Kevin Hughes

Kevin Hughes

Ottawa, Ontario, Canada
1K followers 500+ connections

Activity

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Experience

  • Aiven Graphic
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    http://www.shogun-toolbox.org/

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    Ontario, Canada

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    Ontario, Canada

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    Alberta, Canada

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    Alberta, Canada

Education

  • Queen's University Graphic

    Queen's University

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    Activities and Societies: Queen's First Robotics Club, Kingston Linux Users Group, Intramurals

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Volunteer Experience

  • Open Source Developer

    Shogun Machine Learning Toolbox

    - 2 years 8 months

    Software developer with the Open Source Shogun Machine Learning Toolbox.
    http://www.shogun-toolbox.org/

  • Mentor

    First Robotics

    - 2 years 6 months

    Science and Technology

    Volunteered with a highschool robotics team as the lead technical mentor for programming and controls. Helped students learn about science and engineering through designing and building a robot for an international competition

  • Tutor

    EngLinks Tutoring

    - 2 years 10 months

    Science and Technology

    •Provided tutoring to fellow students in areas of personal strength: Programming, Robotics, Data Structures and Algorithms, Computer Aided Design and Thermodynamics

Publications

  • Video event detection for fault monitoring in assembly automation

    Int. J. of Intelligent Systems Technologies and Applications

    Abstract: A major goal of many manufacturers is to minimise production downtime caused by machine faults and equipment breakdowns. This goal is typically achieved using sensor-based systems that can quickly detect and diagnose machine faults of various types. This paper proposes the use of a video event detection method based on spatiotemporal volumes (STVs) in a fault monitoring application to complement and improve upon existing systems. To detect faults, images are captured using a single…

    Abstract: A major goal of many manufacturers is to minimise production downtime caused by machine faults and equipment breakdowns. This goal is typically achieved using sensor-based systems that can quickly detect and diagnose machine faults of various types. This paper proposes the use of a video event detection method based on spatiotemporal volumes (STVs) in a fault monitoring application to complement and improve upon existing systems. To detect faults, images are captured using a single camera from several different regions of an assembly machine testbed. The motion is segmented in each image creating binary frames which are stacked to build a STV. Normal operation of the machine is modelled by building a STV from several training sequences. New STVs are compared to the model and classified as either normal or faulty behaviour based on a calculated similarity measure. Test results show that the system is very effective on the data sets collected.

    Other authors
    See publication
  • Eigenbackground Bootstrapping

    Tenth Conference on Computer and Robot Vision (CRV)

    Other authors
    • Victor Grzeda
    • Michael Greenspan
    See publication
  • Video Event Detection with STVs: Application to a High Speed Assembly Machine

    41st North American Manufacturing Research Conference (NAMRC)

    Other authors
  • Spatiotemporal Volume Video Event Detection for Fault Monitoring in Assembly Automation

    Proceedings of the 19th International Conference of Mechatronics and Machine Vision in Practice (M2VIP)

    Other authors
    See publication
  • A System For Providing Visual Feedback Of Machine Faults

    Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV)

    Other authors
    See publication
  • Vision Based Fault Detection Of Assembly Automation

    Proceedings of the 7th ASME/IEEE Conference On Mechatronics and Embedded Systems Applications (MESA)

    Other authors

Courses

  • Applied Thermodynamics II

    MECH 330

  • Artificial Intelligence for Robotics

    CS 373

  • Automatic Control Systems

    MECH 350

  • C++ for Programmers

    COMP 306

  • Calculus

    APSC 171

  • Calculus II

    APSC 172

  • Computation Fluid Dynamics

    MECH 444

  • Computer Architecture

    ELEC 274

  • Computer Graphics

    CISC 854

  • Control Systems Design for Robots and Telerobots

    ELEC 848

  • Digital Systems

    ELEC 271

  • Dynamics and Vibration

    MECH 328

  • Electronics for Scientists and Engineers

    PHYS 333

  • Engineering Data Analysis

    STAT 367

  • Fluid Mechanics II

    MECH 341

  • Heat Transfer

    MECH 346

  • Instrumentation and Measurement

    MECH 215

  • Intro to Programming for Engineers

    APSC 142

  • Introduction to Artificial Intelligence

    CS 271

  • Introduction to Computer Science

    CS 101

  • Introduction to Linear Algebra

    APSC 174

  • Introduction to Statistics

    ST 101

  • Machine Design I

    MECH 323

  • Machine Learning

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  • Machine Vision

    ELEC 824

  • Mechatronics Engineering

    MECH 452

  • Numerical Methods

    MATH 272

  • Ordinary Differential Equations

    MATH 225

  • Pattern Recognition

    CISC 859

  • Solid Mechanics II

    MECH 321

  • Visual Basic Applications

    CMIS 214

  • Web Development - Building a Blog

    CS 253

Projects

  • ARPool (Augmented Reality Pool)

    - Present

    ARPool (Augmented Reality Pool) is the next evolution in the game of pool. The system combines advanced computer algorithms, including computer vision, graphics, physics simulation, and even artificial intelligence, to enhance a pool player's experience. In this way, ARPool represents the convergence of traditional games such as pool, with recent advances in computer gaming technology.

    Getting into the technical details, ARPool is what is known as a "projector-camera" system. A data…

    ARPool (Augmented Reality Pool) is the next evolution in the game of pool. The system combines advanced computer algorithms, including computer vision, graphics, physics simulation, and even artificial intelligence, to enhance a pool player's experience. In this way, ARPool represents the convergence of traditional games such as pool, with recent advances in computer gaming technology.

    Getting into the technical details, ARPool is what is known as a "projector-camera" system. A data projector and high resolution video camera are mounted on the ceiling, directly above and pointing down towards the surface of the table. Custom computer vision algorithms have been developed to analyze the balls on the table, determining their position as well as their identity (such as cue ball, etc.). When the player lines up to take a shot, the cue position is also recognized in real-time, and the ball trajectories that will result in that angle are calculated. Finally, the data projector renders the ball trajectory information directly onto the table surface, giving the player information about the success of the shot. All this happens in realtime, so that the player can adjust his shot according to the system display.

    The benefit of the system is two-fold. First, it can be used as a training aid, helping novice and even experienced players determine the best shot to take in a given scenario. The instant replay can then be used to diagnose any errors that may exist in the player's technique. Second, the system enhances (indeed "augments") the game of pool, giving it all of the appeal of a state-of-the-art video game.

    Other creators
    See project

Languages

  • English

    Full professional proficiency

  • French

    Limited working proficiency

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