NVidia Titan X Card problem

#Grub Settings if doesn't starting

Push shift for stop grub and edit(e key)

Replace quit splash with nomodeset if video drive problems

F10 for start with new settings

#add repository 

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

#remove previous drivers

$sudo apt-get purge nvidia-* 

titan@titan:~$ sudo apt-cache search nvidia-3
....
nvidia-340 - NVIDIA binary driver - version 340.102
nvidia-361 - Transitional package for nvidia-367
nvidia-361-dev - Transitional package for nvidia-367-dev
nvidia-367 - Transitional package for nvidia-375
nvidia-367-dev - Transitional package for nvidia-375-dev 
nvidia-370-dev - NVIDIA binary Xorg driver development files
nvidia-370 - NVIDIA binary driver - version 370.28
nvidia-375 - NVIDIA binary driver - version 375.66
nvidia-375-dev - NVIDIA binary Xorg driver development files
nvidia-378-dev - NVIDIA binary Xorg driver development files
nvidia-378 - NVIDIA binary driver - version 378.13
nvidia-381-dev - NVIDIA binary Xorg driver development files
nvidia-381 - NVIDIA binary driver - version 381.22
nvidia-384-dev - NVIDIA binary Xorg driver development files
nvidia-384 - NVIDIA binary driver - version 384.47



$sudo apt-get install nvidia-375  #After installation, execute next commands:

$sudo mv /usr/lib/nvidia-375/libEGL.so.1 /usr/lib/nvidia-375/libEGL.so.1.org
$sudo mv /usr/lib32/nvidia-375/libEGL.so.1 /usr/lib32/nvidia-375/libEGL.so.1.org
$sudo ln -s /usr/lib/nvidia-375/libEGL.so.375.39 /usr/lib/nvidia-375/libEGL.so.1
$sudo ln -s /usr/lib32/nvidia-375/libEGL.so.375.39 /usr/lib32/nvidia-375/libEGL.so.1

$sudo ldconfig  #for verify correct link

PD:

  We can't installed titan x on ubuntu 16, we replaced with Geforce 1070 

  We execute above instructions and that works.

Summary good commands:

$ lspci | grep VGA   #for verify nvidia series

$ lspci -vnn | grep -i VGA -A 12 #for verify kernel driver:nvidia  

$ glxinfo | grep OpenGL | grep renderer #for verify opengl 

References:

[1] https://askubuntu.com/questions/61396/how-do-i-install-the-nvidia-drivers/680826

[2] Instalar driver Nvidia manualmente no Ubuntu 16.04 (pt) https://linuxdicasesuporte.blogspot.com.br/2017/03/instalar-driver-nvidia-manualmente-no.html 

[3] Nvidia drivers on Ubuntu 14.04 http://www.binarytides.com/install-nvidia-drivers-ubuntu-14-04/ 

Grid and Fluids Resources

1) Course , Differences finites
   https://www.math.uci.edu/~chenlong/
   https://www.math.uci.edu/~chenlong/226/

1) https://github.com/rlguy/GridFluidSim3D

Computer Vision Laboratories & Courses

Labs:

Computer Vision Lab http://vision.ece.ucsb.edu/

Oxford https://www.robots.ox.ac.uk/~vgg/projects.html

University Central of Florida (Computer Vision -crowds dataset) http://vision.eecs.ucf.edu/

Courses:

1) Computer Vision: Algorithms and Applications http://szeliski.org/Book/ (2017)
2) University of California https://cseweb.ucsd.edu/classes/sp16/cse152-a/
3) Computer vision course https://courses.cs.washington.edu/courses/cse455/09wi/Lects/

Numeric Analysis

[1] [Book] Computational fluid mechanics and heat transfer
http://inis.jinr.ru/sl/Simulation/Tannehill,_CFM_and_Heat_Transfer,2_ed/

Grid/Mesh Generation

Books

[1] http://servidor.demec.ufpr.br/CFD/bibliografia/Basic_Structured_Grid_Generati.pdf
[2] http://people.inf.elte.hu/fekete/algoritmusok_msc/terinfo_geom/konyvek/Computational%20Geometry%20-%20Algorithms%20and%20Applications,%203rd%20Ed.pdf

SQTPM Software for review code submittion

Resources:
[1] Source code on Perl https://github.com/gptelles/sqtpm

Sunat e Sunarp - Búsqueda de razón social

Sunat http://e-consultaruc.sunat.gob.pe/cl-ti-itmrconsruc/jcrS00Alias

Sunarp https://www.sunarp.gob.pe/bus-personas-juridicas.asp

Journals for Bio Informatics

https://scfbm.biomedcentral.com/track/pdf/10.1186/1751-0473-3-6?site=scfbm.biomedcentral.com

springer computer vision http://www.springer.com/computer/image+processing/journal/11263  11 8.2

IEEE http://signalprocessingsociety.org/publications-resources/ieee-transactions-image-processing 44 4.3
ELSEVIER Pattern recognition https://www.journals.elsevier.com/pattern-recognition/ 47 4.5
ELSEVIER Medical image analysis https://www.journals.elsevier.com/medical-image-analysis/ 56 4.1
IEEE Medical images https://ieee-tmi.org/ 68 3.9
ELSEVIER https://www.journals.elsevier.com/computer-vision-and-image-understanding/ 112 3.2
ELSEVIER https://www.journals.elsevier.com/image-and-vision-computing/ 165 2.6
ELSEVIER https://www.journals.elsevier.com/computer-vision-and-image-understanding/ 189 2.4



http://www.guide2research.com/journals/computer-vision

Web Ftp Online

Sites
[1] https://www.onlineftp.ch/

PDF Signature

Resources:
[1] Make signature and put over pdf  https://www.pdfbuddy.com
[2] Draw signature over pdf https://www.pdffiller.com

Machine Learning Lectures & Tools

References:

[1] Clustering notes: http://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/Clustering.pdf

Datasets:


EMOTIV EPOC+ 14 Channel Mobile EEG https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State#
https://archive.ics.uci.edu/ml/datasets

Resources:

[1] Optimizing parameters of Classifier https://weka.wikispaces.com/Optimizing+parameters
[2] CSV to ARFF online http://ikuz.eu/csv2arff/

VMWare Player Enter Setup

bios.forceSetupOnce = "TRUE"
bios.bootDelay = "5000"

PCA Feature extraction

References:
[1] PCA http://www.visiondummy.com/2014/05/feature-extraction-using-pca/
[2] Reducción de dimensinalidad usando PCA   https://www.coursera.org/learn/clasificacion-imagenes/lecture/PaTVm/reduccion-de-descriptores-pca


[3] Opencv code with explanation for dimentional reduction
    https://stackoverflow.com/questions/27733002/how-to-use-pca-to-reduce-dimension
[4] Distances http://wwwae.ciemat.es/~cardenas/docs/lessons/MedidasdeDistancia.pdf

Distances

References
[1] http://wwwae.ciemat.es/~cardenas/docs/lessons/MedidasdeDistancia.pdf

Lectures Descriptors & datasets

References:

1) BoW summary https://prateekvjoshi.com/2014/08/17/image-classification-using-bag-of-words-model/

2) https://github.com/constanton/bLDFV



Datasets

0) 2D hela https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html
    2D/3D Hela http://murphylab.web.cmu.edu/data/
1) Biomedical flourcence images
http://mivia.unisa.it/datasets/biomedical-image-datasets/hep2-image-dataset/

2) Microscopic/ Histology, Brain, Retinal and more https://sites.google.com/site/lisaywtang/research/descriptors

3) Other for visualization https://grouplens.org/datasets/movielens/ 

Animes online

Bleach
1) Sub English https://www4.animesubhd.net/watch/episode/subbed/bleach-238/
2) Sub Spanish http://jkanimeonline.com/ver/bleach-226.html

Bag of Features and Texture

Notes

What is the difference between SIFT and Dense SIFT

*SIFT consists of both detection and description while dense sift only uses the descriptor in densely sampled locations [1].

*SIFT identifies interest points using Difference of Gaussian Filtering (DoG) before using Histogram of Oriented Gradients (HOG) to describe these interest points, however Dense-SIFT does not identify interest points, it simply divides the image into overlapping cells before using HOG to describe them. since they both use HOG they both produce 128 dimensional feature vectors [1].

*SIFT is typically computed at interest points. Dense SIFT is computed at every pixel, or every kth pixel. HOG is computed for a rectangular cell array where each cell is usually 8x8 pixels. Dense SIFT and HOG are similar in the sense that they both characterize edginess and orientation around pixels, but the computations are different. Jianxiong Xiao's 2x2 HOG is different than normal HOG. The truth is that once you know how these kinds if features work you can get fancy and histogram them differently, change normalization terms, etc and create your own variant. I spoke with Prof Xiao many times about this when we ovarlapped at MIT [2].

*Firstly, Difference of Gaussians (DoG) can be used for estimating Laplacian of Gaussians (LoG), which are useful for finding edges and blobs. DoG is computationally faster so it is used. Overall, the way in which LoG is used for SIFT and HOG is the fundamental difference between these two feature descriptors. Dense SIFT is exactly as it sounds, SIFT computed densely for every pixel in the image and it helps in image registration, pose estimation, object recognition, etc [2].

Resources

1) BoF imlmentation using SURF, IHOG http://www.cvc.uab.cat/~aldavert/plor/software.html

2) Texture video https://www.youtube.com/watch?v=LQBKIi-Xtbc

3) Textons http://webpages.uncc.edu/~yjaved/publications.html


4) Bag of Visual Words implementation (Functional) http://www.codeproject.com/Articles/619039/Bag-of-Features-Descriptor-on-SIFT-Features-with-O

5) Gabor filters histogram, explanation
    http://stackoverflow.com/questions/20608458/gabor-feature-extraction   
6) Filter Banks, Matlab Source Code
    http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html

7) Texture classification using textons  http://courses.media.mit.edu/2008fall/mas622j/Projects/NickLoomis/


References
 [1] https://www.researchgate.net/post/What_is_the_difference_between_SIFT_and_Dense_SIFT
 [2] https://www.quora.com/Computer-Vision-Is-there-a-difference-if-any-between-dense-SIFT-and-HOG

Self-Organizing Map (Resources)

Source code:

1) https://www.codeproject.com/Articles/21385/Kohonen-s-Self-Organizing-Maps-in-C-with-Applicati
2) https://www.codeproject.com/Articles/16273/Self-Organizing-Feature-Maps-Kohonen-maps
3) C++ Sample http://mnemstudio.org/neural-networks-som1.htm
4) GHSOM https://github.com/fffaraz/GHSOM-CPP

Yellow dots, Microdots, how to discover

Resources

1) Tutorial http://blog.erratasec.com/2017/06/how-intercept-outed-reality-winner.html
2) DocuColor Tracking Dot Decode Guide https://w2.eff.org/Privacy/printers/docucolor

Programming books

Resources

1) Ansi C http://www3.fi.mdp.edu.ar/electronica/catedras/ddcm/files/The_C_Programming_Language_KandR.pdf