Enter the world of signal processing: analyze and extract meaning from the signals around us!
With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
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About this course
Advanced calculus, complex algebra, and linear algebra.
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This centre's achievements
2017
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This centre has featured on Emagister for 8 years
Subjects
Systems
Engineering
Frecuency Domain
Time signals
Fourier Transform
Course programme
Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data? Students will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. Students will apply these concepts in interactive MATLAB programming exercises (all done in browser, no download required). Part 1 of this course analyzes signals and systems in the time domain. Part 2 covers frequency domain analysis. Prerequisites include strong problem solving skills, the ability to understand mathematical representations of physical systems, and advanced mathematical background (one-dimensional integration, matrices, vectors, basic linear algebra, imaginary numbers, and sum and series notation). Part 1 is a prerequisite for Part 2. This course is an excerpt from an advanced undergraduate class at Rice University taught to all electrical and computer engineering majors.
Additional information
Richard G. Baraniuk Professor Richard G. Baraniuk grew up in Winnipeg, Canada, the coldest city in the world with a population over 600,000. He studied Electrical Engineering at the University of Manitoba, the University of Wisconsin-Madison, and the University of Illinois at Urbana-Champaign. Dr. Baraniuk joined Rice University in Houston, Texas, in 1993 and is now the Victor E. Cameron Professor of Electrical and Computer Engineering. He is a member of the Digital Signal Processing (DSP) group and Director of the Rice center for Digital Learning and Scholarship (RDLS). Dr. Baraniuk’s research interests lie in the areas of signal, image, and information processing and include machine learning and compressive sensing.
ou'll learn
The Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT)
The Discrete-Time Fourier Transform (DTFT)
The Z-Transform
Introduction to Analysis and Design of Discrete-Time Filters