REU 2009 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing and Decision Making for Intelligent and Secure Environments

REU 2009 Intelligent and Secure Environments • Research projects will involve individual participants working with graduate students and a faculty advisor. • Research Areas: – Intelligent Device Control – Connected Devices – Monitoring – Service Robotics – Multimedia – Environment Security

REU 2009 Intelligent and Secure Environments Goals: • Learn research methodologies • Perform research in the context of an on-going project • Develop agent technologies Course Requirements: • Classroom sessions will cover basic materials • Every student will present her/his research results • Every student will write a report of the work • At the end of the program all results will be presented in an "open-house" workshop

Computer Science and Engineering Department The University of Texas at Arlington Intelligent and Secure Environments

Motivations  Intelligent Environments can  Increase user comfort and safety  Increase productivity while reducing cost  Facilitate aging-in-place  Improve efficiency and cost of health care  Intelligent Environments require  Intuitive user interfaces  Minimally invasive monitoring technologies  Automated decision making

Smart House Face recognition, automated door entry Smart sprinklers Lighting control Door/lock controllers, Surveillance system Robot vacuum cleaner Robot lawnmower Intelligent appliances Climate control Intelligent Entertainment Automated blinds Remote site monitoring and control Assistance for disabilities

UTA MavHome Capabilities  UTA MavHome  Focus on entire home  House perceives and acts  Sensors  Controllers for devices  Connections to the mobile user and Internet  House optimizes goal function  Maximize inhabitant comfort  Minimize cost  Maximize user productivity  Maximize security

Smart Home - An Adaptive Environment  Smart Home is a home environment that adapts to the inhabitants  It has to sense the state of the home and the presence of people  It has to predict their behavior  It has to make decisions in order to automate the home

MavHome Architecture Machine Learning

UTA MavHome Components  Decision Layer  Hierarchical Reinforcement Learning  Information Layer  Reactive / Proactive Information Repository  Predicting inhabitant and house behaviors  Mobility prediction  Communication Layer  Intelligent routing  Supporting location-aware / context-aware services  Specialized Agents  Smart distributed sensor network  Personal service robots  Multimedia agent

Service Robots and Intelligent Environments Assistance for Persons with Disabilities Communication devices and technologies Intelligent assistive devices IT for improved care Information Technologies for Healthcare and Aging Automatic health monitoring Intelligent environments IT to improve uniform communication needs

MavLab Goal: minimize interactions Powerline control of devices Prediction and data mining Decision making Robotics

MavPad UTA apartment housing undergraduate student PDA interface Control of heat/AC, water, blinds, vents, all electrical devices

Example System: MavHome

•Example task: getting up in the morning and taking a shower.

Example System: MavHome •Home learns to automate light activations such as to minimize energy usage without increasing the number of inhabitant interactions

Sample Projects  Computer Vision Technologies  Next generation video  Video stitching  User Interface Technologies  Gesture recognition  3D facial expression and face recognition  Adaptive haptic interfaces  Copse – communication system for law enforcement  Muscle activation interface for motor control  Monitoring and Anomaly Detection Technologies  Behavior modelling and anomaly detection  Fall detection and prevention using mobile sensors

Sample Projects  Service Robotics and Control Technologies  Focus of attention modelling  Humanoid robot control using wireless communication  Assistive wheelchair control  Home service robot localization and control  Location-Aware Computing  Distributed localization using wireless signals  Location-centric services on mobile devices  SLAM using wireless signals  Green Computing  Energy efficient scheduling on multi-core processors