Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.
Big data analytics for the maritime internet of things (IoT)
The Internet of Things (IoT) is an emerging phenomenon that enables ordinary devices to generate sensor data and interact with one another to improve daily life. The maritime world has not escaped to the influence of the IoT revolution. We are in the midst of a technological wave in which vessels are not the only ones carrying sensors (GPS or radar) anymore, but other maritime entities such as cranes, crates, boats, pickup trucks, etc. are being equipped with the same capabilities. This trend constitutes the backbone of the so-called Maritime Internet of Things (mIoT).
This project is about exploiting the tide of sensor data emitted by a myriad of maritime entities in order to improve both internal and collaborative processes of mIoT-related organizations; for instance, think of a Port Authority adjusting its berthing and unloading schedule upon receiving notice that a vessel has been delayed by harsh weather conditions. The challenge addressed by this research project is the generation of actionable intelligence for Decision Support using Big Data analytics. Actionable intelligence includes anomalies, alerts, threats, potential response generation, process refinement and other types of knowledge that improve the efficiency of a maritime-related organization and/or the manner in which it interacts with other similar organizations.
Design of OLED materials for manufacturing and improved product quality
Organic light emitting diodes (OLEDs) present a unique opportunity to produce thinner and more efficient lighting and displays. This will change the way we interact with light. The main barrier to mass adoption of OLEDs is the manufacturing process, due to the need for high throughput while maintaining nanoscale precision. High throughput operation requires materials that can undergo elevated temperature without decomposing. Our objective is to use computational chemistry to model innovative materials that can withstand these elevated temperatures while still providing high performing OLEDs. We will simulate targeted compounds using SOSCIP’s computer cluster examining properties relevant to OLED manufacturing processes. Promising materials will be synthesized and their properties experimentally measured then compared to the simulation results. The most promising materials will then be integrated into OLEDs and characterized by OTI Lumionics in their pilot scale manufacturing line located in Toronto, ON.
End-to-End Machine Learning Based Demand Forecasting
This project aims to implement an end-to-end (E2E) Artificial Intelligence (AI) / Machine Learning (ML) driven demand forecasting predictive model pipeline to increase process efficiencies, help improve forecast accuracy, forecast bias and forecast value added and unlock trapped capacity in the business ensuring improved service, optimum inventory, better production scheduling and better P&L forecasting. As participants in this consortium, Unilever, Larus and SOSCIP bring unique contributions that augment the benefits of the initiative from both scientific and business perspectives. More specifically, Unilever brings unique knowledge of business and operational landscape, curated and diverse data sets and analytics expertise in story building and turning data into insights, Larus brings unique expertise in AI/ML decision making and optimization systems and SOSCIP provides the required high performance computing platforms to run ML algorithms utilizing large data sets with optimal performance.
Forecasting Covid-19 Epidemic in Canada with Spatial-Temporal Models That Exploit Population Behaviour on Twitter
The Covid-19 pandemic is creating unprecedented damage to the public health and world economy. Being able to accurately forecast the spread of Covid-19 is critical for the federal and provincial governments of Canada to devise policies and measures maximally protecting the lives of Canadians and rapidly reviving the Canadian economy. Not only important at present, developing advanced epidemic projection algorithms and techniques also helps prevent future epidemics of other infectious diseases. In this project, we set out to develop advanced forecasting algorithms for the spread of Covid-19 across Canada. Specifically, we will exploit the behaviour information of the population revealed on social media as well as the correlation of the Covid-19 spread across different regions of Canada. Our development will integrate modern AI techniques in data analytics, machine learning and natural language processing with the conventional mathematical models for infectious diseases. The developed algorithms will be hosted on a web platform, which will provide accurate daily predictions of Covid-19 spread and release them to the public. These predictions will assist the governments to strategically adjust policies for the protection of Canadian lives and the revival of the Canadian economy. Individuals, families, schools and businesses will all benefit from this new source of information in their planning processes.
Getting ahead of the curve: a novel way to find people who are likely to be asymptomatic carriers of COVID-19 before they infect others
The spread of COVID-19 has been slowed by physical distancing, self-isolation, lockdowns, masks and travel restrictions. Doing more testing and contact tracing of known cases has helped contain the spread. However, these approaches are ‘behind the virus’ – by at least 5-10 days. We need a way of getting ahead of the virus, before it spreads further. We know that about half the infections are transmitted by people who do not have any symptoms, and this is sufficient to cause a growing epidemic. Therefore, we need to find, isolate and test at least some asymptomatic people. Like intelligence officers hunting hidden terrorists, we need to prevent attacks before they happen, instead of chasing the culprits after the fact. In other words, we need to identify carriers before outbreaks occur, not after. Our team proposes a new way to do this.
The overall goal of this project is to develop models and an intervention prototype to predict which individuals are most likely to be exposed to COVID-19 and are therefore most at risk of onward transmission. We will apply statistical modeling and Artificial Intelligence (AI) methods to demographic, occupational, social networking and geolocation data. In simulations, we will test different ‘smart isolation and testing strategies’ that could be used by public health officials to determine which would most effectively reduce viral transmission. Applying AI-informed strategies could enable partial relaxation of confinement rules for lower-risk segments, and allow greater reopening of economic and social life without risking a second wave and further lockdowns.
Intelligent emergency response using the internet of things (IoT)
Smart mobile devices and wireless Internet access are allowing emergency responders to access and share valuable emergency-related information coming from data repositories, citizens, surveillance cameras, and many other sources. However, because there is no accurate and energy-efficient indoor positioning solution, emergency responders cannot translate that wealth of information into a much-needed situational awareness. This project addresses this unique challenge by using data analytics and the Internet of Things (IoT). More specifically, this project devises an accurate, scalable, energy efficient, robust, and resilient indoor positioning solution using Bluetooth Low Energy (BLE) beacons.
These battery-operated beacons will be deployed in buildings as part of emergency preparedness strategy. When an emergency happens, the communication between these beacons and the smartphones and tablets of emergency responders will give emergency responders the necessary situational awareness at the right time and the right location. This positioning solution is based on a thorough analysis of signal propagation and coverage measurements using machine-learning tools. This solution will then be used to prototype a Location-Based Service (LBS). This software application gives emergency responders necessary and sufficient situational awareness while responding to building emergencies. This LBS is designed and developed based on a comprehensive analysis of buildings’ information, emergency response plans, and emergency records.
This combined repository of information is processed using text mining to identify points of interest at any building and during any emergency and give a statistical model for the workflow of emergency responders between these points. Deploying BLE beacons at these points will help expedite the preparation and execution of emergency response plans, and hence enhance the situational-awareness, efficiency, and effectiveness of emergency responders. This is the first project to combine data analytics and IoT in the public safety domain. Its findings will have a ripple effect on the design and development of software solutions for emergency responders.
Large scale simulation of nanostructured optical surfaces
Plasmonic metasurfaces are engineered human-fabricated surfaces on which there is nanometer scale structure, usually containing metallic features. These nanoscale features can resonantly interact with light through the excitation of electric charge density oscillations, or surface plasmons. As plasmonic metasurfaces have essentially infinite design potential — including designer resonant behaviour and strong nanoscale light field enhancements — there is currently a great deal of interest in using them for a wide range of applications, from flat optical devices to biosensing to enhanced nonlinear optical signals3 to colour production4. A major focus of this project will be to perform large scale computational electrodynamics simulations on the Blue Gene Q to understand and design plasmonic metasurfaces. The work will be connected to projects underway with several Canadian industrial partners, as well as a plan to engage with several others, and will involve multiple trainees at various stages in their education – from undergraduate to postdoctoral fellow.
Thermal imaging for efficient detection of vital signs during COVID-19 pandemic
Early detection of symptoms of COVID-19 infection is of utmost importance. In this project, we are focusing on early detection using thermal imaging cameras that allow for non-contact, non-invasive monitoring of temperature, heart rate and breathing rate. We propose to develop two solutions: 1. to detect early symptoms by detecting an increase in people’s temperature through crowd sensing and 2. to monitor the vital signs of elderly patients continuously using thermal cameras next to the places where they spend most of their time. These solutions will be based on advanced signal processing algorithms and machine learning models.
At the end of the project, we will provide a stand-alone solution that will include a thermal camera, processing hardware and our software and algorithms. This will represent a minimum viable product that will be taken by our industrial partner J&M Group and further developed into a commercial product.
This project is important for Canada because it addresses early detection of COVID-19 of both people who are active and might not know that they have COVID-19 symptoms as well as elderly people who are at their homes or retirement homes and require constant monitoring of their vital non-invasively.
Trade Promotion Forecasting and Optimization
This project addresses two specific challenges related to promotional planning in the consumer-packaged goods sector. The first output will be to develop and evaluate models that statistically predict the impact of Unilever promotions on category and product share across different retailers. This effort will lead to the creation of trade promotion optimization techniques that enables planning of promotional activities. The second output is to simulate potential outcomes of various combinations of promotional programs across different retailers in such a way as to inform investment decisions early in the planning process. Finally, the project aims to develop a systematic approach for allocation of promotional spend that permits continuous adjustment as market and competitor conditions change. These three objectives will be developed using a proof of concept approach: therefore, specific objectives are as follows: 1. Develop a POC that provides models, simulation results and a systematic model for allocation of promotional spend using limited data from a few product categories and customers. 2. Expand the POC to address a larger set of categories and customers. 3. Build on objective two to create an automated model and process.
VISR – NLP for early detection of mental heath issues
VISR a new service offering a simple, effective and kid-friendly tool to help parents keep kids safe on social media; only notifying them when behaviors such as bullying, risky geotagging, and unusual times of use are detected. VISR aims to give parents the tools they need to help them know when there may be an issue they should know about – while building trust and communication with their kids. VISR was founded in July 2014 and launched their beta in April 2015. With already 1000+ users, they are quickly gaining traction. To date VISR has analyzed over 1 million posts, generated 8000+ alerts across 20+ alert categories, and supports 6 major social networks including: Instagram, YouTube, Gmail, Facebook, Twitter and KidsEmail.