Dean’s Research Awards - Faculty of Engineering - University of Alberta

"Scleroderma is an autoimmune connective tissue and rheumatic disease that causes inflammation of the skin and areas of the body." [NIH:https://www.niams.nih.gov/health-topics/scleroderma]. Complications due to this uncurable disease can lead to multiple finger amputations presenting serious challenges to patients for regaining functionality without the use of affordable and robust finger prostheses.
In this applied research project, the student(s) will 3D scan castings of a patient's hands then design, manufacture, and methodically test and analyze various designs. A revised design will be created for beta testing.
The initial starting point with be a former MEC E 460 design project report, and meetings with the patient to ascertain design goals, gain valuable feedback about fit, form, and function. A strong possibility exists to generate a report on invention (ROI) for intellectual property protection.
Students with a strong disposition for "making" are encouraged to apply!

Adverse weather continues to pose significant risks to road safety and traffic flow. Building upon a successfully developed deep learning-based tool for winter road monitoring, this year's project aims to refine and validate this innovative solution. As part of a multi-year collaboration with Iowa DOT, the ultimate purpose is to optimize winter road maintenance practices to improve the safety and mobility of winter travelers. In this regard, the focus will shift to evaluating model performance under various real-world conditions and exploring potential feature augmentations. Students will engage in advanced aspects of Road Weather Information Systems and Intelligent Transportation Systems data management, performance testing, and feature enhancement.

The DRA students will collect the geological and monitoring data from geological surveys and energy regulators in the given jurisdiction and incorporate those data into OpenGeoSys under the supervision of the PhD student. The DRA student will also assist the PhD student to use various geostatistical modeling techniques (e.g., GAN) to digitalize subsurface energy systems in the form of dynamic models for in-situ recovery of oil sands with geological CO2 storage, accounting for all relevant sources of uncertainty. Conditioning, calibration, and history matching the subsurface models with available monitoring data will serve to quantify and reduce uncertainty. The deliverables include (1) classified static and dynamic subsurface data; (2) probabilistic subsurface models considering geological uncertainty; (3) a digital twin history-matched to monitoring data throughout the life cycle of the subsurface energy system.

My group recently created the MecSimCalc platform for creating and sharing computational tools on the web. With a few very simple steps, educators and professionals can create interactive web applications for their students or clients.

The main role of the selected students is to create Engineering Python apps on the online platform: https://mecsimcalc.com/ The student is expected to work with different instructors and researchers to create new Engineering applications on the platform. The students are also expected to convert engineering applications that have been written in different programming languages (such as C++ or matlab) to Python in order to have them available for a larger audience on the MecSimCalc platform.

The project aims to determine if a commercial/consumer grade blue laser can be used to project moving or static patterns (movies or logos) onto a distant screen. This will require some optical design for beam expansion and collimation as well as an investigation of galvanometer laser scanning.

Radio wave propagation models can be derived by physics-based methods (e.g., ray-tracing, full-wave electromagnetic modeling techniques). However, the development of physics-based models demands a high level of relevant expertise and computational resources that can, in practice, be prohibitive for real-time B5G/6G wireless applications. This project aims to explore a data-driven approach that can lead to computationally efficient, high-fidelity radio wave propagation models, without performing a channel simulation, by leveraging advances in deep learning.

The Shankar Group is using high frequency (i.e. microwave) devices and circuits for sensing and diagnostics; a tremendous amount of data is being generated which needs to processed. ECE students are typically aware of Z-parameters, Y-parameters, h-parameters and ABCD parameters used to describe the behavior of low frequency circuits and systems. At GHz and higher frequencies, S-parameters are used. Interested students will be proficient users of python and/or MATLAB, which will be needed to process the data and convert them into scientifically meaningful graphs/plots followed by curve fitting. Some knowledge of machine learning is desirable but not essential. This project provides great introductory hands-on experience in microwave engineering and handling Big Data.

A prerequisite for the deployment of wireless communication systems is the existence of a suitable propagation model, which can provide accurate path-loss predictions of corresponding wireless channels. Computational electromagnetic techniques, such as the finite-difference time domain or ray-tracing methods, are increasingly used to model radio wave propagation for indoor and outdoor environments and can provide accurate results. However, the computational overhead of such deterministic models is huge for large-scale scenarios. The goal of this project is to utilize compressed sensing techniques to reconstruct spatial electric field distribution and alleviate the computational burden of deterministic wireless channel models.

A p-n junction, n-n junction or p-p junction between dissimilar semiconducting materials is called a heterojunction (HJ). Carbon nitride (g-C3N4) is a two-dimensional sheet-like material which is a cousin of graphene. Unlike graphene which is quasi-metallic, C3N4 is semiconducting which enables it to be used as the "active layer" in optoelectronic devices such as diodes, solar cells, photocatalysts and transistors. This project involves forming thin film heterojunctions of carbon nitride with other semiconductors, and then testing the electrical properties of the resulting heterojunctions. This project can accommodate at least 2 students pursuing independent end-goals.

This project aims to advance the accuracy and efficiency of Direction of Arrival (DoA) estimation by leveraging the capabilities of a Non-Uniform Linear Array (NULA) across a range of frequencies. DoA estimation plays a critical role in various fields such as radar systems, wireless communications, and sonar applications. Accurate estimation of the direction from which a signal arrives is crucial for precise spatial awareness and effective signal processing.

Traditionally, uniform linear arrays (ULAs) have been widely used for DoA estimation. However, ULAs have limitations when it comes to resolving closely spaced sources or dealing with frequency diversity. In this project, we propose a novel approach that harnesses the power of a Non-Uniform Linear Array (NULA) to overcome these limitations and achieve enhanced DoA estimation performance.

Organic semiconductors are small molecules or polymers containing pi-conjugation i.e. alternating single and double/triple bonds. Commercial organic light emitting diodes (OLEDs) used in cellphone displays utilize "active layers" made of organic semiconductors. While inorganic semiconductors typically require high temperatures & high vacuum for thin film deposition, thin films of organic semiconductors can be processed close to room temperature directly from liquid solutions. Many organic semiconductors are strongly fluorescent. The Shankar Group has synthesized a number of new organic semiconductors and is looking to have thin films of these organic semiconductors fabricated, optimized and characterized. Characterization involves the use of various imaging and spectroscopic tools such as electron microscopy, fluorescence microscopy, spectrophotometry, Raman scattering, etc. This project can accommodate two or more students, each of whom will work on a different organic semiconductor.

The project aims to determine whether a commercial, consumer grade, blue laser can be used to project moving patterns (movies) on a screen when viewed in the long-wavelength infrared spectral range (as a result of localized target heating). And, if so, what laser power, beam properties, and target material are required. This project will improve your understanding of both lasers and radiative transport.

Surface plasmons (usually just 'plasmons') are quantum quasiparticles. They represent the collective and coherent oscillations of quadrillions of conduction band electrons in metals or metal-like solids. These oscillations propagate at metal-dielectric interfaces. In metallic or quasi-metallic nanoparticles, plasmons are confined to a tiny volume and the squeezing effect of confinement results in a large enhancement of the local electromagnetic field at the surface of the nanoparticle. Such localized surface plasmon resonances (LSPR) can be excited in nanoparticles made of Ag, Au, Cu and Al (and their alloys) by visible light. Plasmonic molecules represent a more complex structure where two or more nanoparticles are in close proximity to each other, thus generating even larger local field enhancements. This project seeks to design, fabricate and characterize plasmonic molecules. Optoelectronic devices exploiting these plasmonic molecules are also planned.

Reconfigurable intelligent surface (RIS) is considered a key enabler for future 6G wireless networks. The electromagnetic properties of an RIS can be dynamically reconfigured to customize the propagation environment. It offers a new degree of freedom in communication system design and has received tremendous interest from the wireless communication community due to its potential in extending signal coverage, enhancing channel capacity, as well as mitigating interference. This project will investigate physics-based wireless channel models to characterize radio links for RIS-empowered environments.

** 2 excellent undergraduate students already applied and had my commitment to supervision. I will NO LONGER take more students for this project.**

Wondering what the future of household robots will be? Curious about when will that date come into reality that personal robots help our everyday lives? Let’s design a house, a robot, and various activities that we hope a robot can do in simulation! Welcome to the research of embodied artificial intelligence!

The state-of-the-art is the Stanford Behavior 1K, a simulator built on the 3D physics engine OmniVerse from Nvidia. However, Behavior 1K lacks ML (machine learning) solution-ready developments regarding datasets, programming interface and benchmarks.

In this DRA project, I will take up to 2 undergraduate students, exploring ML solution-ready development based on Behavior 1K, in specific domains like house cleaning, organization, or deformable object manipulation.

Laser-induced breakdown spectroscopy will be performed on 32 distinct soil samples with varying nitrogen and carbon concentrations. Different machine learning methods, such as multiple linear regression (MLR), polynomial regression, and partial least squares regression (PLSR), will be used to analyze the collected spectra to determine the nitrogen concentration in agricultural soil.

Uncertainty quantification (UQ) in engineering simulations has gained a tremendous interest in the last decade to answer questions related to structural- and systems reliability, global sensitivity analysis, etc. However, accurate computational models in the analysis of electromagnetic designs are often costly. This project will explore efficient techniques such as polynomial chaos expansion (PCE) for the uncertainty quantification of electromagnetic problems.

The project aims to develop an Artificial Neural Network (ANN) capable of
determining the temperature and density of high energy density (HED)plasma
using Cu K shell and L shell spectra. Principle components analysis (PCA) will reduce the dimensionality of the simulated X-ray spectra obtained using atomic simulations codes. Then, PCA components will be used to train an artificial neural network (ANN) model for determining plasma temperature and density.

The most crucial factor in advancing building sustainability is the reduction of energy consumption from fossil fuels. Insulation holds the greatest potential for mitigating CO2 emissions, primarily due to its role in energy conservation. A vital characteristic of insulating materials is their thermal conductivity, which denotes the transmission of energy as heat. In this project, we aim to quantify and compare the thermal properties of various building materials and their impacts on building energy. The objective is to identify the most efficient insulation material suitable for Canadian climates and conditions.

In this DRA project, the student will use a high-speed camera to record drop dynamics impacting a surface to investigate the effect of complex fluids on drop impact outcomes. The skills learned include basic instrumentation, image analysis, and hands-on experimental experience.

In this DRA project, the student will use a high-speed camera to record drop dynamics impacting a surface to investigate the effect of complex fluids on drop impact outcomes. The skills learned include basic instrumentation, image analysis, and hands-on experimental experience.

I am seeking motivated undergraduate students to study the application of computer vision for the analysis of X-ray Computed Tomography (XCT) scans of additively manufactured ceramics (AMCs). In this project we focus on integrity evaluation of AMCs by leveraging data-driven approaches. This project holds great promise for advancing the understanding of AMCs for a wide range of applications, including defense, energy, and manufacturing industries.

The steps of the project include developing an image segmentation method based on deep learning to extract critical features from the XCT scans. This allows for precise identification of microstructural variations. Then, by quantifying these features, the integrity of the AMCs is evaluated, providing insights into their mechanical properties and uncertainties. Finally, multi-scale and stochastic modeling techniques are used to bridge between the macroscopic behavior of AMCs and their microstructure. Proficiency in Matlab is encouraged.

The goal of this project is to design and develop an interactive web platform that offers a dynamic and engaging user experience. The web platform will serve as a central hub for various functionalities, providing users with valuable information, tools, and interactivity. The platform will cater to a wide range of users, from individuals to businesses, and will be designed to be responsive, intuitive, and user-friendly.

The High Load Robotic Platform project aims to design and develop an advanced robotic system capable of efficiently carrying and transporting heavy loads across various terrains and challenging environments. The platform is intended to be versatile, robust, and highly maneuverable, making it suitable for various industrial and logistical applications.

Geo-polymer 3D printing is an innovative manufacturing process that uses geopolymeric materials to create three-dimensional objects. To achieve precise and consistent printing results, the design of the nozzle system plays a crucial role. The nozzle system must be capable of extruding the geo-polymeric material efficiently while maintaining a constant flow rate and ensuring minimal clogging or blockages. This project aims to design and prototype a specialized nozzle system that can handle the unique properties of geo-polymeric materials and enable high-quality 3D printing.

The goal of this project is to design and develop a comprehensive software application and accompanying mobile app for data visualization. The software and app aim to provide users with a user-friendly and interactive platform to analyze, interpret, and present complex data sets effectively. The focus is on delivering a powerful visualization tool that caters to both technical and non-technical users, enabling them to gain insights from the data easily.

Many engineering applications require compressive stiffness and strength. In underwater vehicles, compressive strength resists crushing due to hydrostatic pressure. In the automobile industry, compressive stiffness is essential in car suspension systems. One potential material for these applications are tubular braided composites (TBCs). TBCs are comprised of a textile material (graphite fibres, Kevlar fibres, glass fibres, etc.) encased in a polymer matrix. These advanced materials are increasingly being used in many advanced fields. Although excellent in tension and shear, their performance under compression is comparatively low and not well documented. The objective of this study is to develop a means to test the compressive properties of TBCs and understand the extent of their applications for underwater and automobile applications.

Several kirigami based actuators have been developed that can switch stiffness states and they are required to be tested for stress/strain under different actuation conditions to develop design guidelines for ultimate load and strain capacity of different materials. Mechanical testing would be preferred using a custom linear stage and force probe within our test facilities so building up the capability to do so is the highest priority in this project.

We have developed several manufacturing processes for encapsulated microelectronic systems based on liquid metal cores in rubber tubes. We are looking for a student to help improve a test system for measuring resistance vs. strain over repeated cycles to examine fatigue performance. Development of Arduino code to work with a linear stage, , resistance measurements and force probes is required.

This DRA project concerns machine learning applications to an engineering desalination system. The research tasks include the collection of good literature data and some exposure to machine learning methods.

This DRA project concerns machine learning applications to an engineering desalination system. The research tasks include the collection of good literature data and some exposure to machine learning methods.

Predators leave characteristic tooth marks on their prey. In collaboration with the Dept. of Biological Sciences, we aim to develop a mechanical device to repeatedly mark sample animal bone with an simulated tyrannosaur tooth, and measure simulated bite forces. In doing so, we will help to elucidate the prehistoric ecology of Alberta.

This DRA project investigates the light propagation through bending soft materials under a load shear force, targeting a bio-mechanical application. The research skills learned will include basic numerical simulation concept and experience as well as using a common commercial numerical simulation package.