Gabriel Velez III
Vice President, Research & Development
Professor Ronald Blue
Lehigh College
Neutronics Technologies Corporation (USA)


    Since before the dawn of the industrial era, mechanical emulation of biological functions have been at the forefront of invention. From the electronic eye to airplanes, man has been more and more trying to imitate nature. As early as the silent films, as seen in “Metropolis”, the idea of making a human-like automaton has been a dream. Throughout the years other science fiction examples have come to light, climaxing in the most famous of artificial life, the HAL 2000tm. In recent years there have been many others, including the amusing DATA of Star Trektm fame. This brings us up to today.

    The goal of a human-like automaton was one which has eluded scientists for decades. The obstacle was to model the function of the human brain. Historically speaking one of the biggest roadblocks about the brain is “How does it work?”
Previously, the only man made object which can be deemed as having any form of artificial intelligence was the computer. With the computer as our model of information processing and storage and its binary digital algorithm problems quickly became apparent.

    In this paper we will explore another possibility and model. The theoretical model adopted by Neutronics Technologies Corporation, here after referred to as NTC, is made from several theories with some mainstream science support. The heart of the model was first used by the robot, Little Ricci. By looking at the principles used in the first generation robot, insight will be provided for understanding the second generation robot.


Current theoretical ideas suggest that the brain uses quantum processing rather than memory processing through the firing of neurons in a representational system of informational bits. This orientation for a bit or on/off  system of computer processing of information has unfortunately lead to a mentality that has restricted the development of an effective procedure for artificial intelligence. Conventional quantum mechanics suggests using the particle nature of electrons and their spin characteristics to represent bits of information.

    This generates two problems if you are trying to duplicate the human brain. One is how would you program these functions into a mechanical model of the brain to control the process to read and write these qubits (quantum bits). Another problem is the Heisenberg Principle which states the act of measuring creates uncertainty in other values that must be maintained. For example:  efforts to measure the position of a particle makes the velocity of the particle uncertain.
Yet, there is another way. We first need to go back to the original meaning of quantum mechanics. Quanta energy is released in bundles and these bundles can be collectively added to and subtracted from to provide a mean sum  combination of quanta energy. This is significantly different than looking at the state of a single particle which is still impossible to do.
However,  it is possible to determine the overall additive level of energy which will vary in quantum level amounts. Electricians and electronic technicians do it every day.

    The electron is the focus of our examination because it is a particle of charge which we use and manipulate in electronics. It is believed that the electron is a particle and a wave. The electron is a particle when it is measured one way. An example of this is the CRT or Cathode Ray Tube. Using a heat induced cathode and magnetic fields for directional control an electron can be propelled toward the positively charged screen of a CRT. When the electron strikes a phosphor molecule it causes the phosphor molecule to glow because the electrons quanta energy is absorbed by it.

    Electrons are wave when measured another way. An example of this is the Young double slit experiment. Electrons display the same wave interference pattern as light does. NTC's model assumes that the electron is a perceived particle made up of waves. What this means is that the electron is a compression of energy in an inverted wave, with several wavefronts superposed at a point. Imagine that we have several sources of waves at the frequency of the electron, where the frequency is determined by wavelength, which would be the diameter of the electron times two, (as opposed to the De Broglie mathematical proof where the wavelength is determined by the velocity and mass of the electron. Our model also assumes a stationary electron). These waves are all on the same dimensional plane in our three dimensional plane of x, y, z, where the plane used is the x and z, the two horizontal directions. Now suppose that these waves are carrying energy. At one point, they all collide at the 270 degree point, or the part of the wave which has a peak of -1 (an arbitrary amplitude) on the y axis (Fig 1). This collision can be mathematically viewed as energy moving at the speed of light (the speed of EM radiation) and colliding, or bundling together. When the energy fronts meet, from each other’s point of view the other front is moving at the speed of light times the speed of light. Matter then seems to be a harmonic relationship to the speed of light. Matter could also be viewed as mean sum history of interacting energy waves. In other words, if we take a bundle of energy and it is standing still, and we speed another bundle toward it at the speed of light squared, the same result should occur. This is similar to what happens in particle accelerators, except they cause energy to be released, or uncompressed.

    From a relativistic point of view, this superposed wave is a standing wave. From our point of view it is a soliton or an oscillon. Both of these phenomena support NTC’s  model of the electron. NTC’s model also assumes that the waves continue outward from this central point indefinitely. Otherwise, how can two electrons share the same wave, as will be discussed a few of paragraphs from now?

Fig 1   The point of superposition has the combined compressed amplitude of all the waves. The third physical dimension is the perceived particle. Removal of one wave will result in reduction of charge as has been experimentally measured both at NTC and at other labs. The charge is -(1e).

    Interestingly, this can answer a great many things about the nature of electrons as particles. It is known that an electron has spin. Imagine if you will that the above picture is three pipe cleaners tied together at the center to form a six point star on the two dimensional plane (X and Z, according to the above). The center of the star bulges out in the third dimension. This is the electron. Now, if one were to grab the “star” at opposite points with the thumb and forefinger, on the X axis only, one should be able to spin the entire star around that single pipe cleaner, or axis, which corresponds to a single dimensional wave.
Also, there are particles of 1, 2 and 3 spin, and a variety of ½ spin derivations, which means that a particle looks the same way it does after that many spins. The electron is a ½ spin  particle, which means that it requires the electron to spin twice before it looks like an electron again. At one spin, if our assessment of the electron’s makeup is correct, the electron should look like a positron (Fig 2). This statement is made under the condition that the point of view is in a phased relationship with everything else in our space-time.

    It has recently been discovered that electrons can exist with partial charge. This corresponds with different waves being removed from the superposition. This occurs in thirds. NTC’s research supports this. It has also been discovered that two electrons can share the same “wave” (Fig. 3). This also has been discovered simultaneously in our lab. We call these QuBytes. As an illustration consider the values of the following symbols :, ^, and ). Consider them together as a whole and one sees a smiling face :^). A Qubyte is like a whole that has meaning beyond its parts.

Fig. 2   No.1 is a "side" view of the above  theoretical superposed "negative" charge. Assuming the superposition theory as being correct, the electron should "look" like the black oval in the center of this wave. This would be a peak that is the sum of all the superposed waves. Another prediction is that since an electron is a spin 1/2 particle, then at 1 spin (No.2), the peak will be in the positive of the y axis. Therefore, it should look like a positron. This may support Richard Feynman’s views on the positron.

    It has also recently been demonstrated that particles can display action at a distance. In other words, if some distance separates a pair of particles, or in the case of the experiment, a split photon, and a measurement is done on one of them, the other will respond in kind. The data at this time would suggest that our assessment about waves and particulate matter might be correct. The two pieces seem to be connected by a common wave. The first experiment was done with particles in 1981 at only a few meters distance. The most recent was done at 10 kilometers. For the centennial celebration of Einstein’s famous Theory of Special Relativity paper in the year 2005, this experiment will be conducted at 60 miles distant, looking for this non-local effect.

Fig 3.  In this case, two electrons share both the same wave and spin. This condition is what
we believe exists throughout the circuit. Within the "neutral chamber", only a series of
parallel waves exists on two planes, created by this condition, and form a matrix of waves.
The intensity, or relative amplitude, of the electron waves are determined by how many
 electrons share a single wave. The symmetrical matrix of the silicon in the chamber
assists in this.

    The first two experiments showed no difference in response. This would seem to imply that the propagation of the connecting wave is at the EM speed c. In order to see a delay, therefore, it would seem that a great distance is needed, such that delay in EM propagation exceeds the threshold of perception. The distance from here to the moon would likely be sufficient.These experimental results coupled with the discovery by Planck and Einstein regarding quantum charge and how light can add to the electron’s energy state has led us to develop a quantum analog computer.


    By now you should have some insight into the theoretical basis for the Neutronics Dynamic System, heretofore referred to as NDS. In the NDS a field of charge is set up. This field then varies with information input. The resultant output is the input plus the quiescent state of the charge field. This result is transferred to a similarly set up memory chamber. As time goes on the input is compared to the memory by comparators and either reinforces the initial charge or alters it as input changes. However, before that occurs a copy of the original memory is moved over into the next memory chamber in line, in the manner of a shift register. How long a specific experience is memorized depends on how many memory chambers there are in the string, and how often the experience occurs. Learning results from the associations or correlated information in opposition.

    One point of interest in this system is this: the neutronics chamber is merely the base region of a standard NPN transistor. It has become increasingly apparent that the efforts to reduce the basic mosfet transistor (a transistor which uses a slice of silicon as a channel, and a metal gate for controlling current through the channel, which is insulated from the silicon by a layer of non conductive crystal, which makes it smaller than a bipolar transistor) in microelectronics cannot be done below a certain size. Currently, the lower limit is estimated to be about 60 nanometers, or 100 atoms across. Any smaller than this and electrons begin to display their quantum peculiarities. However, NDS can give us much denser memory and computation capabilities, because we actually use the electrons around each atom. Therefore, it is currently at least 100 times more dense than the projected date of 2010, when current technology would have reached the 60 nanometer transistor.
Since there are fourteen electrons surrounding each  silicon atom, then the possibility of condensation is that much higher, or about 1400 times more dense. Considering the possibility of harmonics and the electron wave strings spreading out from the electron soliton, the count could even be higher.

    Figure 4 shows the matrix of waves within the crystalline matrix of silicon atoms. This would represent one layer of atoms within the silicon chip. Imagine this in 3 dimensions where the chip is about 200 or so atoms thick for a transistor.

Fig. 4    This represents what we believe is set up within the neutronics chamber. The circles represent silicon atoms, and the “waves” represent electron waves. The size of the wavelength and amplitude are exaggerated for image. The crystalline structure of the silicon slice aids in the pattern which allows the alignment of electron waves. Hence no current flow, even though a standing wave structure with information encoded would exist.

    We used the lesson learned by nature in the construction of our first robot Little RICCI by constructing the sensory inputs and outputs in such a way that they would be independent of each other for either side. RICCI stands for Real Independently Controlled Computational Intelligence. Each side of NDS controls the opposite side for motion. Its motion control is independent, while the thought process is CORE operated.

    CORE stands for Correlational Opposition-Ratio Enhanced.  Correlational Opposition is the process of comparing one memory with another. These memories, made up of the combination of sensory input, in this case light, colors, and static field electron waves, are then stored in all memory, but at a reduced amplitude in the form of wavelets. Wavelets of this kind are made up of harmonics of the fundamental main frequency. As such, wavelet memory can be stored in a small package, or the idea of a “parallel in parallel” process. Reference, or clock, waves allow input waves, or experiential stimuli, to interact forming harmonics and these harmonics superpose to form various memory information wavelets. Each harmonic sitting upon each other is the parallel in parallel function. This is also known as an eigenfunction The eigenvalue is used as the output to the control circuit.

    Human, and subsequantly every living thing with neurological networks, memory is therefore aptly compared to a piece of holographic plate, where even if the plate is broken, each piece has the complete 3-D image. This explains why a person can have some minor brain damage, and yet most of their memory remains.

    If one looks at the block diagram in Figure 5, one sees the process that is undergone in NDS/CORE processing. The above explanation of the electron is exploited within this circuit. A static charge is set up by a power source, in our case a single nine volt battery, for all NDS circuits, which battery also acts as the thalamus and the corpus callosum. Since there is no current being drawn, the battery will last its shelf life. It is likely that the field set up within the neutronics chamber theoretically creates a condition which, when the input sensors are not stimulated, recharges the battery.

    The circuitry is designed to be totally passive, meaning that the electron charge wave is passed through the circuit but extremely small current (on the order of 1X10-12 amps), which is not enough to collapse the wave function, is used for motor control. A consequence of using the wave function as opposed to the particle is that radiation cannot decohere the wave, whereas it could destroy the particle.

    The input sensor is a CDS cell. This allows for color and intensity perception. The level of intensity, or the color, is memorized by the string memory. The clock “samples” the input at the NDS, and the input is compared to string memory, which is enhanced by a factor of three to one. The string memory clock is three times faster. This is Ratio Enhancement, which will be expounded later. If the memory already has the same level of information, then the output of the comparator is sent to the motor control circuitry and the robot either turns, or goes forward or backward accordingly. If memory doesn’t have this information, then it is stored therein, to be later either degraded in strength or reinforced by the same or similar sensory input, or experience. The motion in that instance is therefore controlled by the immediate experience, much the same way a person pulls their hand away from a flame when first experiencing the pain.

    As can be seen from the output of the NDS processor, there seem to be five outputs. The NDS processor is also known as a “prism splitter”, where the input is “split” into several slightly different outputs, which each vary in concert with input stimulus. Each output is then memorized by string memory, and compared with the splitter output. The average of the compared outputs is the resultant drive for the motor circuitry. The more outputs and memory strings to compare and average, the more accurate the overall memory is. Also the more fuzzy it can be at the same time, it allowing the circuit to compare slightly different memories with real world input. Where there are similarities, the system will “decide” which is closest and perform its decision making accordingly, while at the same time “learning” something new. The decision is a collective or global within the integration time of the clock cycle, yet all variation and values remain in the wavelet.

    The ratio of the clock of the memory to the sensory input is the determining factor of intelligence in the NDS system.  The faster string memory can be compared to sensory input, the faster a decision can be made by the NDS robot. Consequently, we believe that this is the way the human brain works, and intelligence is based on the ratio of memory to sensory input. This ratio can be anywhere from 2:1 to 1000:1, the higher ratio being better. Modern computer systems’ ratios are 1:1.

    In fact, memory is slower than a CPU and its bus when making comparison to string memory. If you have a 32 bit microprocessor which claims a clock speed of 200 MHz, the memory is still running at about 33 MHz (for a 60 nanosecond simm, using 1/t as the frequency), giving a ratio of .165:1. The actual memory speed being at least ½ for the row and ½ for the column read cycles (in older systems, a multiplex cycle was included, as were refreshes and wait states between RMC cycles). With newer memory configurations it may be even faster, but not much more compared to the clock. The bus (input) is 33 MHz, or as mentioned before, the ratio to memory is 1:1. Not very intelligent, according to these standards.

    String memory is an instantaneous transfer of wavelet function. There is no row/column needed for the wavelet to be stored, just reinforcement. And again, with higher ratios, more wavelets of the event stimulus can be generated thereby reinforcing the overall memory. Decision-making is quicker.

    We were using only three outputs from NDS because Little RICCI was a prototype or demonstration. As a prototype the first had only seven minutes string wavelet memory, or when compared to standard computer memory close to 3.24 trillion bits or qubits. This is like remembering in detail 1056 one-hour movies for one second. This is sufficient for an obstacle course run through for its size (about 2.5’x2.5’x1.5’).

Fig 5.  This is a block diagram of the NDS processor. The sensory input is passed through the NDS circuit and sampled by the rate of the clock divided by thirty. The memory is fed through the comparator at  its clock rate of the clock divided by ten. This is Ratio Enhancement. Theoretically, the greater the ratio of memory to input, the greater the intelligence. Or more accurately, the quicker one can make a decisionabout the sensory input. When memory is compared to input, the comparison then feeds a signal to the motion control circuit. The circuit can either be directly controlled by sensory input for avoidance of immediate "danger", or from string memory for motion of "preferred" location. The machine will therefore have made a decision based on experience. True intelligence.

 As an artificial intelligence, it performed surprisingly and unpredictably. Ricci is a biomimetic robot because its CORE processor is designed to model how the brain works.  Support for the success in the CORE processor in accomplishing this goal is illustrated by the following observation: outdoors, It avoided brightly lit areas and approached a red wagon, slowly at first, then backing away. It then approached closer, until eventually it touched the red wagon and pushed it along. Indoors, Little Ricci was moving along carpet. When it came to carpet of a light color, it stopped and reversed. It appeared as though it was avoiding a cliff. It did all of this with no instruction or programming. It made decisions based on environment.


 In our second-generation project, we will add to the functionality of Little Ricci 1 the ability to sense sound. It will be able to localize sound and respond in kind with its own “voice” that will likely be squawks and squeals. It will likely learn that it can locate obstacles using echolocation. Like the bat, which also has limited vision, it will use its “ears” and perhaps learn from experience a specific high frequency to determine direction and distance of obstacles in front of it. The likelihood of this is due to the nature of any wave structure generating holograms and the observation of echo location systems in animals because of the natural characteristic of sound and Little Ricci 2’s own frequency range.
 Its construction will be similar to Little Ricci 1, with the exception of using condenser microphones with cones for the ear pinnas, and a VCO (voltage controlled oscillator) and small amplifier and speaker for sound output (FIG. 6). Both light input and sound will control its direction. As should be expected, it will take a few minutes for it to learn its environment, but when learned it will maneuver through it more quickly. It should be able to recognize its own voice.


Fig. 6 - The NDS comparator outputs for the visual sensors are directly connected to the motion control module. The aural sensors through NDS go through a sound processor, which in turn goes through the motion control module and a speaker. LR2 will eventually learn its own voice and use it to sonically locate obstacles, and it will have some visual confirmation of the obstacles. All of the NDS modules will run from one 9 volt battery.


    In light of the impending need for new methods of computation and data storage, the NDS solution seems to be the best alternative that seems to be ahead of its time. It will add new dimensionality to existing microtechnology. In other words, instead of concentrating on how we can make a chip smaller, we can concentrate on how can we make it more efficient. NDS is the system that will work.

    It is similar with the old military “trick” of passing several different signals through one component, be they tubes or transistors. Your AM/FM portable radio does the same thing. In the oscillator circuit, one can either use a separate transistor to do the job, or use just one transistor to do two jobs. It also saves a little in battery power in that one transistor is doing two jobs with the same current.

    It will also save money and energy in space programs. With the space program now boasting being able to accomplish missions with a fraction of the cost of previous missions, this should really be a boon to the industry.
Imagine a computer system with far more memory capability which uses no current, is not affected by radiation like humans, can make intelligent decisions based on education and experience which it has been taught, remembers its experiences on missions, placed in probes that can go further due to higher memory density in smaller lighter packages.

    Imagine: disease and malfunction can be emulated and duplicated by NDS and possible cures can be concieved, without risk of damage to any real humans.
    Imagine intelligent automatons that might actually be as caring and “human” as any person we know. Who can learn to do what we do. Who can do a job where it would be dangerous for humans.

“Imagination is more important than knowledge” - Albert Einstein.


Wave Computation - Lee Kent Hempfling
Little Ricci’s 1st Day - Ron and Wanda Blue, Lee Kent Hempfling
The Neutronics Dynamic System - Lee Kent Hempfling
The Error Of Quantum Computation - Lee Kent Hempfling
Correlational Opposition Processing - Ron Blue and Wanda Blue, the above papers found at
Relativity - Albert Einstein
A Brief History Of Time - Stephen Hawking
Taming The Atom - Hans Christian Von Baeyer
Elementary Particles and the Laws of Physics; The reason for Antiparticles - Richard P. Feynman
Lectures by Richard P. Feynman and Steven Weinberg (c) 1987
The Physics Of Waves -  William C. Elmore and Mark A. Heald
Physics The Easy Way - Robert Lerhman
Harmonics - Mark Waller
Who is Fourier - Transitional College of LEX
The Art of Electronics - Paul Horowitz and Winfield Hill
Semiconductor Fundamentals - Robert F. Pierret
The Bipolar Junction Transistor - Gerold W. Neudeck
Electronics Servicing and Technology - November 1996 “Understanding the bipolar transistor” pg. 44
Electronics Servicing and Technology - January 1998 “The end of the mosfet transistor”
Discover Magazine - January 1998 “Score one more for the spooks” pg. 53
Compton’s Interactive Encyclopedia 1995.

Copyright 1998, Gabriel Velez III