OPERATIONAL BRAIN SCANNER CONCEPT

Example: Animals have different frequencies that can be calculated from the following table:

Powerful enough neuron “white noise” generator with a nominal frequency of about 250 Hz might disrupt the brain waves, so none could read them with a distant long wave receiver. The brain pattern filter might fail and long distance reception of brain waves would be prevented. The white noise should be assembled from many neuron like electric signals with slight frequency variation and phase variation. This would interfere with the signals that are normally produced in human brains. The brain patterns would no longer match and tracking would have been impossible, because false data would be gathered and wrong information on brain activity of a particular subject would have been received.

Digital filter development and testing

Digital filters have many advantages over analog filters and no analog filter can match a digital filter fidelity. This is due to a mathematical function that implements a digital filter. An implementation of a digital filter based on a mathematical function can achieve an arbitrary precision limited only by a computer’s processing capabilities. Therefore an analog part of a long wave receiver must consist of an antenna, an analog signal amplifier and a high precision analog to digital converter (ADC) with a resolution of 32 bits or more and high enough sampling rate. Wide enough long wave band must converted to a stream of digital values. This enables correct digital filter operation and enables sufficient precision.

Fidelity of a digital filter depends on a quality of gathered raw digitized data from a long wave receiver and a quality of prerecorded brain patterns that are sampled in a controlled environment. A single digital filter supports each brain pattern. Raw data from the long wave receiver represents a sum of almost infinite number of long wave signals that are received by the receivers a long wave antenna. The main task of the digital filter is to detect only a particular signal if it appears in the environment. A set of digital filters corresponds to a set of digital signals. The goal is to design a narrow digital filter to pass only a desired signal that would correspond to a single neuron impulse of a single living being (subject). A brain pattern is composed of a number of neurons triggering at the same time. No two neurons are located at the same place in brains and no two neurons fire at exactly the same time. A number of time delayed neuron impulses is produced for each thought. There is usually a slight delay in brain activity between each thought. This enables separation of different thoughts. Each thought may be recorded as a unique brain pattern. Because no two living beings have the same brain pattern for the same taught

A successful long distance detection of a weak long wave signal in a natural environment is possible, if the signal is unique, or there is no other such signal present in the environment. A brain pattern of a living being is unique (provided that there are no two living beings that share the same genome present at the same time in the environment – Single egg twins may have similar brain patterns for same thoughts) and quality of a sampling method used is sufficient. A neuron impulse can be decomposed to a number of sine waves present in a certain moment in time, or a sine wave pattern. A moment in time can be defined as a time window. A sliding time window may be used as a basis for a digital filter design.

Digital filter development and testing also benefits from the neuron “white noise” generator, because signal-noise ratio tolerance may be tested in different circumstances. Quality of the long wave receiver could therefore be further improved without a requirement for a frequent areal testing.

A digital filter must be able to filter brain neuron activation patterns of a particular subject (person). A brain neuron activation pattern can only be matched to a particular subject, if it can be associated to a stored brain neuron activation pattern for a particular person. It is therefore important to store brain activation patterns before the long distance reception could be tested.

Digital filter operation principle

Digital filtering through time should be used to detect weak complex long wave signals. Any complex signal is composed of a large number of simple sinusoidal signals. The sinusoidal signals that form a complex signal start with different time delays relative to the first sinusoidal signal. A sliding time windowing technique with a specific time window length must be used to detect a desired complex signal at long distance from the long wave transmitter.

Digital filters may be designed to work similar to a text search engine. However, temporal FFT (Fast Fourier Transform) patterns replace letters. If the first pattern match then the next pattern is checked within the same time window, etc. If all FFT patterns match patterns in a predefined sequence of a desired complex signal, then the complex signal is detected. FFT must be done with extremely high precision, because the existence of particular frequencies through time is more important than actual strength of a signal at a particular frequency.

However, the relative strengths of the signal frequency components should also be observed. All the frequency components in the measured FFT pattern must have the same normalized as the original signal, with an exception. Some noise will be added to each component as the complex signal travels through space. Therefore each signal component strength in a measured FFT pattern may only be greater or equal relative to the original signal component strengths.

Differential filtering is also an important factor. A differential pattern is calculated between two consecutive FFT patterns (FFTPT): FFTPTdif(t)=FFTPT(t)-FFTPT(t-1). This eliminates the FFT background pattern, which relates to constant electromagnetic noise.

Normalization of the FFTPTdif(t) and its frequency components FFTPTdif(t,f) is done in two steps. First normalization of FFT values for each frequency is done through time (txnormalized_FFTPTdif(t)=normalize(FFTPTdif(tx,f)):x=0..time_window_size, ex. 10000, where step = 0,4 ms) and then all the frequency values are normalized between frequencies (normalize(txnormalized_FFTPTdif(t,fx)):x=0..max_frequency_component, ex. 65000) for each time slice. The purpose of such normalization is to assure that a sent signal FFTPTdif may be directly compared to the received signal FFTPTdif’.

Digital filtering and a complex signal detection can be done in the following three steps: Differential filtering is used on the input to transform FFT patterns (FFTPT) into differential FFT pattern (FFTPTdif). Next, normalization of the measured differential pattern is calculated. Differential FFT patterns are then compared to the normalized differential FFT patterns of a desired complex signal. If there is a match, the last step is performed. The absolute values of all the differential patterns must not be lower that differential FFT patterns of the desired signal.

The complex signal match is confirmed, if the above rules hold throughout the observed time window.

Digital filters offline testing

Digital filters should be tested offline (within a computer) before they are used with a communication equipment. There are many statistical and mathematical methods that can simulate all the components of a real life test. The main advantage of offline testing is exclusion of possible flaws in the communication equipment and determination of a signal/noise ratio at which the transmitted signal can still be detected by a receiving device. This also determines a theoretical maximum distance between a transmitter and a receiver when the transmitter power is known in advance and the signal path is not obstructed.

The testing may be performed by drawing an arbitrary signal by hand with a help of a graphics design software. The signal is then digitized the same way as it would be sampled by an ADC. A series of FFTPT is calculated and differential filtering is applied to obtain FFTPTdif(t) sequence. This sequence is then normalized and stored as a reference sequence of the signal. Next, a white noise or other kind of noise generator should implemented in software. The noise is added to the original signal where noise/signal ratio is much higher than one. The noise should be a number of times greater than the signal. This way the signal is superimposed on the noise, which gives the realistic input signal shape. The input signal is then fed to the differential filter and a sequence of FFTPTdif(t)’ is produced. The sequence is normalized and each component of each frequency component FFTPTdif(t,f) is subtracted from each FFTPTdif(t,f)’. The difference must be greater than zero or equal to zero for each frequency component: FFTPTdif(t,f)’- FFTPTdif(t,f) > 0 … for the signal to be identified.

The goal is for the filter to recognize the signal at the approximate time of its appearance with a slight delay. The delay is due to time windowing and it is equal to the signal length. The signal appearance should not be falsely recognized nor missed. If either happens the signal/noise ratio should be increased. This can be done by shortening the virtual distance between transmitter and receiver or strengthening the transmitting power. The signal complexity increase may also help. It is important to note that a complex signal should be composed of at least a thousand sinusoidal signals, which implies that at least 1000 frequencies should appear in its FFTPT though the time window.

Brain Scanner Concept: Digital filter software implementation

Digital filter concept was described in previous chapters. Now, we will take a closer look at practical implementations of algorithms needed for implementation of narrow digital filters. Fast Fourier Transform (FFT) and Inverse FFT (IFFT) algorithms are available in C++ code from different internet webpages. We also need a normalization algorithm to normalize the input sequence (isq) and the reference input sequence (refisq). Also a control software is needed to determine optimal time window size and to calculate differentials between FFT(isq) and FFT(refisq).

FFT and IFFT are based on complex arithmetic. A special care must be taken to correctly implement FFT or IFFT. A good test of FFT and IFFT algorithms is that an input sequence of discrete values is approximately reproduced when IFFT is applied on the FFT transform of the input sequence of discrete values.

There are some important considerations. Input data is digitally sampled to a sequence of discrete real values. A sequence of complex number pairs can be made by simply joining a zero value to each real value. FFT algorithm returns a sequence of complex numbers. Power at a particular frequency is determined by the square root of the sum of the squares of real and imaginary components. However, the whole sequence of complex number pairs is needed to approximate the original input sequence.

Algorithm:

Obtain reference signal FFT:

Digitize a complex reference signal at source to obtain an input sequence. Digitization quality is important. Signal/noise ratio must be very high to gain a pure complex signal sample. Virtually sampled data based on a mathematically defined complex input signal, may also be used instead of a real sampled data. However, a long wave transmitter must be able to reproduce such a signal in ideal conditions.

Normalize the input sequence, so the maximum value is 1.

The time window size (TWS) is equal to the reference signal length.

Perform a differential FFT (dFFT) with time window size TWS on the input sequence to obtain dFFTref(isq).

Store dFFTref(isq) as reference sequence for the desired signal.

Sample and filter long wave signal from the antenna to look for the desired signal:

Digitize an infinite sum of signals from antenna amplifier. No analog filters are used, or low pass only analog filter is used (if this technique is used on brainwaves).

Normalize the input sequence, so the maximum value is 1.

Perform a differential FFT (dFFT) with time window size TWS on the input sequence to obtain dFFT(isq).

Compare dFFT(isq) to dFFTref(isq) as follows: Compare two pairs of complex values from dFFT(isq) and dFFTref(isq). Comparison should be done only for frequencies that are above a certain limit.

The following programming example depicts the above procedure. FFTStorage contain a sound sample that we want to find in FFTLIB.FFTdata sample. The answer is either “Signals match at position” or “Signals DO NOT match”:

long n,m,k,l,xfft_len,match_cnt;

if ((FFTStorage!= null)&&(FFTLIB.SNDdata!= null))

{

xfft_len = FFTStorage.GetLongLength(1);

FFTLIB.FFTdata = null;

FFTLIB.FFTdata = new double[xfft_len];

for (n = 0; n < (FFTLIB.cxdata.LongLength- xfft_len + 1); n+=2) // ++ conversion from 0 -> 2N-1 ==> 1 -> 2N

{

l = 0;

match_cnt = 100; // starting tolerance in number of correct samples vs. number of penalties

for (k = 0; k < xfft_len - 1; k+=2)

{

for (m = 0; m < (xfft_len - 1); m++) FFTLIB.FFTdata[m + 1] = FFTLIB.cxdata[n + m + k];

FFTLIB.FFTransform(FFTLIB.FFTdata, (uint)((xfft_len - 1) / 2), 1); // FFT transform of input sample

FFTLIB.NormalizeCx(FFTLIB.FFTdata, (int)xfft_len - 1); // Normalize FFT transformed data

for (m = 1; m < xfft_len; m++)

if (Math.Abs(FFTStorage[l, m - 1]) > 0.2) // Limiter: Compare only strongest frequences of the reference signal

{

if ((FFTLIB.FFTdata[m] > 0) && (FFTStorage[l, m - 1] > 0)) // BOTH POSITIVE

{

if (FFTLIB.FFTdata[m] < (FFTStorage[l, m - 1] - 0.1)) // Compare imput sample FFT to the reference sample FFT

{

match_cnt -= 2; // penalty ... penalty = 5x revard --> 20% tolerance

if (match_cnt <= 0) goto no_match;

}

else

{

match_cnt++; // revard

}

}

else

{

if ((FFTLIB.FFTdata[m] < 0) && (FFTStorage[l, m - 1] < 0)) // BOTH NEGATIVE

{

if (FFTLIB.FFTdata[m] > (FFTStorage[l, m - 1] + 0.1)) // Compare imput sample FFT to the reference sample FFT

{

match_cnt -= 2; // penalty ... penalty = 5x revard --> 20% tolerance

if (match_cnt <= 0) goto no_match;

}

else

{

match_cnt++; // revard

}

}

else // different signs

{

match_cnt -= 5; // penalty ... penalty = 5x revard --> 20% tolerance

if (match_cnt <= 0) goto no_match;

}

}

}

l++;

}

Lb_Report.Items.Add("FFT patterns match at position: "+Convert.ToString(n)); // Report match

return; // Return to the main program

no_match:;

}

Lb_Report.Items.Add("FFT patterns DO NOT match."); // Report match

}

Operational brain scanner device control and camouflage

It is a great danger for an operational long distance brain scanner device to be abused by a group of people to gain wealth and subdue other people to work for them in a slave like relationships. It might be a good practice for such a group to cloak the brain scanner device in some kind of a realistic computer information department inside of a larger company or institution.

An operational brain scanner device returns mostly text messages (transcriptions of thoughts) that can easily be represented even on old text terminals that would be seemingly a part of an archaic computer system that would still be in operation in the company or institution. Some old trustworthy employees would be working with such a computer system.

It is also possible to distribute messages through a secured web portal to personal mobile phones of other employees working on certain projects within the company or institution. These would raise no suspicion or attention of other people in the observation area within a city or other place where people usually gather or spend their free time. A special mobile application could be developed to enable such message distribution for a particular subject under observation.

Strict rules on using of an operational brain scanner must be applied in a state that poses such a technology to prevent any kind of abuse. A brain scanner device is a piece of equipment that must be controlled by at least two operational managers that have equal rights of decision. If no consensus is reached between then the device should not be used on a particular subject despite previous general decisions on higher levels of decision making. A subject under observation should not be aware of the brain scanner in operation. Data should always be gathered silently to prevent psychological damage to the subject. The only people that could be aware of their brain scanner observation would be brain scanner application developers. They would have their brain patterns solei under their control and safely stored. They would be the only owners of their brain patterns. Nobody one else would be able to track their thoughts but themselves.

Misuse of an operational brain scanner may also cause subjects (living beings) to alter their behaviors to abnormal and/or irrational. For example: If an audio back loop is established between a subject and observer(s), the subject would be invitingly exposed to a telepathic like communication. They would become aware that someone was reading their thoughts before they speak a word (or in case of animals) they make a sound. This would almost certainly cause abnormal and irrational behaviors, because a telepathic communication capability would act as if a subject would have two audio (natural and telepathic) output channels. This could in turn cause the subject to use both channels at the same time. This is not recommended, because it could trigger parallel consciences which could in turn trigger schizophrenia like behavior. There are also other possible misbehaviors that could be caused by a lack of sleep and even awareness of a thought control that tries to respond on any thought rather than on spoken words or sounds. This would prevent imagination and would force straight thinking together with observers. However, other people or animals of the same kind without such a telepathic connection would perceive such behaviors as abnormal, irrational and even dangerous. One simple example is a joke that is head by a subject from an operator or a team of operators. This would cause a subject to start laughing without an obvious cause to the other people and animals without telepathic connections. Such behavior would be perceived as abnormal, irrational and even crazy. It is also very hard for a subject to prevent effects of the telepathic connection.

Now, let’s begin the testing: One of the best practices is that a software developer uses a brain scanner exclusively on himself or herself. He must start testing a scanner device software in an empty room where no other people are present and also any kind of brain waves from outside is blocked. He or she focuses on a single thought that should be detected by the brain scanner. This would produce a specific pattern of low frequency electromagnetic fields that produce long electromagnetic waves that can be picked up by a long wave receiver. The long wave receiver must be a high quality device with extremely low noise. This is the only way to distinguish weak brain signals from the white noise that is produced by electromagnetic background and to create brain signal patterns. However, once the brain wave patterns are recorded or trained to artificial networks the brain waves of a test person can be picked up by the high fidelity long wave receiver from a very long distance, because long wave signals can easily travel long distances. Powerful long wave transmitters may even produce long waves that travel around the World. However, weak long wave signal may nevertheless travel a few km. A designer is therefore able to tune a computer software to scan his brain patters even when he is at a remote location, if he or she has a fast enough digital connection to the high fidelity long wave receiver.

Operational Brain Scanner Management and Reliability Testing

Operational brain scanners must reach high level of reliability and stability. Brain patterns are unique and can be associated with a single human (or other living being).

Database management

A large database may be used to store brain patterns of many people (and other living beings). It is a good practice to assign technical and content administrators to manage such a database. Technical administrators only care about the hardware and software, but they unable to read the contents. Content administrators are only responsible for management of particular collections of brain patterns. They cannot manage hardware or software. It is a good practice that not even content administrators can activate other user’s brain patterns without the user’s explicit consent. The content administrators also maintain brain wave transcriptions for a particular user under their jurisdiction. No transcription should be revealed without user’s explicit consent.

All content of the database brain pattern content must encoded with a reliable cryptographic algorithm. Digital certificates in conjunction with pin codes must be used to assure that particular brain patterns are only available to the rightful (and authorized) users. When an employee that works as a developer changes his occupation his or hers brain patterns must be destroyed by an authorized content administrator to prevent a malicious use of the brain patterns.

A regular and periodic consistency checks of the brain pattern database are required to assure that there can be no information leakage and no implicit change of authority between different content administrators. All changes in database must be authorized by authorized brain scanner team manager.

Context database management

Context database holds information needed to arrange and correctly associate a living being brain pattern activation sequences to a correct context. If a subject is a human then the most common context would be s stream of natural language sentences, but there would also be streams of conceptual objects created by visual and audio perception and memories (textual, visual, audio and conceptual). Each of the streams should be interpreted differently. A human mind nevertheless functions similarly for all humans. The decoding of equivalent brain patterns meaning in a particular brain context remains the same for all people. The context database must therefore be available to all employees that work in teams of developers and developer team managers. The context database must also reflect the knowledge in a knowledge database of the company or organization.

Knowledge database management

As teams of developers are working on practical application for brain scanner machines they should be able to share their technical knowledge. It is a good practice to prevent handwritten communication and paper printouts. A common knowledge database with secured access for all developer teams can prevent data leakage to the public.

There should be a number of topics that are managed by a database administrator. Developers can share knowledge by first selecting the most suitable topic. Next they may also create their own subtopics, or write a use case to a certain topic. The database must enable text search and other faster metadata searches. Each topic must contain a keywords and short description to provide enough metadata for fast database searches. Text search should be user only if other searches return no useful results.

The goal is to share knowledge between team members and between project teams. The highest management may also use this database to estimate a company or organization readiness for brain scanner field operations.

Operations software

Main operations software areas consists of: hardware control software that maintains operation and status of all hardware equipment, diagnostic and statistics software that maintain data on successful telepathic connections and statistical data about the tracked subjects, analytics software that collect new types of brain patterns with yet unknown meaning, a dictionary that stores audio, conceptual or visual descriptions of particular brain pattern types, interfaces to connect brain scanner device output to different presentation devices and even secure web portals, etc.

Operations software is managed by a full information team: hardware and system software administrators, application software engineers, software analysts, application programmers, application testers and operational analysts, developers and testers. The last three roles work on particular use cases that correspond to one or more subjects. Operational tester role is verification of general use case procedures. A use case is tied to a project that is implemented by a dedicated team of experts. The tendency is to implement full project life cycle by a single team, so the use case is maintained and sustained by members of the team as a custom software application.

There are also software programmer teams (usually one team) that maintain the framework software. This operation is not critical from the metadata point of view, but it is crucial to sustain all the use case projects. High reliability of this software must be assured through rigorous developmental, test and foremost preproduction testing procedures. A team of application testers is needed to test the framework software in preproduction environments. Operational testers test custom applications in production environment. An operational developer is usually also an operational tester.

Reliability testing

A brain scanner must be tested before it can be used in a production environment. Brain wave patterns detection filters must be tested to activate only on a subject under an observation. If more subject are involved in the observation then two or more computer processes must be employed one a subject. Brain patterns activation sequences must be analyzed and optionally stored separately for each subject. If the brain scanner is unable to differ between the brain waves from different subjects, the brain scanner cannot be used in a production use case.

Brain pattern activations gathering must be reliable. No brain pattern activation should be dropped in an observed sequence. This implies that no captured thought would be dropped during scanning of a subject and the context of the thinking process of the subject would have been correctly recorded. Rigorous verification test should done prior to production use of the brain scanner.

Long distance testing must also be performed do determine a safe marginal distance to which the brain scanner may be used on a subject. Longer distances may produce faulty sequences of activations of brain patterns for the observed subject. The thoughts would have been incorrectly recorded and any collected thought sequences would be useless.

Short Range Brain Scanner Commercial Applications

A new information age is at our doorstep. Keyboards, mice and other HIDs (human interface devices) will soon be obsolete. They will be replaced by short range brain scanner devices. The devices will resemble long range operational brain scanners but they will be simpler to use and maintain. The devices range will be limited to a few meters. Brain patterns would have to be recorded in a special public isolation rooms that would be available through commercial services. Later on probably some special tools would be available to enable home and office users to record their brain waves.

Telepathic like computer interfaces

A telepathic like computer interface will replace mouse, keyboard and touch pad. A user will upload his prerecorded brain patterns to a special HID interface. Such interfaces will encompass: a short range long wave radio receiver, hardware digitalization support (A/D converter with high enough resolution and sampling rate + supporting hardware) and a microcomputer to decipher brain patterns activation sequences for a particular user. The interface output would be keyboard, mouse and touchpad compatible, so no changes would be needed to a (PC) computer interfaces. Probably a version of USB standard would be used. All processing will be done inside the HID and it would offer a simple replacement for standard devices that we use now. Laptop and tablet computers would no longer need integrated input devices like touch screens, keyboards and mice. They might also need less I/O interfaces (probably USB ports). The devices would be as big as the displays.