ECE PhD Student Receives ACM TECS Best Paper Award 2020
ECE Alumnus Farid Molazem (former ECE PhD Student) and Professor Dr. Karthik Pattabiraman have received the ACM TECS Best Paper Award 2020 for their paper titled “Design-Level and Code-Level Security Analysis of IoT Devices”. The award recognizes the best paper published in the ACM Transactions on Embedded Computing between January 2018 and December 2019. ACM TECS aims to present the leading work relating to the analysis, design, behaviour, and experience with embedded computing systems and is considered the top journal in the embedded computing area. Dr. Farid Molazem is now a software engineer at Google.
ECE Alumnus Farid Molazem (former ECE PhD Student) and Professor Dr. Karthik Pattabiraman have received the ACM TECS Best Paper Award 2020 for their paper titled “Design-Level and Code-Level Security Analysis of IoT Devices”. The award recognizes the best paper published in the ACM Transactions on Embedded Computing between January 2018 and December 2019. ACM TECS aims to present the leading work relating to the analysis, design, behavior, and experience with embedded computing systems, and is considered the top journal in the embedded computing area. Dr. Farid Molazem is now a software engineer at Google.
The paper analyzes the important role the Internet of Things (IoT) plays in different aspects of our lives. Smart grids, smart cars, and medical devices all incorporate IoT devices as key components. The ubiquity and criticality of these devices make them an attractive target for attackers. Therefore, techniques are needed to analyze their security, so that their potential vulnerabilities can be addressed. In this paper Dr. Molazem and Dr. Pattabiraman and introduce two techniques, one at the design-level, and the other at the code-level, to analyze security of IoT devices, and compare their effectiveness. The code-level analysis technique is able to find 3 times more attacks, and complete the analysis in half the time, compared to the design-level analysis technique, with no false positives. They demonstrate their techniques on a widely deployed IoT device, a smart electric meter, and find many real vulnerabilities in it. This work has led to significant interest from industry, and was featured by many news outlets last year.
The award was presented virtually in the Embedded System Week (ESWEEK) 2020 on Wednesday, September 2020.
Congratulations Dr. Molazem and Dr. Pattabiraman!
How You Use This One Smartphone Feature Reveals Your Age
WHETHER OR NOT YOU USE THIS SHORTCUT DEPENDS ON YOUR GENERATION.
Regardless of whether you’re young at heart, there are certain habits that give away your true age. Like signing a text with your name or falling asleep at the very thought of a second glass of wine, some things just signal to the world that you’re over 40 and not afraid to show it. Now, a new study reveals that there’s one thing you do with your smartphone that reveals your age: using a pin number to unlock your phone rather than a fingerprint or facial recognition means you’re of an older generation.
The study, conducted by researchers at the UBC Electrical and Computer Engineering, explored the links between age and smartphone use by remotely tracking smartphone users’ habits. “As researchers working to protect smartphones from unauthorized access, we need to first understand how users use their devices,” explained Konstantin Beznosov, a professor of electrical and computer engineering who supervised the research.
To collect their data, the research team recruited 134 volunteers, ranging in age from 19 to 63, and had them install a custom app to their Android phones for a period of two months. The app logged all of their lock and unlock events, including whether they opted for auto or manual lock, and the movements of the phone at the time of unlocking. This is how they discovered the generational differences in unlocking habits.
The researchers also collected data on the length of user sessions. The team found that in addition to a person’s preferred method for unlocking their phones, the amount of time spent on the phone also correlated with age.
As one report on the study explains, “Analysis showed that older users used their phone less frequently than younger users. For every 10-year interval in age, there was a corresponding 25 per cent decrease in the number of user sessions. In other words, a 25-year-old might use their phone 20 times a day, but a 35-year-old might use it only 15 times.”
So, if you still unlock your smartphone by hand, you can chalk it up to a generational preference. You might even take some pride in it: after all, it’s no wonder young people need a handy shortcut, given how needlessly often they unlock their phones. And for cutting edge technology you can actually use, check out Amazon’s Mind-Blowing New Way to Shop.
Access Original Article by Lauren Gray.
Photo by Thom Holmes on Unsplash
Speeding Up COVID19 Testing with Artificial Intelligence
A University of British Columbia-led study has identified a computer technique that health facilities can use to screen, diagnose and monitor COVID-19 pneumonia more efficiently.
The researchers found that a pre-trained neural network called DarkNet-19 can rapidly and reliably detect COVID-19 on chest X-rays. The network recognized the disease’s imaging patterns on nearly 6,000 chest X-rays with 94 per cent accuracy, outperforming 16 other available networks.
X-rays typically take about five minutes to complete and five minutes to interpret, but the artificial intelligence-enhanced method can provide a “COVID-19 score” — the probability that a patient has the virus — within one minute.
The team also developed a DarkNet-19-based visualization system that highlights the key visual features of the disease and its progression.
“Many hospitals and clinics have become overwhelmed with work during this pandemic, requiring imaging specialists on staff 24/7 to analyze the large number of imaging tests that are being done,” says Mohamed Elgendi, the study’s lead author and an adjunct professor of electrical and computer engineering at the University of British Columbia. “With the help of artificial intelligence, we may be able to optimize the efficiency of X-ray imaging analysis and speed up the COVID screening process around the world.”
When tested against 16 other pre-trained neural networks, DarkNet-19 was found to be not only accurate, but also fast and relatively small in size.
Current gold-standard laboratory tests are expensive and time-consuming, making them impractical for under-resourced health facilities to use. A real-time PCR test, for example, costs approximately CAN$4,000 and has an average turnaround time of three to six days.
In contrast, X-ray tests are widely available and cost about CAN$35 to $40 each. Using DarkNet-19 to analyze these X-rays, doctors could improve throughput and their ability to diagnose COVID-19, the study findings suggest.
“In the earliest stages of COVID-19, chest X-rays often appear normal to the naked eye,” says Savvas Nicolaou, the senior author of the study and the director of emergency and trauma imaging at Vancouver General Hospital. “But in the right clinical context, applying AI-augmented analysis to the same images may reveal the subtle presence of the disease.”
Nicolaou notes, however, that while imaging can assist in COVID-19 screening, it should be used more “as a complementary diagnostic, problem-solving and prognostic tool” in conjunction with clinical evaluation.
Previous research identified pre-trained neural networks that detect COVID-19 with accuracies ranging from 90 per cent to 98 per cent. But those studies examined far fewer sample sizes and were not optimally tested for specificity and reliability.
The study, whose authors also include researchers from Simon Fraser University, the University of Oxford and the Massachusetts Institute of Technology, was recently published in Frontiers in Medicine.
Roberto Rosales Receives UBC President’s Staff Award
Congratulations to Dr. Roberto Rosales, ECE Engineering Services Team Lead, for receiving the 2020 UBC President’s Staff Award for Leadership. The President’s Staff Awards are presented by the university annually and recognize the personal achievements and contributions that staff make to UBC, and to the vision and goals of the University.
Since obtaining his graduate degrees in the Department of Electrical and Computing Engineering (ECE), Roberto Rosales has progressed into a realm of staff leadership that encompasses many roles – researcher, technical consultant, sessional lecturer, manager, mentor, and team leader.
Leading by example, Roberto elevated the ECE Engineering Services Team by cultivating trust, morale and an enthusiasm for enabling research, mentoring undergraduates and serving instructors. The team provides innovative and unprecedented technical and engineering support for both research and teaching, and their model of service delivery demonstrates their commitment to supporting world-class research as well as to provide an outstanding education and experience to our undergraduate students.
An advocate for students’ continued success, Roberto uses his influences to ensure world-class teaching and learning environments. In 2014, Roberto worked with students, faculty and staff to prepare for an external review with the Canadian Engineering Accreditation Board, which included a thorough assessment of labs and facilities with a distinct focus on health and safety. More recently, when the department learned that their teaching epicenter, the MacLeod Building, was due for a multi-year seismic upgrade renovation, Roberto enthusiastically identified the opportunity to enhance the range of technical services that can be provided for teaching and research.
Roberto demonstrated leadership as an inaugural member of the System on a Chip (SOC) Research Lab, recognized by peers as maintaining a professional, constructive, positive, and solution-oriented attitude. He is an expert in his field and is a valuable mentor for graduate and undergraduate students.
See full list of UBC President’s Staff Awards recipients here.
ECE Researchers Receive Nearly $2.9M in NSERC Support
UBC Electrical and Computer Engineering researchers have been awarded nearly $2.6 million from the Natural Sciences and Engineering Research Council of Canada (NSERC) through its Discovery Grants Program.
The Discovery Grants support “ongoing programs of research with long-term goals rather than a single short-term project or collection of projects. These grants recognize the creativity and innovation that are at the heart of all research advances.”
Two UBC Electrical and Computer Engineering researchers also received nearly $300,000 in NSERC Research Tools and Instruments Grants.
The NSERC Research Tools and Instruments Grants “foster and enhance the discovery, innovation and training capability of university researchers in the natural sciences and engineering by supporting the purchase of research equipment.”
183 projects across UBC were awarded a total of $40.1 million from NSERC. For more information about these projects, please see the announcement of UBC’s Office of the Vice President Research and Innovation.
ECE Recipients: Discovery Grants
Beznosov, Konstantin
Novel Physical Protection of Personal Mobile Assets
$205,000
Cheung, Karen
Microfluidics and inkjet for biomedical engineering materials
$230,000
Fels, Sidney
Creating and Evaluating New Media Interfaces for Expression
$240,000
Garbi, Rafeef
Towards Generalizable Reasoned Deep Learning for Efficient Interpretable Medical Image Computing
$230,000
Jatskevich, Juri
Advanced Tools for Modelling and Analysis of Evolving Power and Energy Systems
$275,000
Kamgarpour, Maryam
Stochastic Control for Large-Scale Safety-Critical Systems
$220,000
$120,000 Discovery Accelerator Supplement
$12,500 Discovery Launch Supplement
Leung, Cyril
Energy efficiency and security for wireless communications
$140,000
Marti, Jose
Advanced Hybrid SFA/EMTP Simulator for Seamless Integration of Power Systems Dynamics and EMT Transients
$195,000
Pattabiraman, Karthik
Resilient, Secure, and Programmable Next-Generation Internet of Things (IoT)
$240,000
Rohling, Robert
Advancing towards ubiquitous medical ultrasound
$320,000
Shekhar, Sudip
Integrated Circuits for Large Arrays
$165,000
ECE Recipients: Research Tools and Instruments Grants
Jatskevich, Juri
Hardware and Software for Power-HIL Real-Time Simulation Platform for Integrated AC-DC Energy Systems
$149,988
Tang, Shuo
High-Repetition Rate Laser for Real-Time and 3D Photoacoustic Imaging
$150,000
Sara Hosseinirad Awarded Vanier Canada Graduate Scholarship!
Sara Hosseinirad, a second-year doctoral candidate currently researching an automated closed-loop system of anesthesia, is the recipient of the Vanier Canada Graduate Scholarship.
Sara’s research aims to automate the entire anesthesia process. “The closed-loop anesthesia control has been proven to outperform manual control; however, some technological developments are missing. One of them is the lack of an integrative system that includes the impacts of changes in anesthesia, fluid, cardiac output, etc. on each other.” Sara breaks her solution into two steps. “In the first step, we will design a new depth of hypnosis and analgesia control system and its associated safety system based on a novel, universal pharmacokinetic model of propofol and remifentanil, known as Eleveld model. In the second step, we will investigate multivariable control of the many aspects of anesthesia beyond the depth of hypnosis and analgesia, e.g., cardiac output, arterial pressure, temperature, etc.” She is supervised by Guy A. Dumont and Maryam Kamgarpour.
The research Sara is conducting is a breakthrough in the field of anesthesiology. It would allow the entire anesthesia process to run automatically. This automation would allow anesthesiologists to run several operating rooms simultaneously, all while maintaining a high standard of quality. This is very beneficial for less-equipped hospitals, where there are a lower number of anesthesiology specialties as less anesthesiologist are required. “This research will reduce the post-operation complications by administering just enough anesthetic drugs during surgical operations.”
The Vanier Canada Graduate Scholarship is a prestigious award valued at $50,000 for up to three years. The award was founded in 2008 to attract world-class doctoral students to Canada and establish Canada as a global center in research and higher learning. Recipients require both leadership skills and a high standard of scholarly achievement. Sara was the only recipient from the Electrical and Computer Engineering Department at UBC.
Being the recipient of this award is a great honor and gives Sara well deserved and valuable recognition for all her hard work. She believes it “will open doors of opportunity as a Ph.D. student” and is the beginning of her research path. She says her “future success depends on keeping the momentum going.”
Congratulations Sara on your great achievement!
You can learn more about the Vanier Canada Graduate Scholarship.
ECE Student Pramit Saha leads Imagine Speech Recognition Project
UBC Electrical and Computer Engineering master’s student, Pramit Saha, is working at the forefront of developing speech-related brain-computer interfaces. The Imagine Speech Recognition Project led by Pramit and directed by ECE Professor Sidney Fels in the Human Communication Technologies (HCT) Lab aims to detect speech tokens from speech imagery brain signals. This project has revealed the possible existence of brain imagery footprint related to articulatory movements underlying imagined speech productions
Speech imagery is about representing speech in terms of the unspoken words inside the human brain without them being vocalized. They hypothesize the existence of a brain footprint for the thoughts underlying covert speech, even though the person is not vocalizing. Furthermore, it is possible to detect the imagined words by understanding the intended involvement of the vocal tract and vocal fold, which is internally encoded in the brain signals. Their deep neural network architecture is able to capture information that the brain sends to the tongue, vocal fold, etc, even without there being vocal communication. Interpreting active thoughts from EEG signals can be very challenging. Their carefully designed methodology for learning the EEG manifold includes the computation of a cross-covariance embedding from high dimensional EEG data that successfully captures the joint variability of the electrodes. This allows the classification of the phonological attributes of the imagined words based on the presence/absence of different parts ofd the articulatory system. These categories are then used to identify the imagined speech. Pramit Saha’s and Professor Sidney Fels’ work has the potential to advance the field of speech-related brain-computer interfaces aimed at providing neuro-prosthetic help to those with speech-related disabilities and disorders. It can help users with a way to express their thoughts, which can greatly help in rehabilitation.
Below, Pramit Saha speaks about the importance and potential impact of the work that is being done:
What motivates you to pursue research in this topic?
Speech is the most basic and natural means of communication. However, the neuro-muscular mechanism underlying the production of articulatory speech is extremely complicated, as a result of which, decoding imagined speech by analyzing the noisy brain signals is a highly challenging problem. The primary objective of this research is to understand the discriminative brain signal manifold corresponding to imagined speech that can enable us to relate the brain signals to the underlying articulation mechanism, crucial in designing speech-related brain-computer interfaces. Such interfaces are targeted to provide neuro-prosthetic help for more than 70 million people worldwide who are suffering from speaking disabilities and speech-related neuro-muscular disorders. Decoding their imagined speech will provide them with effective vocal communication strategies for controlling external devices through speech commands interpreted from brain signals. The idea of being able to contribute towards potentially providing people with a better means to communicate and express thoughts without needing to vocalize, thereby increasing the quality of their life, keeps me strongly motivated to pursue research in this field.
How does this project align with your professional goals?
My research goal primarily centers around the investigation of the neural pathways behind expressive communication abilities including speech and gestures. Human speech production is one of the most complex processes within the human motor repertoire, which needs precise coordination of different speech articulators. Such a refined control of articulators is apparently difficult to master. However, it is quite astonishing to me to imagine how we can perform such complex articulation spontaneously without considerable effort by establishing the connection between speech production sites in our brain and the articulators involved in vocalization. The neuro-computational bases behind such articulation are still not well understood and how the speech intent is related to the intended motion of these articulators is an open question in the domain of imagined speech research. In this work, we endeavor to address the issue by developing a hierarchical deep learning-based model that leverages phonological information (involving intended activity of different articulators) embedded in the brain signals to decode the intended speech token.
More details on their work can be found here:
Hierarchical Deep Feature Learning for Decoding Imagined Speech from EEG
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts
Towards Imagined Speech Recognition With Hierarchical Deep Learning
To find out more about Pramit Saha: