Cummins_ - DRUM - University of Maryland
Contents
1. Control 15 002 Comers JOINE Maama Votage VJ 10 ist Gem ion St 6 Repeat steps 4 and 5 until the pictured channel geometry and the modeled channel geometry overlap 7 Exit channel calibration mode by again selecting Channel Geometry 24 3 3 Manipulating Particles l Particles Recording Moe on Enable particle control by selecting Control On Off from the toolbar Controt 149K Camere JOINE Maam Vota V 10 it Gt Get O sA Choose a particle for control by left mouse clicking the active camera view Two circles will appear connected by a black line One circle displays the desired position of the particle while the other displays the actual position of the particle The initial desired position is the point clicked by the mouse 3 Initially a selected particle may exhibit large error with respect to its desired position This error may be attributed to pressure flow or flaws in the device To correct this increase the voltage gain by setting the Gain field on the bottom of the window to a number higher than displayed Controt 16317 Camere JOINE Maam Vota V 10 it cr s Ce ces e ag i 4 Drag the particle within the control area by left mouse clicking either of a particle s positioning circles and dragging the circle in the desired direction 25 5 nn p a Particles may be deleted from the controller by right n mouse clicking either of a particle s po2. Optical Track sierica a 2 1 2 Kalmar eT Sn tice a te al a ot a sb adh he eat at 2A 3 Channel Calibratoni ae ed t mate duda ed dnde pale ed AE ZA 4 Trajectory Manipulation ii g haw Lara a eS 2 1 5 Recorder Pe ae ei R ee et ee bai 22 0 signifi a ld oh Boks 23 peines ui deine 23 1 Ei heC Hannel DEVIC erone a ole iero Cola eda Vea 232 Twelve hannel Deve siriana ia lele ZE AS OC IS A et 3 Multiple Loop Program 3 1 Ea EE E A E EE E EE a te se 5 APPO A e e aa e A E E AR 3 2 1 Optimization 322 MeS asarei es 3 2 3 Graphics 3 2 4 Organization O o AA SI T ns cer aioe SE er Ga Se 3 3 Experiments 3 3 1 Eight Channel Device 3 3 2 Four Channel Device 3 4 Conclusion 4 References A Multiple Loop Program User Manual 11 Table of Figures ee re Ad do do do do do EO CONN Vi BWNRK o Electroosmotic flow double layer Control device Eight channel flow modes Particle Tracker window Channel view Thresholded particle image Particle image matrix Labeled particle image matrix Kalman filter results for tracking a single particle Channel calibration Trajectory Manipulation Prepared eight channel device Matrix synthesis Five particles following a square trajectory Five particles following the letters Multiple Loop program window Particle dragging Four particle channel geometry 111 O 4
3. tA 09 9 lt O QAe 0 0 D 09 0 0000000000000000000000 Figure 1 Electroosmotic flow double layer When a polar fluid like water enters a microchannel an electrical double layer is formed at the interface between the wall of the channel and the fluid as shown in Fig 1 The negatively charged channel walls of the microchannel attract the positive ions of the fluid creating a concentration of positive charges When an electrical field 1s applied to the fluid the fluid of the double layer is displaced moving toward the positive cathode end of the applied current The displacement of the fluid layer drags the entire fluid along with it including all the particles in it regardless of their charge The flows created by this effect are predictable and uniform across the channel making them ideal for particle control and steering 1 3 Control Devices Control devices utilizing EOF are created in a six part process grouped by two functions fabrication and replication During fabrication a mold formed by several intersecting microchannels is formed on a dehydrated silicon wafer During replication a silicone polymer polydimethylsiloxane PDMS is poured over the mold and allowed to solidify The resulting device similar to the one pictured in Fig 2 can then be placed mold side down on a glass microscope slide and filled with deionized water Voltages applied at each channel create EOF in each channel 2 1 4 Control S
4. and can be made to continually run in the program background This was an ideal solution because videoinput analogoutput and all other clocked device objects provide built in timer functionality The camera image would then be automatically updated at a frequency separate and perhaps different from the control frequency because both would be run in their own timers Early tests resulted in frequencies as high as 60 Hz for the videoinput timer and 100 Hz for the analogoutput timer 3 2 3 Graphics To simplify graphics display hggroup and hgtransform objects were employed to group together objects that could be hidden and shown at the same time or rotated and scaled at the same time The hgt ransform object is especially useful in creating a virtual representation of the control device The control device can be easily modeled using actual or normalized units and then scaled rotated and centered over the camera image This simplifies both the display of data in the channel coordinate system and the conversion between coordinate systems Timer functions enabled more extensive use of graphics callbacks to handle mousing 17 events Where these events were handled during each loop iteration in the Single Loop program they are handled using the WindowButtonDragFcn property of the program figure This reduces code size and simplifies the way events are thought about 3 2 4 Organization Due to the shear size of the Single Loop program it was t
5. All extraneous graphic displays were thus disabled or hidden to achieve 15 Hz 3 Multiple Loop Program 3 1 Goal Drawing from experiences and Particle Tracker Camera Particles Recording observations accumulated in developing ROO gt NEO and using the Single Loop program I set out to almost completely rewrite the program This program would be a simplified version of the Single Loop program meaning that 1t would not have trajectory creation or many graphics The focus points of this version of the program were speed size and ease of use Control 14 9Hz Camera 30 3 Hz Maximum EN Gain Es 3 2 Approach Figure 16 Multiple Loop program window 3 2 1 Optimization Using the profile function several time consuming functions were identified and 15 modified for speed l The stop function used to reset the voltage applied to the analog output device took the most time subtracting as much as 10 Hz from the control frequency However it was never needed at all As long as the voltage output device s sampling buffer did not run out it 1s able to run without stopping Alternatively the device can be restarted using start which did not require a stop function call beforehand This was an artifact from older code that used a different voltage output device The drawnow function that flushed the MATLAB event queue and redrew all open figures slowed down the control frequency by a variable amount depending on the
6. capable of performing these three tasks 1t could then be used to perform experiments and troubleshoot problems in the code and in the control model s calculation of voltage 2 3 Experiments 2 3 1 Eight Channel Device To visually verify that the program y Particle Tracker VOR was correctly calculating and applying Se gt View Window Help 300 200 100 0 100 200 voltages an eight channel control device Fig 12 was prepared and equalized on two separate occasions During the first experiment it was i possible to control one particle with limited success near the center of the ASA i Fi device When the particle was dragged j i s 400 500 600 Figure 12 Prepared eight channel device away from the center its error would 10 gradually increase until it could no longer be held by the controller In trying to control more than one particle all the particles were shot off of their desired positions During trials where multiple particles were being controlled it seemed that the wrong channels were being actuated A coordinate system conversion error seemed to be the problem In the existing conversion code the device cross section was assumed to be measured along the diameter rather the radius thus conversion was off by a factor of 2 This explained why particles could be held near the center and it was thought that a combination of this and poor pressure equalization caused multiple part
7. voltage input to each microchannel AMC Aa EREA Individually each particle in the flow has two degrees LESS eg 2 fluid una 5 fluid mode 7 Figure 3 Eight channel flow modes of freedom in its positioning x and y coordinates relative to the center of the the control chamber This means that a device with odd number modes can control N71 2 particles 1 4 3 Optical Feedback The control algorithm described in 1 4 2 assumes no error in the control model or uncertainty in the measurement of mobility constants as well as no sources of noise in the system response To account for any errors or uncertainties that may arise in the system a visual feedback loop 1s created using a camera connected between a computer running image processing software and a microscope used to view the control device A more detailed discussion of the optical feedback algorithm 1s given in section 2 1 2 1 4 4 Accuracy The control system is robust in its ability to overcome deficiencies in mobility measurements ambient noise and a relative disregard for the chemical factors that govern electroosmotic and electrophoretic mobilities for particles In general the accuracy of the system is defined by the resolution of the camera For example each pixel of a 640x480 pixel camera corresponds appoximately to 1 um error 1 5 Project Goal The goal of this project was to develop and refine a graphical user interface GUI that effectively closed the control loop a
8. SS DD NB WN 1 Introduction 1 1 Background Precise manipulation of particles at a microscopic level can be accomplished by a number of means with varying degrees of success and benefits Many biological and pharmaceutical laboratories employ optical tweezers that use dielectrophoresis to trap and move particles 1 Optical tweezers can manipulate hundreds of particles at a time in three dimensional space to within nanometers of each intended position The advantages of such optical trapping systems can not be understated but come at a price Optical tweezers require lasers and delicate optics that require significant power and space relative to less accurate and extensive alternatives Alternative particle trapping systems have been created as alternatives to optical tweezers utilizing electric fields taking advantage of particles with dielectric properties magnetic fields which manipulate particles with magnets attached to them and arrays of microelectromechanical air nozzles that can steer particles along a control surface These solutions can be executed more cheaply and built on a small scale but lack significant steering capabilities The control system described in this section aims to reach some middle ground between the steering capabilities of optical tweezers and the attractive size and scale of the alternatives For a more detailed discussion of the follow introductory descriptions see 1 and 2 1 2 Electroosmotic Flow
9. THE INSTITUTE FOR SYSTEMS RESEARCH ISR TECHNICAL REPORT 2008 16 Graphical User Interface for Automated Biological Cell Manipulation Tasks Cummins Zachary Advisor Shapiro Benjamin and Probst Roland The Institute tor Systems Research Saxo k JAMES CLARK 4 o SCHOOL OF ENGINEERING IRYLAS ISR develops applies and teaches advanced methodologies of design and analysis to solve complex hierarchical heterogeneous and dynamic prob lems of engineering technology and systems for industry and government ISR is a permanent institute of the University of Maryland within the A James Clark School of Engineering It is a graduated National Science Foundation Engineering Research Center www isr umd edu Graphical User Interface For Automated Biological Cell Manipulation Tasks Zachary Cummins Advisors Dr Benjamin Shapiro Roland Probst University of Maryland Institute for Systems Research Research Experience for Undergraduates 2008 Table of Contents l Introd ctiom so bi ds Soe eho ewes a oo bs She ee ees week EP Paella ron 12 Eleciroosmone Elowfssnici ino siria iS ia Control DEVICES iaa tale leale ee 1 4 o CONOS e a e ee a ta e 1 4 1 Modiano 1 42 ASA Mr rd a dd oa ea dde E 1 43 Uy CO AC AMA II Gts BSI GG 1 44 ACTUA A er a A IA E PROVE CH O EEE EE EE E ea ERE 2 Single Loop Procralm at seta a owas aan 2A BxXIStMo Code REVIEW xg ncaa amp aad ait kcah e Bo Gas es cd HO ene ope FE Ged owed 2 1 1
10. a Recording and playback functionality allows users to save replay and analyze experiments 1 1 Requirements The requirements listed below refer to those used in developing and testing the program Other hardware configurations and software versions may work but are not guaranteed 1 1 1 Hardware Processor Intel Pentium 4 3 4 GHz RAM 1 GB DDR2 Hard drive 80 GB WD800JD 7200 RPM Video RAM 256 MB ATI Radeon X600 1 1 2 Software e MATLAB R2007b O Image Acquisition Toolbox Version 3 0 O Image Processing Toolbox Version 6 0 O Data Acquisition Toolbox Version 2 11 1 2 Installation To install extract particletracker zip into a convenient location 1 3 Configuration Particle Tracker uses MATLAB analogoutput and videoinput objects to interface with the control apparatus To use a specific device edit the following files File Output Description Aad ete Sa E Initializes the analog output device applying voltages to the control device epee eae No Output Sends voltages calculated by the control algorithm to the analog output device initvid m videoinput Initializes the camera viewing the control device updatevid m Image Returns the latest frame from the camera Table 1 Configuration files Note Each file contains useful comments and code examples 22 3 Operation 3 1 Running l In MATLAB navigate to the directory containing the Particle Tracker installation To run the program simply run pttool m from the direc
11. ages accuracy and the speed of the control device to the motion of the mouse could be easily increased Multiple particle control likewise worked fairly well when simply holding particles in place Difficulty did occur in moving these particles however Control 144 9Hz Camera 30 2 Hz Maximum Voltage Gain 3 3 1 Four Channel Device Figure 17 Particle dragging The four channel device was especially Particles Recording important to test because it would potentially A serve as a demonstration of the control system s operation for a Nature Protocols protocol paper Unfortunately a working model of four channel control was not available for testing Instead the eight channel model was used using only the voltages calculated for channel 1 top channel 3 Control 15 0Hz Camera 30 2 Hz Maximum Voltage v Gain 1 Figure 18 Four particle channel geometry right channel 5 bottom and channel 7 left This resulted in adequate control of a single particle when the voltage gain was increased However the eight channel flow field matrix is smaller than the control area of the four channel device When directly adapted to the actual control area radius of the four channel 19 device particles must not travel past the boundaries of the flow field matrix Doing so results in program error and termination To account for this either the flow field matrix must be properly scaled or anew model must be
12. complexity of graphics to be recalculated and displayed at each call Removing drawnow also removes the ability to actively use the mouse to interact with program so compromises in the design and display of the GUI were made All extraneous graphics would be hidden by default The get snapshot function used to retrieve the latest frame from the camera for processing limited the maximum control frequency to the frequency of the camera At each call get snapshot waits for the frame to be completed before returning control to MATLAB The preview function used to display images from the camera operates in a similar way getsnapshot was replaced with peekdata which simply takes the number of frames requested from the camera buffer without waiting for a frame to completed This means that when run faster than the camera frequency a frame may only partially be current frame For this application where small velocities and particles are expected 16 this alternative 1s acceptable and does not effect control preview was removed in favor manually updating the CData property of the image object on the display axis with the image retrieved using peekdata This achieves the same thing as preview without waiting for camera to finish acquiring frames 3 2 2 Timers To simulate a multi threaded environment and free the command line up during program operation timer functions were utilized The timer function allows MATLAB to schedule function operation
13. e tracking became a reality for the twelve channel device During early experiments control of two three and four particles was performed with relative success Difficulties came in many forms 1 Pressure flow was very detrimental to the device Unless the pressure of the channel was careful equalized and maintained throughout experiments it was unlikely that the device would display enough stability to either steer particles or simply hold several in position 2 Channel actuation would reverse or fail due to changes in system chemistry and ion buildup This would occur during long experiments and when voltage was manually applied to a specific channel for prolonged periods of time 3 Channels became easily clogged and required thorough cleaning to remove any significant blockage 2 micrometer fluorescent beads were originally used for ease in tracking and visualization but 1 2 micrometer fluorescent beads were settled on 4 Program speed after long experiments gradually decreased to below the desired control frequency of 15 Hz For these reasons experiments with the twelve channel device took on a very delicate dynamic but eventually proved successful Several trajectories were planned by Chaudhary in a 12 simplistic form Each trajectory was a collection of waypoints points used in linearly interpolating the complete trajectory normalized to a radius of 1 unit and each waypoint was associated with a time relative to the be
14. etry users can more easily and accurately line up the modeled channel geometry of the program with the actual channel geometry pictured This is vital for conversion between the camera s pixel coordinate system and the Figure 10 Channel calibration control model s metric coordinate system 2 1 4 Trajectory Manipulation Another key existing feature was the ability to interactively create and manipulate trajectories using SUSIE B zier curve parameterization Modeled after Adobe Photoshop s Adobe Systems Incorporated San Jose CA Path tool users could plot and move points using tools on the trajectory toolbar The shaping tool allowed users to 100 100 Ma 40 500 HI Figure 11 Trajectory manipulation select individual points and modify the curvature around the point This type of trajectory manipulation is ideal for on the fly particle steering and provides the ability to create arbitrarily complex trajectories that would be difficult to create from the command line or from a script file 2 1 5 Recording Recording was a simple matter but especially important in making Particle Tracker useful as an experiment tool At each frame the recording routine would add relevant program data structures to a single cumulative recording structure This structure could then be saved for later analysis or played back within the program In this early version of the recording code channel orientation and scaling information
15. extracted and used 3 4 Conclusion The Multiple Loop program provides an overall better base for continued development of the Particle Tracker program During experiments the control frequency easily reached 30 Hz matching the camera frequency For faster cameras this will mean improved control and the possibility of increasing the number of particles controlled by juggling between different sets of particles As the camera frequency increases the error measured between a particle s measured position and its desired position will decrease and so finer adjustments can be made during each loop iteration A short manual for this program is attached as Appendix B 4 References 1 B Shapiro et al Using Feedback Control of Microflows to Independently Steer Multiple Particles IEEE J Micromech Systems vol 15 no 4 pp 945 956 2006 2 S Chaudhary and B Shapiro Arbitrary Steering of Multiple Particles Independently in an Electro Osmotically Driven Microfluidic System IEEE Trans Control Systems Tech vol 14 no 4 pp 669 680 2006 3 Randall L Eubank A Kalman Filter Primer CRC Press 2006 20 Appendix A Multiple Loop Program User Manual 21 Particle Tracker User Manual 1 Introduction Particle Tracker is a MATLAB demonstration of the cross channel microfluidic control device Users can select particles from live video of the control device and use the mouse to drag particles within the control are
16. ginning of the trajectory s start The number of points in between assured that the trajectory was correctly updated with the control algorithm at each update and were determined by the formula Numberof points Control frequency Hz x Secondsbetweenwaypoints s An example of his type of trajectory 1s a square followed by five particles shown in Fig 14 The black line represents the desired trajectory the red trajectory represents the actual trajectory of each particle and the blue outlined square represents the region of interest in which the particle being controlled was searched for Note that in the bottom right corner of the square that accuracy is especially poor This is due to comparatively poor actuation from one or more of the channels in the bottom right of the image Also note that at points along the trajectories that the actual trajectories seem to loop on themselves This is due to overshoot caused by time delay voltages are being applied for too long forcing particles passed their desired positions Figure JOE File Edit View Insert Tools Desktop Window Help Figure 14 Five particles following a square trajectory 13 Another experiment shown in Fig 15 was done using the trajectory tool to form five letters representing five of the people that have spent time with the project This was a more difficult task and larger errors appeared in letters with more curves in them Note that the trajectories do not use t
17. he B zier shaping feature of the trajectory tool When these curves were employed they resulted in velocities too fast to be handled while steering five particles Instead waypoints were put in place to ensure the same linear fit as employed with the trajectories described above a aaa File Edit View Insert Tools Desktop Window Help a Figure 15 Five particles following the letters B Dr Benjamin Shapiro S Satej Chaudhary M Mike Armani R Roland Probst and Z Zach Cummins 2 4 Conclusion Performing experiments with this program provided many valuable lessons Fundamentally the structure of the program did not lend itself to ease of development which was one of the pillars of choosing MATLAB as the development platform to begin with As needs changed the amount of code grew to many thousand lines which was undesirable for debugging and distribution 14 Trajectory creation essentially failed as a tool Though attractive it became an afterthought during the experiment process When it was used 1t was not fully developed to perform tasks in the experimental setting and so a supplementary waypoint style of trajectory plotting took its place This feature may require functional or experimental necessity and purpose before it is more aptly developed Speed also became an apparent issue during experiments and recording Control frequency would drop to 10 Hz in the presence of more than three particles and trajectories
18. hought that organization would be best handled by organizing structures into classes Object orientation is not one of MATLAB s strengths but the logical organization of functions was attractive Instead as code was being simplified through the other methods described only the directory structure of classes was adopted This meant that functions could be placed in a folder of the same name with an symbol in front of it In this folder another folder entitled private could contain functions only called by the function in the directory above it 3 2 5 Operation Program operation was to be as simplistic as possible following a simple procedure Match channel geometries Select and drag particles with the mouse Record and replay operation 3 3 Experiments With the control code having been debugged during the development of the Single Loop program experiments on the Muliple Loop program were performed largely to verify that it worked with the control system and what if any speed benefits there were 3 3 1 Eight Channel Device Experiments to test that the program worked with the existing control algorithm were done first using an eight channel device This would also verify that the program could be used 18 in related experiments involving nanodots With little effort the eight channel was up Particle Tracker and working By adjusting the gain on the channel cena Parides recordng hs OO MPI Ee volt
19. icle control to fail In the second experiment this correction allowed for one particle to be steered aptly throughout the control area with little problem However when multiple particles were selected control became immediately unstable 2 3 2 Twelve Channel Device Intertwined with the eight channel experiments were twelve channel device experiments with Sate Chaudhary that aimed to control a maximum of five particles at a time These experiments suffered the same characteristics that plagued the eight channel experiments Only one particle could be steered at a time and multiple particle steering was impossible Additionally pressure equalization was of greater importance in reducing disturbances to the control device The apparent obstacle to multiple particle steering was an error in the code that retrieved the flow field matrix as well as the code that returned voltages Both were initially programmed to synthesize matrices following a pattern of stacking all x components on top of all y components The fix was to instead synthesize these matrices by interchanging x and y components as shown in Fig 13 11 However the operations performed here are mathematically equivalent In performing the above fix the actual error was corrected as a consequence of rethinking the functions in question a Correct b Incorrect El x l e Yy 1 e Vy 2 Vx 2 Vil Vio Ve Figure 13 Matrix synthesis Once corrected multiple particl
20. nd gave users the ability to simply and easily interact with a microchannel device To test the efficiency of the control algorithm the program would be able to create and run trajectories for particles to follow To analyze and verify the results the program would be able to record experiments in real time Because of the visual nature of the program it would also be able to output experiments as videos For flexibility in development and distribution it was desired that MATLAB be the primary development and running platform 2 Single Loop Program 2 1 Existing Code Review The Single Loop program was an extension of work that I had done during the Spring 2008 semester ESS During this time my goal was to take a skeleton program originally created by Roland Probst and expand it into a full featured GUI This work was primarily done using previously captured images as a test on the optical tracking and filtering routines described below The main Figure 4 Particle Tracker window view of this program is pictured in Fig 4 The basis for the program was a continuous while loop that would update tracking and control in each iteration 2 1 1 Optical Tracking The optical tracking algorithm uses standard functions provided by the Image Processing Toolbox included with MATLAB Figure 5 Channel view Given a channel image like that shown in Fig 5 and the predicted position of a particle the optical tracking algori
21. ress and ending when Stop is pressed Table 2 Recording toolbar 3 4 2 Playback Display Experiment playback features two useful displays Channel Geometry to give a clearly defined view of the control device and Experiment Data time frequency voltage and gain for analysis Each can be toggled on and off by selecting the corresponding entry of the the Recording menu s Display sub menu 3 4 3 Saving and Loading Experiment data can be saved to a MATLAB mat file for analysis by selecting Save Recording from the Recording menu This data can then be loaded at another time by selecting Load Recording from the Recording menu 3 4 4 Saving Video Playback can be saved as an AVI file by selecting Export to AVI from the Recording Menu 3 4 5 Saving Screenshots Still images can be saved from playback by selecting Save Screenshot from the Recording menu These images are saved in the png format 3 4 6 Clearing Experiment Data Experiments can take up a significant amount of memory causing the program to slow down To release this memory choose Clear Recording from the Recording menu Lal
22. rticle positions involves some unavoidable uncertainties inherent to both the imaging and measuring processes Tracking particles effectively within the camera image 1s done with the aid of a Kalman filter that attempts to reduce measurement uncertainties Additionally the filter is able to predict the position of the particle in the next time step This prediction 1s then used by the optical tracking algorithm to determine which particle among several is the correct particle to measure and subsequently track Kalman Filter 310 312 314 316 318 320 322 324 326 Frame Figure 9 Kalman filter results for tracking a single particle Fig 9 illustrates the use of the Kalman filter in the tracking of a single particle Between frames 317 and 321 the measurement of the particle undergoes a rapid oscillation in the vertical direction the estimated position by contrast remains relatively linear This feature becomes especially important when particles undergo severe oscillation or rapid changes in direction The mathematical derivation and reasoning of the Kalman filter is beyond the scope of this paper A more detailed discussion can be found in 3 2 1 3 Channel Calibration A key feature of the code previously developed BI Firele Tracker i Cerro Came pirata Ti i E n 3 iic Were erie im was the ability to interactively and visually measure and Txe IL orient the channel geometry By visualizing the channel geom
23. sitioning circles To delete all particles at once choose Release All from the Particles menu To delete all but the number of particles capable of being controlled choose Release Extra from the Particles menu 3 4 Recording gt Particle Tracker d IK Particles Recording Save Recording Load Recording Export Eo AWI Save Screenshot Particle Tra Clear Recording ow Channel Geometry w Experiment Data Time 2008 08 04 15 56 27 279 Freq 14 946 Hz ott 4400 40 040 04 0 07 Gain 5 Control 15 0Hz Camera 30 2 Hz Maximum Voltage W Set Gain Set 3 4 1 Toolbar The recording toolbar consists of five tools enabling experiments to be recorded and replayed These tools do not interfere with the tracking and control of particles but may slow the program down when lengthy recordings are in memory 26 Button Action Description 44 Rewind Resets or restarts playback from the first recorded time step Sequentially displays each time step in memory When a recording finishes playback it rewinds to the beginning and replays gt Play ei asa Shales aia del aes Halts the display on the current time step without returning the Il Pause i display to the active camera view Returns the display to the active camera view and any existing zi Stop ate trackers still in the control area Saves each time step to memory beginning from the initial button Record p
24. thm extracts the small region of interest ROI surrounding the predicted position shown as a white outline in Fig 6 The ROI is converted to black and white where any pixel above a certain brightness threshold 1s given a value of 1 and any value below is given a value of 0 illustrated in Fig 7 The brightness threshold 1s determined by averaging the intensity of the brightest pixel with the intensity of the dimmest pixel MA 11111111111 O A EEEE o o oro EEE o ooo E y 10 0 11111110111 DI 1401 4 14 14 1 o Figure 6 Thresholded Figure 7 Particle image Figure 8 Labeled particle image matrix particle image matrix From the resulting black and white image the position of the particle relative to the ROI is calculated by averaging the positions of each pixel with an intensity value of 1 The particle s position with respect to the entire image 1s then known For the event that there are multiple particles present in the same ROI the bwlabel function is used to find blobs of pixels connected to each other Blobs are identified with a numeric identifier put in place of pixels with an intensity value of 1 Fig 8 illustrates the result of bwlabel when applied to the entire image displayed in Fig 5 The particle is uniquely identified by the number 14 bwlabel does well to distinguish between particles but in order to determine which particle is the one of interest a predictive filter 1s useful 2 1 2 Kalman Filter Measuring pa
25. tory view or command line A window similar to that shown in Figure 1 will appear Cortrot 15007 Camera 209 He Maama votage 10 ARA 3 2 Calibrating l Select the Voltage Display On Off button on the ee toolbar to bring up the channel voltage display Ze Manually apply arbitrary voltages to each channel by typing in voltages in the respective channel text fields located in the bottom left corner of the display Applying a variety of positive and negative voltages to each channel tests actuation and 1s useful for visually locating each channel 23 3 5 2 In order to calibrate the conversion from Particle Gop o Tracker s pixel coordinate system to the metric coordinate system of the control algorithm the pictured channel geometry must be matched with the modeled channel geometry provided by the program Select the Channel Geometry toolbar button to initiate the channel calibration mode To adjust the radius and angular orientation of the modeled channel geometry hold the left mouse button down on the white dot lying in Channel 2 and drag until a rough estimate of the scale and rotation of the pictured channel geometry has been reached 4 Partici n Recording Doo eo E To center the modeled channel geometry over the pictured channel geometry hold the left mouse button down on the center of the white cross and drag to the pictured channel geometry s center Di d o ne o
26. was not saved This limited the visual usefulness of the recordings to single program sessions where no changes were made to channel geometry 2 2 Approach The first task was to connect the existing program the external devices used to monitor and control the physical control devices Integration of the camera into the program was achieved 1n three parts 1 Initializing the camera as a MATLAB videoinput object 2 Using preview to automatically update the camera image on the display axis at interval As a downside this capped the program control loop frequency at the image acquisition frequency of the camera 30 Hz at its fastest 3 Retrieving the latest image from the camera using get snapshot The second task was to calculate voltages for each device device based on the error between the measured particle position and the desired particle position This was easily accomplished because each control model associated with the model also had an associated voltage calculation function that followed the flow field matrix inversion scheme described in 2 The third task was to send the calculated voltages to the analog voltage output device that would apply them to a control device This was achieved in two parts 1 Initializing the analog output device as a MATLAB analogoutput object 2 Stopping the object with st op then queuing data voltages into the device s buffer using put data and restarting the object with start Once the program was
27. ystem 1 4 1 Model The Navier Stokes equations model the dynamics of the fluid contained within the control chamber and microchannels The electric fields are modeled by Laplace s equation Electroosmotic slip velocity boundary conditions are enforced at the floor and ceiling of the device while the effects of the channel walls are considered to be negligible The resulting governing equation simplifies to V zo E where Y is the electroosmotic mobility of the particle This equation relates the velocity at any given point in the flow to the electrical field at that point Additionally if the particles themselves are charged or carry surface charges they are effected by a process called electrophoresis Under electrophoresis the particle is transported relative to the flow The velocity due to electrophoresis is similarly V e CE where is the electrophoretic mobility The total velocity for any given particle then is given by V ver u c u and can be determined experimentally or obtained from a reference while is governed by the voltages applied to the control system 1 4 2 Algorithm Each control device is capable of several independent flow modes Four of the modes for an eight channel device QUAD UA od aa A a A are pictured in Fig 3 In order to achieve a desired velocity a RS fida for a given particle some combination of these modes 1s i fluid mode 1 fluid mode 3 calculated in order to determine the necessary
Download Pdf Manuals
Related Search