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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Digitale Signalverarbeitung
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Über diesen Kurs
Was Sie lernen werden
Digital filters, how they work
Digital filter design
Adaptive signal processing
Lehrplan - Was Sie in diesem Kurs lernen werden
Module 2.1 Digital Filters
How digital filters work in time and in frequency.
Module 2.2: Filter Design
Learning how to choose and design the right filter using the z-transform and numerical tools.
Module 2.3: Stochastic and Adaptive Signal Processing
Analyzing and processing random signals and designing filters that adapt to unknown inputs.
Bewertungen
- 5 stars81,20 %
- 4 stars12,08 %
- 3 stars3,35 %
- 2 stars2,01 %
- 1 star1,34 %
Top-Bewertungen von DIGITAL SIGNAL PROCESSING 2: FILTERING
Best material on Signal Processing. Instructors emphasized on concepts. This course is enjoyable
Add some numerical problems and solve them in your video lectures for the better understanding of concepts.
Had to use a lot of outside material - mostly YouTube videos from Rich Radke and Barry Van Veen to complete this course.
Excellent continuation to EPFL's DSP series. Be aware that the 3rd week requires a good foundation in statistics.
Über den Spezialisierung Digitale Signalverarbeitung
This Specialization provides a full course in Digital Signal Processing, with a focus on audio processing and data transmission. You will start from the basic concepts of discrete-time signals and proceed to learn how to analyze data via the Fourier transform, how to manipulate data via digital filters and how to convert analog signals into digital format. Finally, you will also discover how to implement real-time DSP algorithms on a general-purpose microcontroller. The solid theoretical bases provided by the four courses in this specialization are complemented by applied examples in Python, in the form of Jupyter Notebooks; exercises with solutions provide a wealth of examples in order to tackle the weekly homework.

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