Safa Jinje

��ϲ��'s top undergraduate says he fell in love with the process of learning

rahul.kalvapalle — Tue, 25 Jun 2024 14:13:32 +0000

��ϲ��'s top undergraduate says he fell in love with the process of learning

rahul.kalvapalle Tue, 06/25/2024 - 10:13

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Jeff Chen achieved perfect grades in 17 of his 46 courses on his way to graduating with a bachelor of applied science in civil engineering (photo by Bronson Lo)

Safa Jinje

Topic

Our Community

Convocation 2024

Faculty of Applied Science & Engineering

Subheadline

Jeff Chen was awarded the 2024 John Black Aird Scholarship as the top student from an undergraduate degree program at ��ϲ��

Jeff Chen says he's always prioritized his academics, but what really elevated his performance to the next level was pouring his energies into the process of learning and self-improvement – rather than obsessing over the outcome.

“In my first year, I spent a lot of time anticipating the marks I would need to get a 4.0 GPA,” says Chen. “I stopped doing that in second year and cared more about the effort I put in to get the result.”

The approach bore fruit. Chen graduated this June with his bachelor of applied science in civil engineering, achieving 100 per cent in 17 of his 46 courses for an overall average of 96.5 per cent – the highest among his cohort of graduates across ��ϲ��’s three campuses.

His performance earned him the 2024 John Black Aird Scholarship, which is awarded annually to the top student from an undergraduate degree program at ��ϲ��. His stellar academic record also led to him receiving a Governor General’s Silver Medal.

“This honestly wasn’t something I was expecting but I am immensely grateful,” says Chen. “I am treating these awards as a culmination of four years of hard work.”

Chen adopted a methodical process to course work, starting his mornings by writing a list of what he wanted to accomplish that day. The items would be small, such as studying lecture notes for 15-minute increments, and he preferred to spread assignments and projects across several days. “I found that breaking things up helped me not get distracted and everything was continually fresh in my mind,” he says.

Chen also applied the same approach to his pastimes, which include playing chess, hiking and working out. “My hobbies allow for me to set and fulfill goals in the same way I approach my academics,” he says. “I’ve recently gotten into powerlifting, and I always aim to hit higher weights for the squat, bench and deadlift.”

One of the highlights of Chen’s undergraduate career at ��ϲ�� was his fourth-year thesis, which explored the use of deep neural networks – a form of artificial intelligence – to predict the behaviour of structures during earthquakes.

The research focused on the seismic response of structural systems that exhibit non-linear stress-strain behaviour when subjected to strong forces. “Under an earthquake, dynamic forces on the structure are often strong enough that they enter this non-linear range, which has complex, difficult-to-predict behaviour,” Chen says.

The work, supervised by Professor Oh-Sung Kwon of the department of civil and mineral engineering, began when Chen received an undergraduate summer research award from the Natural Sciences and Engineering Research Council of Canada (NSERC).

“The thesis project was completely open-ended and self-directed, unlike my course work, where outcomes are determined by the professors,” Chen says. “I got to explore the intersection between artificial intelligence and structural engineering, and how we can utilize deep neural networks to design better and more resilient structures in the case of disasters.

“I really enjoyed the challenge of working on this research, so I spent a lot of time on it.”

This fall, Chen will take on his next academic challenge as a law student at the University of California, Berkeley. He says he’s looking forward to gaining foundational legal knowledge during his first year of law school before committing to a specific area of law. “I am keeping my options open, but I know my engineering degree will be useful, especially if I go into intellectual property law, since working on patents can be very technical.”

Reflecting on his time at ��ϲ��, Chen says he hopes his experiences will inspire other students to enjoy the journey of higher learning.

“��ϲ�� has opened many doors for me and there are many paths I can take going forward.”

��ϲ�� researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle — Wed, 29 May 2024 14:59:42 +0000

��ϲ�� researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle Wed, 05/29/2024 - 10:59

Cutline

Sandro Papais, a PhD student, is the co-author of a new paper that introduces a graph-based optimization method to improve object tracking for self-driving cars (photo courtesy of aUToronto)

Safa Jinje

Topic

Breaking Research

Artificial Intelligence

Faculty of Applied Science & Engineering

Subheadline

The new tools could help robotic systems of autonomous vehicles better track the position and motion of vehicles, pedestrians and cyclists

Researchers at the ��ϲ�� Institute for Aerospace Studies (UTIAS) have introduced a pair of high-tech tools that could improve the safety and reliability of autonomous vehicles by enhancing the reasoning ability of their robotic systems.

The innovations address multi-object tracking, a process used by robotic systems to track the position and motion of objects – including vehicles, pedestrians and cyclists – to plan the path of self-driving cars in densely populated areas.

Tracking information is collected from computer vision sensors (2D camera images and 3D LIDAR scans) and filtered at each time stamp, 10 times a second, to predict the future movement of moving objects.

“Once processed, it allows the robot to develop some reasoning about its environment. For example, there is a human crossing the street at the intersection, or a cyclist changing lanes up ahead,” says Sandro Papais, a PhD student in UTIAS in the Faculty of Applied Science & Engineering. "At each time stamp, the robot’s software tries to link the current detections with objects it saw in the past, but it can only go back so far in time.”

In a new paper presented at the 2024 International Conference on Robotics and Automation in Yokohama, Japan, Papais and co-authors Robert Ren, a third-year engineering science student, and Professor Steven Waslander, director of UTIAS’s Toronto Robotics and AI Laboratory, introduce Sliding Window Tracker (SWTrack) – a graph-based optimization method that uses additional temporal information to prevent missed objects.

The tool is designed to improve the performance of tracking methods, particularly when objects are occluded from the robot’s point of view.

A visualization of a nuScenes dataset used by the researchers. The image is a mosaic of the six different camera views around the car with the object bounding boxes rendered overtop of the images (image courtesy of the Toronto Robotics and AI Laboratory)

“SWTrack widens how far into the past a robot considers when planning,” says Papais. “So instead of being limited by what it just saw one frame ago and what is happening now, it can look over the past five seconds and then try to reason through all the different things it has seen.” 

The team tested, trained and validated their algorithm on field data obtained through nuScenes, a public, large-scale dataset for autonomous driving vehicles that have operated on roads in cities around the world. The data includes human annotations that the team used to benchmark the performance of SWTrack.

They found that each time they extended the temporal window, to a maximum of five seconds, the tracking performance got better. But past five seconds, the algorithm’s performance was slowed by computation time.

“Most tracking algorithms would have a tough time reasoning over some of these temporal gaps. But in our case, we were able to validate that we can track over these longer periods of time and maintain more consistent tracking for dynamic objects around us,” says Papais.

Papais says he’s looking forward to building on the idea of improving robot memory and extending it to other areas of robotics infrastructure. “This is just the beginning,” he says. “We’re working on the tracking problem, but also other robot problems, where we can incorporate more temporal information to enhance perception and robotic reasoning.”

Another paper, co-authored by master’s student Chang Won (John) Lee and Waslander, introduces UncertaintyTrack, a collection of extensions for 2D tracking-by-detection methods that leverages probabilistic object detection.

“Probabilistic object detection quantifies the uncertainty estimates of object detection,” explains Lee. “The key thing here is that for safety-critical tasks, you want to be able to know when the predicted detections are likely to cause errors in downstream tasks such as multi-object tracking. These errors can occur because of low-lighting conditions or heavy object occlusion.

“Uncertainty estimates give us an idea of when the model is in doubt, that is, when it is highly likely to give errors in predictions. But there’s this gap because probabilistic object detectors aren’t currently used in multi-tracking object tracking.” 

Lee worked on the paper as part of his undergraduate thesis in engineering science. Now a master’s student in Waslander’s lab, he is researching visual anomaly detection for the Canadarm3, Canada’s contribution to the U.S.-led Gateway lunar outpost. “In my current research, we are aiming to come up with a deep-learning-based method that detects objects floating in space that pose a potential risk to the robotic arm,” Lee says.

Waslander says the advancements outlined in the two papers build on work that his lab has been focusing on for a number of years.

“[The Toronto Robotics and AI Laboratory] has been working on assessing perception uncertainty and expanding temporal reasoning for robotics for multiple years now, as they are the key roadblocks to deploying robots in the open world more broadly,” Waslander says.

“We desperately need AI methods that can understand the persistence of objects over time, and ones that are aware of their own limitations and will stop and reason when something new or unexpected appears in their path. This is what our research aims to do.”

��ϲ�� Engineering to launch new certificate in electric vehicle design

Christopher.Sorensen — Mon, 29 Apr 2024 13:22:03 +0000

��ϲ�� Engineering to launch new certificate in electric vehicle design

Christopher.Sorensen Mon, 04/29/2024 - 09:22

Cutline

(photo by Justin Sullivan/Getty Images)

Topic

Faculty of Applied Science & Engineering

Sustainability

The ��ϲ��’s Faculty of Applied Science & Engineering will launch a new electric vehicle design certificate for undergraduate students this fall.

The program aims to prepare the next generation of electric vehicle (EV) engineers for professional and research opportunities in the growing industry.

“EVs have tremendous potential to improve local and global climates, which fits right in with our faculty’s broader goals of sustainability and environmental considerations,” says Professor Dionne Aleman, the faculty’s associate dean of cross-disciplinary programs.

“From battery design to sustainability to infrastructure, engineering is a big part of advancing EV technology. We want engineering students to be able to hit the ground running in this exciting and growing field if they want careers in electrification.”

The widespread adoption of EVs has come into greater focus as governments across the globe develop new policies to achieve net-zero emissions goals by 2050. In Canada, the federal government has set a zero-emissions vehicle sales target by 2035, which will require all new cars, trucks and SUVs to be battery operated.

To meet this demand, the EV industry will require a workforce that can advance all facets of electrification technology, from vehicle design and charging technologies, to battery capacity and thermal management of power systems. This work is multidisciplinary, spanning mechanical, electrical, chemical, industrial, computer and materials science engineering.

While many ��ϲ�� Engineering courses include aspects of EV design, the faculty has delved deeper into EV-focused topics in the past year with two graduate-level courses in electric vehicle systems and thermal science.

The new certificate will expand on these offerings to provide eligible undergraduate students with an understanding of the technical and environmental implications of engineering in EV design. It includes a new course, APS380: Introduction to Electric Vehicle Design.

“The EV Design certificate is the beginning of a start-to-finish investigation into both the design of EVs and their integration into society,” says Matthew Mackay, an associate professor, teaching stream, in the department of mechanical and industrial engineering, who worked with Olivier Trescases, a professor in the Edward S. Rogers Sr. department of electrical and computer engineering (ECE), to design the introductory course.

“Since EVs are inherently multidisciplinary, having students first encounter this content through the certificate and a multidisciplinary design course will expose them to the challenges and knowledge they would not otherwise see through a single-program outlook.”

The APS380 course is a broad-based introduction to EV design, which makes the technical content accessible to students across engineering programs.

“This certificate is part of a multi-year effort to bring EV teaching to our departments. It will take some time for us to grow the capacity of this effort as new lab spaces, lecturers and courses are brought in,” says Mackay.

“But we want this to be an inclusive experience for students. If someone is interested but doesn’t have the required experience, we hope that they can come to see us anyway – there may always be opportunity to join.”

Trescases says there is a growing demand for engineers with specialized EV skills in Ontario.

“In ECE we’ve been teaching core EV topics like electric motors and power electronics to undergraduates for a long time, in courses like ECE314, ECE463 and ECE520. This new multi-disciplinary course takes a more ‘systems/application’ perspective and I think that it will be a great complementary offering for our students,” he says.

“In developing the labs and the course content for APS380 we leveraged elements of our research at [the ��ϲ�� Electric Vehicle Research Centre, or UTEV], our new EV graduate course, as well as the new Porsche EV microcredential that we offer to industry through the School of Continuing Studies.”

��ϲ�� research team hits bullseye with strategy to improve fairness in darts

Christopher.Sorensen — Thu, 11 Apr 2024 15:54:34 +0000

��ϲ�� research team hits bullseye with strategy to improve fairness in darts

Christopher.Sorensen Thu, 04/11/2024 - 11:54

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(photo by Zac Goodwin/PA Images via Getty Images)

Safa Jinje

Topic

Our Community

Faculty of Applied Science & Engineering

Subheadline

Researchers in ��ϲ��'s Faculty of Applied Science & Engineering take home first place at MIT Sloan Sports Analytics Conference

A research team from the ��ϲ�� is rethinking how darts can be played more fairly.

The team, led by Timothy Chan, a professor in the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering, recently took home first place in the 2024 MIT Sloan Sports Analytics Conference Research Papers Competition by designing a new framework to level the playing field in dart games.

“Winning this competition, at the world’s most prominent and competitive sports analytics conference, is a testament to the excellence and ingenuity of our students here at the ��ϲ��,” says Chan, who is also ��ϲ��’s associate vice-president and vice-provost, strategic initiatives.

With millions of players around the world, including an estimated 17 million in the United States, according to the National Sporting Goods Association, the game of darts continues to grow in popularity.

“Darts is a great sport because almost anyone can play and it doubles as a fun mental puzzle,” says Rachael Walker, an alumna who is a co-author of the conference paper along with PhD candidate Craig Fernandes and Chan.

From left to right: Professor Timothy Chan, Rachael Walker and Craig Fernandes are the co-authors of a paper that introduces a new framework designed to level the playing field in dart games (photo by Safa Jinje)

The research, which expands on work Walker did for her undergraduate thesis, focuses on the game of 501 darts, where players begin with a score of 501 and take turns throwing darts at the dartboard. Points are deducted from their total depending on where the darts land. The first player to reach zero wins.

“We looked at 501 darts played in recreational and professional settings,” says Fernandes. “In a recreational setting, the game is often played amongst players that have different skill sets – and when that happens, the stronger player often wins, which can lead to unexciting matches.”

As in golf, the imbalance is often addressed by introducing a system that gives the less-skilled players an advantage so that all players have an equal chance at winning.

“[But] our research first proved that the current approach of giving the weaker player a head start doesn’t actually give all players a fair chance at victory,” says Fernandes. “Instead, we used a Markov decision process to understand the nuances of the game and then come up with a new system that actually leads to mathematical fairness.”

The new framework first determines a player’s skill level by having them throw several darts at the centre of the board before the start of a game. Players are then assigned a skill level based on where their darts land – players who get most of their darts in the centre are determined to be higher skilled, while those whose darts are spread out across the board are deemed less-skilled players who would benefit from an advantage.

The new system gives the lesser-skilled player credits that they can cash in at any point in the game. The credits can be used to claim the outcome of a throw – that is, the region of the board they intend the dart to land in – without physically throwing the dart.

The researchers found that credits create fairness by using a Markov decision process, a mathematical framework that models scenarios where the outcomes are partly in control of the decision-maker and partly random. However, the number of possible decisions and outcomes in darts made the model difficult to implement and solve at scale.

“To accurately model a dart game that assigns an advantage to a single player, we needed to consider over half a million possible game states and hundreds of possible actions at each state,” says Walker.

“In a traditional implementation, you optimize across all states simultaneously, which may require considering billions, or even trillions, of possible outcomes.”

The researchers overcame the challenge of scale by starting simply and slowly adding complexity to the model. The first version did not consider the fact that darts is played in turns of three throws for each player; this helped build intuition and develop implementation tricks that later allowed them to solve the true model.

The first-place finish at the MIT Sloan Sports Analytics Conference was affirming for the researchers.

“It was a very strong competition featuring many major North American sports such as football, baseball and basketball, and a lot of research was focused on generative artificial intelligence and machine learning,” says Fernandes, who presented the research at the Sloan conference.

“Winning with our operations research and optimization approach was exciting for us.”

The team is now looking to implement the framework with collaborators, including local dart leagues, to see it work in practice.

“My lab tackles complex decision-making problems in health care and sports using techniques from operations research,” says Chan.

“The tools we develop are general, so the insights we obtain from solving a problem in darts may then be applied towards solutions in patient scheduling or medical decision-making.”

��ϲ�� Engineering program introduces Black high school students to STEM careers

Christopher.Sorensen — Mon, 12 Feb 2024 20:48:52 +0000

��ϲ�� Engineering program introduces Black high school students to STEM careers

Christopher.Sorensen Mon, 02/12/2024 - 15:48

Cutline

Jadesola Babalola-Lucas, 16, explored her passion for engineering during ��ϲ�� Engineering's Blueprint program last summer (supplied image)

Safa Jinje

Topic

Our Community

Black

Faculty of Applied Science & Engineering

Outreach

Undergraduate Students

Subheadline

The free Blueprint program, open to students who have completed Grades 10 or 11, runs for four weeks during the summer and includes workshops throughout the year

Jadesola Babalola-Lucas, 16, has felt an affinity for math and science since she was in elementary school, but it was not until the summer of 2020 that she discovered her passion for engineering.

“I was stuck at home because of COVID-19 and watching a lot of TV, especially the Smithsonian Channel,” says Babalola-Lucas, who is now in Grade 11. “And I kid you not, I had an epiphany watching Planes That Changed the World.”

She was struck by aeronautical feats involved in the design of aircrafts such as the Lockheed SR-71 Blackbird, the fastest crewed aircraft with air-breathing engines, and the way the engineers overcame their challenges with creativity.

“I began to understand how interdisciplinary engineering really is, and it was at that moment that I realized engineers really do make the world go round,” says Babalola-Lucas. “Who wouldn’t want to be a part of something that big?”

A few years later, Babalola-Lucas stumbled upon a posting for Blueprint, an academic enrichment program run by the ��ϲ��’s Faculty of Applied Science & Engineering for Black high school students who are passionate about science, technology, engineering and math (STEM). She could not pass up the opportunity to apply and step closer to her goal of studying either materials science or mechanical engineering, and then pursuing graduate studies in aerospace engineering.

The free, four-week Engineering Outreach program, which is taking applications for this summer until March 24, introduces students who have just finished Grades 10 and 11 to the engineering design process. Through course offerings, hands-on activities and collaborative projects, participants apply what they have learned to create innovative solutions for global issues.

Students are also connected with mentors, who are current undergraduate engineering students – some of whom have gone through the Blueprint program themselves. The mentors help participants understand the day-to-day experience of studying STEM subjects in university.

Blueprint students continue their engagement with ��ϲ�� Engineering over the next school year through two programs – Prepare for Grade 11 students and My Academic Planning Sessions (MAPS) for Grade 12 students. From October through March, weekly virtual sessions guide the prospective university students through the obstacles they may face during their transitions from high school to university.

Additionally, Blueprint participants who are accepted into a ��ϲ�� Engineering undergraduate program are eligible to receive one of five fully funded entrance scholarships that is renewable for four years.

“Blueprint allows Black students to meet successful individuals with similar racial and academic backgrounds. We do this by inviting Black STEM professionals and current engineering students as guest speakers over the summer and yearlong engagement,” says Cassandra Abraham, a co-ordinator at Engineering Outreach and Blueprint director.

“This representation can be empowering, instilling a sense of belonging and motivating students to pursue STEM careers.”

Students had opportunities to code robots in the Mechatronics and Automation course (photo by Shenile Ellis)

The program is structured to facilitate connection among peers. Students are initially divided into groups based on their hobbies and interests – information that is collected during the onboarding process – to encourage them to communicate with each other and form friendships, says Abraham.

When Shenile Ellis, 17, first applied to Blueprint, the then-Grade 11 student thought it would be a great opportunity to learn more about different engineering disciplines.

While she was already interested in mechanical engineering because she enjoyed 3D modelling and seeing projects come to life, she was keen to meet other Black students who were interested in STEM and get a glimpse of what it is like to learn on a university campus.

“I wasn’t sure I would get in, but when I did, I realized I should never count myself out,” Ellis says. “One of the Blueprint leaders always told us that we were there for a reason and that we are all capable.”

For Christine Martins-Ezeifeaku, 17, some of the most memorable moments from the summer were the hands-on activities that were a part of the two courses: Engineering and Human Health, and Mechatronics and Automation.

“In the biomedical engineering lab, we got to do many things we wouldn’t have had the opportunity to do in a typical high school experience,” Martins-Ezeifeaku says. “We got to diagnose sickle cell anemia and create E. coli bacteria.

“Getting to code the robots in the mechatronics lab and seeing them move after we finished was also an amazing experience.”

Martins-Ezeifeaku, who has always gravitated towards working with computers and sees herself studying computer engineering or computer science at university in the fall, says she knows how important it is to see people like herself in the technology field.

“In Grade 10, I took a computer science course that was taught by a teacher who is a Black woman and I was surprised at first because I wasn’t expecting her,” she says. “I loved that class; it really sparked my interest.

“Everyone who has helped me work towards the technology field has been really encouraging.”

As part of a month-long engineering design challenge, Blueprint participants were placed in groups and tasked with designing a solution to a client’s problem. This culminated with a presentation in front of judges, ��ϲ�� Engineering Dean Chris Yip, graduate students and family members at the Myhal Centre for Engineering Innovation & Entrepreneurship.

“I really enjoyed the project because it allowed me to work on my presentation and teamwork skills,” says Ellis. “The experience also gave us experience that we could use in a first-year engineering design course.”

Babalola-Lucas, meanwhile, was part of a team tasked to create a product that repelled locusts on a farm in Jamaica. Together, they designed and built an ultrasonic 40 kHz speaker with a high-enough frequency to disrupt the locusts, but too high for humans to hear – so it didn’t affect human quality of life or wildlife.

“It was really innovative for us, and we ended up winning the competition, which was an extraordinary moment for me,” Babalola-Lucas says. “The entire experience was also an important lesson because I learned that no engineer works alone. You need to know how to work with anyone and everyone. You need to know how to collaborate. Blueprint taught me that from day one.

“I think any Black student who knows they love STEM should join Blueprint. You’re not only going to be exposed to the different disciplines within engineering, but you’re also going to experience a taste of campus life and learn how to study like a university student. You won’t regret it.” 

With the launch of its first satellite, student team charts a course to new knowledge

rahul.kalvapalle — Fri, 19 Jan 2024 17:44:03 +0000

With the launch of its first satellite, student team charts a course to new knowledge

rahul.kalvapalle Fri, 01/19/2024 - 12:44

Cutline

A Falcon 9 rocket lifts off from Vandenberg Space Force Base on Nov. 11, 2023, carrying a satellite designed and built by the ��ϲ�� Aerospace Team (photo courtesy of SpaceX)

Safa Jinje

Topic

Our Community

Aerospace

Electrical & Computer Engineering

Faculty of Applied Science & Engineering

Graduate Students

Mechanical & Industrial Engineering

Space

Undergraduate Students

UTIAS

Subheadline

“We worked on this project for so long with such a narrow focus that actually seeing it deployed was very rewarding”

Students in the ��ϲ��’s Faculty of Applied Science & Engineering recently gathered in the basement of the Sandford Fleming Building – known to many as “The Pit” – to witness the deployment of HERON Mk. II into space.

The 3U CubeSat satellite, built and operated by the space systems division of the ��ϲ�� Aerospace Team (UTAT), was launched into orbit on a Falcon 9 rocket on Nov. 11, 2023 as part of SpaceX’s Transporter-9 rideshare mission that lifted off from the Vandenberg Space Force Base near Lompoc, Calif.

The feat was entirely student funded with support from ��ϲ�� Engineering through student levies and UTAT-led fundraising efforts.

“The experience of the launch was very surreal,” says master’s degree student Benjamin Nero, HERON’s current mission manger.

“We worked on this project for so long with such a narrow focus that actually seeing it deployed was very rewarding.”

“There are any number of things that could go wrong that might prevent a satellite from deploying,” adds Zachary Teper, a fellow master’s degree candidate who is part of the technical development team working on HERON’s ground station.

“So, watching each of the call outs coming out of the SpaceX mission control, seeing the rocket go up and meet every one of its mission objectives and then finally seeing our satellite get ejected out of the dispenser in the correct trajectory was a big relief – because we knew that it was finally in space and on the right path.”

Members of the UTAT space systems division gather on the sixth-floor roof of the Bahen Centre for Information Technology with the fully assembled ground station (photo by UTAT Space Systems)

Launching HERON – short for High frequency Educational Radio communications On a Nanosatellite – was the culmination of years of teamwork that brought together the efforts of more than 100 students.

HERON Mk. II, the second iteration of UTAT’s spacecraft, was originally designed and built between 2016 and 2018 for the fourth edition of the Canadian Satellite Design Challenge. Since space systems division was formed in 2014, many of the students who worked on the initial HERON design and build have since graduated. But the current operations team continued to develop the satellite and renew the student levy that allowed them to secure their space launch.

“The original objective for HERON was to conduct a biology experiment in space,” says Nero, who joined the team in 2019 during his second year of undergraduate studies. “But because of delays in the licensing process, we were unable to continue that mission objective. So, we re-scoped and shifted our focus to amateur radio communication and knowledge building.”

From left to right: HERON Mk. I (2016), HERON Mk. II Prototype (2018), HERON Mk. II Softstack (2020), HERON Mk. II Flight Model (2021) (photos by UTAT Space Systems)

Once the satellite’s final assembly was completed in 2021, the team began flight model testing and assembling a ground station, while also managing the logistics of the regulatory approvals needed to complete the launch.

“It’s difficult to put something in space, both technically and bureaucratically,” says Nero. “There are a lot of different governments that care about what you’re doing and want to know when and how you’re doing it.”

Getting to space was a significant milestone for the team, but it’s still only the beginning of their work.

“The goal for us as a design team is to start gathering institutional knowledge that we didn’t have before,” says Reid Sox-Harris, an undergraduate student who is HERON’s ground station manager and the electrical lead for UTAT’s next space mission, FINCH (Field Imaging Nanosatellite for Crop residue Hyperspectral mapping).

“We’ve never operated a satellite. So, we’re taking a lot of lessons learned with us through this process.” 

For example, when a satellite is deployed for the first time, the ground control team only has a rough idea of its movement and eventual location. They must simulate the launch to figure out exactly where it is before they can establish a connection. And when they receive new positional data, they must rerun their simulation.

“We have to take into account effects such as air resistance, or the sun’s solar cycles and the gravitational effects of the sun, the moon and the Earth – it’s a fairly complicated simulation,” Sox-Harris says.

Nero adds: “Part of the difficulty with a simulation is that a model is only useful for a certain period. An old estimate could result in as much as a few kilometres of drift from the satellite’s actual position per day.”

HERON’s ground station on the roof of the Bahen Centre (photo by UTAT Space Systems)

The team was not only tasked with designing a ground station capable of communicating with a satellite more than 500 kilometres away, but one that can survive a frigid and snowy Canadian winter.

“For any project, the most important thing you should be doing is testing,” says second-year student Swarnava Ghosh, who primarily works on the ground station software. “One challenge with our ground station currently is that there are too many variables that are not fully tested – and everything needs to be perfect in the chain for the communication to work. If the ground station is not pointing in the right direction, we won’t get a signal and we won’t establish communication. And if the amplifier is not working, then we won’t establish communication.” 

The team is confident that they will ultimately resolve any outstanding issues and establish communications with HERON. More importantly, they will be able to take what they’ve learned and apply it to the next mission.

“With FINCH, we want to make sure the ground station software and satellite can communicate on the ground,” says Sox-Harris. “Right now, there are over 500 kilometres between the satellite and ground station, so we can’t fly up there and test whether a command has worked.”

FINCH is set to launch in late 2025 on a rideshare rocket flight. Its current mission objective is to generate hyperspectral imaging maps of crop residue on farm fields in Manitoba from a low-Earth orbit.

There are many technical developments that are new to FINCH that weren’t applicable to HERON, the team says, including a novel optic system for remote sensing that is being developed by students.

“The risks associated with FINCH are mitigated by the work that is being performed by HERON right now. We’re learning many lessons that will be directly applicable to our next mission, and we’ll continue to learn from HERON for at least another year or more,” says Sox-Harris.

“This means the FINCH mission can be more complicated, it can move faster and ultimately we can have better reliability, which is something that we always strive for in aerospace.”

Researchers use AI to optimize nutrient consistency in donated breast milk

Christopher.Sorensen — Tue, 21 Nov 2023 17:27:29 +0000

Researchers use AI to optimize nutrient consistency in donated breast milk

Christopher.Sorensen Tue, 11/21/2023 - 12:27

Cutline

A new data-driven framework developed by researchers at ��ϲ�� and its hospital partners bypasses the need for a device to analyze donated breast milk (photo by Rogers Hixon Ontario Human Milk Bank)

Safa Jinje

Topic

Breaking Research

Sinai Health

Temerty Faculty of Medicine

Artificial Intelligence

Faculty of Applied Science & Engineering

Nutritional Sciences

Research & Innovation

Subheadline

'The new program ... helps to ensure that each batch of human donor milk meets the protein and calorie needs of preterm infants'

A team of ��ϲ�� researchers is leveraging machine learning to optimize the macronutrient content of pooled human donor milk recipes.

The researchers – led by Timothy Chan, a professor in the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering – introduce their data-driven optimization model in a new paper published in the journal Manufacturing & Service Operations Management.

Chan and his team worked with Mount Sinai Hospital’s Rogers Hixon Ontario Human Milk Bank – which provides donor milk to preterm and sick babies who are hospitalized across Ontario – as well as Deborah O’Connor, a professor in the department of nutritional sciences in the Temerty Faculty of Medicine.

“For a variety of reasons, many hospitalized infants do not have a full supply of mother’s milk. In this instance, human donor milk can be lifesaving particularly as it helps to protect preterm infants from necrotizing enterocolitis, a life-threatening bowel disease,” says Sharon Unger, a neonatologist and the medical director of the Rogers Hixon Ontario Human Milk Bank who is also a professor in Temerty Medicine’s department of nutritional sciences.

“The new program developed by Dr. Chan helps to ensure that each batch of human donor milk meets the protein and calorie needs of preterm infants.”

Currently, many milk banks, including Mount Sinai’s, rely on individual decision-making when pooling donor milk. This presents a significant challenge in producing a consistent donor milk product that contains sufficient macronutrients for premature and sick babies in neonatal intensive care units.

“It takes a lot of time to create these recipes without a defined method,” says Chan, who is also ��ϲ��’s associate vice-president and vice-provost, strategic initiatives.

“While there are studies that show that milk that comes from donors who are early in their postpartum period tends to be more protein-rich, our approach provides a good prediction of the actual macronutrient content that will allow milk bank employees to make better pooling decisions.”

Four beakers hold pooled donor milk at Mount Sinai Hospital’s Rogers Hixon Ontario Human Milk Bank (photo by Rogers Hixon Ontario Human Milk Bank)

Given that milk banks are often non-profit entities operating on lean budgets, a low-cost alternative to obtaining a consistent, nutrient-balanced product could be useful across the entire sector.

While devices known as human milk analyzers can be used to measure the exact macronutrient content of each milk sample at a milk bank, they are costly and require extensive regulatory approval – and are therefore only used by half of all milk banks in North America. On top of that, analyzing every donation is a costly endeavour that is labour- and resource-intensive.

“Our data-driven framework bypasses the need for a device to analyze the donor milk by using an artificial intelligence model to predict the macronutrient content of each donation,” says Rachel Wong, who earned her master’s degree from ��ϲ�� Engineering last year and is a lead researcher of the study.

“In addition, by using an optimization model to choose which donations to pool together, we can increase the consistency of macronutrient content in the donor milk product.” 

The multi-phase study included a one-year implementation trial at the Rogers Hixon milk bank that was designed to test whether AI-informed models could help to fill the gap.

In the first phase, researchers collected the necessary data to create a machine learning model to predict the macronutrient content of the pooled recipes, and then designed an optimization model to create the recipes based on macronutrient requirements – that is, the necessary levels of protein and fat.

The team then created a simulation model to test the method before embarking on an experiment in the milk bank, which took place over 16 months in 2021 and 2022.

“Since our study was performed in the milk bank during regular operating hours, rather than in a controlled environment, there were a number of unexpected challenges that we had to adapt to,” Wong says.

“During the COVID-19 pandemic, the volume of donations fluctuated based on the provincial restrictions – during the lockdown periods there was an unprecedented increase in the number and volume of donations.

“We also needed to adapt the AI decisions that had already been proposed to ensure that we abided with the milk bank’s operating protocols.” 

The last phase of the study began by observing the milk bank’s operation for six months and measuring the fat, protein and bacteria levels in the pooled recipes.

For the following six months, the milk bank used the data-driven optimization framework to create the pooled milk recipes. At the end of the year, the researchers compared the optimized recipes to the previous ones to assess which recipes met the macronutrient targets.

“We found that our pooled recipes met the bar for protein and fat simultaneously up to 75 per cent more often, without compromising other factors like an increase in bacteria,” says Chan. “And it took us 60 per cent less time to make the recipes.”

The team’s optimized recipes also have an added benefit for preterm and sick babies, who have underdeveloped digestive systems that make it especially crucial to ensure that the milk they are consuming isn’t overly rich in protein or fat.

Chan’s team is currently working towards expanding the research to measure other nutrients in human donor milk to see if their models can optimize them. The research has won the 2023 Pierskalla Best Paper Award from the Institute for Operations Research and the Management Sciences (INFORMS) and an Excellence in Quality and Safety award from Sinai Health.

“Our ultimate goal is to show that our tool is applicable to other milk banks,” says Chan. “We would like to design a system that can plug into hospital systems to optimize recipes in a way that is sustainable for milk bank staff.”

Wong says that the entire team is grateful to all those who have made the project possible.

“We couldn’t have done this without all of the mothers who donate to the milk bank and the staff who work incredibly hard to provide donor milk to infants across Ontario and beyond,” she says.

“I hope that this research will provide a framework to help milk banks across North America increase the consistency of macronutrient content in their donor milk product. The eventual end goal would be to see a downstream impact of improved growth and developmental outcomes for the infants that receive this donor milk.”   

AI algorithm improves predictive models of complex dynamical systems

Christopher.Sorensen — Wed, 15 Nov 2023 14:20:37 +0000

AI algorithm improves predictive models of complex dynamical systems

Christopher.Sorensen Wed, 11/15/2023 - 09:20

Cutline

From left to right: Professor Prasanth Nair and PhD student Kevin Course are the authors of a new paper in Nature that introduces a new machine learning algorithm that addresses the challenge of imperfect knowledge about system dynamics (supplied images)

Safa Jinje

Topic

Breaking Research

Artificial Intelligence

Faculty of Applied Science & Engineering

Research & Innovation

UTIAS

Subheadline

Developed by ��ϲ�� researchers, the new approach could have applications ranging from predicting the performance of aircraft engines to forecasting climate change

Researchers at the ��ϲ�� have made a significant step towards enabling reliable predictions of complex dynamical systems when there are many uncertainties in the available data or missing information.

In a recent paper published in Nature, Prasanth B. Nair, a professor at the ��ϲ�� Institute of Aerospace Studies (UTIAS) in the Faculty of Applied Science & Engineering, and UTIAS PhD candidate Kevin Course introduced a new machine learning algorithm that surmounts the real-world challenge of imperfect knowledge about system dynamics. The computer-based mathematical modelling approach is used for problem solving and better decision making in complex systems, where many components interact with each other.

The researchers say the work could have numerous applications ranging from predicting the performance of aircraft engines to forecasting changes in global climate or the spread of viruses.

“For the first time, we are able to apply state estimation to problems where we don’t know the governing equations, or the governing equations have a lot of missing terms,” says Course, who is the paper’s first author.

“In contrast to standard techniques, which usually require a state estimate to infer the governing equations and vice-versa, our method learns the missing terms in the mathematical model and a state estimate simultaneously.”

State estimation, also known as data assimilation, refers to the process of combining observational data with computer models to estimate the current state of a system. Traditionally, it requires strong assumptions about the type of uncertainties that exist in a mathematical model.

“For example, let’s say you have constructed a computer model that predicts the weather and at the same time, you have access to real-time data from weather stations providing actual temperature readings,” says Nair. “Due to the model’s inherent limitations and simplifications – which is often unavoidable when dealing with complex real-world systems – the model predictions may not match the actual observed temperature you are seeing.

“State estimation combines the model’s prediction with the actual observations to provide a corrected or better-calibrated estimate of the current temperature. It effectively assimilates the data into the model to correct its state.”

However, it has been previously difficult to estimate the underlying state of complex dynamical systems in situations where the governing equations are completely or partially unknown. The new algorithm provides a rigorous statistical framework to address this long-standing problem.

“This problem is akin to deciphering the ‘laws’ that a system obeys without having explicit knowledge about them,” says Nair, whose research group is developing algorithms for mathematical modelling of systems and phenomena that are encountered in various areas of engineering and science.

A byproduct of Course and Nair’s algorithm is that it also helps to characterize missing terms or even the entirety of the governing equations, which determine how the values of unknown variables change when one or more of the known variables change.

The main innovation underpinning the work is a reparametrization trick for stochastic variational inference with Markov Gaussian processes that enables an approximate Bayesian approach to solve such problems. This new development allows researchers to deduce the equations that govern the dynamics of complex systems and arrive at a state estimate using indirect and “noisy” measurements.

“Our approach is computationally attractive since it leverages stochastic – that is randomly determined – approximations that can be efficiently computed in parallel and, in addition, it does not rely on computationally expensive forward solvers in training,” says Course.

While Course and Nair approached their research from a theoretical viewpoint, they were able to demonstrate practical impact by applying their algorithm to problems ranging from modelling fluid flow to predicting the motion of black holes.

“Our work is relevant to several branches of sciences, engineering and finance as researchers from these fields often interact with systems where first-principles models are difficult to construct or existing models are insufficient to explain system behaviour,” says Nair.

“We believe this work will open the door for practitioners in these fields to better intuit the systems they study,” adds Course. “Even in situations where high-fidelity mathematical models are available, this work can be used for probabilistic model calibration and to discover missing physics in existing models.

“We have also been able to successfully use our approach to efficiently train neural stochastic differential equations, which is a type of machine learning model that has shown promising performance for time-series datasets.”

While the paper primarily addresses challenges in state estimation and governing equation discovery, the researchers say it provides a general groundwork for robust data-driven techniques in computational science and engineering.

“As an example, our research group is currently using this framework to construct probabilistic reduced-order models of complex systems. We hope to expedite decision-making processes integral to the optimal design, operation and control of real-world systems,” says Nair.

“Additionally, we are also studying how the inference methods stemming from our research may offer deeper statistical insights into stochastic differential equation-based generative models that are now widely used in many artificial intelligence applications.”

��ϲ�� researchers speed up design of next-gen biomedical implants, aerospace materials

Christopher.Sorensen — Tue, 24 Oct 2023 17:34:25 +0000

��ϲ�� researchers speed up design of next-gen biomedical implants, aerospace materials

Christopher.Sorensen Tue, 10/24/2023 - 13:34

Cutline

PhD candidate Xiao Shang operates the directed energy deposition 3D printer that was designed and built in the metal additive manufacturing lab of ��ϲ�� Assistant Professor Yu Zou (photo by Neil Ta)

Safa Jinje

Topic

Our Community

Faculty of Applied Science & Engineering

Subheadline

Study describes a novel end-to-end framework to tailor the microstructure of a widely used titanium alloy in the aerospace and biomedical industries

From bone-like medical implants to stronger, more fuel-efficient aircraft parts, advanced structural materials can take a long time to move from laboratories to industrial applications – but researchers at the ��ϲ�� are harnessing the power of machine learning to fast-track that process.

“Designing microstructures is a key step in materials development,” says Yu Zou, an assistant professor in the department of materials science and engineering in the Faculty of Applied Science & Engineering.

“But traditional materials design, which is based on experiments or simulation methods, could take years – even decades – to identify the right microstructure.”

Zou and his lab group aim to accelerate the discovery of new structural materials. In a new paper, published in Materials Today, Zou’s team describes a novel end-to-end framework used to tailor the microstructure of Ti-6Al-4V, the most widely used titanium alloy in the aerospace and biomedical industries.

“This work could enable materials scientists and engineers to discover microstructures at speeds unseen before, by simply inputting their desired mechanical properties into the framework,” says PhD candidate Xiao Shang, the lead author of the paper.

The researchers began by training two deep-learning models to accurately predict material properties from their microstructures. They then integrated a genetic algorithm with the deep-learning models to close the materials-by-design loop, which allows the framework to design optimal material microstructures with target mechanical properties.

“In less than eight hours, we identified titanium alloy microstructures that showed both the high strength and high stiffness needed to strengthen the structural components of airplanes,” says Shang.

“We also designed titanium alloys with the same chemical compositions as the former but with different microstructures that are about 15 per cent more compliant for biomedical implants compatible with human bones.”

Deep learning models are first established to predict a material’s mechanical properties before a genetic algorithm is used to efficiently search for the optimal material microstructure for given target material properties (Image: Laboratory for Extreme Mechanics and Additive Manufacturing)

The researchers had to overcome several hurdles to develop their deep learning models. That includes generating their own dataset of close to 6,000 different microstructures through simulation – a task made possible by tapping the massive computing powers of super-computers at the Digital Research Alliance of Canada.

“We constantly ran into situations where our selected deep-learning models and/or optimization algorithms just wouldn’t work as well as we expected,” says Shang. “But we were patient and held on to our research plan while actively searching for new approaches to make the models work.”

The research recently won a Poster Prize at the 2023 Accelerate Conference, organized by the Acceleration Consortium at ��ϲ�� – which earlier this year received a historic $200-million grant to support advanced materials discovery using what’s known as self-driving labs. The work is also supported by ��ϲ��’s Data Sciences Institute and Centre for Analytics and Artificial Intelligence Engineering. Both the Acceleration Consortium and Data Sciences Institute are ��ϲ�� institutional strategic initiatives.

“Looking forward, we want to further optimize and improve additive manufacturing technology so that they can continue to advance this new framework,” says PhD candidate Tianyi Lyu, who co-authored the paper along with PhD candidate Jiahui Zhang – both working on metal additive manufacturing.

“We are advancing the quality and reliability of metal additive manufacturing, unleashing its potentials to locally tailor the material microstructure during printing,” adds Zou, who is a member of the Acceleration Consortium.

“For example, with traditional technology, it is close to impossible to tailor biomedical materials for different patients. But we want to enable the future of personalized biomedical implants by making it possible to print the shape and mechanical properties that match a patient’s needs in only a few days.”

��ϲ�� prof leads global collaboration to advance net-zero hydrogen economy

lanthierj — Wed, 04 Oct 2023 20:36:41 +0000

��ϲ�� prof leads global collaboration to advance net-zero hydrogen economy

lanthierj Wed, 10/04/2023 - 16:36

Cutline

From left to right: George Saegh, Mehdi Salakhi, Murray Thomson, Franciska Toth and Luke Di Liddo (photo by Safa Jinje)

Topic

Faculty of Applied Science & Engineering

Global

Graduate Students

Sustainability

Hydrogen is expected to play a crucial role in enabling countries reach net-zero emissions by 2050 – but the technological developments needed for a sustainable hydrogen economy require global collaboration and knowledge sharing, ��ϲ�� researcher Murray Thomson says.

Enter the Global Hydrogen Production Technologies (HyPT) Center.

Thomson, a professor in the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering, is one of four researchers leading the newly established centre – along with professors from Arizona State University in the United States, the University of Adelaide in Australia and Cranfield University in the United Kingdom.

The centre will advance net-zero hydrogen production technologies with the goal of making them more energy-efficient and affordable by reaching US$1 per kilogram. Researchers will also explore the social and environmental changes that are needed to build a global hydrogen economy.

“Our goal is to connect researchers and students worldwide to share insights and work synergistically to create a sustainable energy resource,” Thomson says.

“It is about connecting Canadians who work in hydrogen production and technology, but also connecting Canadians with researchers around the world, which I think is a great benefit to our students to promote new ideas, expertise and approaches.”

The Canadian component of the project will receive $3.6 million over five years from the Natural Sciences and Engineering Research Council of Canada (NSERC) to support student training and mobility.

PhD candidate Mehdi Salaki and master's candidate Franciska Toth work with a microwave-driven pyrolysis reactor (photo by Safa Jinje)

Thomson’s research is focused on methane pyrolysis. He has co-founded a company, Aurora Hydrogen, which is creating low-cost, low-carbon hydrogen production.

“Aurora Hydrogen is growing very quickly,” he says. “We’ve hired 30 people and should have a pilot-scale plant built by the end of the year.”

He is also the methane pyrolysis leader of the new HyPT Center, overseeing one of three technologies the centre aims to advance. The methane pyrolysis subgroup includes researchers from University of Adelaide, University of British Columbia (UBC), Stanford University and University of Cambridge.

“Methane pyrolysis is a process that uses heat to break down natural gas into hydrogen gas and solid carbon particles, so that you don’t produce carbon dioxide. But that carbon is also a useful product,” says Thomson.

“My team at ��ϲ�� is using microwave energy to break apart methane. Stanford and Cambridge are working more on the carbon byproduct side, while Adelaide and UBC are exploring different catalysts.

“We each have a different focus, but by interacting as a group we can work together to provide a more compelling technology.”

The other two hydrogen technologies the centre is exploring are water electrolysis, where water is split into hydrogen and oxygen using electrical energy; and photocatalytic water splitting, which uses sunlight to separate hydrogen and oxygen.

Since both methods require lots of clean water, the centre is also exploring challenges related to this crucial resource.

“Hydrogen production is expected to increase dramatically over the next decade,” Thomson says.

“We have a role to play in better training the next generation of students working in hydrogen energy, in developing the scientific foundations that these hydrogen production technologies are based on, and in ensuring our approaches consume less electricity, use better catalysts and make more efficient use of the carbon and oxygen byproducts.

“The goal is to provide the energy that the world needs with much less greenhouse gas emissions — that is the motivation.”

Safa Jinje

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With the launch of its first satellite, student team charts a course to new knowledge

Researchers use AI to optimize nutrient consistency in donated breast milk

AI algorithm improves predictive models of complex dynamical systems

������ϲ����� researchers speed up design of next-gen biomedical implants, aerospace materials

������ϲ����� prof leads global collaboration to advance net-zero hydrogen economy

��ϲ��'s top undergraduate says he fell in love with the process of learning

��ϲ�� researchers enhance object-tracking abilities of self-driving cars

��ϲ�� Engineering to launch new certificate in electric vehicle design

��ϲ�� research team hits bullseye with strategy to improve fairness in darts

��ϲ�� Engineering program introduces Black high school students to STEM careers

��ϲ�� researchers speed up design of next-gen biomedical implants, aerospace materials

��ϲ�� prof leads global collaboration to advance net-zero hydrogen economy