Signal analysis method and signal analysis module

Abstract

A signal analysis method is described. The signal analysis method includes: receiving an input signal having unknown characteristic signal parameters; determining IQ data being associated with the input signal; determining at least one of the characteristic signal parameters based on the IQ data via an artificial intelligence circuit; and adapting at least one measurement parameter of a measurement instrument based on the at least one characteristic parameter by the artificial intelligence circuit. Moreover, a signal analysis circuit is described.

Claims

1. A signal analysis method, comprising: receiving an input signal having unknown characteristic signal parameters; determining IQ data being associated with said input signal; determining at least one of said characteristic signal parameters based on said IQ data via an artificial intelligence circuit; and adapting at least one measurement parameter of a measurement instrument based on said at least one characteristic parameter by said artificial intelligence circuit, such that the measurement instrument is automatically set to the correct operational mode for the analysis of the input signal, wherein a preliminary reference signal is generated based on said at least one characteristic signal parameter, and wherein said preliminary reference signal is compared with said input signal in order to adapt said at least one measurement parameter.

2. The signal analysis method of claim 1, wherein at least one of said input signal and said IQ data is transformed to frequency domain, thereby generating a transformed signal.

3. The signal analysis method of claim 2, wherein a local maximum of said transformed signal is determined by said artificial intelligence circuit in order to determine a symbol rate of said input signal.

4. The signal analysis method of claim 1, wherein a modulation type of the input signal is determined directly based on said IQ data by said artificial intelligence circuit.

5. The signal analysis method of claim 1, wherein a constellation diagram is determined based on said IQ data, and wherein a modulation type of the input signal is determined by said artificial intelligence circuit.

6. The signal analysis method of claim 1, wherein said preliminary reference signal is generated based on at least one preliminary modulation type, and wherein a constellation diagram determined based on said IQ data is compared against a constellation diagram corresponding to said preliminary reference signal in order to determine said modulation type of said input signal.

7. The signal analysis method of claim 1, wherein an error signal is determined based on said input signal and based on said preliminary reference signal, and wherein said at least one measurement parameter is adapted based on said error signal.

8. The signal analysis method of claim 1, wherein at least one filter parameter of a transmit filter is determined based on said preliminary reference signal.

9. The signal analysis method of claim 8, wherein said at least one filter parameter comprises a roll-off factor.

10. The signal analysis method of claim 1, wherein an error vector magnitude of said IQ data is determined, and wherein said at least one measurement parameter is adapted based on said error vector magnitude by said artificial intelligence circuit.

11. A signal analysis circuit, comprising: an input configured to receive an input signal having unknown characteristic signal parameters; a processing circuit configured to determine IQ data being associated with said input signal; an artificial intelligence circuit configured to determine at least one of said characteristic signal parameters based on said IQ data, and configured to adapt at least one measurement parameter of a measurement instrument based on said at least one characteristic parameter, such that the measurement instrument is automatically set to the correct operational mode for the analysis of the input signal; a signal generator circuit being configured to generate a preliminary reference signal based on said at least one characteristic signal parameter, and wherein said artificial intelligence circuit is configured to compare said preliminary reference signal with said input signal in order to adapt said at least one measurement parameter, and an error circuit configured to determine an error signal based on said input signal and based on said preliminary reference signal, and wherein said artificial intelligence circuit is configured to adapt said at least one measurement parameter based on said error signal.

12. The signal analysis circuit of claim 11, wherein said processing circuit is configured to transform at least one of said input signal and said IQ data to frequency domain, thereby generating a transformed signal.

13. The signal analysis circuit of claim 12, wherein said artificial intelligence circuit is configured to determine a local maximum of said transformed signal in order to determine a symbol rate of said input signal.

14. The signal analysis circuit of claim 11, wherein said processing circuit is configured to determine a constellation diagram based on said IQ data, and wherein said artificial intelligence circuit is configured to determine a modulation type of the input signal based on said constellation diagram.

15. The signal analysis circuit of claim 11, wherein said signal generator circuit is configured to generate said preliminary reference signal based on at least one preliminary modulation type, and wherein said artificial intelligence circuit is configured to compare a constellation diagram based on said IQ data against a constellation diagram corresponding to said preliminary reference signal in order to determine said modulation type of said input signal.

16. The signal analysis circuit of claim 11, wherein said artificial intelligence circuit is configured to determine at least one filter parameter of a transmit filter based on said preliminary reference signal.

17. The signal analysis circuit of claim 16, wherein said at least one filter parameter comprises a roll-off factor.

18. The signal analysis circuit of claim 11, wherein said processing circuit is configured to determine an error vector magnitude of said IQ data, and wherein said artificial intelligence circuit is configured to adapt said at least one measurement parameter based on said error vector magnitude.

19. A signal analysis circuit, comprising: an input configured to receive an input signal having unknown characteristic signal parameters; a processing circuit configured to determine IQ data being associated with said input signal; and an artificial intelligence circuit configured to determine at least one of said characteristic signal parameters based on said IQ data, and configured to adapt at least one measurement parameter of a measurement instrument based on said at least one characteristic parameter, wherein said signal analysis circuit further comprises a signal generator circuit, said signal generator circuit being configured to generate a preliminary reference signal based on said at least one characteristic signal parameter, and wherein said artificial intelligence circuit is configured to compare said preliminary reference signal with said input signal in order to adapt said at least one measurement parameter, wherein said signal generator circuit is configured to generate said preliminary reference signal based on at least one preliminary modulation type, and wherein said artificial intelligence circuit is configured to compare a constellation diagram based on said IQ data against a constellation diagram corresponding to said preliminary reference signal in order to determine said modulation type of said input signal.

Description

DESCRIPTION OF THE DRAWINGS

(1) The foregoing aspects and many of the attendant advantages of the claimed subject matter will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

(2) FIG. 1 schematically shows a block diagram of a signal analysis module, including one or more circuits, according to the present disclosure;

(3) FIG. 2 shows a flow chart of a signal analysis method according to an embodiment of the present disclosure; and

(4) FIGS. 3 to 6 show illustrations of individual steps of the representative signal analysis method of FIG. 2.

DETAILED DESCRIPTION

(5) The detailed description set forth below in connection with the appended drawings, where like numerals reference like elements, is intended as a description of various embodiments of the disclosed subject matter and is not intended to represent the only embodiments. Each embodiment described in this disclosure is provided merely as an example or illustration and should not be construed as preferred or advantageous over other embodiments. The illustrative examples provided herein are not intended to be exhaustive or to limit the claimed subject matter to the precise forms disclosed.

(6) FIG. 1 schematically shows a signal analysis system 10 comprising a signal analysis circuit(s) or module 12, a measurement instrument 14, and a device under test 16.

(7) Generally, the term “module” includes suitable hardware, suitable software, or a combination of hardware and software that is configured to have certain functionality.

(8) In FIG. 1, the signal analysis module 12 is established separately from the measurement instrument 14. For example, the signal analysis module 12 may be established as a suitable measurement device, e.g. as an oscilloscope, as a signal analyzer, or as a computer with suitable software. However, in other embodiments, the signal analysis module 12 may also be integrated into the measurement instrument 14.

(9) In general, the measurement instrument 14 is configured to analyze an output signal generated by the device under test 16, as is indicated by the dotted arrow in FIG. 1.

(10) In some embodiments, the measurement instrument 14 is configured to analyze the output signal of the device under test 16 with regard to perturbations and imperfections comprised in the input signal, such as jitter, noise, an error vector magnitude of the output signal, and/or with regard to other signal properties. In other words, the measurement instrument 14 is configured to analyze the quality of the input signal.

(11) For example, the measurement instrument 14 is established as one of an oscilloscope, a vector network analyzer, a (vector) signal analyzer, and a computer with a suitable measurement application.

(12) The device under test 16 may be any electronic device being configured to generate a modulated output signal comprising a symbol sequence, for example wherein the modulated output signal has a predefined frequency or a predefined frequency spectrum. For example, the device under test 16 is configured to generate the output signal and transmit the output signal via GSM, 3G, 4G, 5G, or based on other wireless communication technologies, such as WLAN or Bluetooth.

(13) In the embodiment shown, the signal analysis module 12 comprises an input 18, a processing circuit or module 20, an artificial intelligence circuit or module 22, a signal generator circuit or module 24, and an error circuit or module 26.

(14) The input 18 is connected to the device under test 16 in a signal transmitting manner Therein and in the following, the term “connected in a signal transmitting manner” is understood to denote a cable-based or wireless connection that is configured to transmit signals between the respective devices or components.

(15) Accordingly, the device under test 16 may be connected to the input 18 via a cable or in a wireless manner. In the latter case, the input 18 may comprise suitable electronic components for receiving a wireless signal transmitted by the device under test 16.

(16) The processing module 20 is connected to the input 18 in a signal transmitting manner. The processing module 20 is connected to each of the error module 26 and the artificial intelligence module 22 in a signal transmitting manner. Moreover, the artificial intelligence module 22 is connected to each of the signal generator module 24, the error module 26, and the measurement instrument 14 in a signal transmitting manner Additionally, the signal generator module 24 is connected to the error module 26 in a signal transmitting manner.

(17) Generally speaking, the signal analysis module 12 is configured to automatically set the correct operational mode of the measurement instrument 14 even if signal properties that are relevant for the analysis of the output signal generated by the device under test 16 are not known. More precisely, the signal analysis module 12 is configured to perform a signal analysis method that is described in the following with reference to FIG. 2.

(18) An input signal having unknown characteristic signal parameters is received by the input 18 (step S1). Generally, the characteristic signal parameters correspond to properties of the signal that need to be known in order to analyze the input signal with high accuracy.

(19) In some embodiments, the characteristic signal parameters comprise one or more of the following parameters: a modulation type of the input signal, a frequency offset of the input signal, a symbol rate of the input signal, a phase offset of the input signal, a timing offset of the input signal, and a transmit filter being associated with the input signal.

(20) Therein, the input signal corresponds to the output signal being generated by the device under test 16, which has been transmitted to the input 18 over the air or via a cable.

(21) The received input signal is forwarded to the processing module 20. IQ data being associated with the input signal is determined by the processing module 20 (step S2). The IQ data comprises both in-phase data (I-data) and quadrature data (Q-data).

(22) The determined IQ data is forwarded to the artificial intelligence module 22. Optionally, the IQ data may also be forwarded to the error module 26.

(23) Moreover, the input signal and/or the determined IQ data is transformed to frequency domain by the processing module 20, thereby generating a transformed signal (step S3).

(24) In other words, a spectrum of the input signal and/or a spectrum of the IQ data is determined automatically by the processing module 20.

(25) The transformed signal is forwarded to the artificial intelligence module 22. Additionally, the processing module 20 may also forward the input signal to the error module 26, possibly after certain pre-processing steps such as sampling. The artificial intelligence module 22 automatically determines the at least one characteristic signal parameter of the input signal, for example all characteristic signal parameters mentioned above (step S4).

(26) Step S4 is described in more detail in the following with reference to FIGS. 3-6. A symbol rate of the input signal is determined by the artificial intelligence module 22 based on the transformed signal.

(27) As illustrated in FIG. 3, the symbol rate of the input signal is located at a local maximum M1 of the spectrum of the input signal or rather of the spectrum of the IQ data being associated with the input signal. Accordingly, the artificial intelligence module 22 may determine the local maximum M1 in order to determine the symbol rate of the input signal.

(28) The artificial intelligence module 22 may determine the local maximum M1 by classical algorithms or by machine learning techniques, such as pattern recognition and/or image analysis.

(29) In some embodiments, an image of the spectrum of the input signal and/or an image of the spectrum of the IQ data may be generated by the processing module 20 based on the transformed signal. The artificial intelligence module 22 may apply image analysis techniques to the generated image in order to determine the local maximum, and thus the symbol rate of the input signal.

(30) As is illustrated in FIG. 4A, the artificial intelligence module may determine a frequency offset or rather a center frequency offset of the input signal based on the transformed signal.

(31) Analogous to the determination of the symbol rate, the artificial intelligence module 22 may employ classical algorithms and/or machine learning techniques in order to determine the (center) frequency offset of the input signal.

(32) As shown in FIG. 4B, the artificial intelligence module 22 may automatically correct for the determined frequency offset of the input signal. In some embodiments, the artificial intelligence module 22 may automatically correct the IQ data for the (center) frequency offset.

(33) As is shown in FIGS. 5A and 5B, the artificial intelligence module 22 may automatically extract portions of the input signal having a valid modulation and may classify the input signal based on the extracted portions.

(34) In some embodiments, the artificial intelligence module 22 may automatically classify the input signal into one of the following categories: burst-signal, continuously modulated signal, and pure noise.

(35) Two examples are illustrated in FIGS. 5A and 5B, which respectively show a plot of an amplitude of the input signal against time, wherein the time is given in units of a symbol length (“sym”).

(36) In the example of FIG. 5A, the input signal is a burst-signal. The artificial intelligence module 22 may automatically identify and extract portions of the input signal that correspond to the individual bursts. Based on the extracted portion(s), the artificial intelligence module 22 may classify the input signal as a burst-signal.

(37) In the example of FIG. 5B, the input signal is a continuously modulated signal. The artificial intelligence module 22 may automatically identify and extract portions of the input signal having a valid modulation. Based on the extracted portion(s), the artificial intelligence module 22 may classify the input signal as a continuously modulated signal.

(38) Moreover, the artificial intelligence module 22 may automatically determine a modulation type or at least a preliminary modulation type of the input signal based on the input signal and/or based on the IQ data. In general, the preliminary modulation type corresponds to a hypothesis about the modulation type of the input signal. This will be explained in more detail below.

(39) The processing module 20 may determine a constellation diagram of the input signal based on the IQ data being associated with the input signal. The artificial intelligence module 22 may then automatically determine the modulation type of the input signal based on the constellation diagram. In general, the constellation diagrams for different modulation types are different from each other. By implication, the modulation type of the input signal can be determined based on the constellation diagram.

(40) The artificial intelligence module 22 may employ classical algorithms and/or machine learning techniques in order to determine the modulation type based on the constellation diagram. In some embodiments, the artificial intelligence module 22 may determine the modulation type of the input signal via pattern recognition techniques that are applied to the constellation diagram.

(41) For example, an image may be generated based on the constellation diagram by the processing module 20. The artificial intelligence module may apply an image analysis technique to the generated image in order to determine the modulation type of the input signal.

(42) Alternatively or additionally, the artificial intelligence module 22 may determine the modulation type of the input signal directly based on the IQ data, for example via suitable machine learning techniques.

(43) The artificial intelligence module 22 may further determine at least one filter parameter of a transmit filter that is associated with the input signal. The input signal is generated by the device under test 16 based on a certain transmit filter having certain filter parameters. In some embodiments, the at least one filter parameter comprises a roll-off factor.

(44) Accordingly, the artificial intelligence module 22 may automatically determine the filter parameters of the transmit filter employed by the device under test 16. This will be described in more detail below.

(45) Summarizing, the signal analysis module 12 determines the characteristic signal parameters of the input signal based on the input signal, the IQ data, and the transformed signal, by automatically extracting relevant features and properties of the input signal as described above.

(46) However, the characteristic signal parameters determined via the signal analysis method described above may be only an estimate of the real characteristic signal parameters of the input signal.

(47) Thus, the signal analysis module 12 may be configured to recursively refine the characteristic signal parameters in order to better match the real characteristic signal parameter of the input signal, as is described in the following with reference to FIG. 6.

(48) In FIG. 6, the steps of the signal analysis method described above are summarized in the box labelled with f.sub.1(x). Based on the input signal, the IQ data, and the transformed signal (summarized as “signal data” in FIG. 6), the artificial intelligence module 22 determines a first set of estimated characteristic signal parameters via the steps S1 to S4 described above.

(49) Based on the first set of characteristic signal parameters, a first preliminary reference signal f.sub.1(x) is generated by the signal generator module 24 (step S5). Generally speaking, the first preliminary reference signal f.sub.1(x) corresponds to an idealized reconstructed input signal, wherein the input signal is reconstructed based on the first set of determined characteristic signal parameters. In other words, the first preliminary reference signal f.sub.1(x) is a hypothesis about the input signal without any perturbations, and thus about the output signal generated by the device under test 16 without any perturbations.

(50) The first preliminary reference signal f.sub.1(x) may be generated based on a mathematical model of the input signal, wherein the characteristic signal parameters of the first set serve as model parameters of the mathematical model.

(51) As is indicated by the arrows in FIG. 1, the first preliminary reference signal f.sub.1(x) is forwarded to both the error module 26 and to the artificial intelligence module 22. The error module determines an error signal based on the first preliminary reference signal f.sub.1(x) and based on the input signal (step S6).

(52) The error signal corresponds to a difference between the input signal and the first preliminary reference signal f.sub.1(x). Thus, the error signal constitutes a measure for the accuracy of the determined characteristic signal parameters.

(53) Based on the first preliminary reference signal f.sub.1(x) and based on the error signal, the first set of characteristic signal parameters may be adapted or rather fine-tuned by the artificial intelligence module 22 (step S7). For example, the at least one filter parameter of the transmit filter may be determined or rather adapted based on the first preliminary reference signal f.sub.1(x).

(54) As already mentioned above, the first preliminary reference signal f.sub.1(x) is associated with a mathematical model of the input signal, wherein the transmit filter is part of that mathematical model. By implication, the model parameters of the mathematical model, for example the filter parameters of the transmit filter, can be determined by comparing the input signal with the preliminary reference signal.

(55) In some embodiments, the at least one filter parameter may be varied such that a deviation of the first preliminary reference signal from the actual input signal is reduced, for example minimized.

(56) Moreover, the determined modulation type of the input signal may be adapted based on the first preliminary reference signal f.sub.1(x) by comparing a constellation diagram corresponding to the first preliminary reference signal f.sub.1(x) against the constellation diagram being associated with the input signal. In other words, the IQ data associated with the input signal is compared against IQ data being associated with the first reference signal f.sub.1(x) having a preliminary modulation type.

(57) If the constellations diagrams do not match, then the first preliminary reference signal f.sub.1(x) may be based on a wrong preliminary modulation type. Accordingly, the modulation type of the first preliminary reference signal has to be adapted.

(58) In order to determine the deviation of the constellation diagram that is based on the preliminary modulation of the first preliminary reference signal f.sub.1(x) from the constellation diagram being associated with the input signal, an error vector magnitude (EVM) of the IQ data being associated with the preliminary modulation may be determined with respect to the IQ data being associated with the input signal.

(59) As a result of the steps S5 to S7 described above, a second set of characteristic signal parameters is obtained, which correspond to a more accurate estimate of the real characteristic signal parameters of the input signal.

(60) Steps S5 to S7 may then be repeated based on the second set of characteristic signal parameters , thereby obtaining a third set of characteristic signal parameters , which correspond to an even more accurate estimate of the real characteristic signal parameters of the input signal.

(61) Steps S5 to S7 may be repeated several times, thereby obtaining a final set of characteristic signal parameters after (n−1) iterations of the steps S5 to S7, wherein n is an integer bigger than 1.

(62) Alternatively, at least one individual signal parameter may be determined in each iteration of steps S5 to S7, while the remaining signal parameters may remain unknown (in that iteration).

(63) Without restriction of generality, e.g. the symbol rate may be determined in the first iteration. In other words, in FIG. 6 would then correspond to the symbol rate of the input signal.

(64) In the second iteration, a second signal parameter is determined based on the first signal parameter , which may e.g. be the type of transmit filter being associated with the input signal.

(65) In a third iteration, a third signal parameter is determined based on the first signal parameter and based on the second signal parameter , which may be the roll-off factor of the transmit filter, etc.

(66) At least one measurement parameter of the measurement instrument 14 is automatically adapted based on the final set of characteristic signal parameters by the artificial intelligence module 22 (step S8).

(67) In other words, the measurement instrument 14 is automatically set to the correct measurement mode for analyzing the input signal.

(68) Summarizing, the artificial intelligence module 22 automatically analyzes the input signal having unknown characteristic signal parameters, and automatically determines the unknown characteristic signal parameters necessary for a substantive analysis of the input signal.

(69) Moreover, the artificial intelligence module 22 automatically sets the measurement instrument to the correct operational mode for the analysis of the input signal.

(70) For this purpose, the artificial intelligence module 22 may use classical algorithms and/or machine learning techniques in order to determine the characteristic signal parameters and in order to adapt the measurement parameters of the measurement instrument 14, for example pattern recognition techniques, image analysis techniques and/or reinforcement learning techniques.

(71) In some embodiments, if the artificial intelligence module 22 is configured for reinforcement learning techniques, the artificial intelligence module 22 acts as an agent, while the measurement instrument 14 is the environment.

(72) Accordingly, the user of the measurement instruments does not have to go through a potentially time-consuming process of manually recovering the characteristic properties of the input signal having unknown properties. Instead, the characteristic properties of the input signal are recovered automatically and the measurement instrument 14 is automatically set to the correct operational mode.

(73) Certain embodiments disclosed herein utilize circuitry (e.g., one or more circuits) in order to implement standards, protocols, models, methodologies or technologies disclosed herein, operably couple two or more components, generate information, process information, analyze information, generate signals, encode/decode signals, convert signals, transmit and/or receive signals, control other devices, etc. Circuitry of any type can be used. It will be appreciated that the term “information” can be use synonymously with the term “signals” in this paragraph.

(74) In an embodiment, circuitry includes, among other things, one or more computing devices such as a processor (e.g., a microprocessor), a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a system on a chip (SoC), or the like, or any combinations thereof, and can include discrete digital or analog circuit elements or electronics, or combinations thereof. In an embodiment, circuitry includes hardware circuit implementations (e.g., implementations in analog circuitry, implementations in digital circuitry, and the like, and combinations thereof).

(75) In an embodiment, circuitry includes combinations of circuits and computer program products having software or firmware instructions stored on one or more computer readable memories that work together to cause a device to perform one or more protocols, methodologies or technologies described herein. In an embodiment, circuitry includes circuits, such as, for example, microprocessors or portions of microprocessor, that require software, firmware, and the like for operation. In an embodiment, circuitry includes one or more processors or portions thereof and accompanying software, firmware, hardware, and the like.

(76) In some examples, the functionality described herein can be implemented by special purpose hardware-based computer systems or circuits, etc., or combinations of special purpose hardware and computer instructions.

(77) Of course, in some embodiments, two or more of these components, or parts thereof, can be integrated or share hardware and/or software, circuitry, etc. In some embodiments, these components, or parts thereof, may be grouped in a single location or distributed over a wide area. In circumstances were the components are distributed, the components are accessible to each other via communication links.

(78) Various embodiments of the present disclosure or the functionality thereof may be implemented in various ways, including as non-transitory computer program products. A computer program product may include a non-transitory computer-readable storage medium storing applications, programs, program modules, scripts, source code, program code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like (also referred to herein as executable instructions, instructions for execution, program code, computer program instructions, and/or similar terms used herein interchangeably). Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non-volatile media).

(79) Embodiments of the present disclosure may also take the form of an apparatus, system, computing device, computing entity, and/or the like executing instructions stored on computer-readable storage media to perform certain steps or operations. The computer-readable media include cooperating or interconnected computer-readable media, which exist exclusively on a processing or processor system or distributed among multiple interconnected processing or processor systems that may be local to, or remote from, the processing or processor system. However, embodiments of the present disclosure may also take the form of an entirely hardware embodiment performing certain steps or operations.

(80) Some embodiments are described above with reference to block diagrams and/or flowchart illustrations of apparatuses, methods, systems, and/or computer program instructions or program products. It should be understood that each block of any of the block diagrams and/or flowchart illustrations, respectively, of portions thereof, may be implemented in part by computer program instructions, e.g., as logical steps or operations executing on one or more computing devices. These computer program instructions may be loaded onto one or more computer or computing devices, such as special purpose computer(s) or computing device(s) or other programmable data processing apparatus(es) or processors to produce a specifically-configured machine, such that the instructions which execute on one or more computer or computing devices or other programmable data processing apparatus or processor implement the functions specified in the flowchart block or blocks and/or carry out the methods described herein.

(81) These computer program instructions may also be stored in one or more computer-readable memory or portions thereof, such as the computer-readable storage media described above, that can direct one or more computers or computing devices or other programmable data processing apparatus(es) or processors to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including computer-readable instructions for implementing the functionality specified in the flowchart block or blocks.

(82) The computer program instructions may also be loaded onto one or more computers or computing devices or other programmable data processing apparatus(es) or processors to cause a series of operational steps to be performed on the one or more computers or computing devices or other programmable data processing apparatus(es) or processors to produce a computer-implemented process such that the instructions that execute on the one or more computers or computing devices or other programmable data processing apparatus(es) or processors provide operations for implementing the functions specified in the flowchart block or blocks and/or carry out the methods described herein.

(83) It will be appreciated that the term computer or computing device can include, for example, any computing device or processing structure, including but not limited to a processor (e.g., a microprocessor), a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a system on a chip (SoC), or the like, or any combinations thereof.

(84) Accordingly, blocks of the block diagrams and/or flowchart illustrations support various combinations for performing the specified functions, combinations of operations for performing the specified functions and program instructions for performing the specified functions. Again, it should also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, or portions thereof, could be implemented by special purpose hardware-based computer systems or circuits, etc., that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

(85) According to some embodiments, many individual steps of a process may or may not be carried out utilizing computer or computing based systems described herein, and the degree of computer implementation may vary, as may be desirable and/or beneficial for one or more particular applications.

(86) The present application may reference quantities and numbers. Unless specifically stated, such quantities and numbers are not to be considered restrictive, but exemplary of the possible quantities or numbers associated with the present application. Also in this regard, the present application may use the term “plurality” to reference a quantity or number. In this regard, the term “plurality” is meant to be any number that is more than one, for example, two, three, four, five, etc. The terms “about,” “approximately,” “near,” etc., mean plus or minus 5% of the stated value. For the purposes of the present disclosure, the phrase “at least one of A and B” is equivalent to “A and/or B” or vice versa, namely “A” alone, “B” alone or “A and B.”. Similarly, the phrase “at least one of A, B, and C,” for example, means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C), including all further possible permutations when greater than three elements are listed.

(87) The principles, representative embodiments, and modes of operation of the present disclosure have been described in the foregoing description. However, aspects of the present disclosure which are intended to be protected are not to be construed as limited to the particular embodiments disclosed. Further, the embodiments described herein are to be regarded as illustrative rather than restrictive. It will be appreciated that variations and changes may be made by others, and equivalents employed, without departing from the spirit of the present disclosure. Accordingly, it is expressly intended that all such variations, changes, and equivalents fall within the spirit and scope of the present disclosure, as claimed.