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  • Internet of Things, building blocks, challenges and research directions

    Room ENG288, 245 Church Street, Toronto, ON, Canada

    Tuesday May 31st, 2016 at 11:30 a.m. Dr. Fatima Hussain will be presenting “Internet of Things, building blocks, challenges and research directions”. Speaker: Dr. Fatima Hussain Day & Time: Tuesday, May 31st, 2016 11:30 a.m. – 12:30 p.m. Location: Room ENG 288 Computer Science Department, George Vari Centre for Computing and Engineering, Ryerson University 245 Church St., Toronto, ON, M5B 2K3 Organizer: IEEE Women in Engineering (WIE), IEEE Magnetics Chapter, IEEE Instrumentation & Measurement/Robotics & Automation Joint Chapter and Computer Science Department Ryerson University Contact: Dr. Maryam Davoudpour Abstract: The Internet of Things (IoT) is a novel paradigm that is rapidly growing in modern wireless telecommunications. IoT means a world-wide network of interconnected objects uniquely addressable, sustainable and enhance able. It is defined as integration of several technologies, and communications solutions based on standard communication protocols. The main strength of the IoT idea is the high impact it will have on several aspects of everyday-life and behavior of potential users. This promising technology comes with great challenges and leads to numerous research directions for industry as well academia. Biography: Fatima Hussain received her PhD and MASc. degree in Electrical and Computer Engineering with specialization in “Wireless Communication” from Ryerson University, Canada. She holds MEng. and BSc. in Electrical and Computer Engineering with specialization in “Controls System” and “Telecommunication Systems”, respectively from University of Engineering and Technology Lahore, Pakistan. She is acting as a reviewer for IEEE Access journal and IET Journal from couple of years. She is working as a post-doctoral fellow in NCART lab, Ryerson University, on a design and implementation of “Smart Parking System”.

  • Optimization and Research: Applications, Opportunities, and Challenges

    Room ENG 288 245 Church St., Toronto, ON, M5B 2K3

    June 20, 2016 at 1:00 p.m. Dr. Shahryar Rahnamayan, Associate Professor in the Department of Electrical, Computer and Software Engineering Faculty of Engineering and Applied Science at UOIT, will be presenting “Optimization and Research: Applications, Opportunities, and Challenges”. Speaker: Dr. Shahryar Rahnamayan Associate Professor Department of Electrical, Computer and Software Engineering Faculty of Engineering and Applied Science, UOIT Day & Time: Monday, June 20, 2016 1:00 p.m. – 2:00 p.m. Location: Room ENG 288 245 Church St., Toronto, ON, M5B 2K3 Organizer: IEEE Women in Engineering (WIE), IEEE Magnetics Chapter, IEEE Instrumentation & Measurement/Robotics & Automation Joint Chapter and Computer Science Department Ryerson University Contact: Dr. Maryam Davoudpour Abstract: In this research seminar, the speaker will explain his recent optimization research work and accomplishments, categorized in the following two main groups of contributions: theoretical/developmental and practical. The first group will cover his contributions in large-scale optimization, opposition-based computation, many-objective optimization, image-based large-scale visualization and interaction, incremental cooperative coevolution, micro-differential evolution, 3D visualization of many-objective Pareto-front, preserving constraint handling, decision making in high-dimensional objective space, and multi-modal optimization. In the practical category, the speaker will explain several real-world applications to demonstrate effectiveness of optimization in medical image processing, renewable energy systems, forensic science, scheduling, and wireless sensors network. This seminar will be beneficial for faculty and students who conduct ‘research in optimization’ or ‘optimization in research’. Biography: Dr. Shahryar Rahnamayan received his B.Sc. and M.S. degrees both with honors in software engineering. In 2007, he received his Ph.D. degree in the field of evolutionary computation from Systems Design Engineering Department, University of Waterloo. Inspired from opposition-based differential evolution algorithm (ODE), more than 450 papers have been published. Before joining to the faculty of engineering and applied science, University of Ontario Institute of Technology (UOIT), Canada, as a tenure-track faculty member, he was a postdoctoral fellow at Simon Fraser University (SFU), Canada. He was granted tenure earlier and also was promoted to an associate professor position in 2013. His research includes evolutionary computation, image processing, machine learning, and opposition-based soft computing. Dr. Rahnamayan was awarded the Ontario Graduate Scholarship (OGS), President’s Graduate Scholarship (PGS), NSERC’s Japan Society for the Promotion of Science (JSPS) Fellowship, NSERC’s Industrial R&D Fellowship (IRDF), NSERC’s Visiting Fellowship in Canadian Government Laboratories (VF), and the Canadian Institute of Health Research (CIHR) Fellowship for two times. He has published more than 100 papers, Dr. Rahnamayan has received several prestigious research grants, such as, NSERC Discovery Grant and also Applied Research and Commercialization Initiative Fund. He recently visited the Michigan State University (MSU) and BEACON Research Center for two years in order to conduct research on large-scale and multi-objective optimization and visualization.

  • The Application of Optimization to Model Predictive Control

    Room ENG 288, 245 Church St., Toronto

    July 4, 2016 at 12:00 p.m. Dr. Ruth Milman, Assistant Professor at UOIT, will be presenting “The Application of Optimization to Model Predictive Control”. Speaker: Dr. Ruth Milman Assistant Professor – Department of Electrical, Computer and Software Engineering Faculty Applied Science and Engineering, University of Ontario Institute of Technology Day & Time: Monday, July 4, 2016 12:00 p.m. – 1:00 p.m. Location: Room ENG 288 245 Church St., Toronto, ON, M5B 2K3 Contact: Dr. Maryam Davoudpour Organizers: IEEE Women in Engineering (WIE), IEEE Magnetics Chapter, IEEE Instrumentation & Measurement/Robotics & Automation Joint Chapter and Computer Science Department Ryerson University Abstract: Model predictive control (MPC) is the application of an optimal control scheme over a finite horizon. At each sample interval a cost function is minimized over a finite horizon and a resulting open loop controller is calculated. The control for the current sample interval is applied and the whole process is repeated at the next sample interval. By repeating the process at each sample interval, the resulting control scheme, which is technically open loop, inherits the benefits of a closed loop controller. These include some stability and robustness properties. By nature, MPC is computationally intensive and only makes sense when a there are constraints which must be enforced by the system. As would be expected, adding constraints into the system even further intensifies the computational requirements. By nature, MPC is an optimal control strategy. If a true optimal control is computed when solving the minimization problem, then the solution is independent of the choice of the optimizer. It is only when time constraints force the need for suboptimal controls to be used that the actual algorithm plays a role in the quality of the resulting controller. Despite (or because of) this, the choice of optimization schemes plays a critical role in the real time application of MPC for a simple but important reason – the computational time it takes to solve for the optimal solution. MPC is a flexible framework which allows for control in the face of both linear or nonlinear systems, and can be applied to systems with either hard or soft constraints. How each problem is set up is critical to the choice of optimizer. These choices can drastically impact the computational effort which is required to solve for the resulting controller. As such, the choice and application of optimization schemes to MPC is of critical importance to the resulting performance of the systems. Biography: Dr. Ruth Milman is an Assistant Professor in the Department of Electrical, Computer and Software Engineering with the Faculty of Applied Science and Engineering at the University of Ontario Institute of Technology. She has been with UOIT since June 2007, where she works in the Department of Electrical and Software Engineering, focusing in the field of control theory. Her research interests include optimization and computationally efficient algorithms for model predictive control as well as the application of both linear and nonlinear MPC to autonomous systems. She has worked on path planning for robotic applications in environments with both moving and stationary obstacles. She has worked extensively in the areas of nonlinear and optimal control theory and has developed algorithms for computation of the optimization problem that underlies Model Predictive control. Prior to coming to UOIT she did post-doctoral research at the University of Toronto from 2005 to 2007. Ruth Milman obtained her PhD in 2004 from the Systems Control Group in the Department of Electrical and Computer Engineering at University of Toronto, Canada. Her dissertation focused on improving the speed and computational efficiency of a Linear Model Predictive Controller. As part of this she developed a novel algorithm for solving the quadratic programming subproblem in MPC. She obtained her MASC in 1997 from the Systems Control Group in the University of Toronto and her BASc (Honours) in Computer Engineering in 1995 from the Faculty of Applied Science and Engineering at the University of Toronto.

  • Teaching and Learning Methods

    Room KHE 225, Ryerson University

    Monday September 12, 2016 at 11:20 a.m. Dr. John Paul Fox, will be presenting “Teaching and Learning Methods”. Speaker: Dr. John Paul Fox Director of the Academic Integrity Office, Ryerson Day & Time: Monday, September 12, 2016 11:20 a.m. – 12:00 p.m. Location: Ryerson, KHE 225 Contact: Maryam Davoudpour Organizer: WIE Abstract: Lesson planning can be a time consuming and needlessly stressful process. In this talk I will discuss strategies for efficiently preparing lesson plans. You will be presented with a framework for lesson planning which can be used to structure any type of lesson, from a 10 minute pre-lab talk to a 3-hour lecture. Attendees are encouraged to think about an upcoming lesson that they will be teaching so that this framework can be applied. Biography: John Paul Fox is the Director of the Academic Integrity Office here at Ryerson. Prior to accepting this position, John Paul worked in the Learning and Teaching Office (LTO) for six years, as an educational developer and as its manager. During this time John Paul was responsible for offering professional development in teaching to Ryerson’s faculty, TAs and GAs. He has an undergraduate degree in genetics from Trinity College Dublin, an MSc in molecular evolution and a PhD in population genetics, both from York University as well as a Masters in Public Police and Administration from Ryerson University. Finally,John Paul is a fellow of SEDA UK.

  • Disaster Scene Reconstruction – Emergency Management Tool

    Ryerson, KHE 225

    Monday September 19, 2016 at 11:00 a.m. Dr. Alex Ferworn, Associate Chair and Graduate Programs Director at Ryerson University, will be presenting “Disaster Scene Reconstruction – Emergency Management Tool”. Speaker: Dr. Alex Ferworn Associate Chair and Graduate Programs Director, Ryerson University Director, Program in Disaster and Emergency Management Day & Time: Monday, September 19, 2016 11:00 a.m. – 12:00 p.m. Location: Ryerson, KHE 225 Contact: Maryam Davoudpour Organizer: WIE, Magnetics, Measurement/Instrumentation-Robotics, Computer Science Department Ryerson University Biography: Prof. Ferworn received his PhD in Systems Design Engineering from the University of Waterloo, his MSc in Computing and Information Science from the University of Guelph and his B.Tech in Applied Computer Science from Ryerson University, where he is a faculty member in the Department of Computer Science, Associate Chair and Graduate Programs Director. He is also Director of a number of Certificate programs including the Program in Disaster and Emergency Management. Ferworn is an adjunct faculty member in the Department of Computing and Software, Faculty of Engineering at McMaster University. Prof. Ferworn has been collaborating with the USAR and CBRNe Response Team (UCRT) of the Ontario Provincial Police since 2005. He has worked extensively with USAR teams in Canada and the United States on a broad range of technology issues related to Computational Public Safety. He does not own a dog.