Digital Signal Processing 3: Analog vs Digital from Coursera

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.

What's inside

Syllabus

Module 3.1: Interpolation and Sampling

From continuous time to discrete time and vice versa.

Module 3.2: Aliasing

What happens when we sample continuous-time signals and problems we should anticipate.

Module 3.3: Multirate Signal Processing

How to change the sampling rate entirely from the discrete-time domain.

Module 3:4: A/D and D/A Conversion

Going from analog to digital, and vice-versa.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores digital signal processing from introductory concepts all the way through advanced topics, like interpolation, downsampling, and more

Provides hands-on examples and demonstrations to help learners connect theory to practice

Suits students with a basic understanding of calculus and linear algebra, as well as programming experience in Python

Taught by Paolo Prandoni and Martin Vetterli, renowned experts in digital signal processing

Covers interpolation, sampling, aliasing, multirate signal processing, and A/D and D/A conversion, which are core concepts in digital signal processing

Reviews summary

Highly rated analog signal processing course

Learners say this course on analog vs digital signal processing is excellent and highly applicable. The mathematically rigorous materials are presented with engaging assignments and well-structured tests that check concepts well. Overall, this course is highly recommended.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Digital Signal Processing 3: Analog vs Digital with these activities:

Review basics of calculus and linear algebra

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Review the fundamentals of calculus and linear algebra to strengthen your mathematical foundation for DSP.

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Review concepts of derivatives, integrals, and limits.
Revisit matrices, vector spaces, and linear transformations.
Solve practice problems to reinforce your understanding.

Explore online tutorials on specific DSP topics

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Seek out supplemental tutorials to deepen your understanding of challenging DSP concepts.

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Identify areas where you need additional clarification.
Search for online tutorials that cover those specific topics.
Follow the tutorials and complete any exercises provided.
Take notes and summarize the key takeaways.

Work through DSP exercises

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Engage in hands-on practice by solving DSP exercises and problems.

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Download the practice exercises from the course website.
Attempt to solve the exercises on your own.
Check your solutions against the provided answer key.
Review any incorrect answers and seek clarification.

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Attend a DSP workshop or conference

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Immerse yourself in the DSP community by attending events where you can learn from experts and connect with others.

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Research upcoming DSP workshops or conferences.
Register for an event that aligns with your interests.
Attend the event and actively participate in discussions.
Follow up with any connections you make.

Join a study group for DSP

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Collaborate with peers to discuss DSP concepts, share knowledge, and work through problems together.

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Find or create a study group with fellow students.
Meet regularly to discuss course material.
Work together on practice problems and projects.
Provide feedback and support to each other.

Develop a visual representation of DSP concepts

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Create visual aids, such as diagrams, charts, or animations, to enhance your understanding of DSP concepts.

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Identify a specific DSP concept or topic.
Brainstorm ideas for how to visually represent the concept.
Create the visual representation using appropriate tools.
Share your visualization with others for feedback.

Build a DSP project from scratch

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Apply your DSP knowledge by designing and implementing a practical project.

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Define the scope and objectives of your project.
Research and select appropriate DSP algorithms and techniques.
Implement your project using a programming language.
Test and evaluate the performance of your project.

Contribute to open-source DSP projects

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Engage with the wider DSP community by contributing to open-source projects.

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Identify open-source DSP projects that align with your interests.
Review the project documentation and codebase.
Identify areas where you can contribute.
Submit pull requests with your contributions.

Career center

Learners who complete Digital Signal Processing 3: Analog vs Digital will develop knowledge and skills that may be useful to these careers:

Signal Processing Engineer

Signal Processing Engineers are responsible for designing, developing, and testing signal processing systems. They need a strong foundation in DSP to understand how signals are processed and how to design systems that can process them effectively. This course provides a comprehensive overview of DSP, covering topics such as Fourier analysis, filter design, sampling, interpolation, and quantization. This knowledge is essential for anyone who wants to work as a Signal Processing Engineer.

See salaries and explore the career path for Signal Processing Engineer

Audio Engineer

Audio Engineers are responsible for recording, mixing, and mastering audio signals. They need a strong understanding of DSP to understand how audio signals are processed and how to create effects and enhancements. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as an Audio Engineer.

See salaries and explore the career path for Audio Engineer

Speech Scientist

Speech Scientists are responsible for studying the production and perception of speech. They need a strong understanding of DSP to analyze speech signals and to develop models of speech production and perception. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Speech Scientist.

See salaries and explore the career path for Speech Scientist

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data. They need a strong understanding of DSP to process and analyze signals. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models. They need a strong understanding of DSP to process and analyze signals. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Electrical Engineer

Electrical Engineers are responsible for designing, developing, and testing electrical systems. They need a strong understanding of DSP to design and analyze signal processing systems. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as an Electrical Engineer.

See salaries and explore the career path for Electrical Engineer

Computer Engineer

Computer Engineers are responsible for designing, developing, and testing computer systems. They need a strong understanding of DSP to design and analyze signal processing systems. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Computer Engineer.

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Software Engineer

Software Engineers are responsible for designing, developing, and testing software systems. They need a strong understanding of DSP to develop signal processing software. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Software Engineer.

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Telecommunications Engineer

Telecommunications Engineers are responsible for designing, developing, and testing telecommunications systems. They need a strong understanding of DSP to analyze and process signals. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Telecommunications Engineer.

See salaries and explore the career path for Telecommunications Engineer

Biomedical Engineer

Biomedical Engineers are responsible for designing, developing, and testing biomedical systems. They need a strong understanding of DSP to analyze and process biological signals. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Biomedical Engineer.

See salaries and explore the career path for Biomedical Engineer

Acoustical Engineer

Acoustical Engineers are responsible for designing, developing, and testing acoustical systems. They need a strong understanding of DSP to analyze and process acoustical signals. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as an Acoustical Engineer.

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Geophysicist

Geophysicists are responsible for studying the earth's physical properties. They need a strong understanding of DSP to analyze and process geophysical data. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Geophysicist.

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Oceanographer

Oceanographers are responsible for studying the ocean. They need a strong understanding of DSP to analyze and process oceanographic data. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as an Oceanographer.

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Astronomer

Astronomers are responsible for studying the universe. They need a strong understanding of DSP to analyze and process astronomical data. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as an Astronomer.

See salaries and explore the career path for Astronomer

Meteorologist

Meteorologists are responsible for studying the weather. They need a strong understanding of DSP to analyze and process meteorological data. This course provides a solid foundation in DSP, covering topics such as Fourier analysis, filter design, and sampling. This knowledge is essential for anyone who wants to work as a Meteorologist.

See salaries and explore the career path for Meteorologist