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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.

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课程大纲

  • Week 1 - Module 1: Basics of Digital Signal Processing
     
  • Week 2 - Module 2: Vector Spaces
     
  • Week 3 - Module 3: Part 1 - Basics of Fourier Analysis
     
  • Week 4 - Module 3: Part 2 - Advanced Fourier Analysis
     
  • Week 5 - Module 4: Part 1 Introduction to Filtering
     
  • Week 6 - Module 4: Part 2 Filter Design
     
  • Week 7 - Module 5: Sampling and Quantization
     
  • Week 8 - Module 6: Digital Communication Systems - Module 7: Image Processing
     
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教师

Paolo Prandoni
Lecturer
School of Computer and Communication Science

Martin Vetterli
Professor
School of Computer and Communication Sciences

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内容设计师

École Polytechnique Fédérale de Lausanne

L’École polytechnique fédérale de Lausanne (EPFL) est une institution universitaire de renommée internationale, spécialisée dans le domaine de la science et de la technologie, située à Lausanne, bien que sur le territoire communal d'Écublens, en Suisse et fondée en 1853, sous le nom d’École spéciale de Lausanne.

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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

评论
4.6 /5 平均值
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最佳评论

A very followable intro to DSP. Gets progressively complex, but fundamentals are thoroughly explained, demonstrated and visualized. A free book is included. `Signal of the Day` examples helped me broaden my understanding--not just audio samples are signals, even weather data for centuries are too. There are python jupyter notebooks to experiment with the concepts. There are few other courses/books that make use of python to teach DSP. They usually write wrapper classes/functions around DSP basics, and we end up doing everything through them. This one is more direct: signals are stored and processed as `numpy` arrays, visualized with `matplotlib`

发布于 2018年2月12日
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发表于 2018年2月13日
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A very good course, but relative hard one. I enjoyed the course but I need to review some of the earlier weeks' materials. Overall I learnt more stuff about DSP.Just to summarize my comments:This course has a different structure than other DSP courses. It focuses on the principles.This course was tough assignments, which nevertheless help you to understand the concepts.Many concepts need reviewing, it is not easy to master them.Guy, thank you for the course.

发表于 2018年2月12日
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A very followable intro to DSP. Gets progressively complex, but fundamentals are thoroughly explained, demonstrated and visualized. A free book is included. `Signal of the Day` examples helped me broaden my understanding--not just audio samples are signals, even weather data for centuries are too. There are python jupyter notebooks to experiment with the concepts. There are few other courses/books that make use of python to teach DSP. They usually write wrapper classes/functions around DSP basics, and we end up doing everything through them. This one is more direct: signals are stored and processed as `numpy` arrays, visualized with `matplotlib`

发表于 2018年2月5日
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The course has great details and concepts to learn. Exams in the course test your knowledge really well. The material and exams are strategically designed and push you to re-analyze the gained knowledge. I am enjoying the difficulties of the course.'Signal Processing for communication by P Prandoni and M Vetterli' is a good text book to refer and study along side the course.I hope you find the course equally interesting!!

发表于 2018年1月26日
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This was a good course, especially for those who are familiar with the topic. In my view, more emphasis could be given to image processing than to communications. Image processing is appealing to a wider audiance.

发表于 2018年1月8日
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I love classes of EPFL. It dives into the concept very deeply while keeping in mind the application. In a nutshell, this class is a must-have for all engineers.