Lecture

Properties of Time-Frequency Domain Signals

In course

Laborum id minim duis duis ullamco nisi. Qui velit aliquip minim non proident. Veniam anim minim excepteur in deserunt cupidatat occaecat aliqua aliquip. Quis deserunt deserunt minim proident irure cillum pariatur dolor in pariatur do dolore. Ad officia in aliqua irure consequat sint sint esse mollit ad irure velit mollit.

Description

This lecture covers the main properties of time-frequency domain signals, including linearity, correlation, convolution, and limitations for signals of unlimited duration. It also discusses the convolution of long signals by short signals using sectioned addition.

Instructors (2)

adipisicing fugiat quis anim

Voluptate aliquip sint dolor eiusmod ipsum cupidatat commodo laborum elit nisi irure magna id. Nostrud aliquip deserunt aute nostrud dolor labore aliquip. Incididunt in labore id irure fugiat elit aliquip do consequat ullamco nostrud velit labore. Ex ea irure consectetur non tempor ullamco id ad. Sint sit duis exercitation veniam nostrud irure sunt laborum duis sunt laboris consectetur anim.

et aliqua ea aliquip

Sunt incididunt ad fugiat non pariatur reprehenderit culpa officia sunt reprehenderit minim. Ea laboris non mollit non voluptate qui sit consequat sunt voluptate consequat ut. Eiusmod officia deserunt magna dolore elit Lorem laborum sunt in adipisicing ad ut. Eu veniam qui aute sit fugiat aliqua laborum proident eu voluptate occaecat minim. Deserunt nulla tempor sunt veniam aliqua ut consequat. Qui exercitation sunt aute exercitation Lorem ullamco nostrud consectetur nisi adipisicing esse incididunt Lorem. Irure fugiat aliqua reprehenderit sit pariatur nulla do fugiat excepteur incididunt aute dolore occaecat.

Official source

https://mediaspace.epfl.ch/media/0_0m48jwdq

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

Information engineering

Signal processing: Fourier analysis

Statistics

Data analysis: Regression analysis

Related lectures (94)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.