Verify the convoliution theorem using Matlab
When using Matlab to verify “Convolution in time domain is multification in frequency domain”, simply doing fft2 is wrong.
Posts by Tag
- domain transform 4
- source imaging 3
- Webpage 2
- deep learning 2
- EM 2
- neural networks 2
- Matlab 1
- Jupyter Notebook 1
- neuro-physiology 1
- Git 1
- DCT 1
- STFT 1
- wavelet 1
- filtering 1
- fft 1
- power spectral density 1
- k-means 1
- Gaussian mixture models 1
- hierarchical clustering 1
- semi-supervised learning 1
- active learning 1
- boosting 1
- naive bayes 1
- regression 1
- perceptron 1
- SVM 1
- eigen 1
- pca 1
- svd 1
- deblurring 1
- pgm 1
- monte carlo 1
- Python 1
domain transform
Wavelet Transform
We know that wavelet transform comes from STFT by changing a single windowing function to a set of windowing functions. \(X(t,\omega) = \int_{-\infty}^{\i...
Short Time Fourier Transform
Why use STFT? We wants to know the frequency components at each time point. Solution: we perform FFT on small blocks. \(X(t,\omega) = \int_{-\infty}^{\...
Discrete consine transform(DCT-II)
Why use DCT DTFS/FFT (We will not detail into the difference between these two at this moment.) use $e^{jwt}$ as the base. And if $x(t)$ is real and even,...
source imaging
Papers I browsed
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EEG source localization
1. Physiology Source of EEG The first a few pages of “François Grimbert, Olivier Faugeras. Analysis of Jansen’s model of a single cortical column. Researc...
Nelder-Mead method/Downhill simplex method
This method is a traditional method for non-linear optimization problem. A simplex is a structure in n-dimensional space formed by n+1 points that are n...
Webpage
Notes on changing the minimal mistakes
I wrote some notes on how to customize your website. Change the sidebar width: If you want to change the image size: goes to _sass and change i...
Notes on changing the academicpages
Other than the notes for changing _config.yml and _pages folder that are already on the academicpages website, here I write down some notes to further custom...
deep learning
Compare GPUs
Comparsion of GPU performance online from Timdettmers
Reading summary for The Brain vs Deep Learning – from Tim Dettmer
Original post: http://timdettmers.com/2015/07/27/brain-vs-deep-learning-singularity/
EM
Expectation maximization
We have observed variable \(x\), latent varivable \(z\) and parameters \(\theta\). The complete log likelihood should be \(l(\theta;x,z) = \log p(x,z | \the...
Clustering
Clustering is aimed at grouping set of objects that have similar “properities”. There are many different ways to define the distance for clustering. The famo...
neural networks
Theoritical and Empirical studies on generalization in Deep learning
Theory: Bound the generalization error Introduction Probably approximately correct (PAC) learning (Valient, 1984) provided a theoretical framework to provide...
Neural Networks
(Many are copied from Sun, 2019 Optimization paper)
Matlab
Tricks while Using Matlab
Using Matlab in Jupyter Notebook Jupyter notebook can combine text and code. When you are study new things and take notes, it could be really helpful. The st...
Jupyter Notebook
Tricks while Using Matlab
Using Matlab in Jupyter Notebook Jupyter notebook can combine text and code. When you are study new things and take notes, it could be really helpful. The st...
neuro-physiology
Reading summary for The Brain vs Deep Learning – from Tim Dettmer
Original post: http://timdettmers.com/2015/07/27/brain-vs-deep-learning-singularity/
Git
Rollback to previous commits
Taken from https://gist.github.com/StefanoMagrassi/6c78b6f48599377a5c805fd58a5cd93d
DCT
Discrete consine transform(DCT-II)
Why use DCT DTFS/FFT (We will not detail into the difference between these two at this moment.) use $e^{jwt}$ as the base. And if $x(t)$ is real and even,...
STFT
Short Time Fourier Transform
Why use STFT? We wants to know the frequency components at each time point. Solution: we perform FFT on small blocks. \(X(t,\omega) = \int_{-\infty}^{\...
wavelet
Wavelet Transform
We know that wavelet transform comes from STFT by changing a single windowing function to a set of windowing functions. \(X(t,\omega) = \int_{-\infty}^{\i...
filtering
Multitaper
The content below is a translation and summary of the wiki page of Multitaper.
fft
Multitaper
The content below is a translation and summary of the wiki page of Multitaper.
power spectral density
Multitaper
The content below is a translation and summary of the wiki page of Multitaper.
k-means
Clustering
Clustering is aimed at grouping set of objects that have similar “properities”. There are many different ways to define the distance for clustering. The famo...
Gaussian mixture models
Clustering
Clustering is aimed at grouping set of objects that have similar “properities”. There are many different ways to define the distance for clustering. The famo...
hierarchical clustering
Clustering
Clustering is aimed at grouping set of objects that have similar “properities”. There are many different ways to define the distance for clustering. The famo...
semi-supervised learning
Some less popular concepts in CMU 10601
Semi-supervised learning: mixing labeled and unlabeled data as training data (no query for labels during training); based on the belief that data has the ...
active learning
Some less popular concepts in CMU 10601
Semi-supervised learning: mixing labeled and unlabeled data as training data (no query for labels during training); based on the belief that data has the ...
boosting
Some less popular concepts in CMU 10601
Semi-supervised learning: mixing labeled and unlabeled data as training data (no query for labels during training); based on the belief that data has the ...
naive bayes
Some basic concepts in CMU 10601
Naive Bayes A linear classifier Generative model: model \(p(X,y)\)
regression
Some basic concepts in CMU 10601
Naive Bayes A linear classifier Generative model: model \(p(X,y)\)
perceptron
Some basic concepts in CMU 10601
Naive Bayes A linear classifier Generative model: model \(p(X,y)\)
SVM
Some basic concepts in CMU 10601
Naive Bayes A linear classifier Generative model: model \(p(X,y)\)
eigen
SVD and PCA
The notes from cmu 17spring-18898:
pca
SVD and PCA
The notes from cmu 17spring-18898:
svd
SVD and PCA
The notes from cmu 17spring-18898:
deblurring
Some concepts in image processing
Ringing artifacts
pgm
Approximate Inference
(Summarized from the lecture notes from cmu spring17-10708, Eric Xing)
monte carlo
Approximate Inference
(Summarized from the lecture notes from cmu spring17-10708, Eric Xing)
Python
Pycharm ignore PEP8 errors
Taken from https://gist.github.com/StefanoMagrassi/6c78b6f48599377a5c805fd58a5cd93d