Ayanda Khoza
Final Year Electrical and Electronic Engineering Student
Summary
Enthusiastic and highly motivated final-year Electrical and Electronic Engineering student with a strong passion for science, technology, and innovation. Possessing a solid foundation in core engineering principles, machine learning, and control systems, demonstrated through diverse projects and research. Eager to leverage strong analytical skills, technical expertise, and a commitment to fostering scientific curiosity as a volunteer judge for the Eskom Science Expo. Proven ability to provide constructive feedback through 2 years of experience tutoring engineering modules.
Experience
Engineering Modules Tutor
University of Johannesburg | Johannesburg, South Africa
2 Years
- Provided academic support and guidance to undergraduate students in various engineering modules.
- Explained complex concepts, assisted with problem-solving, and reviewed coursework to enhance understanding.
- Developed strong communication and feedback skills, adapting explanations to individual learning styles.
- Contributed to improved student comprehension and academic performance in challenging subjects.
Education
Bachelor of Engineering in Electrical and Electronic Engineering
University of Johannesburg | Johannesburg, South Africa
2022 – Present (Final Year Student)
Matric
Tabhane Secondary School | Year of Completion: 2021
Secondary School
Tabhane Secondary School | 2017 – 2021
Primary School
Indanyana Primary School | 2012 – 2016
Skills & Interests
Core Engineering & Technical:
Design & Web:
Projects & Research
MPPT Boost Converter with ESP32
Practical implementation of a Maximum Power Point Tracking (MPPT) boost converter using an ESP32. Features GA-tuned fuzzy logic control, data logging, and a modular design.
Kalman Filter for Airplane Tracking
Applied the Kalman Filter technique in Python to optimize position and velocity estimates for simulated airplane data. Analyzed filter performance and compared results with theoretical expectations.
CNN for Image Classification
Implemented a 5-layer Convolutional Neural Network (CNN) in Python for image classification. Trained and tested the model on a dataset, evaluated accuracy, and analyzed prediction performance.
AI for Energy Systems (TSA Research)
Ongoing research and development for the startup TSA, focusing on applying AI/ML techniques to optimize Power Electronics and Energy Systems. Exploring concepts like intelligent load shedding assistance and renewable energy integration.