Content

1. Software Environment
2. Data Generation
3. Codewords Generation
   1. CPU
   2. GPU
      
4. Support Vector Machine
5. Convolutional Neural Network (pending)

1.Software Environment

The required software are listed below:

• python 2.7 
• ipython 
• scipy
• numpy
• matplotlib
• pillow
• h5py
• scikit-learn
  
• tensorflow

It is recommanded to use linux or mac osx operating sytem. I use ubuntu 16.04. If you use different version of operating system, the command lines below may not work.

To set up  software environment, you might need additional packages:

• pip
• git
  

You can get all these packages installed easily via command line "sudo apt-get install ...(name of the packages) " or  "sudo pip install ... (package names)".  It may also help to protect your system from unexpected mistakes by setting up a virtual environments. The simple tutorial for setting up a virtual environment can be found in the link:

http://docs.python-guide.org/en/latest/dev/virtualenvs/

2.Data Generation

Currently we don't have many images available for training. Thus I use two ways to generate new images.

• For images with a beam, first shift the beam spot to the center of the image, then rotate the image around the center, then cut out a small patch of images that contains the beam. Then arbitrarily chose a position paste the small patch to the new position. Cut out the small patch of image that is covered by the beam spot, and paste it to the original position of the beam spot. This proccedure aims to minimize the influence of our manipulation. It should be notice that to do this, one have to choose a large enough patch. Otherwise, it may not be able to cover the original beam spot.
• For all images, one can merely shift the whole images one or two pixels along x or y axis. In this way, one can create 24 times more data.

3.Codewords Generation