    Intelligent systems speciality

     

    MSc

     

    Course ID Semester Assessment Credit Tantárgyfélév
    VIMIM137 2 2/1/0/v 4  
    3. Course coordinator and department Dr. Dobrowiecki Tadeusz Pawel, Méréstechnika és Információs Rendszerek Tanszék
    4. Instructors
    Dr. Tadeusz Dobrowiecki

     

    Dr. Béla Pataki

     

    Dániel László Kovács

     

    associate professor associate professor

     

    assistant lecturer

     

    DMIT

     

    DMIT

     

    DMIT

     

    5. Required knowledge Artificial intelligence. Programming techniques (Java). Cooperative systems. Embedded systems.

     

    6. Pre-requisites
    Kötelező:
    NEM ( TárgyEredmény( "BMEVIMIMA02" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIMIMA02", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rendek grafikus formában itt láthatók.

    Ajánlott:
    Artificial intelligence (BSc), Cooperation and intelligence (MSc). Machine learning (BSc).

     

    7. Objectives, learning outcomes and obtained knowledge One of the decisive trends nowadays is the appearance of informatics in everyday environments and gadgets. So called ambient environment surrounding the human user reacts to his/her verbal, manual orders, to the gestures, to the face mimics. The environment on its own seeks problems to solve, estimates the emotional state of the humans and on its basis adjusts the environmental parameters. The aim of the subject is a multi level analysis of the information technologies required to realize ambience in the environments, starting from the information infrastructure, up to ambient intelligent spaces encapsulating human users. The subject deals furthermore with the agent based realization of the embedded systems, and with their cooperative behavior. Particular emphasis is put on the intelligence of the sensor networks, on its fusion with the agent based systems. We expect that the students successfully fulfilling the requirements of the subject will have a clear view of the problems of the ambient intelligence and “pervasive computing”, and of the intelligent methods applicable to the embedded systems, will be able to design and analyze intelligent sensor networks, will gain working knowledge of how to analyze problems calling for ambient solutions and how to specify systems to realize ambient intelligent environments.

     

    8. Synopsis I. From traditional AI to ambient intelligence. From traditional AI to ambient intelligence: embedded systems, multi agent systems, wearable computing, pervasive computing and ambient intelligence.

     

    II. Embedded systems basics. Review of S/H technology of embedded systems, characteristic system components.

     

    III. Multi agent systems. Multi-agent systems and cooperation. Emergent and soft-computing methods. Genetic algorithms. Artificial life (Alife). Artificial immune systems. Biologically inspired agent-systems: cellular automaton, swarms (birds, fish, bees, ants), PSO (Particle Swarm Optimization), SDS (Stochastic Diffusion Search).

     

    IV. Intelligence for cooperation. Autonomy and its control. Intelligent scheduling and resource management. Coalition forming and infosphere. Intelligent embedded agents. Service discovery.

     

    V. Agent-human interactions. Agent-user interactions, learning user profile/ behavior, sensing and predicting the emotional state of the user. Problems of agent-human communication.

     

    VI. Intelligent sensor networks. Summary of S/H of (wireless) sensor networks. Motes, protocols, resource management, energy management. Intelligence in sensor networks. Autonomous, reconfigurable, self-organizing mobile sensor networks. Integrating sesnsing, computing, communication, and cooperation. Fault tolerant mobile sensor networks. Biology inspired heterogonous mobile sensor networks. Cooperative control of mobile and static sensor networks.

     

    VII. Information spaces and ambient intelligent environments. Notion of information space. Problems in designing information spaces. Intelligent room, intelligent office, intelligent vehicle, etc. Elements of ambient intelligence. Notion of ambient intelligence, properties, challenges. Components of ambient environments. Placing intelligence. Experimental applications, Ambient Assisted Living, ISL -  Incremental Synchronous Learning, MIT Oxigen Project, iDorm project and its embedded artificial gadgets. Ambient intelligence and disaster management.

     

    Practical knowledge related to the subject is presented within the Embedded intelligent systems Lab.

     

    9. Method of instruction Theoretical part of the curriculum is taught during the lectures. Practical experimentation is supported by the home work, software demonstrations during the lectures, and by the related laboratory practice.

     

    10. Assessment a. During the semester:   ·         A home assignment to be handed out in the mid of the semester. Reports with the solutions are due on the 13th week of the semester. The joint presentation of the solutions and the qualification (assuming the reports are ready) is on the 14th week of the semester. The assignment brings max. 20 points, the required minimum is 40%.

    b. During the supplementary week: advanced oral exam. Students qualify for the final exam with minimal level achievements in home assignment (40 %).

    c. During the examination period: oral exam. Students qualify for the final exam with minimal level achievements in home assignment (40 %). d. Qualification:

    The final mark is based on the number of points collected from the home work and the final exam.

     

     

    11. Recaps Failed home assignment can be handed in until the end of the supplementary week.

     

    12. Consultations As required, upon agreement.
    13. References, textbooks and resources Lecture notes made available at the home page of the subject, suggested electronic literature and additional information, and a web link collection.

     

    Wooldridge, M., An Introduction to Multi-agent Systems, J. Wiley, 2002

     

    Stuart Russell and Peter Norvig: Artificial intelligence. The modern approach, 2nd edition, Prentice Hall, 2001

     

    T. Mitchell: Machine Learning, McGraw-Hill, 1997.

     

    F. L. Bellifemine, G. Caire, D. Greenwood: Developing Multi Agent Systems with JADE, Wiley, 2007

     

    Sensor Modelling, http://www.opengeospatial.org/standards/sensorml

     

    Voting Systems, http://pj.freefaculty.org/Ukraine/PJ3_VotingSystemsEssay.pdf

     

    G. Riva, F. Vatalaro, F. Davide, M. Alcañiz (eds.), Ambient Intelligence. The evolution of technology, communication and cognition towards the future of human-computer interaction, http://www.emergingcommunication.com/volume6.html

     

    Auction Theory, http://ocw.mit.edu/NR/rdonlyres/Engineering-Systems-Division/ESD-260JFall2003/2CECCCEB-0165-42A3-B86A-B4BBA5A6930B/0/l18ch22auctheory.pdf

     

    14. Required learning hours and assignment
    Kontakt óra42
    Félévközi készülés órákra10
    Felkészülés zárthelyire0
    Házi feladat elkészítése12
    Kijelölt írásos tananyag elsajátítása8
    Vizsgafelkészülés48
    Összesen120
    15. Syllabus prepared by

    Dr. Tadeusz Dobrowiecki

    Dániel László Kovács

     

     

    associate professor

    assistant lecturer

     

     

    MIT 

     

    MIT