Difference between revisions of "ESE297 - Intro to Undergraduate Research"

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The Robotics Sensing Training Course was created for students who wish to do Undergraduate Research projects in [[media:Robotic_Sensing_V4.pdf|Robotic Sensing]] under [http://ese.wustl.edu/people/Pages/faculty-bio.aspx?faculty=11 Professor Nehorai], the ESE Department Chair. This course is offered as ESE497 (Section 27) for 2 credits and is typically offered in the spring and summer. Students will learn how to implement sensor array signal processing algorithms on the [[LabVIEW for Robotics|LabVIEW for Robotics Starter Kit robots]] shown above using both Matlab and LabVIEW. Students can then apply this knowledge to individual research projects in Robotic Sensing in subsequent semesters.
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ESE297 - Introduction to Undergraduate Research was created for students who wish to do Undergraduate Research projects in [[media:Robotic_Sensing_V4.pdf|Robotic Sensing]] under [http://ese.wustl.edu/people/Pages/faculty-bio.aspx?faculty=11 Professor Nehorai], the ESE Department Chair. This course is offered as ESE297 for 2 credits and is typically offered in the spring and summer. Students will learn how to implement sensor array signal processing algorithms on the [[LabVIEW for Robotics|LabVIEW for Robotics Starter Kit robots]] shown above using both Matlab and LabVIEW and develop Brain Computer Interface (BCI) algorithms using EEG signals. Students can then apply this knowledge to individual research projects in Robotic Sensing in subsequent semesters. ESE297 does not qualify as an EE elective.
 
== Logistics ==
 
== Logistics ==
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* '''Meeting Time''': Wednesday 8:30-10 pm, Friday 4-5:30 pm in Bryan 316
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* '''Meeting Time''': Fri, 1:30-5:30 in Bryan 316
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* '''Holidays''':  
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* '''Holidays''': Fall Break, Thanksgiving
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* '''Instructor''': Ed Richter
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* '''Instructor''': Ed Richter, Bryan 201E
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* '''Faculty Supervisor''': Arye Nehorai
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* '''T/A''': Stephen Gower (sgower@wustl.edu)
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* '''[[media:Syllabus-sp12.pdf|Syllabus]]'''
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* '''Office Hours''': Mon,Tues 2:30-4 (Ed), Thurs 8-10pm (Steve)
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* '''Expectations''': Each student needs to work 10 hours/week in order to earn the 2 credits for ESE497. In the summer, the expectation is 20 hours/week. That is, students who earn an A will spend many unsupervised hours outside of the class meeting times. In Part 1 of the class, homework will be assigned and is due during the next class meeting. The project (Part2 below) should be completed by the end of the semester.
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* '''[[media:Syllabus-FL15.pdf‎ |Syllabus]]'''
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* '''Expectations''': The work load is estimated to be 10 hours/week if you take it during the Fall or Spring semesters (20 hours/week for a summer semester). That is, students who earn an A will spend many unsupervised hours outside of the class meeting times. Grading is based on your Homework and your Projects. Late work will be accepted with a penalty of 3 points per day. Please see the syllabus for due dates.
  
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== Course Details ==
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= Announcements =
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* Case Study - Study acoustic source localization using Microphone array (see Lecture Notes below)
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* Matlab available for Students now! Send email to support@seas.wustl.edu
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** Demonstrations, Background and Theory
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== Lecture Notes ==
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** Data Acquisition Basics
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* [[Accostic Source Location]]
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** Introduction to Digital Signal Processing Tools
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* [[Data Acquisition Basics]]
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* [[Project1:_Implement_algorithm_using_microphone_array]]
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* [[Signal Processing Basics]]
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* [[Project2a: Implement algorithm with sbRIO robots]]
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* [[Brain Computer Interface (BCI)]], [[media:BCI2000.zip|BCI2000.zip]]
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* [[Project2b: Put the sbRIO robots in motion]]
 
  
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== Lecture Notes ==
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== Projects ==
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* Topic 1: [[media:Presentation_Robotic_Microphone_Array.pdf|Acoustic Source Location Background and Theory]] (Slides 1-19)
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* [[Project1:_Implement_algorithm_using_microphone_array| Project1: Implement algorithm using microphone array]]
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** Additional references:
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* [[Project2:_Triangulation_with_sbRIO_robots|Project2: Triangulation with sbRIO robots]]
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***[http://ese.wustl.edu/ContentFiles/Research/UndergraduateResearch/CompletedProjects/WebPages/fl08/JoshuaYork/index.html Joshua York, Acoustic Source Localization, ESE497, Fall 2008]
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* [[BCI Projects]]
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***[http://ese.wustl.edu/ContentFiles/Research/UndergraduateResearch/CompletedProjects/WebPages/fl09/rms3/index.htm Raphael Schwartz and Zachary Knudsen, Robotic Microphone Sensing: Data Processing Architectures for Real-Time, Acoustic Source Position Estimation, ESE497, Fall 2009]
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== 2013 Upgrade work-around ==
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** Homework 1: Read the material that we discussed in our meeting today and the additional references listed above.
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* Copy [[media:RobotMicSourceLocator.vi|RobotMicSourceLocator.vi]] to RoboticSensing\MicSourceLocator
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** Homework 2: Using this [[media:MicrophoneArrayWithRotation.JPG|figure]], derive the general equations for the source location (x*,y*) which include the rotation of both pair, i.e., the intersection of the 2 lines. Verify that the formula on slide 10 of the lecture notes is correct for the special case where
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* Copy [[media:MoveWheels (Host).vi|MoveWheels (Host).vi]] to RoboticSensing\Examples\MoveWheels (Host).vi (***NOTE*** Change '_' to ' ')
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***y1 = y2 = 0
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* Copy [[media:MoveRobot.vi|MoveRobot.vi]] to RoboticSensing\MicSourceLocator
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***Rotation1 and Rotion2 = 0
 
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***X1=P/2
 
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***X2 = -P/2
 
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* Topic 2: Data Acquisition Basics
 
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** [[media:LabVIEW_Introduction.pdf|LabVIEW Tutorial]]
 
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*** Code up examples in LabVIEW for slides 11, 14, 27, 31, 36, 38, 41. Put each one in a separate VI and demo to me or T/A.
 
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*** Configure LabVIEW options as shown in slides 15-17
 
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*** Exercises 1,2,3
 
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** Homework 3 - Finish Exercises
 
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** Assign Project1 - Simulation
 
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** Homework 4 - Finish ComputeAngle.vi (in Project1 -> RoboticSensing.zip -> micSourceLocator.lvproj -> My Computer -> ComputeAngle.vi) and ComputeIntersection.vi
 
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** Additional Resources
 
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*** Conditionally append values to an array in a loop
 
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*** [http://cnx.org/content/m12220/latest/ How to Create and Array on the Front Panel]
 
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*** [http://zone.ni.com/devzone/cda/tut/p/id/7521 LabVIEW tutorial], [http://www.ni.com/academic/students/learnlabview LabVIEW 101]
 
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** [[media:Data_Acquisition_Basics.pdf|Data Acquisition Basics]]
 
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*** Homework 5 - Finish exercise
 
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*** Homework 6 - Connect wires from A00 and AO1 to AI0+ and AI1+ (remove wire from Banana A to AI0+). Make sure that the Prototyping Power is on. Modify your vi from Homework 5 to collect samples from both AI0 and AI1. Then open  [[media:DelayedChirp2DAC.zip|DelayedChirp2DAC.vi]] and run this vi. You shouldn't modifiy DelayedChirp2Dac.vi. Run your modified Homework 5 vi and zoom in in the time and frequency domain to examine the waveforms in detail. Describe in detail what you see. Measure the difference in time between both channels. Hint: Start and stop your Data Acquisition vi until the entire signal is in the middle of the buffer.
 
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**[[media:CrossCorrelation.pdf| Cross Correlation]]
 
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*** Homework 7
 
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****Plot the Cross Correlation of the 2 channels and see if the peak is shifted from the middle, the number of samples you measured from the previous step.
 
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***** Hints:
 
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****** Functions -> Express -> Signal Analysis -> Conv & Corr -> Cross Correlation
 
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****** This function requires that you extract the 2 channels from the DDT. To do this, use Functions -> Express -> Sig Manip -> From DDT -> Single Waveform -> Channel 0 and then again for Channel 1. Connect the outputs of these to the X and Y inputs.
 
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****** Before you plot the Cross Correlation, extract the 1D array of scalars using the From DDT so that the X-Axis is in samples.
 
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****** Look at the help on the Cross Correlation for details.
 
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**** Plot the Spectrogram of Channel 0.
 
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***** Hint: There is a good Spectrogram example that ships with LabVIEW. Go to Help -> Find Examples... and search for STFT -> STFT Spectrogram Demo.vi. You can copy from this example and paste it into your code.
 
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* Topic 3: Signal Processing Basics
 
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**[[Media:SS_Tutorial_New.pdf|Tutorial]]
 
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***Homework 8- Finish exercise from tutorial.
 
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***[[media:AssignmentForDSPLecture1.pdf|Homework 9]]
 
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***Homework 10- Use the Signal Processing Palette in LabVIEW to generate 2 sinusoid waveforms (Signal Processing -> Waveform Generation -> Sine Waveform) with two different frequencies. Add these together and implement 2 separate filters for this signal (Functions -> Express -> Signal Analysis -> Filter) to extract the original sinusoids. Plot these outputs in the time domain. Also, plot them in the frequency domain (Express-> Signal Analysis -> Spectral). Make sure you can identify the frequencies corresponding to the input sinusoids in the frequency domain. Next, add (as in addition) Gaussian White Noise to the sum of the 2 sinusoids (Signal Processing -> Waveform Generation -> Gaussian White noise). Plot the spectrum  of the unfiltered signal and identify the frequencies corresponding to signal and noise again. Increase the standard deveiation of the WGN and modify your filter to improve the quality of the filtered signal. Also, looking at the sum of the 2 sinusoids and the noise, what is the relationship between the Standard Deviation of the WGN and the amplitude of the noise. Plotting the histogram of the noise (Express -> Signal Analysis -> Histogram) might help? Note: If your graph X-axis is in absolute time instead of seconds, right click on the graph and select Properties -> Display Format -> X-Axis and set it to SI units.
 
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**[[Media:DSP_ESE497.pdf|DSP Lecture by Dr. Jim Hahn]] (for reference)
 
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**[[Media:DSPConfigurations.pdf|DSP Configurations Lecture by Dr. Jim Hahn]] (for reference)
 
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<!--
 
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From Phani
 
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**[[Media:SS_Tutorial_New.pdf|Tutorial]]
 
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***Task 6- Finish exercise from tutorial.
 
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****[[Media:task1.zip|Solution]]
 
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-->
 

Latest revision as of 18:44, 2 October 2015

IMG 1248.jpg

ESE297 - Introduction to Undergraduate Research was created for students who wish to do Undergraduate Research projects in Robotic Sensing under Professor Nehorai, the ESE Department Chair. This course is offered as ESE297 for 2 credits and is typically offered in the spring and summer. Students will learn how to implement sensor array signal processing algorithms on the LabVIEW for Robotics Starter Kit robots shown above using both Matlab and LabVIEW and develop Brain Computer Interface (BCI) algorithms using EEG signals. Students can then apply this knowledge to individual research projects in Robotic Sensing in subsequent semesters. ESE297 does not qualify as an EE elective.

Logistics

  • Meeting Time: Fri, 1:30-5:30 in Bryan 316
  • Holidays: Fall Break, Thanksgiving
  • Instructor: Ed Richter, Bryan 201E
  • T/A: Stephen Gower (sgower@wustl.edu)
  • Office Hours: Mon,Tues 2:30-4 (Ed), Thurs 8-10pm (Steve)
  • Syllabus
  • Expectations: The work load is estimated to be 10 hours/week if you take it during the Fall or Spring semesters (20 hours/week for a summer semester). That is, students who earn an A will spend many unsupervised hours outside of the class meeting times. Grading is based on your Homework and your Projects. Late work will be accepted with a penalty of 3 points per day. Please see the syllabus for due dates.

Announcements

  • Matlab available for Students now! Send email to support@seas.wustl.edu

Lecture Notes

Projects

2013 Upgrade work-around

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