Sampling-Theory Studio

Introduction

sampling an analog signal is a crucial step for any digital signal processing system The Nyquist–Shannon sampling theorem guarantees a full recovery of the signal when sampling with a frequency larger than or equal to the bandwidth of the signal (or double the maximum frequency in case of real signals).

Description

It is a web application that illustrates the signal sampling and recovery showing the importance and validation of the nyquist rate

• Our application have the following features:
  • Visualize and sample an uploaded signal and use the sampled points to recover the original signal
  • Adding noise to the loaded signal and reconstructing it.
  • Prepare mixed signal by adding sinusoidal signals with different frequancy and magnitudes.
  • Sampling and reconstructing the mixed signal.
  • Adding noise to the mixed signal and reconstructing it.
  • Remove any component from the mixed signal.
  • Downloading the reconstructed signal.
  • Resize the signals without missing the UI.

Technology used

Python with streamlit

Task Info

Course: Digital signal processing

Department: Systems and Biomedical Engineering at Cairo University

Semester: 7th SEMESTER

Team Members:

Name	SEC	BN
Saeed Elsayed	1	42
Mazen Tarek	2	13
Maryam Megahed	2	32
Neveen Mohamed	2	49

Screenshots of the web app

Generating sin wave with frequency 4hz and amplitude 2v

Sampling it with 2 fmax(Nyquist rate)

Reconstructing the signal

Adding noise to the generated signal

Reconstructing the signal with its noise by Nyquist rate

Upload ECG signal

Reconstructing the ECG signal