COS 126 Programming Assignment

The Atomic Nature of Matter

Re-affirm the atomic nature of matter by tracking the motion of particles undergoing Brownian motion, fitting this data to Einstein's model, and estimating Avogadro's number.

Historical perspective. The atom played a central role in 20th century physics and chemistry, but prior to 1908 the reality of atoms and molecules was not universally accepted. In 1827, the botanist Robert Brown observed the random erratic motion of wildflower pollen grains immersed in water using a microscope. This motion would later become known as Brownian motion. Einstein hypothesized that this Brownian motion was the result of millions of tiny water molecules colliding with the larger pollen grain particles.

In one of his "miraculous year" (1905) papers, Einstein formulated a quantitative theory of Brownian motion in an attempt to justify the "existence of atoms of definite finite size." His theory provided experimentalists with a method to count molecules with an ordinary microscope by observing their collective effect on a larger immersed particle. In 1908 Jean Baptiste Perrin used the recently invented ultramicroscope to experimentally validate Einstein's kinetic theory of Brownian motion, thereby providing the first direct evidence supporting the atomic nature of matter. His experiment also provided one of the earliest estimates of Avogadro's number. For this work, Perrin won the 1926 Nobel Prize in physics.

The problem. In this assignment, you will redo a version of Perrin's experiment. Your job is greatly simplified because with modern video and computer technology (in conjunction with your programming skills), it is possible to accurately measure and track the motion of an immersed particle undergoing Brownian motion. We supply video microscopy data of polystyrene spheres ("beads") suspended in water, undergoing Brownian motion. Your task is to write a program to analyze this data, determine how much each bead moves between observations, fit this data to Einstein's model, and estimate Avogadro's number.

The data.  We provide ten datasets, obtained by William Ryu using fluorescent imaging. Each run contains a sequence of two hundred 640-by-480 color JPEG images, frame00000.jpg through frame00199.jpg and is stored in a subdirectory run_1 through run_10.

Here is a movie [ avi · mov ] of several beads undergoing Brownian motion. Below is a typical raw image (left) and a cleaned up version (right) using thresholding, as described below.

    frame of polystyrene spheres immersed in water         threshold frame of polystyrene spheres immersed in water

Each image shows a two-dimensional cross section of a microscope slide. The beads move in and out of the microscope's field of view (the x- and y-directions). Beads also move in the z-direction, so they can move in and out of the microscope's depth of focus; this results in halos, and it can also result in beads completely disappearing from the image.

I. Particle identification. The first challenge is to identify the beads amidst the noisy data. Each image is 640-by-480 pixels, and each pixel is represented by a Color object which needs to be converted to a luminance value ranging from 0.0 (black) to 255.0 (white). Whiter pixels correspond to beads (foreground) and blacker pixels to water (background). We break the problem into three pieces: (i) read an image, (ii) classify the pixels (in the image) as foreground or background, and (iii) find the disc-shaped clumps of foreground pixels that constitute each bead.

% java-introcs BlobFinder 25 180.0 run_1/frame00001.jpg
13 Beads:
29 (214.7241,  82.8276)
36 (223.6111, 116.6667)
42 (260.2381, 234.8571)
35 (266.0286, 315.7143)
31 (286.5806, 355.4516)
37 (299.0541, 399.1351)
35 (310.5143, 214.6000)
31 (370.9355, 365.4194)
28 (393.5000, 144.2143)
27 (431.2593, 380.4074)
36 (477.8611,  49.3889)
38 (521.7105, 445.8421)
35 (588.5714, 402.1143)

15 Blobs:
29 (214.7241,  82.8276)
36 (223.6111, 116.6667)
 1 (254.0000, 223.0000)
42 (260.2381, 234.8571)
35 (266.0286, 315.7143)
31 (286.5806, 355.4516)
37 (299.0541, 399.1351)
35 (310.5143, 214.6000)
31 (370.9355, 365.4194)
28 (393.5000, 144.2143)
27 (431.2593, 380.4074)
36 (477.8611,  49.3889)
38 (521.7105, 445.8421)
35 (588.5714, 402.1143)
13 (638.1538, 155.0000)
The program identifies 15 blobs in the sample frame, 13 of which are beads. Our string representation of a blob specifies its mass (number of pixels) and its center of mass (in the 640-by-480 picture). By convention, pixels are measured from left-to-right, and from top-to-bottom (instead of bottom-to-top).

II. Particle tracking. The next step is to determine how far a bead moved from one time step t to the next t + Δt. For our data, Δ t = 0.5 seconds per frame. We assume the data is such that each bead moves a relatively small amount, and that two beads do not collide. (However, we must account for the possibility that the bead disappears from the frame, either by departing the microscope's field of view in the x- or y- direction, or moving out of the microscope's depth of focus in the z-direction.) Thus, for each bead at time t + Δt, we calculate the closest bead at time t (in Euclidean distance) and identify these two as the same beads. However, if the distance is too large (greater than delta pixels) we assume that one of the beads has either just begun or ended its journey. We record the displacement that each bead travels in the Δt units of time.

Write a main() method in BeadTracker.java that takes an integer P, a double value tau, a double value delta, and a sequence of JPEG filenames as command-line arguments, identifies the beads (using the specified values of P and tau) in each JPEG image (using BlobFinder), and prints out (one per line, formatted with 4 decimal places to the right of decimal point) the radial displacement that each bead moves from one frame to the next (assuming it is no more than delta). Note that it is not necessary to explicitly track a bead through a sequence of frames—you only need to worry about identifying the same bead in two consecutive frames.

% java-introcs BeadTracker 25 180.0 25.0 run_1/*.jpg
 7.1833
 4.7932
 2.1693
 5.5287
 5.4292
 4.3962
...

III. Data analysis. Einstein's theory of Brownian motion connects microscopic properties (e.g., radius, diffusivity) of the beads to macroscopic properties (e.g., temperature, viscosity) of the fluid in which the beads are immersed. This amazing theory enables us to estimate Avogadro's number with an ordinary microscope by observing the collective effect of millions of water molecules on the beads.

For the final part, write a main() method in Avogadro.java that reads in the displacements from standard input and computes an estimate of Boltzmann's constant and Avogadro's number using the formulas described above.

% more displacements-run_1.txt        % java-introcs BeadTracker 25 180.0 25.0 run_1/*.jpg | java-introcs Avogadro
 7.1833                               Boltzmann = 1.2535e-23
 4.7932                               Avogadro  = 6.6330e+23
 2.1693
 5.5287
 5.4292  
 4.3962
...

% java-introcs Avogadro < displacements-run_1.txt
Boltzmann = 1.2535e-23
Avogadro  = 6.6330e+23

Output formats. Use four digits to the right of the decimal place for all of your floating point outputs whether they are in floating point format or exponential format.

Running time analysis. Formulate a hypothesis for the running time (in seconds) of BeadTracker as a function of the input size N (total number of pixels read in across all frames being processed). Justify your hypothesis in your readme.txt file with empirical data.

Provided files. You can download the datasets and other files mentioned above as a 67.1 MB zip file. (You can alternatively use the course FTP site.)

Submission. Submit Blob.java, BlobFinder.java, BeadTracker.java and Avogadro.java. Finally, submit a readme.txt file (or partner readme file) and answer the questions including the running time analysis. If your program has any dependencies, submit them as well. However, you do not need to upload the standard libraries, Luminance.java, or the data types from Section 4.3.


This assignment was created by David Botstein, Tamara Broderick, Ed Davisson, Daniel Marlow, William Ryu, and Kevin Wayne.
Copyright © 2005