Hi, I'm Dayna Dranitsaris.
A 3rd-year computer engineering student at Queen's University, with a degree focus in artificial intelligence.
About Me
Hello! I'm Dayna, a 3rd-year computer engineering student at Queen's University specializing in artificial intelligence. My tech journey began in grade 9, where my fascination with code and physical computing was fueled by competing in the Waterloo Centre for Education in Mathematics and Computing (CEMC) contests, including the Fermat (2022), Euclid (2023), and the Canadian Computing Competition (CCC) at both the Junior (2022) and Senior (2023) levels. This foundation seamlessly led to my involvement in a variety of web development and engineering projects in university.
I'm also an active member of the Queen's Engineering community, having joined the Queen's Racing Formula SAE Team and Queen's High Performance Computing Club, volunteering for The Queen's Engineering Society as an orientation leader, and serving as a Challenge Coordinator for the annual Queen's Engineering Competition. I've also taken on the role of Software Developer at the EngSoc Software Development Team (ESSDev).
Skills
Certifications
Projects
Queen's Hyperloop Design Team (QHDT) Machine Vision Sensor System
Built an embedded computer vision system for a Hyperloop prototype to detect and classify track obstructions using a custom-trained YOLOv5 model.
NHL Goal Horn Machine
Built a Raspberry Pi system that triggers LED sequences, audio playback, and LCD score displays based on live NHL game data retrieved via API integration and serial communication.
RFID Scanner System
Built an embedded RFID scanner system using an Arduino and custom LCD to display real-time scanned tag data.
Human Activity Recognition
Preprocessed 100 Hz accelerometer data with noise reduction and 5-second segmentation; engineered statistical features and trained a logistic regression model achieving 94% accuracy (AUC = 0.98). Deployed the model in a Tkinter application for real-time CSV classification and visualization.
Hydroponic Garden Monitoring System
Built a sensor-integrated hydroponic garden system for campus use, collecting real-time pH, temperature, and humidity data via WiFi and displaying it through a React Native mobile application.
Charlene In The City
Built and deployed a professional real estate website using WordPress, HTML, CSS, and JavaScript, integrating IDX (CREA DDF®) listing feeds, configuring SSL/TLS, and monitoring user engagement with Google Analytics.
Automated Powder/Fluid Dispenser
Built an automated powder and fluid dispensing system using an Arduino, IR sensing, and electromechanical actuation, with mechanical components designed in SolidWorks.
Course Grade & Productivity Application
Built an academic performance tracking application in C++ using Qt for the user interface and Firebase/Firestore for real-time data storage. Implemented user authentication, multi-user data structures for managing friends lists, and dynamic data visualization for course statistics.
CPU Project
Designed and implemented a fully functional 32-bit RISC processor in Verilog as part of a team project. Developed the complete datapath including a 16-register file, 32-bit bidirectional bus, arithmetic logic unit (ALU) with 13 operations, and special-purpose registers (PC, IR, MAR, MDR, HI/LO). Integrated a ripple-carry adder and non-restoring divider for arithmetic operations. Created comprehensive testbenches for functional verification and simulated all 13 instruction types using ModelSim and GTKWave. Implemented the finite state machine control unit for instruction fetch, decode, and execute cycles. Designed RAM/ROM memory interfaces and integrated the processor with I/O peripherals for complete system functionality.
Autonomous Vehicle System
Built an end-to-end autonomous vehicle system using a Raspberry Pi, integrating real-time computer vision, motor control (GPIO/PWM), and edge AI inference. Trained a custom YOLOv8 object detection model for multi-class traffic sign recognition and optimized grayscale preprocessing for efficiency. Deployed the model to Google Coral via TensorFlow Lite to enable low-latency, on-device inference and real-time vehicle response.
BMH Interactive Display
Developed a real-time interactive display providing weather, transit routes, events, room availability, news, and a photo booth using a Raspberry Pi 5 and IR touch frame. Built a full-stack application using React, HTML/CSS, Node.js, Flask, and third-party APIs; containerized using Docker and deployed via Vercel. Applied team-based SDLC practices and CI/CD to design, prototype, and integrate hardware and software components.