Computer Science - Student Works

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https://hdl.handle.net/20.500.14078/54

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Now showing 1 - 5 of 9
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    Analyzing factors impacting COVID-19 vaccination rates
    (2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, Mohamad
    Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.
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    OpenCV laptop demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    When we started the project, we had decided to make a program that would use feature matching to recognize a specific image (eg. a poster or sticker), find it’s orientation, and then display some kind of useful AR artifacts in the 3D space of our recognized image. We have implemented this in OpenCV, to show that we have an in-depth understanding of how AR works.
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    Android app demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    For the app, we developed an Android version utilizing Google's ARCore toolkit. The Design students prototyped screens for user profiles, buying art, as well as filtering and browsing functionality. This functionality has not yet been implemented, and we instead chose to focus on the AR screens. The user can browse through and preview different paintings and frames.
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    496 Capstone: AR.t
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.
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    AR.T deliver report
    (2020) Lafitte, Chanelle; Galay, Chance; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.