SURE: Electrical & Computer Engineering

Click on the title for full description of SURE 2018 projects in Electrical, Computer and Software Engineering.

ECSE-001: Attack! Recreating Embedded Systems Security Vulnerabilities
Professor:Brett Meyer
E-mail: brett.meyer [at] mcgill.ca
Telephone: 5143984210
Website

Research Area: Embedded systems; cybersecurity; machine learning.


Description
Modern automotive and aerospace systems now contain 10s, if not 100s of computers, responsible for controlling nearly all aspects of the system, from flight to in-flight entertainment. In an effort to manage costs, weight, and space, these systems are highly integrated, and share a wide variety of on-board hardware resources. Research in embedded systems security has shown that compromising safety-critical systems in cars is a trivial task; aerospace systems are based on the same hardware and design principles, strongly suggesting similar vulnerabilities. In this project, students will collaborate closely with graduate students working to develop and expose and mitigate security vulnerabilities in aerospace systems..

Tasks:
1. Develop models of aerospace systems adhering to ARINC-825 in Matlab Simulink integrating with flight dynamics models in FlightGear. 2. Develop attacks taking advantage of weaknesses in the ARINC-825 specification, and demonstrating how they can manifest in unwanted changes in flight control or other systems. 3. Develop mitigation strategies that demonstrably address these weaknesses.

Deliverables:
1. Detailed instructions and, if necessary, corresponding models and source code, for performing such attacks, including an assessment of the potential consequences, and mitigation strategies (in report form).

Number of positions: 1
Academic Level: No preference

ECSE-002: Neural Networks Designing Neural Networks (NNDNN)
Professor:Brett Meyer
E-mail: brett.meyer [at] mcgill.ca
Telephone: 5143984210
Website

Research Area: Machine learning; electronic design automation


Description
Artificial neural network (ANN) models have become widely adopted as means to implement many complex algorithms, yet there are no systematic ways to derive a network model given a specific application. We have developed a framework for training an ANN to performs response surface modeling to automatically select the structure of the target ANN and find the best sets of trade-offs between model accuracy and cost (time, hardware, power, etc). This can be applied to any cost-constrained systems where machine learning is needed. Work is needed to expand our current infrastructure; opportunities include front-end GUI design, development of new internal cost models, and back-end analytics..

Tasks:
1. With the assistance of graduate students, develop and evaluate extensions to our existing NNDNN infrastructure.

Deliverables:
1. A report, and, if appropriate, source code, detailing the development and evaluation conducted.

Number of positions: 1
Academic Level: No preference

ECSE-003: Test context models for systematic validation of safe behavior for autonomous vehicles
Professor:Daniel Varro
E-mail: daniel.varro [at] mcgill.ca
Telephone: 5145831084
Website

Research Area: Software engineering, cyber-physical systems, model-based systems engineering, autonomous and self-adaptive systems


Description
Deep Neural Networks and other techniques of Artificial Intelligence are quickly penetrating to software components which control the behavior of autonomous vehicles by learning from human reactions paired with camera images and various sensor information. However, existing practice of safety engineering currently used in automotive or civil avionics systems lacks efficient assurance techniques to justify that AI techniques would prevent unsafe situations with a designated level of confidence and reliability. As such, AI techniques are avoided in components which necessitate certification by independent authorities according to the highest level of safety. This SURE project treats the control software provided by AI as a black box component. Instead of validating the intelligent component itself, it aims to systematically validate the different test contexts in which the component under test (CUT) has proven to operate safely. By building test context models while running simulations as tests, we can highlight when the system is operating in a previously unprecedented context (thus potentially outside its safety envelope). Since neither real data nor Monte Carlo simulations can provide sufficient coverage for rare events, the project will aim to exploit automated graph model generation techniques to systematically provide unforeseen text contexts during the validation. This project will be jointly supervised by Prof. Brett Meyer (ECE).

Tasks:
Joint research tasks: * Literature survey (by both students) * Conceptual design of test context models for autonomous vehicles Student 1: * Obtain test data (e.g. from Udacity Self-Driving Car challenge) * Build test context models from simulation results Student 2: * Generate test context models by automatic graph generation techniques

Deliverables:
Joint deliverables * Project report * Conceptual design models for test context models * Software prototype

Number of positions: 2
Academic Level: Year 3

ECSE-004: Generation of consistent graph models by using automated theorem provers
Professor:Daniel Varro
E-mail: daniel.varro [at] mcgill.ca
Telephone: 5145831084
Website

Research Area: Software engineering, Model-based systems engineering, Cyber-Physical Systems, Software tools for safety-critical systems


Description
Graphs are frequently used for knowledge representation in various domains including social networks, graph databases, building information models, systems engineering tools and many more. In the latter case, the certification of design tools used in safety-critical systems such as automotive or avionics is significantly hindered by the lack of tools that would systematically derive consistent and diverse graph models for testing purposes. Interestingly, while there are many efficient algorithms to traverse, query and manipulate graph-based models, the automated and domain-independent synthesis of graph models which are well-typed and consistent (i.e. they satisfy a set of well-formedness constraints) is computationally complex or even undecidable. Existing sophisticated logic solvers (model finders like Alloy, SAT-solvers, SMT-solvers like Z3 developed at Microsoft Research) perform particularly poorly in graph-like domains, failing to generate consistent models with over 100 elements. Recent advances on consistent model generation aim to combine efficient incremental graph queries with multi-objective exploration while repeatedly calling back-end logic solvers to prove unsatisfiability, but SMT and SAT-solvers are still frequently the performance bottleneck. This project will aim to exploit and integrate automated first-order theorem provers (like Vampire) for model generation purposes. This SURE project requires fascination and strong mathematical background in logics, and it may involve international collaboration..

Tasks:
Overview advanced automated first-order theorem provers and the VIATRA Graph Solver and – Integrate a selected theorem prover as back-end solver into graph based model generation techniques– Carry out experimental evaluation on the efficiency of the integrated technique.

Deliverables:
Develop software to integrate a theorem prover into the VIATRA Graph Solver and prepare a manuscript describing the results of experimental evaluation.

Number of positions: 1
Academic Level: Year 3

ECSE-005: High Performance Computational Electromagnetics
Professor:Dennis Giannacopoulos
E-mail: dennis.giannacopoulos [at] mcgill.ca
Telephone: (514)398-7128
Website

Research Area: Computational Electromagnetics


Description
To accurately and efficiently model the electromagnetic fields within sophisticated microstructures of modern engineering systems and devices, high performance computing (HPC) methods, such as parallel and distributed simulations on emerging multicore/manycore platforms, are deemed promising for overcoming current computational bottlenecks. While robust and reliable 3-D automatic mesh generation procedures and solution strategies for electromagnetics are emerging, major computational challenges still remain for effective parallel and distributed 3-D adaptive finite element methods (AFEMs). Uniting AFEMs and HPC methods to achieve high gains in efficiency makes it possible to solve previously intractable problems; however, effective implementation of such techniques is still not well understood. AFEMs for parallel/distributed computing introduce complications that do not arise with simpler solution strategies. For example, adaptive algorithms utilize unstructured meshes that make the task of balancing processor computational load more difficult than with uniform structures..

Tasks:
The students in this project will research and develop efficient parallel and distributed adaptive algorithms for unstructured meshes that use complex data structures for implementing dynamic load balancing strategies for HPC environments such as multicore/manycore architectures. The students’ role will include involvement in all aspects of the engineering research process for this project including actual implementation of algorithms as executable code.

Deliverables:
The students’ are expected to help deliver a functioning, well-documented 3-D parallel automatic mesh generator suitable for use with AFEM refinement criteria, along with documented case study validation & verification examples.

Number of positions: 1
Academic Level: No preference

ECSE-006: Deep Learning for Synthetic Image Generation, Video Game Understanding, and Computer Animation
Professor:Derek Nowrouzezahrai
E-mail: derek [at] cim.mcgill.ca
Telephone: 514 398 3118
Website

Research Area: Computer Graphics; Deep Learning


Description
SURE projects in my group this year will look at novel combinations of computer graphics simulation techniques with deep learning models. Firstly, we are interested in modelling the complex dynamics behind how robots (or virtual humans, e.g., in a video game) walk, run and interact with an environment. Secondly, we want to understand how machine learning can be used to generate believable images of the real-world using only data captured from the internet. Can we generate images indistinguishable from a real photo? Can we come up with smart image editing tools based on these generative approaches? Finally, can we substitute complex simulation engines in video games with black-box machine learning approaches?.

Tasks:
1. Implementing a deep reinforcement learning algorithm for character walking and planning: http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/ 2. Implement the latest work on high-res generative networks for image synthesis, such as the Progressive GANs (https://github.com/tkarras/progressive_growing_of_gans) and pix2pixHD (https://github.com/NVIDIA/pix2pixHD) techniques. 3. Extending typical virtual environments, e.g., those used to train deep reinforcement learning agents, with more realistic physics engines. 4. Learning how video game engines map a user's input to a visual output: implement and extend some promising early results (https://www.cc.gatech.edu/~riedl/pubs/ijcai17.pdf https://www.youtube.com/watch?v=IlOwnxkDL4Q https://www.theverge.com/2017/9/10/16276528/ai-video-games-game-engine).

Deliverables:
1. A working implementation of the DeepLoco architecture. 2. A working implementation of the Progressive GANs architecture. 3. A modular framework for exploratory physics simulations in a virtual testing environment. 4. A prototype black-box video game

Number of positions: 3
Academic Level: No preference

ECSE-007: Non-Intrusive Mobile Experience Sampling Methods for Machine Learning Applications
Professor:Jeremy Cooperstock
E-mail: jer [at] cim.mcgill.ca
Telephone: 5143985992
Website

Research Area: Intelligent Systems


Description
Numerous artificial intelligence projects aim at recognizing high-level psychological concepts such as emotions or anxiety. There is significant interest in doing so in the mobile case, that is, using smartphones or wearable devices. However, these projects are hindered by a lack of large labeled datasets, representative of users’ different contexts, e.g., activities, day of the week, and weather. Although existing mobile experience sampling methods (ESM -- https://dl.acm.org/citation.cfm?id=2935340) allow the collection of self-reports from users in their natural environment, they require disruptive notifications that interrupt the users' regular activity. We have conceptualized a new data collection technique that overcomes this problem, allowing for collection of large amounts of self-reporting data without such interruption. This project aims to explore several designs for the technique, implement the more promising ones, and test them. The result has the potential to contribute significantly to the fields of applied machine learning, user-centered artificial intelligence, and affective computing..

Tasks:
1. Interaction design: Apply user-centered design techniques to the creation of mobile graphical user interface (GUI) layouts that would allow the reporting of different types of data, e.g., current anxiety level on a continuous or Likert scale. 2. Mobile implementation: Android and/or iOS implementation of the preferred interaction designs. 3. Validation: Design and execution of a user study quantifying the improved performance and user experience of the new reporting system in comparison with existing experience sampling methods. This project requires a student with strong mobile development experience (Android and/or iOS) and interests in HCI/UX research.

Deliverables:
1. Paper prototypes or other low-fidelity mock-ups and their validation in user studies. 2. Prototype implementation on mobile platform. 3. User study, forming part of conference or journal paper submission.

Number of positions: 1
Academic Level: No preference

ECSE-008: Haptic Wearables
Professor:Jeremy Cooperstock
E-mail: jer [at] cim.mcgill.ca
Telephone: 5143985992
Website

Research Area: Intelligent Systems


Description
Our high-level objective is to explore the design of wearable haptics as an interaction paradigm in everyday conditions, using wireless devices that can be attached to the body or inserted into regular clothing, capable of sensing human input and delivering richly expressive output to the wearer. In collaboration with an industrial partner, we are building on our experience with foot-ground sensing and actuation to explore this design space with a focus on foot-based haptics, for which potential applications span the gamut from rehabilitation therapy and sports training to information communication, virtual reality, and mobile gaming..

Tasks:
1. Building on our ongoing hardware prototyping efforts, the student will help develop a next generation of body-worn devices that allow for integration of additional physiological, environmental, and movement-related sensors, coupled to the computational resources of our embedded platform. Requires strong analog electrical design experience, and software development skills, ideally suited to robotics club members 2. Haptic effects authoring tools: To produce the haptic sensations experienced by users wearing these devices, whether in the form of discrete patterns of vibration ("tactons") or more continuous textures evocative of interaction with objects or ground surfaces, we are building haptic pattern authoring tools that allow the designer to specify and edit haptic effects at both a high- and low-level of abstraction. Requires strong software design and development experience, and interest in incorporating ideas from recent research literature. 3. Mobile game or activity-based haptic feedback applications: Building on the evolving hardware and software platform, the student will work on the development of compelling applications that employ the haptic modality to convey relevant cues that enhance game play or training. Requires strong software design and development experience, and interest in conducting experiments with human participants.

Deliverables:
1. Next-generation sensor infrastructure for wearables. 2. Haptic effect authoring software for use in experiments and applications. 3. Mobile game or training tool employing haptic feedback.

Number of positions: 3
Academic Level: No preference

ECSE-009: Machine Learning for Breast Cancer Detection Using Radio-Frequency Sensors
Professor:Mark Coates
E-mail: mark.coates [at] mcgill.ca
Telephone: 5143987137
Website

Research Area: Machine learning


Description
Key to successful breast tumor elimination and treatment is early diagnosis. Some women perform self-examinations to detect lumps or changes in their breasts, but without medical training, it can be difficult to distinguish between potential tumors and normal variations in appearance and texture. Over the past eight years, our research team has been working towards the development of a wearable bra device that incorporates a networked array of RF sensors (http://www.compem.ece.mcgill.ca/breast-cancer-detection/). The goal is for women to use this in the home at monthly intervals to provide doctors with an early indicator of potential disease. We have conducted clinical trials with promising preliminary results. The summer project will be dedicated to the development and testing of machine learning algorithms for processing the data that is obtained during scans. The student involved will also be expected to help conduct experiments with the prototype system (recording data using breast phantoms)..

Tasks:
Develop and implement machine learning algorithms for processing the data from scans and detecting the presence of tumours. The two students will work on different algorithmic approaches.

Deliverables:
(1) Software implementing the machine learning algorithms and executing the experiments (2) Research report describing the experiments and their outcomes.

Number of positions: 3
Academic Level: Year 3

ECSE-010: Real-Time Video and Sensor Data Analytics on a Smart-City MultiCom Edge Station
Professor:Tho Le-Ngoc
E-mail: tho.le-ngoc [at] mcgill.ca
Telephone: 514-398-7151
Website

Research Area: Telecommunications and Signal Processing


Description
Successful large-scale Smart City development needs to make sure that the integration of multiple Information and Communication Technology (ICT) and Internet of Things (IoT) solutions is easily and economically deployable, expandable, sharable, controllable, maintainable, and interoperable in terms of hardware & data interfaces, and, of course, well protected from malicious cyber-attacks.

In this multi-segment ongoing program, we will use a real-time IoT Smart-City MultiCom Edge Station testbed deployed on and near McGill downtown campus, with various multi-type sensors such as UltraHD cameras, Traffic Radars, and Environmental Sensors. Data will be collected from all the low and high-data rate sensors, and will be transported over various types of wireless secure networks (900MHz/2.4/5.8/60GHz Wireless Zigbee, BLE, Wi-Fi, LoRa, LTE-M), through multi-protocol gateways, to local data storage and pre-processing IoT Edge stations, this pre-processed, reduced bandwidth data will be also forwarded to centralized Cloud data processing location for further big data global analysis. This prototype testbed will be used to conduct research in Smart Sensor Security and Efficient Data routing, as well as, in flexible distributed data analytics architecture (to reduce sending bandwidth intensive data to Cloud servers), and in IoT resources sharing through virtualization technology.

Students will have a chance to understand various new Smart City IoT concepts (OpenCV, Big Data Analytics, IoT Security, IPv6, etc.), and to be involved in practical real-time system development, deployment and testing of applications.

This particular project aims to build on already existing open source Visual Computing Software modules (such as YOLO: Real-Time Object Detection open source project) to develop new Intelligent Traffic and Public Safety real-time applications for implementation on IoT Edge stations. Utilizing IoT Edge stations, for local storage and data pre-processing, will save scares communications resources (especially if we think about scaled out massive deployment) and significantly reduce the cost of operating only of the centralized Cloud resources. Some of the particular applications to be developed and tested as a prove of concept, are: real-time persons/bicycles/vehicles counter, travel direction and speed detection, fire/smoke event detection, persons and vehicle accident event detections, stop sign violation event detection, and abandoned suspicious object detection. The relevant reduced bandwidth analyzed data, statistics and alarms, besides being send to the centralized Cloud datacenter, would be also displayed on the interactive panel of Smart-city MuliCom station. Pre-processing video data will have the most impact on reducing data bandwidth to the Cloud, however, the same principal would be applied for data collected from potentially massive number of sensor nodes.

Tasks:
Study the general concept of IoT, Big Data, learn how to search for and read scientific papers on a given Video Analysis subject, leverage existing code/libraries and program with OpenCV or Matalab for Visual Analytics applications, learn how to test functional operation and performance of the developed applications, and to collect, document and comment on test results. The following skills and experiences are great assets: software development/testing, Linux, Python, C/C++ coding, experience with tools such as MS Azure, IBM Bluemix or Amazon AWS.

Deliverables:
Real-time demonstration of a developed software application deployed on the IoT Edge testbed, well organized and documented source code, technical report on the developed software application functional operation and conducted test results.  The student will also need to make poster presentation.

Number of positions: 1
Academic Level: Year 2

ECSE-011: Robust Control Applications for Interactive Panels of IoT Smart-City MultiCom Edge Station
Professor:Tho Le-Ngoc
E-mail: tho.le-ngoc [at] mcgill.ca
Telephone: 514-398-7151
Website

Research Area: Telecommunications and Signal Processing


Description
Successful large-scale Smart City development needs to make sure that the integration of multiple Information and Communication Technology (ICT) and Internet of Things (IoT) solutions is easily and economically deployable, expandable, sharable, controllable, maintainable, and interoperable in terms of hardware & data interfaces, and, of course, well protected from malicious cyber-attacks.

In this multi-segment ongoing program, we will use a real-time IoT Smart-City MultiCom Edge Station testbed deployed on and near McGill downtown campus, with various multi-type sensors such as UltraHD cameras, Traffic Radars, and Environmental Sensors. Data will be collected from all the low and high-data rate sensors, and will be transported over various types of wireless secure networks (900MHz/2.4/5.8/60GHz Wireless Zigbee, BLE, Wi-Fi, LoRa, LTE-M), through multi-protocol gateways, to local data storage and pre-processing IoT Edge stations, this pre-processed, reduced bandwidth data will be also forwarded to centralized Cloud data processing location for further big data global analysis. This prototype testbed will be used to conduct research in Smart Sensor Security and Efficient Data routing, as well as, in flexible distributed data analytics architecture (to reduce sending bandwidth intensive data to Cloud servers), and in IoT resources sharing through virtualization technology.

Students will have a chance to understand various new Smart City IoT concepts (OpenCV, Big Data Analytics, IoT Security, IPv6, etc.), and to be involved in practical real-time system development, deployment and testing of applications.

This particular project aims to develop robust and easy to use smart control technology for interactive Smart-City panels on MultiCom Stations. Some of the current technologies used for control of interactive panels utilize touch panel capability of the display screen, however, that technique for large display panels might not be efficient, as the user might not be able to reach all the available control surface, and also might be too close to the big screen to effectively see all the content on the screen at the same time. Bad weather conditions, like rain, snow or extreme cold might also discourage the user from touching the wet screen or removing the winter gloves to control the screen. Other technique could be to use gesture detection by utilizing the camera embedded in the display panel. However, this technique could also be prone to errors of wrong detection or detection interfered by near-by standing or passing-by persons. In this project we want to further investigate these and other methods of interactions, and test their ease of use, accuracy and robustness. Some of the candidate technologies are voice commands recognition, with conjunction of microphone beamforming, and background noise cancelation technologies. Building on the existing technology, for example like Amazon Alexia, which could provide operational capability in multi-language mode, could be very attractive to tourists, or deployment in multi-language cities, like Montreal. In this approach, we could use the microphones built-in the panels itself or the microphone on the user´s Android or iPhone device. Another potential technologies for controlling the interactive panel could be user´s own iPhone or Android device touch screen serving as ¨mouse pad” or as device similar to Wii ¨3D¨acceleretometer sensor. Pairing and wireless connectivity could be done with the help of NFC and BLE technology. Students are also encouraged to come up with other innovative ideas.

Tasks:
Study the general concept of IoT, research and survey existing control technology of interactive panels, tabulate observed advantages and disadvantages of each technology, select one or two promising control technologies for this project, implement the required applications on the interactive panel and user´s device (Android or iPhone), perform rigorous testing with different users, under different conditions. The implemented control application should not compromise security or stability of the interactive panel, should have a time-out features to make sure other users have fair chance of access to the panel, and should have easy but robust pairing feature such as NFC pairing, to make sure only single user at a time can control a given panel. The following skills and experiences are great assets: software development/testing, network security configuration/testing, Linux, Python, C/C++/C#/Java coding, experience with tools such as MS Azure, IBM Bluemix or Amazon AWS.

Deliverables:
Real-time demonstration of a working control application, well organized and documented source code, and user manual and technical report on functionality, number of conducted tests and final observations. The student will also need to make poster presen

Number of positions: 1
Academic Level: Year 2

ECSE-012: Faster Public Safety Response Services through Video Call and Interactive Panel on Smart-City MultiCom Edge Station
Professor:Tho Le-Ngoc
E-mail: tho.le-ngoc [at] mcgill.ca
Telephone: 514-398-7151
Website

Research Area: Telecommunications and Signal Processing


Description
Successful large-scale Smart City development needs to make sure that the integration of multiple Information and Communication Technology (ICT) and Internet of Things (IoT) solutions is easily and economically deployable, expandable, sharable, controllable, maintainable, and interoperable in terms of hardware & data interfaces, and, of course, well protected from malicious cyber-attacks.

In this multi-segment ongoing program, we will use a real-time IoT Smart-City MultiCom Edge Station testbed deployed on and near McGill downtown campus, with various multi-type sensors such as UltraHD cameras, Traffic Radars, and Environmental Sensors. Data will be collected from all the low and high-data rate sensors, and will be transported over various types of wireless secure networks (900MHz/2.4/5.8/60GHz Wireless Zigbee, BLE, Wi-Fi, LoRa, LTE-M), through multi-protocol gateways, to local data storage and pre-processing IoT Edge stations, this pre-processed, reduced bandwidth data will be also forwarded to centralized Cloud data processing location for further big data global analysis. This prototype testbed will be used to conduct research in Smart Sensor Security and Efficient Data routing, as well as, in flexible distributed data analytics architecture (to reduce sending bandwidth intensive data to Cloud servers), and in IoT resources sharing through virtualization technology. Students will have a chance to understand various new Smart City IoT concepts (OpenCV, Big Data Analytics, IoT Security, IPv6, etc.), and to be involved in practical real-time system development, deployment and testing of applications.

This particular project aims to develop new live video call services to connect to 911 Police and 811 Info-Sante call centers. The person at the interactive Smart City panel could place 911 video call and get connected with real police person displayed on the interactive Smart City panel, the user and its surroundings would also be seen by the police officer (through the high definition cameras already installed on the interactive Smart City panel), thus the police person could faster and better assess the emergency of the call and respond accordingly. The same technology concept would also be applied when making 811 video call to remote medical ¨triage¨ nurse. The extra live video information about the person in medical need would speed up and improve the diagnosis. A police or a nurse from the call center could also push relevant content to the display panel in front of the user, for example a local map with the nearest police station, or open, least busy nearby medical center, or display live-saving instructions. Other Smart Data Display applications also will be developed in the project, for example to inform about the air quality or other environmental conditions at the interactive panel site, students are also encouraged to come up with new ideas for smart interactive panel applications, and try them on the real deployed system. These content display applications would be developed for deployment on the interactive panel system itself and could also be pushed to the user’s Android or iPhone device, depending on the application.

Tasks:
Study the general concept of IoT, research and survey existing open video conferencing applications which could be used for this project, survey and investigate existing technology which could provide ability to dispatch video calls to persons in 911 or 811 call centers, and enable the control of the outdoor panel cameras and display content (for example to push content from call center to outdoor panel). Also investigate currently most popular and user friendly techniques to display data content on the outdoor panels. Develop and perform rigorous testing with different users, under different conditions of the selected applications on the outdoor interactive panel and on Android or iPhone devices. NOTE: the implemented applications should not compromise security or stability of the interactive panel. The following skills and experiences are great assets: software development/testing, Linux, C/C++/C#/Java coding, experience with tools such as MS Azure, IBM Bluemix or Amazon AWS.

Deliverables:
Real-time demonstration of the working applications, well organized and documented source code, and user manual and technical report on functionality, number of conducted tests and final observations. The student will also need to make poster presentatio

Number of positions: 2
Academic Level: Year 2

ECSE-013: Multi-Protocol Wireless Network for Sensors in IoT Smart-City MultiCom Edge Station testbed
Professor:Tho Le-Ngoc
E-mail: tho.le-ngoc [at] mcgill.ca
Telephone: 514-398-7151
Website

Research Area: Telecommunications and Signal Processing


Description
Successful large-scale Smart City development needs to make sure that the integration of multiple Information and Communication Technology (ICT) and Internet of Things (IoT) solutions is easily and economically deployable, expandable, sharable, controllable, maintainable, and interoperable in terms of hardware & data interfaces, and, of course, well protected from malicious cyber-attacks.

In this multi-segment ongoing program, we will use a real-time IoT Smart-City MultiCom Edge Station testbed deployed on and near McGill downtown campus, with various multi-type sensors such as UltraHD cameras, Traffic Radars, and Environmental Sensors. Data will be collected from all the low and high-data rate sensors, and will be transported over various types of wireless secure networks (900MHz/2.4/5.8/60GHz Wireless Zigbee, BLE, Wi-Fi, LoRa, LTE-M), through multi-protocol gateways, to local data storage and pre-processing IoT Edge stations, this pre-processed, reduced bandwidth data will be also forwarded to centralized Cloud data processing location for further big data global analysis. This prototype testbed will be used to conduct research in Smart Sensor Security and Efficient Data routing, as well as, in flexible distributed data analytics architecture (to reduce sending bandwidth intensive data to Cloud servers), and in IoT resources sharing through virtualization technology.

Students will have a chance to understand various new Smart City IoT concepts (OpenCV, Big Data Analytics, IoT Security, IPv6, etc.), and to be involved in practical real-time system development, deployment and testing of applications. This particular project aims to test coexistence, performance and reach of various Multi-Protocol Wireless Sensors (Zigbee, BLE, Wi-Fi, LoRa, UHF RFID, LTE-M in 900MHz/2.4/5.8/60GHz frequency bands) for a given real application, and identify what are the potential limitations. The MultiCom Station (with interactive panel) will be a gateway for these various technologies sensor nodes which would be deployed near the stations.

Tasks:
Study the general concept of IoT, and general properties of each of the investigated wireless protocol, help graduate students to configure and communication tests (in real environment) of the selected wireless protocol devices, as well as develop, deploy and test corresponding application (on the MultiCom Edge Station) which uses these wireless sensor nodes under investigation. One example application would be detection of BLE or UHF RFID tagged object passing near the MultiCom Edge Station, and this information could be used to detect lost/stolen items. The alert could be displayed on the station´s panel and/or send to the registered user who reported a given item as lost/stolen. NOTE: the implemented applications should not compromise security or stability of the sensors or MultiCom station. The following skills and experiences are great assets: hardware and software development/testing, Linux, C/C++/C#/Java coding, experience with tools such as MS Azure, IBM Bluemix or Amazon AWS.

Deliverables:
Real-time demonstration of a working developed and tested application/s, well organized and documented source code, and user manual and technical report on functionality, number of conducted tests and final observations. The student will also need to mak

Number of positions: 1
Academic Level: Year 2

ECSE-014: van der Waals heterostructure assembly
Professor:Thomas Szkopek
E-mail: thomas.szkopek [at] mcgill.ca
Telephone: 514 398 3040
Website

Research Area: Nanoelectronic devices and materials


Description
A van der Waals heterostructure is an assembly of layered materials engineered to effect high quality interfaces between layered metals, semiconductors and insulators. Van der Waals heterostructures can be assembled by mechanical micro-manipulation of layered materials using mechanical exfoliation, PDMS stamps, a controlled ambient environment and high-resolution optical microscopy. The end goal of van der Waals heterostructure assembly is the fabrication of novel electronic devices, including transistors operating in the ballistic or hydrodynamic regime of charge transport. The latter regime exploits electron-electron interaction to increase current density in a transistor channel above and beyond the ballistic limit..

Tasks:
Students will exfoliate layers of graphene, hBN, mica and other 2D materials to assemble van der Waals heterostructures suitable for integration into transistor devices.

Deliverables:
Deliver a working protocol and several samples of van der Waals heterostructures in a glove box environment.

Number of positions: 3
Academic Level: No preference

ECSE-015: CANCELLED

ECSE-016: Plasmonic thermocycler for point-of-care medical diagnostics
Professor:Andrew Kirk
E-mail: andrew.kirk [at] mcgill.ca
Telephone: 1542
Website

Research Area: Photonics


Description
The polymerase chain reaction (PCR) is widely used to amplify and identify DNA samples. Most commercial PCR systems require over an hour to produce a result, but we have recently demonstrated a new approach that uses laser heating of gold nanoparticles to drive the reaction. This has allowed us to produce test results in under one minute, and opens the technique up to point-of-care applications. The goal of this SURE project is to improve the current prototype system, in terms of the optics and the electronics/control..

Tasks:
1. Develop an improved optical system design. This will require the use of optical ray-tracing software together with experimental measurements in the lab to design an improved beam-delivery system. Extension to a multichannel system will be considered. 2. Improved electronics and control system: The current system is controlled by an Arduino microcontroller and has limited speed and functionality. The SURE student will evaluate and implement alternatives that may produce improved results.

Deliverables:
1. An improved optical design, preferably with test results 2. An improved microelectronic controller

Number of positions: 2
Academic Level: Year 2

ECSE-017: Visualization of deep learning imaging biomarkers predictive of clinical progression in Magnetic Resonance brain images of patients with progressive Multiple Sclerosis.
Professor:Tal Arbel
E-mail: arbel [at] cim.mcgill.ca
Telephone: 5143988204
Website

Research Area: Computer vision/medical image analysis


Description
Multiple Sclerosis is the most common neurodegenerative disease affecting young people. Currently, there is no cure. There is a significant unmet need to define robust and sensitive outcome predictors for progressive MS, defined as progressive worsening of neurological function (accumulation of disability) over time. Prof. Arbel is part of an interdisciplinary collaborative research network, comprised of a set of researchers from around the world, including neurologists and experts in MS, biostatisticians, medical imaging specialists, and members of the pharmaceutical industry. The team recently received a Collaborative Network Award by the International Progressive MS Alliance (IPMSA). The objectives of the grant include: (1) the federation of the first large Magnetic Resonance Image (MRI) progressive MS dataset (~40,000 patients over time) from hospitals around world and from almost all large phase 3 clinical trials for progressive MS and (2) the development of new Magnetic Resonance Imaging (MRI) biomarkers for predicting Multiple Sclerosis disability progression for use in clinical trials. Professors Arbel and Precup (School of Computer Science) are currently developing new machine learning techniques to automatically discover (MRI) markers for disability prediction in progressive MS and as an outcome measure in early phase trials to facilitate drug discovery. Specifically, their teams have begun to develop new deep learning frameworks that are completely data-driven, in which latent image features are identified using large amounts of imaging data. Supervised learning will result in the identification of features predictive of future clinical progression..

Tasks:
The goals of the project are to explore methods to visualize the resulting imaging biomarkers associated with clinical progression in order to permit their clinical interpretation by neurologists. The student will work closely with graduate students and Research Associate in Prof. Arbel’s lab and with members of the collaborating teams, particularly at the Montreal Neurological institute.

Deliverables:
The student will develop software tools for the visualization of imaging biomarkers that are associated with clinical progression in progressive MS. The algorithm will be developed and tested on the large federated dataset of real MS patients from patients from different centers and clinical trials

Number of positions: 1
Academic Level: Year 3

ECSE-018: Transformation of carbon dioxide into value-added chemicals
Professor:Ishiang Shih and Zetian Mi
E-mail: ishiang.shih [at] mcgill.ca zetian.mi [at] mcgill.ca
Telephone: 5143987147
 

Research Area: Advanced materials and nanotechnology


Description
The transformation of carbon dioxide (CO2) into value-added chemicals through artificial photosynthesis is a highly sought-after process for the transition to a carbon-neutral economy. Molecular beam epitaxially grown III-nitride nanowires have demonstrated exceptional performance in photo-reducing CO2 molecules into more useful products such as syngas, methane, methanol, formic acid, etc. However, this process often requires the presence of expensive noble metal co-catalysts. Moreover, multi-carbon products, which are more desirable due to their higher energy density, are difficult to produce. The end goal of this project is to identify a cheap, earth-abundant co-catalyst that can catalyse the CO2 reduction reaction and optimise the deposition process for III-nitride nanowires, to produce high efficiency and high selectivity photoelectrochemical devices.

Tasks:
1. Identify an inexpensive, earth-abundant co-catalyst for CO2 reduction 2. Develop and optimize a deposition procedure for co-catalyst loading onto III-nitride nanowires 3. Characterize samples using photoelectrochemistry, gas chromatography, nuclear magnetic resonance spectroscopy and scanning electron micrography

Deliverables:
1. High selectivity photoelectrochemical device for CO2 transformation 2. Documented procedure for loading co-catalysts onto nanowire devices 3. Manuscript for journal paper submission

Number of positions: 
Academic Level: 

ECSE-019: Minimum mean-square demodulation and error control decoding for non-orthogonal modulation schemes
Professor:Jan Bajcsy
E-mail: jan.bajcsy [at] mcgill.ca
Telephone: 514-398-7462
 

Research Area: Telecommunication and Signal Processing


Description
5G wireless cellular systems will provide ultra-high rate transmission for emerging integrated services such us ultra high-definition sstreaming video, 3D television, online gaming, machine-to-machine communication for billions of devices, support for smart homes and smart cities. Spectrally efficient frequency division multiplexing (SEFDM), non-orthogonal multiple access (NOMA), faster-than-Nyquist modulation and multi-antenna (MIMO) systems are the next generation technologies considered for 5G to enable ultra high data rates over physical communication channels for multiple users. This project will explore design and implementation of receivers based on minimum mean-square (MMSE) solutions to recover data from wireless transmission over non-orthogonal channels.

Tasks:
Implement, test and obtain results for MMSE receivers for non-orthogonal modulation systems that also use error control codes. Testing and simulation of their performance is to be done under practical wireless channel conditions – fading, additive noise, co-antenna, intersymbol and multi-user interference.

Deliverables:
Final poster presentation, real-time demonstration of implemented systems and final technical report documenting in detail successful implementation of MMSE receivers for MIMO, SEFDM, NOMA and FTN transmission systems. Properly documented source code, simulation results and BER curves.

Number of positions: 1 
Academic Level: 3 

Click on the title for full description of SURE 2018 Mechanical Engineering projects that are open to students in ECSE.

MECH-031: Flight Testing, Hardware Interfacing for Unmanned Aerial Vehicles
Professor:Meyer Nahon
E-mail: Meyer.Nahon [at] mcgill.ca
Telephone: 514-398-2383
Website

Research Area: Unmanned Aerial Vehicles. Dynamics and Control


DescriptionThe Aerospace Mechatronics Laboratory currently houses several unmanned aerial vehicles: both quadrotor platforms and model fixed-wing aircraft. Research is currently ongoing with all these platforms with the overall objective to develop autonomous unmanned aerial vehicles. For example, some of the research ongoing with quadrotors aims to integrate wind sensing into our flight platform, and use this data for close-loop feedback, and for its own sake (e.g. monitoring local wind conditions). The fixed-wing aircraft are used for the development of autonomous acrobatic flight through obstacle fields. A SURE student is sought with strong interest and aptitude for research in the areas of robotics, mechatronics and aerial systems. Depending on the status of the above projects, the student is expected to contribute to experimental testing of components and to flight tests with these platforms. In addition, the student will be involved with interfacing new sensors into the platforms, for the purposes of acquiring data and for closed loop control. Some programming experience would be useful for the development of a real-time hardware-in-the-loop simulation. The student is expected to assist with hardware interfacing, programming, conducting experiments, and processing the data.

Tasks:
The tasks will be varied and could accommodate a mechanical, electrical or software engineering student; but ideally someone with experience in all aspects. Tasks will include some interfacing of sensing hardware with microprocessors; programming; some CAD modeling; some Matlab/Simulink modeling; and finally, experimental testing.

Deliverables:
Assist in the improvement of autonomous flight performance of our quadrotor and fixed-wing aircraft under closed-loop control.

Number of positions: 1
Academic Level: Year 3