Patent Application
20210408825
This patent application relates to solar energy resource assessment systems and more particularly to a decomposition of broadband direct normal and diffuse horizontal irradiances from spectral global horizontal irradiance measurements and multi-wavelength spectral clearness indices for establishing solar energy resource assessments.
Solar irradiance is the power per unit area received from the Sun in the form of electromagnetic radiation, either across a wavelength range or as reported in the wavelength range of a measuring instrument. Solar irradiance is often integrated over a given time period in order to report the radiant energy emitted into the surrounding environment during that time period. This integrated solar irradiance is called solar irradiation, solar exposure, solar insolation, or insolation. Solar irradiance at the Earth's surface is a function of the Earth's distance from the Sun, the solar cycle, the tilt of the measuring surface, the height of the sun above the horizon, and atmospheric conditions.
Solar irradiance affects plant metabolism and animal behavior, including human behaviour. The measurement of solar irradiance has several important applications, including for example solar resource assessment, solar power plants, photovoltaic system monitoring, heating and cooling loads of buildings, climate modeling and weather forecasting.
Sunlight at the earth's surface is typically represented by three irradiances, the global horizontal irradiance (GHI), the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI). Of the three, GHI measurements are by far the most common because they only require a relatively inexpensive, low maintenance pyranometer statically mounted on a flat surface. In contrast, obtaining measurements of DNI and DHI requires both a pyrheliometer and a pyranometer (with a shadow ball assembly) mounted to a solar tracker. For cost-sensitive applications, such as obtaining measurements at solar cell deployments etc. it is possible to use “tracker-less” options to derive the GHI, DNI and DHI with a single instrument such as a rotating shadow band radiometer or a shadow-mask pyranometer but the resultant measurements have a higher uncertainty than the aforementioned methods.
An alternative convenient option is to solely measure the GHI and then use an irradiance decomposition algorithm to derive the DNI and/or DHI. These models vary in complexity and generally have a relatively high uncertainty. For example, root mean square (RMS) errors for DNI retrieval of −85 W/m2′ at hourly resolution. Accordingly, for an unbiased distribution this represents a standard deviation of DNI of average daily DNI of 2,000 W/m2 this represents an RMS error of 4.25% and at 4,000 W/m2 2.1%. As evident from
Accordingly, it would be beneficial to establish an improved methodology based upon an improved decomposition algorithm allowing for improved accuracy in derived solar irradiance measurements in conjunction with a low cost non-moving part spectral pyranometer supporting spectral global irradiance measurements and spectral clearness indices.
Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.
It is an object of the present invention to mitigate limitations within the prior art relating to solar energy resource assessment systems and more particularly to a decomposition of direct normal and diffuse horizontal irradiances from spectral global horizontal irradiance measurements and multi-wavelength spectral clearness indices for establishing solar energy resource assessments.
In accordance with an embodiment of the invention there is provided a system comprising:
a spectral measurement device comprising:
In accordance with an embodiment of the invention there is provided a system comprising:
a processing system comprising a processor, a memory and computer executable instructions stored within the memory where the computer executable instructions when executed by the processor configure the processor to perform a process comprising the steps of:
In accordance with an embodiment of the invention there is provided a system comprising:
a processing system comprising a processor, a memory and computer executable instructions stored within the memory where the computer executable instructions when executed by the processor configure the processor to perform a process comprising the steps of:
In accordance with an embodiment of the invention there is provided a system comprising:
a processing system comprising a processor, a memory and computer executable instructions stored within the memory where the computer executable instructions when executed by the processor configure the processor to perform a process comprising the steps of:
Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.
Embodiments of the present invention will now be described, by way of example only, with reference to the attached Figures, wherein:
The present invention is directed to solar energy resource assessment systems and more particularly to a decomposition of broadband direct normal and diffuse horizontal irradiances from spectral global horizontal irradiance measurements and multi-wavelength spectral clearness indices for establishing solar energy resource assessments.
The ensuing description provides representative embodiment(s) only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the embodiment(s) will provide those skilled in the art with an enabling description for implementing an embodiment or embodiments of the invention. It being understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims. Accordingly, an embodiment is an example or implementation of the inventions and not the sole implementation. Various appearances of “one embodiment,” “an embodiment” or “some embodiments” do not necessarily all refer to the same embodiments. Although various features of the invention may be described in the context of a single embodiment, the features may also be provided separately or in any suitable combination. Conversely, although the invention may be described herein in the context of separate embodiments for clarity, the invention can also be implemented in a single embodiment or any combination of embodiments.
Reference in the specification to “one embodiment”, “an embodiment”, “some embodiments” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment, but not necessarily all embodiments, of the inventions. The phraseology and terminology employed herein is not to be construed as limiting but is for descriptive purpose only. It is to be understood that where the claims or specification refer to “a” or “an” element, such reference is not to be construed as there being only one of that element. It is to be understood that where the specification states that a component feature, structure, or characteristic “may”, “might”, “can” or “could” be included, that particular component, feature, structure, or characteristic is not required to be included.
Reference to terms such as “left”, “right”, “top”, “bottom”, “front” and “back” are intended for use in respect to the orientation of the particular feature, structure, or element within the figures depicting embodiments of the invention. It would be evident that such directional terminology with respect to the actual use of a device has no specific meaning as the device can be employed in a multiplicity of orientations by the user or users.
Reference to terms “including”, “comprising”, “consisting” and grammatical variants thereof do not preclude the addition of one or more components, features, steps, integers, or groups thereof and that the terms are not to be construed as specifying components, features, steps or integers. Likewise, the phrase “consisting essentially of”, and grammatical variants thereof, when used herein is not to be construed as excluding additional components, steps, features integers or groups thereof but rather that the additional features, integers, steps, components or groups thereof do not materially alter the basic and novel characteristics of the claimed composition, device or method. If the specification or claims refer to “an additional” element, that does not preclude there being more than one of the additional element.
A “pyranometer” as used herein and throughout this disclosure may refer to, but is not limited to, a type of actinometer used for measuring solar irradiance on a planar surface and it is designed to measure the solar radiation flux density (W/m2) from the hemisphere above within a wavelength range, for example 300 nm to 3 μm.
A “pyrheliometer” as used herein and throughout this disclosure may refer to, but is not limited to, an instrument for measurement of direct beam solar irradiance.
As noted above it would be beneficial to establish an improved methodology based upon an improved decomposition algorithm allowing for improved accuracy in derived solar irradiance measurements in conjunction with a low cost non-moving part spectral pyranometer supporting spectral global irradiance measurements and spectral clearness indices. To date the majority of decomposition algorithms have been based on a clearness index. which is a unitless measure of the atmosphere's clearness, derived from the product of the local GHI and the airmass divided by the extraterrestrial irradiance. Some models have improved the decomposition results by also including the atmospheric turbidity and total column water vapor (i.e. the precipitable water vapor) into the calculations. The additional insight from local atmospheric parameters translates to improved model performance.
A comprehensive way to assess the atmospheric conditions is to use local spectral irradiance data. which historically has been difficult to obtain. as it requires several co-located field spectroradiometers. However, with the advent of compact, field deployable, relative low cost spectral pyranometers, such as the “SolarSIM-G” developed by the inventors and sold by Spectrafy Inc. of Ottawa, Canada, it is now possible to obtain full-range spectral and broadband GHI data from a single compact. low power instrument with no moving parts. Accordingly, the inventors have established a methodology exploiting temporally based (e.g. one minute) spectral measurements using such a spectral pyranometer to derive spectral clearness indices at multiple wavelengths. These are then employed as predictors within the novel decomposition algorithm according to an embodiment of the invention.
A: Spectral Pyranometer
A spectral pyranometer such as the SolarSIM-G is an instrument for resolving the global spectral and broadband irradiance over a predetermined wavelength range, for example 280 nm≤λ≤4000 nm as described below. Accordingly, the SolarSIM-G combines the capabilities from multiple instruments such as a spectroradiometer and a pyranometer all in one single compact housing.
Referring to
In
Accordingly, the SolarSIM-G is an instrument that combines a multi-filter radiometer with an advanced radiative transfer model to derive in real-time full-range spectral and broadband global irradiances under all sky conditions. The SolarSIM-G measures the global spectral irradiance using hard-coated narrow bandpass filters paired with silicon and indium gallium arsenide calibrated detectors. The center wavelengths for the 9 channel SolarSIM-G employed are given in Table 1 along with the atmospheric parameters or conditions that these wavelengths are targeted at. The SolarSIM-G also senses the ambient temperature, humidity and atmospheric pressure. These radiance and environmental measurements then fed into the inventor's radiative transfer model to derive the spectral and broadband GHI in the 280 nm≤λ≤4000 nm range.
Now referring to
Second functional block 330B relates to the other sensors within the SolarSIM-G 330 including, but not limited to, ambient temperature, ambient pressure, ambient humidity, internal temperature, internal humidity, and accelerometer. The outputs of these being also coupled to the electronic functional block 330C.
The electronic functional block 330C therefore receives multiplexed digital data relating to the multiple wavelength channels and digital data from multiple environmental sensors. These are processed by a microcontroller within the electronic functional block 330C via a software algorithm or software algorithms stored in memory associated with the microcontroller. The electronic functional block 330C also implements one or more communication protocols such that the raw and/or processed data are pushed to or pulled to a host computer, in this instance a remote server 310 via a network 320. The remote server 310 may process the data from the SolarSIM-G 330 or stores processed data from the SolarSIM-G 330. This data may include, but is not limited to, global spectral irradiance (horizontal or titled), direct spectrum, diffuse spectrum, spectral water vapour, aerosols, and ozone absorption profiles. Optionally, the data acquired by the SolarSIM-G 330 is processed directly onboard the SolarSIM-G 330 prior to being transmitted to the remote server 310 or another device via the network 320. Accordingly, the SolarSIM-G 330 may employ one or more wireless interfaces to communicate with the network 320 selected from the group comprising, but not limited to, IEEE 802.11, IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU-R 5.150, ITU-R 5.280, and IMT-1000. Alternatively, the SolarSIM-G 330 may employ one or more wired interfaces to communicate with the network 320 selected from the group comprising, but not limited to, DSL, Dial-Up, DOCSIS, Ethernet, G.hn, ISDN, MoCA, PON, and Power Line Communication (PLC).
A software block diagram for the software algorithm of a SolarSIM-G is depicted in
Next in fifth block 450 the diffuse spectral irradiance is estimated and then employed to generate a refined reconstructed solar spectrum in sixth block 460 which is then employed to reconstruct the final global spectrum, diffuse and direct spectra as well as the atmospheric absorption profiles for water, ozone, and aerosols in seventh block 470. As the global spectrum is a combination of the direct and the diffuse spectral irradiances, the first reconstruction will not be perfect, as we are not taking the diffuse irradiance into account. However, the reconstructed proxy spectrum allows estimating the aerosols, water vapour and ozone content in the atmosphere, which in turn allow a better approximation of the diffuse irradiance. The approximated diffuse irradiance is then subtracted from the proxy global solar spectrum and reconstruction is performed once again, which gives the direct component of the global spectral irradiance. Addition of the estimated diffuse spectral irradiance to the direct component yields the global spectral irradiance.
B: Spectral Reconstruction Algorithm
The spectral reconstruction algorithm according to embodiments of the invention comprises three steps:
Accordingly, referring to
Referring to
Accordingly, based upon exemplary process flow 700 the GHI is derived from real-time multi-wavelength spectral data obtained with the SolarSIM-G.
C: Experimental Data Set
In order to verify the improvements from the novel methodology established by the inventors spectral and broadband irradiance data were obtained from five “stations” across a range of environments as outlined in Table 2: Four stations form part of the Canadian Spectral Irradiance Network operated by Spectrafy whilst the fifth was operated by the Institute of Atmospheric Physics in the People's Republic of China. Each station being equipped with a SolarSIM-G as manufactured by Spectrafy Inc. together with a second device, a SolarSIM-D2 also manufactured by Spectrafy Inc. The SolarSIM-D2 providing a versatile device providing the functionalities of a pyrheliometer, a spectroradiometer, a sun photometer and an ozone spectrophotometer, all in a single compact rugged unit. The raw data was acquired with one-minute resolution by a datalogger. and subsequently sent to a central server for processing and storage.
The SolarSIM-D2 provides the spectral and broadband DNI in the 280 nm≤λ≤4000 nm range together with spectral AOD in the 280 nm≤λ≤4000 nm range, total column ozone and the precipitable water vapor. The SolarSIM-G delivers the spectral and broadband GHI in the 280 nm≤λ≤4000 nm range. By combining the measurements from both instruments, the inventors computed the spectral and broadband DHI in the 280 nm≤λ≤4000 nm range.
The data sets from each location end by 1 Dec. 2019 and vary in length from 9 to 24 months. Aggregated they are equivalent to almost seven years of acquired data at one-minute granularity. The data sets were carefully screened and validated. Data for solar elevation angles less than 100 were excluded to minimize horizon perturbations and to avoid any effects from shadowing. Data taken during periods of snow, rain or maintenance were likewise excluded. Three of the stations, Egbert. Ottawa. and Varennes, operate under similar atmospheric conditions. Their mean AODs at λ=500 nm (AOD500) as listed in Table 2 being near or at 0.1. The other two stations show greater diversity in atmospheric conditions. The Devon station has slightly heavier AOD500 loading at 0.14, whilst the Xianghe station in China has a mean AOD500 of 0.48. The combined data set represents a diverse range of environmental conditions representative of many locations around the world.
D. Global Irradiance Decomposition
DNI and DHI data may be extracted from the SolarSIM-G's GHI data. The algorithm for its extraction as described below and depicted in respect of exemplary process flow 800 in
D1: Decomposition Algorithm
S
GHI,CLR(λ)=SDNI,CLR(λ)·m−1+SDHI,CLR(λ) (1)
κ(λ)=SGHI(λ)/SGHI,CLR(λ) (2)
I
DNI,MOD=α1,X·IGHI·m+α2,X·IDNI,CLR+ΣI=1,I≠79βI,X·κ(λI) (3)
I
DHI
=I
GHI
−I
DNI
·m
−1 (4)
D2: Spectral Clearness Index
We start by defining the “clear-sky” spectral global horizontal irradiance by Equation (1) where SDNI,CLR(λ) and SDHI,CLR(λ) are the modelled “clear-sky” spectral DNI and spectral DHI respectively, in the 280 nm≤λ≤4000 nm range, and m is the optical air mass. The DNI is obtained through a parameterized direct beam transmittance model such as presented by the inventors within “Design Principles and Field Performance of a Solar Spectral Irradiance Meter” (Solar Energy, Vol. 133, pp. 94-102, 2016), except the aerosol transmittance is generated by fixing the AOD at λ=500 nm to 0.05 with its spectral dependence defined by two Angstrom exponents of 0.98 and 1.22 for wavelengths λ<500 nm and λ>500 nm respectively. The spectral ozone and water vapor transmittance functions are generated from the total column ozone and precipitable water vapor content obtained by the SolarSIM-G measurements. Finally. the modelled “clear-sky” spectral DHI in the 280 nm≤λ≤4000 nm is based upon a predetermined model, for example R. Bird et al. “Simple Solar Spectral Model for Direct and Diffuse Irradiance on Horizontal and Tilted Planes at the Earth's Surface for Cloudless Atmospheres” (J. Climate and Applied Meteorology, Vol. 25, pp. 87-97, 1986).
Accordingly, for a more comprehensive measure of the atmosphere's clearness the inventors define κ(λ) as given by Equation (2) as the spectral clearness where SGHI(λ) is the measured spectral GHI as derived by the SolarSIM-G, and SGHI,CLR(λ) is the modelled “clear-sky” spectral GHI. computed from Equation (1).
D3. Classification of Sky Conditions
The inventors have established a crucial insight leveraged in their decomposition algorithm which is based upon similar atmospheric conditions correlate with sky conditions. Accordingly, a classification can be employed as discussed above wherein an exemplary classification is presented in Table 3. Within this the inventors categorize sky conditions into seven classes based on the values of the spectral clearness indices κ(λ1) and κ(λ9), which are established using optical channels 1 and 9 of the SolarSIM-G respectively. These two channels were chosen by the inventors as after analysis the spectral clearness indices at these wavelengths show the strongest sensitivity to sky conditions. Channel 1 was chosen by the inventors because it is the most sensitive to small changes in the diffuse irradiance, whilst channel 9 was chosen because it is the least sensitive to the clear-sky diffuse irradiance and to the aerosol absorption of the direct beam. As a result. channel 9 is the most sensitive channel to cloud absorption and scattering and is accordingly a reasonable estimator of the clouds' optical depth that obscure the sun.
As indicated in Table 3 the inventor's classification of sky clarity is quantified by index ranges. It would be apparent that beneficially, this inventive dual-wavelength spectral classification can be automatically established and employed within instruments, systems and software exploiting embodiments of the invention. Whilst the embodiments of the invention described employ two channels for sky condition determination it would be evident that 3, 4, or more wavelengths may be employed within other embodiments of the invention.
Based on the AOD500 data from all stations the inventors established that values for κ(λ9) of 0.75 and above correlate with an unobstructed sun disk for over 95% of the data. When the sky is free of clouds. the values of κ(λ1) can be used to further characterize the sky as either “very clear”. “clear”, or “hazy”. When the sky is cloudy. but the sun disk is not obscured, the GHI in some cases can exceed the solar constant. This is the special case of lensing. where κ(λ9) is found to exceed 1.05.
The inventors also established that values of κ(λ9) below 0.75 correlated with a sun obstructed by the clouds for over 90% of the data. as determined by the SolarSIM-D2's AOD500 measurements at each station. Decreasing values of κ(λ9) were found by the inventors to correlate well with cloud optical depth. allowing “thin” clouds, “thick” clouds, and completely overcast conditions to be identified by their κ(λ9) ranges.
D4: Computation of DNI and DHI
For a specific sky condition X (as defined in Table 3) the decomposition of the modelled DNI may be parameterized as given by Equation (3) where IGHI is the measured broadband GHI; IDNI,CLR is the integral of the modeled “clear-sky” spectral DNI, SDNI,CLR(λ), in the 280 nm≤λ≤4000 nm; α1,X and α2,X are unitless coefficients for the broadband predictors, IGHI and IDNI,CLR, respectively; PI, is a set of eight coefficients for spectral clearness indices at the center wavelengths of all SolarSIM-G's optical channels, except for the water vapor channel, i.e. channel 7. This channel is excluded because variation in the total column water vapor is already captured within the IGHI and IDNI,CLR variables. The α and β coefficients for each sky condition X were determined using a multivariate ordinary least squares linear regression algorithm that minimized the difference between the modelled DNI and the measured DNI time series from all stations at the same time. These coefficients are presented in
E: Analysis
The performance of the inventive decomposition algorithm was established by comparing modeled DNI and DHI time series at each station against their corresponding references values. The reference DNI was determined from the SolarSIM-D2 measurements at each station whilst the reference DNI was computed from Equation (4) using the reference DNI and GHI. as derived by the SolarSIM-D2 and the SolarSIM-G, respectively, at each station. The inventors have assumed that any differences between reference instruments and derived DNI and DHI values are dominated by the limitations of the decomposition algorithm. Therefore. reference instrument measurement uncertainties were not included in the comparative analysis (i.e. reference DNI and DHI data were assumed to be true).
Accordingly, the inventors evaluated the decomposition algorithm by calculating various statistical estimators from the difference between the modeled and reference DNI and DHI time series for each station. First and second graphs 1000A and 1000B in
As can be seen from first graph 1000A in
The modeled DHI propagates the errors from the modeled DNI. as per Equation (4). The extended error range for Varennes, Ottawa. Egbert stations is about ±21 W/m2, while the MBE and the RMSE are +1 W/m2 and 14 W/m2 respectively. For Devon station the MBE was approximately −3 W/m2 with the extended error range stretching from −28 W/m2 to +17 W/m2, while the RMSE was 15 W/m2. Similar to the DNI retrieval, the Xianghe station saw the highest error spread with the extended error range varying from −52 W/m2 to +39 W/m2, with negligible MBE and the RMSE of 27 W/m2.
As noted previously the prior art methodologies yield an RMSE of −85 W/m2. Accordingly, for Xianghe station with high variations in aerosol conditions due to changes in pollution the RMSE from the inventive algorithm according to an embodiment of the invention yields an RMSE of 27 W/m2, or approximately 30% of the prior art. For stations without such variations in aerosol conditions the RMSE was 15 W/m2, or approximately 17% of the prior art.
The inventors also computed the integrated energy per unit surface area errors for the entire DNI and DHI datasets at each station and compared them against the corresponding reference values. The DNI and DHI integrated energy errors were less than 1% and 2%, respectively, at each station. This is an important result as it suggests that even in high aerosol environments, such as Xianghe, the novel decomposition algorithm according to an embodiment of the invention can accurately provide the estimate of the DNI and DHI solar resource potential.
Accordingly, the novel decomposition algorithm demonstrates a significant improvement over state-of-the-art decomposition algorithms, even with a one-minute resolution data set. Furthermore, exploiting a spectral pyranometer such as the SolarSIM-G provides a compact, low cost, non-moving part system solution which presents an alternative to other tracker-less methods for obtaining all three components of sunlight. such as rotating shadow band radiometers and shadow-mask pyranometer. It is expected that the decomposition algorithm can be further improved as more data becomes available from the existing measurement stations and future installations worldwide in order to refine the coefficients.
Potentially, different coefficient sets may be established in different deployment environments such as those with high aerosols/variations in aerosol conditions versus those without such aerosols and/or variations in aerosol conditions.
Within the embodiments of the invention described above specific wavelengths have been defined associated with specific aspects of the process. It would be evident that these specific wavelengths are nominal centre wavelengths for optical filters or other optical spectrometry methods of establishing optical intensity at these nominal centre wavelengths. Further, it would be evident that optical filters or other optical spectrometry methods would perform these measurements with a nominal wavelength range around these nominal centre wavelengths. Within other embodiments of the invention certain wavelengths defined above may be varied and/or augmented with other wavelengths associated with a characteristic being determined. For example, multiple wavelengths may be employed for specific aerosols or different absorption bands of an aerosol or other component of the atmosphere may be employed.
Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Implementation of the techniques, blocks, steps and means described above may be done in various ways. For example, these techniques, blocks, steps and means may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above and/or a combination thereof.
Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
Furthermore, embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages and/or any combination thereof. When implemented in software, firmware, middleware, scripting language and/or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium, such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures and/or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters and/or memory content. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
For a firmware and/or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software codes may be stored in a memory. Memory may be implemented within the processor or external to the processor and may vary in implementation where the memory is employed in storing software codes for subsequent execution to that when the memory is employed in executing the software codes. As used herein the term “memory” refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.
Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and/or various other mediums capable of storing, containing or carrying instruction(s) and/or data.
The methodologies described herein are, in one or more embodiments, performable by a machine which includes one or more processors that accept code segments containing instructions. For any of the methods described herein, when the instructions are executed by the machine, the machine performs the method. Any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine are included. Thus, a typical machine may be exemplified by a typical processing system that includes one or more processors. Each processor may include one or more of a CPU, a graphics-processing unit, and a programmable DSP unit. The processing system further may include a memory subsystem including main RAM and/or a static RAM, and/or ROM. A bus subsystem may be included for communicating between the components. If the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD). If manual data entry is required, the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth.
The memory includes machine-readable code segments (e.g. software or software code) including instructions for performing, when executed by the processing system, one of more of the methods described herein. The software may reside entirely in the memory, or may also reside, completely or at least partially, within the RAM and/or within the processor during execution thereof by the computer system. Thus, the memory and the processor also constitute a system comprising machine-readable code.
In alternative embodiments, the machine operates as a standalone device or may be connected, e.g., networked to other machines, in a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. The machine may be, for example, a computer, a server, a cluster of servers, a cluster of computers, a web appliance, a distributed computing environment, a cloud computing environment, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. The term “machine” may also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
The foregoing disclosure of the exemplary embodiments of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many variations and modifications of the embodiments described herein will be apparent to one of ordinary skill in the art in light of the above disclosure. The scope of the invention is to be defined only by the claims appended hereto, and by their equivalents.
Further, in describing representative embodiments of the present invention, the specification may have presented the method and/or process of the present invention as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. In addition, the claims directed to the method and/or process of the present invention should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the present invention.
This application claims the benefit of priority from U.S. Provisional Patent Application 63/044,633 filed Jun. 26, 2020.
| Number | Date | Country | |
|---|---|---|---|
| 63044633 | Jun 2020 | US |
