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Nearly Sample Optimal Sparse Fourier Transform in Any Dimension - RIPless and Filterless

Offered By: IEEE via YouTube

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Signal Processing Courses Computational Complexity Courses Fourier Transform Courses Algorithm Optimization Courses

Course Description

Overview

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Explore a groundbreaking IEEE conference talk on the development of a nearly sample-optimal sparse Fourier transform applicable to any dimension, without the need for Restricted Isometry Property (RIP) or filtering techniques. Delve into the innovative research presented by Vasileios Nakos, Zhao Song, and Zhengyu Wang as they discuss their novel approach to signal processing and data analysis across multiple dimensions.

Syllabus

(Nearly) Sample Optimal Sparse Fourier Transform in Any Dimension; RIPless and Filterless


Taught by

IEEE FOCS: Foundations of Computer Science

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