Linear filters | Signal processing
In signal processing it is useful to simultaneously analyze the space and frequency characteristics of a signal. While the Fourier transform gives the frequency information of the signal, it is not localized. This means that we cannot determine which part of a (perhaps long) signal produced a particular frequency. It is possible to use a short time Fourier transform for this purpose, however the short time Fourier transform limits the basis functions to be sinusoidal. To provide a more flexible space-frequency signal decomposition several filters (including wavelets) have been proposed. The Log-Gabor filter is one such filter that is an improvement upon the original Gabor filter. The advantage of this filter over the many alternatives is that it better fits the statistics of natural images compared with Gabor filters and other wavelet filters. (Wikipedia).
Isolating a logarithm and using the power rule to solve
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations
Solving a natural logarithmic equation using your calculator
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations
Nicki Holighaus: Time-frequency frames and applications to audio analysis - Part 1
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 Analysis and its Applications
Bruno Olshausen: "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 1" Bruno Olshausen, UC Berkeley Institute for Pure and Applied Mathematics, UCLA July 24, 2012 For more information: https://www.ipam.ucla.edu/pro
From playlist GSS2012: Deep Learning, Feature Learning
Stéphane Mallat: "Scattering Invariant Deep Networks for Classification, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "Scattering Invariant Deep Networks for Classification, Pt. 2" 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
Karlheinz Gröchenig: Gabor Analysis and its Mysteries (Lecture 1)
The lecture was held within the framework of the Hausdorff Trimester Program Mathematics of Signal Processing. In Gabor analysis one studies the construction and properties of series expansions of functions with respect to a set of time-frequency shifts (phase space shifts) of a single fu
From playlist HIM Lectures: Trimester Program "Mathematics of Signal Processing"
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
Jean-Claude Risset: Sound, music and wavelets in Marseille: A reminder of early sonic [...]
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
Martin Vetterli: Wavelets and signal processing: a match made in heaven
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
Where do logarithmic graphs come from
👉 Learn all about graphing logarithmic functions. A logarithmic function is a function with logarithms in them. The graph of the parent function of a logarithmic function usually takes its domain from the positive x-axis. To graph a logarithmic function, it is usually useful to first graph
From playlist How to Graph Logarithmic Functions | Learn About
How to graph the logarithmic functions
👉 Learn all about graphing logarithmic functions. A logarithmic function is a function with logarithms in them. The graph of the parent function of a logarithmic function usually takes its domain from the positive x-axis. To graph a logarithmic function, it is usually useful to first graph
From playlist How to Graph Logarithmic Functions | Learn About
Solving an natural logarithmic equation using properties of logs
👉 Learn how to solve natural logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a natural logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the l
From playlist Solve Logarithmic Equations
Solving an logarithmic equation
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations
Thomas Serre: "Deep Learning in the Visual Cortex, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "Deep Learning in the Visual Cortex, Pt. 2" Thomas Serre, Brown University Institute for Pure and Applied Mathematics, UCLA July 25, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-
From playlist GSS2012: Deep Learning, Feature Learning
Bruno Olshausen: "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 2" Bruno Olshausen, UC Berkeley Institute for Pure and Applied Mathematics, UCLA July 25, 2012 For more information: https://www.ipam.ucla.edu/pro
From playlist GSS2012: Deep Learning, Feature Learning
Solving a natural logarithmic equation
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations
What does a logarithmic graph look like
👉 Learn all about graphing logarithmic functions. A logarithmic function is a function with logarithms in them. The graph of the parent function of a logarithmic function usually takes its domain from the positive x-axis. To graph a logarithmic function, it is usually useful to first graph
From playlist How to Graph Logarithmic Functions | Learn About
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations
Palina Salanevich - STFT Phase retrieval: robustness and generative priors - IPAM at UCLA
Recorded 02 December 2022. Palina Salanevich of Utrecht University Department of Mathematics presents "STFT Phase retrieval: robustness and generative priors" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Phase retrieval is the non-convex inverse problem
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Solving a logarithm with a fraction
👉 Learn how to solve logarithmic equations. Logarithmic equations are equations with logarithms in them. To solve a logarithmic equation, we first isolate the logarithm part of the equation. After we have isolated the logarithm part of the equation, we then get rid of the logarithm. This i
From playlist Solve Logarithmic Equations