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

Captures and replays signals on a PS/2 interface (DATA and CLOCK lines). A "capture" is a sequence of GPIO readings taken at short intervals, effectively logging the entire timeline of PS/2 pin states during recording. Each capture can be replayed - emulating the original signal.

Compressive Sensing and Optimization Framework to reconstruct Faraday Depth signals

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).

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.

Captures and replays signals on a PS/2 interface (DATA and CLOCK lines). A "capture" is a sequence of GPIO readings taken at short intervals, effectively logging the entire timeline of PS/2 pin states during recording. Each capture can be replayed - emulating the original signal.

Semester Project 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.

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.

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.

Project assignment for course Introduction to Telecommunications at ECE NTUA

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

Sampling and reconstruction studio with composer

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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.

🚀 Astride simplifies multi-cloud deployments, enabling teams to manage resources efficiently and enhance application scalability.