Instrumentation & Measurement

Prerequisites: You do not need to have attended the earlier talks. If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. Longer Abstract: At the risk of being non-standard, Jeff will tell you the way he thinks about this topic. Both "Game Trees" and "Markoff Chains" represent the graph of states through which your agent will traverse a path while completing the task. Suppose we could learn for each such state a value measuring "how good" this state is for the agent. Then competing the task in an optimal way would be easy. If our current state is one within which our agent gets to choose the next action, then she will choose the action that maximizes the value of our next state. On the other hand, if our adversary gets to choose, he will choose the action that minimizes this value. Finally, if our current state is one within which the universe flips a coin, then each edge leaving this state will be labeled with the probability of taking it. Knowing that that is how the game is played, we can compute how good each state is. The states in which the task is complete is worth whatever reward the agent receives in the said state. These values somehow trickle backwards until we learn the value of the start state. The computational challenge is that there are way more states then we can ever look at. Speaker(s): Prof. Jeff Edmonds, Virtual: https://events.vtools.ieee.org/m/287737

This series of 5 beginner friendly workshops will teach students how to create their own indie game in Unity. We will teach the building blocks and best practices to create a shooter including creating the player, creating enemies, collectibles, effects, and more! All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a showcase with small prizes at the end of the workshop series. - Quick review of last week’s progress (10 minutes) - Introduction to the Package Manager & Post Processing package (10 minutes) ● Apply post processing effects to camera (20 minutes) - Implement camera shaking (20 minutes) - Break (10 minutes) - Building our project (10 minutes) ● QnA (40 minutes) Virtual: https://events.vtools.ieee.org/m/287756

Prerequisites: You do not need to have attended the earlier talks. If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. Longer Abstract: A randomly chosen bit string cannot be compressed at all. But if there is a pattern to it, eg it represents an image, then maybe it can be compressed. Each pixel of an image is specified by one (or three) real numbers. If an image has thousands/millions of pixels, then each of these acts as a coordinate of the point where the image sits in a very high dimensional space. A set of such images then corresponds to a set of these points. We can understand the pattern of points/images as follows. Maximum Likelihood assumes that the given set of points/images were randomly chosen according a multi-dimensional normal distribution and then adjusts the parameters of this normal distribution in the way that maximizes the probability of getting the images that we have. The obtained parameters effectively fits an ellipse around the points/images in this high dimensional space. We then reduce the number of dimensions in our space by collapsing this ellipse along its least significant axises. Projecting each point/image to this lower dimensional space compresses the amount of information needed to represent each image. Virtual: https://events.vtools.ieee.org/m/289240

This series of 5 beginner friendly workshops will teach students how to create their own indie game in Unity. We will teach the building blocks and best practices to create a shooter including creating the player, creating enemies, collectibles, effects, and more! All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a showcase with small prizes at the end of the workshop series. Quick review of last week’s progress (10 minutes) Introduction to the Package Manager & Post Processing package (10 minutes) ● Apply post processing effects to camera (20 minutes) Implement camera shaking (20 minutes) Break (10 minutes) Building our project (10 minutes) QnA (40 minutes) Virtual: https://events.vtools.ieee.org/m/287758

Prerequisites: You do not need to have attended the earlier talks. If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. Longer Abstract: Suppose you have a distributions of random images of cats. Suppose you want to learn a neural network that takes uniformly random bits as input and outputs an image of a cat according to this same distribution. One fun thing is that this neural network won't be perfect and hence it will output images of "cats" that it has never seen before. Also you can make small changes in the network input bits and see how it changes the resulting image of a cat. The way we do this is with Generative Adversarial Networks. This is formed by having two competing agents. The task of the first agent, as described above, is to output random images of cats. The task of the second is to discern whether a given image was produced by the true random distribution or by the first agent. By competing, they learn. If we have more time in the talk then we will talk about Convolutional & Recurrent Networks which are used for learning images and sound that are invariant over location and time. Virtual: https://events.vtools.ieee.org/m/289241

How might the machine learning make the world a better place? How might it make the world worse? I have some thoughts. Likely you do too. Virtual: https://events.vtools.ieee.org/m/289243

"Women in Leadership", a collaboration between IEEE Toronto Section, Gybo Robotics, and Humber College. Co-sponsored by: Humber College Speaker(s): Dr. Azadeh Yadollahi Virtual: https://events.vtools.ieee.org/m/306228

Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on Autonomous Electric Combat Vehicles, presented by Prof. Zeinab El-Sayegh. Abstract: Current literature pertaining to multi-steerable mobile platforms and the progression of military vehicles in the past few decades suggest a lack of effort in pursuing advanced technologies in this joint area. As a result, a novel 1:6 scaled 8x8 electric combat vehicle prototype that features eight independently driven and steerable wheels is designed and developed. The intent is to create a scaled model for future autonomous vehicle navigation and control research in off road terrains. Starting with the mechanical design, this talk will discuss the details of the chassis, suspension, driving and steering systems development. The electronics necessary for vehicle actuation is implemented with custom nodes and topics created for hardware communication within the Robot Operating System (ROS). Lastly, path planning and obstacle avoidance abilities are implemented to achieve autonomous navigation. The result of this work is a fully functional and instrumented robotic platform with a modular software architecture. Vehicle design analysis, performance and autonomous navigation abilities are experimentally tested with promising results. This talk will also cover the future of autonomous electric combat vehicles. Speaker(s): Zeinab El-Sayegh, PhD, PEng Register: https://events.vtools.ieee.org/m/316156 Biography: Dr. Zeinab El-Sayegh is an Assistant Professor in the Department of Automotive and Mechatronics Engineering. She completed her postdoctoral fellowship and Ph.D. in Mechanical Engineering at Ontario Tech University. She received her master’s degree in Mechanical Engineering from the University of Concordia, Montreal. Formally, worked at Volvo Group Trucks Technology Gothenburg, Sweden as a vehicle analyst. And, as a combustion analyst in Siemens aero-derivative gas turbines, Montreal. Her research interests are related to on-road and off-road vehicle design, and autonomous and hybrid electric vehicle simulation. She is involved in research related to tire-terrain interaction in cooperation with Volvo Group Truck Technology and NSERC.