Informações principais
Sobre o conteúdo
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.
Programa de estudos
- 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
Instrutores
Paolo Prandoni
Lecturer
School of Computer and Communication Science
Martin Vetterli
Professor
School of Computer and Communication Sciences
Criador do conteúdo
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.
Plataforma
A Coursera é uma empresa digital que oferece um curso on-line massivo e aberto, fundado pelos professores de computação Andrew Ng e Daphne Koller Stanford University, localizado em Mountain View, Califórnia.
O Coursera trabalha com as melhores universidades e organizações para disponibilizar alguns dos seus cursos on-line e oferece cursos em várias disciplinas, incluindo: física, engenharia, humanidades, medicina, biologia, ciências sociais, matemática, negócios, ciência da computação, marketing digital, ciência de dados. e outros assuntos.Cours
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.