Belkin, Finken, Walczak
Multispectral Camera System
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Multispectral Camera MDR report
S.Belkin, EE, A.C.Finken. EE, M.Walczak, EE

hobbyists curious about their surroundings.
Abstract— Multi-spectral cameras capture images through
special optical filters, essentially band-pass filters, which allow
only a certain range of wavelengths through while blocking the
rest. By carefully selecting these optical filters, the small range of
wavelengths that were allowed through can show certain optical
characteristics of the target image. These optical characteristics
can indicate what substances, and even concentrations, are present
within the target. The multi-spectral camera will be operated by a
microcontroller system. This system will position an individual
filter in front of a monochrome camera and an image will be
captured and displayed to the operator.
I. INTRODUCTION
Most common CCD cameras capture images that display the
entire visible spectrum that a human eye can detect [1]. These
images containing the entire visible spectrum display the
wavelength ranges between approximately 400-700 nanometers
[2]. Having an image that displays the entire visible spectrum
in one image masks certain visible “information” that may be
present within the target being imaged, since all spectra are
combined; capturing the spectra in individual bands, while
blocking out others, allows for finer details to emerge. This
detail can show the spectral signature [3] of the objects that are
captured in the image, such as the moisture content within a
plant, or the minerals present in soil [2].
One interesting application of hyperspectral imaging is in the
field of mineralogy. Figure 1 shows the “reflectance spectra of
(the mineral) pyroxene as a function of grain size. As the grain
size becomes larger, more light is absorbed and the reflectance
decreases. Note the trace tremolite contamination (a silicate
mineral [6]) causing the narrow absorption features near 1.4
and 2.3 µm [7].” Hyperspectral imagery can be used not only
to determine the size of particulates within a sample, but also if
that sample is pure, or contains other substances.
The field of hyperspectral imagery is growing in popularity
because of its ability to capture image data of the earth’s
surface with non-contact, non-disruptive, aerial or space-based
imagery. Rough and otherwise dangerous terrain, as well as
large surface areas, can be imaged in multiple small spectral
wavelength bands, which can reveal certain spectral signatures
contained within [5].
Multispectral imagery relates to hyperspectral imagery in that
they both capture images using optical filters that filter out
certain spectral bands. Hyperspectral imagery uses tight
spectral bands for each image it captures, such as 10nm wide
bands, which are usually in continuous sequence. Multispectral
imagery tends to use a little bit wider spectral bands that are not
necessarily in a continuous sequence, but defined in chunks,
like the ‘visible spectrum’ or the ‘near-IR’ spectrum [4]. In
essence, there is not a large distinction between these two terms Fig. 1. Reflectance Spectra of Mineral Pyroxene as a Function of Grain
Size.
of spectral imagery.
The significance of having the ability to obtain this type of
selective spectrum information from an image, which is nondestructive to the environment and relatively easily captured,
can be very useful. Knowing what material is present simply
by studying a multispectral image is an extremely powerful and
useful tool for many different scientific fields, such as
agriculture and mineralogy, as well as for non-professional
Hyperspectral cameras have the resolution to identify
materials such as minerals and vegetation on the surface of the
earth based only on their spectral signature [3]. These spectral
signatures are matched to actual specific materials and
compiled into libraries to be used as references. This type of
spectral library has been created for minerals, plants, manmade materials, vegetation stress, and many other materials [5].
Multi-spectral imaging has been in use for many years but
had been relatively cost prohibitive since the technology was
Belkin, Finken, Walczak
Multispectral Camera System
new and that it was generally used on aircraft, which left this
technology primarily in the hands of government agencies, such
as NASA and the US Geological Survey (USGS). However, its
usefulness has spurred the development of lower cost camera
devices, which has allowed many new industrial uses, but still
not very affordable for non-professional hobbyists or novice
scientists. The primary goal of the SDP13 Multi-Spectral
Camera system is to develop a system that will be very useful,
while also being relatively affordable. Affordability of such a
powerful imagery tool will have a very positive societal impact
by enabling laypeople to view and learn about their
surroundings in a very personal and individual way.
Individuals with such knowledge will be better able to make
informed decisions about their immediate environment because
this knowledge was previously unavailable to them.
II. DESIGN
A. Overview
Our multispectral camera system will have a USB CMOS
sensor camera attached to a filter wheel assembly, which was
donated by Seahorse Bioscience, containing multiple
interference type, narrow band wavelengths optical filters. On
the other side of the filter wheel will be a monofocal, manual
iris, and manual zoom lens assembly.
The intended
functionality will be to capture images of geological objects
from a mobile ‘Mars Rover’ type of platform, from distances
anywhere between .3 meters to approximately 2 meters. A
microcontroller device, either a Raspberry Pi, or an Arduino,
will command a stepper motor driver to rotate the filter wheel
for the selection of the optical filters. While the camera system
is positioned at one particular geological object, an image will
be captured through each filter. The resulting images can then
be analyzed with spectral libraries in an attempt to determine
the composition of the object [14]. The block diagram of the
multispectral camera is shown in Figure 2.
Fig. 2. Block Diagram of Multispectral Camera System.
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contains various band-pass optical filters through which the
images will be taken. The microcontroller is responsible for
positioning the filter wheel and controlling the camera.
The general specifications for the Mightex brand camera
include USB connectivity, 1/3” CMOS imaging sensor, and Cmount monofocal lens. The camera will be controlled using
Linux platform for controlling the system. The lens is a Pentax
brand C-mount mono-focal manual iris lens with a 12.5mm
focal length. The filter wheel contains 8 filter locations, has Cmount connections for the camera and lens, and has a stepper
motor for positioning the lens. The microcontroller board must
be able to support the camera and run the code for the image
collection.
B. Optics – Lens and Camera
The lens and camera units form the imaging component of
the multi-spectral camera system. In order to choose these
components correctly, knowledge of certain photographic
principles needed to be learned, such as f-stop, sensor size, iris
functionality. The correct combination of parameters will
allow an optimum image to be taken. Optical imperfections
such as geometric, color, and radiometric aberrations will need
to be dealt with during image analysis. The analysis of these
aberrations will be attempted with software simulation.
A digital monochromatic camera was chosen for our system
because it has better spatial resolution than a color camera
does. The lower resolution in a color camera is due to the fact
that they need to interpolate, (also called demosaicing), the
color that is captured within each individual pixel with the
pixels that are directly surrounding it, which reduces the
sharpness of an image’s contrast, called ‘color aliasing’ or
‘edge artifacts’. A Bayer filter, which is a checkerboard of red,
green, and blue filters, (in a 25%, 50%, 25% ratio, respectively,
Figure 3), sits on top of the cameras’ sensor, one color filter
covering one individual pixel, and blocks out the wavelengths
of the other two colors (see Figure 4). Each pixel in a
monochromatic camera contains the intensity of the light
captured, regardless of the wavelength, while the pixels in a
color camera, (which also has a monochromatic sensor but with
a Bayer Filter over the top of it), contain the light intensity only
of the filtered wavelengths [16].
Interference filters (also called dichroic filters) reject certain
wavelengths of light while allowing others to pass through.
The rejection of certain wavelengths occurs because these
filters are constructed with different layers (either material or
coatings), each having a different index of refraction. Certain
wavelengths travelling from a lower index of refraction will be
reflected by material with a higher index [8]. This is also
termed “destructive interference” [15].
The optical lens and the camera form the optical part of the
project. This portion is responsible for capturing the images.
Snell’s Law gives the relationship between the light
The filter wheel is located between the camera and the lens. It incidence angle and refraction between two types of lens media
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Multispectral Camera System
with different indices of refraction. Snell’s equation is used to
determine the index of refraction of a given lens; n1*sin(θ 1) =
n2*sin(θ 2), where n1 and n2 are the indices of refraction and θ
1 and θ 2 are the angles from the normal of the incident and
refracted waves, respectively [13].
Fig. 3. Bayer Filter.
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the most cost effective USB camera was CMOS from
Mightex.com. CMOS cameras require less power to operate
[17], which may play a role in their being able to be utilized
with only USB connectivity.
“In a CCD sensor, every pixel's charge is transferred through
a very limited number of output nodes (often just one) to be
converted to voltage, buffered, and sent off-chip as an analog
signal. All of the pixel can be devoted to light capture, and the
output's uniformity (a key factor in image quality) is high. In a
CMOS sensor, each pixel has its own charge-to-voltage
conversion, and the sensor often also includes amplifiers,
noise-correction, and digitization circuits, so that the chip
outputs digital bits. These other functions increase the design
complexity and reduce the area available for light capture. With
each pixel doing its own conversion, uniformity is lower. But
the chip can be built to require less off-chip circuitry for basic
operation [17].”
An alternative to out multispectral camera could be an off
the shelf industrial style camera that are most commonly used
for aerial-type of imagery applications such as detecting forest
fires or observing oil traces near oil or gas drilling operations
[4][5].
The f-stop setting is a parameter of the iris/aperture diameter
within the lens assembly. An iris is a light blocking device that
determines how much light enters into the lens assembly and
Another choice of filters are called ‘absorptive filters’ ultimately onto the camera sensor or film. A smaller f-stop
because they absorb the light of wavelengths that they are number allows more light into the lens, while a larger number
allows in less light. Generally, a higher f-stop (allowing in less
designed to stop from passing through [8][15].
light) produces sharper images [18].
Interference filters were determined to be a better choice for
Using a ‘ray trace’ diagram, see Figure 11, it can be shown
our design because they almost completely reflect unwanted
wavelengths rather than the less effective absorptive type filters that a bi-concave lens with a -50mm focal length would extend
[8][15]. Our images will contain more accurate filtered out the focal length to a suitable distance, which would put
spectral responses by using the interference type filters that most, if not all, of the image fully onto the bi-convex lens. This
diagram also shows that a bi-convex lens having a focal length
block most of the wavelengths they are intended to block.
of about +18mm should place the focused image directly onto
A common type of aberration is called “transverse chromatic the cameras’ sensor. The unknown factors are how the optical
aberration (TCA) which occurs when red, yellow, and blue filters will affect the focal length, if at all, of the camera
wavelengths focus at separate points in a vertical plane [9]”. system.
Chromatic aberrations occur because the “index of refraction in
Utilizing the thin lens equation, 1/(focal length) = 1/(object
a medium varies with the wavelength [10]”. This type of
aberration can be avoided by using an achromatic lens [11], distance) + 1/(image distance), where the object distance equals
which usually consists of two or even three lenses cemented the Minimum Object Distance of our Pentax lens, which is
together, which corrects for the misalignment of the focal point 300mm, and the -50mm focal length of the bi-concave lens, we
differences of each wavelength [12]. Our multispectral camera can determine that the image distance produced from the biutilizes a mono-chromatic imaging sensor viewing an object concave lens should be positioned at -60mm along the optical
through narrow bandwidth filters; therefore, no chromatic path.
misalignment will be present due to the narrow spectrum range
being used for each image.
C. Filter Wheel
The chosen multispectral camera design uses a filter-wheel
We chose a CMOS camera instead of a CCD camera because
consisting
of six or more optical bandpass interference filters.
of our specification of utilizing a USB camera for our system;
Through the positioning of individual filters into the optical
Fig. 4. Theory of Bayer Filter Blocking the Two Non-Filter Colors.
Belkin, Finken, Walczak
Multispectral Camera System
path, the electromagnetic spectrum can be selectively acquired
by the CMOS camera sensor and captured as an image. The
technology used in this system includes the filter wheel, bipolar stepper motor, stepper motor driver, and a variety of
bandpass optical filters. The bandpass interference filters only
transmit a certain wavelength band and block the others
[19].The filter wheel came with several unidentified filters
which needed to be analyzed; through the use of a
spectrometer, dark room, and a white light source, we were
able to identify the different wavelength spectrums of these
filters[20]. The graphs produced by the spectroscopic software
shows the wavelength ranges of the filters; Figure 8 and Figure
9 show two different wavelengths that the filters allow pass,
while blocking all others.
The main objective that has yet to be achieved with the
filter wheel is its ability to rotate the filters into the field of
view by using a stepper motor. A stepper motor turns a
specific amount of degrees when given a sequence of voltage
levels. A linear actuator produces linear motion, back and forth
along one axis, when given a voltage. Our filter wheel has a 4
pin stepper motor in it which must be bipolar as there aren’t
any possible configurations. After doing a back EMF test in
which you short leads together and check if it is hard to move
the motor it made it possible to identify phase A and phase B
[21]. Below is a basic image of a bipolar stepper motor.
Fig. 5. Picture of Bipolar 4 wire stepper motor [22]
The next part for the stepper motor is to fully explore the
alternatives available to drive it. The alternatives being the
stepper motor driver, H-bridge or Darlington Array
[23][24][25]. The stepper motor driver can drive a bipolar
stepper motor with 4, 6 or 8 wires. The stepper motor has to be
powered with an Arduino or alternate microcontroller.
Similarly the H-Bridge needs to be drive by a micro controller
circuit but it’s an alternate way to approach the motor know
that it is functional. An H-bridge allows the polarity of the
power applied to each end of each winding to be controlled
independently [24]. The Darlington array with the use of a
microcontroller can also be used to drive a 4 wire stepper
motor. The Darlington array is a compound structure consisting
of two bipolar transistors connected in a particular way that the
current is amplified by both transistors [26]. These design
alternatives will be further explored and adjusted with the
stepper motor provided to us in the filter wheel.
D. Microcontroller
A microcontroller is a small computer on an integrated circuit
(IC). Microcontrollers are widely used in projects due to their
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small size and it being capable of accomplishing a lot of
tasks. The use a microcontroller involves writing a program
on a computer, downloading it on the microcontroller and it
will be able to control a robotic device. We decided to use a
microcontroller because it will be take care of positioning the
filter wheel as well as controlling the operation of the camera.
There are different types of microcontrollers available. But
for the scope of our project, a Raspberry Pi (RPi) is the one
we will use.
Fig. 6. Picture of a Raspberry Pi model B
The Raspberry Pi is a 700MHz ARM small computer that
runs on custom Debian Linux [19]. Its primary programming
language is Python. As seen from the Fig. 6 above, the RPi has
two USB ports for a mouse and a keyboard, an HDMI port to
connect to a computer, a micro USB port for power, and
Ethernet jack and General Programming Input Output (GPIO)
pins. We chose a Raspberry Pi because it is a small, capable
computer that does almost anything a desktop computer can do.
The microcontroller block is responsible of controlling the
filter wheel and the camera. The microcontroller will have a
safe guard that will stop the filters using the necessary step size
to position them into the cameras field of view. To achieve this
block, we had to learn the Python programming language. We
ran a few programs on the RPi using python to gain more
proficiency in the language. We wrote a Python code that
should be able to turn the stepper motor of the filter wheel with
the proper step sizes.
To use the RPi to control the filter wheel, we had to use a
stepper motor driver. We decided to use a stepper motor driver
to prevent damage to filter wheel from to the high current
supplied from the RPi and also to have an easier control of
stepping and direction. We ordered the EasyDriver stepper
from SparkFun for its flexibility. The whole circuit was
connected as shown below.
Belkin, Finken, Walczak
Multispectral Camera System
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possible from our final images, and running camera tests to
prove out our results.
The team has been very good on communication. We share
important files and documents through a shared Dropbox. We
talk regularly via text messages and emails. Each member of
our team is working very closely from now on with the
microcontroller since the project is now heavily centered on
that. And with the expertise we got from working on our
individual parts, we expect to be able to produce the needed
results by CDR.
Fig. 7. Stepper motor of the filter wheel connected to
stepper driver and GPIO of RPi.
The stepper motor driver (red board) was connected to the
stepper motor and a 5V source. The pins connected to the RPi
correspond to ground, direction and step. We tried to test the
above circuit with a python code in lab but encountered a
technical problem. The Raspberry Pi froze. One of the
drawbacks of using a RPI is it very delicate GPIO pins. The
GPIO pins are directly connected to the Broadcom of the RPi
which is the heart of the RPi. Basically, there is no protection
with those pins. A wrong voltage down the wrong pin could
damage the RPi. [27]. The RPi froze during testing and
wouldn’t turn back on. Through the online trouble shooting
website of the raspberry pi, we were advised to let it off for a
while. Due to the fact that we were very behind in our project, Fig. 8. Filter Spectrum Wavelength 510 – 525nm
we decided to use an Arduino board as a backup to the RPi.
An Arduino is another type of microcontroller. It is an open
source single board microcontroller that was derived from the
Wiring platform. The Arduino board has an Atmel AVR
processor [28]. We used the Arduino Uno as it was readily
accessible in the lab. We wrote an Arduino code to control the
stepper motor based on similar codes in the Arduino library.
We switched the RPi for the Arduino in Fig 7 utilizing the
necessary pins. Unfortunately, we were not successful in
getting the filter wheel to turn and couldn’t pinpoint the source
of error. To use the RPi to control the camera, we installed the
Linux driver of the Mightex camera on the RPi. We haven’t
been able to test it as the RPi froze.
III. PROJECT MANAGEMENT
Our MDR goals are shown in the Gant chart in Figure 10.
We have accomplished the goals of choosing and ordering the
components needed to build our multispectral camera system.
The remaining goals for MDR focus around the integration of
the different parts with the microcontroller. The parts include
integration of the stepper motor driver with the microcontroller
to control the wheel rotation. The second part is the integration
of the CCD camera with the microcontroller through the use of
Linux, the optical aberration calibrations to remove as many as
Fig. 9. Filter Spectrum Wavelength 433 – 438nm
IV. CONCLUSION
Our project is moving forward. We have almost all the parts
needed and have a lot of research done to get the right parts.
We did not get all of our MDR deliverables but we have
learned a lot all this semester and are will double our efforts to
meet our CDR goals.
Belkin, Finken, Walczak
Multispectral Camera System
Our plan for the future is to get more practical work done as
much of what we did was in theory and research. We are going
to assemble all the parts together and make it work. We expect
some difficulties with the camera if we don’t get a different
driver for the camera. We anticipate difficulties using the
delicate GPIOs of the RPi. We also expect a hard time coding
since we are all EEs. Despite all these issues, we will get there
with hard work and team work.
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[6] Tremolite, mindat.org, [Online] 2012, URL
http://www.mindat.org/min-4011.html (Accessed: 4 Dec
2012).
[7] R Clark, et al., Imaging Spectroscopy: Earth and Planetary
Remote Sensing with the USGS Tetracorder and Expert
Systems, speclab.cr.usgs.gov, [Online] 2003, URL
http://speclab.cr.usgs.gov/PAPERS/tetracorder/ (Accessed: 4
Dec 2012).
Lifelong learning technologies that were/are used include the
use of Matlab, which is a tool that was evaluated early in the
design of the multispectral camera system and may be used
extensively in the analysis and image manipulations required as
part of the ultimate goal.
[8] Optical Filters, edmundoptics.com, [Online] 2012, URL
http://www.edmundoptics.com/learning-andsupport/technical/learning-center/applicationnotes/optics/optical-filters. (Accessed: 8 Dec 2012).
Another life-long learning tool, which is really only a skill, is
the use of ‘ray tracing’ as a way to determine the optical
characteristics of an imaging system. Once a basic familiarity
is obtained, it can be utilized as a tool.
[9] Chromatic and Monochromatic Optical Aberrations,
edmundoptics.com, [Online] 2012, URL http://www.
edmundoptics.com/learning-and-support/technical/learningcenter/application-notes/optics/chromatic-and-monochromaticoptical-aberrations/ (Accessed: 8 Dec 2012).
[10] A Roorda. (2011), “OPTICAL ABERRATIONS
Laboratory 7”, URL http://vision.berkeley.edu/
We would like to thank Seahorse Bioscience for donating the roordalab/VS203BWebsite/Labs/VS203B_LAB7.pdf. (Date
downloaded 8 Dec 2012).
filter wheel that we are currently using for our senior design
project.
[11] J Alves., Correcting and Preventing Chromatic Aberration,
[Online] 2010 URL http://www.tutorial9.net /tutorials/
photography-tutorials/correcting-and-preventing-chromaticREFERENCES
aberration. (Accessed: 8 Dec 2012).
[1] C McFee. “An introduction to CCD operation”,
(mssl.ucl.ac.uk), [Online] 2012, URL http://www.mssl.ucl. [12] Why Use an Achromatic Lens?, edmundoptics.com,
ac.uk/www_detector/ccdgroup/optheory/ccdoperation.html#beh [Online] 2012, URL http://www.edmundoptics.com/ learningaviour, (Accessed: 4 Dec 2012).
and-support/technical/learning-center/applicationnotes/optics/why-use-an-achromatic-lens. (Accessed: 8 Dec
[2] R Smith. (2012, Jan). “Introduction to Hyperspectral 2012).
Imaging,” URL http://www.microimages.com/ documentation/
Tutorials/hyprspec.pdf. (Date downloaded 4 Dec 2012).
[13] E Weisstein. Snell’s Law, scienceworld.wolfram.com
[Online] 2007, URL http://scienceworld.wolfram.com/
[3] Space Computer Corporation, (2007). “An Introduction to physics/SnellsLaw.html (Accessed: 8 Dec 2012).
Hyperspectral Imaging Technology,” URL https://www.
spacecomputer.com/documents/Introduction_to_HSI_Technolo [14] USGS Digital Spectral Library, speclab.cr.usgs.gov,
gy.pdf. (Date downloaded 4 Dec 2012).
[Online] 2007, URL http://speclab.cr.usgs.gov/spectral-lib.html
(Accessed: 8 Dec 2012).
[4] J Hintz. (2001, Jan). "Using Multi-spectral Imagery to
Help Fight Fires” Earth Observable Magazine. [Online]. 10(1), [15] Light Filtration, olympusmicro.com, [Online] 2012, URL
Available: http://web.archive.org/web/20081013192544/
http://www.olympusmicro.com/primer/lightandcolor/ filter.html
http://www.eomonline.com/
(Accessed: 9 Dec 2012).
Common/Archives/2001jan/01jan_hintz.html
[16] Fluid Imaging Technologies, (2010). “Color Versus Black
[5] J Ellis. (2001, Jan). "Searching for Oil Seeps & Oil& White Cameras,” URL http://www.fluidimaging. com/
impacted Soil with Hyperspectral Imagery,” Earth Observable bae35316-08b9-4861-beb0-1c39079e41fb/download.htm
Magazine. [Online]. 10(1), Available:
(Date downloaded 9 Dec 2012).
http://web.archive.org/web/20080723151224/
http://www.eomonline.com/Common/Archives/2001jan/01jan_ [17] CCD vs. CMOS, teledynedalsa.com, [Online] 2012, URL
ellis.html
http://www.teledynedalsa.com/
corp/markets/CCD_vs_CMOS.aspx (Accessed: 9 Dec 2012).
ACKNOWLEDGMENT
Belkin, Finken, Walczak
Multispectral Camera System
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[18] P Hill. The Easy Guide To Understanding Aperture (f
Stop) [Online] 2010, URL http://www.redbubble.com/
people/peterh111/journal/5725038-the-easy-guide-tounderstanding-aperture-f-stop (Accessed: 9 Dec 2012).
[25] Admin (2011, Mar). Driving a Bipolar Stepper motor
with Arduino and ULN2803AG. eLABZ.com [Online].
Available: http://elabz.com/driving-a-bipolar-stepper-motorwith-arduino-and-uln2803ag/
[19] Interference Filter, Optometrics.com [Online] 2009, URL
http://www.optometrics.com /interference_filter.html
(Accessed: 9 Dec 2012).
[26] D. A. Hodges (1999, Jan). “Darlington’s Contributions to
Transistor Circuit Design.” Berkeley [Online] Available:
andros.eecs.berkeley.edu/~hodges/DarlingtonCircuit.pdf (Date
downloaded 7 Dec 2012).
[20] BWSpec, Nano Ram Website [Online] 2011, URL
www.nanoram.com/bwspec-2/ (Accessed: 9 Dec 2012).
[21] Avayan (2011, Dec). “Having Fun With Brushless
Motors.” EBLDC Magazine [Online]. Available:
www.ebldc.com/?p=253
[22] M Colin. (2010, Nov). “Stepper Motor Controller,”
Talking Electronics Newsletter [Online]. Available:
www.talkingelectronics.com/projects/Stepper%20Motor%20C
ontroller/StepperMotor.html
[27] Raspberry Pi, [online] 2012, .Accessed:10 Dec. 2012)
<http://www.raspberrypi.org>
[28] “Arduino” Wikipedia. Foundation, Inc. 22 July 2004.
Web. 10 Dec. 2012.
< http://en.wikipedia.org/wiki/Arduino>
[23] D.Thompson (2010, May). “EasyDriver 4.2 Tutorial.”
Dan Thompsons Blog [Online]. Available:
http://danthompsonsblog.blogspot.com/2010/05/easydriver-42tutorial.html
[24] B. Stephnes (2006, July). “Stepper Motor Intro,”
Northwestern University Mechatronics Design Laboratory
[Online]. Available: mechatronics.mech.northwestern.edu/
design_ref/actuators/stepper_intro.html
Figure 10 - Gant Chart
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Multispectral Camera System
Figure 11 - Ray Trace of Multispectral Camera System
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