/dsatools

Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mode decomposition (EMD); empirical wavelet transform (EWT); Hilbert vibration decomposition (HVD) and many others.

Primary LanguageJupyter NotebookMIT LicenseMIT

Digital Signal Analysis (DSA) library for python. The library includes such methods of signal analysis and signal parameter estimation as arma-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mode decomposition; empirical wavelet transform; Hilbert vibration decomposition and many others.

For install use

pip install dsatools

For cite you may use

Ronkin, Mikhail V., et al. "Numerical analysis of adaptive signal decomposition methods applied for ultrasonic gas flowmeters." AIP Conference Proceedings. Vol. 2425. No. 1. AIP Publishing LLC, 2022.

Google schoar; research gate

bibtex

@inproceedings{ronkin2022numerical,
 title={Numerical analysis of adaptive signal decomposition methods applied for ultrasonic gas flowmeters},
 author={Ronkin, Mikhail V and Kalmykov, Alexey A and Polyakov, Stanislav O and Nagovicin, Viktor S},
 booktitle={AIP Conference Proceedings},
 volume={2425},
 number={1},
 pages={130009},
 year={2022},
 organization={AIP Publishing LLC}
}

Also paper on the topic of using dsatools: Ronkin, Mikhail, and Dima Bykhovsky. "Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption." Sensors 23, no. 1 (2023): 533.

Google schoar; research gate; mdpi

bibtex

@article{ronkin2023passive,
 title={Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption},
 author={Ronkin, Mikhail and Bykhovsky, Dima},
 journal={Sensors},
 volume={23},
 number={1},
 pages={533},
 year={2023},
 publisher={MDPI}
}