Signal Conditioning System for Analog Processing of Dynamic Light Scattering Signals

Citation Author(s):: David Miranda (Universidad Industrial de Santander, Bucaramanga, Colombia)

Said Pertuz (Universidad Industrial de Santander, Bucaramanga, Colombia)

Julian Jerez (Universidad Industrial de Santander, Bucaramanga, Colombia)
Submitted by:: David Miranda
Last updated:: Sun, 05/11/2025 - 12:52
DOI:: 10.21227/skfv-mp79
Data Format:: *.txt *.ipynb *.m *.mat *.cpp *.STL *.IGS *.pdf *.png *.xlsx

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Categories:

Analog signal processing

Keywords:

light scattering

Analog-to-digital conversion

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Abstract

This dataset accompanies the article "Signal Conditioning System for Analog Processing of Dynamic Light Scattering Signals", and contains the processed data and Python scripts used for signal analysis. The data includes voltage signals generated by a Maxwell demon-based random signal generator and acquired through a conditioning system designed for breast tissue analysis using Dynamic Light Scattering (DLS). The dataset supports key stages of the processing pipeline, including histogram generation, Gaussian fitting, power spectral analysis, and digital notch filtering for 60 Hz noise reduction. Figures in the corresponding paper were generated from these scripts, which also include functions for entropy and signal-to-noise ratio (SNR) calculations. This resource facilitates reproducibility and provides a reference implementation for future studies involving analog signal conditioning and biomedical signal analysis.

Instructions:

This dataset includes a Jupyter Notebook (.ipynb) that contains all the necessary code and processing steps to reproduce the figures and results described in the associated publication. To utilize the dataset:

Ensure you have Python installed, preferably via a scientific distribution such as Anaconda.
Open the Jupyter Notebook environment on your system.
Launch the notebook file data_processing_paper.ipynb.
Execute the notebook cells sequentially to load the data, apply processing steps (e.g., filtering, histogram generation, Gaussian fitting, and power spectral analysis), and visualize the results.

No additional setup is required. All key functions and processing stages are embedded within the notebook for ease of use and reproducibility.