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Research Projects

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(FoRCE): Powering clinical trials research through a secure and integrated data management platform
Collaborators: Queen's University & Indoc Research
Cybersecurity Digital Media Health

(FoRCE): Powering clinical trials research through a secure and integrated data management platform

Critical care units are one of the most data-rich environments in clinical settings, with data being generated by advanced patient monitoring, frequent laboratory and radiologic tests, and around-the-clock evaluation. There are substantial opportunities in linking data that are collected as a part of such clinical practice with data collected in a research setting, such as genome wide studies or comprehensive imaging protocols. However, security and privacy issues have historically been a significant barrier to the storage, analysis, and linkage of such biomedical data. Further, disparate technologies hinder collaboration across teams, most of which lack the secure systems required to enable federation and sharing of these data. This is particularly true when clinical practice or research designs require close to real time analysis and timely feedback, such as when dealing with streamed medical data or output from clinical laboratories. Current commercial and research solutions often fail to integrate different data types, are incapable of handling streaming data, and rely solely on the security measures put in place by the organizations that deploy them.

This proposal seeks to build FoRCE (Focus on Research and Clinical Evaluation), a scalable and adaptable add-on module to the existing Indoc Informatics platform that will address the critical gaps in cybersecurity and privacy infrastructure within shared clinical and research settings, while fulfilling important unmet needs for both the clinical and research communities. FoRCE will provide the secure architecture and processes to support the collection, federation and sharing of data from distributed clinical settings, including critical care units, clinical laboratories, and imaging facilities. The proposed platform will address several key issues including security considerations, infrastructure and software requirements for linkage, and solutions for handling streaming real time medical data, and ensuring regulatory and ethics compliance when linking diverse medical data modalities in a clinical setting.

FoRCE will be designed and developed with broad applicability in mind, and will therefore allow the different data types from numerous technologies and across multiple disease states to utilize the platform. The long term impact of FoRCE on improving the health of Ontarians is of course dependent on its utilization within research and clinical settings. An initial project which will utilize the platform as part of the testing and validation of FoRCE includes Dr. Maslove’s integrated approach to merging genomic and physiologic data streams from the ICU in the context of clinical research. FoRCE will enable Dr. Maslove’s team of critical care researchers to move beyond predictors of survival to focus on predictors of response to therapy, so that clinical trials in the ICU can be optimized to produce actionable evidence and personalized results. This will lead to better allocation of ICU resources, which in Canada cost nearly $3,000 per patient per day – $3.72 billion per year.

Industry Partner(s): Indoc Research

Academic Institution: Queen's University

Academic Researcher: David Maslove

Platform: Cloud, Parallel CPU

Focus Areas: Cybersecurity, Digital Media, Health

A cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease
Collaborators: Western University & Tracery Ophthalmics
Digital Media Health

A cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease

Age Related Macular Degeneration is the leading cause of irreversible blindness in Canada and the industrialized world, yet there are no treatments for the vast majority of patients. Led by Tracery Ophthalmics inc, and working with Translatum Medicus inc (TMi) and academic partners at the Robarts Research Institute, Western University, and the “High Risk Dry AMD Clinic” of St Michael’s Hospital, we will engage SOSCIP’s Cloud Analytics platform, including servers, software and human resources, to accelerate the search for new treatments.

Specifically, Tracery has developed a novel functional imaging method, “AMD Imaging” (AMDI) that has already generated unprecedented pictures of the retina (the film of the eye) that include both known and unknown “flavours” of disease (the phenotype). These complex images will be compared against an individual’s genetic makeup (their genotype) and their concurrent illnesses, medications, and lifestyle history (their epigenetics). Further, Tracery’s imaging will help identify particular patients that will benefit from TMi’s drug development program, and ultimately help doctors choose which treatment will work best. Over the course of two years, we will involve increasing numbers of medical experts and their patients to generate and amass AMDI images, evaluating them over time and against other modalities.

Ultimately, through the “I3” program, we will work with IBM to train Watson and the Medical Sieve to recognize and co‐analyse complex disease patterns in the context of the ever‐expanding scientific literature. In short, we will leverage cloud‐based computing, to integrate image‐based and structured data, genomics and large data analytic to unite global users. We anticipate that this approach will significantly accelerate drug development, providing personalized treatment for the right patient at the right time.

Industry Partner(s): Tracery Ophthalmics

Academic Institution: Western University

Academic Researcher: Ali Khan

Co-PI Names: Filiberto Altomare, Louis Giavedoni & Steven Scherer

Platform: Cloud

Focus Areas: Digital Media, Health

A dynamic and scalable data cleaning system for Watson analytics
Collaborators: McMaster University & IBM Canada Ltd.
Cybersecurity Digital Media

A dynamic and scalable data cleaning system for Watson analytics

Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. It is estimated that poor data quality cost organizations between 15% and 25% of their operating budget. Existing data cleaning solutions focus on identifying inconsistencies that do not conform to prescribed data formats assuming the data remains relatively static. As modern applications move towards more dynamic search analytics and visualization, new data quality solutions that support dynamic data cleaning are needed. An increasing number of data analysis tools, such as Watson Analytics, provide flexible data browsing and querying abilities. In order to ensure reliable, trusted and relevant data analysis, dynamic data cleaning solutions are required. In particular, current data quality tools fail to adapt to: (1) fast changing data and data quality rules (for example as new datasets are integrated); (2) new data governance rules that may be imposed for a particular industry; and (3) utilize industry specific terminology and concepts that can refine data quality recommendations for greater accuracy and relevance. In this project, we will develop a system for dynamic data cleaning that adapts to changing data and rules, and considers industry specific models for improved data quality.

Industry Partner(s): IBM Canada Ltd.

Academic Institution: McMaster University

Academic Researcher: Fei Chiang

Platform: Cloud

Focus Areas: Cybersecurity, Digital Media

Active learning for automatic generation of narratives from numeric financial and supply chain data
Collaborators: Ryerson University & Unilever Canada Inc.
Advanced Manufacturing Digital Media

Active learning for automatic generation of narratives from numeric financial and supply chain data

Large enterprises compile and analyze large amounts of data on a daily basis. Typically the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by financial analysts can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand automating some of the manual processing requires more involved artificial intelligence techniques.

In our project we aim to solve one of these harder to automate tasks. In fact analyzing textual data using Natural Language Processing (NLP) techniques is one of the standardized methods of data processing in modern software tools. However the vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives.

Since the generation of text is a domain-dependent and non-trivial task, the automated generation of narratives requires novel research to be useful in an enterprise environment. In this project we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in the executive level of companies. Upon successful completion of the project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher-level data fusion skills.

Industry Partner(s): Unilever Canada Inc.

Academic Institution: Ryerson University

Academic Researcher: Ayse Bener

Co-PI Names: John Maidens

Platform: Cloud, GPU

Focus Areas: Advanced Manufacturing, Digital Media

Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality
Collaborators: University of Guelph & Greenland International Consulting
Cities Clean Tech Digital Media Water

Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality

Watershed modeling is widely used to better understand processes and help inform planning and watershed management decisions. Examples include identifying impacts associated with land use change; investigating outcomes of infrastructure development, predicting effects of climate change. The proposed project will see the evolution of a desktop based watershed modeling and decision support system to a web based tool that will allow greater access by decision makers and stakeholders. By this means we will advance the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.

The proposed software evolution will take advantage of high performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. The result is expected to be a dramatic reduction in simulation run times. Reduced run times will facilitate the use of automatic calibration routines used to conduct model setup, reducing costs. It will also enable rapid response if the simulation were to be re-run by a request through the web-based user interface. The designed web-based tool will be used by decision and policy makers in the watersheds that contribute to Lake Erie to understand the sources of pollution especially phosphorus which is a major contributor to Lake Erie eutrophication problems and develop policies in supporting a wide variety of watershed planning and ultimately help achieve the Federal and Ontario government commitments to reduce 40% phosphorus entering Lake Erie by 2025.

Industry Partner(s): Greenland International Consulting

Academic Institution: University of Guelph

Academic Researcher: Prasad Daggupati

Platform: Cloud

Focus Areas: Cities, Clean Tech, Digital Media, Water

Advancing video categorization
Collaborators: Seneca College & Vubble Inc.
Digital Media

Advancing video categorization

Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble is collaborating with Dr. Vida Movahedi from Seneca’s School of Information and Communication Technology to develop a machine-learning algorithm that will automatically output highly probable categories for videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This video identification research project will be Vubble’s first step in understanding how to automate the identification of accurate video. The need for automation of videos curation is prevalent, as video is quickly becoming the world’s dominant form of media consumption, particularly for digital native younger audiences. Furthermore, the results of the applied research will aid Vubble in moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake news video created from archival footage.

Industry Partner(s): Vubble Inc.

Academic Institution: Seneca College

Academic Researcher: Vida Movahedi

Platform: Cloud

Focus Areas: Digital Media

Agile computing for rapid DNA sequencing in mobile platforms
Collaborators: York University & Canadian Food Inspection Agency (CFIA)
Health

Agile computing for rapid DNA sequencing in mobile platforms

DNA can now be measured in terms of electronic signals with pocket-sized semiconductor devices instead of 100-pound machines. This has started to transform DNA analysis into a mobile activity with the possibility to track and analyze the health of organisms at unprecedented levels of detail, time, and population. But the remote cloud-based computer services currently needed to process the electronic signals generated by these miniature DNA-meters cost over $100 to complete an initial analysis on one human genome and consume over 1000 Watts of power. Also, the cost of wirelessly transmitting measured data to these cloud-based analyzers can exceed $1000 per human genome. Further, reliance on external high-performance compute services poses a greater risk for compromising the security of the DNA data collected. This project proposes the construction of a specialized high-performance miniature computer – the agile base caller (ABC) – built from re-configurable silicon chips that can connect directly to the DNA-meter and analyze the DNA it measures in real-time.

The ABC, by virtue of its size, will preserve the mobility of emerging DNA measurement machines, and will enable them to analyze data for less than $1 while consuming less than 10 Watts. These cost/power performance improvements will significantly drop the barriers to the application of genomic analysis to non-laboratory settings. For example, they will allow continuous monitoring of Canada’s food supply for the presence of harmful biological agents with the possibility of cutting analysis delays from weeks to hours.

Industry Partner(s): Canadian Food Inspection Agency (CFIA)

Academic Institution: York University

Academic Researcher: Sebastian Magierowski

Platform: Cloud

Focus Areas: Health

Agile real time radio signal processing
Collaborators: University of Toronto & Thoth Technology Inc.
Digital Media

Agile real time radio signal processing

Canadian VLBI capability has been missing for a decade. Jointly with Thoth Technology Inc we propose to restore domestic and international VLBI infrastructure that will be commercialized by Thoth Technology Inc. This project will implement and optimize multi-telescope correlation and analysis software on the SOSCIP BGQ, Agile and LMS platforms. The resulting pipeline package will allow commercial turnkey VLBI delivery by Thoth Technology Inc to domestic and international customers into a market of about $10 million/year

Industry Partner(s): Thoth Technology Inc.

Academic Institution: University of Toronto

Academic Researcher: Ue-Li Pen

Platform: Cloud, Parallel CPU

Focus Areas: Digital Media

An economics-aware autonomic management system for big data applications
Collaborators: York University & IBM Canada Inc.
Cities Digital Media

An economics-aware autonomic management system for big data applications

Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.

The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.

Industry Partner(s): IBM Canada Inc.

Academic Institution: York University

Academic Researcher: Marin Litoiu

Platform: Cloud

Focus Areas: Cities, Digital Media

Analyzing geospatial patterns in the cloud: application to the mineral exploration and mining in Canada
Collaborators: Western University & Osisko Mining Corporation
Digital Media Mining

Analyzing geospatial patterns in the cloud: application to the mineral exploration and mining in Canada

Industry Partner(s): Osisko Mining Corporation

Academic Institution: Western University

Academic Researcher: Neil Banerjee

Co-PI Names: Leonardo Feltrin

Platform: Cloud

Focus Areas: Digital Media, Mining

Automated cytogenetic dosimetry as a public health emergency medical countermeasure
Collaborators: Western University & Cytognomix Inc.
Health

Automated cytogenetic dosimetry as a public health emergency medical countermeasure

Biodosimetry is a useful tool for assessing the radiation dose received by an individual when no reliable physical dosimetry is available. Our Automated Dicentric Chromosome Identifier software (ADCI) has been developed to automate dose estimation of gamma and X-ray radiation exposures. Biodosimetry laboratories currently process these data manually, and the capacity to handle more than a few samples at the same time would quickly overwhelm the laboratories. The software has been developed to handle radiation exposure estimation in mass casualty or moderate scale radiation events. Federal biodosimetry and clinical cytogenetic laboratories have automated systems to collect digital chromosome images of cells with and without chromosomes exposed to radiation. We have developed advanced image segmentation and artificial intelligence methods to analyze these images. ADCI identifies dicentric chromosomes (DCC), a widely recognized, gold standard hallmark of radiation damage. The number and distribution of DCCs are determined and compared with a calibration curve of known radiation dose. Our ADCI software can also generate these calibration curves. Our software computes the dose received of one or more test samples and generates a report for the user. The desktop version of ADCI contains an easy-to-use graphical user interface to perform all of these functions. The supercomputer version of this software proposed here will be optimized to determine the dose for many samples simultaneously, which would be essential in the event of a mass casualty.

Industry Partner(s): Cytognomix Inc.

Academic Institution: Western University

Academic Researcher: Joan Knoll

Co-PI Names: Mark Daley

Platform: Cloud, Parallel CPU

Focus Areas: Health

Big cardiac data
Collaborators: Western University & London X-ray Associates
Cybersecurity Health

Big cardiac data

We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.

Industry Partner(s): London X-ray Associates

Academic Institution: Western University

Academic Researcher: Li Shuo

Platform: Cloud, GPU

Focus Areas: Cybersecurity, Health

Big data analysis and optimization of rural and community broadband wireless networks
Collaborators: University of Ottawa & EION Inc.
Cities Digital Media Energy

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.

Industry Partner(s): EION Inc.

Academic Institution: University of Ottawa

Academic Researcher: Amiya Nayak

Co-PI Names: Octavia Dobre

Platform: Cloud

Focus Areas: Cities, Digital Media, Energy

Big data analytics for the maritime internet of things (IoT)
Collaborators: University of Ottawa & Larus Technologies Inc.
Advanced Manufacturing

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.

Industry Partner(s): Larus Technologies Inc.

Academic Institution: University of Ottawa

Academic Researcher: Emil Petriu

Platform: Cloud, GPU

Focus Areas: Advanced Manufacturing

Cloud-based Framework for Analysing COVID-19 Data
Collaborators: Queen's University & Kings Distributed Systems
COVID-19

Cloud-based Framework for Analysing COVID-19 Data

Edge computing (EC) has shown to have a significant impact on offloading traffic/computations/AI tasks from backhaul links/servers and improving users’ Quality of Service (QoS). It is anticipated that EC in general and its use to implement AI at the edge (a.k.a. edge intelligence, EI) will be necessary components of all digital business by 2022 and that 40% of large enterprises will integrate EC/EI into their production systems by 2021.

The objective of the proposed research is to transform the already existing yet latent computing resources into on-demand clusters of distributed EC/EI workers, with a focus on distributed learning and intelligence. The proposed research will develop foundational elements of the COVID-19 and future pandemic analysis platform using KDS’s Distributed Compute Protocol (DCP). DCP allows massive computing resources to be accessed in parallel.

The proposed DCP COVID-19 project will support the Looking Glass project, a free and open platform to better inform remediation strategies, public health interventions, and vaccine campaign strategies against COVID-19 – as well as time strategies to relax lockdown measures during the recovery phase – for municipalities, state/federal authorities by modelling transmission patterns of diseases based on reports from epidemiologists fused with economic report data, and high-resolution mobility data.

Identifying disease clusters, spatial patterns, and methods of transmission of endemic, epidemic and pandemic diseases are essential to inform policymakers, programs, and interventions at both local and global scales. Health authorities depend on alerts provided by front-line employees or by members of the public when there is a disease cluster. The recent emergence of the COVID-19 as a global pandemic is one example of a critical public health threat that challenged management systems.

Industry Partner(s): Kings Distributed Systems

Academic Institution: Queen's University

Academic Researcher: Hassanein, Hossam

Platform: Cloud

Focus Areas: COVID-19

Cloud‐based data analytic platform for real‐world evidence generation
Collaborators: University of Waterloo & Roche Canada
Cybersecurity Health

Cloud‐based data analytic platform for real‐world evidence generation

Randomized controlled trials (RCTs) are considered the gold standard for supportive clinical evidence, however RCTs rarely describe the effectiveness of an intervention in real life practice. As such, regulators, payers, and health‐care providers are turning towards real world data (RWD) to understand how well an intervention performs in clinical practice.

The best source of RWD is source data – that is, data that are collected at the interface of the patient and the health care system, as well patient monitoring and self‐reported data captured directly within the patients’ home environment. Unfortunately, these data are stored in different and distinct systems that are not well integrated and in formats that do not allow for rapid analytic capabilities. The University of Waterloo in partnership with Roche Canada, are therefore proposing to develop the “CARE” (Clinical Analytics for Real‐World Evidence) platform.

The “CARE” platform will be a holistic cloud‐based data analytic solution featuring a large central repository for consolidating data obtained from disparate data systems (including data from electronic medical records, lab data, physician transcriptions, and patient monitoring devices and self‐reported surveys). The “CARE” platform will serve as a central hub for researchers that includes integrated and sophisticated data analytics, access control, security and study management tools in order to curate data for research and clinical purposes. This project begins the initial steps to address relevant research objectives in lung cancer by providing the tool and infrastructure required to process and analyze the enormous amount of scattered oncology data within an institution and across multiple institutions. Ultimately, this work will make it possible to mine currently siloed and/or unstructured data across the system and produce data‐driven insights in order to deliver the right care to the right patient at the right time through scientific innovation and research excellence.

Industry Partner(s): Roche Canada

Academic Institution: University of Waterloo

Academic Researcher: Helen Chen

Co-PI Names: Plinio Morita

Platform: Cloud

Focus Areas: Cybersecurity, Health

Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
Collaborators: University of Toronto
Advanced Manufacturing Clean Tech Energy

Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation

According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.

Industry Partner(s):

Academic Institution: University of Toronto

Academic Researcher: Ted Sargent

Co-PI Names: Aleksandra Vojvodic

Platform: Cloud, GPU, Parallel CPU

Focus Areas: Advanced Manufacturing, Clean Tech, Energy

Computational support for big data analytics, information extraction and visualization
Collaborators: York University & IBM Spectrum Computing
Cities Digital Media Energy Water

Computational support for big data analytics, information extraction and visualization

The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:

IBM Platform:

  • Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
  • Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
  • Design algorithms to determine which are suitable in such an environment.
  • Identify commercialization venues in application domains.
  • Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts

Industry Partner(s): IBM Spectrum Computing

Academic Institution: York University

Academic Researcher: Aijun An

Co-PI Names: Amir Asif

Platform: Cloud

Focus Areas: Cities, Digital Media, Energy, Water

Computer vision powered digital twin for tracking manual manufacturing processes
Collaborators: University of Windsor & IFIVEO CANADA INC.
Business Analytics

Computer vision powered digital twin for tracking manual manufacturing processes

Over 70% of tasks in manufacturing are still manual; therefore, over 75% of the variation in manufacturing comes from human beings. Human errors were the primary driver behind $22.1 billion in vehicle recalls in 2016. Currently, when plant operators want to gain an understanding of their manual processes, they send out their highly paid industrial engineers to run time studies. These studies produce highly biased and inaccurate data that provides minimal value to manufacturing teams. This project aims to develop a computer vision powered digital twin prototype that is ready to test on the client’s site, which helps manufacturing plant operators gain unprecedented visibility into their manual production operations, allowing them to optimize their worker efficiency while maximizing productivity. This will be done by automated data generation using computer vision, conversion of raw data into useable information, visualization of information using standard Business Intelligence methodologies and lastly, prediction of future plant performance based on historical information, as well as information about other market drivers.

Industry Partner(s): IFIVEO CANADA INC.

Academic Institution: University of Windsor

Academic Researcher: Afshin Rahimi

Platform: Cloud, GPU

Focus Areas: Business Analytics

Continuous vital sign monitoring using intelligent bed sheet
Collaborators: York University & Studio 1 Labs Inc.
Advanced Manufacturing Health

Continuous vital sign monitoring using intelligent bed sheet

Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.

Industry Partner(s): Studio 1 Labs Inc.

Academic Institution: York University

Academic Researcher: Laura Nicholson

Platform: Cloud, GPU

Focus Areas: Advanced Manufacturing, Health

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