Innovative Projects Realized

Digital Pre-Distortion for Concurrent Multiband and Multiple Antenna Transmission

The proposed project aims at the design and implementation of low complexity digital pre-distortion (DPD) algorithms for multiband and multiple input multiple output (MIMO) wireless transmitters. The power amplifier (PA) is one of the major sources of power dissipation in wireless base stations. The DPD techniques enable the PA to operate in a more efficient power level resulting in more energy efficient wireless networks. To address the ever increasing demand for higher data rates, the MIMO and the carrier aggregation techniques are being adopted by the current and upcoming wireless standards. The requirement for concurrent transmission over multiple bands in carrier aggregation schemes and over multiple antennas in MIMO schemes poses a great challenge to the design of feasible DPD algorithms. The multiband/MIMO DPD algorithms currently available in the literature are computationally too complex to be implemented in practice. TEKTELIC believes that the proposed research project will result in low complexity and yet effective DPD designs that can be used in the company’s future multiband and MIMO radio products.

Pipe replacement and rehabilitation optimization method

The research project will develop a pipe replacement and rehabilitation optimization method. The research group is currently developing machine learning models to predict future water main failure. Data has been collected from 13 cities across Canada and represent different conditions and settings. This data is already being cleaned and analysed by a team of graduate students. This project will focus on one specific city as a case study to develop an optimization method. This method will build upon the predictive models being developed and will establish a risk framework for replacement and rehabilitation prioritization, accounting for economic, social and environmental impacts.

Solid Waste Management Best Practices: Cost-effective options to sustainably manage solid waste in the Peace River Regional District

Concerns over climate change have led public and private institutions to change the way they conduct business. Initiatives include ways to reduce greenhouse gas emissions and working toward a net zero carbon society. Recycling and composting programs have been around for decades, with the goal to reduce the amount of solid waste entering the landfills. The natural decomposition of food and green wastes in landfills results in the emission of methane, a potent greenhouse gas. Plastics in the environment find their way into the food chain and even human tissues. Solid waste (SW) diversion is a strategy that reduces landfilling and greenhouse gases, and recovers energy and materials.
The PRRD is located in northeast BC, a distance of 800+ km to major recycling centers. The PRRD is seeking to implement a SW diversion strategy that sees recyclables processed in the region, avoiding long-range transport, with a novel waste to energy plant. The environmental impact must be assessed to determine the economic and environmental feasibility. An economic evaluation has already been performed.
For food wastes, anaerobic digestion (AD) has been selected. The process captures >95% of methane emissions.

Representation Learning in Knowledge Graphs

Mastercard is looking to make transactions simple and secure for its customers and vendors across the globe by harnessing the power of graph learning. In this project, the power of graph-based deep learning will be utilized to develop a system that can detect fraud entities, anomalous communities, etc. This track will enable card users and online buyers and sellers to enjoy safe and secure digital transactions. The final goal of knowledge representation learning (KRL) from graphs is to obtain a universal information rich embedding that can be used under multiple applications. The benefit to the partner organization includes a greater understanding of the domain of Knowledge Graphs, identification of gaps in the current area of research, and the possession of a system that can detect fraud entities with better accuracy compared to the state-of-the-art systems in the field.

Predicting Perfect Matches Between Organizations

Successful partnerships between non-profit organizations and funders are fundamental for organizations’ abilities to concentrate on their mission, and for founders to see their philanthropic investments align with their values and make real impacts. Matching founders and organizations has traditionally been done informally through personal connections, networks, and expensive prospecting software and databases. Just like a dating app for philanthropic organizations, Orgmatch matches funders and non-profits together based on attribute-based representations. This research project will explore the use of Artificial Intelligence/Machine Learning technologies to perform this matching Non-profit organizations are fundamentals to a healthy society and the philanthropic relationships with the funders sustaining them are essential to their survival. A tool to rapidly and successfully match them would allow both parties to focus on results and real impacts to the community.

Engaging stakeholders to evaluate alternatives in energy system planning through agentbased automated negotiation Year Two

The objective of this project is to improve long-term planning of Alberta’s electricity system infrastructure in collaboration with an industry sponsor, Alberta Electricity System Operator (AESO). The project aims at engaging stakeholders in a regional planning exercise in southern Alberta by a detailed exploration of their perspectives when evaluating energy system alternatives. This was identified by AESO as being a pressing gap in their current practice. The objective will be achieved through the development of an agent-based negotiation model that will act as a virtual laboratory in which computer agents, acting on behalf of stakeholders, will examine scenarios of energy system development and negotiate to find the alternative that satisfies them the most, considering economic, social and environmental aspects. The project will allow AESO to engage in a transformation in decision making and will deliver knowledge that will support them in achieving their mandate by improving their planning process.

AI For Real-Time Embedded Applications: Honeypot Learning and Predicting for OoD Attack Patterns

Today’s automobile is more than a mechanical tool; it contains a myriad of computers, sensors, IoT, and embedded nodes. The embedded system is the heart of a vehicle’s electronic system because of its versatility and flexibility. Furthermore, these systems are becoming increasingly sophisticated and interconnected, both to each other and to the Internet. However, with great convenience also comes great concerns about the security and privacy of our digital assets. Unfortunately, the security implications of this complexity and connectivity have mostly been overlooked, even though ignoring security could have disastrous consequences.

The attacks on embedded systems are evolving and becoming more complex and destructive. In this project, we develop a security incident response system for embedded systems designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in computational power, memory, and energy overhead. Furthermore, this project’s overcoming will help Thales identify the problem and associated cyber threat risks and pave the way for more efficient hardware/software solutions.

Survival of Indigenous Healing Systems in Colonial India

We will study the efforts of traditional physicians to preserve their Indigenous healing system against the oppressive forces of western colonialism. Ayurveda is one of the oldest holistic healing systems in the world and has been practiced in India for more than 2,000 years. However, under the British rule and the spread of colonial medicine Ayurveda was discredited and demoralized. Our hypothesis is that to safeguard their Indigenous knowledges and practices, traditional physicians turned into entrepreneurs and focused on industrializing the production of pharmaceutical products as a path to protecting Ayurvedic medicine. Their success was key to the development of the medical pluralism policy in India and for the World Health Organization recommendation for the integration of traditional medicine into national health care systems to achieve universal health coverage. Their experience can help us reflect on Indigenous healing systems in Canada and what can be done to ensure the survivance (survival + endurance) of traditional knowledges and their use for the benefit of Canadian citizens.

AI to increase statistical power in clinical neuroimaging

Many studies worldwide use neuroimaging to investigate whether there are abnormalities in the structure or function of the brain that underlie mental disorders. The sample sizes in these studies are limited because of the high cost of neuroimaging and difficulties in recruiting large numbers of patients if the disease is rare. This is a problem because clinical researchers need to investigate correlations between imaging metrics and a wide array of demographic and clinical variables, which traditionally requires large sample sizes to obtain good statistical power. This project will thus harness artificial intelligence (AI) to reduce the many variables under consideration in a real-world clinical neuroimaging study of bipolar disorder to just those that are most important. This will boost the statistical power of the study and inform future clinical neuroimaging studies of the best approach to analyze richly-characterized yet small datasets.

Polymer seed film coating mixed with a micronized natural mineral (zeolite) as management tools against pea root rot disease

Root rot disease especially Aphanomyces root rot has led to decrease pea acreage in the prairies due to yield losses of up to 70 percent in wet years. Current fungicides are few and have very limited efficacy on the target pathogen, but may negatively affect other living organisms in the environment. Therefore, there is the need to develop effective tools against root rots in a more effective and natural way. One graduate student will develop a polymer-based seed coating with antifungal properties to protect pea seedlings from root rot disease. The second graduate student will assess the survival of aphanomyces spores in the presence of new developed coatings and other gemination processes. This will be the first step in a collaborative research effort to tap into industry know-how and develop solutions that will benefit producers and the pulse industry.

Engaging stakeholders to evaluate alternatives in energy system planning through agentbased automated negotiation

The objective of this project is to improve long-term planning of Alberta’s electricity system infrastructure in collaboration with an industry sponsor, Alberta Electricity System Operator (AESO). The project aims at engaging stakeholders in a regional planning exercise in southern Alberta by a detailed exploration of their perspectives when evaluating energy system alternatives. This was identified by AESO as being a pressing gap in their current practice. The objective will be achieved through the development of an agent-based negotiation model that will act as a virtual laboratory in which computer agents, acting on behalf of stakeholders, will examine scenarios of energy system development and negotiate to find the alternative that satisfies them the most, considering economic, social and environmental aspects. The project will allow AESO to engage in a transformation in decision making and will deliver knowledge that will support them in achieving their mandate by improving their planning process.

Influence of chronic low back pain on aperture crossing behaviours during goal-directed locomotion

Low back pain (LBP) is one of the most prevalent musculoskeletal conditions globally; it has claimed the number one spot for
global burden, the number of years lived with disability, and health condition requiring the most rehabilitation services. Despite the
commonness of LBP, there is a lack of understanding on the deficits associated with chronic LBP (pain experienced for more than
three months), making diagnoses and treatment challenging. To better inform proper management of chronic LBP, the proposed
study will investigate how chronic LBP influences pathway selection while walking toward a goal. The aims of the study are to 1)
determine how those with chronic LBP differ in their navigational behaviours compared to back-healthy controls (i.e. do they show
protective mechanisms and avoid pathways that would require body rotation?) and 2) measure any altered motor control
behaviours (walking speed, step length, step width, stability). Results from the study will not only address a research gap, but they
can be used to better inform sub-type diagnoses, return to work protocols to avoid re-injury or poor performance, and treatment
practices.