Patent Description:
Communicating with an underwater vehicle can be challenging especially when the underwater vehicle is submerged. Very low frequency (VLF) signals have been used for communications. A very low frequency signal can penetrate at least <NUM> meters into ocean water. This type of signal is typically used to communicate with underwater platforms such as submarines.

This type of signal attenuates as the signal travels through the ocean. As result, the use of very low frequency (VLF) signals has limited bandwidth that can result in low data rates.

For example, it would be desirable to have a method and apparatus that overcome a technical problem with low data rates that occur in communications using very low frequency signals.

<CIT>, in accordance with its abstract, states: Described is a cognitive signal processor for signal denoising and blind source separation. During operation, the cognitive signal processor receives a mixture signal that comprises a plurality of source signals. A denoised reservoir state signal is generated by mapping the mixture signal to a dynamic reservoir to perform signal denoising. At least one separated source signal is identified by adaptively filtering the denoised reservoir state signal. The signals to be denoised have very high frequencies above <NUM>. In the paper "<NPL>,a a very-low-frequency (VLF, <NUM>-<NUM>, extendable to <NUM>) magnetic field-based quantum receiver for the part of the electromagnetic spectrum is proposed.

In the paper of <NPL>, quantum sensing techniques are described for underwater communications.

According to the present disclosure, a communication system and a method for processing low frequency signals as defined in the independent claims are provided. Further embodiments of the claimed invention are defined in the dependent claims. Although the claimed invention is only defined by the claims, the below embodiments, examples, and aspects are present for aiding in understanding the background and advantages of the claimed invention.

An embodiment of the present disclosure provides a communications system comprising a low frequency receiver, a denoiser, and a signal extractor. The low frequency receiver receives low frequency signals in which a communications signal is expected. The denoiser is in communication with the low frequency receiver. The denoiser denoises the low frequency signals received from the low frequency receiver. The denoising results in a generation of denoised signals. The signal extractor is in communication with the denoiser. The signal extractor extracts the communications signal from the denoised signal , wherein the denoiser is a cognitive signal processor comprising a reservoir computer with a reservoir, the reservoir being configured to denoise the low frequency signals.

In the present disclosure, a communications system comprises a quantum magnetometer very low frequency receiver, a cognitive signal processor, a filter system, and a signal extractor. The quantum magnetometer very low frequency receiver detects very low frequency signals. The cognitive signal processor is in communication with the quantum magnetometer very low frequency receiver. The cognitive signal processor denoises the very low frequency signals received from the quantum magnetometer very low frequency receiver. The denoising results in a generation of denoised signals. The filter system is located in a neural network in the cognitive signal processor. The filter system comprises at least one of a bandpass filter or a band reject filter. The signal extractor is in communication with the cognitive signal processor. The signal extractor extracts a communications signal from the denoised signal.

In yet another embodiment of the present disclosure, a method processes low frequency signals. The low frequency signals in which a communications signal is expected are received by a low frequency receiver. The low frequency signals received from the low frequency receiver are denoised by a reservoir of a cognitive signal processor comprising a reservoir computer. The denoising results in a generation of denoised signals. The communications signal is extracted from the denoised signal by a signal extractor.

In still another embodiment not according to the claimed invention, a computer program product for processing very low frequency signals comprises a computer readable storage medium having program code embodied therewith. The program code are executable by a computer system to cause the computer system to perform a method of receiving, by a low frequency receiver, low frequency signals in which a communications signal is expected; denoising, by a denoiser, the low frequency signals received from the low frequency receiver, wherein the denoising results in a generation of denoised signals; and extracting, by a signal extractor, the communications signal from the denoised signal.

The illustrative embodiments recognize and take into account one or more different considerations as described herein. For example, increasing the signal-to-noise ratio (SNR) in a communications system can increase at least one of the bit rate or depth at which very low frequency signals can be received.

As used herein, the phrase "at least one of," when used with a list of items, means different combinations of one or more of the listed items can be used, and only one of each item in the list may be needed. In other words, "at least one of" means any combination of items and number of items may be used from the list, but not all of the items in the list are required. The item can be a particular object, a thing, or a category.

Increasing the signal-to-noise ratio in radio frequency signals can be achieved by reducing noise in signals. In one illustrative example, a denoiser can be used to increase the signal-to-noise ratio in very low frequency signals. A denoiser has a nonlinear time varying very filter that can be configured to remove thermal noise from signals, such as very low frequency (VLF) signals. Very low frequency signals do not have a large bandwidth compared to electronic wideband receiver applications. In other words, the illustrative embodiments recognize and take into account that very low frequency communications have a signal bandwidth that is relatively low as compared to other applications using more conventional communications bandwidths.

Configuring a denoiser to remove noise and increase the signal noise ratio (SNR) for communications using very low frequency signals for communications using very low frequency signals takes less processing resources as compared to other communications implementations that use broader ranges of frequencies. As a result, a hardware implementation can be more easily achieved. For example, an integrated circuit such as a field programmable gate array (FPGA) can be configured with just a few nodes for a neural network using fewer processing resources as compared to wider bandwidth applications. Thus, an illustrative example can be implemented in field programmable gate array (FPGA) currently used for other types of communications using less resources.

Therefore, with recognizing and taking into account these different considerations, illustrative examples can provide a method, system, apparatus, and computer program product for processing radio frequency signals in such as very low frequency (VLF) signals that increases at least one of the bit rate or depth at which very low frequency signals can be received.

With reference now to the figures in particular with reference to <FIG>, an illustration of a communications environment is depicted in accordance with an illustrative embodiment. In communications environment <NUM>, submarine <NUM> is located below surface <NUM> of ocean <NUM>.

As depicted, submarine <NUM> includes a communications system <NUM> that enables receiving and sending communications while located underwater in ocean <NUM>. In the illustrative example, these communications can be achieved using very low frequency (VLF) signals <NUM>. In this example, a very low frequency signal is an electromagnetic signal that has a frequency from <NUM> to <NUM>. These types of signals can typically penetrate water to a few tens of meters, enabling submarine <NUM> to communicate at shallow depths. For example, with a traditional antenna operating at <NUM>, submarine <NUM> can communicate at a depth of about <NUM>. With a gas magnetometer operating at <NUM>, submarine <NUM> can receive very low frequency signals at a depth of about <NUM>.

In illustrative example, greater depths for communications are achieved by submarine <NUM> using communications system <NUM> as compared to currently available communications systems. In this illustrative example, communications system <NUM> is configured to provide increased signal-to-noise ratio (SNR) that enables submarine <NUM> to communicate using at least one of an increased bit rate and detecting transmissions or an increased depth. This increased performance can be enabled in communications system <NUM> through the use of a receiver and a denoiser in communications system <NUM>.

In this example, the denoiser can reduce noise by <NUM> dB to <NUM> dB resulting in an increased signal-to-noise ratio. With this increased signal-to-noise ratio, submarine <NUM> can be submerged deeper, the bit rate can be increased, or submarine <NUM> can be submerged deeper with an increased bit rate. For example, depths greater than <NUM> can be achieved in which communications can be received by submarine <NUM>. As another example, the bit rate can be increased by <NUM> times the bit rate for the same bandwidth that is currently available.

With reference now to <FIG>, an illustration of a block diagram of a communications environment is depicted in accordance with an illustrative embodiment. In this example, communications environment <NUM> is an environment in which platform <NUM> can communicate using communications system <NUM>. In this example, platform <NUM> is an underwater platform <NUM>.

In this illustrative example, underwater platform <NUM> can take a number of different forms. For example, underwater platform can be selected from a group comprising a stationary underwater platform, a mobile underwater platform, a submarine, a submersible vehicle, an autonomous underwater vehicle, an underwater research station, an underwater habitat, an underwater drone, and an underwater remotely operated vehicle. Communications system <NUM> in <FIG> can be implemented using Communications system <NUM> depicted in this figure.

Communications system <NUM> can provide communications via radio frequency signals <NUM> such as low frequency signals <NUM>. Low frequency signals can be selected from at least one of extremely low frequency (ELF) signals <NUM>, ultra low frequency (ULF) signals <NUM> very low frequency signals <NUM>, or other signals having a frequency of about <NUM> or less. An extremely low frequency (ELF) signal has a frequency range from <NUM> to <NUM>. An ultra low frequency signal has a frequency in a range from about <NUM> to <NUM>. A very low frequency (VLF) signal has a frequency in a range from <NUM> to <NUM>, which corresponds to wavelengths from <NUM> to <NUM> respectively.

In this illustrative example, underwater platform <NUM> can receive low frequency signals <NUM> while submerged in water <NUM>. In this illustrative example, water <NUM> can be, for example, an ocean, a sea, a lake, or some other body of water.

In this illustrative example, communications system <NUM> comprises a number of different components. As depicted, communications system <NUM> comprises low frequency receiver <NUM>, denoiser <NUM>, signal extractor <NUM>, and computer system <NUM>.

Computer system <NUM> is located in platform <NUM>. In this illustrative example, low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM> can be located in computer system <NUM>. In other words, these functional blocks can be components implemented as part of computer system <NUM>. These components can be at least one of hardware components or software components in computer system <NUM>.

At least one of low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM> can be implemented in software, hardware, firmware or a combination thereof. When software is used, the operations performed by at least one of low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM> can be implemented in program code configured to run on hardware, such as a processor unit. When firmware is used, the operations performed by at least one of low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM> at least one of low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM> can be implemented in program code and data and stored in persistent memory to run on a processor unit. When hardware is employed, the hardware can include circuits that operate to perform the operations in at least one of low frequency receiver <NUM>, denoiser <NUM>, and signal extractor <NUM>.

In the illustrative examples, the hardware can take a form selected from at least one of a circuit system, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware configured to perform a number of operations. With a programmable logic device, the device can be configured to perform the number of operations. The device can be reconfigured at a later time or can be permanently configured to perform the number of operations. Programmable logic devices include, for example, a programmable logic array, a programmable array logic, a field programmable logic array, a field programmable gate array, and other suitable hardware devices. In some illustrative examples, quantum circuits can be used to implement the processes in these components. Additionally, the processes can be implemented in organic components integrated with inorganic components and can be comprised entirely of organic components excluding a human being. For example, the processes can be implemented as circuits in organic semiconductors.

Computer system <NUM> is a physical hardware system and includes one or more data processing systems. When more than one data processing system is present in computer system <NUM>, those data processing systems are in communication with each other using a communications medium. The communications medium can be a network. The data processing systems can be selected from at least one of a computer, a server computer, a tablet computer, or some other suitable data processing system.

As depicted, low frequency receiver <NUM> can be implemented using any type of receiver system that can receive radio frequency signals <NUM> such as low frequency signals <NUM>. In one illustrative example, low frequency receiver <NUM> is quantum magnetometer very low frequency receiver <NUM>.

A quantum magnetometer is a device that measures the strength and direction of magnetic fields based on the spin of subatomic particles such as nuclei and unpaired valence electrons. The spin of nuclei and unpaired valence electrons is associated with the magnetic strength and orientation of a magnet that produces a magnetic field. Quantum magnetometers includes proton magnetometers, overhauser magnetometers, optically pumped magnetometers, cesium magnetometer, and potassium magnetometer.

In this example, the magnetic fields can be used for communications. The magnetic fields in low frequency signals <NUM> are less prone to attenuation in water <NUM> than other using higher frequencies. Quantum magnetometer very low frequency receiver <NUM> can be configured to receive very low frequency signals in magnetic fields that encompass frequency in range of <NUM> Hz to <NUM>.

In this illustrative example, low frequency signals <NUM> are received in which communications signal <NUM> is expected. In one example, communications signal <NUM> can be a low frequency signal that contains information <NUM>. This information can be, for example, data, program code, voice, images, or other suitable types of information.

In this illustrative example, denoiser <NUM> is in communication with low frequency receiver <NUM>. In other words, denoiser <NUM> can receive low frequency signals <NUM> detected by low frequency receiver <NUM>.

Denoiser <NUM> can be implemented using cognitive signal processor <NUM>. Cognitive signal processor <NUM> is a data processing system that uses neural network in the form of a reservoir. Denoiser <NUM> operates to denoise low frequency signals <NUM> received from low frequency receiver <NUM>.

In this illustrative example, denoiser <NUM> operates to predict low frequency signals <NUM> that will be received. The prediction can be for several samples ahead of low frequency signals <NUM> that have been received for processing. In this illustrative example, the prediction performed by denoiser <NUM> does not predict noise <NUM> in low frequency signals <NUM>. As result, noise does not pass through denoiser <NUM>.

This denoising in generating of denoised signals <NUM> results in denoised signals <NUM> that have a reduction in noise <NUM> as compared to noise <NUM> in low frequency signals <NUM> received from low frequency receiver <NUM>. The reduction in noise <NUM> may not eliminate all noise but provides an improvement in reducing the level of noise <NUM> that make it easier to identify communications signal <NUM>. For example, the reduction of noise <NUM> increases signal-to-noise ratio (SNR) <NUM> in denoised signals <NUM>.

For example, noise <NUM> in denoised signals <NUM> can have a reduction in noise from about <NUM> dB to <NUM> dB when using low frequency receiver <NUM> in the form of quantum magnetometer very low frequency receiver <NUM> and denoiser <NUM> in the form of cognitive signal processor <NUM> to receive low frequency signals <NUM> in the form of very low frequency signals <NUM>. As a result, signal-to-noise ratio <NUM> can in a manner that allows for underwater platform <NUM> to be located deeper under water <NUM>, increasing the bit rate, or a combination thereof when receiving very low frequency signals <NUM>.

As depicted, signal extractor <NUM> is in communication with denoiser <NUM>. Signal extractor <NUM> receives denoised signals <NUM> from denoiser <NUM>. Signal extractor <NUM> extracts communications signal <NUM> encoding information <NUM> from the denoised signals <NUM>. In this manner, communications system <NUM> can receive information <NUM>.

With reference to <FIG>, an illustration of a filter system in a cognitive signal processor is depicted in accordance with an illustrative embodiment. In the illustrative examples, the same reference numeral may be used in more than one figure. This reuse of a reference numeral in different figures represents the same element in the different figures.

The illustrative example, filter system <NUM> can be implemented in denoiser <NUM> to further increase the reduction of noise <NUM> in low frequency signals <NUM>. As depicted, filter system <NUM> comprises a set of filters <NUM>. The set of filters <NUM> can be selected from least one of bandpass filter <NUM>, band reject filter <NUM>, or some other suitable type of filter that can be implemented within denoiser <NUM>.

Bandpass filter <NUM> can be configured to pass very low frequency signals <NUM> in frequency range <NUM>. For example, bandpass filter <NUM> can passes low frequency signals <NUM> in frequency range <NUM> in which communications signal <NUM> is expected be present in low frequency signals <NUM> received from the low frequency receiver <NUM>.

As another example, band reject filter <NUM> rejects frequency range <NUM> in low frequency signals <NUM> in which interference <NUM> is present. In one example, interference <NUM> can be direct current (DC) bias <NUM> present from the magnetic field of the earth. This direct current bias shows up as a zero frequency because the magnetic field of the earth does not change quickly relative to other types of magnetic fields such as those used for communications.

In this illustrative example, band reject filter <NUM> provides for bias rejection to reject and undesired signal separate from reducing noise using bandpass filter <NUM>. In this manner, undesired signals that are known can be rejected.

The rejection of frequency range <NUM> removes direct current bias <NUM> in low frequency signals <NUM>. In this illustrative example, direct current bias <NUM> can be present in low frequency signals <NUM> detected by low frequency receiver <NUM> that is a magnetic field-based receiver such as quantum magnetometer very low frequency receiver <NUM>. With magnetometer type receivers, the magnetic field of the earth has a frequency of zero varies very little and is treated as a constant that manifests as direct current bias <NUM>.

In other examples interference <NUM> can take other forms such as intentional hostile jamming in addition or in place of direct current bias <NUM>.

Filter system <NUM> can include multiple bandpass filters to allow the passage of different frequency ranges in which communications signal <NUM> may be present in very low frequency signals <NUM>. Filter system <NUM> can also include multiple band reject filters to reject frequency ranges <NUM> for at least one undesired noise, artifacts, or other undesired frequencies such as direct current bias <NUM>.

As result, filter system <NUM> can be configured to operate to pass at least one of a set of frequency ranges <NUM> or reject a set of frequency ranges <NUM>. Filter system <NUM> in denoiser <NUM> can provide a complex filter configuration having a mix of bandpasses in some frequencies and band rejects in other frequencies.

In this illustrative example, filter system <NUM> can be implemented using filter designs <NUM> that describe filters <NUM> in filter system <NUM> using poles <NUM> and zeros <NUM>. The placement of poles <NUM> and zeros <NUM> describe the behavior of a filter in filter system <NUM>.

In this illustrative example, the input signals applied to filter can have some desired shape as an output. This output and input relationship can be written as transfer function <NUM>. The output of the transfer function is an amplitude response of the filter at different frequencies.

For example, transfer function <NUM> can be represented as the ratio of two polynomials. For example, transfer function <NUM> can be as follows:<MAT> where N(s) in the numerator and D(s) in the denominator. In this expression, the roots of the polynomial in the denominator D(s) are poles <NUM>. The roots of N(s), located in the numerator, are zeros <NUM>. In other words, the zeros of transfer function <NUM> are values of s that makes N(s)=<NUM>, which in turns makes the transfer function H(s)=<NUM>. The poles of the transfer function H(s) are values of s that makes the D(s)=<NUM>, which in turns makes the transfer function H(s)=infinity.

When illustrating the transfer function of H(w) of frequency w with a plot of frequency versus amplitude response of the filter, the zeroes appear at frequencies where the response is minimal on the plot, and the poles appear at frequencies where the response is peaked in the plot. For example, a bandpass filter can have a pole at the center frequency and two zeroes at the cut off frequencies.

Poles <NUM> in transfer function <NUM> are also affected by the quality factor Q, which indicate how close to perfect a filter or filter component can be for a particular design. As Q increases, the filter is considered to be better with lower the losses. For an inductor or capacitor, Q is the ratio of the reactance to the resistance. In this case, the Q of an inductor equals to wL/R, and the Q of a capacitor equals to <NUM>/wCR, where R in both instance is the resistance, while w is the frequency, C is the capacitance, and L and R are the reactance.

The quality factor Q can be substituted into transfer function <NUM> to calculate poles <NUM> and zeroes <NUM> for given transfer function <NUM>. Thus, in w and Q affect the value of poles <NUM> and location of poles <NUM> on the frequencies versus amplitude response plot.

For example, for the illustration of a frequencies versus amplitude response plot of a second-order lowpass filter, varying the frequency w changes the pole's distance from the origin, decreasing the Q moves the poles towards each other, and increasing the Q moves the poles in a semicircle away from each other and toward the frequency axis.

Poles <NUM> and zeros <NUM> can be used to configure denoiser <NUM> to provide desired bandpass and band reject characteristics in processing low frequency signals <NUM> to obtain denoised signals <NUM>. The use of filter system <NUM> can further increase the ability of denoiser <NUM> to reduce noise in low frequency signals <NUM>.

Turning to <FIG>, an illustration of a cognitive signal processor is depicted in accordance with an illustrative embodiment. In this figure, an example of one implementation for cognitive signal processor <NUM> is shown.

Cognitive signal processor <NUM> comprises a number of different components. As depicted, cognitive signal processor <NUM> comprises reservoir computer <NUM>, delay embedding <NUM>, and weight adaptation <NUM>.

Reservoir computer <NUM> can be implemented using currently known reservoir computing techniques and architectures. For example, reservoir computer using a recurrent neural network with individual nonlinear units and is capable of storing information. The nonlinearity describes the response of each unit to input enabling solving complex problems. In this illustrative example, reservoir computer <NUM> comprises reservoir <NUM> and readout layer <NUM>.

Reservoir <NUM> contains nodes <NUM>. Connections <NUM> between nodes <NUM> have connectivity weights <NUM>. In this illustrative example, connectivity weights <NUM> define the strength of connections <NUM> between nodes <NUM>.

In reservoir <NUM>, information can be stored using nodes <NUM> as reservoir states <NUM> in reservoir state space <NUM> in reservoir computer <NUM> by connecting nodes <NUM> in reservoir <NUM> in recurrent loops with previous input in the next response.

In this example, reservoir <NUM> can receive input <NUM>. Input <NUM> can be low frequency signals <NUM> that may include a desired signal such as communications signal <NUM>. In this example, low frequency signals <NUM> are noisy signals that can also include an expected communication signal. In other words, low frequency signals <NUM> in input <NUM> can include both noisy signals and desired communications signals.

In this example, low frequency signals <NUM> in input <NUM> are mapped to reservoir states <NUM> in reservoir <NUM>.

Mapping low frequency signals <NUM> in input <NUM> to reservoir states <NUM> in reservoir state space <NUM> can provide high dimensional state space representation <NUM> of low frequency signals <NUM> in input <NUM> in reservoir <NUM>.

In this illustrative example, readout layer <NUM> receives output <NUM> from reservoir <NUM>. Readout layer <NUM> includes neural network layer <NUM> that can perform a linear transformation on output <NUM> from reservoir <NUM>. In some illustrative examples, readout layer <NUM> can be comprised of more than one neural network layer.

Readout layer <NUM> can be configured through training to generate output <NUM> in response to input <NUM>. Weights <NUM> in this layer can be set by during training by analyzing the spatial temporal patterns of reservoir <NUM> after excitation by known inputs used as training data. This training can be performed off-line or online during processing of input signals.

In the illustrative example, the training continues during processing of input signals to enable reservoir computer <NUM> operate adaptively in predicting outputs. In other words, reservoir computer <NUM> can operate as an adaptive predictable filter to filter low frequency signals such as very low frequency signals and ultra low frequency signals.

Output <NUM> comprises predicted low frequency signals <NUM>. These predicted low frequency signals are a prediction of low frequency signals <NUM> that will be received. In this illustrative example, this prediction does not predict noise <NUM>. As result, noise <NUM> does not pass through reservoir computer <NUM>. In this example, predicted low frequency signals form denoised signals <NUM>. Denoised signals <NUM> in output <NUM> can be processed to extract communication signal <NUM>.

In this illustrative example, delay embedding <NUM> creates a temporal record of reservoir states <NUM> in reservoir computer <NUM>. Delay embedding <NUM> can be used to perform delay embedding on input <NUM> and send the delay embedded input signals into reservoir computer <NUM>. Delay embedding <NUM> can apply delay embedding be applied to reservoir states <NUM> to provide history of reservoir dynamics.

In this illustrative example, weight adaptation <NUM> adapts output <NUM> from reservoir computer <NUM> through setting weights <NUM> in a gradient descent to produce a prediction of an input signal at a step in time in the future in the form of predicted low frequency signals <NUM>. With the noise in input <NUM> being inherently random and unpredictable, predicted low frequency signals <NUM> generated is free of noise to form denoised signals <NUM>.

Error <NUM> between the predicted low frequency signals <NUM> and low frequency signals <NUM> in input <NUM> is used by weight adaptation <NUM> to further modify weights <NUM> in readout layer <NUM> in reservoir computer <NUM> using an iterative process. In this example, the weights <NUM> can be adjusted for each new input sample. This process is iterative in a manner that will adapt to a changing input signal, not just iteration to a fixed signal.

Further, filter system <NUM> can be implemented in reservoir computer <NUM> provide desired filtering. This desired filtering can include implementing at least one of bandpass filter <NUM> or band reject filter <NUM>.

In this illustrative example, poles <NUM> for filters in filter system <NUM> can be implemented in reservoir computer <NUM> by setting connectivity weights <NUM> between nodes <NUM>. The implementation of a pole in poles <NUM> involves using two nodes in nodes <NUM>.

In this example, zeros <NUM> for a filter can be implemented as weights <NUM> in readout layer <NUM>. These different weights can be set by weight adaptation <NUM>.

In this manner, filter system <NUM> can be implemented within reservoir computer <NUM> to include bandpass filter <NUM> to pass very low frequency signals <NUM> in frequency range <NUM> in which the communications signal <NUM> is expected be present in the very low frequency signals <NUM> received from low frequency receiver <NUM>. Filter system can also be implemented in reservoir computer <NUM> to implement band reject filter <NUM> to reject frequency range <NUM> in very low frequency signals <NUM> in which the direct current (DC) bias <NUM> is present from the magnetic field of the earth.

Thus, in this illustrative example, cognitive signal processor <NUM> operates as an adaptive predictable filter to remove noise and other undesired artifacts from low frequency signals input into cognitive signal processor <NUM>. Cognitive signal processor <NUM> outputs denoised signals <NUM> from which communications signal <NUM> can be extracted.

Further, parameters <NUM> in reservoir computer <NUM> can be set in readout layer <NUM>. Parameters <NUM> can include, for example, learning rate <NUM>, forgetting rate <NUM>, and filter length <NUM>.

The setting of these parameters can depend on various factors. For example, increasing learning rate <NUM> can be increased to increase responsiveness, but accuracy can be reduced from lower noise reduction. As learning rate <NUM> increases, reservoir computer <NUM> will more closely follow input <NUM>. However, the reduction of noise <NUM> in low frequency signals <NUM> decreases. Further, with low frequency signals <NUM> having a relatively low bandwidth as compared to other types of communication signals, learning rate <NUM> can be reduced, which also aids in reducing noise <NUM>.

Forgetting rate <NUM> is used to decay the states of filtering by reservoir computer <NUM>. If forgetting rate <NUM> is not set, the reservoir computer <NUM> adapts to low frequency signals <NUM> but when low frequency signals <NUM> disappear, reservoir computer <NUM> can still generate output <NUM> as a smooth representation of the earlier low frequency signals.

In this illustrative example, forgetting rate <NUM> can be set so that a single dropout change in signal modulation is recognized anytime that is only a fraction of the signal bandwidth. For example, the fraction can be a <NUM>/signal bandwidth. As the forgetting rate <NUM> decreases, a longer latency is present in recognizing when a signal has dropped out or ends.

In this illustrative example, filter length <NUM> defines the number of samples used to generate predicted low frequency signals <NUM>. In one illustrative example, <NUM> samples can be used to obtain a desired level of accuracy in predicted low frequency signals <NUM>. If the speed at which low frequency signals <NUM> change decreases, filter length <NUM> can be increased. When low frequency signals <NUM> increase in the speed at which they change, filter length <NUM> can be decreased.

Further, filter length <NUM> can be selected to also provide for oversampling. Oversampling involves sampling beyond a Nyquist required sampling rate.

In the illustrative examples, communications using low frequency signals <NUM> can have bandwidths of less than several thousand Hertz. With these bandwidths, increasing the filter length is not an issue with respect to implementing these processes to obtain a desired level of accuracy in predicted low frequency signals <NUM>.

The illustration of parameters <NUM> in this example is provided as an illustration of some of parameters <NUM> that can be implemented in readout layer <NUM>. Other parameters are also present with settings that can also be made in processing low frequency signals <NUM> in input <NUM>. These parameters are selected as examples of parameters <NUM> that can be used to increase the accuracy in predicted low frequency signals <NUM> for denoised signals <NUM> in output <NUM>.

In one illustrative example, one or more technical solutions are present that overcome a technical problem with at least one of receiving very low frequency signals at desired deaths in the ocean or at desired rates. As a result, one or more technical solutions can provide a technical effect in enabling a greater reduction of noise that increases the signal-to-noise ratio in a manner that enables at least one of receiving very low frequency signals at a greater depth or increased bit rate.

The illustration of communications environment <NUM> and the different components in communications environment <NUM> in <FIG> is not meant to imply physical or architectural limitations to the manner in which an illustrative embodiment may be implemented. Other components in addition to or in place of the ones illustrated may be used. Some components may be unnecessary. Also, the blocks are presented to illustrate some functional components. One or more of these blocks may be combined, divided, or combined and divided into different blocks when implemented in an illustrative embodiment.

For example, communications system <NUM> can be implemented in platform <NUM> that operates in other locations other than under water <NUM>. For example, platform <NUM> can be some other type of platform other than underwater platform <NUM>. For example, the platform <NUM> can be, an aircraft, a surface ship, a tank, a personnel carrier, a train, a spacecraft, a space station, a satellite, an automobile, a power plant, a bridge, a dam, a house, a manufacturing facility, building and other suitable platforms.

Further, communications system <NUM> can include other components not depicted in this block diagram. For example, communications system <NUM> can include an antenna, power source, or other suitable ones. One illustrative example is described in which communications signal <NUM> is received with very low frequency signals <NUM>. In other illustrative examples, communications signal <NUM> can be received with extremely low frequency (ELF) signals <NUM> or ultra low frequency signals <NUM> when low frequency receiver <NUM> can receive extremely low frequency (ELF) signals <NUM> or ultra low frequency signals <NUM>.

In one illustrative example, reservoir <NUM> has connections <NUM> that are fixed connection. These fixed connections form an array of passive oscillators with user defined resonant frequencies and quality (Q) factors.

With this example, input <NUM> is mapped into the impulse responses of the oscillators in reservoir <NUM>. Readout layer <NUM> combines the delayed output values of each oscillator output with adaptable weights that are determined online via the gradient descent update equation. The quadratic error signal to be minimized during the gradient descent update is the L2 norm of the delayed output and input. This corresponds to a short time signal prediction process. This algorithm implements an online learning process where the number of delays and time delay values between delayed taps are estimated from the delay embedding theory implemented in delay embedding <NUM>.

The delayed reservoir computer output will converge to the input signal in input <NUM>. By this process most of the noise is eliminated from the output signal in output <NUM>. In this illustrative example, delay embedding <NUM> is used to apply delay embedding for the reservoir state functions and used to predict the input signal by adaptively combining the delayed versions of the states. Since the noise is not predictable this process generates a denoised version of the input signal. Reservoir <NUM> with delay embedded states for embedded states <NUM> can be configured to have the same behavior as reservoirs with delay embedded inputs. The time history of these reservoir states can be used to perform short-term predictions of observations such as predicting the noisy input signal.

Turning now to <FIG>, an illustration of a graph of a low frequency signals processed by a cognitive signal processor to generate a denoised signal is depicted in accordance with an illustrative embodiment. As depicted, graph <NUM> plots data for very low frequency signals with X axis <NUM> representing frequency in kHz and y-axis <NUM> representing magnitude in decibels (dB). Data points <NUM> represent a fast Fourier transform of input very low frequency signals input into a cognitive signal processor such as cognitive signal processor <NUM> shown in <FIG> and <FIG>. Data points <NUM> illustrate output very low frequency signals output in response to processing input very low frequency signals.

As depicted in graph <NUM>, section <NUM> illustrates a spike in the low frequency signals that represents a communication signal that now stands out from the noise in other data points and data points <NUM> because of an increased signal to noise ratio. Data points <NUM> do not show the communications signal.

Filtering to remove a DC bias caused by the Earth's magnetic field is not shown in this particular example. As depicted, section <NUM> in data points <NUM> represent a DC bias caused by the Earth's magnetic field. This DC bias is also seen in section <NUM> in data points <NUM>.

With reference to <FIG>, an illustration of the spectrogram of a low frequency signals input into a cognitive signal processor is depicted in accordance with an illustrative embodiment. In graph <NUM>, a spectrogram of input very low frequency signals is depicted. Data points <NUM> for input very low frequency signals are provided in graph <NUM> with X axis <NUM> representing frequency in kHz, Y axis <NUM> representing time in seconds, and z-axis <NUM> representing magnitude in dB. The ratio of power to frequency in dB/Hz in data points <NUM> is represented by scale <NUM>.

In graph <NUM>, a DC bias is interference <NUM> that is visible at <NUM> in data points <NUM>. This DC bias is caused by the Earth's magnetic field having a frequency of zero. In this illustrative example, the frequency is zero but shown at -<NUM> because of a <NUM> shift in the data during processing.

In this illustrative example, the communications signal is signal of interest <NUM> and has a frequency of -<NUM> in the very low frequency signals. The communications signal in this example has a strength of -<NUM> dB but is not seen within the noise in input very low frequency signals represented by data points <NUM>.

In this illustrative example, input very low frequency signals represented by data points <NUM> in graph <NUM> are processed using a cognitive signal processor such as cognitive signal processor <NUM> shown in <FIG> and <FIG>. In <FIG>, an illustration of the spectrogram of output low frequency signals resulting denoising input very low frequency signals using a cognitive signal processor is depicted in accordance with an illustrative embodiment. As depicted, graph <NUM> represents a spectrogram of output very low frequency signals generated in response to very low frequency signals for data points <NUM> in <FIG>. Data points <NUM> for output very low frequency signals are provided in graph <NUM> with X axis <NUM> representing frequency in kHz, Y axis <NUM> representing time in seconds, and z-axis <NUM> representing magnitude in decibels (dB). The ratio of power to frequency in dB/Hz in data points <NUM> is represented by scale <NUM>.

In this illustrative example, the DC bias represented by interference <NUM> in data points <NUM> at.

Turning next to <FIG>, an illustration of a flowchart of a process for processing low frequency signals is depicted in accordance with an illustrative embodiment. The process in <FIG> can be implemented in hardware, software, or both. When implemented in software, the process can take the form of program code that is run by one of more processor units located in one or more hardware devices in one or more computer systems. For example, the process can be implemented in computer system <NUM> in Communications system <NUM> in <FIG>.

The process begins by receiving, by a low frequency receiver, low frequency signals in which a communications signal is expected (operation <NUM>). The process the noises, by a denoiser, low frequency signals received from the low frequency receiver, wherein the denoising results in a generation of denoised signals (operation <NUM>).

The process extracts, by a signal extractor, the communications signal from the denoised signal (operation <NUM>). The process terminates thereafter.

With reference now to <FIG>, an illustration of a flowchart of a process for denoising the noisy low frequency signals is depicted in accordance with an illustrative embodiment. The process illustrated in <FIG> is an example of one implementation for operation <NUM> in <FIG>.

The process passes the low frequency signals in a frequency range in which the communications signal is expected in the low frequency signals received from the low frequency receiver (operation <NUM>). The process terminates thereafter. In other words, only a portion of the low frequency signals received from the low frequency receiver are passed through during the noisy signals. This frequency range can be continuous or discontinuous frequency range. In other words, gaps may be present in frequency range to further fine tune what frequencies are passed for further processing.

In <FIG>, an illustration of a flowchart of a process for denoising the low frequency signals is depicted in accordance with an illustrative embodiment. The process illustrated in <FIG> is an example of one implementation for operation <NUM> in <FIG>.

The process rejects the low frequency signals in a frequency range in which a direct current bias is present such that the direct current bias is removed from the low frequency signals (operation <NUM>). The process terminates thereafter. In operation <NUM>, artifacts or constant noise can be removed from the low frequency signals. These artifacts can be, for example, a direct DC bias caused by the magnetic field of the earth having essentially a zero frequency.

The flowcharts and block diagrams in the different depicted embodiments illustrate the architecture, functionality, and operation of some possible implementations of apparatuses and methods in an illustrative embodiment. In this regard, each block in the flowcharts or block diagrams can represent at least one of a module, a segment, a function, or a portion of an operation or step. For example, one or more of the blocks can be implemented as program code, hardware, or a combination of the program code and hardware. When implemented in hardware, the hardware can, for example, take the form of integrated circuits that are manufactured or configured to perform one or more operations in the flowcharts or block diagrams. When implemented as a combination of program code and hardware, the implementation may take the form of firmware. Each block in the flowcharts or the block diagrams can be implemented using special purpose hardware systems that perform the different operations or combinations of special purpose hardware and program code run by the special purpose hardware.

In some alternative implementations of an illustrative embodiment, the function or functions noted in the blocks may occur out of the order noted in the figures. For example, in some cases, two blocks shown in succession may be performed substantially concurrently, or the blocks may sometimes be performed in the reverse order, depending upon the functionality involved. Also, other blocks may be added in addition to the illustrated blocks in a flowchart or block diagram.

Turning now to <FIG>, an illustration of a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system <NUM> can be used to implement computer system <NUM> in <FIG>. In this illustrative example, data processing system <NUM> includes communications framework <NUM>, which provides communications between processor unit <NUM>, memory <NUM>, persistent storage <NUM>, communications unit <NUM>, input/output (I/O) unit <NUM>, and display <NUM>. In this example, communications framework <NUM> takes the form of a bus system.

Processor unit <NUM> serves to execute instructions for software that can be loaded into memory <NUM>. Processor unit <NUM> includes one or more processors. For example, processor unit <NUM> can be selected from at least one of a multicore processor, a central processing unit (CPU), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a network processor, or some other suitable type of processor. Further, processor unit <NUM> can may be implemented using one or more heterogeneous processor systems in which a main processor is present with secondary processors on a single chip. As another illustrative example, processor unit <NUM> can be a symmetric multi-processor system containing multiple processors of the same type on a single chip.

Memory <NUM> and persistent storage <NUM> are examples of storage devices <NUM>. A storage device is any piece of hardware that is capable of storing information, such as, for example, without limitation, at least one of data, program code in functional form, or other suitable information either on a temporary basis, a permanent basis, or both on a temporary basis and a permanent basis. Storage devices <NUM> may also be referred to as computer-readable storage devices in these illustrative examples. Memory <NUM>, in these examples, can be, for example, a random-access memory or any other suitable volatile or non-volatile storage device. Persistent storage <NUM> can take various forms, depending on the particular implementation.

For example, persistent storage <NUM> may contain one or more components or devices. For example, persistent storage <NUM> can be a hard drive, a solid-state drive (SSD), a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage <NUM> also can be removable. For example, a removable hard drive can be used for persistent storage <NUM>.

Communications unit <NUM>, in these illustrative examples, provides for communications with other data processing systems or devices. In these illustrative examples, communications unit <NUM> is a network interface card.

Input/output unit <NUM> allows for input and output of data with other devices that can be connected to data processing system <NUM>. For example, input/output unit <NUM> can provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Further, input/output unit <NUM> can send output to a printer. Display <NUM> provides a mechanism to display information to a user.

Instructions for at least one of the operating system, applications, or programs can be located in storage devices <NUM>, which are in communication with processor unit <NUM> through communications framework <NUM>. The processes of the different embodiments can be performed by processor unit <NUM> using computer-implemented instructions, which can be located in a memory, such as memory <NUM>.

These instructions are program instructions and are also referred to as program code, computer usable program code, or computer-readable program code that can be read and executed by a processor in processor unit <NUM>. The program code in the different embodiments can be embodied on different physical or computer-readable storage media, such as memory <NUM> or persistent storage <NUM>.

Program code <NUM> is located in a functional form on computer-readable media <NUM> that is selectively removable and can be loaded onto or transferred to data processing system <NUM> for execution by processor unit <NUM>. Program code <NUM> and computer-readable media <NUM> form computer program product <NUM> in these illustrative examples. In the illustrative example, computer-readable media <NUM> is computer-readable storage media <NUM>.

Computer-readable storage media <NUM> is a physical or tangible storage device used to store program code <NUM> rather than a media that propagates or transmits program code <NUM>. Computer readable storage media <NUM>, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Alternatively, program code <NUM> can be transferred to data processing system <NUM> using a computer-readable signal media. The computer-readable signal media are signals and can be, for example, a propagated data signal containing program code <NUM>. For example, the computer-readable signal media can be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals can be transmitted over connections, such as wireless connections, optical fiber cable, coaxial cable, a wire, or any other suitable type of connection.

Further, as used herein, "computer-readable media <NUM>" can be singular or plural. For example, program code <NUM> can be located in computer-readable media <NUM> in the form of a single storage device or system. In another example, program code <NUM> can be located in computer-readable media <NUM> that is distributed in multiple data processing systems. In other words, some instructions in program code <NUM> can be located in one data processing system while other instructions in program code <NUM> can be located in one data processing system. For example, a portion of program code <NUM> can be located in computer-readable media <NUM> in a server computer while another portion of program code <NUM> can be located in computer-readable media <NUM> located in a set of client computers.

The different components illustrated for data processing system <NUM> are not meant to provide architectural limitations to the manner in which different embodiments can be implemented. In some illustrative examples, one or more of the components may be incorporated in or otherwise form a portion of, another component. For example, memory <NUM>, or portions thereof, can be incorporated in processor unit <NUM> in some illustrative examples. The different illustrative embodiments can be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system <NUM>. Other components shown in <FIG> can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program code <NUM>.

Some features of the illustrative examples are described in the following Examples. These Examples are examples of features not intended to limit other illustrative examples.

Example <NUM> A communications system comprising:.

Example <NUM> The communications system according to Example <NUM> further comprising:
a reservoir in the denoiser, wherein the reservoir the low frequency signals.

Example <NUM> The communications system according to one of Examples <NUM> or <NUM> further comprising:
a bandpass filter in the reservoir in the denoiser, wherein the bandpass filter passes the low frequency signals in a frequency range in which the communications signal is expected in the low frequency signals received from the low frequency receiver.

Example <NUM> The communications system according to <NUM> further comprising:
a band reject filter in the reservoir in the denoiser, wherein the band reject filter rejects the low frequency signals in a frequency range in which a direct current bias is present such that the band reject filter removes the direct current bias in the low frequency signals.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, or <NUM> further comprising:
a filter system in the denoiser, wherein the filter system comprises at least one of a bandpass filter or a band reject filter.

Example <NUM> The communications system according to Example <NUM>, wherein the filter system passes at least one of a set of frequency ranges or rejects a set of frequency ranges.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>, wherein the low frequency receiver selected from one of magnetometer very low frequency receiver, and a quantum magnetometer very low frequency receiver, or a magnetic field-based quantum receiver.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>, wherein the denoiser is a cognitive signal processor.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>, wherein the low frequency signals comprises at least one of extremely low frequency (ELF) signals, ultra low frequency signals, or very low frequency signals.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM> further comprising:
an underwater platform, wherein the low frequency receiver, the denoiser, and the signal extractor are located in the underwater platform.

Example <NUM> The communications system according to Example <NUM>, wherein the underwater platform is selected from a group comprising a stationary underwater platform, a mobile underwater platform, a submarine, a submersible vehicle, an autonomous underwater vehicle, an underwater research station, an underwater habitat, an underwater drone, and an underwater remotely operated vehicle.

a filter system in a neural network in the cognitive signal processor, wherein the filter system comprises at least one of a bandpass filter or a band reject filter; and
a signal extractor in communication with the cognitive signal processor , wherein the signal extractor extracts the communications signal from the denoised signal.

Example <NUM> The communications system according to Example <NUM>, wherein the bandpass filter passes the very low frequency signals in a frequency range in which the communications signal is expected in the very low frequency signals received from the quantum magnetometer very low frequency receiver.

Example <NUM> The communications system according to one of Examples <NUM> or <NUM>, wherein the band reject filter rejects the very low frequency signals in a frequency range in which a direct current bias is present such that the band reject filter removes the direct current bias in the very low frequency signals.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, or <NUM>, wherein the cognitive signal processor comprises the neural network in a reservoir computer, a delay embedding, and a weight adaptation.

Example <NUM> The communications system according to one of Examples <NUM>, <NUM>, <NUM>, or <NUM> further comprising:
a platform, wherein the quantum magnetometer, the cognitive signal processor, and the signal extractor are located in the underwater platform.

Example <NUM> The communications system according to Example <NUM>, wherein the platform is selected from a group comprising a underwater platform, a stationary underwater platform, a mobile underwater platform, a submarine, a submersible vehicle, an autonomous underwater vehicle, an underwater research station, an underwater habitat, an underwater drone, and an underwater remotely operated vehicle, an aircraft, a surface ship, a tank, a personnel carrier, a train, a spacecraft, a space station, a satellite, an automobile, a power plant, a bridge, a dam, a house, a manufacturing facility, and a building.

Example <NUM> A method for processing low frequency signals, the method comprising:.

Example <NUM> The method according to Example <NUM>, wherein denoising comprises:
passing the low frequency signals in a frequency range in which the communications signal is expected in the low frequency signals received from the receiver.

Example <NUM> The method according to one of Examples <NUM> or <NUM>, wherein denoising comprises:
rejecting the low frequency signals in a frequency range in which interference is present such that the interference is removed from the low frequency signals.

Example <NUM> A computer program product for processing very low frequency signals, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a computer system to cause the computer system to perform a method of:.

Thus, the illustrative embodiments provide a method, apparatus, system, and computer program product for processing low frequency signals. A communications system comprises a low frequency receiver, a denoiser, and a signal extractor. The low frequency receiver receives low frequency signals in which a communications signal is expected. The denoiser is in communication with the low frequency receiver. The denoiser denoises low frequency signals received from the low frequency receiver. The denoising results in a generation of denoised signals. The signal extractor in communication with the denoiser. The signal extractor extracts the communications signal from the denoised signal.

With the use of a low frequency receiver and a denoiser a reduction of noise can be achieved that enables communication using at least one of an increased bit rate and detecting transmissions or an increased depth. With very low frequency implementations, the use of a quantum magnetometer very low frequency receiver with a cognitive signal processor can increase the reduction in noise. Further, with the implementation of a filter system having at least one of a bandpass filter to pass frequencies in which a communications signal is expected or reject frequencies in which artifacts such as a DC bias is expected. As a result, an increase in at least one of the bit rate or depth at which very low frequency signals can be received can occur.

The description of the different illustrative embodiments has been presented for purposes of illustration and description and is not intended to be exhaustive or limited to the embodiments in the form disclosed. The different illustrative examples describe components that perform actions or operations. In an illustrative embodiment, a component can be configured to perform the action or operation described. For example, the component can have a configuration or design for a structure that provides the component an ability to perform the action or operation that is described in the illustrative examples as being performed by the component. Further, To the extent that terms "includes", "including", "has", "contains", and variants thereof are used herein, such terms are intended to be inclusive in a manner similar to the term "comprises" as an open transition word without precluding any additional or other elements.

Many modifications and variations will be apparent to those of ordinary skill in the art.

Claim 1:
A communications system (<NUM>) comprising:
a low frequency receiver (<NUM>) configured to receive low frequency signals (<NUM>) in which a communications signal (<NUM>) is expected;
a denoiser (<NUM>) in communication with the low frequency receiver (<NUM>), wherein the denoiser (<NUM>) is configured to denoise the low frequency signals (<NUM>) received from the low frequency receiver (<NUM>), wherein the denoising results in a generation of denoised signals (<NUM>); and
a signal extractor (<NUM>) in communication with the denoiser (<NUM>), wherein the signal extractor (<NUM>) is configured to extract the communications signal (<NUM>) from the denoised signals (<NUM>);
wherein the denoiser (<NUM>) is a cognitive signal processor (<NUM>) comprising a reservoir computer (<NUM>) with a reservoir (<NUM>), the reservoir (<NUM>) being configured to denoise the low frequency signals (<NUM>).