Robert T. Leskovar,
Metod Škarja, Igor Jerman
BION Institute
Stegne 21, SI-1000 Ljubljana, Slovenia
Web: www.bion.si
E-mail: Robert.Leskovar@bion.si
Abstract
In the article an original approach is presented for detection of interactions, which appear as coherent light oscillations, between biofields and an ambient light. Introduction section connects theories from biology and physics and drives to the definition of the biofield. Coverage of related works follows and shows their principles and disadvantages. Our current, ongoing research is presented in the third section, with the stress on general noise estimation and trustworthy biofield signal derivation.
There are many collective phenomena like the laser, where - when a certain threshold is reached - all the atoms oscillate together in phase. The mathematical theory describing such collective phenomena is of sufficient generality that it predicts the emergence of global order under very different circumstances. Obviously, something similar is involved in the living organisms, too.
Many modern biological theories suggest that the coherence of the organism is closely tied up with its energetic status, that is with the way energy is stored and readily mobilized [ 1 ]. In the description of many macroscopic biological functions, like the coordination of the movements of the four limbs in animal locomotion, the concept of coherence is already subsumed, or taken for granted.
According to Fröhlich, influences of metabolic pumping on macromolecules (such as proteins and nucleic acids) and cellular membranes in organisms build up into collective modes (i.e. coherent excitations) of both electromechanical oscillations (phonons) and electromagnetic radiations (photons) that extend over macroscopic distances within the organism and perhaps also outside the organism [ 2 ].
This theory has been extended by a number of theoretical physicists (e.g. Vitiello, Giudice), who show that such coherent excitations can arise under the most general conditions of energy pumping and energy sharing, and that once established, they are stably maintained [ 3 ].
Czech group of scientists, lead by Pokorny, have been measuring radiations from the endogenous electromagnetic (EM) field within organisms, which confirm Fröhlich's predictions, although at somewhat lower frequencies [ 4 ].
Indirectly, the theory was also empirically confirmed through the phenomenon of dielectrophoresis (movements of small electrically polarized particles in fluid in the oscillatory EM field) [ 5 ].
The endogenous EM field may therefore be considered as a fact, it is only not yet clear whether such field is coherent or not, which is theoretically predicted but not yet proved.
There are also many claims that organisms emit and receive EM signals in biocommunication, although these signals are difficult to detect below the visible range. German biophysicist Fritz Popp is one of the pioneers in detecting ultraweak photon emission from living systems. He and many others since, have found that all organisms emit light ('biophotons') at ultraweak intensities, which are strongly correlated with the cell cycle and other functional states [ 6 ]. The emitted light typically covers a wide band (200nm to 900nm) around the optical range and the limitation is set by the photon-detecting devices. There are approximately equal numbers of photons throughout the range. Biological light is, taken as a whole at all wavelengths, very far from thermal equilibrium, because photon energy currents, measured at short wavelengths, are higher than equilibrium ones even for the factor of 1040 [ 7 ].
From the research of biophotons it is proposed by a strong group of scientists lead by Popp that the photons are held in a coherent form in the organism, and when stimulated, they are emitted coherently [ 8 ].
When looking for other evidence for the coherence of the EM field within each organism we have to mention at least two. Vitiello proposes that endogenous coherent bioelectomagnetic field causes molecules to behave as 'sensitive' particles that exhibit a sort of intelligence and the connectedness with the whole organism [ 9 ]. Populations of synchronously developing fruitfly embryos can undergo phase-correlated collective light emission minutes to hours after a single brief light stimulation [ 1 ].
According to quantum field theory there is a coherent EM field not only in living but also in non-living matter (like water). We denote this coherent EM field as a biofield .
Regarding the emission of coherent oscillations, which could serve as a basis for their detection, Fröhlich maintains that because of the usual resonant transfer of energy characterizing coherent oscillations and being almost without loss, the emission should normally be very small, achieving higher values only at defects on inner surfaces.
Biofield therefore does not propagate as a normal EM field (i.e. does not radiate), because it is very compact.
From our experiments it seems as if biofield in certain time frames induces creation of new photons and coherently interacts with the surrounding light. Whereas this interaction is not as strong when non-living matter is involved, it becomes stronger when before-mentioned coherent excitations in living organisms are present.
A well-known approach for visualizing biofields of living organisms was discovered by inventors Semyon and Valentina Kirlian in 1939. Since then, the technique has been researched and refined by independent labs and health practitioners throughout the globe. Kirlian photography involves a high frequency, high voltage, ultra low current and the object being photographed. In traveling through and reacting with the complex systems of living organisms, this influx of electrical energy amplifies and makes visible the organisms' biological and energetic exchange. The subject and the plate or the film interact to produce a corona of multifrequency energy waves, which are captured by the camera.
There were also diagnostic systems developed by Peter Mandel and Dr. Korotkov. Kirlian photography has been extensively studied in reputable labs and has shown consistent correlation with many emotional and physical states.
There are also disadvantages in using Kirlian photography. The main disadvantage lies in the method itself, since it is a contact photography. It is not possible to photograph a whole being or other three dimensional objects or a space. This type of biofield photography is also sensitive to physical conditions of fingers and environment (like skin humidity, thickness and softness; length and thickness of finger nails; temperature; etc.).
Another approach, known as Polycontrast Interference Photography (PIP), was introduced in late 1980s by a British inventor Harry Oldfield. Oldfield's method bases on similar principles as ours, but uses a different approach and he has different hypotheses and conclusions.
The method itself is simple. PIP shows levels of brightness in the picture coded with artificial contrast colours. When a digital camera takes an image, the image includes pixels of various intensities of red (R), green (G) and blue (B) channels, which are combined in a certain real-like colour. Then this method converts RGB colours to grayscale, thus getting indexed palette of 256 shades of gray, with 0 being black and 255 being white. Each pixel has an index, which corresponds to its real brightness value. This palette is then replaced by a prepared palette, which has on same indexes different, artificially designated colours, which are grouped together according to Oldfield's system of correlating levels of brightness with levels of energy balance in organisms.
Although there have been some interesting results shown [ 10 ], this method is quite error prone, since little changes in object's position, ambient light and consequent shading may produce strong changes in resultant colours. Correlating these colours with an "energy balance and well-being" of a person, as his method for interpreting colours suggests, could be in many cases erroneous. It is also not always clear which light bands are results of subtle shades in an ambient and which are results of interaction of the biofield with an ambient light.
Figure 1 shows two images of the same hand with the same ambient lighting conditions. On the second image hand and the whole body were very slightly moved (for a few mm). There was no other change involved. This slight move resulted in change of ambient light bands. There are two sections marked on these images. Upper marked region on the first image could be misinterpreted using Oldfield's system as a health problem area, because of a darker colour area, and the lower marked region could be misinterpreted as an energy field, radiating from fingers. On the second image these regions are different and no conclusions like those from the first image could be made. These changes of person's position were very slight, bigger ones result in even more differences in resultant colours and light bands.
Figure 1
: PIP method - a very slight change of a subject's position may lead to a false
interpretation in otherwise controlled conditions – the "dark area" and
the "energy
radiation" on the first image are none of these on the second
While, according to biophotonics, living beings emit ultraweak photons that are not easily detected, on the other hand we have found out that interactions of a biofield with an ambient light produce more or less coherent light oscillations that can be detected and even intensified.
So light oscillations around the observed system serve us as an indicator of interactions between its biofield and the ambient light.
The first phase of our research was called Differential Contrast Photography (DCP) and was described in [ 11 ]. It was intended for detecting very subtle changes of ambient luminosity and magnifying them. Many experiments with stress were performed on young beans and yeast cultures. The purpose of the stress was to induce changes in the state of the whole bean and consequently in its biofield, which could eventually be reflected and detected via the changes of surrounding light fluctuations. Conclusions from the DCP phase of our research were that according to the results there is a strong indication that ambient light oscillations may be influenced by organism's physiological processes.
Encouraged by the results from the first phase we started the second phase less than a year ago and it is still going on. This phase of research is intended to identify trustworthy biofield signals, dump influences of different types of noise and, later, estimate coherence domains and levels.
Current results from the second phase are:
With the application DVBAnalysis we were able to estimate the level of noise in photos.
First let us describe major sources of noise in digital photography:
RN varies unpredictably , both in time and across the image frame (is not coherent!) . Since RN is uncorrelated, it can be reduced effectively by image averaging , as CCD astronomers do .
While it is possible to do averaging with our DVBProcess application, generally we do not do it because we want to observe also instantaneous oscillations, which sometimes differ from photo to photo. Random noise can be clearly distinguished from these oscillations, as they appear as areas of higher densities of pixels of same or similar colours. Figure 2 demonstrates such areas.
Figure 2 : On this image areas of
higher densities of pixels of same or similar colours can be seen
FPN varies in time but is rooted in physical structure inhomogeneities among the CCD pixels and thus exhibits a fixed pattern across the image frame.
Dark current noise (DCN) is the most commonly visible form of FPN . It results when electrons leak into CCD pixels from the surroundings. At constant temperature and exposure time, each pixel's dark current electron load varies randomly about a mean.
It is possible to reduce DCN by subtracting dark currents from an image, with a dark frame or an average of several dark frames, as CCD astronomers often do.
In our case most of DCN is removed in the process of image subtraction, as the images are shot at almost the same temperature and with the same exposure, which makes the dark current stay (nearly) the same through all the series.
The general noise , caused by different factors, was calculated as follows. First we took a series of photos of the colour pattern. We processed them (subtracted successive photos) to get oscillations. After a sector analysis (sectors suppress noise, since they average pixels contained) it is interesting to see ( Table 1 ) that sector oscillations across colour channels are not correlated much to the original colours.
|
Gray |
R |
G |
B |
Gray |
0,38 |
0,27 |
0,25 |
0,32 |
R |
|
0,61 |
0,06 |
-0,01 |
G |
|
|
0,53 |
0,22 |
B |
|
|
|
0,53 |
Table 1 : Correlations among sector luminosities of the image with the colour pattern and the image with fluctuations from the colour pattern
After we calculated average luminosity levels for gray (which is average of colour channels), R, G and B channels across all 400 sectors (of size 51*38=1.938 pixels each) in the image and across all differences in the series, we calculated also standard deviations in the same way.
Resultant strict noise level was calculated for gray as:
,
where m is the mean of sector luminosities across all the sectors and all the images with fluctuations, and s is standard deviation of sector luminosities across the same domain.
Thus the general noise was identified at 2,69 luminosity levels. Any oscillations above these level ( that are not results of differences in shadings or subtle movements of an observed system, of course ) may be considered with very high certainty as the results of interactions between biofields and an ambient light. According to statistics only about 2-3 out of 1000 sectors (about 0,26%) could be misinterpreted with the above noise level.
Now, with noise level estimated, we are able to show only sectors with luminosity levels that are higher than noise. These show trustworthy biofield signals.
Our experiments also indicate that Tesla generator induces permanent change of coherence of biofields in its environment. When at a certain time we turn the generator on and off, the results of its coherency induction can be seen for a certain amount of time, often for half a minute. During this time resulting oscillations vary.
We denote this influences as changes of coherence because influences cause large, continuous regions and disconnected areas of higher density of oscillations to appear, which cannot be caused by chance or noise as it appears.
To prevent outside inductive influences on the camera we shielded the camera and grounded separately the shield and the camera body. Figure 3 shows four images that present major results from the process of a digital visualisation of a biofield of a hand (1600 sectors, noise level is 2,72 in this case). Fourth image shows sectors, which have luminosity levels, calculated from a processed series of images, above noise. Luminosities of all sectors that are not on the edges of the hand represent trustworthy biofield signals.
Figure 3 : Major results from a process of digital visualisation of biofields; 1st (top left) is the original image, 2nd (top right) the processed image, 3rd (middle) the sector analysis image and 4th (bottom) the image showing sectors with luminosity levels above noise
According to our current empirical results there is a strong indication that the field of organisms is coherent, but further extensive research in this direction should be done.
Since we have minimized physical (displacement, lighting and inductive) disturbances and have calculated the noise level, we are able to identify genuine biofield signals.
Next phases of the current research will involve research and development of a filter system, which would magnify light oscillations, deeper analyses of fluctuations and estimation of coherence domains and levels.
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