Signal-Processing

A structured collection of signal processing algorithms, simulations, and tutorials implemented in MATLAB and Python. The repository covers spectral analysis, wavelets, filtering, resampling, time-series denoising, and related foundational techniques used in modern communication systems and electronic signal workflows.

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Key Topics

• Spectral Analysis
  FFT, windowing, PSD estimation, spectral leakage analysis.

• Wavelet Processing
  Discrete wavelet transforms, denoising, multi-resolution decomposition.

• Time-Series Denoising
  Reduction of noise using wavelets, filtering, and transform methods.

• Convolution & Filtering
  FIR/IIR filter implementation, kernel-based convolution, smoothing.

• Resampling & Interpolation
  Downsampling, upsampling, anti-aliasing, interpolation methods.

• Complex Signal Processing
  I/Q signals, analytic representations, Hilbert transforms.

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Repository Structure

Signal-Processing/
├── MATLAB/
│ ├── Spectral/
│ ├── Wavelet/
│ ├── TimeSeriesDenoising/
│ ├── Convolution/
│ └── Resample/
│
├── Python/
│ ├── spectral.ipynb
│ ├── wavelet.ipynb
│ ├── convolution.ipynb
│ ├── denoising.ipynb
│ └── resampling.ipynb
│ ├── data/
├── docs/
└── README.md

Each subdirectory contains modular examples illustrating the theory and implementation of each technique.

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Getting Started

Requirements

MATLAB:

• Any recent version (R2020+ recommended)

Python: Install via:

pip install numpy scipy matplotlib pywt jupyter

Installation

git clone https://github.com/AliArabi2022/Signal-Processing.git
cd Signal-Processing

MATLAB: Open .m files directly.

Python: Run notebooks via:

jupyter notebook

Usage

Example workflows:

Frequency Domain Analysis Apply window functions → compute FFT → evaluate spectrum.

Wavelet Denoising Perform DWT → thresholding → reconstruct clean signal.

Resampling Downsample/upsample → compare aliasing → apply anti-aliasing filters.

Filtering Design FIR/IIR filters → apply convolution → analyze stability.

Each folder contains comments, equations, and plots to make the concepts accessible.

Roadmap

Planned additions:

Filter design toolbox (MATLAB + Python)

Adaptive filtering (LMS, NLMS, RLS)

Real-time signal streaming examples

Kalman filtering and state-space signal processing

Machine learning + signal processing examples

Expanded documentation with derivations and problem sets

Contributing

See CONTRIBUTING.md for guidelines on pull requests, style, and code organization.

License

This project is released under the MIT License. See the LICENSE file for details.

Maintainer

Ali Arabi Researcher in Communication Systems & Signal Processing GitHub: AliArabi2022

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