MIT License

项目介绍

MVRonkin/dsatools 是一个托管在 GitHub 上的开源项目,主要使用 Jupyter Notebook 编写,采用 MIT License 许可证,目前获得 145 个 Star、27 次 Fork。

仓库全名
MVRonkin/dsatools
开源许可证
MIT License
Star 数
145
Fork 数
27

README 摘要

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…

完整内容见 GitHub 原始 README 或本页 。

常见问题

dsatools 用什么语言开发?

主要使用 Jupyter Notebook 编写,采用 MIT License 许可证,目前有 145 个 Star。

dsatools 可以免费商用吗?

仓库采用 MIT License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 dsatools 的源码?

可以直接克隆仓库:git clone https://github.com/MVRonkin/dsatools.git,默认分支为 master;安装与运行步骤见仓库 README。

dsatools 最近还在维护吗?

仓库最近一次更新为 2023年3月15日,当前没有未关闭的 Issue;Star 数为 145,可作为社区活跃度的参考。

关于

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.

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

语言占比

Jupyter Notebook
Python

贡献者

README

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}
}

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