Intelligent Environments by yaosaigeng

VIEWS: 3 PAGES: 39

									Intelligent Environments


 Computer Science and Engineering
  University of Texas at Arlington

              Intelligent Environments   1
Course Overview
   Course website
       http://ranger.uta.edu/~holder/courses/cse
        6362.html
   Major topics
       Sensors, Networks, Database
       Prediction, Decision-Making
       Robotics
       Privacy and Security

                   Intelligent Environments    2
Course Overview
       Readings, lectures, quizzes
       Homeworks
         HW1:   Sensors
         HW2:   Networks
         HW3:   Database
         HW4:   Prediction and Decision-Making



                    Intelligent Environments      3
Course Overview
   Presentation topics
       Architectural design
       Human-computer interfaces
       Visualization
       Smart materials
       Energy efficiency
       …


                  Intelligent Environments   4
Course Overview
   Project
       Simulated intelligent environment
            Sensors
            Network
            Database
            Prediction and decision-making
       Scenario-based design
       Project demonstration

                       Intelligent Environments   5
Course Overview
   Invited Speakers
       …




               Intelligent Environments   6
Intelligent Environments

         Introduction




           Intelligent Environments   7
Definitions
   Intelligent
       Able to acquire and apply knowledge
       Knowledge is more than data
   Environment
       Surroundings
   Intelligent Environment
       An environment able to acquire and apply
        knowledge about you and your surroundings in
        order to improve your experience.

                       Intelligent Environments        8
Definitions
   “Improve your experience”
       Comfort
       Security
       Efficiency
       Productivity




                   Intelligent Environments   9
IE Scenarios
   Your house learns your living patterns in
    order to optimize energy efficiency.
       Turn down the HVAC when you are gone
   Your house learns that you like to sleep later
    on Saturdays.
       Postpone morning events (e.g., coffee-maker,
        alarm, shades, …)
   Your house adapts to the entertainment
    center settings of each inhabitant
       Volume, favorite channels

                     Intelligent Environments          10
IE Scenarios (cont.)
   Your car collects information about its
    environment as you drive
       Theatre locations, times, ticket availability
       Restaurant locations, cuisine, mean wait
        time
       Gas stations, facilities
       Emergency care, closest, facilities
       Recommendations based on learned
        preferences and destination prediction

                    Intelligent Environments       11
More IE Scenarios
   ???




          Intelligent Environments   12
Intelligent Environments

           Projects




           Intelligent Environments   13
IE Projects: Academic
   UTA MavHome Smart Home
   Georgia Tech Aware Home
   MIT Intelligent Room
   MIT House_n
   Stanford Interactive Workspaces
   UC Boulder Adaptive House


               Intelligent Environments   14
IE Projects: Commercial
   General Electric Smart Home
   Microsoft Easy Living
   Philips Vision of the Future




                Intelligent Environments   15
Georgia Tech Aware Home
   Perceive and assist occupants
   Aging in Place (crisis support)
   Ubiquitous sensing
       Scene understanding, object recognition
       Multi-camera, multi-person tracking
       Context-based activity
   Smart floor
   www.cc.gatech.edu/fce/ahri
                   Intelligent Environments       16
MIT Intelligent Room
   Support natural interaction with room
       Speech
       Gesture
       Movement
       Context
   Numerous projects
   www.ai.mit.edu/projects/iroom
   Supported by MIT Project Oxygen (pervasive
    computing)
       oxygen.ai.mit.edu
                    Intelligent Environments   17
MIT house_n
   MIT Department of Architecture
   Dynamic, evolving places that respond
    to the complexities of life
   New technologies
   New materials
   New design strategies
   architecture.mit.edu/house_n

               Intelligent Environments   18
Stanford Interactive
Workspaces
   Large wall and tabletop interactive
    displays
   Scientific visualization
   Mobile computing devices
   Computer-supported cooperative work
   Distributed system architectures
   graphics.stanford.edu/projects/iwork

               Intelligent Environments   19
UC Boulder Adaptive House
   Infer patterns and predict actions
   HVAC, water heater, lighting
   Goals
       Reduce occupant manual control
       Energy efficiency
   Nice simulation
   www.cs.colorado.edu/~mozer/house

                  Intelligent Environments   20
General Electric Smart Home
   Appliance control
   Climate control
   Energy management
   Lighting control
   Security
   Consumer Electronics Bus (CEBus)
   www.ge-smart.com
               Intelligent Environments   21
Microsoft Easy Living
   Camera-based person detection and tracking
   Geometric world modeling for context
   Sensor fusion
   Authentication
   Distributed systems
   Ubiquitous computing
   research.microsoft.com/easyliving


                 Intelligent Environments   22
Philips Vision of the Future
   Less obtrusive technology
       Heart controller
   Lots of gadgets
       Interactive wallpaper
       Control wands
       Intelligent garbage can
   www.design.philips.com/vof

                   Intelligent Environments   23
UTA MavHome Smart Home
   Focus on entire home as a rational agent
   Goals
       Maximize comfort and productivity of inhabitants
       Minimize cost
       Ensure security
   Reasoning and adaptation
   ranger.uta.edu/smarthome



                     Intelligent Environments         24
UTA MavHome Smart Home




        Intelligent Environments   25
UTA MavHome Projects
   CSE Projects
        MavHome Agent Design (Cook, Holder, Huber, Kamangar)
        Predicting inhabitant and house behaviors (Cook, Holder)
        Robot assistance (Huber, Cook)
        Web monitoring and control (Kamangar)
        Distributed sensor fusion (Kamangar)
        Database monitoring (Chakravarthy)
        Multimedia traffic for entertainment and security (Yerraballi)
        Intelligent routing, mobility prediction (Das)
   Cross-Disciplinary Projects
        Smart materials and structures (Civil Engineering)
        Nano structures (Electrical Engineering)
        Device communication (Telcordia Technologies)


                           Intelligent Environments                       26
MavHome Sponsors
   National Science Foundation ($1.2M)
   UTA to fund house
   Nortel, $100K to Das for research
   Friendly Robotics, robot donation
   Potential
       NIH (assistance for people with disabilities)
       DARPA (military applications)
       Ericsson, Motorola, Nokia, Dallas Semiconductor

                     Intelligent Environments         27
Proposed MavHome Location
   Southeast corner of UTA Blvd and Davis
            Nedderman
            Hall




                Intelligent Environments   28
MavHome FloorPlan (1st floor)




          Intelligent Environments   29
MavHome FloorPlan (2nd floor)




          Intelligent Environments   30
Intelligent Environments

          Challenges




           Intelligent Environments   31
IE Challenges
   Sensors
       Type
       Number
       Interference
       Autonomous
       Active vs. Passive
       Communication
       Interface

                   Intelligent Environments   32
IE Challenges
   Networking
       Wired vs. Wireless
       Protocol(s)
       Bandwidth
       Organization




                   Intelligent Environments   33
IE Challenges
   Data storage
       Size
       Query rate
       Active vs. Passive
       Decision-making
       Communication



                   Intelligent Environments   34
IE Challenges
   Prediction and Decision-Making
       Dynamic, temporal patterns
       Data relevance
       Sensor fusion
       Real-time
       Autonomy



                  Intelligent Environments   35
IE Challenges
   Robotics
       Mechanical capabilities
       Learning
       Safety
   Privacy and Security
       Unwanted surveillance
       “Break-ins”


                   Intelligent Environments   36
IE Challenges
   System architecture
       Agent-based vs. monolithic
       Hierarchical vs. flat
       Distributed vs. centralized control
   Systems integration
       Plug-n-play everything
       Existing appliances


                    Intelligent Environments   37
IE Design: Smart Home
   Physical home design
       New vs. retrofit
       Home architecture
       Materials
   Sensors, Networking, Database
   Prediction and Decision-making
   System architecture

                  Intelligent Environments   38
My Smart Home




             ?
        Intelligent Environments   39

								
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