Theoretical and experimental development of this technology started in the late 19th
century by a man named Nikola Tesla. It was further studied by scientist J. Lakhovsky,
who experimented with RF effects on animals and plants. Later, the American researcher
software of the NLS Device.
The first biofield detection devices used manual data entry and manual detection. The
biofield matrix detection actively involved the operator whose brain through the use of
low-frequency vibrations was tuned into and resonant with the biofield magnetic vortex
states of the patient. In other words, the operator's energetic state was coherently
synchronous with the patients and had a sensing-amplifying effect. Original results were
reported by the deviation of the L-shaped frame in the hand of the operator on a special
Fleyndera scale (this scale is tied into the analysis software of the current Non-Linear
System).
However, this method of biofield matrix detection was too subjective. This eventually led to
the creation of the first electronic L-shaped frames called trigger sensors. Trigger sensors
automatically sense the magnetic vortex states (biofield matrix information) of the patient.
Developers fed these signals into a computer database until over 100,000 patients with
over 1000 health processes had been analyzed and stored in the database. Distant
biofield matrix detection experiments were carried out by VN Kravkov in the 20th century .
Under the guidance of Prof. B. Togatova, biofield matrix detection experiments were also
carried out on various semiconductor structures.
The NLS device method of analysis was developed at the Russian Institute of Practical
Psychophysics. Every organ and every cell has associated with it a biofield magnetic
vortex state which is stored in the computer memory and can be displayed on the
computer screen as a graph. These graphs represent the conditions of information
exchange between the organ biofield and the environment. Every pathological process
with distinctive age and sex has its own distinctive biofield graph stored in the computer
memory. After reading the biofield frequency characteristics of a patient, the system
compares the degree of their spectral similarity with biofields of healthy and pathologically
affected subjects, or the biofields of infection agents, to obtain the closest biofield process
or tendency. The culmination of all of this science led to the NLS. The NLS device is
capable of detecting the biofield frequencies without human intervention, as well as,
balancing the biofield imbalances.