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Message ID: 14114     Entry time: Sun Jul 29 23:15:34 2018
Author: pooja 
Type: Update 
Category: Cameras 
Subject: Developing CNN 

Aim: To develop a convolutional neural network that resolves mirror motion from video.

Input : Previous simulated video of beam spot motion in pitch by applying 4 sine  waves of frquencies 0.2, 0.4, 0.1, 0.3 Hz  and amplitude ratios to frame size to be 0.1, 0.04, 0.05, 0.08 where random uniform noise ranging 0.05 has been added to amplitudes and frequencies. This is divided into train (0.4), validation (0.1) and test (0.5).

Model topology:

  • Number of filters = 2
  • Kernel size = 2
  • Size of pooling windows = 2
  •                                        ----->         Dense layer of 4 nodes  ---->    Output layer of 1 node 

         Activation:                      selu                                                  linear

Batch size = 32, Number of epochs = 128, loss function = mean squared error

Optimizer: Nadam ( learning rate = 0.00001, beta_1 = 0.8, beta_2 = 0.85)

Plots of CNN output & applied signal given in Attachment 1. The variation in loss value with epochs given in Attachment 2.

This needs to be further analysed with increasing random uniform noise over the pixels and by training CNN on simulated data of varying ampltides and frequencies for sine waves.

Attachment 1: conv_nn_varying_freq_amp_1.pdf  106 kB  Uploaded Mon Jul 30 00:28:41 2018  | Hide | Hide all
conv_nn_varying_freq_amp_1.pdf
Attachment 2: conv_nn_varying_freq_amp_2.pdf  39 kB  Uploaded Mon Jul 30 00:28:53 2018  | Hide | Hide all
conv_nn_varying_freq_amp_2.pdf
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