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.

Semester Project for course Introduction to Telecommunications at ECE - NTUA

Sampling and reconstruction studio with composer

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.

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.

This repository enhances EEG signal reconstruction using CycleGAN to improve brain-computer interface (BCI) signals. By translating between MRI and EEG domains with Continuous Wavelet Transform (CWT), it aims to enhance low-quality or noisy EEG data.

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

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

Project assignment for course Introduction to Telecommunications at ECE NTUA

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

Compressive Sensing and Optimization Framework to reconstruct Faraday Depth signals

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