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.

Implementation of Linear Prediction Coding (LPC) for speech signal analysis, including LPC reconstruction, prediction error, autocorrelation recovery, Levinson-Durbin recursion, and comparison of Autocorrelation and Covariance methods.

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.

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.

How much of a time series survives its image? Reconstruction of time series from recurrence plots (binary, unsigned, signed), GASF, GADF, MTF and XIRP images across seven datasets: decoders, fidelity, robustness, cost and classification utility.

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

Semester Project for course Introduction to Telecommunications at ECE - NTUA

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

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.

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.

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.

Sampling and reconstruction studio with composer

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

CL-SCA: covariance learning via successive convex approximations

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