Innovations Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Tensor based machine learning with human computer interaction applications

In this research, first we will using tensor network techniques speedup the processing in neural network since computational cost is a major bottleneck in neural network based deep learning. Note that the weight matrix in each layer of neural network is with huge size, because of millions of parameters. This may cause large time complexity to calculate all the parameters. To overcome the large time complexity, we could use tensor decomposition to calculate the low-rank weight matrix, to reduce a large amount of parameters, therefore to reduce the time complexity. The tensor networks include Hierarchical Tucker (HT), Tensor Train (TT), CP, and Tucker decomposition etc. Second, we propose to develop tensor based algorithms to solve spatiotemporal corrupted problem, complex background problem in human action recognition (HAR) and denoising problems in medical images. The human action videos are represented as a high-dimensional tensor, and we can solve the spatiotemporal corrupted problem by tensor completion, and recurrent neural network to deal with the missing frames problem; also we could denoise the high-dimensional medical images by tensor completion.

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Faculty Supervisor:

Xiao-Ping Zhang

Student:

Chengcheng Jia

Partner:

Huawei Canada

Discipline:

Engineering - biomedical

Sector:

Professional, scientific and technical services

University:

Ryerson University

Program:

Accelerate

Optimizations of AC/DC Converters for Powering 5G Network

The telecommunication industry is rapidly evolving towards providing faster services to accommodate the global surge in network demand. The next-generation (5G) network aims to increase the current network speed by more than twentyfold. This transition brings enormous benefits for the economy and society. A main challenge faced by network providers, however, is that the existing infrastructure cannot fulfill the new requirements of the 5G network. For example, the future 5G small cells need to be more powerful than the conventional 4G network architecture. Thus, further research and development are required in powering future 5G network. The research and development at Alpha Technologies Ltd. in Burnaby is currently focussed on developing new two-stage power converter for powering 5G small cells with power 3KW. The work involves layout of power converters optimized for electrical and thermal performance. In addition, the converter will be evaluated in terms of its EMI performance.

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Faculty Supervisor:

Majid Pahlevani

Student:

Fatemeh Mardani Boroujeni

Partner:

Alpha Technologies Ltd.

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

Queen's University

Program:

Accelerate

Vibration Analysis: Fault detection, classification, prediction

Vibration analysis is probably the most widely used technique to perform health monitoring of mechanical machinery. Specifically, we are interested in monitoring ‘Vibrating screens’, machines that are for example used by the mining industry to sort aggregate by size. Over the last 10 years the research group of Dr. v. Mohrenschildt has developed hardware, software and theory to accomplish this. The goal is to further the understanding of feature extraction and classification to perform effective predictive maintenance. Several activities contribute to this overall initiative: Data Mining: perform feature extraction on the data sets we recorded data sets to obtain machine health information. These topics are very timely as recent successes in machine learning sparked significant interest in industry and academia. Develop methods to perform additional machine measurement: impact based modal analysis: determine the resonance frequencies of a machine and bearing analysis again with the goal to determine bearing health.

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Faculty Supervisor:

Martin V Mohrenschildt

Student:

Hassan Elaghoury;Elizabeth Hofer

Partner:

Haver & Boecker

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Defining the Transmission Season and Determining the Gastropod Intermediate Hosts of Crenosoma vulpis, the Fox Lungworm

Crenosoma vulpis (Fox Lungworm) is a metastrongyloid nematode parasite infecting the lungs of wild and domestic canids. It is a frequent cause of chronic respiratory disease in dogs in Atlantic Canada. Dogs and foxes acquire the infection from ingesting terrestrial gastropods which contain the infective third larval stage (L3) of the parasite. Nothing is known on which gastropod species serve as a source for natural infections. Determining the gastropod species involved in natural infections and defining the transmission season will further our understanding of the parasite epidemiology which is important in devising preventive control strategies.

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Faculty Supervisor:

Gary Conboy

Student:

William Robbins

Partner:

Bayer Inc Animal Health

Discipline:

Medicine

Sector:

Manufacturing

University:

Program:

Accelerate

A Regular Solution Based Model for Evaluating Asphaltene Stability of Upgraded Heavy Oils – Year two

Most of the heavy oil and bitumen produced in Western Canada is transported through pipelines to refineries in North America. Prior to transportation, the high viscosity of those fluids must be reduced by either dilution with a light solvent or upgrading. The high costs associated with handling diluents has increased the interest in upgrading; that is, the thermal conversion of high viscosity heavy oil or bitumen into a less viscous product. Upgraded heavy oils and bitumen require less solvent prior to pipeline transportation and have a higher market value compared to diluted heavy oil or bitumen. However, the changes in chemical composition of the fluid during upgrading can trigger the precipitation of heavy components which can then deposit on surfaces and cause fouling.
The aim of this study is to develop and test a Regular Solution based approach to model the precipitation of heavy components from upgraded fluids. An existing model will be modified as necessary based on a comprehensive database collected from a pilot plant. The proposed approach will be a valuable tool in the simulation and scaling of the BituMax™ partial upgrading process being developed by NEXEN Energy ULC.

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Faculty Supervisor:

Harvey William Yarranton

Student:

Francisco Ramos-Pallares

Partner:

CNOOC Petroleum North America ULC

Discipline:

Engineering - chemical / biological

Sector:

Mining and quarrying

University:

University of Calgary

Program:

Elevate

Intelligent Analytics for Dynamic Events in a Smart City

Artificial Intelligence (AI) research has grown rapidly in recent years as the result of faster computers and better algorithms. AI models can be trained to automate the decision process and provide results. However, if the model is not properly or sufficiently trained, the outcome will likely be unpredictable and inaccurate. Besides, training data is not easily available in a lot of applications. To address these issues, our strategy is to integrate classical Computer Vision (CV) algorithms and Deep Learning (DL) techniques. CV can provide solutions without training data. CV knowledge is also valuable to select significant features, which is necessary to train AI model. Our strategy takes into account of the trade-off between CV and DL, and can benefit a wide range of applications, including healthcare data analytics, event monitoring and pattern classification in general. The objective of this proposal is to support intelligent business operations in a smart city environment.

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Faculty Supervisor:

Irene Cheng

Student:

Harsh Sharma;Frincy Clement;Harshal Soni;Jatin Dawar;Xinli Cai

Partner:

AltaML

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Alberta

Program:

Accelerate

Design and development of a grating-based flow-cytometer

Flow cytometry is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles. A sample containing cells or particles is suspended in a fluid and injected into the flow cytometer instrument. The sample is focused to ideally flow one cell at a time through a laser beam and the light scattered is characteristic to the cells and their components. Cells are often labeled with fluorescent markers so that light is first absorbed and then emitted in a band of wavelengths. Tens of thousands of cells can be quickly examined and the data gathered are processed by a computer.”[Wikipedia]
Modern commercial cytometers typically employ bulk optics and capillary-based flow cells. Furthermore, they often employ a collection of specialized filters, mirrors, one to several lasers, and can have more than 10 separate highly sensitive PMT (Photomultiplyer tube) detectors.
Diffraction grating is an optical component with a periodic structure that splits and disperses light into its constituent wavelengths (colors), much like the formation of a rainbow when light hits a prism. TO BE CONT’D

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Faculty Supervisor:

Ray G DeCorby

Student:

Seyed Azmayesh-Fard

Partner:

Lab Chip Technologies Corporation

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Development of Technologies and Methods for Metabolite collection and processing

Metabolomics is an emerging field of research that provides insight into health and disease by studying the levels of various small molecules (metabolites) in the body. In this project, we are developing new tools that will improve and standardize methods for collecting and stabilizing fecal samples for metabolomics studies based on the analysis of fecal samples. Ultimate goals of the project are improved workflows for analyzing fecal samples for metabolomics studies, and kits which will permit easy home-collection of samples. The kits will also stabilize the samples at room temperature. This will provide new methods which can be used to study various diseases and/or the health of gut bacteria, or develop new diagnostic tests for identifying diseases in patients.

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Faculty Supervisor:

James Harynuk

Student:

Seo Lin Nam;Kieran Tarazona

Partner:

DNA Genotek

Discipline:

Chemistry

Sector:

Manufacturing

University:

University of Alberta

Program:

Accelerate

Comprehensive Study on Dielectric Properties of Rat Brain Tissue Following an Ischemic Stroke at 0.5-10 GHz.

Stroke happens when a blood vessel in the brain, is blocked by a clot (ischemic stroke) or bleed due to a vessel rupture (hemorrhagic stroke). The dielectric properties of healthy brain tissue are different from the one that is affected by stroke. In this study, these properties are measured in rat’s brain by an electromagnetic probe at frequency range 0.5-10 GHz. Subsequently, the changes are related to the type and level of brain injuries. One of the applications of this research is early diagnosis of different types of stroke and traumatic brain injuries by electromagnetic imaging that significantly impacts the effectiveness of the treatment.

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Faculty Supervisor:

John G Mielke

Student:

Atefeh Sadat Zarabadi

Partner:

Discipline:

Epidemiology / Public health and policy

Sector:

Manufacturing

University:

University of Waterloo

Program:

Characterizing the Mode of Action of Novel Boron-Containing Antifungal Agents for Crop Protection

Fungal pathogens of agriculturally significant crops pose a serious threat against global food security. This is exacerbated by the limited classes of fungicides that are commercially available for the farmers and the rapid emergence of resistance against the existing fungicides. Furthermore, resistance against agricultural fungicides can poses serious threat to human health as it can provide cross-resistance to the antifungal drugs that are used in the clinics world-wide. The objective of this project is to discover novel boron-containing fungicides and identify their mechanism of action through chemical genomic analysis. This project will address the urgent need to discover novel fungicides against plant fungal pathogens and identify new approaches to treating fungal infections.

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Faculty Supervisor:

Leah E. Cowen

Student:

Sang Hu Kim

Partner:

Boragen Inc

Discipline:

Biochemistry / Molecular biology

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Stability and antioxidant properties of pharmaceutical and nutraceutical products

Nutraceuticals are being used ubiquitously, but seldom undergo rigorous testing. This study is focused on two primary aspects of studying natural health products, specific cannabis and its oils. The first objective is to determine the shelf life (product stability) by performing a series of tests to accelerate the ageing process in order to provide an estimation of shelf life. Currently, cannabis oils have arbitrary assignments of expiry dates, if at all. The other key focus involves studying the antioxidant benefits (or pro-oxidant detriment) imparted by cannabis products. Lastly, studies on white blood cells (leukocytes) will be carried out to determine if there are any detrimental or beneficial effects of cannabis products on these critical immune cells.

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Faculty Supervisor:

Arno Siraki

Student:

Dinesh Babu

Partner:

Applied Pharmaceutical Innovation

Discipline:

Pharmacy / Pharmacology

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Assessment of indoor environmental quality in the school building

Indoor environmental quality in schools is one of the major issues that should be considered. Indoor air quality (freshness and cleanness of air inside the building) and thermal comfort (the comfortable condition when no one is feeling either too hot or too cold) in schools can have significant impact on children’s health, learning and productivity. The purpose of this research is to evaluate indoor environmental quality (indoor air quality, thermal comfort, lighting, and acoustics) in First Nation School. To achieve this goal we conduct long-term measurements of indoor air quality and thermal comfort parameters (CO2, temperature, and relative humidity) and ask the teachers to participate in voluntary and anonymous questionnaire survey about their perception of parameters of indoor environmental quality. The data will be shared with the partner organization to develop different retrofit packages to improve indoor environment quality and energy efficiency of the school building depending on potential to achieve goals such as energy savings, CO2 reduction, cost savings, improvement in thermal comfort by using Building Information Modeling optimization tool.

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Faculty Supervisor:

Miroslava Kavgic

Student:

Narubayeva Saule

Partner:

ProCS

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

University of Manitoba

Program:

Accelerate

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