Content Coming Soon
Content Coming Soon
Content Coming Soon
Common Black College App, EDU Inc., Numerous Historically Black Colleges and Universities
Web Application Development, Cloud Services, Software Development
Python, Django, Flask, NodeJS, GoLang, AWS, Javascript, AngularJS
Content Coming Soon
Content Coming Soon
Content Coming Soon
NexxChair
Web Application Development, iOS Application Development, Cloud Services
Python, Django, AWS, Swift
Content Coming Soon
Content Coming Soon
Content coming soon
MyCoinvest
Web Application Development, iOS Application Development, Cloud Services, Architecture Design, Cryptocurrency
Python, NodeJS, AWS, AngularJS, Objective-C, Swift, Docker
Content Coming Soon
Content Coming Soon
Blue Ventures
Web Application Development, iOS Application Development, Cloud Services, Architecture Design
Python, NodeJS, Swift, AWS, MongoDB, Nats.io, Docker
The Dash Collector project was a multi-semester project during my undergraduate tenure at Georgia Tech which focused on developing a system to predict the driver of a vehicle based on the driver’s driving pattern.
The first portion of the project involved the development of an iOS application to connect with the car’s OBD port, making use of the Kiwi 2 OBD2 Diagnostic Scanner. The developed application connects wirelessly with the device to access the OBD port of the vehicle, and asynchronously sends requests to the car to receive necessary information using the various PIDs described here: https://en.wikipedia.org/wiki/OBD-II_PIDs#Standard_PIDs. The application saves the data locally using Core Data, as well as sends the data to the RESTful API if the user has network access. If access to the internet is not available, the application will send the data once network access has been re-established.
Beyond the iOS application, I also developed a Web Application to collect the data from the iOS application using a RESTful API, as well as login and signup. Through the web application, various user types can be differentiated based on their credentials and give the capability of monitoring various types of users, vehicles, and thus driving styles. In addition to the aggregation of the vehicle data, the web application also leverages several machine learning algorithms to preprocess the data and train models for each user based on each of their individual driving sessions. Based on the data collected, the system is capable of making a determination of the driver based on the various drivers within the system, and thus send a notification after a specified time, identifying the expected driver as well as the accuracy of the determination.
Future work includes more data cleanup and event-based predictions on critical events like starts and stops, turns, etc.
The research continued into a focus on work with drone technologies to uniquely identify a drone by tracking its flight pattern. The research focused on creating two different types of flight signatures: the first signature is based on data collected from sensors on the drone, and a second signature based on data collected from an on-ground camera. This research is in its infancy.
I had the opportunity to build the entire system, including an iOS application and web application, from the ground up. From the web application perspective, the backend was composed of Python Django and a PostgreSQL database. The frontend was built using AngularJS to display statistics, information, show progress and estimations, initialize jobs, and provide alerts to users. The system is also load balanced using AWS and Nginx to automatically scale based on the overall system usage.
Georgia Institute of Technology, SRI International
Web Development, Security, iOS Development, Machine Learning
Python, Django, Scikit-Learn, Nginx, AngularJS, Objective-C, AWS
The goal of the School Bus Tracker was to develop a system that would easily allow students, parents, teachers, and administrators locate school buses associated with a school system, receive notifications of events such as traffic, bus changes, and other emergencies, and easily yet securely locate students within a route. The system was designed to satisfy various user types, and thus, the data returned to each user was based on a secure sign-up process that has been verified by the student and the school system. For a student, they would be able to locate the bus that is assigned to them, as well as potentially locate family members if given proper permissions. Parents/Guardians are given the ability to easily locate each of their children once given permission. School administrators are presented with the capability to view the entirety of their system, locate each of the students as well as each of the buses.
The test system consisted of a BeagleBone Black, fitted with an RFID reader, a GPS hat, and a cellular network (provided by Verizon). Once a student boards onto and off of a bus, they tap the RFID reader with their chip-enabled student id, which is then updated in real-time to the system. This gives everyone the ability to have an up to date view of the location of students. At a specified interval, the bus updates the system with its current location. The bus is also able to provide the system with emergency events. Finally, the system leverages Google Maps to notify of events such as traffic.
The system architecture leverages AWS IoT (Amazon Web Services’ Internet of Things) platform to easily pass the data to the database (PostgreSQL) as well as scale the system. A diagram of the architecture can be seen below. It also makes use of Python Django and AngularJS for authentication as well as the Web Portal where users can log in to view their relevant information.
The School Bus Tracking application was developed in conjunction with my Senior Design Project at Georgia Tech, where I lead the various software efforts. I was the primary architect of the backend system, where I made use of AWS IOT, an elastic load balancer on several fronts, and the web backend developed using Python Django. I also lead and oversaw the development of the Web Frontend developed using AngularJS. Finally, I helped in various on the BeagleBone Microcontroller, helping the device to successfully connect to and interact with the system.
Steelcase, Georgia Tech
Web Development, AngularJS, Python Django, AWS, AWS IOT, Nginx, Load Balancer, iOS Application
Javascript, NodeJS, AngularJS, Python Django, Nginx, AWS, AWS IOT