Key Information
About the content
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.
Syllabus
- 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
Instructors
Paolo Prandoni
Lecturer
School of Computer and Communication Science
Martin Vetterli
Professor
School of Computer and Communication Sciences
Content Designer
The École polytechnique fédérale de Lausanne (EPFL, English: Swiss Federal Institute of Technology in Lausanne) is a research university in Lausanne, Switzerland, that specialises in physical sciences and engineering.
One of the two Swiss Federal Institutes of Technology, the school was founded by the Swiss Federal Government with the stated mission to:
Educate engineers and scientists to the highest international standing
Be a national center of excellence in science and technology
Provide a hub for interaction between the scientific community and the industry
EPFL is considered one of the most prestigious universities in the world for engineering and sciences, ranking 17th overall and 10th in engineering in the 2015 QS World University Rankings; 34th overall and 12th in engineering in the 2015 Times Higher Education World University Rankings.
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
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`
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.
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`
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!!
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.
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.
