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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dsp3
课程评论：没有评论
课程名称：数字信号处理3：模拟与数字 课程概述：数字信号处理（DSP）是工程学的一个分支，在短短几十年内，促进了前所未有的人际沟通和按需娱乐的发展。通过重新构建电子学、电信和计算机科学的原理，DSP成为了数字革命的核心，带来了CD、DVD、MP3播放器、手机和无数其他设备。 本课程的目标是从基础学习数字信号处理的基本知识。我们将从离散时间信号的基本定义开始，逐步深入傅里叶分析、滤波器设计、采样、插值和量化，构建一个足够完整的DSP工具集，以详细分析一个实际的通信系统。课程中将定期使用实践示例和演示，以缩小理论与实践之间的差距。 为了更好地利用本课程，建议学生具备基本的微积分和线性代数知识；课程将提供多个编程示例，采用Python笔记本形式，但您也可以使用自己喜欢的编程语言来测试课程中描述的算法。 课程大纲： 1. 模块3.1：插值与采样 - 描述：从连续时间到离散时间及反向转换。 2. 模块3.2：信息失真（别名现象） - 描述：在采样连续时间信号时会发生什么以及我们应预见的问题。 3. 模块3.3：多速率信号处理 - 描述：如何在离散时间域中完全更改采样率。 4. 模块3.4：A/D与D/A转换 - 描述：从模拟信号到数字信号的转换，以及反向过程。
Name:Module 3.1: Interpolation and Sampling
Description:From continuous time to discrete time and vice versa.
Name:Module 3.2: Aliasing
Description:What happens when we sample continuous-time signals and problems we should anticipate.
Name:Module 3.3: Multirate Signal Processing
Description:How to change the sampling rate entirely from the discrete-time domain.
Name:Module 3:4: A/D and D/A Conversion
Description:Going from analog to digital, and vice-versa.
Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.
