Bridging the digital divide in Saskatoon: Exploring the benefits of providing free Wi-Fi to regions and demographics with low access
It is increasingly difficult for individuals who lack reliable, regular access to
the Internet to fully participate in society and obtain critical information. DICE
and the City of Saskatoon are collaborating on a pilot program to investigate whether
the installation of Wi-Fi into one neighbourhood that meets selection criteria based
on equity analysis and access data is successfully providing reliable service to the
selected area.
There are two distinct areas of research that will be undertaken by the Sask Polytech interns. First, they will investigate the impact of building materials on Wi-Fi signal strength, assist in troubleshooting issues with the installed system, and provide statistics on the Wi-fi system’s performance. Second, they will perform analysis on the utilization of the system. The investigators are not interested in any specific types of data but rather interested in the amount of data being transferred as a demonstration of service utilization. The intersection of these two research areas will help determine how to better design future instances based on neighbourhood building materials and Wi-Fi data usage. If the pilot program is successful, the results from this research will be used to inform future Wi-Fi installations in other neighbourhoods.
Partner organziation
Funding source
There are two distinct areas of research that will be undertaken by the Sask Polytech interns. First, they will investigate the impact of building materials on Wi-Fi signal strength, assist in troubleshooting issues with the installed system, and provide statistics on the Wi-fi system’s performance. Second, they will perform analysis on the utilization of the system. The investigators are not interested in any specific types of data but rather interested in the amount of data being transferred as a demonstration of service utilization. The intersection of these two research areas will help determine how to better design future instances based on neighbourhood building materials and Wi-Fi data usage. If the pilot program is successful, the results from this research will be used to inform future Wi-Fi installations in other neighbourhoods.
Partner organziation
Funding source
Combining machine learning/artificial intelligence with voice and gesture control on a virtual reality platform
Titan1Studios (T1S) is a cross-media company specializing in television/film, publishing
and video games. They are now expanding their services into the gaming market, as
there is significant overlap between their current skillset and that required for
video game development. The studio has set out an ambitious plan to incorporate multiple
aspects of artificial intelligence into a virtual reality (VR) platform that will
push the envelope of current technologies available in VR gameplay.
This includes voice recognition, gesture-based commands, advanced machine learning/artificial intelligence for non-player characters, and dynamic difficulty adjustment based on assessment of the player’s skill. Although aspects of these components have been demonstrated in the research literature on an individual basis, there has been little research on how to incorporate them together on a single platform, (e.g., a current, low cost, off-the-shelf VR system). DICE will collaborate with T1S in developing the software platform necessary to achieve this goal.
Partner organization
Titan1Studios
Funding sources
Titan1Studios
This includes voice recognition, gesture-based commands, advanced machine learning/artificial intelligence for non-player characters, and dynamic difficulty adjustment based on assessment of the player’s skill. Although aspects of these components have been demonstrated in the research literature on an individual basis, there has been little research on how to incorporate them together on a single platform, (e.g., a current, low cost, off-the-shelf VR system). DICE will collaborate with T1S in developing the software platform necessary to achieve this goal.
Partner organization
Titan1Studios
Funding sources
Titan1Studios
Data amalgamation and analysis across agricultural data silos
In partnership with Mera Development Corp., DICE is developing a sub system for Mera’s
Asset Data Management (ADAM) platform. Specifically, the proposed applied research
will develop a system capable of performing data aggregation and migration across
multiple corporate and sensor databases while maintaining or introducing relationships
between the data points. The system infrastructure will collect, scan and import data
to a NoSQL database (Lake). That aggregated data will be used to support machine learning
(ML) and Artificial Intelligence (AI) algorithms found in ADAM. Previous work on a
prototype focused on data migration and preprocessing of data. This work will set
the foundation for allowing individuals without extensive data expertise the ability
to migrate and preprocess data. The application will benefit anyone who needs to control
data aggregation strategies and aid in the application of ADAM functionality to improve
and optimize their agricultural and food manufacturing processes.
Partner organization
Mera Development Corp.
Funding source
Partner organization
Mera Development Corp.
Funding source
Developing localization systems for autonomous equipment operation
The adoption of remotely piloted aircraft systems (RPAS) is the focus of several initiatives
at the federal and provincial level in Canada. This emerging technology opens up new
avenues for many industries critical to the Canadian economy including agriculture,
forestry, mining, oil and gas, industry, logistics, security and defense. Further,
the capabilities of emerging remote sensing platforms support key national objectives
including exploration of the Arctic, rapid response to hazardous situations such as
floods and wildfires, and management of waterways.
The impact of these technologies is only beginning to be felt in the area of inspection for critical infrastructure. The maintenance of critical infrastructure is essential to the functioning of our economy, and yet inspection can be difficult over long distances such as pipelines and power lines; in harsh environments such as nuclear reactors; or in difficult to access locations such as under water. Remote sensing on remotely piloted platforms allow for all of this and more. The National Research Council (NRC) has active partnerships with Transport Canada and the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ) to establish national research initiatives exploring new applications and key challenges to the adoption of these emerging technologies.
The proposed Canadian Unmanned and Remote Sensing Innovation Network (CURSIN) combines the expertise of three leading colleges across the country and supports critical infrastructure providers and partners in adopting RPAS and remote sensing. Mohawk College, the Southern Alberta Institute of Technology and Saskatchewan Polytechnic offer complimentary skills and resources in key research themes including RPAS operations, beyond visual line of sight (BVLOS) applications, photogrammetry and bathymetry of critical infrastructure, data analytics for LiDAR and sensor data collected using RPAS platforms. In partnership with SMEs and large operators across the country, this new network will accelerate the adoption of RPAS in Canadian industry by leveraging leading edge RPAS systems and sensors, and by training a new generation of pilots and experts in remote sensing.
Partner organization
Applied Research and Technology Partnership
Funding sources
Applied Research and Technology Partnership
The impact of these technologies is only beginning to be felt in the area of inspection for critical infrastructure. The maintenance of critical infrastructure is essential to the functioning of our economy, and yet inspection can be difficult over long distances such as pipelines and power lines; in harsh environments such as nuclear reactors; or in difficult to access locations such as under water. Remote sensing on remotely piloted platforms allow for all of this and more. The National Research Council (NRC) has active partnerships with Transport Canada and the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ) to establish national research initiatives exploring new applications and key challenges to the adoption of these emerging technologies.
The proposed Canadian Unmanned and Remote Sensing Innovation Network (CURSIN) combines the expertise of three leading colleges across the country and supports critical infrastructure providers and partners in adopting RPAS and remote sensing. Mohawk College, the Southern Alberta Institute of Technology and Saskatchewan Polytechnic offer complimentary skills and resources in key research themes including RPAS operations, beyond visual line of sight (BVLOS) applications, photogrammetry and bathymetry of critical infrastructure, data analytics for LiDAR and sensor data collected using RPAS platforms. In partnership with SMEs and large operators across the country, this new network will accelerate the adoption of RPAS in Canadian industry by leveraging leading edge RPAS systems and sensors, and by training a new generation of pilots and experts in remote sensing.
Partner organization
Applied Research and Technology Partnership
Funding sources
Applied Research and Technology Partnership
Duesytech
Duesytech is a product of Audio Concepts Inc., which provides tech matching services
to general customers and technicians along with an online store. The intent is to
provide technical matching services to general customers and technicians while enhancing
the traditional online shopping experience.
Traditionally this is done through the application of a recommender system. However, given the sparse nature of the current data set the application of self-supervised recommendation is anticipated to yield better recommendations. By applying machine learning to match what customers’ needs for technical services with technicians, Audio Concepts will be able to move Duesytech forward to provide customers and technicians with more robust technical matching services as well as enhanced online shopping experience.
Partner organization
Audio concepts Inc.
Funding sources
Audio Concepts Inc.
Traditionally this is done through the application of a recommender system. However, given the sparse nature of the current data set the application of self-supervised recommendation is anticipated to yield better recommendations. By applying machine learning to match what customers’ needs for technical services with technicians, Audio Concepts will be able to move Duesytech forward to provide customers and technicians with more robust technical matching services as well as enhanced online shopping experience.
Partner organization
Audio concepts Inc.
Funding sources
Audio Concepts Inc.
Enterprise Resource Planning platform for small- to medium-sized enterprises
Using the Agile software development approach, DICE and Levis Media Inc. are collaborating
on the research and development of an inventory management system that targets small
to medium-sized enterprises (SMEs) in numerous industries underserved by current systems.
With enterprise resource planning (ERP) software, an organization can manage their entire business. These products contain many modules such as accounting, human resources, inventory management, etc. This software comes with a huge price tag so Levis Media Inc. strives to create an affordable solution for SMEs.
Partner organization
Levis Media Inc.
Funding sources
Levis Media Inc.
With enterprise resource planning (ERP) software, an organization can manage their entire business. These products contain many modules such as accounting, human resources, inventory management, etc. This software comes with a huge price tag so Levis Media Inc. strives to create an affordable solution for SMEs.
Partner organization
Levis Media Inc.
Funding sources
Levis Media Inc.
Grading wheat using machine learning
Grain quality determines the prices growers are paid for their grains. Canadian Grain
Commission grading factors are an important part of the pricing models, but additional
premiums can be obtained by achieving certain quality parameters.
Grains that achieve these parameters can be segmented and mixed with varying qualities of grains. If done correctly, the average price of the grain can be raised, however if done incorrectly or accidentally, the premium pricing on grains can be lost.This system relies on accurate grain samples being completed prior to mixing grain. Grain growers face several challenges related to the quality of their grain and they are beginning to look to precision agriculture for solutions. Except for harvest data such as location, moisture and weight, current methods of obtaining harvest data are often aggregated. For example, grain samples are taken for offsite testing after grain has been moved into a grain cart or a grain bin. Often this means that the grain isn’t uniformly mixed before obtaining a sample which can result in test result variations. This can result in mispricing of grain or even rejection of a delivery. It also means that location, moisture and weight data are stored separately from the remaining quality data.
Ground Truth Ag is working with Sask Polytech to create a grain grading tool that reflects quality data more accurately at the time it is harvested. This will allow growers to market their grains with confidence. This project focuses on using machine vision tools for accurate identification of damaged Canada Western Red Spring Wheat kernels.
Partner organization
Funding sources
Grains that achieve these parameters can be segmented and mixed with varying qualities of grains. If done correctly, the average price of the grain can be raised, however if done incorrectly or accidentally, the premium pricing on grains can be lost.This system relies on accurate grain samples being completed prior to mixing grain. Grain growers face several challenges related to the quality of their grain and they are beginning to look to precision agriculture for solutions. Except for harvest data such as location, moisture and weight, current methods of obtaining harvest data are often aggregated. For example, grain samples are taken for offsite testing after grain has been moved into a grain cart or a grain bin. Often this means that the grain isn’t uniformly mixed before obtaining a sample which can result in test result variations. This can result in mispricing of grain or even rejection of a delivery. It also means that location, moisture and weight data are stored separately from the remaining quality data.
Ground Truth Ag is working with Sask Polytech to create a grain grading tool that reflects quality data more accurately at the time it is harvested. This will allow growers to market their grains with confidence. This project focuses on using machine vision tools for accurate identification of damaged Canada Western Red Spring Wheat kernels.
Partner organization
Funding sources
Grain quality, traceability and decision-support system for the agriculture sector
Canadian growers can market their own grain. With this comes the need to monitor the
time, price and place they wish to sell their grain. To determine the price of grain
one must be able to determine the quality of the grain and the quantity. To grade
the quality of the grain, samples must be taken to identify characteristics such as
damage caused by insects, moisture or temperature. Grain sampling can be a time-consuming
process because traditionally a representative sample requires a person to take a
sample from grain flowing from the auger at regular time intervals.
VeriGrain Sampling Inc. has produced a system to automatically perform grain sampling and data management. They want to build on to their mobile app to provide further functionality for growers, buyers and consumers of the agriculture industry. This applied research project focuses on increasing grain quality identification and traceability as well as providing a Decision Support System. VeriGrain will work with DICE to create this product, which will maximize profits for the agriculture industry.
Partner organization
Funding sources
VeriGrain Sampling Inc. has produced a system to automatically perform grain sampling and data management. They want to build on to their mobile app to provide further functionality for growers, buyers and consumers of the agriculture industry. This applied research project focuses on increasing grain quality identification and traceability as well as providing a Decision Support System. VeriGrain will work with DICE to create this product, which will maximize profits for the agriculture industry.
Partner organization
Funding sources
Improving the user experience using distributed analytics and user modeling
The amount of online information being created and consumed continues to increase
dramatically. Mobile applications have become the preferred way to quickly access
information individuals need. However, when accessing large volumes of data online
on devices with limited sized screens, memory and processors, limiting the information
to what meets the user need is required. One of the common strategies of accomplishing
this is to match an individual's preference for something with the preferences of
other users for that same item.
Those that have similar preferences can then be used to decide what else might be useful. In a shopping application, it usually appears as the statement, "others that bought this item also bought...". Refresh Enterprises, Inc. (Refresh) is a marketing firm that operates in the interactive marketing niche. The company's goal is to create a flexible framework that supports multi-device access to an adaptive and personalized presentation of content and resources. Working with DICE, Refresh will work on a prototype or proof-of-concept mobile application they have created to help users navigate the complex space of mental wellness information and services. This project will modify this common strategy to not only look at how users are similar but how the information itself is similar. The group will explore how this modified strategy can produce better recommendations. The modified strategy will tax the mobile device's abilities further, so ways of using Internet resources to share the load will also be looked at.
Partner organization
Refresh Enterprises, Inc.
Funding sources
Refresh Enterprices, Inc.
Those that have similar preferences can then be used to decide what else might be useful. In a shopping application, it usually appears as the statement, "others that bought this item also bought...". Refresh Enterprises, Inc. (Refresh) is a marketing firm that operates in the interactive marketing niche. The company's goal is to create a flexible framework that supports multi-device access to an adaptive and personalized presentation of content and resources. Working with DICE, Refresh will work on a prototype or proof-of-concept mobile application they have created to help users navigate the complex space of mental wellness information and services. This project will modify this common strategy to not only look at how users are similar but how the information itself is similar. The group will explore how this modified strategy can produce better recommendations. The modified strategy will tax the mobile device's abilities further, so ways of using Internet resources to share the load will also be looked at.
Partner organization
Refresh Enterprises, Inc.
Funding sources
Refresh Enterprices, Inc.
Laying the foundation for an Intelligent Restaurant Management System
DICE and Globex Management collaborated on the research and development of an Intelligent
Restaurant Management System (IRMS).
Globex Management is a hospitality-based organization that has developed a software platform to build detailed menus for food service organizations. Globex wishes to extend the capabilities of their software to create an IRMS. The purpose of this project was to further develop the IRMS to automatically enter invoices, control the shrinkage and spoilage of food, and enable easy entry of inventory. All these features will allow for prices of menu items to reflect the real costs of ingredients. This will allow restaurants to effectively maximize profits.
Partner organization
Globex Management
Funding sources
Globex Management
Globex Management is a hospitality-based organization that has developed a software platform to build detailed menus for food service organizations. Globex wishes to extend the capabilities of their software to create an IRMS. The purpose of this project was to further develop the IRMS to automatically enter invoices, control the shrinkage and spoilage of food, and enable easy entry of inventory. All these features will allow for prices of menu items to reflect the real costs of ingredients. This will allow restaurants to effectively maximize profits.
Partner organization
Globex Management
Funding sources
Globex Management
Particle size classification for gravel aggregate using machine learning
DICE and McIntosh Sand & Gravel are working to develop a digital method that is cost
effective for determining gravel source particulate size using image analysis technique.
McIntosh Sand & Grave is a Saskatoon based gravel crushing and processing provider operating mainly in the Outlook area of Saskatchewan with work spanning from Swift Current to Saskatoon area and growing. They provide technical services related to gravel investigations including drone surveys, GEM electromagnetic induction surveys, monitoring, and gravel crushing, hauling and processing solutions for both private industry and municipal governments. With a workforce approaching nine personnel they have a diverse degree of experience and backgrounds to draw from in solving problems with innovative solutions both internally and for their clients.
Partner organization
Funding sources
McIntosh Sand & Grave is a Saskatoon based gravel crushing and processing provider operating mainly in the Outlook area of Saskatchewan with work spanning from Swift Current to Saskatoon area and growing. They provide technical services related to gravel investigations including drone surveys, GEM electromagnetic induction surveys, monitoring, and gravel crushing, hauling and processing solutions for both private industry and municipal governments. With a workforce approaching nine personnel they have a diverse degree of experience and backgrounds to draw from in solving problems with innovative solutions both internally and for their clients.
Partner organization
Funding sources
Using knowledge graphs and AI to recognize similarities between politicians
IOTO is working in partnership with Sask Polytech's DICE to help them discover how
machine learning and big data techniques can be used to conceive political career
metrics and comparisons. This project demonstrates how political data can be standardized,
collected, stored, and presented in order to support insightful cross-jurisdictional
comparison of governance performance.
In data analysis, there are differing approaches taken with respect to the discovery
of information and patterns within large data sets. IOTO International Inc. is a non-partisan
analytics company based in Vancouver, BC that specializes in the use of AI to gain
insights from political data and deliver high-quality factual political content at
low cost. IOTO's work is recognized as being innovative by UNESCO’s International search Centre on Artificial Intelligence.
The company has gathered and classified large municipal, provincial, state and national
datasets and is in the process of obtaining more political data by using Natural Language
Processing of political audio. By determining how to classify and store political
and governance data, IOTO will be able to provide media and other organizations with
more robust analytics on legislators’ actions and performance in government.
Partner organization
IOTO International Inc.
Funding sources
IOTO International Inc.
Using machine learning to locate geological deposits
DICE and geological experts at Peter Lucas Project Management Inc. are collaborating
on the development of a digital system to locate aggregate sources with a large gravel
content.
Gravel is a non-renewable resource that is used in almost every aspect of construction. The identification of sources of gravel and the corresponding management of it is important to many different industries. The project examined the use of machine learning (ML) and neural network algorithms to determine the probability of a location on a map containing gravel. This resulting technology helps to find more sources, increase supply, save costs and reduce greenhouse gases due to minimized hauling. The work done in this project expands on previous collaborations between DICE and Peter Lucas Project Management Inc. that have shown promise.
Partner organization
Funding sourcse
Gravel is a non-renewable resource that is used in almost every aspect of construction. The identification of sources of gravel and the corresponding management of it is important to many different industries. The project examined the use of machine learning (ML) and neural network algorithms to determine the probability of a location on a map containing gravel. This resulting technology helps to find more sources, increase supply, save costs and reduce greenhouse gases due to minimized hauling. The work done in this project expands on previous collaborations between DICE and Peter Lucas Project Management Inc. that have shown promise.
Partner organization
Funding sourcse
Previous projects
Summary of proposals for public release: