Stationary wavelet transform

Wavelets: a mathematical microscope

Wavelet transform is an invaluable tool in signal processing, which has applications in a variety of fields - from hydrodynamics to neuroscience. This revolutionary method allows us to uncover structures, which are present in the signal but are hidden behind the noise. The key feature of w

From playlist Fourier

Understanding Wavelets, Part 2: Types of Wavelet Transforms

Explore the workings of wavelet transforms in detail. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr You will also learn important applications of using wavelet transforms with MATLAB®. Video Transcript: In the previous session, we discussed wavelet co

From playlist Understanding Wavelets

Understanding Wavelets, Part 1: What Are Wavelets

This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts ca

From playlist Understanding Wavelets

Effects of signal nonstationarities on the Fourier power spectrum

This is part of an online course on foundations and applications of the Fourier transform. The course includes 4+ hours of video lectures, pdf readers, exercises, and solutions. Each of the video lectures comes with MATLAB code, Python code, and sample datasets for applications. With 3000

From playlist Understand the Fourier transform

The Two-Dimensional Discrete Fourier Transform

The two-dimensional discrete Fourier transform (DFT) is the natural extension of the one-dimensional DFT and describes two-dimensional signals like images as a weighted sum of two dimensional sinusoids. Two-dimensional sinusoids have a horizontal frequency component and a vertical frequen

From playlist Fourier

Lecture: Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT)

This lecture details the algorithm used for constructing the FFT and DFT representations using efficient computation.

From playlist Beginning Scientific Computing

An introduction to the wavelet transform (and how to draw with them!)

The wavelet transform allows to change our point of view on a signal. The important information is condensed in a smaller space, allowing to easily compress or filter the signal. A lot of approximations are made in this video, like a lot of missing √2 factors. This choice was made to keep

From playlist Summer of Math Exposition Youtube Videos

The stationarity assumption of wavelet convolution

This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.

From playlist NEW ANTS #3) Time-frequency analysis

The Discrete Fourier Transform

This video provides a basic introduction to the very widely used and important discrete Fourier transform (DFT). The DFT describes discrete-time signals as a weighted sum of complex sinusoid building blocks and is used in applications such as GPS, MP3, JPEG, and WiFi.

From playlist Fourier

Stéphane Mallat: "Scattering Invariant Deep Networks for Classification, Pt. 3"

Graduate Summer School 2012: Deep Learning, Feature Learning "Scattering Invariant Deep Networks for Classification, Pt. 3" Stéphane Mallat, École Polytechnique Institute for Pure and Applied Mathematics, UCLA July 19, 2012 For more information: https://www.ipam.ucla.edu/programs/summer

From playlist GSS2012: Deep Learning, Feature Learning

Stéphane Mallat: High dimensional learning from images to physics

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist 30 years of wavelets

Guy Nason: A test for local white noise (and the absence of aliasing) in locally stationary...

Abstract: This talk develops a new test for local white noise which also doubles as a test for the lack of aliasing in a locally stationary wavelet process. We compare and contrast our new test with the aliasing test for stationary time series due to Hinich and co-authors. We show that the

From playlist Probability and Statistics

Professor Stéphane Mallat: "High-Dimensional Learning and Deep Neural Networks"

The Turing Lectures: Mathematics - Professor Stéphane Mallat: High-Dimensional Learning and Deep Neural Networks Click the below timestamps to navigate the video. 00:00:07 Welcome by Professor Andrew Blake, Director, The Alan Turing Institute 00:01:36 Introduction by Professo

From playlist Turing Lectures

Michael Unser: Wavelets and stochastic processes: how the Gaussian world became sparse

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist 30 years of wavelets

Empirical Mode Decomposition (1D, univariate approach)

Introduction to the Empirical Mode Decomposition - EMD - (one-dimensional, univariate version), which is a data decomposition method for non-linear and non-stationary data. This video covers the main features of the EMD and the working principle of the algorithm. The EMD is briefly compar

From playlist Summer of Math Exposition Youtube Videos

Electrical Engineering: Ch 19: Fourier Transform (2 of 45) What is a Fourier Transform? Math Def

Visit http://ilectureonline.com for more math and science lectures! In this video I will explain the mathematical definition and equation of a Fourier transform. Next video in this series can be seen at: https://youtu.be/yl6RtWp7y4k

From playlist ELECTRICAL ENGINEERING 18: THE FOURIER TRANSFORM

Stéphane Mallat - Apprentissage par invariants en grande dimension

Apprentissage par invariants en grande dimension : de l’image ou de la musique à la chimie quantique Huawei-IHÉS Workshop on Mathematical Sciences Tuesday, May 5th 2015

From playlist Huawei-IHÉS Workshop on Mathematical Sciences

Stéphane Mallat: "Scattering Invariant Deep Networks for Classification, Pt. 1"

Graduate Summer School 2012: Deep Learning, Feature Learning "Scattering Invariant Deep Networks for Classification, Pt. 1" Stéphane Mallat, École Polytechnique Institute for Pure and Applied Mathematics, UCLA July 18, 2012 For more information: https://www.ipam.ucla.edu/programs/summer

From playlist GSS2012: Deep Learning, Feature Learning

Fourier Transforms: Discrete Fourier Transform, Part 3

Data Science for Biologists Fourier Transforms: Discrete Fourier Transform Part 3 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton

From playlist Fourier

Learning Multiscale Physics with Deep Neural Networks - Mallat - Workshop 2 - CEB T3 2018

Stephane Mallat (College de France/ENS) / 26.10.2018 Learning Multiscale Physics with Deep Neural Networks ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitte

From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology