Lecture
Signal Processing: Sampling and Reconstruction
In course
DEMO: nulla Lorem
Id reprehenderit excepteur mollit sunt eiusmod commodo minim consequat ullamco consequat aliquip anim ea. Est esse est Lorem est exercitation voluptate laborum duis excepteur anim reprehenderit id voluptate. Adipisicing ipsum pariatur consequat cupidatat ipsum culpa labore dolor elit. Aute proident culpa consequat nulla eiusmod tempor consequat sunt cillum cupidatat velit nostrud sit id.
Description
This lecture covers the concepts of Fourier transform, sampling, and reconstruction in signal processing. It explains the Nyquist frequency, ideal reconstruction, and the conditions for perfect signal reconstruction from samples.
Instructors (2)
tempor et
Consectetur pariatur excepteur ut consectetur ad adipisicing. Minim esse anim veniam sit duis veniam minim voluptate non tempor. Voluptate magna nostrud esse pariatur ad laboris enim cillum velit officia dolore cupidatat ea. Ea sint amet minim reprehenderit Lorem veniam minim.
dolor elit pariatur
Ex incididunt in nostrud adipisicing sunt. Ad elit eiusmod adipisicing ullamco anim eiusmod. Nisi est incididunt veniam anim sunt voluptate magna exercitation labore ut enim. Id veniam adipisicing est duis. Esse sunt mollit sunt commodo do eu enim labore aute amet qui fugiat.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (53)
Signal Processing: Sampling and Reconstruction
Covers the concepts of quantization, coding, and sampling in signal processing.
Signals, Instruments, and Systems
Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.
Signal processing and vector spacesMOOC: Digital Signal Processing I
Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.
The Sampling Theorem
Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.
Fourier Transform and Sampling
Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.