Innovative Projects Realized

Developing nature-based interlocking barriers for protection against climate change-induced erosion and flooding

The joint research’s primary goal is to pursue the most optimized design and materials of new interlocking barriers to prevent coastal erosion and reduce flooding, which are growing concerns of coastal communities like Newfoundland and Labrador. The research results will support investors and industry to have access to a cost-effective and efficient barrier to protect their natural resources and cultural heritage in a sustainable way. The growing force of nature, including sea-level rise and climate change, will affect coastal areas more than other inland jurisdictions, so house owners, businesses, and provincial government will be able to protect their own or public properties and infrastructures via an easy-to-use structure in an environmentally friendly manner.

Pairing Meteorological and Power Data for Marine Hybrid Electric Boats

Research will be conducted to determine the best way to visualize and operationalize electric and diesel engine performance data collected in real-time for vessel owners and operators. The boats involved in this research work are candidates for using hybrid electric/diesel drive. The methods of displaying data will be explored to determine the best way to visualize the interaction of the various data points in real time. The goal is to research the characteristics of a “fit-bit” for a vessel, tracking vessel performance and supplying feedback in real-time. Prior research has shown that feedback on the fuel use of a vessel in real time can cause a reduction in fuel use of 15%.

Water Protection and Participatory Film Making: Empowering connections among intercultural groups, Indigenous youth, water, culture, tradition, and technology

The shared goal of researching how filmmaking can be an effective tool in contributing to water protection efforts, and in promoting safe water access for Indigenous communities, has brought together groups from different cultures, sectors, and countries (Canada and Australia). This internship, with the industry partner River Voices Productions will allow the intern to conduct interviews (with individuals and groups) to produce an academic article focused on best practices in innovative research methodologies like filmmaking with groups.

More specifically, during the interviews (to be conducted online because of COVID-19), the intern will show film footage gathered from previous research activities that were conducted in 2019 with a group of Indigenous youth from Northern Quebec. Highlights will include clips from: a 10-day cultural canoe expedition, a trip to Australia to meet Indigenous communities there, visits to a hydro-electric dam and a desalination plant, participating in water testing and water ceremonies, as well as learning about traditional stories and water policy making.

The research interviews will relate to innovative research practices like film making that maximize sharing research knowledge in adaptive and novel ways; findings from this work will be disseminated in an academic publication.

Developing a machine learning-based diagnostic strategy to detect early onset of double negative prostate cancer by integrating SEMA3C-associated genomic variations and blood biopsies

The main goal of this research project is to study if a protein named SEMA3C can be a biomarker for early detection of an aggressive and lethal form of prostate cancer, named Double Negative Prostate Cancer (DNPC). To test if SEMA3C is a contributing factor in the progression of DNPC, we will compare SEMA3C level in tissues from patient and healthy individuals. Then, we will study if SEMA3C level changes in parallel to genetic variations, happening in tumors with cancer growth. If successful, the results can help us figure out SEMA3C’s correlation to contributing genetic factors that are involved in DNPC growth and resistance to drugs. This new diagnostic strategy will improve patient care and management and may improve survival of men with one of the worst forms of prostate cancer.

Nano-Structured Adsorbents for Water Remediation systems: Removal and Biodegradation of Contaminants

According to world health organization (WHO) 80% of the diseases are water borne! Providing clean and affordable water then is one of the modern-times hurdles. Finding new remedies could mean saving thousands of lives. Therefore, my research expertise lies in the area of creating new strategies to remove hazardous contaminants saying heavy oil, hydrocarbons, heavy metal and pharmaceutical discharges from water. Through my research, I apply facile eco-friendly strategies for preparing advanced nano-based materials. These synthesized nano-assemblies will further be used to design the backnone of adsorbents, either in the form of membranes or sponge, so that they are capable of removing different pollutants from water. Their potential can also be examined for removing dyes and pharmaceutical discharges from water since they are widespread pollutants in water.

Improving Competency-Based Medical Education through Technology

Competency-based medical education (CBME) is an approach to training that combines educational objectives and time-based learning. CBME is currently being rolled out in residency training programs across Canada and represents the biggest change in medical education since the early 1900s. This project aims to determine perspectives of current residents in CBME- and non-CBME-based residency training programs at Memorial University of Newfoundland.

A low-cost wearable tele-health monitoring system for chronic and infectious diseases

Continuous monitoring of health allows for early diagnosis and tracking of disease progression, thus enabling timely medical intervention. However, continuous monitoring of the patients’ health is conventionally conducted in hospitals or long-term care facilities with expensive, bulky, and unobtrusive systems, which require skilled professionals to operate. In this work, we propose an affordable, small-in-size, and wearable smart system that will not only enable in-home monitoring of key health parameters such as body temperature, heart rate, blood oxygen level, blood pressure, respiratory rate, cough, and lung health continuously, without hampering the users’ daily activities but also evaluate their health by using intelligent software. This system will allow for early diagnosis of chronic and infectious diseases while reducing the frequency of visits to the doctor’s office. Furthermore, this system will be immensely useful in a pandemic situation like COVID-19 by facilitating self-monitoring or remote monitoring of symptoms in non-clinical settings, thus enabling the efficient and effective use of healthcare services capacity. The collaboration with Ortho Biomed Inc. will allow me to receive hands-on experience on and to develop the skills required for bringing a product from the research bench to the market.

A Unified Hardware / Algorithm Approach to Secured Machine Learning for Cyber-Physical Systems: Applications in Autonomous Vehicles and Connected Autonomous Vehicles Networks

The advancement of artificial intelligence (AI) systems has enabled development systems such as autonomous vehicles (AVs). However, like any other technology, AI systems suffer from security vulnerabilities, and they can be easily fooled by a smart adversary. Malicious attacks on AI systems in safety-critical system such as AVs can be life-threatening or result in financial harms. Unfortunately, the research on defensive methods against adversarial attacks on AI systems is at its infancy, and there is a lack of proper understanding of the inherent security vulnerabilities in these systems. In this research, we use a unified hardware / software approach to develop secure AI systems for AVs and connected autonomous vehicles. We develop specialized hardware architectures and algorithms, so that mitigation algorithms can be performed faster and more efficiently. We believe that improving the security through a unified software / hardware approach is essential in enabling the use of machine learning in safety-critical systems.

Utilizing Remote Sensing and Machine Learning to Improve Poultry Farm Productivity

Within the Canadian context, poultry farmers are constrained by regulations that predetermine chicken prices and market supply. As a result, they are limited in the approaches they can take to improve the profitability of their operations. Within this regulatory framework, farmers must rely on measures that can be applied on their farms to improve chicken’s growth performance while reducing production costs. In this project, we aim to find the effective approach to utilizing remote sensing and machine learning to improve poultry farm productivity. Specifically, we would like to identify which network/sensor configuration is best suited to meet remote sensing needs on rurally located chicken farms. In addition, we plan to design a machine learning based scheme to analyze data from farm sensors in order to identify any environmental concerns (e.g. barn humidity and/or temperature is too high or low) and to suggest actions for poultry farmers accordingly. Finally, we attempt to assess how data collected from remote sensing equipment can be used to increase performance and/or growth of chickens.

Infrastructure Sensor-based Automated Driving: Development and Demonstration

In this proposal, we intend to answer how infrastructure sensors can be used for autonomous driving. Using infrastructure sensors make automated driving safer, more simplified, and cost effective especially for multiple autonomous vehicles operating in known environments such as large residential/commercial complexes and resorts. Infrastructure sensors replace the main onboard vehicle perception sensors with infrastructure sensors mounted on the side of the road, for example on light posts. The information sent by all the sensor nodes to the cloud is used to localize all the autonomous and non-autonomous vehicles, pedestrians, and all other objects on the road for path planning and path tracking of any number of autonomous vehicles on the road. To enable infrastructure sensor-based automated driving, we will extend our navigation, object detection/classification, and motion planning/control modules from vehicle-installed sensors to infrastructure-installed sensors.

Mapping Indigenous Businesses in the Agriculture/Agri-food Sector in Canada

This research project sets out to conduct a comprehensive literature review on Indigenous agriculture in Canada. We collect information about different sources of data that could be put in use to provide insight into Indigenous engagement in the Agriculture/Agri-food sector across the country. We will review the current Statistics Canada’s databases to explore data overlaying options to compile new datasets on Indigenous engagement in the Agriculture/Agri-food sector. Using these data, we will map Indigenous agribusinesses across Canada. The results will inform policy makers of what data is available and what information will need to be collected in the future.

Understanding Indigenous Graduate Students and Their Motivations to Pursue a Career in Research

The research project that will be conducted by the intern will enable the creation of a questionnaire that will later be deployed to the indigenous researcher (and graduate student) community. Most of the activities will involve gathering and analysis information from databases (i.e., Aboriginal People’s Survey) and interviews (and focus groups) to ensure that we are asking the right questions. The aim is to better understand the enablers and constraints facing indigenous researchers in Canada, in order to help design interventions that could boost the former and mitigate the latter. The intern will work closely with both the academic and partner mentors to complete the work.