|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dsp1
课程评论：没有评论
课程名称：数字信号处理1：基本概念与算法 课程概述：数字信号处理（DSP）是工程学的一个分支，在短短几十年间实现了前所未有的人际沟通与按需娱乐水平。通过将电子学、电信和计算机科学的原则重组为一个统一的范式，DSP成为了数字革命的核心，带来了CD、DVD、MP3播放器、手机及无数其他设备。本系列四门课程将从基础开始学习数字信号处理的基本概念。课程内容将涵盖离散时间信号的基本定义、傅里叶分析、滤波器设计、采样、插值和量化，构建出足够完整的DSP工具集，以详细分析实际通信系统。课程将定期用实际示例和演示来桥接理论与实践之间的差距。 为了充分利用这门课程，建议学习者具备基础微积分和线性代数的知识；课程中将提供若干编程示例，使用Python笔记本进行演示，但学习者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： - 模块1.1：数字信号处理基础 - 描述：介绍数字信号处理的符号和基础知识 - 模块1.2：信号处理与向量空间 - 描述：将信号建模为适当向量空间中的向量，使用线性代数表示信号处理 - 模块1.3：傅里叶分析基础 - 描述：傅里叶变换和频域的基本概念 - 模块1.4：进阶傅里叶分析工具 - 描述：深入探索傅里叶分析的更多内容
Name:Module 1.1: Digital Signal Processing: the Basics
Description:Introduction to the notation and basics of Digital Signal Processing
Name:Module 1.2: Signal Processing Meets Vector Space
Description:Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.
Name:Module 1.3: Fourier Analysis: the Basics
Description:The fundamental concepts behind the Fourier transform and the frequency domain
Name:Module 1.4: Fourier Analysis: More Advanced Tools
Description:Delving deeper in the world of Fourier analysis.
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. In this series of four courses, you will 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.
