Pinned Repositories
CGAL-Polygon-Optimization
From Point Sets to Optimization and Evaluation: In this project series, we have executed three projects, each focusing on a fundamental aspect of polygon manipulation using CGAL.
Eigenfaces-for-Face-Recognition
Eigenfaces method for face recognition using PCA (Principal Component Analysis) under varying lighting conditions.
Memory-Management-Simulation
This project implemented in C++ aims to evaluate and compare the performance of three different memory allocation algorithms: best fit, worst fit, and the Buddy algorithm. The simulation is designed to provide insights into how these algorithms perform under various scenarios.
Music-Classification-with-Neural-Networks
This project focuses on classifying 1-second music samples into four genres (classical, hip-hop, rock, and blues) using neural networks with two different audio data representations: MFCCs and mel-spectrograms.
Quantum-Fourier-Transform-via-variational-quantum-circuits
This project is a research focused on utilizing variational quantum circuits (VQC) to simulate the Quantum Fourier Transform (QFT), a fundamental operation in quantum computing.The primary objective is to develop an efficient VQC-based approach to perform QFT, with a particular emphasis on optimizing circuit parameters using classical algorithms.
CGAL-Polygon-Optimization
EvangeliaS's Repositories
EvangeliaS/CGAL-Polygon-Optimization
From Point Sets to Optimization and Evaluation: In this project series, we have executed three projects, each focusing on a fundamental aspect of polygon manipulation using CGAL.
EvangeliaS/Eigenfaces-for-Face-Recognition
Eigenfaces method for face recognition using PCA (Principal Component Analysis) under varying lighting conditions.
EvangeliaS/Memory-Management-Simulation
This project implemented in C++ aims to evaluate and compare the performance of three different memory allocation algorithms: best fit, worst fit, and the Buddy algorithm. The simulation is designed to provide insights into how these algorithms perform under various scenarios.
EvangeliaS/Music-Classification-with-Neural-Networks
This project focuses on classifying 1-second music samples into four genres (classical, hip-hop, rock, and blues) using neural networks with two different audio data representations: MFCCs and mel-spectrograms.
EvangeliaS/Quantum-Fourier-Transform-via-variational-quantum-circuits
This project is a research focused on utilizing variational quantum circuits (VQC) to simulate the Quantum Fourier Transform (QFT), a fundamental operation in quantum computing.The primary objective is to develop an efficient VQC-based approach to perform QFT, with a particular emphasis on optimizing circuit parameters using classical algorithms.