Installing Tensor Flow (Problems)

Main guide
https://www.tensorflow.org/install/install_sources

Cannot find bazel. Please install bazel.
Solution https://docs.bazel.build/versions/master/install-ubuntu.html

Monitoring Nvidia card

$watch -n 1 nvidia-smi #to obtain continuous updates without filling the terminal with output

$sudo pip install gpustat
$gpustat
 
$sudo pip install glances
$sudo pip install glances[gpu]
$glances

Resources:
[1] Cuda 7.5 http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda_7.5.18_linux.run
[2] Install Cuda 7.5 http://www.r-tutor.com/gpu-computing/cuda-installation/cuda7.5-ubuntu

Vertex Smooth

Resources:
[1] http://www.stat.wisc.edu/~mchung/papers/HBM2001/HBM2001diffusion.html

[2] http://www.stat.wisc.edu/~mchung/papers/HBM2001/chung.2001.OHBM.pdf
[3] http://www.stat.wisc.edu/~mchung/papers/HBM2001/chung.2001.OHBMposter.pdf

[4] http://docs.salome-platform.org/5/smesh/user/smoothing_page.html
[5] https://www.youtube.com/watch?v=TQcS2q_rAHQ
     https://i.ytimg.com/vi/TQcS2q_rAHQ/maxresdefault.jpg
[6] http://grasshopperprimer.com/en/1-foundations/1-5/1_surface-geometry.html

HPC - High Performance Computing

[1] SLURM https://www.hpc.cam.ac.uk/using-clusters/running-jobs

[2] Module

Latex Desing

Resources
[1] Draw Complex Networks https://github.com/hackl/tikz-network/

Execute VTK Samples on ubuntu

VTK5.10
$locate vtkXOpenGLRenderWindow.cxx
$gedit  ... vtkXOpenGLRenderWindow.cxx
Uncomment next line
#define GLX_GLXEXT_LEGACY

$ CC=/usr/bin/gcc-5 CXX=/usr/bin/g++-5 cmake ..


cmake -DVTK_DIR=/home/DRIVE/Backup2018/Downloads2017/tools2/VTK5.10.1/build .

VTK7.1
$cmake -DVTK_DIR=/home/DRIVE/Downloads/tools/VTK7.1/build .

Resources
[1] How to write in vtk format https://github.com/arborworkflows/Visomics/blob/master/Base/voIOManager.cpp
[2] https://vtk.org/download/

Computer Graphics - Grid Structures

$sudo apt-get install freeglut3-dev
$sudo apt-get install libxmu-dev
$make

Resource:
[1] corner table exercice https://www.cc.gatech.edu/~lena007/cs4451_p2/corner_tab.html

Books online repositories

Library Genesis

Resources:
[0] Main reference https://en.wikipedia.org/wiki/Library_Genesis
      http://93.174.95.27/ 
[1] Mathematic, Biology and others https://libgen.pw (*)
[2] Mathematic and others http://libgen.io (*)
[3] http://gen.lib.rus.ec/
[4] zlibrary https://b-ok.org/ 
[5] sci-hub.tw
[6] https://whereisscihub.now.sh/
[7] Art books https://www.artubook.com (*)

(*) My favorite list


https://marcusnunes.me/posts/livros-gratuitos-da-springer/

Español

'A ver' y 'haber' se pronuncian de la misma forma, pero es habitual confundirlas y utilizarlas de manera incorrecta a la hora de escribir. Por eso, a continuación procedemos a distinguirlas tal y como marca la Real Academia Española (RAE):


A ver: Se trata de la secuencia constituida por la preposición a y el infinitivo verbal ver. Se utiliza en los siguientes casos:
        Para pedir al interloculor que nos deje ver algo: - Mira mi coche. - ¿A ver?
        Para dejar claro una cierta expectación: A ver cuándo viene la ayuda.
        Para llamar la atención sobre algo: A ver, ¿por qué no no vinisteis ayer a casa?
        En los casos en los que equivale a 'claro' o 'naturalmente': ¡A ver! Estaremos allí sin falta.
        En los casos que lleva delante la conjunción si, expresa, bien expectación, curiosidad o interés, a veces también un reto; otras temor o sospecha; y deseo o mandato: ¡A ver si me traes lo que te he pedido!

Haber: Puede tratarse de un verbo o un sustantivo:
        Como verbo se emplea como auxiliar seguido de un participio, para formar los infinitivos compuestos de la conjugación: Debe haber faltado a clase.
        También como verbo se emplea como infinitivo del impersonal que indica la presencia o existencia de lo designado por el sustantivo que lo acompaña: En su casa debe haber mucho dinero.
        Como sustantivo masculino su significado es "conjunto de bienes o caudales de una persona": En su haber contaba con muchos títulos.

Create Partition and Format for Fix HD

$sudo fdisk /dev/sdb #commands d, n, w

$sudo mkntfs /dev/sdb1

$mkfs.ntfs -f /dev/sdb1 

$dmesg | grep sd

$sudo lshw -C disk

Skype on Ubuntu and Centos

Skype on Ubuntu 16

 [1] http://ubuntuhandbook.org/index.php/2017/03/install-skype-5-0-for-linux-ubuntu-16-04/

Skype on Centos 6.5

When i tried install skype on centos 6.5 i received next message:

error: Failed dependencies:                                       
        alsa-lib >= 1.0.23 is needed by skype-4.2.0.13-fc16.i586  
        libQtWebKit.so.4 is needed by skype-4.2.0.13-fc16.i586    
        libstdc++.so.6(GLIBCXX_3.4.15) is needed by skype-4.2.0.13-fc16.i586
        qtwebkit is needed by skype-4.2.0.13-fc16.i586  

then i tried next commands and works very well. 

1) Install EPEL if you haven't already.
#yum install http://dl.fedoraproject.org/pub/epel/6/i386/epel-release-6-8.noarch.rpm

2) Download the RPM here
#wget http://www.bromosapien.net:8080/others/skype-4.2.0.11-4.el6.i686.rpm

3) Install it as so
#yum install skype-4.2.0.11-4.el6.i686.rpm

4) If you receive an error about a GPG key, you may import my key as necessary.
#wget http://www.bromosapien.net:8080/others/SYRKIT-GPG-KEY.pub
#rpm --import SYRKIT-GPG-KEY.pub

References:
[1] http://community.skype.com/t5/Linux/CentOS-RHEL-6-Skype-4-2-RPM-Installation-Steps/td-p/1740485

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

Convert PowerPoint to EPS

When you draw some design and you like to put in your paper

1. Open your design on Powerpoint, then save as .wmf(windows meta file)
2. go to https://cloudconvert.com/anything-to-eps
3. Load you .wmf file and convert to eps
4. Download result

Persian Language

Courses

1) http://www.persianlanguageonline.com/learn/beginner
2) https://persianlanguage.anu.edu.au
3) https://www.nyu.edu/gsas/dept/mideast/virtualpersian/index.html

Fiducial Land Mark (Face Keypoints)

Facial Keypoints
[1] https://github.com/sunsided/facial-keypoints
[2] kaggle https://www.kaggle.com/c/facial-keypoints-detection/data


Resources:
1) Source Code Matlab/C++
http://cmp.felk.cvut.cz/~uricamic/flandmark/

Autodominio

Recursos
1) http://www.ayuda-psicologia.org/2013/12/salirse-siempre-con-la-suya-o-el.html

LBP Descriptor resources

Lectures

Texture descriptor (pt) http://wiki.icmc.usp.br/images/d/d7/Dip10_imagedescription-texture.pdf



Resources

1) 2015 LBP variant library https://github.com/carolinepacheco/lbplibrary
2) University OULU  http://www.cse.oulu.fi/CMV/Downloads/LBPMatlab
3) Upsala University http://www.cb.uu.se/~gustaf/textureDescriptors/

Different kind of Image Medical Datasets

References:
[1] Neuromorph https://datamed.org/repository_list.php
[2] http://neuroangio.org/anatomy-and-variants/anterior-cerebral-artery/

Tools for text to speech

[1] use TensorFlow http://hts.sp.nitech.ac.jp/?Download#zf6c5f7c

Common datasets for image processing and computer vision

Resources:

[1] Hela https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html

OpenCV 2.4 Fix cvCreateGLCM()

Changes for cvCreateGLCM(), see [1] for more details, and [2] has code ready for recompile.

Resources/References:

  [1] http://intercontineo.com/article/723743881/

  [2] https://drive.google.com/open?id=0B8teK-3L4sK2Tll1Y2FyWmNCc3M