COSC2021OCHS54486 COSC
Type: Undergraduate
Author(s):
Delaney Ochs
Computer Science
Barbara Amoros
Computer Science
Steve Priest
Computer Science
Trieu Truong
Computer Science
Marko Vulovic
Computer Science
Advisor(s):
Krishna Kadiyala
Computer Science
Bingyang Wei
Computer Science
Location: Zoom Room 2, 01:34 PM
View PresentationHomeopathy is a holistic natural system of medicine and helps patients recover from all types of illnesses naturally, while strengthening their immune system and increasing their energy and vitality. The Hygieia Homeopathy Clinic provides basic knowledge of homeopathy to their patients. Patients use their time-tested methods to trust in their own body’s recovery functionality. The main problem of the website is their patient’s inability to search the website for knowledge and protocols about homeopathy. Other problems with the website include the ability of patients to view and make appointments, purchase vitamins and supplements, and payment information. The Smart Homeopathy Doctor App Senior Design 2021 Team’s goal is to provide their clients a fully functional mobile app for easier content viewing, appointment making, shop, and patient messaging. Furthermore, the website needs to facilitate easy communication between the doctor and patients. The Smart Homeopathy Doctor App is a mobile application. Its primary function is to allow users to query a server-hosted database. The content of the database includes publicly available, non-sensitive data such as FAQs pertaining to homeopathy. The administrator performs database CRUD operations. Over the course of the project, our team has refined our time management skills and honed our Peer Review skills. We communication better by not only updating others on our progress but also asking members for help. We also learned Ionic Framework with Angular for our front-end user experience and learned to store our database in Firebase.
COSC2021RAMIREZ4645 COSC
Type: Undergraduate
Author(s):
Damon Ramirez
Computer Science
Nick Bell
Computer Science
Joe Donoghue
Computer Science
Zach Macadam
Computer Science
Cuong Nguyen
Computer Science
Advisor(s):
Bingyang Wei
Computer Science
Location: Zoom Room 2, 01:18 PM
View PresentationOur goal is to create a user friendly dashboard with data related to the current COVID-19 pandemic. This includes an interactive map, charts, and numbers presented to the user in a simplified manner. The data spans every county in the United States. Beyond just being a COVID-19 Tracker, our tool will be available as an API that can be used with any other state and county specific data.
COSC2021RUELAS29731 COSC
Type: Undergraduate
Author(s):
Ben Ruelas
Computer Science
Hy Dang
Computer Science
Trang Dao
Computer Science
Dorian Dhamo
Computer Science
Minh Nguyen
Computer Science
Advisor(s):
Bingyang Wei
Computer Science
Location: Zoom Room 2, 02:31 PM
View PresentationIdentifying new and cutting-edge investment strategies is a crucial step in establishing any large business within its relative industry. Fort Capital, whose primary investment focus is on industrial-grade buildings, is taking an innovative and insightful approach to geographic understanding. Fort Capital aims to identify trade routes used by major market players, such as Amazon and Walmart, to find the areas where industrial warehouses and large-scale distribution centers are in highest demand. To locate such trade routes, identifying the main travelers on these routes is essential, and Truck Detective aims to do exactly that. Using machine learning and artificial intelligence models such as a deep neural network, Truck Detective enables Fort Capital to detect, with high accuracy, the location of big rig trucks, and can additionally help identify where they came from or where they are heading. This, in turn, illuminates geographically important areas with promising investment opportunities for Fort Capital.
COSC2021TRUONG2357 COSC
Type: Undergraduate
Author(s):
Quang Truong
Computer Science
Advisor(s):
Bo Mei
Computer Science
Location: Zoom Room 3, 12:46 PM
(Presentation is private)Vehicle Re-identification, which aims to retrieve matching vehicles across different cameras, is a challenging problem in Intelligent Transport System due to different factors such as illumination conditions, occlusions, and video resolution. Numerous studies are proposing the use of Deep Neural Networks, a recent advance in Artificial Intelligence, thanks to their exceptional feature embedding extraction. However, Deep Neural Networks perform poorly on cross-domain settings. Furthermore, vehicle re-identification training data is relatively limited because public videos are only accessible to the authority only. Our study tackles the above challenges by utilizing several state-of-the-art techniques on domain learning to expand the model's generalization capability. Our research shows that we can outperform other state-of-the-art models by large margins on popular vehicle re-identification benchmarks.
ENGR2021HERENDEEN60975 ENGR
Type: Undergraduate
Author(s):
Jim Herendeen
Engineering
Advisor(s):
Robert Bittle
Engineering
Location: Zoom Room 6, 12:54 PM
View PresentationThe purpose of this project is to create a closed loop system that will enable a continuous drying cycle of mined limestone through a rotating cylindrical dryer. Our client, Lhoist North America, has tasked us with designing this system, and our biggest issue has been putting together the system on a limited budget. We have determine that the most efficient method of designing the system is to used scrapped equipment that Lhoist has available and reconfiguring it for our design, rather than buying a new system. Another challenge we have faced is the method of transporting the mined limestone due to its sand-like qualities. We believe that the most effective method of designing the system will be by altering scrapped material from Lhoist’s scrapyard to complete a closed loop system of the limestone for the rotary dryer.