signal-reconstruction

[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).

A full-stack signal reconstruction platform that degrades and restores real-world audio and scientific time-series data using robust numerical interpolation and classical DSP techniques.

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

Code and experimental data for reconstructing noise-buried superoscillations and demonstrating sub-bandwidth range resolution

MATLAB tool for signal reconstruction using Fast Fourier Transform (FFT). Analyzes time-domain data, computes Mean Squared Error (MSE), and visualizes the trade-off between signal fidelity and harmonic compression.

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 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.

CL-SCA: covariance learning via successive convex approximations

Demonstrates Pulse-Code Modulation (PCM) — sampling, quantisation, and reconstruction of a composite analogue signal (6 Hz + 19 Hz). Implements Nyquist sampling, uniform quantisation (16 levels), FFT spectrum analysis, and ideal low-pass filter reconstruction. MATLAB and Python implementations included.

Semester Project for course Introduction to Telecommunications at ECE - NTUA

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.

Transformer-based signal reconstruction framework using neural processes, Gaussian Process priors, and in-context learning for sparse waveform prediction.

Sampling and reconstruction studio with composer

Compressed Sensing reconstruction in MATLAB — OMP, L1 minimization, DCT, speech and image recovery from sub-Nyquist measurements

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