IEEE Kingston Section -

October 4th, 2022

The EMB/RA/CS Societies Joint Chapter of IEEE Kingston Section and Ingenuity Labs Research Institute are proud to present the following hybrid technical talk:

TITLE:

Flying Flat Out: Fast Multirotor Flight Using Vision-Based Navigation in Real-World Environments

SPEAKER:

Dr. Melissa Greeff

Department of Electrical and Computer Engineering

Queen’s University

Kingston, ON

TIME & VENUE:

1:30 – 2:30pm, Tuesday October 18, 2022

In-Person: Ingenuity Labs, Mitchell Hall (Room 395), Queen’s University

Online: https://queensu.zoom.us/j/97760737047?pwd=dHB1ME5FZzhld2VLRnQ1UExqSEVwdz09

Meeting ID: 977 6073 7047, Passcode: Ingenuity

ABSTRACT:

Multirotor unmanned aerial vehicles (UAVs) are mechanically simple and highly maneuverable robots that are suitable to a wide range of applications such as infrastructure inspection, transportation, search-and-rescue missions, and mapping operations. To support these applications, my research vision is to expand reliable autonomous multirotor navigation beyond lab demonstrations to real-world environments. To enable this, in the first part of this talk, we present computationally efficient control algorithms, by exploiting a property of the dynamics knowns as differential flatness. We exploit this property to enable efficient prediction and safe learning using online data. We develop safe high-performance control by accounting for nonlinear and unknown dynamics in a computationally tractable way. In the second part of this talk, we explore some of the challenges to high-speed autonomous vision-based flight. Real-world environments may be GPS-denied and vision-based navigation, relying predominantly on an onboard camera, is a lightweight and cost-effective alternative. Most standard controllers are perception-agnostic and tend to assume (i) the action computed by the controller has no effect on the ability of vision-based navigation to determine location and (ii) perfect state estimation is obtained. These assumptions often limit the reliability and performance of perception-agnostic controllers for autonomous vision-based flight. In the second part of this talk, we present perception-aware control algorithms that account for partial knowledge of the environment and plan despite imperfect state estimation. These approaches are validated through outdoor experiments on a DJI M600 multirotor where we demonstrate autonomous vision-based flight at speeds up to 10 m/s. Finally, I will discuss some of the open gaps to robust high-performance autonomous multirotor navigation.

BIOGRAPHY:

Melissa Greeff joined Queens as an Assistant Professor in the department of Electrical and Computer Engineering and is a faculty affiliate with the Vector Institute for Artificial Intelligence. She obtained her BASc in Engineering Science and her PhD from the University of Toronto where she was a course instructor for Linear Algebra. Her research interests include aerial robots, vision-based navigation, and safe learning-based control. She has published in various international robotics and control systems venues including IEEE Robotics and Auto. Letters, Annual Review of Control, Robotics, and Autonomous Systems, ICRA, IROS and CDC. She has helped co-organize various workshops on safe robot learning and benchmarking at various international conferences.

HOSTS:

Dr. Joshua Marshall joshua.marshall@queensu.ca

Dr. Keyvan Hashtrudi-Zaad khz@queensu.ca

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August 3rd, 2022

Title: Optimization of DSP-Based Optical Communication Links Beyond 100Gbps

Speaker: Tony Chan Carusone

Professor, University of Toronto

Chief Technology Officer, Alphawave IP Group

Date: Monday, August 8th

Time: 2pm

Location: Walter Light Hall Rm 302, Queen’s University

Abstract:

Progress in computation and communication is increasingly bottlenecked by integrated circuit I/O. Previously reserved for communication over 100’s of kilometres, today optical links are widely viewed as the primary solution for chip-to-chip links above 100 Gbps and up to 1 km. Meanwhile, CMOS technology scaling has led us toward integrated circuit transceivers that are, essentially, complete modems: thin but critical analog front-end circuits and a large custom DSP. This presentation will describe how to co-design of DSP transceivers with a thin but critical analog front-end and the associated optical components to create optical links serving future datacentre communication needs. As an example, a 4-PAM CMOS linear TIA designed in a FinFET technology consuming less than 50 mW and co-packaged alongside photodiodes is presented. The circuits and packaging are co-designed to maximize the passive front-end BW. Experimental results confirm the integrated optical fibre receiver operates up to 160-Gb/s using a single wavelength with a suitable DSP.

Bio:

Tony Chan Carusone has been a faculty member at the University of Toronto since completing his Ph.D. there in 2002. He has co-authored eight award-winning papers on chip-to-chip and optical communication circuits, ADCs, and clock generation. He has also been a consultant to industry since 1997. He is currently the Chief Technology Officer of Alphawave in Toronto, Canada.

Dr. Chan Carusone was a Distinguished Lecturer for the IEEE Solid-State Circuits Society 2015-2017 and served on the Technical Program Committee of the International Solid-State Circuits Conference from 2015-2021. He co-authored the latest editions of the classic textbooks “Analog Integrated Circuit Design” along with D. Johns and K. Martin, and “Microelectronic Circuits” along with A. Sedra and K.C. Smith. He has served as Editor-in-Chief of the IEEE Transactions on Circuits and Systems II: Express Briefs, an Associate Editor for the IEEE Journal of Solid-State Circuits, and is now Editor-in-Chief of the IEEE Solid-State Circuits Letters. He is a Fellow of the IEEE.

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November 3rd, 2019

The EMB/RA/CS Societies Joint Chapter of IEEE Kingston and Queen’s Ingenuity Labs Research Institute are proud to present the following invited lecture:

UNCERTAINTY ASSESSMENT FOR DEEP NETWORKS: MAKING AUTONOMOUS DRIVING PERCEPTION AWARE OF ITS OWN LIMITATIONS

Date: Wednesday November 20^th, 2019.

Time: 12:30 – 1:30 PM

Location: Mitchell Hall, Room 395, Queen’s University

Speaker: Prof. Steve Waslander, University of Toronto Institute for Aerospace Studies (UTIAS). Director, Toronto Robotics and Artificial Intelligence Laboratory (TRAILab).

Light Refreshments: 1:30 – 2:00PM, Mitchell Hall, Room 395, Queen’s University

Abstract

Most autonomous vehicle perception approaches are primarily reliant on modern deep neural networks (DNNs). DNNs have shown breakthrough performance or object detection, tracking and prediction, scene segmentation, vehicle localization and mapping, providing accurate bounding boxes for vehicles and pedestrians, lane boundaries and signage over extensive datasets and on-road testing. Yet, these networks are not uniformly consistent in the quality of their perception outputs, and much can be gained by accumulating evidence over time. In this talk, I will lay out our progress in 3D object detection to improve detection accuracy for a range of sensor configurations, and demonstrate the effects of adverse weather on these approaches. Further, I will describe our approach to providing reliable uncertainty estimates for network outputs that enable proper Bayesian inference when incorporating prior information and tracking object motion through time.

Speaker Bio:

Prof. Steven Waslander is a leading authority on autonomous aerial and ground vehicles, including multirotor drones and self-driving cars. He received his B.Sc.E.in 1998 from Queen’s University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics, where as a graduate student he created the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), the world’s most capable outdoor multi-vehicle quadrotor platform at the time. He was recruited to Waterloo from Stanford in 2008, where he founded and directs the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous drones and autonomous driving through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination. His work on autonomous vehicles has resulted in the Autonomoose, the first autonomous vehicle created at a Canadian University to drive on public roads. His insights into autonomous driving have been featured in the Globe and Mail, Toronto Star, National Post, the Rick Mercer Report, and on national CBC Radio. In 2018, he joined the University of Toronto Institute for Aerospace Studies (UTIAS), and founded the Toronto Robotics and Artificial Intelligence Laboratory (TRAILab).

To attend this seminar, RSVP by clicking this Link

For more information, please contact Dr. Joshua Marshall or Dr. Keyvan Hashtrudi-Zaad

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July 4th, 2019

The Joint Communications & Computer Chapter of IEEE Kingston Section is proud to present the following IEEE Lecture:

APPLICATION OF COMPRESSED SENSING THEORY TO RADAR SIGNAL PROCESSING: TUTORIAL AND RECENT DEVELOPMENTS

Date: Thursday July 11^th, 2019.

Time: 10:30 – 11:30 AM

Location: Royal Military College of Canada, Kingston, Room S4214

Speaker: Dr. Soheil Salari

Abstract

During the last decade, the emerging technique of compressed sensing has become a popular subject in signal processing and sensor systems since it can reduce the sampling rate and computational complexity of practical systems without performance loss. The technique of compressed sensing has been successfully applied in signal acquisition, image compression, and data reduction. Based on compressed sensing theory, the original radar echo can be sampled at a lower rate, and then the detection and imaging can be implemented. Although the theory of compressed sensing has been investigated for some radar and localization problems, several important questions have not been answered yet. This presentation introduces the main principle of compressed sensing theory, and then reviews some recent developments in the application of the compressed sensing theory to radar signal processing.

Speaker Bio:

Soheil Salari received all degrees in electrical engineering: Ph.D. from K.N. Toosi University of Technology in 2007, M.Sc. from K.N. Toosi University of Technology in 2001, and B.Sc. From University of Kerman in 1998. He held various research/teaching/engineering positions in Iran until 2011. Since 2011, he has served several research appointments with University of Ontario Institute of Technology (UOIT), University of Toronto, Queen’s University, and RMCC. He also collaborated in several industrial projects. Currently, he is working for the government of Canada as a research scientist. His role has been to carry out research, develop new capabilities, and provide technical advice on topics of artificial intelligence, target tracking, and data fusion. His research interests are in the areas of wireless communications, radar and localization, compressed sensing, digital signal processing, machine learning and artificial intelligence, and optimization theory.

This seminar is open to the general public with free admission and refreshments.

For more information, please contact Dr. François Chan, chan-f@rmc.ca

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