Day 19 Blog

Today in the morning I took measurements with the Shack-Hartmann wavefront sensor. The rest of the morning was spent working on the code. For the Shack-Hartmann sensor I need 3 different code parts. One to analyze the the intensity data, the other to measure the position data and the final (and hardest) is one that takes the centroid data, does a bunch of stuff to it to get the displacement data, then uses that to get the wavefront slope data.

At 11 all of the interns took a bike ride to U of R and ate lunch there before coming back to RIT around 1:30. The bike ride was a lot of fun and a good way to interact with the other interns.

After the bike ride I went back to coding and finished the position code and started the slope code.

Outline

• Title Slide
• Summary
• What is going to be discussed in the presentation
• Basics of wavefront sensing
• what a wavefront is,
• Purpose of what I am doing
• What is an optical differentiation sensor
• diagram and explanation
• prototype
• Comparison between OD and Shack-Hartmann sensor
• OD has better dynamic range, can look at polychromatic sources
• Important to make sure quality of image is the same
• Explanation of Signal to Noise Ratio
• Important to compare different sensors
• Shows how much of original image is preserved
• Equation for the SNR of OD and SH sensor
• From paper by Oti
• Explanation of terms
• Derivation
• Numerical calculation of SNR of both sensors
• Estimation for number of photons and ensemble average
• Actual calculation
• Analysis of numerical calculation
• Oti Graph
• Difference in Airy rings
• Atmospheric turbulence term for Shack-Hartmann sensor
• Graph and explanation
• Centroid error term for Shack-Hartmann sensor
• Graph and Explanation
• Larger project and Deformable mirror
• To develop a sensor to compete with the Shack-Hartmann
• SNR of camera for broadband
• graphs and analysis
• SNR of camera for lasers
• graphs and analysis
• Different way of calculating SNR
• graphs and analysis
• Blur and mean functions
• code and results
• Analysis of camera SNRs
• Conclusion    

Day 18

Today I analyzed the SNR graph from yesterday. The second linearity is the reason for that large dip in SNR, which is very surprising since in everything I have read so far there time where more photons means less SNR.  This afternoon we had a very interesting discussion from Bob Cremens, who works on analyzing forest fires. In the afternoon I worked on by outline and updating my abstract for the final presentation.

Tomorrow I will be working on data from the Shack-Hartmann sensor. I started writing code in order to be able to analyze the data tomorrow, but this code seems complicated to it will probably take a few days to completely iron out.

Day 17 Blog

In the morning we had a meeting with Jie before she left about our plans for the future. She told me to see if the weird stuff I saw yesterday was still present with another camera.

Once the meeting was over, we set up the other camera and analyzed the data for the new camera, unfortunately even after setting it up the data we got from it was meaningless. Today I also turned the graphs of variance into graphs of SNR.

Day 16: Some more data

In the beginning of the morning I wrote the sort that I talked about yesterday. The following graph is a comparison of the sorted data(orange) and the data from the sensor(blue), as well as a zoomed in version to show what the sort algorithm(and blur) does to the data

The data is now close enough to a function in order for me to be able to analyze it through matlab. Based on theory as well as our broadband results, we are expecting some sort of linear relationship between the mean and the variance. A pure derivative of the data is meaningless due to the noise that I still can't remove from the data, but matlab has a linear regression which I ran on the two obvious sections of the data. Since there were questions about the validity of the data with the turbulence, we got new (and better data). Which confirmed that there were some major influences to the data at some point in the data. (Graph is data with sort and blur algorithm).

Day 15: (actual data)

Today in the morning I took a bunch of data from the Shack-Hartmann sensor. The first trial I did involved me taking a bunch of data with broadband light shined onto paper over the sensor. I varied both the intensity and the exposure time and found that the variance seemed to be linear with the amount of photons. We tried to do the same experiment with the laser, but was our result due to there being pixelation of the paper. As a result, we tried keeping the intensity the same and measuring how the image varied, and I got

Due to the huge variation , I wrote a blurring program which gave me slightly better results, but not simplified enough to let me analyze it.

At the end of the day I turned the points into dots instead of lines and examined it and found that the problem stems from

so tomorrow I will be writing code that will sort the x vector and removes any values that repeat, as well as go through the y vector and for each x value that is the same, average all of the y values that correspond to that one point.

Day 14 Summary

Today everyone but our PHD student (who has her own project on how heat is transferred with very high powered lasers) is working on a new deformable mirror that the lab has received. Currently my task is to look at the SNR from the results that we get from the mirror, but as I can't do that until we have the mirror set up, I am looking for more equations that could explain the SNR of the Shack-Hartmann sensor. Today ended up being another day looking at papers, but tomorrow I am going to be analyzing some baseline values for the signal to noise ratio. Tomorrow I will be looking at data from the sensors, so at the end of the day I started writing some preliminary code to look at tomorrow's data.

Day 13 Summary

In the morning we had a reporter come and interview us. She asked what we did and what connections were made in the lab. I forget what newspaper she was from. We also had a photographer come and take pictures of us 'in action' which for me is just in front of a computer. Due to the long hours yesterday I left a little after lunchtime.

Day 12 Summary

Today was the presentation and the trip to the observatory. I finished the powerpoint in time for the presentation, I wasn't sure to what detail I should go into for the presentation. Bob showed up for my presentation which was nice, I think overall the presentation went well and I had the proper understanding of what was going on. Most of the comments were just on small details in the presentation. One mistake that I made was I calculated the error with the full photon count instead of just the photon per microlens. I also have to verify the units in some of the equations. Jie also told me that some of the detail I went into was good for the update but not needed in a final presentation. After my presentation Zach and Aaron each did their presentation, which were on different tasks in the same greater project that I am working under. Both of their presentation were very good and I learned a bunch about the project. Zach is working on a matlab model of the OD and has error under 1/10 of a wave. Aaron is working on the setup and has good results with the two sensors, as well as plans for how to improve the measurements.

Day 11 Summary:

Tomorrow is our presentation and the trip to the observatory, so today was spent mostly working on the presentation. Since tomorrow we are going to 11pm, I am planning to show up around noon so I have to finish up the powerpoint today. I didn't do much other than work on the powerpoint.

Day 10 Summary:

Today in the morning we went over our abstracts, the lack of specifications did show up in the abstracts, which had wildly different lengths and styles, but all seemed pretty good. It was suggested that I edit my abstract to use less technical vocabulary. At the very end of Yesterday Jie came into our lab and went over what we had done. She also told the non-REU students that we had to do a presentation on Tuesday. Since I've only been here two weeks I'm not sure how the presentation will go or exactly what to put on it, but I'm sure I can figure it out. The rest of the day was spent going over the papers and looking at the two terms from yesterday. I worked on the term from Yesterday, but I either get a meaningless graph or a really long symbolic equation, neither of which is useful. The other equation I managed to parse through and it seems to be off by a scaling factor, I have the same shape as the paper. I am planning to focus on the presentation tomorrow.

Day 9 Summary

Today was spent working on the Shack-Hartmann noise term. I've done a little more work with the atmospheric turbulence coefficient and seeing how different parameters affect it. Jie told the nonREU students that we had to prepare a presentation by Tuesday, but I wanted to finish up my data as much as possible today. During a rereading of the Shack-Hartmann paper, I found two more terms to explore. One of the terms involves the filter size, but the math is very complicated and I don't have experience with symbolic formula in Matlab. By the end of the day the graph I have seems to be odd variations on zero which is not the correct shape for the graph.

The Optical Differentiation sensor is a newer sensor can be adapted to different dynamic ranges and work with multicolored light sources. The signal to noise ratio is important to measure in order to determine how much of the original wavefront is preserved throughout the sensor.  By comparing the SNR of the Optical Differentiation sensor to the Shack-Hartmann sensor, one of th e most common sensors, we can determine how competitive the Optical Differentiation sensor is. The paper by Oti that compares these two sensors includes a graph of the ratio between the SNR’s of both sensors, but the equations in that paper do not match that graph. The purpose of this project is to generate an equation  that better models the Signal to Noise ratio of both the Optical Differentiation and Shack Hartmann sensor in order to improve the equations in the paper to recreate the graph in that paper.

Day 8 Summary

Today I spent some of the morning helping out Jason and John in the preform lab with their work on the way people look at images, which went longer than expected due to some technical difficulties. Afterwards we visited Angela in her lab and she explained what she has been working on. Once I got back to my lab I figured out the K value I worked on yesterday by writing a Matlab script for the equation and figuring out what part of the equation was giving me the infinity and retyping it until it output a correct value. I'm pretty sure that the current value is right since it is real, and all of my previous results have been complex. The current value is  1.5038 but dependent on the Fried's parameter I choose, which I'm taking from the paper but the actual value might be different.

Day 7 Summary

Today I worked mainly on solving for a constant term that accounts for atmospheric turbulence for the Shack-Hartmann sensor. If somehow this constant is around 2, then I will have the right values for the amount of sensors per area required in order to have the OD sensor be equal to the SH sensor. The problem is that the term is two long integrals over each other, and by the end of the day I have gotten various results from 1.1 to .6 to infinity, which is where I am at at the end of the day. Even if this value is perfect, I still have to find a formula that varies with photons, which as of yet I don't have for either the OD sensor or the SH sensor. We spent the afternoon going through a rough draft of the REU's presentation.

Day 6 Summary

Today was spent working on a paper describing the error in the Shack-Hartmann sensor. This paper is more clear in what it is doing, but also longer and more mathematically complicated than the original paper. In this paper I did find the noise equation used in the original paper, and this paper does explain the issues with that, the rest of the day was spent slowly working through this paper and looking at other papers to find a more accurate Shack-Hartmann variance. I also looked at a masters thesis which had a model for the Shack-Hartmann sensor, but some of the files needed for the model weren't available and with my current knowledge I couldn't generate them.

Day 5 Summary

Since yesterday I finished most of the equation in the paper, the morning was spent just going over the derivations that I had done previously. I also did more work in matlab, at this point I understand the basic syntax and the way matlab works, but I still spend much of my time looking up commands. After lunch Jie came into the lab and went over what I had done during the first week, the discussion was very useful and allowed me to understand much better what my goals were in this lab.

Afterwards Jie told me to look at the papers that cite my paper. Up until now I had only looked at the papers that were cited in the paper. Unfortunately none of the papers use the equations explicitly, and most only refer to it as a simple comparison of the OD and Shack-Hartmann sensor.

Day 4 Summary

At this point I feel that I am getting into the rhythm of the lab and I get along with everyone in the lab really well. Today was spent mostly on the Signal to Noise Ratio paper, and I think that I derived as much as I can today. I found one error in the paper where there I think one equation is off by a factor of two, which was confirmed by the PHD student in the lab.  One thing that took by a very long time to figure out is the graph which I was tasked with reproducing had an x-axis variable which was never explicitly mentioned in the paper, and even when it was found the equations given did not seem to create the graph in question. It turns out after a lot of help that the graph was the result of a complicated computer simulation and not the equation in the paper, which provided a minimum value for the ratio between the OD and HS sensor. I don't think that I am going to be able to properly simulate both of the sensors, so I am hoping that I can move on to my final project tomorrow.

Day 3 Summary

Today was the first day spent entirely in the lab, mornings in the lab are much more quiet than afternoons, and I just took notes on specific section of a textbook on signal to noise ratios in optics and their sources. For lunch, the REU students and some of the interns attended an hour or so long talk by Dr.Easton on his work on historical documents. While the talk was long, the content of Easton's work is very interesting and it was fun learning about the different processes he uses and the progression of those processes over time. After lunch I figured out the derivation of the first signal to noise ration in the paper, and spent the rest of the day looking at the Matlab code for the OD sensor.

Day 2 Summary

Today the first half of the day was spent in the Red Barn participating in team-building activities. All of the activities were fun, but the 3 hours or so seemed excessive given that my internship is more about my interaction with the people in my lab than the other internships. Regardless the activities were enjoyable and it was useful to have these activities the first few days in order not to become overwhelmed with the internship. Afterwards I grabbed lunch and returned to the lab. Most of my time was spent learning Matlab, a programming language that I will be working in, and working on the concepts in the sensor paper. I managed to derive the variance equation for the OD sensor which I was very excited about doing in the second day there.

Day 1 Summary

July 6, 2015

Today we officially started the internship, I had previously come in to get an introduction to the lab and a bunch of introductory materials, including a series of 6 lectures on probability and probability function which I took notes on prior to entering the internship. We started off the day with a full tour of the CIS building with a short introduction for each lab. After that we were placed in a large room and tasked with creating a powerpoint with an introduction of each of us, explanations on how to run skype and ImageJ and set up our blogs. After a lunch provided by the center, we each returned to our labs. I am the only intern in my lab, but all of the other students here are very helpful and will stop what they are doing to help me. After showing up in the lab I was given a paper on signal to noise ratio in the two sensors I will be working with, the Optical Differentiation sensor and the Shack-Hartmann sensor. The rest of the day was spent learning the math and concepts I would need for the internship.