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.
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배울 내용
The nature of discrete-time signals
Discrete-time signals are vectors in a vector space
Discrete-time signals can be analyzed in the frequency domain via the Fourier transform
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강의 계획표 - 이 강좌에서 배울 내용
Module 1.1: Digital Signal Processing: the Basics
Introduction to the notation and basics of Digital Signal Processing
Module 1.2: Signal Processing Meets Vector Space
Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.
Module 1.3: Fourier Analysis: the Basics
The fundamental concepts behind the Fourier transform and the frequency domain
Module 1.4: Fourier Analysis: More Advanced Tools
Delving deeper in the world of Fourier analysis.
검토
- 5 stars69.85%
- 4 stars20.38%
- 3 stars5.73%
- 2 stars1.69%
- 1 star2.33%
DIGITAL SIGNAL PROCESSING 1: BASIC CONCEPTS AND ALGORITHMS의 최상위 리뷰
The state space approach to explaining the basics of signal processing has been handled well.
very good course, but it require some math and a brief reading of a book in signals, there are only few courses in coursera that are challenging, this is one of them, 10/10
I material the very challenging, but with some time and effort it can be very fun and also rewarding
The math was extremely difficult. But I made it through.
디지털 신호 처리 특화 과정 정보
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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