Download 12. lecture

12. lecture
Laser remote sensing and control
The previous lectures of the course may have strengthened the feeling that lasers are applied in
biophysics to study the phenomena of the microworld only. No doubt about the importance of lasers
in the biophysics of molecules and atoms, but several applications can be listed in the domains of
larger sizes, as well. The use of lasers in remote sensing of biophysical parameters exhibits a
demonstrative example. Here again, the selectable color, high power and parallelism of the beam are
the major required properties of the radiation that can be fulfilled by lasers in the practice.
Lidar for remote sensing. Laser remote sensing (LRS) is the general term to describe the procedure
to gain physical information on systems from a large distance with the aid of lasers. The technique is
also referred to as LIDAR: light detection and ranging. The principle of ranging is simple: light
from a laser strikes the system of interest and the returning light is detected by a telescope and
analyzed. The technology is developing from the 1970's but especially the rapid advances in laser
technology and computers over the past ten years opened up a wide variety of applications. Most
commonly LRS is used in atmospheric and environmental studies applied from an airplane, minivan
or satellite, measuring concentrations of pollutants, mapping cloud formation and monitoring
agricultural crops for stress and phytoplanktons in the seas.
In addition to ranging, lidar systems can provide information about a) the target (spatial
information i.e. mapping and imaging) and b) the transmission path (e.g. atmospheric lidar can
measure concentration of elements in the atmosphere) expressed by the lidar equation:
R




I r ( R,  )  I 0  


(
R
,

)

exp

2


(
r
,

)
d
r

2


4 R
0


A
(12.1)
where I0 is the emitted power of the laser pulse, Ir is the received signal power from transmitted laser
pulse after scattering/reflecting from the target, η is the optical transmission efficiency of all optical
components, A is the effective collection area of the optical receiver, R is the slant range to the target
from sensor (the third factor on the right-hand-side assumes parallel beam which suffers reflection
on the target in all directions) , β is the reflectance or backscattering coefficient (can include either
Rayleigh-, Mie- or Raman scattering and fluorescence) and σ is the extinction (including scattering)
coefficient of the medium (Fig. 12.1). The distance-dependence of the transmission efficiency of the
atmosphere between sensor and target can be approximated by exp (-2·σR), where σ ~ 0.15/km for
10 km visibility. The transmission efficiency, however, shows strong wavelength-dependence both
in the air and in different layers of the ocean (Fig. 12.2).
The ranging techniques include a) pulsed time-of-flight and b) continuous wave (CW)
methods. By direct detection time of flight (Δt), the laser emits a short pulse which travels to the
target and is reflected back to the receiver. The range is determined by R = c·Δt/2, where c is the
speed of propagation of the light. The Δt time interval can be measured with a precision of 67 ps
(corresponding to 1 cm range precision). In CW systems, either the amplitude is modulated and the
phase shift between the received and the transmitted beams is measured or the frequency is
modulated (chirped) and the received signal is mixed with the transmitted signal.
The spatial characteristics of the returned pulse can give information about the target. The
emitted laser radiation is a diverging Gaussian beam whose spot size (footprint) at given range is
typically offered by the radius of the contour where the intensity has fallen to 1/e2 of the intensity of
the peak (Fig. 12.3). As the target characteristics influence the pulse shape, the return pulse shape is
a result of interaction of several reflected (Gaussian) beams. Sloping or rough terrain produce wider
return pulses. Multiple targets separated by small distances (e.g. the leaves of a tree) generate
complex waveforms.
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Biophysical parameters of vegetation. Measuring the biophysical characteristics of vegetation
structure in mass forests and quantifying the canopy cover are major challenges in remote sensing
and have high impact on ecology. Scanning lidar remote sensing systems directly measure the
distribution of vegetation material along the vertical axis and can be used to provide threedimensional, or volumetric, characterizations of vegetation structure. Scanning lidars are used to
characterize the physical structure of canopies including the one-dimensional canopy height, the
total volume and spatial organization of vegetation material and the empty space within the forest
canopy.
Figure 12.3 shows the time-resolved backscatter intensity response of a lidar pulse (shown at
the top of the profile) to either a fraction of a tree (small footprint) or several trees (large footprint)
illuminated by the laser pulse whose energy distribution is illustrated. It is easy to see that there is a
correlation between the material in the path of the pulse and features of the waveform. The fact that
the energy intensity varies with position complicates the interpretation of the waveform. Anyhow,
several layers and therefore groups of return pulses can be distinguished (Fig. 12.4). Discrete-return
lidar data are one of the most intuitive forms of lidar data available. They contain sets, or clouds of
‘points’ with each point having a 3D coordinate describing its spatial relationship with the sensor (or
some other coordinate reference system). Figure 12.5 shows examples of discrete-return lidar pointclouds. Each point will generally, though not always, relate to the surface of some physical objects
(here a leaf or a stem) that obstructs the path of the laser pulse. From the distribution of these points
and their density, we can draw assumptions about the distribution of the structures (e.g. trees) in the
medium being surveyed.
Two out of tremendous number of examples of the lidar’s application of canopy
characterization will be selected here. Figure 12.6 shows the results of the canopy height in the
lowlands of Amazonia. The instrument was flown at an average altitude of 1600 m. The system used
a 1064 nm wavelength laser and had a pulse repetition frequency (PRF) of 70 kHz, the scanner was
operated in multi-pulse mode and the scan half-angle was 27 degrees. 291 million returns were
recorded for the study area (with only 3.1% of returns coming from the ground), with an average
density of 2.1 returns/m2. The nominal footprint size was 24 cm. Figure 12.7 illustrates a single line
of waveform data from a waveform instrument, the Scanning Lidar Imager of Canopies by Echo
Recovery (SLICER). The colors in the image represent the density of material (i.e. scattering
elements like leaves and stems) in the sensed medium (forest), which is closely related to the raw
intensity of the return that is recorded (density of the canopy).
Lidar can be used to estimate the yield of the corps (Fig. 12.7). The spectral- and intensity
dependence of the back scattering of the lidar pulse from the plants in the field depends on the
physiological state of the vegetation and the area of plants of different developments can be
visualized by a lidar scan. Based on these pictures, the farmer can decide what methods
(arrangement of resources, fertilization, watering, etc) to apply to maximize the yield of the
vegetation.
Biophysical parameters of urban trees. Airborne lidar has been used extensively to estimate
biophysical parameters of trees in forested ecosystems. The quantification of biophysical parameters
of urban trees is important for urban planning, for improvement of air quality and for assessing
carbon sequestration and ecosystem services. At the same time, urban trees are subjected to different
stresses. They often grow on compacted soil, are subjected to intense pruning, have very little space
in which to grow and are improperly staked. These factors affect the rate of growth as well as the
shape and form of the trees, which makes the ability to predict biophysical parameters such as tree
height, biomass, and crown dimensions more difficult than with forests, where trees are more
homogenous in their growth and form for a particular locality and species. Unlike traditional forests,
where trees experience a change in growth and allocation after certain events like thinning, low
density of trees in urban environments reduces potential competition for light and other resources.
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Fluorescence lidar in vegetation. This is a special case of LRS since instead of investigating scatter
properties one monitors the laser-induced fluorescence (LIF) of photosynthetic systems. The LRS
system in principle consists of a laser as excitation source and a telescope for detection (Fig. 12.9).
The excitation and the detection of the fluorescence are commonly from a very similar angle in order
to discriminate between excitation and other effects and to use the same alignment when the set-up
moves. By means of a set of mirrors the excitation and detection light are guided such as to come
from the same direction. Because detection is done over large distances the measured signals will be
very small therefore great care has to be taken to obtain a good signal-to-noise ratio (S/N). The
intense and monochromatic light is needed to excite over great distances and to discriminate between
the several processes induced by light in plants. Ideally this is provided by a wide range tuneable
laser but one with a few lines like an Argon laser will work too. While aligning the set-up the laser is
set to a minimal output power. The laser beam is guided through a pinhole to positive lenses L1 and
L2 and into the telescope by the mirror M1. The fixed mirror in the telescope projects the beam in a
forward direction. A larger or smaller surface of a leave of a plant can be illuminated by adjusting
the distance between the two lenses L1 and L2. The emitted fluorescence from the leaf is detected by
a large parabolic mirror (M2) which focuses the incoming light onto a fixed mirror which guides the
light through mirror M3 outside the excitation/detection arm onto a monochromator / photomultiplier
(PM) system which can measure the spectrum of the fluorescence. By tuning mirror M1, maximal
signal can be obtained for a certain setting of the PM and monochromator. Because the scattered
excitation light is very intense great care has to be taken not to set the monochromator on the
excitation wavelength, to avoid saturation of the PM. Since the scattered excitation light is always at
higher energy than the fluorescence light (see Stokes shift) it can be separated from the fluorescence
spectrum.
Leaves are green because red and blue light are absorbed to a large extent while green light is
not; (it's mainly scattered). When light is not absorbed it can also not lead to fluorescence; therefore
488 nm excitation leads to a higher fluorescence yield than the 514 nm excitation (Fig. 12.10). Also
the ratio of PS I to PS II fluorescence changes with the wavelength of the excitation. This is because
the amount of chlorophyll a and b is different for both photosystems. PS II has a higher chlorophyll
b/chlorophyll a (Chl b / Chl a) ratio then PS I. Additionally, chlorophyll b absorbs more 488 nm light
than chlorophyll a. Therefore, the PS II has a higher fluorescence yield at this excitation wavelength.
Most likely, the difference in pigment content is responsible for the difference shown here.
Laser control. Lasers can be used to control processes in many fields of life sciences including
neuroscience and some exotic area like subconscious influencing of humans by modulated
microwave radiation of maser.
Control of genes by laser light. The final goal is the optical control of genes, i.e. to turn genes "on"
and "off" with light inside neurons and developing embryos with high spatial and temporal control.
As a first step, a method was elaborated for incorporating a photoactive blocking group in the middle
of a DNA (or RNA) oligonucleotide (Fig. 12.11). In this way, the primer extension by DNA
polymerase can be modulated by using UV laser light and the photoactivation can be monitored by a
built-in fluorescent reporter molecule. Further developments would include the control of protein
translation in the ribosome using similarly caged RNA and blocking groups to mask the messenger
RNA start codon. The light-sensitive labels would mask to prevent translation until photocleavage.
Optogenetics refers to the integration of optics and genetics to achieve gain (excitation) or loss of
function (inhibition) of well-defined events within specific cells of living tissue. A nice example is
the introduction of microbial opsin genes to achieve optical control of defined action potential
patterns in specific targeted neuronal populations within freely moving mammals or other intactsystem preparations (Fig. 12.12). The mammalian brain is an intricate system in which tens of
billions of intertwined neurons—with multitudinous distinct characteristics and wiring patterns—
exchange precisely timed, millisecond-scale electrical signals and a rich diversity of biochemical
messengers. How this system can it be partly controlled by light? In 1979, Francis Crick recognized
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the need of precise control of activity of one cell type while leaving the others unaltered because
electrodes cannot readily distinguish different cell types. Crick later speculated that light might be a
relevant control tool, but without a concept for how this could be done. In an initially unrelated line
of research, bacteriorhodopsin had been identified as a microbial single-component, membranebound and light-activated ion pump together with other members of this microbial opsin family such
as halorhodopsins and channelrhodopsins that transport various ions across the membrane in
response to light. The solution has emerged from the basic genetic research on these microorganisms
that rely on light-responsive opsin proteins. By inserting opsin genes into the cells of the brain,
flashes of light may trigger (or block) specific neurons on command (Fig. 12.13). This technology
permits to conduct extremely precise and targeted experiments in the brains of living, freely moving
animals, which electrodes and other traditional methods do not allow. The optogenetics is already
yielding potentially useful insights into the neuroscience and psychiatric disorders such as
depression or schizophrenia.
The optics of the setup (Fig. 12.14) is discussed here in more detail. To stimulate the channel
proteins and the photoconversion, diode-pumped solid state (DPSS) lasers were used: 473 nm
wavelength and 100 mW maximum power for the channelrhodopsin2 (ChR2), 532 nm wavelength
and 200 mW maximum power for the halorhodopsin (Halo/NpHR) and 405 nm wavelength and 100
mW maximum power for the photoconversion. The beams from the 473 nm and 532 nm lasers were
aligned to a common path by a dichroic beamsplitter. The beam from the 405 nm laser was aligned
to the common path with a retractable mirror. The laser beam was expanded using a telescope
composed of two plano-convex lenses and incident onto a 1,024 × 768 pixel digital micromirror
device (DMD) attached to a mirror mount. Using a series of mirrors, the laser was aligned such that
the reflected beam for the ‘on’ state of the DMD was centered on the optical axis of the illumination
pathway. The plane of the DMD was imaged onto the sample via the epifluorescence illumination
pathway of the microscope using an optical system composed of two achromatic doublet lenses. A
dichroic filter was used to reflect 405 nm, 473 nm or 532 nm laser light onto the sample while
passing wavelengths used for dark-field illumination (λ > 600 nm). An emission filter was used to
prevent stray laser reflections from reaching the camera. The dichroic and emission filters were
mounted in a custom filter cube in the microscope filter turret.
An outstanding demonstration of the power of optogenetics is the manipulation of the actions
of nematode worms without wires or electrodes i.e. control and steer live worms by laser light only
(Fig. 12.15). The neural system of the worm is relatively simple as C. elegans has only 302 neurons
and can be genetically modified (for comparison, a human being has roughly 100 billion neurons).
Each of these genetically modified neurons is associated with a certain behavior, like moving
forward or backward or even reproducing (laying eggs). The light-sensitive opsin proteins make
those neurons sensitive to different colors of light, allowing to stimulate or to inhibit those neurons
with lasers of different wavelengths.
Thought transmission based on microwave auditory effects. Since the 1940’s it is known that certain
RADAR pulses can be perceived as a knocking sound. This phenomenon was named microwave
auditory effect. Several studies argue for the audibility of modulated microwave energy if irradiated
into the head. Also known are methods of affecting emotions with acoustical or electrical stimulation
and the application of acoustical signals for the subconscious influencing of humans. The concept of
thought transmission represents a further development of these findings and inventions.
The modulated electromagnetic beam is directly coupled into the organism of the receiving
person including the head, big cerebral cortex, inner ear, auditory nerves, or other nerves. Depending
on the type of signal of modulation, this coupling can cause an intended change of the thoughts of
the receiving person. In general, the change of thoughts is only statistically effective, e.g., the
probability for certain thoughts is increased or decreased in a predictable way. However, in some
cases the change may be deterministic.
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Carrier frequencies around 100 MHz – 100 GHz appear to be particularly suitable because
the electromagnetic waves of these frequencies travel linearly, can be focused, penetrate air and
walls of buildings, and induce currents in the outer layers of the human body. For a good efficiency
of thought transmission, the carrier frequency may match the resonance frequency of body parts, e.g.,
certain nerves. The modulation frequency should be in the range of 1 Hz – 100 MHz. For example,
voice signals in the audible range of 16 Hz – 20 kHz may contain components in the MHz region
when transformed into pulse sequences (Fig. 12.16). Low frequencies (infra sounds) in the range of
1 Hz – 60 Hz are known to be effective in mood influencing.
The thought transmission is carried out by sending a sequence of microwave pulses of which
the intensities correlate with the sound intensity of certain keywords. The microwave beam induces
currents in the cerebral cortex of the receiving person which change the probability of certain
thoughts. In contrast to stunning shots with electromagnetic guns, the intensity of radiation is much
lower and may be below or just above the level of conscious perception. A large number of weak
correlations between stimuli and thoughts can be used. While 100 independent stimuli may cause a
2% probability of a certain thought only, the combinations of these stimuli may cause 87%
probability of that thought.
Take-home messages. Remote sensing is based on the time and intensity analysis of the returned
signal either back scattered or emitted as induced fluorescence from the target. Terrestrial, airborne
and spaceborne lidar systems can map large area in forests, in fields (agriculture) or under the sea
and predict the physiological state of the vegetation. Lasers can be used to control essential
biological reaction among genes and in the neurons of living animals. Microwave auditory and
remote control of thought transmission by modulated maser beams might play role in brain research,
in analysis of neural networks in the brain and in the treatment of diseases.
Home works
1. Why are pulse lasers instead of continuous wave (CW) lasers commonly used for lidar systems?
2. Laser pulses illuminate a certain distance range along the direction of propagation. What should
be the duration of the laser pulse if the pulse shall illuminate a distance range of 0.5 m in air? Which
range would be illuminated in water with the same pulse duration?
3. What is the intensity loss of a returned lidar beam from a target of 50% reflectance at a distance of
5 km under good visibility conditions (the attenuation coefficient is 0.15/km)?
References
Lefsky M.A., Cohen W.B., Parker G.G, Harding D.J. (2002) Lidar Remote Sensing for Ecosystem
Studies, BioScience 52 (No. 1) 19-30.
Rupesh Shrestha, Randolph H. Wynne (2012) Estimating Biophysical Parameters of Individual
Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar. Remote
Sens. 4, 484-508.
Ofer Yizhar, Lief E. Fenno, Thomas J. Davidson, Murtaza Mogri, and Karl Deisseroth (2011)
Optogenetics in Neural Systems, Neuron 71, 9-34.
Andrew M Leifer, Christopher Fang-Yen, Marc Gershow, Mark J Alkema & Aravinthan D T
Samuel (2011) Optogenetic manipulation of neural activity in freely moving Caenorhabditis
elegans. Nature methods, 1-8
Bengt Nölting: Methods in Modern Biophysics, Chapter 11, Springer-Verlag Berlin 2004.
Links
Photosynthesis on Internet:
http://www.life.uiuc.edu/govindjee/photoweb/
Learning photosynthesis:
http://photoscience.la.asu.edu/photosyn/
LIDAR links:
http://www.lidar.com/links.htm
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Fig. 12.1. Lidar: principle
of operation. The emitted
laser light passes through
the absorption (and
scattering) layer of the
intermittant medium, is
reflected from the target
and returns to the receiver.
Fig. 12.2. Wavelength dependence of the atmospheric transmission and the diffuse water attenuation
coefficient in oceans. The sharp lines are due to narrow IR absorption bands of water vapor and
chemical compounds of the air. The diffuse attenuation coefficient of water depends strongly (note the
logarithmic vertical scale) on the types of the coast and the ocean (the types are not specified here).
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Fig. 12.3. Difference
between retrieved
waveforms for small (left)
and large (right) lidar
footprints.
Fig. 12.4. Principle of airborn LIDAR that records 1st, 2nd and last returns and intensity
for each pulse.
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Fig. 12.5. Lidar point
clouds obtained from
different perspectives. The
numbers in the figures
describe the number of
points in each cloud. Each
point within the point
cloud is essentially derived
from an intensity timeseries.
Fig. 12.6. Canopy height in different types of Amazonian forests in the lowland. Sample areas of 300·300
m extracted from the 2 m spatial resolution for a) terra firme forest, b) floodplain forest, c) swamp forest
and d) regenerative forest. The instrument was flown at an average altitude of 1600 m. The system uses a
1064 nm wavelength laser and has a maximum pulse repetition frequency (PRF) of 200 kHz. For this
acquisition, the PRF was set at 70 kHz, the scanner was operated in multi-pulse mode, the scan half-angle
was 27 degrees and a 50% sidelap was acquired between flight lines. 291 million returns were recorded
for the study area (with only 3.1% of returns coming from the ground), with an average density of 2.1
returns/m2. The nominal footprint size was 24 cm.
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Fig. 12.7. Canopy structure
along a 4 km transect in the
Andrews Experimental Forest
in Oregon USA. Ground
topography and the vertical
distribution of canopy material
is shown in color codes. Each
column is the width of one
laser pulse waveform.
Fig. 12.8. LIDAR used to
estimate the yield rates on
agricultural fields. This
technology will help farmers
improve their yields by directing
their resources toward the highyield sections of their land.
Fig. 12.9. Scheme of a Laser Remote Sensing (LRS) set-up to excite and to measure
chlorophyll fluorescence from a remote leaf of a tree. Notations: MC, monochromator;
PM, photomultiplier; >>, lock-in-amplifier; M, mirror; L, lense.
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Fig. 12.10. Room temperature
fluorescence spectra of a plant
leave obtained by blue (488
nm) and green (514 nm)
excitations. The participation
of the two photosystems (PS I
and PS II) in the fluorescence
spectrum depends on the
excitation wavelength.
Fig. 12.11. Principle of light control of
genes. A photoactive blocking group (red)
is incorporated in the middle of a DNA (or
RNA) oligonucleotide. The UV laser light
will remove the block and the primer
extension by DNA polymerase (KF) can be
continued (transcription). The
photoactivation is monitored using a
fluorescent reporter (green).
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Fig. 12.12. Principle of light control of
fireing of neurons. A light-sensitive ion
channel (right inset, top) is incorporated in
genetically engineered neurons in rodents.
When exposed to blue light delivered by a
laser through a fiber-optic cable, the
channel opens, allowing positively charged
sodium ions to rush into the cell (right
inset, bottom). This in turn triggers the cell
to fire, transmitting a signal to cells
downstream in the neural circuit.
Fig. 12.13. Principle of optogenetics in
neuroscience. Targeted excitation (as with
a blue light–activated channelrhodopsin) or
inhibition (as with a yellow light–activated
halorhodopsin), conferring cellular
specificity and even projection specificity
not feasible with electrodes while
maintaining high temporal (actionpotential scale) precision.
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Fig. 12.14. Two-Laser setup for optogenetic stimulation. Two solid-state lasers are coupled into a single
fiberoptic cable for twocolor modulation. A fast laser shutter is used to control the output of the yellow
(593.5 nm) laser, due to its slow analog modulation. Beam pick-offs allow for online monitoring of laser
output by photodetectors. An optical fiber commutator enables animals to freely move in the behavior
apparatus without fiber twisting or breakage. A fiberoptic cable connects from the commutator to a
fiberoptic implant consisting of a metal ferrule with a permanently attached fiberoptic cable that extends
into the target region.
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Fig. 12.15. High-resolution optogenetic control of freely moving C. elegans. (a) An individual worm
swims or crawls on a motorized stage under red dark-field illumination. A highspeed camera images
the worm. Custom software instructs a DMD (digital micromirror device) to reflect laser light onto
targeted cells. (b) Images are acquired and processed at ~50 FPS (frames-per-seconds). Each 1,024 ×
768 pixel image is thresholded and the worm boundary is found. Head and tail are located as maxima
of boundary curvature (red arrows). Centerline is calculated from the midpoint of line segments
connecting dorsal and ventral boundaries (blue bar) and is resampled to contain 100 equally spaced
points. The worm is partitioned into segments by finding vectors (green arrows) from centerline to
boundary, and selecting one that is most perpendicular to the centerline (orange arrow). Targets
defined in worm coordinates are transformed into image coordinates and sent to the DMD for
illumination (green bar). (c) Schematic of body-wall muscles. Anterior, to left; dorsal, to top. Bending
wave speed of swimming worm expressing Halo/NpHR (halorhodopsin) in its body-wall muscles
subjected to green light (10 mW mm−2) outside or inside the worm boundary (n = 5 worms,
representative trace). (d) Schematic of HSN (motor neurons whose stimulation evokes egg-laying
events). A swimming worm expressing ChR2 (channelrhodopsin-2) in HSN was subjected to blue
light (5 mW mm−2). Histogram, position at which egg-laying occurred when a narrow stripe of light
was slowly scanned along the worm’s centerline (n = 13 worms). Once an egg was laid, the worm was
discarded (from Leifer et al. 2011).
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Fig. 12.16. Scheme of a handheld thought transmission device consisting of a maser, a detector, a
headset with microphone and electronics. The carrier frequency of the maser is modulated by the
voice (keywords) of the observer. By a display and handle, the receiving person can be tracked (from
Nölting 2004).
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