[EUSIPCO 2024] Python implementation of "ASTRIDE: Adaptive Symbolization for Time Series Databases"

Compressive Sensing and Optimization Framework to reconstruct Faraday Depth signals

This repo provides source code for optimizing sensor sampling locations in wireless sensor networks using spatiotemporal autoencoder.

Phase retrieval is an applied problem in the field of frame theory that describes recovering the phase of a signal given linear intensity measurements. We give examples of the codes for algorithmic phase retrieval, specifically the Gerchberg-Saxton and PhaseLift methods.

This repository contains the implementation for the paper "Enhancing EEG Signal Reconstruction in Cross-Domain Adaptation Using CycleGAN", presented at the 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS).

Sampling and reconstruction studio with composer

Semester Project for course Introduction to Telecommunications at ECE - NTUA

This repository contains MATLAB codes developed in 2018 to simulate the proposed model in Atakan, B., & Gulec, F. (2019). "Signal reconstruction in diffusion-based molecular communication." Transactions on Emerging Telecommunications Technologies, 30(12), e3699.

SCRNet is a deep neural network architecture designed to handle compressed and noisy character images signals. Tailored for tasks like character recognition and image signal restoration, SCRNet integrates classification and reconstruction pathways, enhancing performance and robustness through their synergistic interaction

From a continuous time signal get minimum required sampling frequency to allow the reconstruction of the signal and application of the reconstruction formula of the sampling theorem.

Project assignment for course Introduction to Telecommunications at ECE NTUA

A desktop application illustrating the signal sampling and recovery showing the importance and validation of the Nyquist rate.

desktop application that demonstrates signal sampling and reconstruction, emphasizing the Nyquist–Shannon sampling theorem. It allows users to explore the effects of different sampling frequencies on signal reconstruction and understand aliasing.