Winston-Salem State Graduate School Projects 2022-2024

Computer Vision Fire detection Capstone Thesis
Advanced Database Project (Coldfusion, PostGRESQL) (Unfinished) Adv Software Engineering Project (Front and Back End working with AI Recommender System)
First place Nationwide Hackaton 2023 Spring

MSc Computer Science Summary

I am a graduate in Computer Science with a strong foundation in both theoretical and applied aspects of the field. My academic journey has been enriched by a diverse set of advanced courses and hands-on projects, culminating in a thesis focused on computer vision.

Key Areas of Expertise:

Computer Vision: Completed a thesis in computer vision, demonstrating proficiency in image processing, pattern recognition, and deep learning techniques.
Advanced Databases: In-depth knowledge of database systems, including design, optimization, and the use of advanced database management systems.
Advanced Algorithms: Strong understanding of algorithmic design and analysis, with the ability to develop efficient and scalable solutions.
Software Engineering: Expertise in advanced software engineering principles, including software development lifecycle, design patterns, and testing methodologies.
Operating Systems: Comprehensive knowledge of advanced operating system concepts, including concurrency, memory management, and kernel development.
Computer Architecture: Proficiency in advanced computer architecture, focusing on hardware-software interactions, pipelining, and parallelism.
Networking: Graduate-level understanding of networking principles, protocols, and the design of scalable and secure networks.
Programming Languages and Compilers: Advanced skills in Java, Python, and Prolog, with experience in compiler construction and language design.
Data Analytics: Expertise in the Python data science stack, including data manipulation, statistical analysis, and machine learning.

Firefighting: Applying Deep Learning Algorithms for Real-time Fire DetectionWildfires in North America present a grave danger, leading to extensive damage. In this paper, we proposed a deep learning method to rapidly detect wildfires using drones or ground cameras in remote locations. The performance of the proposed system was assessed on public datasets, focusing on accuracy and energy efficiency. The study explores the possibility of running the proposed algorithm on low-power devices like NVIDIA Jetson Nano for real-time fire identification in remote regions.

Thesis Firefighting Deep Learning Algorithms by Aaron Linder.pdf