This repository contains a series of Jupyter Notebooks that provide practical insights into fundamental concepts in signal and image processing. The notebooks are designed to offer hands-on examples with Python code, making it easier to understand and apply these concepts in real-world scenarios.

1. signal_processing.ipynb

   • This notebook covers the basics of signal processing, with a focus on signal conditioning and sensor calibration. It includes Python code for simulating signal conditioning for a temperature sensor and a basic sensor calibration using known reference values.

2. image_processing.ipynb

   • This notebook delves into image processing techniques. It includes examples of applying Fourier Transforms to images and implementing various edge detection methods such as Sobel, Canny, and Laplacian filters.

To get started, clone this repository and ensure you have Jupyter Notebook installed. You can install Jupyter using Anaconda or with pip:

pip install notebook

Once installed, navigate to the cloned directory and launch Jupyter Notebook:

jupyter notebook

• NumPy
• Matplotlib
• OpenCV-Python

You can install these dependencies via pip:

pip install numpy matplotlib opencv-python

Contributions to this repository are welcome. Feel free to fork the repo and submit pull requests with enhancements or additional examples.

This project is licensed under the MIT License - see the LICENSE file for details.

Feel free to contact me for any questions or feedback regarding this repository.