ECE 700 Topic 6 - Master of Engineering Project
This is a project course, designed exclusively for MEng students. Students will carry out a research project over one academic term, under the direct supervision of an ECE faculty member. At the end of the term, a written Project Report has to be submitted, which will be evaluated and marked by the Supervisor.
Eligibility and Guidelines:
- MEng students from ECE Department only (MASc and PhD students are NOT eligible).
- Coursework average ≥ 80%, after at least 3 courses.
- No RA or GRS is paid.
- The course is not transferrable to the ECE MASc program.
Course enrolment process is as follows:
- Projects that are available with ECE faculty members are listed below.
- Students should contact the faculty member, and the faculty member shall confirm allocating the project to the student.
- Faculty member will notify Susan King (MASc/MEng Coordinator), who will issue a Permission Number to the student for registering in the course.
Projects Available for Winter 2019 (the list will be updated as projects become available).
Project #1: Fault Detection in Hybrid HVDC Grids
Supervisor:
Dr.
Sahar
Azad
Email: s3azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4006
High voltage direct current (HVDC) grids, where a number of point-to-point HVDC links are connected together in a meshed configuration, have recently gained substantial attention in Europe, China and Canada. These HVDC grids enable the bulk and low-loss transfer of power and allow for large integration of renewable resources. As future HVDC grids will be built by different manufacturers, various types of converters will be operating in the same grid. The creation of such hybrid HVDC grids will bring forth significant technical challenges. One significant challenge is the hybrid HVDC grid protection. This project revolves around developing a relaying algorithm for hybrid HVDC grids.
Project #2: Backup Protection of Hybrid HVDC Grids
Supervisor:
Dr.
Sahar
Azad
Email: s3azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4006
High voltage direct current (HVDC) grids, where a number of point-to-point HVDC links are connected together in a meshed configuration, have recently gained substantial attention in Europe, China and Canada. These HVDC grids enable the bulk and low-loss transfer of power and allow for large integration of renewable resources. As future HVDC grids will be built by different manufacturers, various types of converters will be operating in the same grid. The creation of such hybrid HVDC grids will bring forth significant technical challenges. One significant challenge is the hybrid HVDC grid protection. This project revolves around developing a backup protection system for hybrid HVDC grids.
Project #3: Protection of Modernized Distribution Systems
Supervisor:
Dr.
Sahar
Azad
Email: s3azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4006
The conventional protection strategies and protective relays in the electric power distribution systems have been developed based on the characteristics of large centralized generation systems, i.e., synchronous generators. The existing protection systems are not designed taking into account the different behaviour of electronically‐interfaced Distributed Energy Resources (DERs), e.g., renewables and energy storage systems. This project aims to enable reliable protection of the modernized distribution systems with increased penetration of electronically‐interfaced DERs, especially the large‐scale wind and solar power plants.
Project #4: Hybrid Residential Buildings
Supervisor:
Dr.
Sahar
Azad
Email: s3azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4006
Currently, the electric networks of all residential buildings use alternating current (AC) power. As solar photovoltaic (PV), battery energy storage systems (BESS) and a large number of loads such as light emitting diodes (LEDs), electric vehicle charging, various electronics at home (laptops, smartphones, printers, etc) and newer generation of major appliances such as heating, ventilation and air conditioning systems use direct current (DC), a converter is required to convert the AC to DC and connect these DC loads and energy sources to the AC electric network. Such a conversion will result in electrical losses and less reliable electric systems. A novel solution is to use a hybrid AC-DC electric network, which can provide efficient and reliable energy to residential buildings. In this project, various structures for a hybrid residential building will be investigated. The main objective is to perform a cost-benefit analysis to identify the optimal structure.
Project #5: Studying the Energy Efficiency of a Wearable Wireless Sensor
Supervisor:
Sagar
Naik
Email: snaik@uwaterloo.ca
Phone:
519-888-4567
x35313
Location:
EIT
4174
There is significant interest in wearable devices to monitor the health and well-being of people. In addition, there is much interest in gesture based communication supported by wireless sensors. The objective of this project is to study the energy performance of a wearable wireless sensor module that has been designed for gesture recognition. In this project, the student will: (i) adapt a test bench for measuring the power cost on smartphones to the measurement of the power cost of the wearable sensor; (ii) design some representative use cases of the sensor; (iii) measure the power cost of executing those use cases; and (iv) estimate the life time of the battery of the wireless sensor. The student will closely work with a PhD
Project #6: Detection of Anomalous Behavior of Wireless Devices
Wireless devices, namely, smartphones, IoT (Internet of Things) devices, and wireless sensors, are finding widespread applications in personal communication, monitoring of critical infrastructure, and even human bodies for healthcare applications. In many cases, the monitored data is represented in the form of a time series. The devices may report unexpected behavior or even behave abnormally because of various reasons. The objective of this project is to design anomaly detection techniques by using time-series data and applying machine learning techniques. The student will work on a test bench to measure the power cost of wireless devices and simulate some representative causes of anomalous behavior of wireless devices. Finally, anomaly detection algorithms will be applied on actual data obtained in a lab environment. The student will closely work with a PhD student and a Master’s student.
Supervisor:
Prof.
Sagar
Naik
Email:
snaik@uwaterloo.ca
Phone:
519-888-4567
x35313
Location:
EIT
4174
Project #7: Detection of Defects in Polymer Insulators
Supervisor:
Dr.
Ayman
El-Hag
Email: ahalhaj@uwaterloo.ca
Phone:
519-888-4567
x
31431
Location:
EIT-4016
During manufacturing process, polymer insulators may suffer from undetected defects like air void in the polymeric matrix, crack in the fiber glass core and/or inclusion of metallic particles. This project investigates the ability of using electric field measurement and simulation to detect those defects.
Project #8: Simultaneous Measurement of Low and High Frequency Discharges on the Surface of Polymer Insulators
Supervisor:
Dr.
Ayman
El-Hag
Email: ahalhaj@uwaterloo.ca
Phone:
519-888-4567
x
31431
Location:
EIT-4016
Electrical discharges can eventually damage the surface of polymer insulators. There are two main types of discharges that occur on the insulator surface, i.e. dry band arcing (low frequency) and partial discharge (high frequency). This project deals with the development of simultaneous measurement system for both discharges.
Project #9: Comparison of Quantum and Classical Computing Performance on Max-cut Optimization Problems
Supervisor:
Prof.
Na
Young
Kim
Email: nayoung.kim@uwaterloo.ca
Phone:
519-888-4567
x
x30481
Location:
RAC
2101
Survey of computational optimization problems, which can be mapped to quantum computers whose performance can be compared with classical computing algorithms. Max-cut problems have been studied to summarize the current status between quantum and classical computing algorithms. Perform a benchmarking problem using IBM Q, Rigetti, and quantum neural network cloud services with respect to classical algorithms. Consider machine learning algorithms to be incorporated to improve computation speeds.
Project #10: Energy-Efficient Data Analytics
Supervisor:
Prof.
Wojciech
Golab
Email: wgolab@uwaterloo.ca
Phone:
519-888-4567
x
x
32029
Location:
DC
2528
This project investigates the use of general-purpose graphics processing units (GPGPUs) to analyze data from the smart grid. The work will involve developing code using CUDA C/C++, optimizing the code, and executing performance experiments using a Tesla K40 GPGPU on a real world data set. A solid background in optimization or data mining is required.
Project #11: Multi-Core Algorithms for Persistent Memory
Supervisor:
Prof.
Wojciech
Golab
Email: wgolab@uwaterloo.ca
Phone:
519-888-4567
x
x
32029
Location:
DC
2528
This project aims to develop concurrent data structures that leverage emerging non-volatile main memories to protect data against power outages and system crashes. The research will involve developing multithreaded code using C/C++, and executing performance experiments on a multi-core computer. Strong programming skills and prior exposure to concurrency are required.
Project #12: Blockchain Scalability
Supervisor:
Prof.
Wojciech
Golab
Email: wgolab@uwaterloo.ca
Phone:
519-888-4567
x
x
32029
Location:
DC
2528
Distributed ledgers, also known as blockchains, promise to transform society by enabling nearly frictionless trading of digital assets. In this project, the student will participate in the design an implementation of a private blockchain with a focus on scalability in the presence of Byzantine faults. Strong programming skills and prior exposure to distributed systems are required.
Project #13: Hardware Implementation of a Neural Network based on Stochastic Computing
Supervisor:
Prof.
Vincent
Gaudet
Email: vcgaudet@uwaterloo.ca
Phone:
519-888-4567
x
84016
Location:
EIT
3036
In this project, you will simulate, design, and implement an artificial neural network with arithmetic operations based on stochastic computing. After software simulation of a floating-point, and then bit-true implementation, you will design the hardware, which will be based on a field-programmable gate array platform.
Project #14: Comparison of Quantum and Classical Computing Performance on Max-cut Optimization Problems
Supervisor:
Prof.
Na
Young
Kim
Email: nayoung.kim@uwaterloo.ca
Phone:
519-888-4567
x
x30481
Location:
RAC
2101
Survey of computational optimization problems, which can be mapped to quantum computers whose performance can be compared with classical computing algorithms. Max-cut problems have been studied to summarize the current status between quantum and classical computing algorithms. Perform a benchmarking problem using IBM Q, Rigetti, and quantum neural network cloud services with respect to classical algorithms. Consider machine learning algorithms to be incorporated to improve computation speeds.
Project #15: Antenna Measurements
Supervisor
#1:
Prof.
S.
Safavi-Naeini
Email: safavi@uwaterloo.ca
Phone:
519-888-4567
x
32822
Location:
E5
4029
Supervisor
#2:
Prof.
G.
Shaker
(Adjunct)
Email: gshaker@uwaterloo.ca
Phone:
519-888-4567
x37267
Location:
EIT
3123
The goal of this course project is to familiarize students with antenna concepts that affect the performance of wireless devices. The students will learn how to perform basic antenna measurements in an anechoic chamber. The students will then need to propose a setup that characterizes/demonstrates the properties of an antenna system and provide detailed material to explain these properties. These properties include (but are not limited to) impedance, efficiency, frequency of operation, bandwidth, gain, polarization, beam width, RCS, MIMO performance. Students will be encouraged to use their report towards an international competition organized by the IEEE Antennas and Propagation Society (note: a team from UW won the competition in 2016).
Project #16: Antenna Design for the Internet of Things
Supervisor
#1:
Prof.
S.
Safavi-Naeini
Email: safavi@uwaterloo.ca
Phone:
519-888-4567
x
32822
Location:
E5
4029
Supervisor
#2:
Prof.
G.
Shaker
(Adjunct)
Email: gshaker@uwaterloo.ca
Phone:
519-888-4567
x37267
Location:
EIT
3123
The goal of this course project is to empower students with antenna design skills to meet the increasing demand for custom wireless internet of things (IoT) devices. The students will decide upon a given IoT application. The students will then use a conceptual CAD model for the IoT device and utilize numerical computer aided design tools to design a suitable antenna solution.
Project #17: Building a Radar System
Supervisor
#1:
Prof.
S.
Safavi-Naeini
Email: safavi@uwaterloo.ca
Phone:
519-888-4567
x
32822
Location:
E5
4029
Supervisor
#2:
Prof.
G.
Shaker
(Adjunct)
Email: gshaker@uwaterloo.ca
Phone:
519-888-4567
x37267
Location:
EIT
3123
The goal of this course project is to help students understand basic radar concepts. The course spans topics of applied electromagnetics, antennas, RF design, analog circuits, digital signal processing, machine learning, and artificial intelligence. Students will decide upon a radar application (whether for autonomous drones/robots/vehicles or in the general theme of sensing for healthcare). Students will then get to work towards building a simulation model of their own radar system.
Project #18: Alloys Classifications by Applying Different Machine Learning Techniques
Supervisor:
Sebastian
Fischmeister
Email: sfischmeister@uwaterloo.ca
Phone:
519-888-4567
x
33694
Location:
E5
4112
Project description is not provided for confidentiality reasons.
Project #19 Active Learning Based Crowdsourcing Engine
Modern deep learning scheme requires a staggering number of data with ground-truth labels, which are often collected through human subjective tests. In this project, the student will implement a program that could actively select the most informative samples (images or videos) to label on a crowdsourcing platform. Through the project, the student will gain knowledge of active learning and hands-on experience of large-scale data collection.
Supervisor:
Prof.
Zhou Wang
Email:
zhou.wang@uwaterloo.ca
Phone:
519-888-4567
x35301
Location:
E5
5113
Project
#20:
Next
Generation
Video
Compression
This
project
involves
exploration
of
rate
distortion
optimization
(RDO)
methods
specifically
for
next
generation
video
compression
(codec).
State-of-the-art
video
encoding
algorithms
employ
rate-distortion
(RD)
optimization
methods
to
achieve
competitive
coding
performance.
With
many
RDO
methods
at
hand,
it
is
necessary
to
conduct
a
comprehensive
comparison
across
different
RDO
methods.
The
project
will
provide
student
with
a
great
opportunity
to
gain
hands-on
experience
of
next
generation
video
encoding
technologies
and
state-of-the-art
RDO
techniques.
Supervisor:
Prof.
Zhou Wang
Email:
zhou.wang@uwaterloo.ca
Phone:
519-888-4567
x35301
Location:
E5
5113
Project #21: Ancillary Services in HVDC Grids
High voltage direct current (HVDC) grids, where a number of point-to-point HVDC links are connected together in a meshed configuration, have recently gained substantial attention in Europe, China and Canada. The offshore HVDC networks facilitate the integration of renewable energy resources. The large integration of renewable energy resources results in various phenomenon such as reduced grid inertia. This project revolves around ancillary services that can be provided through HVDC grids such as frequency regulation and oscillation damping to mitigate the adverse impact of renewables on the stability of the power system.
Supervisor:
Prof. Sahar
Azad
Email:
s3azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4017
Project #22: Video Content Acquisition and Classification
The project is on a real-time media access system that enables a vast amount of viewers to obtain the content snapshots upon their requests. The project concerns the programming of Intel DE10 development board and content classification implementation based on convolutional neural networks.
Pre-requisite: Android and Linux programming.
Supervisor:
Prof.
Pin-Han
Ho
Email:
p4ho@uwaterloo.ca
Phone:
519-888-4567
x32452
Location:
EIT
4161
Project #23: Machine Learning (ML) based Resource Allocation in 5G Mobile Systems
The project is on developing a machine learning algorithm for resource block allocation for users under flexible frequency division duplex (FDD). The student has to understand the issues and mathematical model of the mobile system resource allocation as well as machine learning algorithms, and the integration of them.
Supervisor:
Prof.
Pin-Han
Ho
Email:
p4ho@uwaterloo.ca
Phone:
519-888-4567
x32452
Location:
EIT
4161
Project #24 Application of Machine Learning in Power Transformers’ Assessment
Power transformers are vital to the secure operation of the power system. Different electrical and chemical tests are conducted to assess their insulation integrity but those tests will add to their running cost. This projects aim in applying different state-of-the-art machine learning algorithms to optimize the needed number of tests to assess the transformer insulation health condition. Both supervised (ANN, SVM, KNN, …) and un-supervised (clustering) machine learning algorithms will be applied and comparison between the efficiency of the different techniques will be investigated. The project requires the use of either Python or ready packages like WEEKA.
Supervisor:
Prof.
Ayman
El-Hag
Email:
ahalhaj@uwaterloo.ca
Phone:
519-888-4567
x31431
Location:
EIT
4016
Project #25: Energy Harvesting Wireless Sensor Networks
In this research project, we will design a prototype wireless sensor network running on harvested energy for the purpose of monitoring the natural corrosion of underground steel pipes supplying water to individual homes. In this case, the source of the harvested energy will be the physical phenomena being monitored. We plan to set up two practical corrosion protection units in the lab, harvest energy from the corrosive environment of underground steel pipes, temporarily store the harvested energy in storage elements, and periodically run the wireless sensors to report the sensed data to the cloud. By the end of the term, we expect to design and implement a small-scale wireless sensor network running on harvested energy. The M. Eng. student will work in collaboration with an MASc student.
Supervisor:
Prof.
Sagar
Naik
Email:
snaik@uwaterloo.ca
Phone:
519-888-4567
x35313
Location:
EIT
4174