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Entry  Thu May 23 15:37:30 2019, Milind, Update, Cameras, Simulation enhancements and performance of contour detection 6x
    Reply  Sat May 25 20:29:08 2019, Milind, Update, Cameras, Simulation enhancements and performance of contour detection residue_normalised_x.pdfresidue_normalised_x.pdfresidue_normalised_x.pdfpredicted_motion_x.pdfnormalised_comparison_y.pdf
       Reply  Wed Jun 12 22:02:04 2019, Milind, Update, Cameras, Simulation enhancements  simulated_motion0.mp4simulated_motion0.mp4simulated_motion0.mp4
          Reply  Mon Jun 17 14:36:13 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
             Reply  Tue Jun 18 22:54:59 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
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                   Reply  Tue Jun 25 22:14:10 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
                      Reply  Thu Jun 27 20:48:22 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking readme.txtframe0.pdfLearning_curves.pngMotion.png
                         Reply  Thu Jul 4 18:19:08 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking Motion.pdfError.pdfLearning_curves.pdf
                            Reply  Mon Jul 8 17:52:30 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking Motion.pdf
                               Reply  Tue Jul 9 22:13:26 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking predicted_motion_first.pdfpcdev5_time.png
                                  Reply  Wed Jul 10 22:32:38 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking 
                               Reply  Mon Jul 15 14:09:07 2019, Milind, Update, Cameras, CNN LSTM for beam tracking cnn-lstm.pngfft_yaw.pdfyaw_motion.pdf
                                  Reply  Fri Jul 19 16:47:06 2019, Milind, Update, Cameras, CNNs for beam tracking || Analysis of results 7x
                                     Reply  Sat Jul 20 12:16:39 2019, gautam, Update, Cameras, CNNs for beam tracking || Analysis of results 
                                        Reply  Sat Jul 20 14:43:45 2019, Milind, Update, Cameras, CNNs for beam tracking || Analysis of results frame0.pdfsubplot_yaw_test.pdfintensity_histogram.mp4network2.pdf
                                           Reply  Wed Jul 24 20:05:47 2019, Milind, Update, Cameras, CNNs for beam tracking || Tales of desperation saturation_percentage.pdf
                   Reply  Thu Jul 25 00:26:47 2019, Milind, Update, Cameras, Convolutional neural networks for beam tracking centroid.pdfsubplot_yaw_test.pdf
          Reply  Mon Jun 17 22:19:04 2019, Milind, Update, Computer Scripts / Programs, PMC autolocker 
             Reply  Mon Jul 1 20:18:01 2019, Milind, Update, Computer Scripts / Programs, PMC autolocker 
                Reply  Tue Jul 2 12:30:44 2019, Milind, Update, Computer Scripts / Programs, PMC autolocker 
                   Reply  Sun Jul 7 17:54:34 2019, Milind, Update, Computer Scripts / Programs, PMC autolocker 
          Reply  Tue Jun 25 23:52:37 2019, Milind, Update, Cameras, Simulation enhancements  
             Reply  Mon Jul 1 20:11:34 2019, Milind, Update, Cameras, Simulation enhancements  
Message ID: 14667     Entry time: Wed Jun 12 22:02:04 2019     In reply to: 14638     Reply to this: 14678     14680   14698
Author: Milind 
Type: Update 
Category: Cameras 
Subject: Simulation enhancements  

Today, Rana asked me to work on improving simulations based on the ideas we discussed last week. As of the previous elog the simulation accomodated only

  1. Simulation of Gaussian beam spot.
  2. Arbitrary motion.

Today, I added the simulation of point scatterers.

What?

The image on the sensor (camera) is produced in roughly the following steps.

  1. Motion of the Gaussian beam on the optic (X,Y coordinates) which is what has been simulated so far.
  2. Reflection from the surface of the optic which can be modeled using knowledge of the BRDF has not been included as of this elog as I wish to do a little more reading before doing so.
  3. Reflection from point scatterers (dust particles burnt into the optic surface by the laser and so forth) which are characterised as peaks (impulses) in the TIS vs position plot. The laser beam is incident nearly normally on the optic and this behaviour is independent of the angle of observation. This is what has been added to the simulation.

How?

  1. Increased the frame resolution to 720 x 480.
  2. Defined an array of the same size and set values of at most "num_scatter" number of points at random positions to values determined randomly between 1 and "scatter_amp" + 1 where scatter_amp is non-negative.
  3. Multiplied the resulting array by the resulting Gaussian beam. The motivation was to imitate the bright specks obtained on various camera feeds in the lab. Physically, this also implies normal incidence and normal observation which is not the real case at all. I shall add these features in a day or two.

Herewith, in attachments #1, #2, #3 I am attaching videos obtained by varying scattering amplitude and number of scattering points in a vain attempt to reproduce this data. I shall work more on this simulation on Friday.

 


Scripting stuff:

  1. Previous elogs detail how to take gige images at various exposure times. I am still waiting on Kruthi to use the script.
  2. Tomorrow I shall work on the scripting software to interact with the GigE and take video for a fixed duration etc. I shall also begin working on a script to autolock the PMC based on what Rana showed me on Monday. I will also take a look at the the contents of this elog and try to pick up from there. I hope to make significant progress by the next lab meeting.

Neural network stuff:

GANs for simulation:

  1. Other than putting the physics into simulation i.e the first portion of this elog, GANs can be trained to generate images similar to the original data. I am unfamiliar with training GANs and the various tricks that are used specifically for them. I will do a bit of reading and make an update by Friday. As of now, the data I plan to use is this and I will train it using the GTX 1060 on my machine.

Networks for beam tracking:

  1. I will use the architectures suggested in this work with a few modifications. I will use MSE loss function, Adam optimizer and my local GPU for training.
Attachment 1: simulated_motion0.mp4  12 kB  Uploaded Thu Jun 13 00:53:38 2019
Attachment 2: simulated_motion0.mp4  12 kB  Uploaded Thu Jun 13 00:55:09 2019
Attachment 3: simulated_motion0.mp4  15 kB  Uploaded Thu Jun 13 00:56:06 2019
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