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Intelligent Environments

Intelligent Environments. Computer Science and Engineering University of Texas at Arlington. Course Overview. Course website http://ranger.uta.edu/~holder/courses/cse6362.html Major topics Sensors, Networks, Database Prediction, Decision-Making Robotics Privacy and Security.

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Intelligent Environments

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  1. Intelligent Environments Computer Science and Engineering University of Texas at Arlington Intelligent Environments

  2. Course Overview • Course website • http://ranger.uta.edu/~holder/courses/cse6362.html • Major topics • Sensors, Networks, Database • Prediction, Decision-Making • Robotics • Privacy and Security Intelligent Environments

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

  4. Course Overview • Presentation topics • Architectural design • Human-computer interfaces • Visualization • Smart materials • Energy efficiency • … Intelligent Environments

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

  6. Course Overview • Invited Speakers • … Intelligent Environments

  7. Intelligent Environments Introduction Intelligent Environments

  8. 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

  9. Definitions • “Improve your experience” • Comfort • Security • Efficiency • Productivity Intelligent Environments

  10. 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

  11. 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

  12. More IE Scenarios • ??? Intelligent Environments

  13. Intelligent Environments Projects Intelligent Environments

  14. 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

  15. IE Projects: Commercial • General Electric Smart Home • Microsoft Easy Living • Philips Vision of the Future Intelligent Environments

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. General Electric Smart Home • Appliance control • Climate control • Energy management • Lighting control • Security • Consumer Electronics Bus (CEBus) • www.ge-smart.com Intelligent Environments

  22. 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

  23. 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

  24. 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

  25. UTA MavHome Smart Home Intelligent Environments

  26. 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

  27. 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

  28. Proposed MavHome Location • Southeast corner of UTA Blvd and Davis Nedderman Hall Intelligent Environments

  29. MavHome FloorPlan (1st floor) Intelligent Environments

  30. MavHome FloorPlan (2nd floor) Intelligent Environments

  31. Intelligent Environments Challenges Intelligent Environments

  32. IE Challenges • Sensors • Type • Number • Interference • Autonomous • Active vs. Passive • Communication • Interface Intelligent Environments

  33. IE Challenges • Networking • Wired vs. Wireless • Protocol(s) • Bandwidth • Organization Intelligent Environments

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

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

  36. IE Challenges • Robotics • Mechanical capabilities • Learning • Safety • Privacy and Security • Unwanted surveillance • “Break-ins” Intelligent Environments

  37. 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

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

  39. My Smart Home ? Intelligent Environments

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