PCP Notebooks: A Preparation Course for Python with a Focus on Signal Processing
PCP Notebooks: A Preparation Course for Python with a Focus on Signal Processing
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PCP 笔记本：专注于信号处理的 Python 准备课程
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sebastian Rosenzweig
中科院分区：
文献类型：
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作者：
Meinard Müller;Sebastian Rosenzweig
由于机器学习的快速发展和开源软件的日益重要性，Python已成为许多科学领域研究和教育的主要计算机编程语言。虽然许多工程专业的硕士学生拥有不同编程语言的编程技能，如MATLAB，C/C++或Java，但他们在使用Python和许多相关的软件框架方面往往缺乏经验。PCP笔记本通过为Python准备课程（PCP）提供开源教育材料，同时使用信号处理作为实践编程概念的激励和有形应用程序，有助于缩小这一差距。以开放访问的Jupyter笔记本框架为基础（Kluyver等人，2016年），PCP笔记本由交互式文档组成，其中包含可执行代码，教科书般的解释，数学公式，绘图，图像和声音示例。假设有一些一般的编程经验和数字信号处理的基本知识，PCP笔记本电脑的目的是服务于几个目的。首先，他们介绍了在参加信号处理课程的实验室课程或使用更高级的信号处理工具箱时所需的Python编程的基本概念。此外，笔记本回顾了信号处理中所需的中心数学概念，包括复数、指数函数、信号和采样以及离散傅里叶变换。课程的另一个目标是让学生熟悉软件开发和可重复研究的现代工具。提供交互式和结构良好的材料，可用于课程或自学，我们希望PCP笔记本电脑在促进教育和研究多媒体工程和超越作出宝贵的贡献。
Due to the rapid developments in machine learning and the growing importance of opensource software, Python has become the predominant computer programming language for research and education in many scientific fields. While many engineering students on the Master’s level have programming skills in different programming languages such as MATLAB, C/C++, or Java, they are often less experienced in using Python and the many associated software frameworks. The PCP notebooks contribute to closing this gap by offering open-source educational material for a Preparation Course for Python (PCP) while using signal processing as a motivating and tangible application for practicing the programming concepts. Building upon the open-access Jupyter notebook framework (Kluyver et al., 2016), the PCP notebooks consist of interactive documents that contain executable code, textbook-like explanations, mathematical formulas, plots, images, and sound examples. Assuming some general programming experience and basic knowledge in digital signal processing, the PCP notebooks are designed to serve several purposes. First of all, they introduce basic concepts of Python programming as required when participating in lab courses in a signal processing curriculum or when working with more advanced signalprocessing toolboxes. Furthermore, the notebooks recap central mathematical concepts needed in signal processing, including complex numbers, the exponential function, signals and sampling, and the discrete Fourier transform. Another goal of the course is to familiarize students with modern tools for software development and reproducible research. Providing interactive and well-structured material that may be used in a course or for self-study, we hope that the PCP notebooks make a valuable contribution in fostering education and research in multimedia engineering and beyond.