Activity
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I’ve never been great at accepting recognition, but this is a big one, so I'm making a point to share it. FIRST Robotics Canada Team 2609 Beaverworx…
I’ve never been great at accepting recognition, but this is a big one, so I'm making a point to share it. FIRST Robotics Canada Team 2609 Beaverworx…
Liked by Kevin Hughes
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We're excited to announce the Limited Availability launch of Aiven Apps! Aiven Apps allows you to unify your code and data, and deploy stateless…
We're excited to announce the Limited Availability launch of Aiven Apps! Aiven Apps allows you to unify your code and data, and deploy stateless…
Liked by Kevin Hughes
Experience
Education
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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
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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
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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
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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 authorsSee publication -
Eigenbackground Bootstrapping
Tenth Conference on Computer and Robot Vision (CRV)
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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)
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A System For Providing Visual Feedback Of Machine Faults
Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV)
Courses
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Applied Thermodynamics II
MECH 330
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Artificial Intelligence for Robotics
CS 373
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Automatic Control Systems
MECH 350
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C++ for Programmers
COMP 306
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Calculus
APSC 171
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Calculus II
APSC 172
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Computation Fluid Dynamics
MECH 444
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Computer Architecture
ELEC 274
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Computer Graphics
CISC 854
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Control Systems Design for Robots and Telerobots
ELEC 848
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Digital Systems
ELEC 271
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Dynamics and Vibration
MECH 328
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Electronics for Scientists and Engineers
PHYS 333
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Engineering Data Analysis
STAT 367
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Fluid Mechanics II
MECH 341
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Heat Transfer
MECH 346
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Instrumentation and Measurement
MECH 215
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Intro to Programming for Engineers
APSC 142
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Introduction to Artificial Intelligence
CS 271
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Introduction to Computer Science
CS 101
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Introduction to Linear Algebra
APSC 174
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Introduction to Statistics
ST 101
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Machine Design I
MECH 323
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Machine Learning
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Machine Vision
ELEC 824
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Mechatronics Engineering
MECH 452
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Numerical Methods
MATH 272
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Ordinary Differential Equations
MATH 225
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Pattern Recognition
CISC 859
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Solid Mechanics II
MECH 321
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Visual Basic Applications
CMIS 214
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Web Development - Building a Blog
CS 253
Projects
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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 creatorsSee project
Languages
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English
Full professional proficiency
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French
Limited working proficiency
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I once watched a VP refuse a $10k raise for a Lead DevOps Engineer. "It's outside the band," he said. "We have to hold the line," he said. The…
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