Electrical and Computer Engineering

SURE: Electrical & Computer Engineering

Electrical
& Computer
Engineering

 

ECSE-001: Integrated electro-optic devices in semiconductors and smart materials

Professor David Plant

david.plant [at] mcgill.ca
(514) 398-2989
http://www.photonics.ece.mcgill.ca/Plant/dvp_research/dvplant_research.html

Research Area

Integrated electro-optic devices in semiconductors and smart materials

Description

We depend on computers and telecommunication for most aspects of our daily life. As more activities and services operate through the internet (e.g. apps, media, networking, cloud computing), the demand for technological improvements in the speed, size and efficiency of the networks has pushed the electronics to its limits. As a next step, photonics (light) is being implemented in digital systems at every scale – from vast intercontinental fiber optic networks to nano-photonic devices, where electronics is now the control mechanism. Our group develops nanoscale devices that manipulate the flow of light in integrated chips so that processors can analyze data encoded in the light. This requires the testing, analysis, and measurements of our fabricated devices as well as learning the appropriate software tools to perform these tasks. The project offers the unique opportunity to gain hands-on, experimental experience in opto-electronics and material platforms. The concepts will be applied to different projects that have previously ended in publications from internships. Due to the steep learning curve, each student will be mentored by the graduate student responsible for the projects in their interest.

Tasks

Depending on the project chosen, tasks can involve measuring the modulation and propagation of light and electricity in different devices. Interns will have the chance to test and evaluate the ability of opto-electronic devices to manipulate the flow of light. This can also include microscopy (SEM, AFM, SNOM, HSI) techniques, analog electronic circuit designs, polymer synthesis techniques, and other skills as needed.

 

Deliverables

Experimental analyses with accompanying measurement protocols and code. A report detailing the developments to the project and explaining the contributions made.

Number of positions

2

Academic Level

Year 3

ECSE-002: High Performance Computational Electromagnetics

Professor Dennis Giannacopoulos

dennis.giannacopoulos [at] mcgill.ca
(514) 398-7128
http://www.compem.ece.mcgill.ca/index.html

Research Area

Computational Electromagnetics / Software Development

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

Year 2

ECSE-003: Computer Graphics and Machine Learning Reproducibility Study

Professor Derek Nowrouzezahrai

derek [at] cim.mcgill.ca
5143983118

Research Area

Computer Graphics Applied Machine Learning Deep Reinforcement Learning Numerical Methods

Description

This year I'm trying something different. The goal of each SURE project will be to reproduce a published research result from either the computer graphics (SIGGRAPH/SIGGRAPH Asia) or machine learning (NeurIPS, ICML, ICLR, CVPR) fields. Interested students should propose a list of three papers that interest them and, in each case, quickly outline (in one sentence) what their research/implementation methodology will be.

Tasks

1. read and internalize the mathematical and technical details of a top-tier published research result in the fields of computer graphics and/or machine learning 2. independently implement the technique and reproduce a paper-quality result 3. [optional] propose, implement and report on an extension of the work

 

Deliverables

1. a technical report, written in an academic style, outlining the research problem, proposed solution and results 2. a clean, well-documented open-source implementation of the reproduced research results

Number of positions

3

Academic Level

Year 3

ECSE-004: Hardware implementation of a microgrid controller

Professor François Bouffard

francois.bouffard [at] mcgill.ca
5143982761

Research Area

Electric power and energy

Description

The goal of this project is to implement the dispatch control functionality of a microgrid controller on an embedded computing platform. Given the limited CPU and memory of these platforms, other implementations of the control will have to be explored (e.g., decision trees). The control function to be implemented will have to aggressively work to reduce the carbon footprint of a microgrid while meeting other technical constraints. The student team will have to prepare a demonstration showcasing how the controller behaves in response to changing operating conditions. Students are expected to have some power and energy background. The project will involve coding in Matlab and/or Python.

Tasks

Students will work together to: * Implement a control policy for energy dispatching functionalities of a microgrid controller using an embedded computing platform (e.g., Arduino Uno, Raspberry Pi) * Document the implementation process * Prepare a showcase demonstration of the workings of the controller

 

Deliverables

* Validated embedded hardware implementation of a microgrid dispatch controller * Documentation * Demonstration script

Number of positions

2

Academic Level

No preference

ECSE-005: Mobile Mixed-Methods Data Collection for Machine Learning Applications

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
http://srl.mcgill.ca

Research Area

Intelligent Systems/Mobile Interaction

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 (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 the collection of large amounts of self-reporting data without such interruption. Starting from an existing prototype implementation of this data collection technique, this project aims to extend the self-reported data with quantitative data collection capabilities, i.e., smartphone sensor data and physiological signals. The objective of these modifications is to enable use of this data collection framework in practical machine learning applications. The outcomes of this project have the potential to contribute significantly to the fields of applied machine learning, user-centered artificial intelligence and affective computing.

Tasks

1. Mobile implementation: Integration and testing of the new data collection channels in the current Android implementation. 2. Validation: Design and execution, with support from a graduate student, of a user study quantifying the performance and user experience of the updated system in comparison with existing data collection frameworks. Interface design: Implementation of new interfaces that allow the collection of subjective self-reports. This project requires a student with strong Android development experience and interests in HCI/UX research.

 

Deliverables

Deliverables include a prototype implementation on a mobile platform and a user study that will constitute part of a conference or journal paper submission.

Number of positions

1

Academic Level

No preference

ECSE-006: Haptic Wearables

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
http://srl.mcgill.ca

Research Area

Intelligent Systems/Haptic Interaction

Description

Our lab works on the design of wearable haptic 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. We are particularly interested in applications to rehabilitation therapy, sports training, information communication, virtual reality, and mobile gaming. The projects described under the "research tasks" below relate to advanced applications involving such novel wearable devices.

Tasks

1. Haptic device for sensory reeducation applications: Nerve damage, frequently caused by injury, can result in the loss of sensorimotor functions in certain parts of the hand. After suturing the nerve, unpleasant sensations on contact, including tingling and electric shocks are often felt. Following nerve regrowth, it is necessary to re-train the brain to interpret the signals from these nerves correctly. This project involves the design of haptic devices to help in this process of sensory reeducation, which can involve two phases, depending on the severity of the loss of sensitivity: relearning how to localize sensations, and differentiation of shapes and textures in the identification of objects. Students should be comfortable working with an Arduino or similar microcontroller platform to interface with sensors and effectors. Mechatronics or robotics experience would be a valuable bonus. 2. Rich haptic effects for the OR and ICU: At present, the operating room (OR) and intensive care unit (ICU) are noisy environments, exacerbated by frequent alarms. Regardless of whether the alarms are valid or false, all command attention, raise stress, and are often irrelevant to the responsibilities of individual clinicians. To cope with these problems, we are investigating the possibility of using audio only for those alarms that should be announced to the entire team, but delivering other alarm cues individually, through haptics vibrations. In this project, you will work on the design and evaluation of haptic effects to represent various physiological parameters such as patient heart rate or blood pressure in a self-explanatory manner, that is, the effects should not require clinicians to expend significant cognitive effort interpreting the signals. 3. Temperature based haptics: Although haptics is typically associated with vibration mechanisms to simulate different effects and textures, temperature also has an important role to play, and can be used to enrich these, for example, in the context of simulating contact with water or snow. It is also possible to create surprising effects such as a burning sensation, even when the temperature difference is quite small. This project involves the design and testing of a temperature control system for a portable haptic interface, ideally a shoe, that can be used in multimodal VR simulations. Students should have practical electronics design and assembly experience, and be comfortable working with an Arduino or similar microcontroller platform.

 

Deliverables

1. Prototype implementation involving hardware and software implementation. 2. Haptic-alarm delivery system, tested in simulation of clinical context. 3. Temperature-controllable shoe, verified through user study.

Number of positions

3

Academic Level

Year 1

ECSE-007: Haptic Wayfinding

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
https://srl.mcgill.ca

Research Area

Intelligent Systems

Description

Today we rely intensely on the smartphone screen and map‐based interfaces to orient ourselves in the world. But what about moments when a screen is not available, or when battery life is extremely low? And what about visually impaired that have no easy access to a digital map at all? Haptic interfaces can provide a sense of connection with others far away without the need for a visual interface. Using a pre‐built smartphone application that shares the location of nearby users, students will create a haptic feedback system to find others nearby. The smartphone app consists only of a black screen, so students will need to use force feedback and vibration controls to help guide the user.

Tasks

1. Design exploration of haptic feedback for wayfinding, shared presence and proximity 2. Canvas of known haptic interactions and gestures

 

Deliverables

1. Prototype haptic app forked from an open‐source iOS or Android boilerplate 2. Written report comparing iPhone and Android smartphones at a technical level to understand limitations as they relate to haptics

Number of positions

1

Academic Level

Year 1

ECSE-008: Autonomous Drone Landing

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
http://srl.mcgill.ca

Research Area

Intelligent Systems

Description

Advances in image processing and object detection enable cameras to detect roads, buildings and street signs with relative ease. But when it comes to making meaning of objects from high above, existing solutions may not go far enough. Using machine learning and a database of satellite imagery, students will collect and tag data for use in an autonomous drone. Ultimately the drone will be able to rate several potential landing surfaces, such as building rooftops, and choose between them to establish a safe landing zone.

Tasks

1. Research into existing autonomous drone solutions 2. Gather training data and help evolve image classification model

 

Deliverables

1. Image database optimized for safe landing in a rural environment 2. Written suggestions on potential enhancements for the machine learning model

Number of positions

1

Academic Level

Year 1

ECSE-009: Population Distribution Across Town

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
http://srl.mcgill.ca

Research Area

Intelligent Systems

Description

In days after a natural disaster, supplies are distributed often unevenly and inequitably. Certain populations, for example those with access to a vehicle and steady income, can more easily stockpile extra supplies at home and are less susceptible than others. But when relief workers arrive in a region, they often do so without deep knowledge of the local population. To help mitigate risk, students will work on a predictive model to understand the geographic distribution of citizens across a city or rural zone.

Tasks

1. Identify unique predictive traits that can be used to identify where people would most likely gather in the aftermath of a disaster 2. Use demographic and venue data to suggest areas of town that would be most likely to be neglected when it comes to supply distribution

 

Deliverables

1. Chart of predictive indicators along with level of confidence 2. Computer script that, when given a zip/postal code, can aggregate demographic and venue data from multiple providers to identify level of risk in the wake of a disaster

Number of positions

1

Academic Level

Year 1

ECSE-010: ecentralized Identity

Professor Jeremy Cooperstock

jer [at] cim.mcgill.ca
5143985992
http://srl.mcgill.ca

Research Area

Intelligent Systems

Description

In just the last few years, distributed ledgers have created several viable ways to store identity in a secure, decentralized manner. With cryptographic keys it is possible to create decentralized applications that do not depend on any single source of truth and, moreover, enable new levels of resilience and anonymity. However, such solutions lack intuitive user interface controls that typically go along with the familiar username & password design pattern. Students will be tasked to create design concepts to manage their own credentials in a decentralized database, and to create a proof of concept to retrieve forgotten credentials to sign‐in to a demo application.

Tasks

1. Research on existing solutions from various distributed ledgers (Cardano, Iota, etc.) and decentralized databases (GunDB, HyperDB, CouchDB, etc.) 2. Wire framing, sketching and prototyping for credential management

 

Deliverables

Single‐page web application demonstrating sign‐on and retrieval of lost credentials without the need for username & password

Number of positions

1

Academic Level

Year 1

ECSE-011: Nano light emitters

Professor Songrui Zhao

songrui.zhao [at] mcgill.ca
5143983244

Research Area

Photonic devices, Semiconductor nanostructures, Molecular beam epitaxy

Description

Compared to the rapid growth of data traffic, the performance of semiconductor devices - as the most fundamental ingredient for modern information and communication technologies, improves highly asymmetrically slowly, opening a large unsustainable gap. It is imperative to reduce this gap by innovating novel electronic and photonic devices. In this context, this summer project aims to look for solutions to the yet-missing high efficiency semiconductor light emitting technologies at very short wavelengths, which are pertinent to many emerging applications including non-light-of-sight communications, sensing, sterilization, among others. Today, such light sources have to rely on toxic mercury and fluoride gases, which have low efficiency, bulky size, and are clearly not sustainable. In this project, it will exploit low-dimensional semiconductor nano light emitters that are precisely engineered at an atomic level, and further understand the optical emission characteristics of such light emitters, including pinpointing device performance limiting factors. This summer project opens to U3 and above, with basic knowledge related to electrical conduction, heat, and light. Students in this project will learn how modern light emitting devices work, how to fabricate and characterize these devices, as well as how to improve their performance and tailor them for various emerging applications such as artificial reality.

Tasks

1. Literature review and understand optical properties of GaN based light emitting devices 2. Learn and perform device fabrication 3. Device performance characterization, regarding to their electrical, optical, and thermal properties

 

Deliverables

Identify device performance limiting factors and degradation mechanism

Number of positions

1

Academic Level

Year 3

ECSE-012: High efficient nanowire light emitting diodes and lasers

Professor Songrui Zhao

songrui.zhao [at] mcgill.ca
5143983244

Research Area

Photonics, low-dimensional semiconductors

Description

Despite demand for more compact, environmentally-friendly, and reliable ultraviolet (UV) sources, the performance of UVLEDs has remained limited. This is due to several factors such as the presence of a high density of defects and dislocation, inefficient p-type doping, and low light extraction efficiency. To date, the best-attained wall plug efficiency (WPE) for UVLEDs (emitting wavelength<340 nm) is below 15% at 20 mA. And the WPE value drastically drops with further decreasing the wavelength. Consequently, conventional planar UVLED structures face tremendous challenges and new device scheme to achieve high efficient UVLEDs is urgently needed. The dislocation-free AlGaN nanowires have attracted much attention and many breakthroughs have been achieved such as the report of the shortest wavelength of an electrically-pumped deep UV laser diode emitting at 239 nm by adopting AlGaN nanowires. Furthermore, III-nitrides in the form of nanowires can be virtually grown on any substrates, such as silicon, metal, and graphene, paving the way towards the realization of integrated photonics applications. The end of this project is to design, grow, fabricate high-efficient and high output power deep UVLEDs (emission wavelength at 265 ~280 nm), and possibly demonstrate edge emitting UV laser diode.

Tasks

1. Design and optimize the structure of the AlGaN-based nanowires using commercial software or MATLAB coding 2. Conduct the material growth and material characterization such as scanning electron microscopy, x-ray diffraction, etc. 3. Fabricate nanowire devices and test its electrical and optical properties of the devices such as electroluminescence and photoluminescence

 

Deliverables

1. High efficient UVLED emitting around 265 nm to 280 nm 2. Documented experimental details and procedures for design, growth, fabrication nanowire devices 3. Manuscript for journal paper submission

Number of positions

1

Academic Level

Year 3

ECSE-013: Visualization of deep learning imaging biomarkers predictive of clinical progression in Magnetic Resonance brain images of patients with progressive Multiple Sclerosis

Professor Tal Arbel

arbel [at] cim.mcgill.ca
5143988204

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

2

Academic Level

Year 3

ECSE-014: Machine Learning in Broadband Wireless Access Communications

Professor Tho Le-Ngoc

tho.le-ngoc [at] mcgill.ca
514-398-5252

Research Area

Telecommunications and Signal Processing

Description

In this multi-segment on-going research project, we consider how to design a broadband wireless access communications system that can adaptably adjust itself to the continuously changing complex environment by using machine learning (ML) techniques. We aim to explore the potential of applying ML techniques to harvest relevant environmental information for improving the resource allocation, performance and operation of the corresponding broadband wireless access communications system. Relevant environmental information can include weather (e.g., rain, snow, fog, temperature, etc.), terrain (e.g., user locations, relative positions, buildings, obstacles, etc.), propagation (e.g., power, frequencies, etc.), social relationships (e.g., user groups, social networks, etc.) Various ML-based algorithms within a prototype testbed will be developed for the specific topics such as 3D channel modelling/estimation, hybrid ARQ, hybrid massive-MIMO precoding/beamforming, etc., to demonstrate the effectiveness of the Artificial Intelligence (AI)-augmented systems in terms of performance benchmarks such as energy consumption, increase in achievable capacity, reduction in interference, etc. Students will have a chance to understand various new concepts and development tools in both wireless communications (channel modelling, antenna array, beam forming, MIMO, etc.) and machine learning (deep neural network, reinforcement leaning, etc.), and to be involved in practical prototype development, and testing. As an example, one sub-project aims to make use of both the terrain and weather information available in many sources such as Google map, online meteors, to develop a ML-based channel estimator for dynamic resource allocation in a broadband wireless access communication system.

Tasks

Study the general concepts of ML and wireless communications. Learn how to search for and read scientific papers on a given signal processing or machine learning methods. Investigate Matlab toolboxes, PyTorch, Keras, Tensorflow, and DSP/FPGA hardware for possible applications to algorithm/prototype implementation. Assist in implementation and testing algorithms/prototypes, and in collecting, documenting and commenting the test results. The following skills and experiences are great assets: software development/testing, antenna design, Matlab, Python, VHDL, etc.

 

Deliverables

Demonstration of a developed software/hardware testbed, well organized and documented source code and design, technical report on the developed software/hardware functional operation and conducted test results. The student will also need to make a poster presentation.

Number of positions

3

Academic Level

Year 2

ECSE-015: Ultra High-Speed Orbital Angular Momentum Multi-Input Multi-Output (OAM-MIMO) Wireless Communications

Professor Tho Le-Ngoc

tho.le-ngoc [at] mcgill.ca
514-398-5252

Research Area

Telecommunications and Signal Processing

Description

With several significant progress in wireless communications technologies, high bandwidth communications are now available for users at low costs. However, with the massive deployment of Internet of Things (IoT) applications such as Smart City, current wireless standards may not be sufficient. As the multiple orthogonal resources such as frequency, time and space are already almost exhaustively explored, it is difficult to further increase the capacity to support more users at a higher speed. In this context, Orbital Angular Momentum (OAM), which explores the orthogonal topology charges of electromagnetic waves, is considered to be one of the candidates for solving the high bandwidth demand issue. In this multi-segment ongoing program, we aim to build a communication testbed prototype that combines OAM helical waveform with MIMO technology for ultra high-speed wireless communications. This prototype testbed will be then used to conduct research in antenna design and digital signal processing algorithms to enhance the wireless communication speed. Based on this testbed, testing can be executed through both numerical simulations and measurements. Students will have a chance to understand various concepts in wireless communications (antenna design, wireless channel simulation, resource allocation algorithms) and to be involved in practical system design, development and testing. In particular, this projects aims to develop a testing environment to conduct measurements and numerical simulations on OAM-MIMO to investigate the potential limitations. The testbed will be used to test various antenna designs and digital signal processing algorithms of the future research.

Tasks

Study the general concept and characteristics of Orbital Angular Momentum at radio frequency. Learn how to search for and read scientific papers on a given signal processing or antenna design subject. Leverage Matlab, antenna designs, DSP/FPGA hardware to implement the testbed platforms for OAM. Learn how to test functional operation and performance of the developed testbed, and to collect, document and comment on the test results. The following skills and experiences are great assets: software development/testing, antenna design, Matlab, VHDL, etc.

 

Deliverables

Demonstration of a developed software/hardware testbed, well organized and documented source code and design, technical report on the developed software/hardware functional operation and conducted test results. The student will also need to make a poster presentation.

Number of positions

2

Academic Level

Year 2

ECSE-016: Smart Hybrid Massive-MIMO 3D Active Antenna Array for next generation Full-Duplex Communications

Professor Tho Le-Ngoc

tho.le-ngoc [at] mcgill.ca
514-398-5252

Research Area

Telecommunications and Signal Processing

Description

Smart Hybrid Full Duplex Massive MIMO (Multi-Input Multi-Output) Active Antennas Arrays are considered for the next generation 5G Internet-of-Things mobile broadband communications standard. Using a massive number of antenna elements can (i) help to adaptively create narrow beams continuously steered to follow the target user while avoiding interference from other users, (ii) increase the reach distance, and (iii) increase the capacity of the system (especially useful when new massive number of IoT machines/devices will be deployed). The smart antenna system will know how to follow the mobile user based on (i) a hybrid 2-stage digital and RF precoding structure to reduce the complexity of the digital system, and (ii) Full Duplex operation for simultaneous transmission/reception over a frequency slot to reduce frequency spectrum utilization as well as latency. In this on-going project, we investigate, design and test new promising antenna 3-Dimentional structures (such as metamaterials, EBG, Dielectric filled, etc.), with integrated Power and Low Noise Amplifiers, as well as RF combiner and smart DSP based control sub-system. The hardware testbed consists of powerful multi-FPGA, multi-microprocessor, and RF Analog-to-Digital Converter (ADC) and Digital-to-Analog Converter (DAC) modules to be programmed with digital signal processing algorithms to generate the transmit and process the received real wireless communication signals. Students will have a chance to understand Hybrid Full Duplex Massive MIMO systems, antenna design, material selection techniques, RF signal combining techniques, and improve their DSP design skills and more.

Tasks

Study the general concept of Full Duplex massive MIMO, radio-wave propagation, antenna design and simulation; learn the operation of the antenna design and simulation CAD tools HFFS, Matlab, PCB/DSP/FPGA design tools; prepare the simulation set-ups; assist graduate students and/or research associates to evaluate/analyze simulation results.

 

Deliverables

A technical report on antenna design and simulation results, analyzing and discussing the observed antenna characteristics and its meaning/limitations on the performance and practical applications.

Number of positions

3

Academic Level

Year 2

ECSE-017: Massive-MIMO Self-Interference Channel Characterization

Professor Tho Le-Ngoc

tho.le-ngoc [at] mcgill.ca
514-398-5252

Research Area

Telecommunications and Signal Processing

Description

Multi-Input Multi-Output (MIMO) has been considered in wireless systems such as LTE, WiMAX, Wi-Fi and Full–Duplex (Transmission and Reception in the same frequency band and at the same time) massive-MIMO is considered for the next generation IoT driven mobile telecommunications standard. In this on-going project, we measure and characterize 3.5GHz (for wider signal penetration application) and 60GHz (for wider bandwidth, high peak data rates applications) MIMO Self-Interference channels over different practical scenarios in order to understand the implications on the design requirements of the MIMO Full-Duplex systems, in particular on the design requirements of RF Self-Interference Canceller. Some of the types of considered channel environments: controlled free-space (e.g., in anechoic chamber), simulated rich scattering (e.g., reverberation chamber), or practical indoor and outdoor environments. Students will have a chance to understand MIMO systems, Self-Interference Canceller design, Self-Interference and Intended Signal channel measurements, and to work with real-life measurement facilities and testbeds.

Tasks

Study the general concept of massive-MIMO, radio-wave propagation in free-space and in rich-scattering environments; learn the operation principle of measurement equipment/facilities such as vector network analyzer, spectrum analyzer, vector signal generators, anechoic chamber, reverberation chamber and/or MIMO testbeds; prepare measurement set-ups; assist graduate students and/or research associates to evaluate/analyze measurement/simulation results (e.g., using Matlab).

 

Deliverables

A technical report on measured data, characterizing different types of Self-Interference and Intended Signal Channels, analyzing and discussing the observed characteristics and its meaning/usefulness in the design of Full Duplex RF Self-Interference Canceller.

Number of positions

1

Academic Level

Year 2

ECSE-018: Deep Neural Network (DNN)-based Linearization for Power Amplifiers

Professor Tho Le-Ngoc

tho.le-ngoc [at] mcgill.ca
514-398-5252

Research Area

Telecommunications and Signal Processing

Description

For power efficient operation RF Power Amplifiers (PA) should operate near the saturation region, but that creates non-linear behavior and distortion of the sensitive complex signal of the modern broadband wireless communication system. Typical approach to balance power efficiency and performance is to use PA linearizers. Non-Linear Power Amplifier with Memory is challenging to linearize using conventional models and techniques. In this project we will investigate, develop, simulate and test Deep Neural Network (DNN)-based Algorithms to linearize typical Power Amplifiers with Memory.

Tasks

Study about Power Amplifier characteristics, characterization, measurements and Subsequent Modeling in Matlab. Learn about typical PA parameters such as: gain compression (P1dB compression point), Amplifier Saturation Output Power (P3dB), IMD, IIP3, OIP3 (input/output third-order intercept points), AM/AM and AM/PM distortion. And also about other concepts such as: ACPR (for modulated signals, like QPSK or QAM) and Error vector magnitude (EVM) (for modulated signals, like QPSK or QAM). Review literature other conventional and new ML based PA linearization techniques.

 

Deliverables

A technical report on developed DNN structure for PA linearization which includes theory review, simulated and measured results.

Number of positions

1

Academic Level

Year 2

ECSE-019: Graphene ion-sensitive field effect transistor fabrication and testing

Professor Thomas Szkopek

thomas.szkopek [at] mcgill.ca
514 398 3040
http://carbon.ece.mcgill.ca

Research Area

Nanoelectronics

Description

Large area graphene field effect transistors functionalized with ionopore layers can be used for sensing a variety of ions. While the sensitivity and detection limit of graphene ion sensitive field effect transistors (ISFETs) approach laboratory assay performance, there remain open questions about noise, reliability and reproducibility that are pre-requisite to broad application of graphene ISFETs.

Tasks

Students will fabricate and test graphene ISFETs with different ionophore / lipophilic salt mixtures. Laboratory testing will be conducted to measure sensitivity, selectivity, reproducibility, and noise spectral density.

 

Deliverables

Students will deliver a detailed report of fabrication methods used, test protocols employed, and test results.

Number of positions

2

Academic Level

Year 3

ECSE-020: Automation of 2D material identification and manipulation

Professor Thomas Szkopek

thomas.szkopek [at] mcgill.ca
514 398 3040
http://carbon.ece.mcgill.ca

Research Area

Nanoelectronics

Description

The exfoliation of 2D materials from bulk crystals of layered materials is a labour intensive process. Manual inspection of optical microscopy images, followed by manual manipulation of exfoliated flakes is required to assemble van der Waals heterostructures. The goal of this project is to implement automation of this labour intensive process using computer assisted image analysis and automation of sample manipulation hardware.

Tasks

Using quantitative analysis of optical reflection contrast, the student will implement automated image analysis to identify exfoliated flakes and register them against lithographic alignment marks. The student will interface the software with hardware for automated stage movement. In the second phase of the project, the student will develop an automated PDMS stamp controlled by stepper and peizo actuators to pick and place exfoliated 2D material flakes.

 

Deliverables

The student will deliver a functional software and hardware package, including detailed documentation of the structure and functionality of the combined system.

Number of positions

1

Academic Level

No preference

ECSE-021: A measurement-based method to identify forced oscillation in power systems

Professor Xiaozhe Wang

xiaozhe.wang2 [at] mcgill.ca
5143981749
https://arxiv.org/pdf/1506.00934v3.pdf

Research Area

Power Engineering

Description

Small signal instability related to inter-area electromechanical oscillations can lead to catastrophic blackouts. For instance, the US Western Electricity Coordinating Council (WECC) observed an outage where an unstable mode caused an oscillation to grow out of control in 1996. To make it even worse, forced oscillations from, e.g., cyclic loads or generator turbines, may be introduced to power systems. The project aims to develop a measurement-based method to detect forced oscillation by exploring the statistical properties of phasor measurement unit (PMU) data. The student is expected to learn power system modeling, study the mathematical formulation of different oscillation mechanisms, and develop statistical methods to diagnose forced oscillations. The student will also gain hands-on experience by building up models and conducting simulations in power system analysis software. The student is expected to know the fundamentals of power systems and has mathematical backgrounds in linear algebra, linear systems, ordinary differential equations, statistics, probability, and stochastic process. It is required that the student is comfortable using Matlab and it is preferred that the student is proficient in programming.

Tasks

1. study the mathematical formulations of different oscillation mechanisms 2. develop a statistical method to identify forced oscillation 3. test the method in the benchmark power system 4. compare the method with state-of-art methods

 

Deliverables

A conference paper summarizing the method for the identification of forced oscillation.

Number of positions

1

Academic Level

Year 3

ECSE-022: Automated generation of consistent and realistic domain-specific graph models

Professor Daniel Varro

daniel.varro [at] mcgill.ca
5145831084

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 machine learning techniques to provide domain-specific guidance for model generators in order to derive more realistic models. This SURE project requires strong mathematical background in logics and programming.

Tasks

  • Investigate various machine learning (ML) techniques to characterize domain-specific graph models
  • Overview the VIATRA Graph Solver
  • Integrate a selected ML techniques as heuristics for automated graph model generation
  • Carry out experimental evaluation on the efficiency of the integrated technique.

 

Deliverables

"1) Evaluate how well various machine learning (ML) techniques characterize domain-specific graph models 2) Develop software to integrate ML techniques as heuristics into the search process of the VIATRA Graph Solver 3) Prepare a manuscript describing the results of experimental evaluation."

Number of positions

2

Academic Level

Year 2

ECSE-023: Machine Learning Algorithms for Radio-Frequency Breast Cancer Detection

Professor Mark Coates

mark.coates [at] mcgill.ca
514 718 7137
http://www.compem.ece.mcgill.ca/breast-cancer-detection/

Research Area

Machine learning and statistical signal processing

Description

Key to successful breast tumour 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 tumours 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. With regard to this application, this project will include the design of algorithms that provide a decision about the need for additional scans. These algorithms will take advantage of the longitudinal nature of the data, address the class imbalance (many more healthy scans than cases with tumors), and incorporate aspects of transfer learning. The project will focus on the development of novel machine learning algorithms (classification, anomaly detection, and regression) that can provide an assessment of the uncertainty associated with decisions and can provide explanations for the decisions.

Tasks

1. The student will implement several state-of-the-art machine learning algorithms and assess their capability to detect the presence of a tumour using synthetic, phantom, and clinical datasets. 2. The student will propose and implement modifications to improve the performance of the algorithms.

Deliverables

1. Software implementing the researched algorithms 2. Technical report describing conducted data analysis and results

Number of positions

1

Academic Level

Year 3

ECSE-024: Neural Networks Designing Neural Networks (NNDNN)

Professor Brett Meyer

brett.meyer [at] mcgill.ca
5143984210

Research Area

Machine learning, computer engineering, optimization.

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; there is particular interest in automotive, aerospace, and other mobile devices. 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

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

Deliverables

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

Number of positions

1

Academic Level

No preference