REAL TIME. 7/10/11 Now Digitized: Sense/Observe Collect/Distribute Analyze Simulate Live, Full-scale Giga-Places Peta-Bytes, Tera-OPS Femto-time Observe.

Slides:



Advertisements
Similar presentations
Gordons Personal View of Personal Computing: before the PC Vintage Computer Society 27 September 1998 Gordon Bell
Advertisements

Digitally-Bypassed Transducers: Interfacing Digital Mockups to Real-Time Medical Equipment Scott Sirowy*, Tony Givargis and Frank Vahid* This work was.
15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois Intelligent Agents in Design Zbigniew Skolicki Tomasz.
A Component Based Programming Framework for Autonomic Applications Hua Liu, Manish Parashar, and Salim Hariri ICAC ‘04 John Otto Wi06 CS 395/495 Autonomic.
Information and Communication Technology for Inquiry Based Science Education.
Component-Based Software Engineering Oxygen Paul Krause.
Department of Computer Science and Electrical Engineering.
John McCarthy. biography Was born in Boston, Massachusetts on September 4, Family of two Irish immigrants, John Patrick and Ida Glatt McCarthy.
Digital Systems Emphasis for Electrical Engineering Students Digital Systems skills are very valuable for electrical engineers Digital systems are the.
CS538: Advanced Topics in Information Systems. 2 Secure Location transparency Consistent Real-Time Available Black Box: Distributed Storage [GMM] ? Data.
Gordon’s Personal View of The Early Days of Digital… DECWorld, 16 June 2001 Gordon Bell
 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Linking the Real World Manfred.
ICPCA 2008 Research of architecture for digital campus LBS in Pervasive Computing Environment 1.
Research Directions for the Internet of Things Supervised by: Dr. Nouh Sabry Presented by: Ahmed Mohamed Sayed.
Intelligent Agents: an Overview. 2 Definitions Rational behavior: to achieve a goal minimizing the cost and maximizing the satisfaction. Rational agent:
Vanderbilt University Vibro-Acoustics Laboratory Distributed Control with Networked Embedded Systems Objectives Implementation of distributed, cooperative.
U.S. Army Research, Development and Engineering Command Unclassified – Unlimited Distribution Considerations for adaptive tutoring within serious games:
Computing in Atmospheric Sciences Workshop: 2003 Challenges of Cyberinfrastructure Alan Blatecky Executive Director San Diego Supercomputer Center.
Vital Connections - Engineering What are the general characteristics of an engineer? What are the general characteristics of an engineer? What do engineers.
Chen Cai, Benjamin Heydecker Presentation for the 4th CREST Open Workshop Operation Research for Software Engineering Methods, London, 2010 Approximate.
Introduction to Operation Research
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Grid & Utility Computing: Do they really mean Pervasive Services ICPS 2006 – Panel Manish Parashar TASSL, Rutgers University, Piscataway, NJ USA.
Japanese State of the Art and Perspective on ADI Toshiyo aTamura Department of Gerontechnology National Institute for Longevity Sciences.
Monitoring, Modelling, and Predicting with Real-Time Control Dr Ian Oppermann Director, CSIRO ICT Centre.
Informal Learning, Cyberlearning and Innovative Education Diana G. Oblinger, Ph.D.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 11: Artificial Intelligence Computer Science: An Overview Tenth Edition.
Distributed Virtual Environments Bob Marcus. Networked Virtual Environments Agenda 10:00 Forterra Systems (Mike Macedonia) - Dealing with Zillionics 11:00.
FOREWORD By: Howard Shrobe MIT CS & AI Laboratory
Perspectives on Cyberinfrastructure Daniel E. Atkins Professor, University of Michigan School of Information & Dept. of EECS October 2002.
Gordon’s Personal View of The Early Days of Digital… DECWorld, 16 June 2001 Gordon Bell
Futures Lab: Biology Greenhouse gasses. Carbon-neutral fuels. Cleaning Waste Sites. All of these problems have possible solutions originating in the biology.
Frankfurt (Germany), 6-9 June 2011 M. Khederzadeh Power & Water University of Technology (PWUT) Tehran, IRAN. M.Khederzadeh – IRAN – RIF S3 – Paper 0066.
ICCS WSES BOF Discussion. Possible Topics Scientific workflows and Grid infrastructure Utilization of computing resources in scientific workflows; Virtual.
CENG334 Introduction to Operating Systems 1 Erol Sahin Dept of Computer Eng. Middle East Technical University Ankara, TURKEY URL:
Context in Ubiquitous Computing. Context sensing.
CyberInfrastructure for Network Analysis Importance of, contributions by network analysis Transformation of NA Support needed for NA.
Computers, part of your life – Grade 11
Introduction of Intelligent Agents
Presented by Darshan Balakrishna Shetty. Contents Internet of Things? Sample IoT devices What's Smart? Why Now? IoT in Power Grids and Homes Smart Grid.
1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.
REVEAL Instrument Overview What is REVEAL? Prototype strap-down (vehicle-independent) instrumentation system REVEAL provides the traditional (manned) airborne.
1 MIS in Practice Types of Information Systems (IS)
Scientific Workflows for the Sensor Web ICT for Earth Observation Anwar Vahed.
Current & Future States of Medical Device Systems PastPresentFuture Medical Device Systems Paul L. Jones Senior Systems/Software Engineer Office of Science.
REU 2007 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.
What are Smart Sensors/Actuators ? Sensor/Actuator either senses environment or activates motors, solenoids. –Motion detectors and light level sensors.
Towards the New Framework Programme Vienna, 26 March 2001 Horst Forster European Commission WI0.
Exercise 17. No.1  (Worse) Closely examining the test results, the final trial was delayed by the laboratory manager for another two week.  (Better.
Urban Infrastructure and Its Protection Responding to the Unexpected Interest Group Report.
Computer Science and Engineering Department The University of Texas at Arlington MavHome: An Intelligent Home Environment.
Intelligent Agents Chapter 2. How do you design an intelligent agent? Definition: An intelligent agent perceives its environment via sensors and acts.
Cyberinfrastructure Overview of Demos Townsville, AU 28 – 31 March 2006 CREON/GLEON.
Introduction to Earth Science THE SCOPE OF EARTH SCIENCE.
Big Data Quality Challenges for the Internet of Things (IoT) Vassilis Christophides INRIA Paris (MUSE team)
Presenter: Prof. Dimitris Mourtzis Advanced Manufacturing: Industry 4.0 and Smart Systems.
Marketing Research & the Internet Research processes Types of data.
Configuring pacemaker while 2kms away. Person is travelling in driverless car.
CECS 474 Computer Network Interoperability Notes for Douglas E. Comer, Computer Networks and Internets (5 th Edition) Tracy Bradley Maples, Ph.D. Computer.
Virtual Laboratory Amsterdam L.O. (Bob) Hertzberger Computer Architecture and Parallel Systems Group Department of Computer Science Universiteit van Amsterdam.
Overview Introduction to Operating Systems
Intelligent Agents Chapter 2.
RESEARCH, EDUCATION, AND TRAINING FOR THE SMART GRID
Data Warehousing and Data Mining
PH Systems Require More than IT
Pervasive Computing Happening?
Gordon’s Personal View of Personal Computing: before the PC
CONTROL SYSTEM AN INTRODUCTION.
Gordon’s Personal View of Personal Computing: before the PC
Presentation transcript:

REAL TIME

7/10/11 Now Digitized: Sense/Observe Collect/Distribute Analyze Simulate Live, Full-scale Giga-Places Peta-Bytes, Tera-OPS Femto-time Observe Learn Decide Act Interoperable, pervasive networking Ubiquitous sensors Real-time, massive analysis Automated learning Vast, evolving knowledge stores

What is “real time”? Real Time Systems: Live, always-on, reactive, interactive, stable, controllable, adaptive Then What If? Now In Time Too Late

7/10/11 Then What If? Now Later What If? Rate -1

Time Frames and Operation Rates Observation time – measure, discriminate Analysis time – communicate, contextualize, understand Decision time – choose, formulate, distribute Action time – energize,drive 7/10/11

Sample rates vary with applications Pico to milli- seconds Speed of a process Days to weeksDecades Stock trading incl’d programmed trading Automobile controls engines to driverless control Water resources measurement, modeling & controls SCIENCE SurveillanceMemory recallClimate change Content distribution Power grid demand

It all begins with simulation… Simulator with Human Sensors* Model of System e.g. airplane *Sensors may be people

MIT Whirlwind (c1951) first real time computer. Begot SAGE (Semi-Automatic Ground Environment)

Sample rate versus utility Pico to milli- seconds Speed of a processDays to weeksDecades Stock trading incl’d programmed trading Automobile controls engines to driverless control; Power grid demand Water resources measurement, modeling & controls SCIENCE SurveillanceMemory recallClimate change Content distribution

Wes Clark, Lincoln Laboratory, and LINC, first PC. LINC: Laboratory Instrument Computer c Real time processing for bio-medical research.

Real time Sensing e.g. laboratory control, simulators REAL WORLD System Sensors* Observers, Model Builders e.g. Scientists *Sensors may be people Channel to effect behavior

LINC 2007 Vintage Computer Fair (At age 45 … turns 50 next year)

Some other real time computers Whirlwind: Bright Boys 1962 LINC that influenced PDP-5 8/1963 PDP-5 for interfacing a Canadian research reactor to a PDP-4 (controller) RT-11 from OS/8 begot CPM >> DOS RSX-11

Sensing… REAL WORLD System Sensors* Observations by human operators *Sensor/effector may be people Sharing of data To effect change

Plain old closed loop control Real time Control REAL WORLD System Model of REAL WORLD System Sensors* Effectors* Advice (t+1) Controller (Processing, policies and people) *Sensor/effector may be people

Real time and Control with/wo Real World Modeling REAL WORLD System Model of REAL WORLD System Sensor s* Effector s* Advice (t+1) Controller (Processing, policies and people) *Sensor/effector may be people Delays and Noise