Development of a light detection system for bidirectional measurements
over the solar spectrum and sun course simulations with scale models
by
Courtney A. Browne
S. B. Mechanical Engineering
MIT, 2004
SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
SCIENCE MASTER OF MECHANICAL ENGINEERING (S.M.M.E.)
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2006
C 2006 Massachusetts Institute of Technology. All rights reserved.
Signature of Author:
Department of Mechanical Engineering
June 7, 2006
Certified by:
Certified by:
,-06
lDC
Marilyne Andersn
Assistant Professor of Building Technology
Thesis Advisor
Leon R. Glicksman
Professor of Building Technology and Mechanical Engineering
Thesis Reader
Ac~ep-c
MA SSACHUSETTS INSTTUTE
OF TECHNOLOGY
JU L14 2006
MeIni
~pKjvv
BARKER
Development of a light detection system for bidirectional
measurements over the solar spectrum and sun course
simulations with scale models
*Courtney Browne
June 5, 2006
Abstract
The use of natural light in building structures can increase energy efficiency and lead
to more sustainable architecture. To encourage such use of natural light, a dual experimental device is being developed at MIT to help evaluate the effectiveness of various
daylighting approaches, to be used as a goniophotometer for materials and coatings analysis and as a heliodon for studying scale models. The goniophotometer will be used to
conduct detailed assessments of the bidirectional transmission or reflecting distribution
function ("BT(R)DF") properties of building materials, using a CCD camera to produce
a luminance map of the emerging light distribution. The heliodon mode will be used to
as an educational tool to perform qualitative evaluations of shadow patterns by simulating sunlight illumination on scale models. This thesis focuses on several aspects of this
larger project. This thesis first describes the design of an illumination system appropriate for both functions of the joint goniophotometer/heliodon. This thesis then describes
the design and manufacture of a light collection system for the goniophotometer mode,
specifically the design and fabrication of an acrylic semi-ellipsoid with a half-mirrored
coating that focuses the collected light at the CCD camera used for collection and analysis. Finally, this thesis describes the calibration of the light detection system (the color
CCD camera) to make its spectral sensitivity match that of the human eye. With this
calibration, the CCD camera will be useful not only as a component of the goniophotometer/heliodon system, but may also be adapted to serve as a freestanding multi-point
luminance meter for the characterization of BT(R)DFs for various materials of interest.
Acknowledgements
This thesis is based upon work jointly supported by the National Science Foundation
under Grant No. 0533269 and by the Massachusetts Institute of Technology. Any opinions, findings and conclusions or recommendations expressed in this thesis are those of the
author and do not necessarily reflect the views of the National Science Foundation (NSF).
I would like to thank my advisor, Dr. Marilyne Andersen, for her guidance throughout
this project. Additionally, I would like to thank American Tooling and Engineering, Inc.,
Spartech PDC, and Tanury Industries for all of their patience and their wonderful service
in fabricating the semi-ellipsoid. Also, Steve Kaye at Kayelites was incredibly helpful
in determining the correct spotlight for our application. Furthermore, I am grateful to
Zofia Gajdos for her thoughtful editing of my horrific grammar and sentence structure
and Victor Lum for his patient help with the programming of the beam shaper. Finally, I
would like to thank my husband, Ira Phillips, for his support- from keeping me company
on long nights to explaining MATLAB programming- I couldn't have done the project
without you!
1
Contents
1
Introduction
1.1 The need for natural light . . . .
1.1.1 Economic concerns . . . .
1.1.2 Psychological effects . . .
1.1.3 Physiological effects . . . .
1.1.4 Rendering concerns . . . .
1.2 Existing light directing strategies
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Existing technology
2.1 Existing goniophotometers . . . . . . . . . . . . . . . . . . .
2.1.1 Scanning goniophotometers . . . . . . . . . . . . . .
2.1.2 Projection goniophotometers . . . . . . . . . . . . . .
2.2 Shortcomings of existing goniophotometers . . . . . . . . . .
2.3 Existing Heliodons for Scale Models Solar Simulation . . . .
2.3.1 Current Heliodons . . . . . . . . . . . . . . . . . . .
2.4 Development of an ellipsoidal goniophotometer and heliodon
3
Design and Development of the architecture of the goniophotometer/heliodon
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system
29
3.1 Principle of goniophotometer/helidon operation . . . . . . . . . . . . . .
29
3.2 The dual illumination system . . . . . . . . . . . . . . . . . . . . . . . .
30
3.2.1 Placement of light sources . . . . . . . . . . . . . . . . . . . . . .
3.2.2 The heliodon light source . . . . . . . . . . . . . . . . . . . . . . .
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3.2.3 The goniophotometer light source . . . . . . . . . . . . . . . . . .
3.2.4 Beam Shaper . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
4
Data collection
4.1 Development of a Semi-ellipsoid
4.2
4.1.1
4.1.2
Principle . . . . . . . . . . .
Design . . . . . . . . . . . .
4.1.3
Development
4.1.4
Validation . . . . . . . . . .
. . . . . . . .
Calibration of a color CCD camera to behave as a multipoint luminance
m eter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1
Spectral Calibration . . . . . . . . . . . . . . . . . . . . . . . . .
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4.2.2
4.2.3
Spectral calibration for continuous spectra . . . . . . . . . . . . .
Photometric Calibration . . . . . . . . . . . . . . . . . . . . . . .
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5
Conclusion
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6
Appendix
6.1 Positioning the Dedolight.
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6.2
Positioning the beam shaper . . . . ..
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75
6.3
Matlab Code
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6.3.1
Speclntegrate.m . . . . . . . ..
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6.3.2
6.3.3
calcam.m . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
turnOffBadPixels.m . . . . . . . . . . . . . . . . . . . . . . . . . .
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6.3.4
6.3.5
datamean.m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
compare.m . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
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6.3.6
opentif.m
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6.3.7
integrate.m
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84
6.3.8
6.3.9
6.3.10
6.3.11
6.3.12
photo2radio.m
radio2photo.m
rad2XYZ2.m
rgb2XYZ.m .
XYZ2rgb.m .
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List of Figures
1.1
The Kresge Chapel at MIT uses natual light in novel and beautiful ways.
1.2
The light in Kresge Chapel emerges from the moat to dance on the interior
walls. Unfortunately, electric lights, which are often used, cancel out this
... . ......................
....
effect . . . . . .
The altar in Kresge Chapel has a diffuse skylight over it which makes the
white marble appear almost supernatural. . . . . . . . . . . . . . . . . .
This advanced fenestration system has reflective blinds which can redirect
light in useful ways [Andersen et al., 2005b]. . . . . . . . . . . . . . . . .
This light pipe has a reflector in the dome to collect light from the exterior
of the building and send it down the pipe. In addition, the reflective
coating is located at the back of the dome to maximize collection of light
1.3
1.4
1.5
1.6
2.1
2.2
2.3
2.5
2.6
14
14
16
at low sun angles [oikos@ , 2006] . . . . . . . . . . . . . . . . . . . . . . .
17
The anadolic system is mounted on the exterior of the building, extending
into the interior. Light ducts allow the light to exit, permitting it to penetrate deep within the space. This photograph shows an implemented
anidolic system at Ecole Polytechnique Federale de Lausanne (EPFL)
[Scartezzini and Courret, 2002]. . . . . . . . . . . . . . . . . . . . . . . .
18
The ISE Goniophotometer is a scanning goniophotometer in which the
light detector rides on a rail which is rotated about the sample [ApianBennewitz and von der Hardt, 1998]. . . . . . . . . . . . . . . . . . . . .
pab@opto Goniophotometer is commercially available and allows flexibility
in detecter and light source selection [Apian-Bennewitz, 2006]. . . . . . .
The concept of the goniophotometer at the Universite de Rennes 1 in
France. A reflective box fits over the sample as the projection device
[D eniel, 2002]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4
13
The EPFL Goniophotometer uses a six-sided structure as its projection
mechanism. The CCD camera images each of these screens and combines
the results to characterize all incident angles [Andersen et al., 2005a]. . .
The LBNL goniophotometer uses a half-mirrored, hollow hemisphere as its
projection device. The CCD camera images the interior of the hemisphere
and is able to record data for all incident angles in one data collection
[Ward , 1992]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The heliodon built by the PEC group of UC Berkeley at the PG&E Energy
21
22
23
24
25
Center in San Francisco and a scale model being tested with the heliodon
[U C Berkeley, 2006].
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
27
3.1
3.2
3.3
3.4
3.5
3.6
The structure of the goniophotometer/heliodon [Ljubicic, 2005]. . . . . .
The final room layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
If the light source is too big or mounted too low, the heliodon itself might
block its light path. The light rays from the spotlight will hit the back
side of the heliodon instead of the mirror. If this is the case, then the light
will never reach its intended destination on the front side of the heliodon.
Mole Richardson Mole Beam . . . . . . . . . . . . . . . . . . . . . . . . .
An illuminance map of the Molebeam. The dip in intensity in the center
30
31
is due to the HMI bulb in the spotlight . . . . . . . . . . . . . . . . . . .
33
The shadow test- a transparent ruler was held three inches and one foot off
the ground in sunlight, outdoors, at noon and with the Molebeam on the
heliodon. a) outdoors, 1', b) outdoors, 3", c) Molebeam, 1', d) Molebeam,
3"
3.7
3.8
3.9
32
33
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
Spectrum of natural sunlight from [Wikipedia, 2006f] and the spectrum of
the Molebeam (the jagged curve) measured with an Ocean Optics spec36
trom eter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The emission spectrum of a xenon lamp compared to natural light [xen, 2006].
The dotted line is natural light and the solid line is the xenon spectrum.
36
of
the
and
the
specifications
The xenon lamps available from Hamamatsu
L2274 150 watt lamp we purchased [AIM Digital Imaging, 2006b]. ....
37
3.10 Principle of point source and paraboloid . . . . . . . . . . . . . . . . . .
3.11 The xenon lam p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
38
3.12 400 watt Dedolight spotlight . . . . . . . . . . . . . . . . . . . . . . . . .
39
3.13 Spectrum of the 400 watt Dedolight (the jagged curve) compared to the
spectrum of sunlight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
3.14 Illuminance map of the Dedolight . . . . . . . . . . . . . . . . . . . . . .
40
3.15 The shadow test- a transparent ruler was held three inches and one foot
off the ground under sunlight, at noon, outdoors and with the Dedolight
on the goniophotometer. a) outdoors, 1', b) outdoors, 3", c) Dedolight, 1',
. . . . . . . . . . . . . . . .
41
3.16 The apparent beam on the goniophotometer with the beam shaper . . . .
3.17 The beam shaper "shapes" the light from the Dedolight so that the apparent beam on the goniophotometer is always circular. . . . . . . . . . . . .
3.18 The motion control chip circuit . . . . . . . . . . . . . . . . . . . . . . .
42
d) D edolight, 3" . . . . . . . . . . . . . . .
4.1
4.2
4.3
4.4
4.5
Optical principle of ellipses. Light from one focus is emitted, reflected off
the mirrored ellipsoid surface, and focused back to the other focus. . . . .
The sample and CCD camera with fisheye lens are located at the foci of
the semi-ellipsoid, along the major axis of the base. . . . . . . . . . . . .
The holding mechanism for the CCD camera. This setup allows the camera's fisheye lens to be flush with the table at the focus of the semi-ellipsoid
The final dimensions of the semi-ellipsoid . . . . . . . . . . . . . . . . . .
The data sheet sent to the fabrication company. . . . . . . . . . . . . . .
5
43
43
47
48
48
49
50
4.6
4.7
4.8
4.9
Four holes were placed on the edge of the semi-ellipsoid to accurately
position it on the goniophotometer. Two are located on the flange between
the semi-ellipsoid and the lip and serve to accurately position the semiellipsoid. The other two holes are found on the lip and their purpose is to
attach the semi-ellipsoid to the goniophotometer. . . . . . . . . . . . . .
51
The general reflection of aluminum as a function of wavelength [Griot, 20061. 52
In reflection experiments (a), light travels through the semi-ellipsoid, reflects off the sample, and is focused with the semi-ellipsoid to the camera.
In transmission experiments (b), light travels through the sample and is
focused with the semi-ellipsoid to the camera. . . . . . . . . . . . . . . .
52
The CIE Standard Colorimetric Observer functions: how a human perceives the color of monochromatic light. These curves indicate how sensitive the human eye's receptor's are for red, green, and blue for the visible
spectrum of light [Wikipedia, 2006d]. . . . . . . . . . . . . . . . . . . . .
4.10 The composite V(A) curve gives the overall sensitivity of the human eye
to radiation at different wavelengths [Wikipedia, 2006e]. It is not divided
into three colors as with Figure 4.9. . . . . . . . . . . . . . . . . . . . . .
4.11 Spectral calibration experimental setup. Light passes from a Labsphere
tungsten-halogen calibrated light source through a Spectral Products CM 110
im monochromator to a Labsphere SRS-99-010 pure white reflectance
standard. Reflected light is measured with a Minolta LS-110 luminance
meter and a Kappa DX20 color CCD camera. . . . . . . . . . . . . . . .
4.12 The RGB sensitivity of the CCD camera (top) and its associated errors in
averaging pixels (bottom ). . . . . . . . . . . . . . . . . . . . . . . . . . .
4.13 The Labsphere tungsten-halogen source does not emit light in wavelengths
close to the U V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.14 The RGB sensitivity of the CCD camera (top) and its associated errors
in averaging pixels (bottom) when the xenon source is used instead of the
tungsten-halogen source. Note that the signal starts at 380 nm instead
of 430 nm (as in Figure 4.12) because the xenon source emits these wavelengths and the tungsten-halogen source does not. In future work, the
xenon source should be used for camera calibration in order to obtain an
accurate portrayal of camera behavior for these wavelengths. . . . . . . .
4.15 New spectral calibration experimental setup with the spectrometer instead
of the lum inance m eter . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.16 k is determined by dividing the luminance for the monochromatic light
at a given wavelength by the Y value determined in the above procedure.
This k value can now scale the X, Y and Z values so that they will compare
to what the camera imaged. . . . . . . . . . . . . . . . . . . . . . . . . .
4.17 The smaller curve (based on the RGBs the camera captured) must be
mapped onto the larger curve (the XYZs derived from the amount of light
present) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
56
56
57
59
60
61
63
66
67
4.18 The proposal of how the CIE Standard Observer function curves should be
divided by the filter system. Within these ranges, the XYZ values will be
unique, allowing monochromatic light with the same XYZs to be assigned
to a certain wavelength. The XYZs of quasi-monochromatic light should
be the summation of the XYZs of monochromatic light from these ranges.
69
The commands written for the camera calibration and how they relate.
SpecIntegrate is the root command that calls all of the other functions. .
76
6.1
7
List of Tables
2.1
A comparison of current goniophotometers in terms of their features how
they experimentally assess fagade components and fenestration systems
[Andersen and de Boer, 2006.
. . . . . . . . . . . . . . . . . . . . . . . .
8
26
Chapter 1
Introduction
1.1
The need for natural light
Buildings often use electrical energy to light a space when natural light could be used
instead. Recently, there has been an increased interest in understanding sophisticated
daylighting strategies, such as advanced fenestration systems, because of their ability to
exploit natural light (from the sun) for use in buildings. Advanced fenestration systems
can redirect natural light so that it is used more efficiently within a building. They might
block light at eye level which can cause glare and instead redirect it to the ceiling where
it can reflect, diffusing within the room. Using natural light has obvious advantages.
It reduces the need for electric lighting, which lowers energy costs and benefits the environment. Also, humans are more productive and healthy when they are exposed to
natural light rather than electric light. Natural light is the obvious choice; however, new
strategies are needed to increase its effective use.
1.1.1
Economic concerns
In 2001, it was estimated by the US Department of Energy, Energy Information Administration (EIA), that residential and commercial buildings used 39% of the United States'
total energy consumed [US Department of Energy, 2006]. Even in China, an emerging
economy, buildings account for 27.6% of the country's energy usage [Lam et al., 2006.
In the United States, estimates vary from 25-40% of the 39% being due to use of artificial lighting [Krarti et al., 2005] [Li and Tsang, 2005]. In China, two-thirds of the 27.6%
is due to electric lighting and heating and HVAC systems [Lam et al., 2006]. In Israel,
typical office buildings on a summer day use as much as a third of their total energy
consumed on artificial lighting [C.E.Ochoa and Capeluto, 2006].
In Southeast Asia, experts believe effective use of daylight could result in an overall energy reduction of 20% [Zain-Ahmed et al., 2002]. Even simple daylighting strategies such
as altering the size of a window can result in a reduction of 10% [Zain-Ahmed et al., 2002].
Commercially available daylight systems that are properly installed in areas with considerable daylight can result in savings of up to 45% [Granderson and Agogino, 2006].
Another study showed that daylighting strategy in conjunction with a "daylight-linked
automatic lighting control" which adjusts the amount of electric lighting in response
9
to the amount of natural light in a space, energy savings can be as much as 70%
[Li and Lam, 2003].
Additionally, use of natural light can decrease energy consumption in terms of cooling
and heating a building. In the summer, heat gains from electric lighting can cause a large
percentage of the total building cooling load. Because electric lighting produces heat
which can be alleviated by a building's HVAC system, the use of natural lighting provides
the potential to decrease this cooling load [Li and Lam, 20011. Furthermore, in the winter,
natural light can provide solar gains through the windows, decreasing a building's heating
load. However maximizing natural light does have disadvantages; solar gains that were
desirable in the winter can become a nuisance in the summer, increasing a building's
cooling load [Franzetti et al., 2004]. However, even after taking these factors into account,
it is estimated that, in general, any savings in electric lighting due to natural light usage
results in a subsequent reduction of one-third of the cost of operating the HVAC system
due to the reduced heating and cooling loads [Lam et al., 2006]. For optimal reduction
in HVAC and electric lighting costs, all of these variables must be taken into account
when designing a lighting strategy for a specific building [Franzetti et al., 2004]. But
most importantly, one must have the tools to analyze all variables in the equation.
1.1.2
Psychological effects
People are drawn to light; they will choose the more illuminated path when traveling
around a barrier [Taylor and Socov, 1974]. Another study indicated that people tend to
sit where there is more light, finding that people in a cafeteria preferred to sit facing
bright areas. When the pattern of light was changed to highlight a different area, more
people sat in the newly illuminated areas [Rea, 2000]. In a classroom setting, subjects
of a study would cross a room to sit at an illuminated desk. However, when the desk
was not highlighted, the subjects would most often sit at the desk closest to the door
[Yorks and Ginthner, 1987].
Natural light helps humans to maintain mental stability. For example, serotonin, a
neurotransmitter, has long been used to treat depression and aggressive behavior. A study
in Australia found a direct correlation between the luminosity of a given day and serotonin
levels [Bj6rksten et al., 2005]. Furthermore, lack of exposure to natural light may cause
symptoms of seasonal effective disorder (SAD). This condition occurs primarily during
the winter when individuals are not exposed to sufficient sunlight. Symptoms of SAD
include "overeating, oversleeping, carbohydrate cravings and weight gain, withdrawal
from friends and family, decreased sex drive, lack of energy, and in severe cases, feelings
of clinical depression" [Women's Health Weekly, 2005]. Unfortunately, symptoms similar
to those of SAD can occur any time during the year if a person spends too much time
indoors, where there is often not enough natural light. Even though people can use
expensive machines to simulate natural light to prevent this disorder, the obvious and
easiest solution is to maximize ones exposure to natural light.
Finally, people are more productive in areas lit by natural light. Companies have begun to recognize that an efficient use of energy and daylighting promotes their corporate
image and provides a better environment for their workers which can result in increased
productivity [Li and Lam, 2001]. A study of Elementary school students in 2003 found
10
that learning rates of students in classrooms with the most daylight were as much 21%
better than students in classrooms with the least daylight [Group, 2003b]. In an office
setting studies have shown that "Call Center" workers in cubicles with the best possible window view processed phone calls 6-12% faster than workers with no view at all
[Group, 2003c]. Additionally, the same study found that office workers with the best
views performed 10-25% better on tests assessing memory recall and mental function
than those office workers who had no view. Studies of department store sales indicated
that stores utilizing skylights to improve natural lighting within the store experienced
sales of as much as 40% more than those not exposed to natural light [Libby, 2003].
Additionally, during the California power crisis in 2001 when retailers in the state were
required to operate their stores at half lighting power, stores from this same department
store chain with skylights experienced an average of 5.5% increase in sales over their
usual totals [Group, 2003a]. These studies demonstrate the importance of natural light
in mental health and productivity.
1.1.3
Physiological effects
People are biologically drawn to natural light. Not receiving sufficient exposure to natural light can cause adverse physiological effects. For example, lack of exposure to natural
light, and more specifically, UV wavelengths, reduces the production of vitamin D. Vitamin D is important in many bodily functions. For instance, it aids in calcium uptake,
which is essential for strong bones. Studies have shown that in northern latitudes (where
there is less access to sunlight because of the suns solar course), vitamin D deficiency is
endemic. Vitamin D deficiency has also been associated with rickets (a childhood disease that deforms bones), osteoporosis, rheumatoid arthritis, multiple sclerosis, and even
cancer. Studies have also shown that rickets and the other diseases mentioned above are
much more common at higher latitudes, which suggests that a lower exposure to natural
light can, by affecting vitamin D production, cause illness. Although vitamin D is found
in certain foods, like fish, it is most efficiently made when the human body is exposed
to natural light [Raymond and Adler, 2005]. This is another reason that exposure to
sunlight is necessary for our health.
Additionally, exposure to sunlight helps to set our "biological clock." Many complex
organisms are governed by their circadian clock which prepares their bodies for activities that occur throughout the day. Mammals have evolved so that they are biologically
prepared to accomplish specific tasks at particular times of the day. For instance, predatory animals must have sufficient energy for hunting supplied to their muscles during the
time of day that they typically hunt. These animals have evolved to function on an approximate 24 hour cycle corresponding to the sun's course [Albrecht and Eichele, 2003].
Humans also perform better when their circadian clocks are regulated properly. Humans
appear to require exposure to natural light to notify their bodies that it is time to work.
Conversely, melatonin, a neurotransmitter responsible for regulating human sleep cycle, is
produced in response to a given day's photoperiod (the duration of light throughout that
day) [Sumovai et al., 2004]. These examples clearly indicate the importance of exposure
to natural light for human physiological health.
Finally, natural lighting is more comfortable to the human eye because physiologically,
11
we are better able to process white light emitted by the sun [Verilux, 2006]. In incandescent lighting, the spectrum is comprised primarily of red and near-infrared wavelengths.
Therefore, it has a warm glow. In fluorescent lighting, something that has become more
popular with new energy efficient light bulbs, the light often appears to have a blue tinge.
This effect, due to the short wavelengths that comprise many fluorescent lights, can cause
eye strain. The short wavelengths have scattering characteristics that make it difficult for
human eyes to focus [W.S. Strouse Watt, 2006]. Additionally, electric lighting can exhibit
"flickering," an effect resulting from variation in voltage supplied to the light source; this
can cause further eye strain in humans. When magnetic or electronic ballasts are used to
supply electricity to the fluorescent bulbs, the frequency of these fluctuations increases to
a degree that they are much less noticeable to humans [CCOHS, 2006]. A study concluded
that use of these ballasts decreased complaints of eye strain by 50%. Additionally, workers on higher levels of an office building who were exposed to more natural light because
of less sunlight obstruction from neighboring buildings complained of fewer symptoms,
like headaches, associated with eye strain from electric lighting [Wilkins et al., 1989].
Although technology is improving, natural light is still best.
1.1.4
Rendering concerns
Natural light and light sources that mimic it are considered by many to have a better
quality of light. The spectrum of a light source effects the way one perceives colors under
that light source. If two objects have the same pigment, an observer might perceive their
color differently under two different light sources [Rea, 20003. If the light source has a
blue-ish tinge (as with many fluorescent lamps), objects viewed under this light can also
have a blue-ish tinge depending on the objects properties and comparative environment.
Natural lighting allows one to view colors under conditions human are most adept at
processing. Skylights are often used in retail situations to increase sales because the
natural light they provide improves color rendition of the goods for sale [oikos@, 2006]
[Heschong et al., 2002].
In museum, artwork is most often lit with light from windows or light sources that
simulate natural light. However, in one instance, an artist, Dan Flavin used fluorescent
lights to highlight the harsh, minimalist ideas in his artwork [Scott, 2006]. He realized
that this light source would alter the appearance of his artwork.
Additionally, it is important that the level of light be appropriate in a space for the task
intended to be performed in that space. In an office environment, increased use of computers means that different light levels are needed for one work space [Osterhaus, 2005].
A computer needs a lower light level to maintain contrast, while a desk needs higher light
level in order to read printed pages. However, these two tasks are often performed by
the same person in the same workspace. If the visual conditions of the workspace are
inappropriate, it may be difficult to do one's work. Light redirecting strategies can be
employed to direct light to the desk, and redirect it away from the computer monitor, but
presently, there is not a clear strategy on how to implement the light directing systems
[Osterhaus, 2005]. Information about how the light directing strategies, such as advanced
fenestration systems will add to this knowledge, aiding in better working conditions.
Another beneficial effect of natural light is that it can enhance the space's aesthetic
12
Figure 1.1: The Kresge Chapel at MIT uses natual light in novel and beautiful ways.
value. The sunlight will highlight different portions of the space as the sun moves throughout the day. It may filter through parts of the building, leaving beautiful patterns where
it finally hits the ground. At the chapel at MIT (known as Kresge Chapel, Figure 1.1)
designed by Eero Saarinen, this effect is at its height.
The chapel is surrounded by a small moat. Inside the chapel, there is a gap between
the outer wall and a small interior wall that rises only 3 feet. The gap between the
exterior wall and this interior wall is glazed so that there is a horizontal hidden window.
These windows allow the reflection off the water in the moat to penetrate within the
chapel. To further exaggerate the light reflection, the walls are built with uneven bricks
so that the light has something extra on which to bounce off (Figure 1.2).
Even with this beautiful effect, electric lighting is often used in the chapel. Unfortunately, when artificial light is used, the overhead electric lighting cancels out the lighting
from the hidden window and prevents it from reverberating off the walls in its intended,
beautiful display (Figure 1.2).
Furthermore, there is an expansive, diffuse skylight over the altar (Figure 1.3). The
placement and quality of the skylight makes it seem as though the light is supernatural.
In addition, its supernatural quality is exaggerated by the use of white marble in the
altar, which makes the light seem brighter than it actually is. Also, there is a sculpture
which hangs behind the altar and under the skylight that reflects the light from above.
These techniques use natural light in a novel and beautiful way. With better understanding of natural light, more advanced effects might even be possible.
13
Figure 1.2: The light in Kresge Chapel emerges from the moat to dance on the interior walls. Unfortunately, electric lights, which are often used, cancel out this effect.
Figure 1.3: The altar in Kresge Chapel has a diffuse skylight over it which makes the white marble
appear almost supernatural.
14
1.2
Existing light directing strategies
Although it is important for humans to be exposed to natural light during the day, it
is often not possible for them to be outdoors in order to experience this light. Some
may have indoor jobs or find it too cold to go outside. Fortunately, windows can provide
natural light to indoor spaces, although the light they can provide is limited by factors
such as the building and window geometry, and a building's orientation and exterior
environment. Conventional windows alone rarely permit natural light to penetrate deep
within an enclosed space.
Because of these limitations, designers have turned to light directing strategies to
increase the amount of usable natural light within a building. A large variety of strategies
have been developed.
Technologies to utilize diffuse light
While natural light in buildings is desirable, direct sunlight through a window can be
a nuisance. A study of office workers found that performance decreased by 15-21% in
cubicles with greater glare potential [Group, 2003a]. In these situations, occupants will
often block the light with shading devices, decreasing or eliminating natural light, and
thus, its benefits. However, if the direct light can be converted into diffuse light, occupants
will be less likely to use shading devices to block the light.
For example, anisotropic coatings on windows allow for angular selection of light which
can decrease direct solar radiation while permitting transmission of a large portion of
diffuse natural light [Sullivan et al., 1998].
"Smart glazings," a special coating on windows, change their behavior in response
to different environmental stimuli. They can block light in the summer when the solar gains are undesirable and permit light to travel through the window in the winter when its cooler. A type of smart glazing, holographic optical elements (HOE), allow solar control by reflecting or redirecting light incident on a window coated with
HOE [James and Bahaj, 2005b]. Direct sunlight is reflected and diffuse light is allowed
to pass through, improving occupant comfort in terms of light level and temperature
[James and Bahaj, 2005a]. The holograms are printed on transparent films and sandwiched between two panes of glass and can be used for a variety of purposes such as
"gratings, zone plates, lenses, mirrors and any other type of optical element"
[H.F.O. Muller, 2005]. Use of HOE can reduce the temperature in a sunroom by 6.10
when as little as 61% of the windows are coated [James and Bahaj, 2005a].
Technologies that redirect light
Reflective coatings on blinds reduce absorption of light when it strikes the blinds, allowing
the light to bounce off the material and travel deeper into the space (Figure 1.4). These
are often used in advanced fenestration systems [Andersen et al., 2005b], a particular
interest to the area of research this thesis covers.
Furthermore, laser-etched glass and prismatic panels can reorient the direction of
incoming light so that it will hit the ceiling and reflect farther into the room, moving
light deeper into the space [Andersen, 2004] [Sweitzer, 1993].
15
--..--
.
Figure 1.4: This advanced fenestration system has reflective blinds which can redirect light in useful
ways [Andersen et al., 2005b].
Another technology, "microstructured sun-shading devices" are microstructures that
are partially coated to have special reflective, absorptive, and transmissive properties and
can be used in glazings such as a light-shading device. Their use allows redirection of
light in desirable ways [Walze et al., 20053.
Technologies that reflect unwanted light
Low-emissivity glazings are another example of interesting light redirecting strategies;
they permit visible natural light while reflecting infrared [Sweitzer, 1993.
Other devices to select or redirect light
While the following devices are not directly characterizable by this line of research, they
are important in advanced daylighting strategies.
"Light pipes," tubes mounted on the exterior of a building that have a reflective
coatings, let direct light from the sun bounce within the pipe with little absorption,
allowing the light to exit from the pipe farther into the space than traditional windows
permit (Figure 1.5). At the end of the pipe, there is a diffuser which spreads the light
throughout the space. This technology was invented to address the shortcomings of
skylights- light pipes do not have to be mounted directly above the area to be lit; they
can direct light to the area through reflective coatings within the pipe [oikos@, 2006].
Anidolic light ducts are another light directing strategy which transmits diffuse light
further into a building than is possible with windows (Figure 1.6). They are similar
to light pipes in that they collect light from the exterior of a building and, through a
reflective conducting structure, transmit light farther into the building than it would
16
Figure 1.5: This light pipe has a reflector in the dome to collect light from the exterior of the building
and send it down the pipe. In addition, the reflective coating is located at the back of the dome to
maximize collection of light at low sun angles [oikos@, 2006]
travel with windows. However, anidolic light ducts are mounted on the side of a building,
rather than on the roof, as with light pipes. Also, they can utilize diffuse light while light
pipes exploit direct light. Through careful design of the shape of the system, the number
of reflections within the system can be reduced, and thus, the effectiveness increased
(every time a light ray reflects, it loses some of its energy to absorption). Simulations of
this system suggested that the daylight factor could be increased by a factor of as much
as 2.7 in urban settings with the use of these systems[Scartezzini and Courret, 2002].
Although several light directing methods exist to be used in daylighting strategies,
studies have shown that only 10% of commercial buildings employ tactics to increase
natural light [Krarti et al., 2005]. On the other hand, as much as 50% use energy efficient lighting fixtures [Krarti et al., 2005]. These statistics clearly show that commercial
building designers and operators are interested in energy efficient strategies for their
buildings. But, due to a lack of information on daylighting strategies, building designers
and operators are reluctant to incorporate the technology into their buildings. Although
simple raytracing can produce generalizations about how these special coatings redirect
light and some have even been characterized by empirical measurement devices, it is likely
that they could be used more often and more effectively in buildings if more information
were available about all varieties.
17
\
Roller bhnd
0A7
gL-
.....9
..........
Arydolk cekwnts
......
Double
Figure 1.6: The anadolic system is mounted on the exterior of the building, extending into the interior.
Light ducts allow the light to exit, permitting it to penetrate deep within the space. This photograph shows an implemented anidolic system at Ecole Polytechnique F6d6rale de Lausanne (EPFL)
[Scartezzini and Courret, 2002].
18
Chapter 2
Existing technology
2.1
Existing goniophotometers
To plan effectively for daylighting strategies, we must have information about how solar
shading or daylight redirecting systems will behave under different lighting conditions.
In particular, physical properties such as directional reflectance and transmission information on advanced fenestration systems will further daylighting design. Manufacturers of these advanced lighting systems must be able to test their products to validate
and to improve upon them. Additionally, architects and building managers need general information about these systems in order to use them effectively in building design.
Also, programmers of light simulation software can integrate this information into their
programs to produce better lighting simulations, and thus, aid the manufacturers, architects, and building managers by providing them with more accurate lighting information
[Andersen and de Boer, 2006].
Simulation and modeling techniques have been performed multiple times to forecast
energy savings for different daylighting strategies [Sullivan et al., 1998],
[Li and Lam, 2001], [Franzetti et al., 2004]. However, quantitative experimental information is needed to maximize the utility of these models; experimental characterization can
give insight to systems that are not fully understood, improving the simulation and modeling techniques. Measurement devices are generally able to achieve acceptable accuracy
(specific accuracies discussed in the following chapter), but they are not perfect. However, even partially flawed experimental data is important. Reflection and transmission
characteristics of some materials are not known because their properties are too difficult
to accurately model mathematically. Experimental data can help fill this void. These
approaches taken together can improve understanding of these materials more than either
approach could on its own.
For advanced fenestration systems, the quantitative information can be described by
the photometric property described by Bidirectional Transmission (or Reflection) Distribution Functions (BTDFs or BRDFs). A BT(R)DF conveys the emerging light distribution for a given incident direction for a given material. Mathematically, it is expressed
as
L
1
BT(R)DF=-=ou[]
E
steradian
19
where
emerging luminance =[-2
and Es = incident illuminance =[2"]
BT(R)DFs are angle-dependent in both incidence and transmission or reflection
([Andersen and Scartezzini, 2003]). It gives the ratio of light that a given surface reflects
or transmits back into its environment along a particular direction to the amount of light
arriving at that surface. Several methods for determining the BT(R)DFs are currently
in use. In this project, we are developing a new type of goniophotometer to measure the
directional reflectance and transmittance used in calculating BT(R)DFs.
There are several types of goniophotometers, but all operate by collecting information
about emerging luminance for a given direction. There are two main ways to accomplish
this task. One is to scan all points in space for emerging luminance from a sample by
moving a detector. The other is to collect this data with the aid of a stationary multipoint detector in combination with a light collection system, thus reducing the data
acquisition time.
2.1.1
Scanning goniophotometers
Scanning goniophotometers are the most common. They can be easier to develop since
the detector signal can be easily converted into luminance. However, this strength is also
the source of the scanning goniophotometer's weakness. Because these goniophotometers
use a mobile detector, the data produced cannot be continuous. Therefore, data for the
areas in between the sample points must be interpolated. The possibility exists that
highly dynamic sets would not be properly characterized because of this interpolation
[Andersen and de Boer, 2006].
The first goniophotometer was developed at Lawrence Berkeley National Laboratory
(LBNL). It was used to test multi-layer fenestration systems. This data was used in
solar heat gain simulations. Comparison to existing data sets for solar heat gain values showed that the data obtained from the goniophotometer helped the simulations to
achieve accuracy within 10% [Andersen and de Boer, 2006].
At Fraunhofer Institute for Solar Energy Systems (ISE), their original goniophotometer consisted of two parallel ground steel shafts used as rails on which a linear detector rides around the sample of interest (Figure 2.1). They produced a second design
that improved upon the track system for the detector, reducing the measurement error
[Apian-Bennewitz and von der Hardt, 1998]. They were able to improve upon LBNL's
design by allowing flexibility in the dimensions of the sample to be tested. Also, they
worked on refining angular resolution in the data acquisition. Although the work on
improving angular resolution was successful, it did increase the data acquisition time.
The results obtained from this goniophotometer were tested against Ulbricht integrating
sphere measurements, and it was found that there was only a 20% discrepancy. Also,
when the goniophotometer data was judged against data from ray-tracing simulations,
another estimation tool in the field, the differences were as low as 5% for small incident
angles. However, the discrepancies increased up to 61% when data was compared for angles greater than 60' [Apian-Bennewitz and von der Hardt, 1998]. Currently, researchers
at ISE are working to improve their goniophotometer's performance with a better light
source and a moveable CCD camera as the data collection device, rather than a discrete
20
I 111
.
- -
__
; !pi_
__
-_
__7 1_
-
_
--
-
motor for moving detecour Iider
g detector kr
xeni...
lamipX
motor for vertical axis
Figure 2.1: The ISE Goniophotometer is a scanning goniophotometer in which the light detector rides
on a rail which is rotated about the sample [Apian-Bennewitz and von der Hardt, 1998].
detector [Andersen and de Boer, 2006].
pab@opto is an optical consulting company who have designed and produced a commercially available scanning goniophotometer(Figure 2.2). It is unique in that it allows
flexibility in detection devices and light sources used with the system
[Apian-Bennewitz, 2006].
At the University of Technology Sydney (UTS), they developed a goniophotometer
that operates in a similar way to the ISE goniophotometer. The TNO Building and
Construction Research in Delft, The Netherlands, also built a similar design. Their
machine was meant to characterize transparent insulating (TI) materials. Its data was
tested against data from an integrating sphere and the difference was only 10% for most
samples (although it was as high as 20% for others) [Andersen and de Boer, 2006].
Researchers at Cardiff University, UK proposed a novel twist to existing goniophotometers. They would like to add spectral assessment capabilities to future machines,
allowing one to perform an analysis of wavelength-selective glazings. In their existing
machine, the transmitted light in this goniophotometer is focused, with the aid of an offaxis parabolic reflector, to a optical fiber bundle [Breitenbach and Rosenfeld, 1998]. This
advancement permits sample sizes much larger than any existing goniophotometer. The
measurements obtained from this goniophotometer were compared to integrating sphere
results and the difference was about 10% [Andersen and de Boer, 2006].
Finally, researchers at the Technical University in Berlin (TUB) have built a goniophotometer with a spiral, scanning design [Aydinli, 1996]. However, it is now being replaced
by a new system of multiple sensors on a rotating arc [Andersen and de Boer, 2006].
2.1.2
Projection goniophotometers
Data collection of all incident or emerging angles utilizing scanning goniophotometers such
as those described above can be quite time consuming, taking anywhere from four to thirty
days to obtain [Andersen and de Boer, 2006]. Most scanning devices take approximately
21
__ __
I
Figure 2.2: pab@opto Goniophotometer is commercially available and allows flexibility in detecter and
light source selection [Apian-Bennewitz, 2006].
four days, but a device at DTU in Denmark (which is actually the device developed
at Cardiff University which they bought) takes up to thirty days due to the spectral
characterization [Andersen and de Boer, 2006]. A device that can detect multiple points
simultaneously, rather than just individual points, would accelerate this process. A CCD
camera is such a device and can be calibrated for use as a multiple-point luminance meter
when used in conjunction with a light collection device. In goniophotometers that use
this approach, light from the sample is reflected or transmitted onto a projection surface
on which the multi-point luminance meter is trained. The CCD camera images this
scene, and through a camera calibration process, is able to extract directional luminance
information. This technology is much faster than scanning goniophotometers, taking
about eight hours rather than several days [Andersen and de Boer, 2006]. In addition,
this approach has an added advantage of capturing a continuous scene, allowing it to
characterize high dynamic data sets accurately. However, the reliability of this system is
effected by the accuracy of the calibration procedure of the CCD camera [Andersen, 2004]
[Andersen and de Boer, 2006].
Presently, three projection goniophotometers have been built and are in operation: one
at LBNL for computer graphics applications, one at the Universit6 de Rennes 1 in France
to improve photo-realistic rendering, and one at EPFL to test advanced fenestration
systems. All use a CCD camera to capture data, but their projection surfaces differ
[Andersen and de Boer, 2006].
At Universit6 de Rennes, Deniel created a goniophotometer with a cube as projection
mechanism (Figure 2.3) [Deniel, 2002]. A spatial calibration was required to convert the
data into hemispherical coordinates needed for BT(R)DF characterization.
Andersen at Ecole Polytechnique F6derale de Lausanne (EPFL) in Lausanne, Switzer22
Light
source
I
I
I
-
/
01
yr
a
in
I
'4
'4
'4
N
,
Out
/
/
Sample
Figure 2.3: The concept of the goniophotometer at the Universit6 de Rennes 1 in France. A reflective
box fits over the sample as the projection device [Deniel, 2002].
23
Figure 2.4: The EPFL Goniophotometer uses a six-sided structure as its projection mechanism. The
CCD camera images each of these screens and combines the results to characterize all incident angles
[Andersen et al., 2005a].
land devised a goniophotometer that required only six data collections (Figure 2.4)
[Andersen, 2004] [Andersen et al., 2005a]. Reflection and transmission of light from the
sample traveled to a triangular, diffuse screen, six of which surrounded the sample. A
CCD camera imaged each screen, and after six 60' movements of the screen and camera
system, data from all incident angles was collected. As with the device at Universit6
de Rennes, a spatial calibration was necessary to convert the data into hemispherical
coordinates (In fact, this is the case for all projection goniophotometers.). Although this
method was more than two orders of magnitude faster than using a standard scanning
goniophotometer, it still took around eight hours to collect data from a sample. It was
recognized that a system which allows the camera to image all incident angles at once
would accelerate the process even further.
In the early 1990s, Ward created a goniophotometer that was able to reduce the number
of data acquisitions to one (Figure 2.5) [Ward, 1992]. A half-mirrored hemisphere was
attached to the goniophotometer and the light source shone through the hemisphere to
the sample, placed approximately along the central axis of the hemisphere. Light would
pass through the hemisphere to the sample and reflect off or transmit through the sample
into the interior of the hemisphere. The reflected or transmitted light would be collected
by the mirrored hemisphere and then, due to geometry of a hemisphere, be reflected from
the sample to a fisheye lens of the CCD camera, which was also placed roughly at the
central axis of the hemisphere. The CCD camera with the aid of the fisheye lens would
collect all of the data in one acquisition.
In order to obtain the most precise measurements, the camera and sample would both
be located exactly along the central axis of the hemisphere, a physical impossibility.
24
Front View
metal
Side View
cunerweight
stand/base
assmiblv
Source support
sample
=
Collimlated
liuht
source
~~
Sample/target
holder
---CCD camera
(
with fisheve lens
source
calcra
tal f-silvered
optical hemisphere
c isae
Half-silvered
plastic hemisphere
Metal stand
Figure 2.5: The LBNL goniophotometer uses a half-mirrored, hollow hemisphere as its projection device.
The CCD camera images the interior of the hemisphere and is able to record data for all incident angles
in one data collection [Ward, 1992].
Therefore, a complicated spatial calibration was performed to extract meaningful data.
On the other hand, if the hemisphere were replaced by a semi-ellipsoid, this shortcoming
could be eliminated, allowing for more precise data acquisition without the aid of spatial
conversion.
All of this information has been summarized and compiled in Table 2.1, which is an
excerpt from [Andersen and de Boer, 20061.
2.2
Shortcomings of existing goniophotometers
Current goniophotometers have several shortcomings. Data collection using scanning goniophotometers is a very slow process and only allows for discrete data measurement,
affecting its ability to measure highly dynamic data sets. Projection goniophotometers
allow for more rapid data acquisition and characterization of highly dynamic data sets,
but current models can still be improved to improve the time of data acquisition, allowing
measurement of much more data. Furthermore, except for the goniophotometer at Cardiff
University [Breitenbach and Rosenfeld, 1998], all current technology lack the capability
to provide information about how the color spectrum of light is redirected. However, the
time needed for this device to measure spectral information is too long to make the measurements practical and extensive. Even so, this information allows the characterization
of spectrally selective materials, an area that that should be explored further experimentally [Andersen and de Boer, 2006]. Additionally, when light enters a building through
a window, it carries heat with it. This is mainly due to near-infrared wavelengths of
light traveling through the window. No method exists to directly determine the solar
gains through a given material in a directional and time efficient manner. Because different materials reflect visible light and near-infrared wavelengths in different ways, it is
important to consider these materials's infrared transmissivity and the subsequent heat
gains when designing buildings that utilize these light-redirecting elements. Without this
consideration, it is possible to spend more on cooling a building than the savings from
25
Institute
BTDF
BRDF
Coverage
Direc
kcp
LBNL, USA
/
-
discrete
sample
ISE, Germany
/
/
discrete
BTF
pabRopto,
Germany
/
/
discrete
sample
TUB, Germany
/
-
discrete
sample
TNO, The
Netherlands
/
/
discrete
not publ.
-
-4 days
fsphere
DTU, Denmark
/
-
discrete
±0.50
/
-30 days
fsphcrc, g-val., an.
-
/
Time
Validation
~4 days
g-val. comp. (10%)
'-4 days
old: fsphcre (5%-61%);
new: tinder devlpmt
-4 days
not published
- 4 days
under devlpmt
Replica
(10%-20%)
-
model (-11%)
UTS, Australia
/
/
discrete
sample
EPFL,
/
/
full
sample
-
-~4 days
not published
8 hours
fsphere, an.model,
Switzerland
MIT, USA
ray-trac., BTDF
comp. (2%-14%)
/
/
full
sample
/
< 10 min
under devlpmt
Table 2.1: A comparison of current goniophotometers in terms of their features how they experimentally
assess fagade components and fenestration systems [Andersen and de Boer, 2006].
having a natural lighting system [Scartezzini, 2003]. The latter two improvements were
proposed by Andersen in her PhD thesis [Andersen, 2004].
2.3
Existing Heliodons for Scale Models Solar Simulation
When designing a daylighting strategy, it is also important to consider how a building's
structure interacts with sunlight throughout the day. Architects use certain generalizations (i.e. south facing windows are desirable for sunlight exposure, etc.) but these
provide insufficient information to predict how a building will be affected by sunlight.
Tools have been developed to give a designer a more accurate idea of how his or her
building will behave when exposed to sunlight.
A heliodon is a machine that allows sunlight simulation on scale models for a given
time of day and year at a certain place on earth. Often for beginning architecture courses
at educational institutions (for which the MIT device will be used), scale models of ones
building designs are built before CAD models are even approached. Although modern
light simulation software permits a similar study of a building design, heliodons allow
a quick investigation of scale models that could take hours of input (building the CAD
model, assigning materials, etc.) to a computer program. However, once architecture
courses start to require the construction of a CAD model for ones building design, light
simulation software can be a faster approach than heliodon studies. Additionally, CAD
models are more precise than scale models, so the information obtained from simulations
which use them can be more accurate. However, heliodons still provide the designer with
a physical, in-person view of the shadows on their scale model, a more intuitive approach
26
-
Figure 2.6: The heliodon built by the PEC group of UC Berkeley at the PG&E Energy Center in San
Francisco and a scale model being tested with the heliodon [UC Berkeley, 2006].
than computer programs. Taken together, simulations with a CAD model and shadow
tests of a scale model with a heliodon can provide more insight than either approach
alone.
In a heliodon, a "sun" is attached in a fixed position relative to the heliodon and
the heliodon is rotated to simulate the desired orientation relative to the sun. Using a
heliodon, a designer can determine how far natural light will likely penetrate into his
or her building at a certain time of year over the course of a day, given the building's
location.
2.3.1
Current Heliodons
One such example of a heliodon was built at the PG&E Energy Center in San Francisco
by the PEC group of UC Berkeley (Figure 2.6). It consists of an articulated table which
can rotate appropriately relative to a spotlight (the "sun") that is mounted on the ceiling
30 feet from the table. The spotlight is used in order to approximate collimated light (the
rays exiting the spotlight are quasi-parallel), a characteristic that the actual sun exhibits.
A camera is mounted to the moving platform to capture the change in shadows on the
exterior of the scale model through the heliodon's range of motion [UC Berkeley, 2006].
Heliodons can either be automated or manual; the heliodon at PG&E is automated.
Manual versions can be much less expensive because they do not require a complicated
control system or an advanced lighting system to mimic the sun. They can often be
used outdoors where the real sun, or even a cloudy day, can be characterized. However,
automated versions are often far more practical because they can simulate the Earth's
motion in a continuous fashion, providing a more complete characterization.
27
2.4
Development of an ellipsoidal goniophotometer and heliodon
Because the information gathered from goniophotometers and solar simulators are both
relevant to daylight design, it would be valuable to combine their functions into one
machine while also improving on the shortcomings of existing goniophotometers. This
project proposes to design and build a goniophotometer that will improve upon current
designs in the following ways: it will allow for rapid data collection, be able to detect
color information from the light emitted from the sample in order to characterize spectrally selective materials, and be able to measure heat information from the near infrared
spectrum to aid in balancing daylight considerations with their associated solar gains. In
addition, the structural design of this goniophotometer allows it to be used as a heliodon
as well. This goniophotometer/heliodon will be useful for conducting detailed assessments of the light redirecting properties of materials such as window coatings and novel
blind systems and also performing sunlight simulations on scale models.
The research described here focuses on several aspects of the overall project. A dual
illumination system is described in Chapter 3, Section 3.2, with one light for the heliodon
and one for the goniophotometer. For the goniophotometer application, the light collected
must be focused at the CCD camera. As discussed in Chapter 4, Section 4.1, an acrylic
semi-ellipsoid with a half-mirrored coating has been designed and fabricated for this
purpose. The light collected must then be analyzed. The procedure for this is described
in Chapter 4, Section 4.2. A color CCD camera is calibrated such that it has the same
spectral sensitivity over the visible range as the human eye and is able to produce a
luminance map of the captured scene. This calibration will permit the use of the camera
as a multi-point luminance meter and will allow it to be used eventually for the collection
of the light redirecting properties (or BT(R)DFs) of various materials of interest.
28
Chapter 3
Design and Development of the
architecture of the
goniophotometer/heliodon system
3.1
Principle of goniophotometer/helidon operation
To compile BT(R)DFs for use in effective daylighting design, it is necessary to know
the directional reflectance and transmittance of light for a given material for different
orientations of an advanced fenestration system. For architects to use a heliodon for
shadow simulation on their scale model, the heliodon must be able to simulate all locations
on Earth at different times of the day and the year. The goniophotometer/heliodon aims
to produce these various conditions through mechanical means.
This goniophotometer/heliodon simulates different illumination conditions via its rotation about two axes. As shown in Figure 3.1, the altitude axis runs through the side
supports of the machine and the azimuthal axis is normal to it. These two axes allow
the goniophotometer to simulate any location on Earth at a given time of year and at
a certain time of day. A computer interface has been developed that allows the user
of the goniophotometer/heliodon to automatically move the machine to the proper location for the given study. Further information about the detailed design, fabrication
and implementation of the structure of the machine can be found in [Ljubicic, 2005] and
[Clifford, 2006].
3.2
The dual illumination system
The ideal lighting design system for the goniophotometer/heliodon would have only one
light source, simplifying the experimental setup. However, practically speaking, it is
impossible to have one source for both applications. The beam produced by the light
source for the heliodon would be far too big for the goniophotometer application, flooding
the entire goniophotometer/heliodon surface (desirable for the heliodon application, but
not for the goniophotometer application). If the same light source were used for both
applications, an awkward system of diaphragms would have to be implemented to shape
29
Figure 3.1: The structure of the goniophotometer/heliodon [Ljubicic, 2005].
the beam down to the size necessary for the goniophotometer application. An attempt
was made to do this, but it proved to be unfeasible due to size constraints. In addition to
requiring a smaller beam, the goniophotometer requires a more uniform source (in terms
of light levels across the sample surface), a requirement easily met using a small beam
size. However, it proved impossible to achieve sufficient uniformity of the light using the
beam size necessary for the heliodon. Because of these difficulties, it was decided that
two separate light sources would be used, one for the heliodon application and one for
the goniophotometer application, would be used.
3.2.1
Placement of light sources
The next step was to determine the placement of the sources within the lab space. There
were several constraints for the placement of the larger light source for the heliodon.
The heliodon light source must provide nearly collimated light, so the degree of spread
must not be more than 50 which allows for approximation of the light as collimated
(justification for this assertion is discussed in Section 3.2.2), but the source itself must
be as small as possible to reduce costs. Therefore, the light must travel a long distance
in order to diverge enough to flood the entire surface of the heliodon. In addition, the
large light source must be carefully placed to prevent the heliodon from blocking the
light emitted from it when the heliodon is in a fully vertical position. Also, the space
available for the experimental setup was limited to a 12' by 14' room that had already
been constructed, so all of these constraints had to be met within a small space.
It was realized that a mirror could be used to increase the virtual beam distance of
the larger light source to the heliodon, and that the light source could be mounted on the
ceiling to prevent the heliodon blocking its light path. A Microsoft Excel optimization
with the solver function was performed to calculate the optimal location for the light
source and the mirror. It was found that the optimal location of the source was on the
ceiling above the heliodon and that the mirror would be most useful mounted in the corner
of the room, near the ceiling, opposite the spotlight, and rotated to a slight angle (60).
The goniophotometer/heliodon was placed in one corner of the room, 56 inches diagonally
30
Figure 3.2: The final room layout
from the corner, with enough space around it so that a person could still walk behind
it. The normal position of the goniophotometer to the spotlight is 21.930 off the vertical.
The layout of the room was modeled using Solidworks to verify that the proposed solution
fit within the room's space constraints (Figure 3.2). The goniophotometer light source
was not included in this optimization because the anticipated size was small. A mirror
system was not needed to produce the beam diameter necessary for goniophotometer
tests. Therefore, the goniophotometer source could be placed in the corner of the room
with its light beam perpendicular to the goniophotometer is in a vertical position.
3.2.2
The heliodon light source
Each light source had to be carefully selected by taking into consideration specific constraints in terms of color temperature, collimation, illuminated area, and spectral profile
such that its properties resembled the characteristics of the sun as closely as possible.
The light source for the heliodon had to be selected symbiotically with the room layout
design. If the source were too small, the degree of spread required to reach the necessary
spot size at the heliodon's surface would be greater than 5'. If it were too big, it would be
too expensive. If it were too big or mounted incorrectly, the heliodon itself might block
31
Figure 3.3: If the light source is too big or mounted too low, the heliodon itself might block its light
path. The light rays from the spotlight will hit the back side of the heliodon instead of the mirror. If
this is the case, then the light will never reach its intended destination on the front side of the heliodon.
its light path (Figure 3.3).
After researching available options, HMI (Hydrargyrum medium-arc iodide) spotlights
were selected as an appropriate choice for this application. HMIs are a mercury-halide
discharge short-arc lamps that exhibit a spectrum and color temperature similar to natural light. Using the Excel optimization to test available spotlight sizes for feasibility in
the overall design, it was found that a light source with an eighteen inch diameter would
work. The HMI source selected was available from Mole Richardson (Figure 3.4). The
spotlight has a minimum 5' spread, 5600 K color temperature and was rated at 6200 lux
at 25 feet (more than adequate for this project- since the light must travel less than 25
feet).
Once the spotlight was purchased and installed, a validation of the expected behavior
was performed. By using a lux meter, the uniformity of the light source on the surface of
the heliodon was found to vary by 7.4%, ranging from 17,000 to 22,880 lux (Figure 3.5) if
a small non-uniformity is not considered). This small non-uniformity is produced by the
HMI bulb itself in the spotlight; it blocks light emerging from the back of the spotlight.
Therefore, there is a dark area in the spotlight (the trough in Figure 3.5) which is the
32
Figure 3.4: Mole Richardson Mole Beam
x 10
Illuminance map of the Molebeam
2.5
X le
3,
2.5,
.2
1.5
1.5
E
0.5
0
40
0.5
10
0
00
20
310
Inches
40
0
Figure 3.5: An illuminance map of the Molebeam. The dip in intensity in the center is due to the HMI
bulb in the spotlight
vertical length of the spot and approximately five inches in width, meaning that it is only
about 4% of the total area of the spot. This area has a low value of 10,000 lux. Since the
heliodon is used mostly for qualitative measurements, this non-uniformity is undesirable,
but acceptable.
This illuminance map describes the illuminance of the Molebeam on the heliodon's
surface when the light beam is perfectly perpendicular to the heliodon surface. When the
heliodon is at it's most extreme position (its surface is parallel to the light beam), it is
estimated (by performing a calculation based on the behavior of light) that the variance
in light over the surface will be as much as 30%. However, this variance will only occur in
this extreme case for very grazing angles. Therefore, the light farthest from the spotlight
will not actually be generating shadows because the scale model itself will block this light
from reaching the farthest surface of the heliodon. The scale model will be experiencing
the sun as if it were sunrise, so it will actually only be subject to light from the front half
33
of the heliodon surface. Therefore, the variance of the light the scale model is subjected
to is actually only about 16% for this situation. At these light levels (10,000 lux and
above), humans are not able to perceive a difference between something that is a fifth as
bright or even a third as bright [AIM Digital Imaging, 2006a]. Since the heliodon is to
be used for qualitative measurements, these contrasts are considered acceptable.
A shadow test was performed to confirm that shadows produced by the Mole-Richardson
spotlight are similar to those produced by sunlight. Outside at noon on a sunny day, a
transparent ruler with dark gradations was held at three inches and then one foot above
the ground. The same ruler was held at three inches and then one foot above the surface
of the heliodon in its noon configuration. As shown in Figure 3.6, the shadows produced
by the Molebeam are less sharp than those produced by the sun, but they are comparable.
This qualitative assessment verifies that the light exiting the Molebeam is will produce
shadows similar to the sun.
Finally, the intensity of the light source as a function of wavelength (its spectrum)
was tested with the aid of an Ocean Optics spectrometer (Figure 3.7) which can measure
spectra up to 880 nm. The spectrum was found to be similar to other HMI lamps, and
although it is not an exact match to natural sunlight, it is recognized as a good substitute
for natural light. As mentioned previously, the Molebeam has a color temperature of 5600
Kelvin (compared to 6000 Kelvin from the sun) and the shape of its spectral curve roughly
matches that of natural light. However, it does lack wavelengths in the red range of the
visible spectrum when compared to natural light.
3.2.3
The goniophotometer light source
As with the light source for the heliodon, the light source for the goniophotometer must
mimic sunlight as closely as possible. It must be collimated (up to 5' of spread- justification for this value is discussed later in this section), have a color temperature similar
to the sun (approximately 6,000 Kelvin), have a spectral output similar to the sun, and
be uniform in its emission such that all areas are illuminated equally. These constraints
are even more important for the goniophotometer light source since it will be used for
quantitative measurements rather than the mostly qualitative measurements taken with
the heliodon source.
Although HMI lamps offer characteristics similar to sunlight, xenon lamps are superior.
They emit a more even spectrum which is closer to natural light (Figure 3.8) [xen, 2006].
They were much too expensive to use for the large heliodon source, but it was thought that
they could be utilized in the goniophotometer application. However, integrated systems
were cost prohibitive. Hence, it was decided that a light source would be designed and
built.
The design for the small source included a paraboloid with an aluminum reflective
coating. An L2274 150 watt xenon bulb (Figure 3.9) was purchased from Hammamatsu
which would approximate a point source. The design principle specified the point source
would sit at the paraboloid focus so that the paraboloid would collimate all exiting light
rays exiting the paraboloid (Figure 3.10).
This design was fabricated, assembled and tested (Figure 3.11). Initially, the results
were quite poor. Because the paraboloid was focusing the light and not completely
34
Figure 3.6: The shadow test- a transparent ruler was held three inches and one foot off the ground in
sunlight, outdoors, at noon and with the Molebeam on the heliodon. a) outdoors, 1', b) outdoors, 3", c)
Molebeam, 1', d) Molebeam, 3"
35
200
400 600 800
Wavelength (nm)
1000
Figure 3.7: Spectrum of natural sunlight from [Wikipedia, 2006f] and the spectrum of the Molebeam
(the jagged curve) measured with an Ocean Optics spectrometer
0100--
60-
... 20
300
-600 750
Wavelength (nm)
450
Figure 3.8: The emission spectrum of a xenon lamp compared to natural light [xen, 2006]. The dotted
line is natural light and the solid line is the xenon spectrum.
36
0
MAETA BASE
Figure 3.9: The xenon lamps available from Hamamatsu and the specifications of the L2274 150 watt
lamp we purchased [AIM Digital Imaging, 2006b].
Figure 3.10: Principle of point source and paraboloid
37
Figure 3.11: The xenon lamp
collimating it, it was clear that the bulb was not placed precisely at the focal point of
the paraboloid. Although the design allowed for fine adjustments, the exact position
of the xenon bulb's arc point was unknown due to manufacturing variations in xenon
bulbs. Also, because the cathode and anode (between which the arc point is created)
are encased in the bulb, measurements to determine this position were not completely
accurate. Additionally, the arc point is not a perfect point source. Therefore, it was
impossible to exactly place the arc point of the xenon lamp at the focal point of the
paraboloid even with trial and error, which was a requirement of the system. The behavior
improved, but not to the degree required by the system.
Furthermore, the xenon bulb itself exhibited strange diffraction properties through its
glass. With the paraboloid's focusing, these diffractions were quite noticeable and caused
a large variation in intensity of the spot. With methods available, it was impossible to
correct for these diffractions. Unfortunately, due to non-uniformities in the bulb and
adjustments needed to place the bulb with respect to the paraboloid, an acceptable
uniformity and collimation was never achieved.
After this setback, research began to find an integrated HMI source with the desired
optics. Even though the spectral characteristics of xenon bulbs are superior in terms of
mimicking natural light, integrated systems were still too expensive to consider. Therefore, HMI sources were reconsidered. The 400 watt Dedolight was found to meet the
requirements of the system (Figure 3.12). The Dedolight has a similar color temperature
and spectrum to sunlight due to its HMI bulb. It also produces a uniform beam with a
small degree of spread.
With data provided by Dedolight's manufacturer, it was confirmed that the Dedolight
has a spectrum similar to other HMI sources, which is considered to be a sufficient
substitute of natural light (Figure 3.13). As the figure shows, the Dedolight does not
have as much intensity in the red and near-infrared range. Because of this characteristic,
the light source does not produce as much heat as natural light. Also, the Dedolight has
a color temperature of 5600 Kelvin, another similarity to natural light which has a color
38
Figure 3.12: 400 watt Dedolight spotlight
temperature of 6000 Kelvin at noon [Wikipedia, 2006c].
With focusing, it is able to achieve a high level of uniformity. Over a 15 cm diameter,
the illuminance of the source was determined (with the aid of a LMT Pocket-Lux photometer) to vary by 8%, which is commonly considered acceptable in optics applications.
Figure 3.14 shows how the illuminance varies over the entire diameter. For extreme angles of the goniophotometer, this variance rises to as much as 30%. However, the only
way to improve this error is to have a spotlight with a smaller degree of spread. For HMI
spotlights, 50 is the smallest degree of spread available, so this is the best situation for
our given financial constraints.
A shadow test was also performed on the Dedolight. Just as with the Molebeam for the
heliodon, shadows of a transparent ruler were compared for sunlight and the Dedolight.
The images in Figure 3.15 confirm that they were similar. The gradations on the ruler
are easily viewed in the shadow when the ruler is held at three inches. At a foot, the
ruler becomes blurry in both cases. However, the behavior of the ruler in the sunlight is
superior to the Dedolight, as is expected since the sun only has a 0.25' spread, while the
Dedolight has a 10' spread.
3.2.4
Beam Shaper
For the goniophotometer application, it is important that the apparent beam on the
material being characterized is the correct size. When the goniophotometer rotates on its
altitude axis, the apparent beam will expand, becoming ellipsoidal. Without intervention,
the apparent beam will flood the surface of the goniophotometer when it is rotated to an
extreme angle (nearly parallel to the ground). The apparent beam will be the wrong size
39
200
1000
400 600 800
Wavelength (n)
Figure 3.13: Spectrum of the 400 watt Dedolight (the jagged curve) compared to the spectrum of sunlight.
Illuminance map
of Dedolight
x 10 4
1.34
1.32
-1.3
1.4
1.28
a 1.31
1.26
CD
1.2,
1.24
5
4
4
3
2
2
Figure 3.14: Illuminance map of the Dedolight
40
Figure 3.15: The shadow test- a transparent ruler was held three inches and one foot off the ground
under sunlight, at noon, outdoors and with the Dedolight on the goniophotometer. a) outdoors, 1', b)
outdoors, 3", c) Dedolight, 1', d) Dedolight, 3"
41
15 cm
15 cm
t
r'
Figure 3.16: The apparent beam on the goniophotometer with the beam shaper
unless something is put in its path to stop this excess light from reaching the table.
To stop this occurrence, a "beam shaper" has been developed (Figure 3.17). It consists
of an aluminum diaphragm that is rotated to the same angle as the altitude axis of the
table. From this action, the light beam from the Dedolight is "shaped" into the correct
size (Figure 3.16). The hinged flaps shown in the figure and in Figure 3.17 are intended
to block light when the beam shaper is rotated to grazing angles. Without these flaps,
the beam shaper diaphragm would have to be several feet long to block these grazing
angles. They are a passive system which can be flipped depending which way the beam
shaper is faced so that they always block unwanted light.
The rotation of the beam shaper is controlled with a stepper motor and a fabricated
motion control chip (Figure 3.18) which allows the motor to find a "home" position. Code
written in Visual Basic specifies the angle to which the beam shaper travels. The degree
of accuracy of the angle to which it can travel is determined by the amount of "steps"
to which the motor can discretely travel. For this particular motor, there are 1720 steps,
which means that there are approximately 0.21 degrees per step (1720p,). Therefore,
this fraction is the maximum resolution of this rotation.
The beam shaper must mimic the altitude axis of the goniophotometer in angle in
order to "shape" the beam.
The beam shaper is placed in the path of the Dedolight to the goniophotometer. It
is as close to the goniophotometer as possible. However, when using the goniophotometer/heliodon as a heliodon, the beam shaper must be moved out of position so that it
does not block the light path of the heliodon light source bouncing off the mirror. In the
future, a mechanism will be built which will allow the beam shaper to be moved easily
and replaced reliably in its correct position. Until that mechanism has been developed,
instructions for replacing the beam shaper can be found in Appendix 6.2.
42
Figure 3.17: The beam shaper "shapes" the light from the Dedolight so that the apparent beam on the
goniophotometer is always circular.
Sc
R2<
out
U,
RL
C1
L§7RI
*
_
Figure 3.18: The motion control chip circuit
43
R3
Also, when using the beam shaper, the spotlight must be focused on the beam shaper
rather than the goniophotometer surface in order to image the beam shaper. Otherwise,
the beam shaper will not work. Because of this constraint, beam uniformity is compromised. It is quite uniform at the beam shaper, but it has spread again once it reaches the
goniophotometer. To maintain acceptable beam uniformity, the actual size of the beam
on the goniophotometer's surface must be larger than the 15 cm originally specified. It
is large enough so that the center of the beam has sufficient uniformity, while the outer
edges decrease in intensity. This outer edge will be dealt with by applying a black velvet
diaphragm around samples. This diaphragm will absorb the light in this outer ring, effectively making the beam diameter a uniform spot of 15 cm. The optimal configuration was
determined by manually moving the spotlight relative to the beam shaper and focusing
it. From this trial and error procedure, the best setup was found to be a inner uniform
spot of 8% uniformity (as discussed previously) and an outer diameter of approximately
25 cm.
Validation
In this configuration, the beam shaper was able to shape light so that the apparent beam
on the goniophotometer is circular. However, future work must be completed to integrate
the beam shaper into the goniophotometer system. The code which controls the motion
of the beam shaper must be coupled to the code used to control the altitude axis of the
goniophotometer. Also, it was noticed that for extreme angles (near horizontal position),
the beam shaper did not make the apparent beam completely circular. A calibration
procedure must be performed to account for this irregularity.
Additionally, there are many features of the beam shaper which introduce an error
into it moving to the right position. For example, the mechanism for finding "home"
consists of a light diode, which when the light beam of the diode is broken, the circuit
is broken and the beam shaper can "know" where it is. A tab exists on the edge of the
beam shaper which breaks this diode beam. However, this tab is not negligible in size; it
is 0.03125" in thickness. Therefore, there is a slight discrepancy in its use because when
entering the diode beam, it cuts the beam with its front surface. When exiting, it exits
last with its back surface. Therefore, there is a slight delay in the signal which would
not occur if this tab were of negligible thickness. This thickness causes a discrepancy of
approximately 1'. However, it can be accounted for in the code.
Furthermore, the resolution of the angle, as discussed earlier, can cause a build-up
of errors over time. At this time, it is programmed to move to one angle and then
another with each calculation of degrees to steps occurring independently for each angle.
Therefore, if it is given the command to go to 600 and goes to 59.80', and then is given
the command to go to 1200, it will go to 119.60'. However, if the user "homes" the beam
shaper before each experiment, the errors will not accumulate. This homing is possible
within the user interface of the beam shaper and is recommended for its proper use.
Alternatively, the code could be altered to relate the degrees to steps for each final angle
rather than the intermediate ones.
Finally, until the beam shaper can be reliably placed in the exact position every time
after it has been removed for heliodon tests, there is will be an error associated with
44
replacing it. It might not be in the exact same position every time. In fact, this error
is expected to be much more significant than all other errors in the system. Once a
mechanism must be has been installed which can replace the beam shaper in the same
position every time, this error will be eliminated thereby vastly reducing this major source
of error.
Additionally, the beam on the goniophotometer need not be perfectly circular. The
primary purpose of the beam shaper is to prevent the light from entirely flooding the
goniophotometer's surface. Even with these errors, the beam shaper performs this task.
A more accurate measure of the beam shaper's performance its to measure the beam
exiting it onto the goniophotometer. This test was performed in Section 3.2.3 and it was
confirmed that the uniformity of the beam was 8%, which is considered acceptable for
optical applications.
45
Chapter 4
Data collection
To improve upon previous goniophotometer designs, we wanted to reduce the data acquisition time as well as provide a way to evaluate the spectral selectivity of various
materials. Most goniophotometers use a scanning principle to create a three-dimensional
profile of the light reflected or transmitted through a sample. At EPFL and LBNL, researchers were able to reduce the data acquisition time by projecting the light reflected
off of or transmitted through the sample onto a screen or a shell and measuring the luminance on these projection surfaces with a multi-point luminance meter [Ward, 1992]
[Andersen, 2004]. At LBNL, they used a CCD camera fitted with a fisheye lens so that
simultaneous imaging of the entire interior of the shell was possible. Like Ward and Andersen, we also decided to use a projection device (a hollow semi-ellipsoid) and a CCD
camera with a fisheye lens. For CCD cameras to provide accurate information about
BT(R)DFs, it they must be calibrated so that pixel intensity levels can be associated
with physical properties of light. Several methods have been devised by researchers to relate the pixel value a camera records with a luminance value. Once applied, the resulting
three-dimensional luminance map, can be used to calculate BT(R)DFs can be derived .
In this project, the camera setup captures an entire panorama panorama of either
reflected or transmitted light through a sample. This is possible with the aid of a light
collection device (a hollow semi-ellipsoid with a half-mirrored coating) which focuses the
light from the sample to the camera. The design, fabrication and installation of the semiellipsoid and the calibration procedure for the CCD camera are described in the following
chapter.
4.1
4.1.1
Development of a Semi-ellipsoid
Principle
An ellipse is an algebraic curve where the sum of the distances from any point on the
curve to two fixed points (the "foci") is constant. Optically, any light generated at one
focus of an ellipse or ellipsoid (an ellipse spun around it's major axis or minor axis so
that it is a three-dimensional object) will emit in all directions and be focused reflected
back onto the other focus of the ellipse if the interior of the ellipse is mirrored (Figure
4.1).
46
Figure 4.1: Optical principle of ellipses. Light from one focus is emitted, reflected off the mirrored
ellipsoid surface, and focused back to the other focus.
For this project, the sample is placed at the center of the rotating platform of the
goniophotometer. A CCD camera fitted with a fish-eye lens is embedded in the table
(Figure 4.3). Attached above the table is a semi-ellipsoid shell, which is mirrored on
the inside to behave as a one-way mirror. In reflectance measurements, light passes
through the shell to the sample where it is then reflected into the interior of the shell.
In transmission experiments, light travels through the sample and is reflected within the
shell. Because the sample is located at one focus of the semi-ellipsoid light reflects off
the mirrored surface to the other focus, where the camera lies (Figure 4.2). The sample
and camera can be approximated as point sources as required by the ellipsoid principle
because they are small relative to the overall size of the ellipsoid. The fisheye lens permits
full-scene imaging.
4.1.2
Design
Several factors were considered when determining the geometry of the semi-ellipsoid. The
semi-ellipsoid needed to be small enough so that it would not interfere with goniophotometer rotation, while big enough to allow one to approximate the non-finite sample as
a point source. The following characteristics describe these constraints more specifically.
" The major axis was held constant at 1150 mm since this was the maximum allowable
dimension for it to fit on the existing goniophotometer.
" Two constraints limited the size of the minor axis.
1. It must be sufficiently small such that the goniophotometer can rotate through
a full 360' with the goniophotometer attached. If the semi-ellipsoid were too
tall, it would interfere with this rotation.
2. It must be large enough such that the difference between the major axis and
focal distance is at least 10 times the sample diameter. This is needed so that the
sample will approximate a point source which is required to satisfy the optical
principle of using the semi-ellipsoid.
47
Camera
Sample
Figure 4.2: The sample and CCD camera with fisheye lens are located at the foci of the semi-ellipsoid,
along the major axis of the base.
Figure 4.3: The holding mechanism for the CCD camera. This setup allows the camera's fisheye lens to
be flush with the table at the focus of the semi-ellipsoid
48
Figure 4.4: The final dimensions of the semi-ellipsoid
o The focal distance must be maximized so that light emitted directly from the sample
to the camera is minimized.
These factors were optimized in a Microsoft Excel spreadsheet. The final design specified a major axis of 1150 mm and a minor axis of 1122.50 mm (Figures 4.4 and 4.5).
Additionally, the ellipsoid was selected to be made of acrylic since it is the most
transmissive plastic and is non-polarizing [Wikipedia, 2006a]. Acrylic transmits at least
90% of light throughout the UV, visible, and infrared spectrum (the typical value for
transparent acrylic). Also, to minimize distortions within the plastic, a refractive index
no greater than 1.5 was specified. Again, this value was the best for acrylic which would
minimize distortions. Finally, the thickness of the part needed to be kept as thin and
uniform as possible to maintain the refractive index of 1.5. An exact specification for the
thickness was not given, as it was informed by the manufacturing process.
4.1.3
Development
Due to the large size of the designed semi-ellipsoid, it was necessary to have an outside
company fabricate the part. An aluminum tool for thermoforming was machined by
American Tooling and Engineering, Inc. Using this tool, six semi-ellipsoids were made
by Spartech PDC by pulling melted acrylic sheets over the tool surface. An analysis of
these parts is discussed in Section 4.1.4.
To be able to accurately position the semi-ellipsoid on the goniophotometer- (which
is a vital step in data collection), -four holes were drilled into the lip of the semi-ellipsoid
(Figure 4.6). Two of the holes serve to place the semi-ellipsoid in an accurate position
on the goniophotometer (within 0.01"). They are fitted onto pegs protruding from the
goniophotometer's rotating surface. The pegs on the goniophotometer and holes in the
semi-ellipsoid have been very accurately placed to guarantee that the semi-ellipsoid's
49
2F~ (~.
0.395 i). thr
ra
~
____
WLle
i)HI - 5C
M
Hollow Semi-ellipsold
Courtney Browne
MIT
2 0.395 'n. thr hoe
Dirnensions irtn m niess
Figure 4.5: The data sheet sent to the fabrication company.
50
othemise
rreed
Securini hole
Stile hp
Plac e-findei
hole in flaiie
Figure 4.6: Four holes were placed on the edge of the semi-ellipsoid to accurately position it on the
goniophotometer. Two are located on the flange between the semi-ellipsoid and the lip and serve to
accurately position the semi-ellipsoid. The other two holes are found on the lip and their purpose is to
attach the semi-ellipsoid to the goniophotometer.
position with respect to the goniophotometer is correct.
The other two holes on the lip of the semi-ellipsoid are used to attach it to the goniophotometer. These holes correspond in position to two other holes on the rotating surface
of the ellipsoid. Bolts travel through both sets of holes and are secured by a nut. From
these two sets of holes, the semi-ellipsoid is placed accurately on the goniophotometer
and secured.
After the semi-ellipsoid was fabricated, it was sent to Tanury Industries to apply a
reflective coating. Again, this part of the project was outsourced because MIT did not
contain facilities to complete the coating.
Aluminum was chosen as the coating material because it reflects the complete spectrum
of white light in a uniform manner (around 90% reflection for an opaque coating-, Figure
4.7 [Griot, 2006]). We considered other coating materials such as inconel which gives a
more uniform reflection and transmission through the spectrum. However, it has a lower
overall reflection and transmission than aluminum (because it has a higher absorption).
Also, and inconel coating was cost prohibitive, so we decided to use aluminum.
If the percentage of reflection for a coating is 90%, the absorption and transmission
percentages sum to only 10% (the total of reflection, transmission and absorption must
equal 100%). For reflection experiments where light travels through the semi-ellipsoid to
the sample and then reflects to the lens (Figure 4.8), transmission and reflection had to
be balanced so that as much light as possible would reach the camera. The light is first
reduced when it passes through the ellipsoid (transmission). It is reduced again when
it bounces off the sample into the interior of the semi-ellipsoid and is reflected off the
coating.
Light at the camera
=
(%transmission)(%reflection)(original light level of Dedolight)
Therefore, reflection and transmission percentages would both ideally be 50%. How51
-
typkaf reNctame curwos
100
normal Inddence
~90
400
1000
800
600
WAVELENGTh IN NANOMETERS
Figure 4.7: The general reflection of aluminum as a function of wavelength [Griot, 2006].
Camera
Sample
Sample
b)
a)
Camera
Figure 4.8: In reflection experiments (a), light travels through the semi-ellipsoid, reflects off the sample,
and is focused with the semi-ellipsoid to the camera. In transmission experiments (b), light travels
through the sample and is focused with the semi-ellipsoid to the camera.
52
----------
ever, this is not possible in practice. There must be some absorption, and the reflection
and transmission percentages can rarely be perfectly balanced. Tanury Industries is still
working to determine the exact ratio they can achieve.
In transmission experiments, however, light will not need to travel through the semiellipsoid; light will only be reflected within the semi-ellipsoid itself (Figure 4.8). Therefore,
it is useful to maximize reflection and minimize transmission. Since Spartech was able
to make more than one useful shell for the project, we decided that two shells would
be coated-: one to be used for reflection experiments (balancing transmission and reflection as much as possible) and one to be used for transmission experiments (maximizing
transmission).
4.1.4
Validation
Because thermoforming is not a controlled process, the semi-ellipsoid is expected to have
imperfections. These imperfections will impact the effectiveness of the part. If the semiellipsoid is slightly the wrong size, light that is expected to travel from the sample to
the camera might not reach the camera because the semi-ellipsoid reflects it elsewhere.
Additionally, when air gets caught between the tool and plastic during the thermoforming
process, small bubbles can occur. These small surface imperfections will cause the same
effect as an ellipsoid that is the wrong size; - light will not be reflected back to the other
focus of the ellipse. We believe that the primary source of error in the semi-ellipsoid's
ability to reflect will be due to these small surface imperfections. This hypothesis was
confirmed by an inspection of the semi-ellipsoids after they were fabricated. They were
uniform in shape, but there were many minor surface imperfections. The manufacturer
used both cast and extruded acrylic in fabrication, and the extruded acrylic exhibited
less distortion than the cast versions. In the best two parts, we estimated these surface
imperfections to be less than 1% of the total surface area of the semi-ellipsoid. In a
worst-case scenario, the light reflected off of these surface imperfections will not reach
the camera at all. Therefore, less than 1% of light from the sample does not reach the
camera due to these surface imperfections. Even with this worst-case scenario, we believe
this percentage to be negligible. Therefore, the part should perform effectively.
A more exact calculation of the error can be performed after the semi-ellipsoids have
been aluminum coated. Unfortunately, this part of the project has not been completed
at this time. Once coated, we propose the following procedure for this error calculation
after the ellipsoids have been coated.
" A perfect reflector is used as a sample on the goniophotometer.
" The camera records, with the fisheye lens, the grey levels (0-255) of the light reflected
off the sample to the semi-ellipsoid to the camera.
" This signal is averaged.
" If the semi-ellipsoid, perfect reflector and light source were all completely uniform,
every pixel recorded by the camera would have the same level. Therefore, the perfect
reflector's uniformity or error (O-reflector) must be characterized. One might utilize
a standardized reflector in which this has been characterized by the manufacturer.
53
The light source's uniformity (Ulight source) has been characterized and can be found
in Section ??. Since these error sources multiply to give the total error, the total
error for a given position for the system is as follows.
=mtotal 1
where mtotal,
components.
mreflector,
reflector )2
7total
reflector
mlght
source,
+
( ('light source )2
mlight source
±
(,semi-ellipsoid
)2
msemi--ellipsoid
and msemi-ellipsoid are the averages of the given
" The standard deviation from the average of the pixels imaged by the camera can
be set equal to the total error (-total). The error associated with the semi-ellipsoid
(csemi-ellipsoid) can be solved for in this equation.
" This signal
noise ratio for the semi-ellipsoid will inform the calibration procedure for the
semi-ellipsoid. Spatial calibration procedures must be developed to account for this
error.
4.2
Calibration of a color CCD camera to behave as a multipoint luminance meter
For BT(R)DFs to be extracted from the image that the CCD camera captures when viewing the interior of the ellipse, the camera must be calibrated to behave as a multipoint
luminance meter. To do so, the picture is converted, pixel by pixel, into luminance values.
Calibrating a CCD camera to behave as a multi-point luminance meter has been performed in many other projects. More specifically, this process has been performed many
times for black-and-white cameras, with some examples described in [Andersen, 2004]
[Bellia et al., 2002]. The general principle is to calibrate the camera so that it has the
same sensitivity to light as the human eye, and then find the relation between the pixel
values of this calibrated camera to luminance values to be applied to images the camera
captures in the future.
Conversion of a color camera to behave as a multi-point luminance meter has been
the focus of more recent projects. For example, at LBNL, Inanici and Galvin used a color
HDR camera to capture light levels of a high dynamic range [Inanici and Galvin, 20043.
This procedure is possible because a HDR camera takes images for several integration
times and combines them into one image so one can see light levels of a larger range than
with regular cameras. At Ecole Nationale des Travaux Publics de l'Etat, Dumortier used a
Nikon Coolpix camera in which the integration time could be adjusted to capture this high
dynamic range [Dumortier et al., 2004]. Because luminance can be directly derived from
RGB values via a conversion to XYZ tristimulus values (another system for describing
colors in which the Y value is the measure of luminance [Wikipedia, 2006b]) both groups
used the camera's output to directly determine the luminance of a given pixel rather than
experimentally determining this relationship. They actually did not perform the spectral
calibration which would give the camera the same sensitivity to light as the human
eye. Therefore, their measurements ranged in error of 10-20% [Inanici and Galvin, 20043
[Dumortier et al., 20043.
54
For this project, we want to be able to evaluate the spectral selectivity of a sample.
Therefore, we used a color camera- a Kappa DX20 color CCD camera. This camera
allows adjustment in integration time to capture a large range of light levels. However,
since we want to deduce spectral selectivity, the camera needed to be calibrated so that
it had the same sensitivity to light as the human eye. From this calibration, the camera
will be able to evaluate the spectrum of light after it has interacted with a sample. After
this step has been completed, the same method used by Inanici and Galvin can be used
to convert RGB values into luminance for a given pixel [Inanici and Galvin, 2004].
4.2.1
Spectral Calibration
Often a camera does not have the same sensitivity to colors as the human eye. It will
be more efficient at imaging some wavelengths and less efficient at others. Therefore, to
determine the relationship between luminance and pixel values required to convert the
CCD camera into a multi-point luminance meter, the camera must be calibrated such
that it has the same sensitivity to colors as the human eye. When the color of light is
taken into consideration, this spectral sensitivity is specified by the CIE Standard Colorimetric Observer functions (Figure 4.9), which have been experimentally derived from
physiological measurements of how a human "standard observer" perceives monochromatic light [Wikipedia, 2006d]. It has been normalized to show the relative sensitivity
of a human to different wavelengths of monochromatic light. If one can make a camera
have the same sensitivity to this light as shown in this figure, the camera can be used
to determine luminance values a human observer would perceive directly from the RGB
values it captures, via a conversion to XYZ tristimulus values in which the Y value is
actually luminance. For this project, the calibration of the camera so that it has the
same sensitivity to colors as the human eye has been termed the "spectral calibration."
In previous works, researchers completed this task with the aid of physical filters
[Andersen, 2004], an impossibility for this project because, unlike previous projects, our
goal is to characterize light not just in intensity, but in color as well. This innovation
will allow the description of spectrally selective materials and light source variation. For
the spectral calibration, other projects only had to match one sensitivity curve (the V(A)
curve shown in Figure 4.10) for gray levels instead of three. Physical band-pass filters
(the types of filters used in applications such as these) often block all wavelengths above
or below a prescribed wavelength. Therefore, if physical filters were used, by matching
one curve, the other wavelengths would be filtered out. To circumvent this problem, a set
of three filters (one for each of the CIE Standard Colorimetric Observer functions- Figure
4.9) could have been used. We explored the possibility of coating three fish-eye lenses with
very thin filters to achieve this goal and found that the coatings were possible. However,
this approach would increase the data acquisition time because the fisheye lenses would
have to be changed in the middle of an experiment to obtain information about how a
sample behaves to the full visible spectrum of light.
Other projects using color cameras have ignored this spectral calibration and determined the degree of reliability one can expect from the measurements with an uncalibrated camera. As mentioned before, the measurements can be off by as much as 20%
[Inanici and Galvin, 2004] [Ward 1992].
55
CIE Standard Observer Curves
2
Red
Green
- - - Blue
I'
I
C)
U)
I
I
I
1.5
I
-0
I
0
CO)
=3
72
I
1
I
I
I
I
I
I
I
I:
E
co
-I.
w)
C,-
0.5
I
I
'I
2
I
0
400
450
500
550
600
650
Wavelength (nm)
700
750
Figure 4.9: The CIE Standard Colorimetric Observer functions: how a human perceives the color of
monochromatic light. These curves indicate how sensitive the human eye's receptor's are for red, green,
and blue for the visible spectrum of light [Wikipedia, 2006d].
Photometric sensitivity of the human eye
1
0.8
0.6
E
cz 0.4
0.2
0
400
500
600
Wavelength (nm)
700
Figure 4.10: The composite V(A) curve gives the overall sensitivity of the human eye to radiation at
different wavelengths [Wikipedia, 2006e). It is not divided into three colors as with Figure 4.9.
56
Figure 4.11: Spectral calibration experimental setup. Light passes from a Labsphere tungsten-halogen
calibrated light source through a Spectral Products CM110 Im monochromator to a Labsphere SRS-99010 pure white reflectance standard. Reflected light is measured with a Minolta LS-110 luminance meter
and a Kappa DX20 color CCD camera.
Ignoring the spectral calibration or using physical filters were not seen as an appropriate solutions because we wanted a very accurate measurement device that is quick in
its data collection (After much research, it was determined that our method is actually
not much faster than other methods. However, it will be able to characterize spectrally
selective materials, an innovation in this field. The time of data collection will be discussed later in this paper). Therefore, we decided that a new approach would be used.
We proposed that a computer program could take the place of physical filters by altering
the information the camera captured such that the final data were the same as if physical
filters were present. This approach was termed a "virtual filter." In this approach, the
"virtual filter" will alter the RGB values that the camera captures so that the camera
has the correct response to the light levels present.
To complete the spectral calibration, the relationship between the RGB values the
camera observes and the RGB values that a human would perceive must be determined.
To accomplish this task, an experiment is set up in which a Kappa DX20 color
CCD camera and a Minolta LS-110 luminance meter are pointed at a Labsphere SRS99-010 pure white reflectance standard with an average reflectance factor of 98% over
the visible spectrum at approximately the same distance and angle. A Labsphere calibrated tungsten-halogen light source is passed through a Spectral Products CM110 }m
monochromator (which can divide white light into monochromatic spectra) and hits the
reflectance standard. The camera images and the luminance meter takes a reading of
the monochromatic light (Figure 4.11). In order to calibrate what the camera observes
of the monochromatic light (in terms of RGB values) to what light the luminance meter
measures, the following experimental procedure was performed. First, what the camera
observes must be determined. Then, what the camera should observe is calculated and
57
compared.
Determining what the camera observes
" An image is captured with the Kappa CCD camera of wavelengths at intervals of
5 nm in visible light range (380-780 nm) as divided by monochromator. This 5 nm
division is appropriate to characterize the spectrum; anything greater would leave
out information, while anything finer would not add new information
[American Society for Testing and Materials, 20011. The settings for the camera are
as follows:
- 2.2 second integration time
- 0 gain
- 4095 HI
- 1200 LO: By altering HI and LO, one can change the range of levels which the
camera images. With this setting, a level of "0" corresponds to no light.
- 100 gamma (input'Y= output: -y is a percentage that describes the contrast. For
a -y of 100, the output pixel value is directly proportional to the input.)
- 1000 for R, G, and B
" The image file produced by the camera is imported into MATLAB with "imread.m,"
a function in the image processing toolbox. This function returns a matrix of intensity values for the three color channels (red (R), green (G), and blue (B)). Since we
are using an 8-bit camera, the intensity values range from 0 to 255. To manipulate
these pixel values, they must be converted to signed integers. This procedure has
been automated in the "opentif.m" file (Appendix 6.3.6).
" A single light intensity value is extracted from each image for each color channel
(R, G, and B) by means of an averaging algorithm implemented in MATLAB (see
Appendix 6.3.4).
This algorithm averages a prescribed range of pixel values. This range was determined by examining the spot created by the monochromator. Its size and uniformity
informed the choice of range; it appeared as though there were more than 10 pixels
of similar levels, so the range was chosen as 10. Additionally, the twelfth through
second pixels were averaged, throwing out the top two pixels to discard aberrantly
high, possibly false signal. The curve of these RGB values with their associated
errors due to this averaging can be found in Figure 4.12. As is shown in the figure,
the error bars are quite large (as much as 30% for some wavelengths) in relation to
the overall data set. This data seems to indicate that the uniformity of this spot is
not very good or that there were less than 10 pixels of acceptable uniformity. Examination of the spots imaged by the camera confirmed that the spot did decrease
in intensity at the outer edges such that there were less than 10 pixels of uniform
intensity. For future experiments, these errors can be reduced by having a larger
spot size with more pixels from which to sample so that this decrease in intensity
58
Camera Calibration for Threshold of 5
A
200
C,
0
Red
Green
Blue
100.0
coA
400
-
200 ---
--
450
500
650
700
750
650
600
550
Wavelength (nm)
700
750
600
550
R
Green
-Blue
100
0
400
450
500
Figure 4.12: The RGB sensitivity of the CCD camera (top) and its associated errors in averaging pixels
(bottom).
does not effect the results.
In addition, the Kappa DX20 color CCD camera appeared to have many pixels
that exhibited signal even when darkness was imaged (over 100 in some extreme
cases). We learned this was inherent camera behavior for long integration times
and the camera rising in temperature during operation. We deemed these false signals invalid and discarded them via the MATLAB functions turnOffBadPixels.m
and compare.m (see Appendices 6.3.3 and 6.3.5 for more information about how
these algorithms work). In the final experiment shown in Figure 4.12, this algorithm
eliminated 0.4% of total image pixels which were consistently exhibiting false levels
among many dark images.
o To be able to compare colors from light of different intensity levels, each RGB value is
converted to XYZ tristimulus values in which the Y value is a measure of luminance
[Wikipedia, 2006b]. XYZ values scale linearly with light intensity, while RGB values
do not [Rea, 2000]. Without this conversion, light comprised of the same wavelengths
but different intensity would have completely different RGB values and one would
not be able to compare light from different light sources that have the same color but
different intensity. For this situation, the XYZ values will be the same, just scaled
to reflect the light intensity. This conversion allows the recognition of two colors of
different intensities as the same color, while an RGB comparison would not. More
about this recognition is discussed at the end of this section and in Section 4.2.2.
The conversion is completed with the following equations [Inanici and Galvin, 2004]
59
Relative Intensity of the Labsphere tungsten-halogen lamp
0.15C 0.10-.1
0
0.050
400
450
500
550
600
650
Wavelength (nm)
700
750
Figure 4.13: The Labsphere tungsten-halogen source does not emit light in wavelengths close to the UV.
[Rea, 2000] [American Society for Testing and Materials, 2001].
X = k(0.412424R + 0.357579G + 0.180464B)
Y = k(0.212656R + 0.715158G + 0.072186B)
Z
=
k(0.019332R + 0.119193G + 0.950444B)
These equations have been automated in the MATLAB code rgb2XYZ.m which can
be found in Appendix 6.3.11.
In Figure 4.12, it appears as though the camera is only sensitive starting around 430
nm. However, this is not the case. Its sensitivity actually starts around 380 nm, as does
the human eye. This discrepancy is due to the light source used in these experiments; it
produces very little light in the wavelengths near the UV range (Figure 4.13). To verify
this assumption, we used the xenon source (spectrum found in Figure 3.8) discussed in
section 3.2.3 to perform the same experiment outlined above. The xenon source generates
wavelengths from UV to infrared, including all of the visible range. From this experiment,
the camera's sensitivity was determined for the entire visible range (380-780 nm) (Figure
4.14). As is evident in the figure, the error bars (due to the averaging function) for the
xenon lamp are much smaller (between 1-2%). This is due to the larger and more even
spot the xenon lamp was able to produce through the monochromator. However, it was
not as intense as the spot for the tungsten-halogen experiment, so longer integration time
was needed (8.1 seconds), which increased the number of bad pixels thrown out.
In order to characterize the error of the camera, in future work, this experiment should
be repeated with the xenon source at least three times to improve the probability that
the errors are accurately characterized. Because great care has been taken to eliminate
systematic errors (false signal due to the camera heating up, camera settings, etc.), the
variability between the results of these experiments can best be attributed to noise.
60
Camera Calibration for Threshold of 5
250
-
200 -
150 -
0
260
400
450
500
400
450
500
550
600
650
700
750
550
600
650
700
750
-
200 150 100S0
50
Wavelength (nm)
Figure 4.14: The RGB sensitivity of the CCD camera (top) and its associated errors in averaging pixels
(bottom) when the xenon source is used instead of the tungsten-halogen source. Note that the signal
starts at 380 nm instead of 430 nm (as in Figure 4.12) because the xenon source emits these wavelengths
and the tungsten-halogen source does not. In future work, the xenon source should be used for camera
calibration in order to obtain an accurate portrayal of camera behavior for these wavelengths.
61
This noise should be noted to characterize the final reliability of the camera within the
goniophotometer system.
Additionally, the errors due to the averaging function should be characterized as a
function of wavelength and intensity. We do not have enough experimental data at this
point to determine this relation. However, since the error in the averaging function is
related to spot uniformity, we expect that the percent error will be related to intensity
since it affects spot uniformity. Also, the errors could be greater because the intensity
of the signal is of similar magnitude to the noise in the system. This error will help
determine how reliably one can assign future XYZ values to the measured value from this
experiment. How this assigning occurs is discussed in the next section.
Determining what the camera should observe
In order to find the X, Y and Z tristimulus values the camera should observe to compare
with the derived XYZ values the camera captured, the amount of light (radiance) that
reaches the camera for each wavelength in 5 nm intervals from 380-780 nm must be
determined. Originally, this quantity was to be determined with a luminance meter placed
at an angle and distance to the reflectance standard similar to the angle and distance of
the camera to the reflectance standard. However, it was difficult to get accurate readings
with the luminance meter because it is difficult to keep ones hand steady to take the
reading. Measurements varied by more than 100% for very low light levels. Also, the
Labsphere tungsten-halogen calibrated light source produced very little light in the blue
range. Since the luminance meter is calibrated to have the same sensitivity as the human
eye (Figure 4.9), it was not very sensitive in this range, which compounded the problem.
Therefore, a spectrometer which can be kept steady easily was used to collect radiance
as a function of wavelength. This task can be accomplished if the luminance for white
light is measured, which is possible with the luminance meter because the intensity of
white light in much greater than the intensity of blue light generated by the Lapsphere
light source. The fluctuations in the values for the white light are around 5%. Our
spectrometer measures irradiance, which is a radiometric instead of photometric value.
Therefore, the data the luminance meter collected needed to be converted into radiometric
units for proper comparison. The relation between photometric and radiometric units are
related by the composite V(A) curve (Figure 4.10).
780nm
#photo
=
>
683 * V(A) * #(A),rdioA(A)
A=380
where
#photo
=
flux [lumens] and ca/rdi= flux [watts]
This relation can be used to convert any radiometric quantity (radiance, irradiance, etc.)
into its corresponding photometric quantity (luminance, illuminance, etc.).
Furthermore, the spectrometer must be placed along the axis of the monochromator
and light source so that it is directly in its path (allowing it to detect sufficient light
62
Figure 4.15: New spectral calibration experimental setup with the spectrometer instead of the luminance
meter
levels) rather than in the same position as the camera, as was the case with the luminance
meter (Figure 4.15). Placing the spectrometer in the path of the monochromator allows
the determination of the relative light flux exiting the spectrometer; provided that the
spectrometer is measuring some light, the size of the relative curve, and thus, the position
of the spectrometer relative to the monochromator, is not important.
A conversion is needed to relate the light detected by the spectrometer to the light
it would detect if it were in the same position as the camera or luminance meter. This
conversion is accomplished by relating the total amount of light recorded by the luminance
meter at the center of the spot produced by the monochromator to the amount of light
measured by the spectrometer, as a function of wavelength. This measurement can be
done with the luminance meter for bright, white light because it is around 20 1 for
white light instead of 0.1 - for blue light. In the case of the blue light, the error was
larger than the measured value, which meant the data is unreliable. Once the relationship
between the luminance reading and the spectrometer reading has been determined, all
light measured by the spectrometer in the path of the light source can be converted to
light the camera sees. This experiment is limited by the reliability of the luminance
reading for white light. Although a reliable reading can be taken (with an error of less
than 10%), better equipment which reduces this error would improve the reliability of
the experiment. The following mathematical procedure was performed to complete this
task.
e Variables:
total luminance at the luminance meter =
-
Lphoto,Ium =
-
L(A)photo,lum=
lumens 2
_
steradiai*m2]
luminance at the luminance meter, as a function of wavelength
steradian*m
-
L(A)radiolum =
[watts
radiance at the luminance meter, as a function of wavelength
63
=
-
E(A)radio,spec =
length
(Eradio =
relative irradiance at the spectrometer, as a function of wave[a"s], but since this is a relative value, units are not important.)
Note that these values exist for white and monochromatic light.
* Measured:
- Lphoto,lum
of white light
-
E(A)radio,spec of
-
E(A)radio,spec
white light
of monochromatic light
* Determine a for white light
Lradio,um
Eradio,spec
which defines the relationship between the light present at the luminance meter and
the light present at the spectrometer.
- E(A)radio,spec must be integrated over all wavelengths.
/780nm
E(A)radio,specdA
Eradio,spec =
A=380
- LphotoIum must be converted to L(A)radio,lum (photo2radio.m- Appendix 6.3.8)
780nm
Lphoto,Ium =
683 * V(A) * L(A)radio,lumA(A)
A=380
Because of the summation, it is difficult to isolate L(A)radio,lum. However, this
is possible because the relative radiance at the luminance meter is known. It is
the same as the relative radiance at the spectrometer; distance and angle will
not affect the relative radiance. However, to find the absolute radiance, which is
needed for determining ce, some algebraic manipulation is required. L(A)radio,lum
can be isolated from the equation by determining the wavelength at which it at
a maximum. Then each term of the summation includes L(A)radio,lum,max and
the fraction, (3), of the maximum it is at that wavelength.
L(A)photolum = L(A)radio,lum,max[683V(A) 3 8 0 3380 + ... + 683V(A) 78 o3 78 0]
L(A)radio,um,ma
L(A)photo,lum
=683V(A) 38 0 038 0 +
L(A)radio,lum,380 =
...
+ 683V(A) 78 0Q 78 0
3 8 0L(A)radiomax
L(A)radio,lum,385 =/ 3 385L(A)radio,max...
64
L(A)radio,um,780 =
3
780L(A)radio,max
From this procedure, the absolute radiance at the luminance meter (or camera)
can be determined. The above procedure has been automated in Appendix
6.3.8. Next, the total radiance of white light at the luminance meter must be
found by summing under the found curve to determine a.
780nm
Lradio,lum
L(A)radio,IumdA
-=
A=380
Finally, ce can be determined.
Lradio,lum
Eradio,spec
* This a must be applied to monochromatic light for each wavelength in 5 nm intervals from 380-780 nm measured by the spectrometer to determine the radiance of
monochromatic light that the camera observes.
L(A)radio,lum
-
aE(A)radio,spec
* Now the absolute radiance of monochromatic light that the camera observes has been
determined. The color of this light (X, Y, and Z) can be derived with the following
procedure (can be calculated with MATLAB file found in Appendix 6.3.10):
780nm
X
=
k EL(A)radio,lumT(A)AA
A=380nm
780nm
Y = k EL(A)radiolumg(A)AA
A=380nm
780nm
Z
=
k
L(A)radioIum7Z(A)AA
A=380nm
Where
- X, Y, Z = tristimulus values
-
spectral radiance distribution of monochromatic light from the
source, derived from previous procedure
L(A)radio,lum =
- !(A), Y(A), i (A) = spectral tristimulus value given for CIE standard observer in
The IESNA Lighting Handbook (Figure 4.9) [Rea, 2000]
65
Determining K Value
0)
cTz
01
400
450
500
550
600
650
700
750
400
450
500
550
600
650
700
750
550 600
650
Wavelength (nm)
700
750
2-
0
IiI-
I
C1
400
450
500
Figure 4.16: k is determined by dividing the luminance for the monochromatic light at a given wavelength
by the Y value determined in the above procedure. This k value can now scale the X, Y and Z values so
that they will compare to what the camera imaged.
- k
=
normalizing factor which makes Y equal the luminance for a given wave-
length ([Inanici and Galvin, 2004]).
Figure 4.16 shows how k was determined.
L(A)radio,spec -
The
L(A)phOt,Spc
(derived from
procedure outlined below) for monochromatic light was divided
by Yk.1 from the above summation. From this, the constant which relates
L(A)photo,spec and Yk=1 was found (k), which can be used to convert X, Y, and Z
into normalized values based on the amount of luminance present in the system.
To derive L(A)pho,,, 8 pc from L(A)radio,sp,,ec, complete the following summation for
each monochromatic spectra.
780nm
L(A)photo,Ium
S1
683 * V(A) * L(A)radio,lumA(A)
A=380
e Each XYZ value the camera observes can be compared to a value that the human eye
would render, based on the amount of light present, as determined in the procedure
above (Figure 4.17). This plot was rendered with SpecIntegrate.m (see Appendix
6.3.1).
Now that this relationship has been determined, there must be a way to apply this
relationship to images the camera captures. Ideally, the camera would capture an image,
and based on the pixel values, new pixel values would be assigned to account for the way
66
Camera XYZ and Expected XYZ values
based on radiance present
A M.
M.
M.
E.
E.
x E.
2-
I
:3
> 1.5 N
CO
Red
Green
Blue
Red
Green
Blue
1
0.5
400
450
500
650
550
600
Wavelength (nm)
700
750
Figure 4.17: The smaller curve (based on the RGBs the camera captured) must be mapped onto the
larger curve (the XYZs derived from the amount of light present)
the human eye would image the scene based on the amount of light present, rather than
how the camera imaged the scene. However, the camera does not capture the exact same
pixel values from acquisition to acquisition (due to slight variations in photons interacting
with the camera's sensor, slight variation in light levels, etc.). The average due to the
averaging function will be similar, but the exact pixel values will be different. This
anomaly is the main reason for the averaging function. Therefore, the exact relationship
between XYZs the camera captures to expected XYZs based on light levels derived in
the procedure above is unlikely to occur more than once. However, this relationship map
can be created, allowing the conversion of XYZ values the camera captures to proper
XYZ values based on the light present. The following procedure is a proposal for how
this assignment would occur.
" The ratio between the X, Y, and Z values is determined as a unique ratio for each
wavelength. This unique ratio is associated with the expected XYZs based on the
amount of light present (Figure 4.17). There will be a ratio for each 5 nm increment
from 380-780 nm.
" Ranges of the unique ratios are created for each of the monochromatic light experiments. The size of these ranges is statistically determined from the errors associated
with the averaging function to ensure correct placement of the experimental XYZ
values in the correct range (Section 4.2.1).
" Monochromatic light is imaged with the camera. The ratio between these experi67
mental X, Y, and Z values is found and they are placed in the appropriate range.
The experimental XYZ values will be a multiple of the original XYZ values from
which the unique ratios were determined. New XYZ values can be assigned after
multiplying the expected XYZ values based on the amount of light present by the
multiple the experimental XYZs are of the original ratios.
This approach is limited by the certainty of placing experimental XYZ values into
these ranges. There will be an error associated with this assignment. Additionally, there
will be an error associated with scaling the XYZ values with the appropriate multiple.
These errors must be determined experimentally after the characterization is complete
to determine the overall reliability of this assigning method. However, we expect these
errors to be negligible. Additionally, this approach allows for spectral characterization
of camera RGB values (via conversion to XYZ tristimulus values), which has not been
previously accomplished.
4.2.2
Spectral calibration for continuous spectra
Although this procedure is a novel approach, it does have a shortcoming. It only "maps"
light the camera sees to what the human eye would see for monochromatic light. In order
to make the procedure valid for all combinations of light, one must be able to identify
what amount of monochromatic light is a sum of the total continuous spectrum.
This separation is not specifically covered by this project. However, if a mechanism
were available to filter continuous spectra into monochromatic light, the computer based
spectral calibration would work. The information about how monochromatic light behaves with a sample could be combined to determine the response of the entire spectrum.
However, the filter system need not filter as finely as the spectral calibration required.
It must only filter to a degree which allows classification of the quasi-monochromatic
light into the original monochromatic divisions. We propose that this division would be
most appropriate at the peaks of the CIE Standard Observer function curves (Figure
4.18) because the XYZs in these ranges should be unique. From this point, the XYZ
values of the quasi-monochromatic light will be a sum of XYZs for the monochromatic
light within that quasi-monochromatic range. It has been proposed that the intensity
of these wavelengths can be determined by guessing what combination of the XYZs for
monochromatic light add up to the XYZs for quasi-monochromatic light. The range of
XYZs to try in this guessing are determined from the ranges mentioned in the previous
section. For instance,
XYZquasi-mono(550-560nm) = XYZmno(550nm)+XYZmono(555nm)+XYZmono(560nm)
If this summation can be determined, then the monochromatic content of the quasimonochromatic light can be determined. From there, the multiple of the ratios can be
applied to the XYZs based on the amount of light present.
For example, if the quasi-monochromatic light produced by the filter system ranged
from 550-570 nm, and the original XYZ values from the spectral calibration were as
68
CIE Standard Observer Curves
Red
Green
- - - Blue
2
a)
a)a,
0,
1.5
-0
0
1
II%
-a E
2 a)
0.5
----
0
400
450
500
650
600
550
Wavelength (nm)
700
750
Figure 4.18: The proposal of how the CIE Standard Observer function curves should be divided by the
filter system. Within these ranges, the XYZ values will be unique, allowing monochromatic light with
the same XYZs to be assigned to a certain wavelength. The XYZs of quasi-monochromatic light should
be the summation of the XYZs of monochromatic light from these ranges.
69
follows
550 nm: X =2, Y =5, Z =4
555 nm: X
=
1, Y =4, Z
=
6
560 nm: X
=
5, Y
=
2
565 nm: X
=
3, Y
570 nm: X = 2, Y
3, Z
=
5, Z =3
3, Z =-1
=
then the unique ratios of these monochromatic light experiments would be
550 nm: X:Y = 1, Y:Z = , X:Z = 1
555 nm: X:Y-=1, Y:Z =Z, X:Z =
560 nm: X:Y =,
Y:Z =-1, X:Z<= 5
565 nm: X:Y= , Y:Z =j,
570 unm: X:Y
=
j,
33
Y:Z
=
X:Z-= 3
3, X:Z= 2
The camera would be used experimentally to determine the spectral selectivity of a given
sample in the 550-570 nm range. If the sample transmits light that the camera images
as XYZ values of 3, 9, and 10 respectively. The ratios would be
X:Y =1, Y:Z=-, X:Z =These ratios do not fit in any of the ranges. However, it is clear that the XYZ values
from 550 and 555 nm sum to equal these new XYZ values.
XYZ 5 50 5-
70 nm
= XYZ5 50 lnm + XYZ 5 5 5 nm
X:3=2+1
Y:9=5+4
Z: 10= 4 + 6
Therefore, it is likely that the material is blocking 560-570 nm wavelengths and per-
mitting 550-555 nm.
Although the errors for this procedure have not yet been determined, it is recognized
that they can be minimized by selecting ranges which permit the reliable determination of these summations. However, smaller ranges mean longer data acquisition times.
Therefore, the error should be balanced with the desired data acquisition time.
70
4.2.3
Photometric Calibration
After the spectral calibration has been performed, the camera has the same sensitivity to
light as the human eye when one can subdivide the spectrum into quasi-monochromatic
spectra with the filtering system. Next, a calibration must be performed which relates
the RGB values the camera captures to luminance (the "photometric calibration"). The
experimental setup is the same as in the spectral calibration. The RGB levels of white
light are determined as a function of luminance level for a given integration time. The luminance level can be altered by varying the distance of the light source to the reflectance
standard or with filters. The range of integration times for this experiment is governed
by what integration time the camera needs to image the lowest light level the luminance
meter can measure and what integration time the camera needs to image (without saturating) the light the Dedolight produces at the particular distance of the setup and
after a reflection off the semi-ellipsoid. Once this relationship has been determined for all
integration times within this range, a pixel value can be used to determine the luminance
at that pixel for a given integration time.
Luminance can be derived from RGB values, if one knows the relationship between
luminance and integration time for the camera [Inanici and Galvin, 2004].
Y
=
k(0.212656R + 0.715158G + 0.072186B)
The Y value (of X, Y and Z) is equal to luminance.
Lphoto
The k is determined experimentally from the photometric calibration. It relates the
luminance the camera measures for a given integration time to the luminance that is
present and is unique for each combination of luminance and integration time (k*).
k*
=
Lpresent
Ldetermined with the camera
In order to determine the k for each integration time, the luminance for the white light
at a given distance should be determined, along with the Yk.1 value, based on the RGBs
the camera images at that distance. Then their quotient should be taken according to the
above relation and the k* value can be determined for a given luminance and integration
time.
*=
Lhoto
0.212656R + 0.715758G + 0.072186B
This procedure should be repeated for all relevant integration times and luminance
levels. After this constant has been determined for each integration time, luminances can
be derived directly from RGB values the camera captures. Code was written in MATLAB
(Appendix 6.3.11) which converts RGB into XYZ values. In future work, the k values for
different integration times must be integrated into this code.
71
Presently, the k value is a constant in the code (in rad2XYZ2.m - Appendix 6.3.10)
which is inputted when the function is called. It must be rewritten to allow the assignment
of a k based on an integration time. By listing k as a function of integration time in
an array in the code, and changing the input to be integration time, one can call the
appropriate k by inputting the integration time.
This experiment should also be repeated three times to determine the errors in measurement. By averaging the results from the three experiments, the error associated with
relating luminance to RGB values can be determined. The error arises from discrepancies in the RGB values the camera captures and the luminance values measured with
the luminance meter. When the results from three consecutive experiments are averaged, the mean (m) and standard deviation (a) of the luminance and RGB values can be
determined. The mean (m) and standard deviation (7) of the RGB values are as follows.
2
a2
URGB
2
MiRGB = 0. 2 12 6 5 6MR + 0. 7 151 5 8 MG + 0.0 7 2 18 6 mB
There will be a different CRGB and MRGB for each k*. Additionally, there will be a
different, corresponding ML and cL for each k*. The total error, O'k-, will be
ik* = nk*
(
GL
_L)2 +
mL
(
RGB
)2
mRGB
Once the photometric calibration has been completed, luminance values can be directly
derived, pixel by pixel from RGB values the camera captures with the aid of the filtering
system discussed in section 4.2.2. Within the entire goniophotometer system, the camera
will image the interior of the semi-ellipsoid and through a spatial conversion, be able to
characterize the BT(R)DFs which emitted from the sample. However, the speed of the
data acquisition is limited by this filtering system because a measurement must be taken
within each filtered range (Figure 4.18) to determine the spectral selectivity of a material.
The exact errors expected from this system have yet to be determined, but we expect
they will arise from the range chosen for the filters which will directly effect the certainty
of XYZ assignment as discussed in section 4.2.2 and the errors associated with the relation
between luminance and RGB values as discussed previously in this section.
72
Chapter 5
Conclusion
The work described in this thesis has focused on selecting and installing the light sources
for the two applications of our device and setting up the CCD camera used for collection
and analysis of the data produced by the goniophotometer application. A light source for
each application (heliodon and goniophotometer) was successfully selected and installed
in the room layout and they were found to exhibit a uniformity over their beam diameter of 7.4% (for the heliodon source) and 8% (for the goniophotometer source), a color
temperature similar to sunlight (5600 Kelvin) and a spectrum that is considered to be a
suitable simulator of sunlight.
In addition, this thesis features the creation of a projection device used in data collection. The developed projection device is an acrylic semi-ellipsoid, which fits over the
rotating surface of the goniophotometer and functions to focus light emitted from the
sample to the CCD camera such that the light can be characterized as a BT(R)DF. The
acrylic semi-ellipsoid has been fabricated, but due to delays in the outsourcer's schedule,
the coating of it has not yet been completed. In future work, the semi-ellipsoid must
be tested with the goniophotometer to characterize the system and coatings. A rough
estimation of the error associated with the semi-ellipsoid has been approximated to be
less than 1%. When it is coated, a more accurate characterization will be completed to
determine its reliability within the goniophotometer system.
Finally, the thesis discusses the calibration of a color CCD camera to behave as a multipoint luminance meter. This portion is the most innovative; although other researchers
have used color CCD cameras as multi-point luminance meters, they have neglected the
spectral calibration which gives the camera the same sensitivity to light as the human
eye. Without this calibration, the measurements the camera takes can be off by 10-20%
[Dumortier et al., 2004] [Inanici and Galvin, 2004]. A new approach has been proposed
in this thesis that will allow the spectral calibration involving a system which filters the
light into quasi-monochromatic spectra to permit the extraction of spectral data from
RGB values, and eventually a calibration which relates pixel value to luminance. The
errors associated with this calibration have yet to be determined. However, we expect
that there will be errors associated with the entire system to will be less than the 10%
accomplished by [Dumortier et al., 20041 and (Inanici and Galvin, 2004] since we will be
completing a spectral calibration.
Once complete, the goniophotometer will be the only one of its kind. In addition to
73
performing BT(R)DF characterization as the existing goniophotometers do, it will be
able to characterize spectrally selective materials and directional near infrared transmission through materials. With this new system, databanks of BF(R)DF information can
be quickly and easily created. This data will aid in advanced daylighting design; the
BT(R)DFs can be inputted into computer simulation programs to inform a building's
design. Generalized properties of these materials can be better determined so that building managers are more willing to use the materials. This information will foster better
understanding of novel materials and thus, promote sustainability in future design and
construction.
There is still much to complete on this project. The performance of the semi-ellipsoid
must be determined with the procedure outlined in Section 4.1.4. The camera calibration
must be finished, but the framework is in place to collect the data and quickly analyze
it (Section 4.2). An near infrared camera must undergo a similar calibration so that the
goniophotometer can evaluate near infrared transmission and reflection through these
materials of interest. Finally, all of these systems must be integrated to foster use amongst
building designers.
74
Chapter 6
Appendix
6.1
Positioning the Dedolight
The Dedolight is placed along the axis of the room with its light rays parallel to the
floor and perpendicular to the goniophotometer in its vertical position. Because the
Dedolight's proper position is in the middle of the lab, it is forseeable that it will be
bumped slightly out of position on occassion. Therefore, the following steps should be
performed occassionally to ensure that the Dedolight is in the proper position:
" Spotlight stand must be placed on painted markers in lab room.
" Check that the center of the lens is 36" off the ground. If not, loosen the knob on
the spotlight right above the stand and adjust the height. Retighten the knob.
" With the goniophotometer in a horizontal position, shine the Dedolight to the corner
of the room. Make sure that the light passes the goniophotometer and hits the corner
of the room in a centered way.
" Check that the the spotlight is set to have a 100 of spread (which is ok, because the
iris selects the central rays of the spotlight, meaning that those outputed do have 50
spread.
* The condenser lens should be retracted to its full extent.
6.2
Positioning the beam shaper
The following procedure should be performed when the beam shaper is returned to its
proper position for the goniophotometer application:
" Place the beam shaper within the marks painted on the floor.
" Make sure that the Dedolight has been focused properly by confirming that the lens
from the Dedolight is imaged on the beam shaper (it should have a textured look,
as the lens in the Dedolight is slightly textured).
75
SpecInteg rate6.m
photo2radio.m
radio2photo.m
rad2XYZ2.m
integrate.m
calcam.m
turnOffBadPixels.m
datamean.m
compare. m
opentif.m
Figure 6.1: The commands written for the camera calibration and how they relate. SpecIntegrate is the
root command that calls all of the other functions.
"
Adjust the iris so that the beam of light from the Dedolight is only slightly bigger
than the opening on the beam shaper. There is a piece of tape on it currently to
show where the iris should be adjusted.
" Adjust the height of the Dedolight so that the beam is centered on the goniophotometer. It should just graze the top opening of the beam shaper.
6.3
Matlab Code
This Appendix contains the MATLAB code created for the camera calibration.
ordered by the root command and the files that that command calls (Figure 6.1).
6.3.1
It is
Speclntegrate.m
SpecIntegrate determines XYZ values a human eye would render, based on the amount
of light present.
clear all;
close all;
cd .. ;
cd 22;
thres = 5;
A = calcam(thres);
sizeA = size(A);
plotxyz-result =
for i = 1:sizeA(1)
temp = cat(2,A(i,1),rgb2xyz([A(i,2) A(i,3) A(i,4)]));
plotxyz-result = cat(1,plotxyz_resulttemp);
end;
76
cd .. ;
cd CameraCalibration;
rho = 0.987173;
luminancemeter_wl = 21.5;
darkfilename = 'black.txt';
[A B] = textread(dark-filename,'Xf %f',
dark-spectrum
[A B];
clear A;
[A B];
for i = 1:2048
spectrum(i)
end;
clear A;
%Read in two column vectors:
% wavelength, relative irradiance
clear B;
white-filename = 'O.txt';
[A B]= textread(white-filename,'Xf %f',
spectrum =
2048);
clear B;
2048);
%Read in two column vectors:
% wavelength, relative
% irradiance
XCreat e matrix C by combining A and B
= spectrum(i)-darkspectrum(i,2);
radio-output = photo2radio(luminance-meter-wl,spectrum);
radianceluminance-meter = radio-output(:,1:2)
%Find radiance with
% photo2radio
% Take wavelength and radiance
radiance-spectrometer(:,1) = spectrum(:,1);
radiance-spectrometer(:,2) = spectrum(:,2) * rho / pi;
alpha = integrate (radiance luminancemeter)/integrate (radiance-spectrometer);
figure(5);
plot(radiance-spectrometer(:,1),radiancespectrometer(:,2),"'r');
hold on;
%Loop to Process Files
for i = 1:81
wavelength = 375 + 5 * i;
%Create File Names
filename = [num2str(wavelength) '.txt'];
[A B]= textread(filename,'f .f', 2048);
C = [A B];
clear A;
clear B;
%Create matrix C
for
j
= 1:2048
C(j,2) = C(j,2) - dark-spectrum(j,2);
end;
C_irr = C;
luminance(i,1) = wavelength;
luminance(i,2:3) = alpha * radio2photo(C-irr);
C(:,2)
=
C(:,2)*rho/pi;
%convert
77
to radiance
plot(C(:,1),C(:,2));
C(:,2) = C(:,2) * alpha;
XYZ(i,2:4)=rad2XYZ2(C(:,1:2),1);
XYZ(i,1)=wavelength;
k = luminance(i,2) / XYZ(i,3);
XYZ(i,2:4)=rad2XYZ2(C(:,1:2),k);
end;
figure(1);
plot(XYZ(:,1),
hold on;
plot(XYZ(:,1),
plot(XYZ(:,1),
XYZ(:,2),
'r');
XYZ(:,3),
'g');
XYZ(:,4),
'b');
xlabel('Wavelength (nm)');
ylabel('tristimulus (XYZ) values');
title('Expected XYZ values based on radiance present');
figure(2);
subplot(2,1,1);
hold off;
plot(plotxyz-result(:,1),plotxyzresult(:,2),r)
axis([350 800 0 1]);
hold on;
plot(plotxyz-result(:,1) ,plotxyzresult(:,3),'g');
plot (plotxyz-result(:,1) ,plotxyz-result(:,4),'b');
ylabel('Measured/Converted Chromaticity Coordinates');
title(['Camera Calibration for Threshold of ' num2str(thres)]);
x_bar
=
y-bar =
z_bar
=
[ 0.0002 0.0007 0.0024 0.0072 0.0191 0.0434 0.0487 0.1406 0.2045 0.2647
0.3147 0.3577 0.3837 0.3867
0.0805 0.0411 0.0162 0.0051
0.2365 0.3042 0.3768 0.4516
1.0142 1.0743 1.1185 1.1343
0.6475 0.5351 0.4316 0.3437
0.0409 0.0286 0.0199 0.0138
0.0010 0.0007 0.0005 0.0004
0.0000 1 ;
[ 0.0000 0.0001 0.0003 0.0008
0.0387 0.0496 0.0621 0.0747
0.2536 0.2977 0.3391 0.3954
0.8752 0.9238 0.9620 0.9822
0.8689 0.8256 0.7774 0.7204
0.2835 0.2283 0.1798 0.1402
0.0159 0.0111 0.0077 0.0054
0.0004 0.0003 0.0002 0.0001
0.0000 1];
[ 0.0007 0.0029 0.0105 0.0323
1.5535 1.7985 1.9673 2.0273
0.3707
0.0038
0.5298
1.1240
0.2683
0.0096
0.0003
0.3430
0.0154
0.6161
1.0891
0.2043
0.0066
0.0002
0.3023
0.0375
0.7052
1.0305
0.1526
0.0046
0.0020
0.0895
0.4608
0.9918
0.6583
0.1076
0.0037
0.0001
0.0045
0.1063
0.5314
0.9991
0.5939
0.0812
0.0026
0.0001
0.0088 0.0145
0.1282 0.1528
0.6067 0.6857
0.9973 0.9824
0.5280 0.4618
0.0603 0.0441
0.0018 0.0012
0.0000 0.0000
0.2541 0.1956 0.1323
0.0714 0.1177 0.1730
0.7938 0.8787 0.9512
0.9597 0.8563 0.7549
0.1122 0.0813 0.0579
0.0031 0.0022 0.0015
0.0001 0.0001 0.0001 0.0000
0.0214 0.0295
0.1852 0.2199
0.7618
0.9556
0.3981
0.0318
0.0008
0.0000
0.8233
0.9152
0.3396
0.0226
0.0006
0.0000
0.0860 0.1971 0.3894 0.6568 0.9725 1.2825
1.9948 1.9007 1.7454 1.5549 1.3176 1.0302
78
0.7721 0.5701 0.4153 0.3024 0.2185 0.1592 0.1120 0.0822 0.0607 0.0431
0.0305
0.0000
0.0000
0.0000
0.0000
0.0000
wavelength =
0.0206
0.0000
0.0000
0.0000
0.0000
];
0.0137
0.0000
0.0000
0.0000
0.0000
0.0079
0.0000
0.0000
0.0000
0.0000
0.0040
0.0000
0.0000
0.0000
0.0000
0.0011
0.0000
0.0000
0.0000
0.0000
% CIE standard observer data
[380 385
460 465
540 545
620 625
700 705
780];
390
470
550
630
710
395
475
555
635
715
400
480
560
640
720
405 410 415
485 490 495
565 570 575
645 650 655
725 730 735
420
500
580
660
740
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
(tristimulus values)
425
505
585
665
745
430
510
590
670
750
435
515
595
675
755
440
520
600
680
760
445
525
605
685
765
subplot(2,1,2);
hold off;
plot(wavelengthx_bar,'r--');
axis([350 800 0 2.5]);
hold on;
plot(wavelength,ybar,'g--');
plot(wavelength,z-bar,'b--');
xlabel('Wavelength (nm)');
ylabel('CIE Standard Observer Tristimulus Values');
figure(3);
subplot(1,1,1);
plot(plotxyz-result(:,1),plotxyz-result(:,2) ,'r');
axis([350 800 0 2.5]);
hold on;
plot(plotxyz-result(: ,1),plotxyz-result(:,3),'g');
plot (plotxyz-result(: , 1),plotxyz.result(:,4),'b');
ylabel('Camera XYZ/Light Source XYZ');
plot(XYZ(:,1), XYZ(:,2), 'r--');
plot(XYZ(:,1), XYZ(:,3), 'g--');
plot(XYZ(:,1), XYZ(:,4), 'b--');
xlabel('Wavelength (nm)');
ylabel('tristimulus (XYZ) values');
title('Camera XYZ and Expected XYZ values based on radiance present');
figure(4);
subplot(3,1,1);
plot(luminance(:,1),luminance(:,2));
ylabel('Luminance');
subplot(3,1,2);
plot(XYZ(:,1),XYZ(:,3));
hold on;
plot(540,0.02,'*r');
ylabel('Y');
subplot(3,1,3);
plot(XYZ(:,1),XYZ(:,3)./luminance(:,2));
ylabel('Y/Luminance')
79
0.0000
0.0000
0.0000
0.0000
0.0000
450
530
610
690
770
455
535
615
695
775
for i = 1:81
k(i,1)=luminance(i,1);
k(i,2)=luminance(i,2)/plotxyz-result(i,3);
end;
figure(6);
subplot(3,1,1);
plot(k(:,1),luminance(:,2));
ylabel('luminance');
subplot(3,1,2);
plot(k(:,1),plotxyz-result(:,3));
ylabel('Y');
subplot(3,1,3);
plot(k(:,1),k(:,2));
ylabel('k');
6.3.2
calcam.m
Calcam.m plots camera sensitivity.
function calresult = calcam(thres)
%calcan(thres) returns vector
[wavelength r g b rSD gSD bSD]
% thres is a noise threshold for discarding bad pixels.
%Calculates calibration curve for a given directory of data
/Assumes noise files are 'noisel.tif' and 'noise2.tif'
IAssumes image files are '420.tif' '480.tif' etc.
%Outputs output.txt with columns of wavelength rgb and rgbSD
i = 1;
noiseArray
while(1)
tempName = ['noise' num2str(i) '.tif'];
try imread(tempName);
catch break;
end;
noiseArray = strvcat(noiseArray, tempName);
i = i + 1;
end;
calresult = El;
for i = 1:81
wavelength = i*5 + 375;
imagel = [num2str(wavelength) '.tif'];
try calresult = cat(1,calresult,
[wavelength turnOffBadPixels(imagei,noiseArray,thres)]);
end;
end;
try
dlmwrite('output.txt', calresult,
end;
'delimiter',
80
'\t',
'newline', 'pc');
sizeCal = size(calresult);
subplot(2,1,1);
hold off;
plot(calresult(:,1),calresult(:,2),'r');
axis([calresult(1,1) calresult(sizeCal(1),1) 0 260]);
hold on;
plot(calresult(:,1),calresult(:,3),'g');
plot(calresult(:,1),calresult(:,4),'b');
ylabel('8-bit Intensity');
title(['Camera Calibration for Threshold of ' num2str(thres)]);
subplot (2,1,2);
hold off;
errorbar(calresult(: , 1) ,calresult(: ,2) ,calresult(: ,5), 'r');
axis([calresult(1,1) calresult(sizeCal(1),1) 0 260]);
hold on;
errorbar (calresult (: ,1) , calresult (: ,3), calresult (: , 6), 'g');
errorbar(calresult(:,1),calresult(:,4),calresult(:,7),'b');
xlabel('Wavelength (nm)');
ylabel('8-bit Intensity');
6.3.3
turnOffBadPixels.m
turnOffBadPixels.m takes information from compare.m to determine if pixels are "bad"
(see section 6.3.5 for how this determination is performed). If it is a bad pixel, and it
throws it out.
function output = turnOffBadPixels(tifname,noiseArray,thres)
imagel = opentif(tifname);
badpix = compare(noiseArray,thres);
numbad = size(badpix);
for i = 1 : numbad(1)
imagel(badpix(i,1),badpix(i,2),badpix(i,3)) = 0; % turns off bad pixels
end;
image-uint8 = uint8(imagel);
% image(image-uint8); % images the matrix
outputfile = ['minusNoise' tifname];
imwrite(image-uint8,outputfile,'TIFF');
output = datamean(outputfile);
6.3.4
datamean.m
Datamean.m selects the twelfth through second highest pixel value in an image for each
channel and averages them to determine an overall value for the pixel. The twelfth and
second pixel were selected as the range to reflect the amount of pixels the camera imaged
81
of monochromatic light. The second highest pixel value was to throw out any pixel
that might be falsely too high. This range can be altered in the code to reflect other
experimental situations. It also determines the standard deviation for the average of the
pixels selected.
function doutput = datamean (tifname);
imagel = opentif(tifname);
sizeOfTIF = size(imagel);
%array of matrix dimensions (row, col)
totalSize = sizeffTIF(1) * sizeOfTIF(2);
R = reshape (image(: , :,1) , 1,sizeOf TIF(1)*size0f TIF (2));
G = reshape (imagel(: , ,2),1,sizeOf TIF(1)*size0f TIF(2)) ;
B = reshape (imagel(:,
,3) ,1,sizeOf TIF(1)*size0f TIF(2));
Rorder = sort(R);
Gorder = sort(G);
Border = sort(B);
%reshape into one long row
%sort
%control pixel selection criteria here
IMPORTANT!!!!!H!
% pick out desired data points between j and k
% this lets you set how many pixels you want the program to average
Rordertrunc = Rorder(totalSize-12:totalSize-2);
Gordertrunc = Gorder(totalSize-12totalSize-2);
Bordertrunc = Border(totalSize-12:totalSize-2);
M_R = mean(Rordertrunc);
M_G = mean(Gordertrunc);
M_B = mean(Bordertrunc);
M = [MR MG MB];
stdR = std(Rordertrunc);
stdG = std(Gordertrunc);
stdB = std(Bordertrunc);
STD =
[stdR stdG std_B];
MMaxR = max(Rordertrunc);
MMaxG = max(Gordertrunc);
MMaxB = max(Bordertrunc);
MMax =
[MMaxR MMaxG MMaxB];
doutput =
6.3.5
[M STD MMax];
compare.m
compare.m takes dark images (images taken without light present, named by the user
noisel.tif, noise2.tif, etc.) and compares the pixels to see if there is a signal. If all of the
dark images have signal at the same pixel above a certain threshold (set as "thres" by the
82
user), turnOffBadPixel.m (see next section) assumes it is a "bad" pixel, and it throws it
out.
function badPixels = compare(noiseArray, thres)
%compare(image1,image2,threshold)
%Shows bad pixels for two noise images above a threshold
%Returns bad pixels
size(noiseArray);
sizeArray
noiseImage = [1;
for i = 1:sizeArray(i)
noiseImage(:,:,:,i) = opentif(noiseArray(i,:));
end;
%compares Rdatasinti and Rdatasint2 to see if the bogus pixels are in the
%same position
sizeOfTIF = size(noiseImage);
badPixels = [];
% shows the i,j position of the bad pixel
in (RGB)
% k is what channel it's
for i = 1 : sizeOfTIF(1)
sizeOfTIF(2)
for j = 1
for k= 1 :3
for m = 1:sizeArray(1)
if noiseImage(i,j,k,m) <= thres
break;
end;
if m == sizeArray(1)
badPixels = cat(1,badPixels,[i
j
k]);
end;
end;
end;
end;
end;
numBad = size(badPixels);
numberBad = numBad(1);
percentBadPixels = numBad(i) / (sizeOfTIF(i)
%determines percentage of bad pixels
6.3.6
* size~fTIF(2)
* 3);
opentif.m
opentif.m automates the opening of image files. It also changes the unsigned integers
outputed from imread.m to signed integers needed for data manipulation.
function tifimage = opentif(tif)
%returns row x col x rgb double matrix of tif file
83
A = imread(tif);
tifimage = double(A);
integrate.m
6.3.7
integrate.m performs a numerical integration.
function result = integrate(func)
%input is x, y
%output is integrated result
integral = 0;
for i = 1:2048
if func(i,1) >= 380
if func(i,1) <= 780
integral = integral + func(i,2)
*
(func(i+1,1)-func(i-1,1))
/
2;
end;
end;
end;
result = integral;
6.3.8
photo2radio.m
photo2radio converts photometric units to radiometric units.
function result = photo2radio(photo, spectrum)
%input is luminance and spectrum of light (wavelength,
%output is wavelength and radiance and irradiance
V
=
irradiance or radiance)
[ 0.0000 0.0001 0.0001 0.0002 0.0004 0.0006 0.0012 0.0022 0.0040 0.0073
0.0116 0.0168
0.1390 0.1693
0.8620 0.9149
0.8700 0.8163
0.2650 0.2170
0.0170 0.0119
0.0005 0.0004
0.0000];
0.0230
0.2080
0.9540
0.7570
0.1750
0.0082
0.0002
0.0298
0.2586
0.9803
0.6949
0.1382
0.0057
0.0002
0.0380
0.3230
0.9950
0.6310
0.1070
0.0041
0.0001
0.0480
0.4073
1.0000
0.5668
0.0816
0.0029
0.0001
0.0600
0.5030
0.9950
0.5030
0.0610
0.0021
0.0001
0.0739
0.6082
0.9786
0.4412
0.0446
0.0015
0.0000
0.0910
0.7100
0.9520
0.3810
0.0320
0.0010
0.0000
0.1126
0.7932
0.9154
0.3210
0.0232
0.0007
0.0000
rho = 0.987173;
multiplier = 683;
integral = 0;
for i = 1:2048
if spectrum(i,1) >= 380
if spectrum(i,1) <= 780
integral = integral +
(V(floor(spectrum(i,1)/5)-75) +
(V(floor(spectrum(i,1)/5)-75+1)-V(floor(spectrum(i,1)/5)-75)) *
mod(spectrum(i,1),5) / 5 ) *
spectrum(i,2) * (spectrum(i+1,1)-spectrum(i-1,1)) / 2;
84
end;
end;
end;
integral = integral * multiplier;
correctionfactor = photo /
integral;
result(:,1) = spectrum(:,1);
result(:,2) = correction-factor * spectrum(:,2);
result(:,3) = correction-factor * spectrum(:,2) * pi / rho;
6.3.9
radio2photo.m
radio2photo.m converts radiometric units into photometric units.
function result = radio2photo(radio)
%input is irradiance
%output(1) is luminance
Xoutput(2) is illuminance
V
=
[ 0.0000 0.0001 0.0001 0.0002 0.0004 0.0006 0.0012 0.0022 0.0040 0.0073
0.0116 0.0168
0.1390 0.1693
0.8620 0.9149
0.8700 0.8163
0.2650 0.2170
0.0170 0.0119
0.0005 0.0004
0.0000];
0.0230
0.2080
0.9540
0.7570
0.1750
0.0082
0.0002
0.0298
0.2586
0.9803
0.6949
0.1382
0.0057
0.0002
0.0380
0.3230
0.9950
0.6310
0.1070
0.0041
0.0001
0.0480
0.4073
1.0000
0.5668
0.0816
0.0029
0.0001
0.0600
0.5030
0.9950
0.5030
0.0610
0.0021
0.0001
0.0739
0.6082
0.9786
0.4412
0.0446
0.0015
0.0000
0.0910
0.7100
0.9520
0.3810
0.0320
0.0010
0.0000
0.1126
0.7932
0.9154
0.3210
0.0232
0.0007
0.0000
rho = 0.987173;
multiplier = 683;
radio-integral = 0;
for i = 1:2048
if radio(i,1) >= 380
if radio(i,1) <= 780
radio-integral = radio-integral +
(V(floor(radio(i,1)/5)-75) +
(V(floor(radio(i,1)/5)-75+1)-V(floor(radio(i,1)/5)-75)) *
mod(radio(i,1),5) / 5 ) *
radio(i,2) * (radio(i+1,1)-radio(i-1,1)) / 2;
end;
end;
end;
result(1) = multiplier * rho / pi * radiointegral;
result(2) = multiplier * radiointegral;
85
6.3.10
rad2XYZ2.m
rad2XYZ2.m converts radiance to XYZ values.
function XYZ = rad2XYZ2(spectrum, k)
% CIE
standard observer data (tristimulus values)
x_bar = [ 0.0002
0.3147
0.0805
0.2365
1.0142
0.0007
0.3577
0.0411
0.3042
1.0743
0.0024
0.3837
0.0162
0.3768
0.0409
0.0010
0.0000
[ 0.0000
0.0387
0.2536
0.8752
0.8689
0.2835
0.0159
0.0004
0.0000
[ 0.0007
1.5535
0.7721
0.0305
0.0000
0.0000
0.0000
0.0000
0.0000
0.0286
0.0007
];
0.0001
0.0496
0.2977
0.9238
0.8256
0.2283
0.0111
0.0003
];
0.0029
1.7985
0.5701
0.0206
0.0000
0.0000
0.0000
0.0000
0.0199
0.0005
1.1185
0.6475 0.5351 0.4316
y-bar
z_bar
=
0.0487
0.3023
0.0375
0.7052
1.1343 1.1240 1.0891 1.0305
0.3437 0.2683 0.2043 0.1526
0.0138 0.0096 0.0066 0.0046
0.0004 0.0003 0.0002 0.0001
0.0072
0.3867
0.0051
0.4516
0.0191
0.3707
0.0038
0.5298
0.0434
0.3430
0.0154
0.6161
0.1406
0.2541
0.0714
0.7938
0.9597
0.2045 0.2647
0.1956 0.1323
0.1177 0.1730
0.8787 0.9512
0.8563 0.7549
0.1122 0.0813 0.0579
0.0031 0.0022 0.0015
0.0001 0.0001 0.0000
0.0003 0.0008 0.0020
0.0621 0.0747 0.0895
0.3391 0.3954 0.4608
0.9620 0.9822 0.9918
0.7774 0.7204 0.6583
0.1798 0.1402 0.1076
0.0077 0.0054 0.0037
0.0002 0.0001 0.0001
0.0045 0.0088 0.0145 0.0214
0.1063 0.1282 0.1528 0.1852
0.5314 0.6067 0.6857 0.7618
0.9991 0.9973 0.9824 0.9556
0.5939 0.5280 0.4618 0.3981
0.0812 0.0603 0.0441 0.0318
0.0026 0.0018 0.0012 0.0008
0.0001 0.0000 0.0000 0.0000
0.0105
1.9673
0.4153
0.0137
0.0000
0.0000
0.0000
0.0000
0.1971
1.9007
0.1592
0.0011
0.0000
0.0000
0.0000
0.0000
0.0323
2.0273
0.3024
0.0079
0.0000
0.0000
0.0000
0.0000
0.0860
1.9948
0.2185
0.0040
0.0000
0.0000
0.0000
0.0000
0.3894 0.6568
1.7454 1.5549
0.1120 0.0822
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0000 0.0000
0.0295
0.2199
0.8233
0.9152
0.3396
0.0226
0.0006
0.0000
0.9725 1.2825
1.3176
0.0607
0.0000
0.0000
0.0000
0.0000
1.0302
0.0431
0.0000
0.0000
0.0000
0.0000
0.0000 0.0000
1;
redoutput = 0;
green-output = 0;
blueoutput = 0;
for i = 1:2048
if spectrum(i,1) >= 380
if spectrum(i,1) <= 780
red-output = redoutput +
(x-bar(floor(spectrum(i,1)/5)-75) +
(x-bar(floor(spectrum(i,1)/5)-75+1)-x-bar(floor(spectrum(i,1)/5)-75)
) * mod(spectrum(i,1),5) / 5)spectrum(i,2) * (spectrum(i+1,1)spectrum(i-1,1)) / 2;
green-output = green-output +
(y-bar(floor(spectrum(i,1)/5)-75) +
(y-bar(floor(spectrum (i,1)/5)-75+1)-y-bar(floor(spectrum(i,1) /5)-75)
) * mod(spectrum(i,1),5) / 5 ) * spectrum(i,2) * (spectrum(i+1,1)spectrum(i-1,1)) / 2;
blue-output = blueoutput +
86
(z-bar(floor(spectrum(i,1)/5)-75) +
(z-bar(floor(spectrum(i,1)/5)-75+1)-z-bar(floor(spectrum(i,1)/5)-75)
) * mod(spectrum(i,1),5) / 5 ) * spectrum(i,2) * (spectrum(i+1,1)spectrum(i-1,1)) / 2;
end;
end;
end;
X = k * redoutput; % sums the products for all wavelengths
Y = k * green-output;
Z = k * blue-output;
XYZ =
[X Y ZI;
6.3.11
rgb2XYZ.m
rgb2XYZ.m converts RGB values into XYZ values [Inanici and Galvin, 2004] [Wikipedia, 2006g].
Basically, it performs this matrix operation:
X = k(O.412424R + 0.357579G + 0.180464B)
Y = k(0.212656R + 0.715158G + 0.072186B)
Z = k(0.019332R + 0.119193G + 0.950444B)
function xyz = rgb2xyz(rgb)
%function rgb2xyz([red green blue])
returns
[X
a = 0.055;
gamma = 2.4;
rgb = rgb / 255;
for i = 1:3
if rgb(i) > 0.04045
g(i) =
((rgb(i)+a)/(1+a))^gamma;
else
g(i) = rgb(i)/12.92;
end;
end;
transformMatrix =
[0.412424 0.357579 0.180464;
0.212656 0.715158 0.072186;
0.019332 0.119193 0.950444];
xyz = transformMatrix
xyz = xyz';
* g';
87
Y Z]
6.3.12
XYZ2rgb.m
XYZ2rgb.m converts XYZ values into RGB values [Inanici and Galvin, 20041. This code
was not specifically used in this project, but it can be used in the future to post-process
images from XYZs into RGBs so they can be reimaged by MATLAB.
function output = xyz2rgb(input)
%function rgb2xyz([X Y Z]) returns
[red green blue]
a = 0.055;
gamma = 2.4;
A
[ 3.2406 -1.5372 -0.4986;
=
-0.9689
1.8758
0.0557 -0.2040
%
0.0415;
1.0570];
input = input/80;
RGB = A *
input';
RGB(RGB<0)=0;
RGB(RGB>1)=1;
for i = 1:3
if RGB(i) <= 0.0031308
RGB(i) = 12.92*RGB(i);
else
RGB(i) = (1+a) * RGB(i)^(1/gamma)
end;
-
a;
end;
output = RGB';
output=round(output*255);
output(output<O)=0;
output(output>255)=255;
88
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