BACKGROUND

The need of accurate and quick but affordable diagnostic medical devices with mobility is becoming more important for routine or periodic check up in a person's busy life. In this perspective, several personal electronic devices that are communication or infotainment oriented have been exploited to be used as health care monitoring device. Few examples of electronic devices may include Smartphone, tablet etc. These electronic devices are becoming cheaper by the day and also possess enormous capability for computing due to the application of multi-core CPU, high resolution and high dynamic range of camera, sufficient storage, wireless communication and excellent quality display.

On the other hand, the need of quick monitoring of sugar or salt contents in bodily fluid like urine and blood as a bio marker in routine is inevitable to have preventive care for health conditions. One of the popular, inexpensive and quick methods is paper micro- fluidic assay based Point of Care (POC) devices. In the POC devices, the diagnostic health parameters are derived from colorimetric parameters of assay. Currently, the colorimetric analysis is performed by an instrument using color analyzer or customized video camera with a computer. However, presently there is no affordable, mobile and self-diagnostic care instrument.

Few challenges associated with consistent quantitative measurement using a Smartphone or tablet is mentioned herein. The consistent quantitative determination of colorimetric parameters needs to be optimized for photography in ambient conditions, since the color image is highly dependent on ambient lighting or flash lighting conditions. Further, the resolution of the image captured in any camera system is dependent not only on focusing

i and lighting conditions, but also on the distance at which the image is captured. This raises the issue of imaging at known height or at same height which is difficult to assure or otherwise restrict the usage only to trained experts. The need of low power consumption is another issue that restricts computations that are needed for geometric image analysis for coiorimetry. In addition, the evaluation of small color changes in the POC color paper assay is difficult. This is because capturing minor level chemical changes in the POC color paper assay before and after dipping into the bodily fluid in question by the Smartphone or tablet camera optics may not be accurate. Further, the calorimetric calibration of the POC color strip that are mentioned on the assay may not hold accurately though these calibrations are carried out in factory controlled environment. This is because the colorimetric parameters naturally drift due to aging of color. Therefore, there is a need for a method and a device for performing colorimetric analysis of a test fluid to evaluate associated physiological parameters.

SUMMARY

One or more shortcomings of the prior art are overcome and additional advantages are provided through the present disclosure. Additional features arid advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

Accordingly, the present disclosure relates to a method of performing colorimetric analysis of a test fluid to evaluate associated physiological parameters. The method comprising the step of receiving a first image of a test strip captured at a first height before dipping the test strip into the test fluid, and a second image of the test strip captured at a second height after dipping the test strip into the test fluid. Based on analysis of the captured first and second images, a plurality of first and second geometric parameters respectively associated with the test strip is determined. The method further comprising the step of deriving an image resizing factor based on the plurality of first and second geometric parameters and generating one of a resized first image and a resized second image based on the image resizing factor thus derived. Upon generating the resized first and second images, first and second caiorimetric values respectively associated with the resized first and the resized second images are determined. Based on the first and second caiorimetric values and one or more determined caiorimetric values, a change in luminosity is determined based on which one or more physiological parameters associated with the test fluid are evaluated. Further, the present disclosure relates to a system for performing colorimetric analysis of a test fluid to evaluate associated physiological parameters. The system comprises at least an image capturing device configured to capture one or more images of a test strip and a processor coupled with the image capturing device. The system further comprises a memory communicatively coupled with the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to receive a first image of the test strip captured by the image capturing device at a first height before dipping the test strip into the test fluid, and a second image of the test strip captured at a second height after dipping the test strip into the test fluid. Based on the plurality of first and second geometric parameters, the processor is configured to derive an image resizing factor and generate one of a resized first image and a resized second image based on the image resizing factor thus derived. The processor is further configured to determine a first and second caiorimetric value respectively associated with the resized first and the resized second image. Based on first and second caiorimetric value and determined caiorimetric values, the processor is configured to determine a change in luminosity and evaluate one or more physiological parameters associated with the test fluid based on the change in luminosity thus determined.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed embodiments. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

Figures 1A, IB and 1C illustrate an exemplary embodiment of a calorimetric device for capturing image of test strip in accordance with some embodiment of the present disclosure; Figure 2illustrates an exemplar}' scheme to convert each RGB pixel in to xyY representation in accordance with an embodiment of the present disclosure;

Figure 3 illustrates an exemplary block diagram of a calorimetric device in accordance with some embodiments of the present disclosure;

Figure 4 illustrates an exemplar}' representation of geometric parameters involved in capturing an image in accordance with an embodiment of the present disclosure;

Figure 5 illustrates an exemplary 3D representation of chromaticity xyY model in accordance with an embodiment of the present disclosure;

Figure 6 illustrates an exemplary geometrical representation of calibration and determination of color calorimetry in accordance with an embodiment of the present disclosure; and

Figure 7 illustrates a flowchart of an exemplary method of performing colorimetric analysis of a test fluid to evaluate associated physiological parameters in accordance with some embodiments of the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

The present disclosure relates to a calorimetric device and a method of performing coiorimetric analysis of a test fluid to evaluate associated physiological parameters .In one embodiment, the calorimetric device comprises an image capturing device configured to capture first and second images of a test strip at one or more heights before or after dipping the test strip in the test fluid. Based on the analysis of the captured first and second images of the test strip, the calorimetric device determines one or more geometric parameters associated with the test strip based on which an image resizing factor is determined. The caiorimetric device further generates a resized first image and a resized second image using the image resizing factor based on which one or more caiorimetric values associated with the resized first and second images is determined. A plurality of device dependent pixel values of the resized first and second images are determined by the caiorimetric device based on RGB pixel values derived from the resized first and second images using normalization and gamma correction techniques. An intensity value for each normalized RGB pixel values is computed for each derived RGB pixel values to determine linearized gamma corrected device dependent RGB pixel values based on predetermined gamma correction factor, thus reducing the computations to determine the gamma correction factor each time. Upon determining, the plurality of device dependent pixel values are converted into corresponding plurality of device independent pixel values. The caiorimetric device then determines a change in luminosity based on determined caiorimetric values and predetermined caiorimetric values based on which the caiorimetric device generates a caiorimetric curve. The newly generated caiorimetric curve indicates the changes in the physiological parameters associated with the test fluid from manufacturing time to the time of using the test strip. Thus, the caiorimetric device and associated method enables efficient computation of caiorimetric values and caiorimetric curve of the test strip to determine the physiological parameters involving less number of computations and storage memory. In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

Figures 1A, IB and 1C illustrate an exemplar}' embodiment of a caiorimetric device for capturing images of a test strip in accordance with some embodiment of the present disclosure. Figure 1A illustrates the caiorimetric device 101 configured with an image capturing device or image sensor 102 such as camera. The caiorimetric device 101 may be an electronic device configured to perform caiorimetric analysis based on the images of the test strip captured by the image sensor 102. As an example, the electronic device can be any device capable of capturing images, processing the image and providing display. The electronic device may include, but not limited to, smart phone, tablet and Personal Digital Assistant (PDA) etc. In another example, the electronic device may be a handheld portable device.

The caiorimetric device 101 is positioned at a first height or distance 103A from a background 104 that is holding the test strip. The image seiisorl02flashes light with light rays 105 on the POC color strip alternatively referred as assay or test strip on the top surface of the test strip 108,In another embodiment, an external light source may be used for flashing light on the test strip 108. The light rays 105 from the image sensor 102 or the external light source have a solid angle of exposure 106. The caiorimetric device 101 is configured to capture a first image of the test strip 108 via the image sensor 102 positioned at the first height and obtain calibration curve with 3-D chromaticity model using data associated with the captured first image. The first image, the calibration curve and data associated with the first image are stored in memory and printed and/or supplied as specification corresponding to the POC assay for future use.

In another embodiment, the caiorimetric device 101 is positioned at a second height or distance 103B from the background 104 just before using the test striplOSfor analysis as illustrated in Figure IB. FigurelB illustrates a similar embodiment as Figure lA. The caiorimetric device 101 is configured to capture the second image of the test strip via the image sensor 102 positioned at the second height and obtain calibration curve with 3-D chromaticity model using data associated with the captured second image. The second image, the calibration curve and data associated with the second image are stored in memory and printed and/or supplied as specification corresponding to the POC assay for future use. In one embodiment, the second height 103B is more than the first determined Figure 1C illustrates an embodiment of image capturing of the given test strip after dipping or soaking with test fluid associated with a person for evaluating physiological parameters of the person. The test strip 108 is dipped or soaked in the test fluid that changes corresponding colorimetric values that yields physiological parameters associated with the test fluid.

In one embodiment, the calorimetric device 101 is configured to determine physiological parameters based on colorimetric curve derived from calorimetric values associated with the captured images of the test strip 108. Figure 2 illustrates an exemplary scheme to convert each RGB pixel of the captured images into colorimetric values in xyY representation. The calorimetric device lOlis a typical calorimetric device connected with the image sensor 102 as illustrated in Figure 3. The calorimetric device 101 comprises a processor 302, a memory 304, and an I/O interface 306. The I/O interface 306 is coupled with the processor 302 and an I/O device. The I/O device is configured to receive inputs via the I/O interface 306 and transmit outputs for displaying in the I/O device via the I/O interface 306.

The calorimetric device 101 further comprises data 308 and modules 310. In one implementati n, the data 308 and the modules 310 may be stored within the memory 304. In one example, the data 308 may include test strip image312, geometric parameters 314, image resizing factor 316, calorimetric values 318, physiological parameters 320 and other data 322. In one embodiment, the data 308 may be stored in the memory 304 in the form of various data structures. Additionally, the aforementioned data can be organized using data models, such as relational or hierarchical data models. The other data 218 may be also referred to as reference repository for storing recommended implementation approaches as reference data. The other data 322 may also store data, including temporary data and temporary files, generated by the modules 310 for performing the various functions of the calorimetric device 101.

The modules 310 may include, for example, a geometric parameters determination module (GPDM) 324, a calorimetric value determination module (CVDM) 326, a luminosity change determination module (LCDM) 328 and a physiological parameter evaluation module (PPEM) 330. The modules 310 may also comprise other modules 332 to perform various miscellaneous functionalities of the calorimetric device 101. It will be appreciated that such aforementioned modules may be represented as a single module or a combination of different modules. The modules 310 may be implemented in the form of software, hardware and/or firmware. In operation, the calorimetric device 101 is configured to receive factory calibrated calorimetric values of the test strip 108. The calorimetric device 101 is further configured to receive the first and the second images 312 of the test strip 108 from the image sensor 102 and determine geometric parameters associated with the test strip 108. In one embodiment, the GPDM 324 determines one or more geometric parameters 314 of the test strip 108 associated with the captured first and second images 312 of the test strip 108. Fig¾ire4 illustrates an exemplary representation of geometric parameters involved in capturing the first and second images 312 of the test strip 108.

In one embodiment, the GPDM 324 obtains a third image by extracting the image portion corresponding to the test strip 108 from the captured first image and determine a plurality of first geometric parameters associated with the third image thus obtained. In one example, the first geometri parameters may include radius ri, length l _a , breadth b _a , or area A _a , perimeter P _a , of the test strip 108 associated with the third image. The GPDM 324 further obtains a fourth image by extracting the image portion corresponding to the test strip 108 from the captured second image and determine a plurality of second geometric parameters associated with the fourth image thus obtained. In one example, the second geometric parameters may include radius TM, length lb, breadth bt _> , or area At,, perimeter Ρ· ₀ , of the test strip 108 associated with the fourth image. Based on the plurality of first and second geometric parameters thus determined, the calorimetric device 101 derives the image resizing factor 316 to generate one of a resized first and a resized second image.

In one embodiment, the CVDM 326 determines the image resizing factor 316 S based on the comparison of the plurality of the first and the second geometric parameters 314 as illustrated below. s —— ifi _a > l otherwise s _b ~—or

lb " ^' la

s _b - ¾f fc _a > ¾ otherwise s„ - ¾r s _ft =—if /ϊ _σ > 1 _¾ otherwise s _b -—or

^ft "

¾ ^:::: ¾f¾ > P _b otherwise s _b - ^™

ft ' a

In one embodiment, if the image resizing factor S _b 316 is determined to be equal to ' Γ, then the CVDM 326 performs no resizing of the second image. In another embodiment, if the image resizing factor 316s _b is determined to exceed value ' Γ, then the CVDM 326 obtains resized new image I _b based on the image resizing or scale factor 316and any appropriate interpolation technique. The CVDM 326 is further configured to determine calorimetric vales 318 from one of the resized first and second images. In one embodiment, the CVDM 326derives one or more RGB pixel values associated with each pixel of the resized first and the resized second image and determines a plurality of device dependent pixel values of the resized first and the resized second images based on each of the one or more derived RGB pixel values. The C VDM 326 determines the plurality of device dependent pixel values of the resized first and the resized second images by normalizing and applying gamma correction technique. In one aspect, the CVDM 326 normalizes the one or more derived RGB pixel values based on predetermined reference pixel values for example, 100 pixels, to obtain one or more normalized RGB pixel values(i? _c G _c B _c ). Upon normalization, the CVDM 326 calculates intensity value (c) of each of the one or more derived RGB pixel values based on a predetermined gamma correction factor represented in terms of a predetermined intensity value. In one example, the predetermined gamma correction factor is chosen within the range of 1.7 to 2.7. Gamma correction factor represented in terms of predetermined intensity value enables low number of computations during the conversion of device dependent pixel values into device independent pixel values. Based on the intensity value of each of the one or more derived RGB pixel values thus calculated, the CVDM 326 determines one or more linearized gamma corrected device dependent RGB pixel values C _f corresponding to each of the one or more normalized RGB pixel values (R _c G _c e).

In one example, the CVDM 326 determines the linearized gamma corrected device dependent RGB pixel values (βι G> Bf) for each of the one or more normalized RGB pixel values from the reference set (R _c G _c B _c ) obtained using the transformation described below in equation (1):

As illustrated in equation (1), the predetermined gamma correction factor is 2.4.

Upon determining the linearized gamma corrected device dependent RGB pixel values(/?j 6 B ), the CVDM 326 converts the plurality of linearized gamma corrected device dependent pixel values into a corresponding device independent pixel values (X Y Z)associated with the resized first and the resized second images using the transformation as illustrated below in equation (2);

......(2)

The CVDM 326 then determines the first and the second calorimetric values 318represented in 3-D chromaticity model( x >' Y) using the transformation as illustrated below in equation (.3):

Upon determining the first and the second calorimetric values, the LCDM 328 determines the change in luminosity based on the first and the second calorimetric values318. In one embodiment, the LCDM 328 generates a calibration curve corresponding to each of the first and the second calorimetric values 318 and determines a corresponding first and a second luminosity values. In one example, the exemplary 3D representation of chroraaticity xyY model corresponding to one of the first and the second calorimetric values is illustrated in Figure 5. The curve 502 represents the Calibration curve (indicative curve) of intended test fluid and the curve 504 represents the luminance value of 2-D chroraaticity point in (xy) space. Further, the curve 506 represents the xy space.

As illustrated in Figure 5, consider the point M with a coordinate (x _M , y _M that is obtained from a measurement made using the test striplOS. Two points P and Q are identified with corresponding co-ordinate points ( ρ, Υρ) and (XQ, YQ) belonging to calibrated and stored curve is obtained by searching two nearest points to the just measured point (x _M , y _M ) in the chromaticity space (xy) as shown in FigiireS and Figure 6. Figure 6 illustrates an exemplary geometrical representation of calibration and determination of color calorimetry. In order to find appropriate chromaticity point (xo' Vo) belonging to calibration curve as shown in Figure 6, a triangle is formed with vertices points (x _P , y _P ), (XQ- YQ ) ^an d (x _M , y _M )as shown in Figure Sand Figure 6.

The LCDM 328 is configured to determine the distance the two points (x _P , y _P ) and {XQ, YQ) and determine the orthogonal distance d _M0 to the measured point (x _M , y _M ) and line joining points (x _P , y _P )dXi.d(x _Q , y^). Based on the determined distance dq _P and the orthogonal distanced _{M 0} , the LCDM 328 determines the first luminosity value in 3D-chrornaticity point (x _P , y _P , Y _P ) and point Q, YQ , ^projecting from 2-D Chromaticity' point (x _P , y _P ) and ροίηΐ(χρ, YQ) , respectively. The LCDM 328 further determines the second luminosity value in 3D Chromaticity point {x _Q , y ₀ , Y ₀ ) projected from 2-D chromaticity point (x ₀ , y ₀ ) which is derived out of points (x _M , y _M ) ,

Jp) ^an d (½» )¾)· Using the computed first and second luminosity values, the LCDM 328 determines the difference in luminosity based on which the one or more physiological parameters 320 are determined. In one embodiment, the PPEM 330 determines the one or more physiological parameters 320 based on the respective calibration curves, associated difference in luminosity values and predetermined calorimetric values determined at the time of manufacturing.

Thus, the calorimetric device 101 determines the physiological parameter associated with the test fluid with low number of computations, involving less time and memory.

Figure 7 illustrates a flowchart of a method of performing coiorimetric analysis of a test fluid to evaluate associated physiological parameters in accordance with some embodiments of the present disclosure.

As illustrated in Figure 7, the method 700 comprises one or more blocks implemented by the processor 302 for performing coiorimetric analysis of a test fluid to evaluate associated physiological parameters. The method 700 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the method 700 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 700. Additionally, individual blocks may be deleted from the method 700 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 700 can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 702, receive images of test strip. In one embodiment, the calorimetric device 101 is configured to receive factory calibrated calorimetric values of the test strip 108. The calorimetric device 101 is further configured to receive the first and the second images 312 of the test strip 108 from the image sensor 102 and determine geometric parameters associated with the test strip 108.

At block 704, determine geometric parameters associated with the test strip. In one embodiment, the GPDM 324 obtains a third image by extracting the image portion corresponding to the test strip 108 from the captured first image and determine a plurality of first geometric parameters associated with the third image thus obtained. In one example, the first geometric parameters may include radius r _{1 ?} length L, breadth b _a , or area A _a , perimeter P _a , of the test strip 108 associated with the third image. The GPDM 324 further obtains a fourth image by extracting the image portion corresponding to the test strip 108 from the captured second image and determine a plurality of second geometric parameters associated with the fourth image thus obtained. In one example, the second geometric parameters may include radius I ^' M, length lb, breadth bi„ or area A _b , perimeter P _b , of the test strip 108 associated with the fourth image. Based on the plurality of first and second geometric parameters thus determined, the caiorimetric device 101 derives the image resizing factor 316 to generate one of a resized first and a resized second image.

At block 706, generate resized images of test strip. In one embodiment, the CVDM 326 determines the image resizing factor 316s _b based on the comparison of the plurality of the first and the second geometric parameters 314 and generates resized images based on the image resizing factor 316.1n one embodiment, if the image resizing factor Sb316 is determined to be equal to ' Γ, then the CVDM 326 performs no resizing of the second image. In another embodiment, if the image resizing factor 316s _b is determined to exceed value ' Γ, then the CVDM 326obtains resized new image / _¾ based on the image resizing or scale factor 316and any appropriate interpolation technique.

At block 708, determine caiorimetric values of resized images. In one embodiment, the CVDM 326 is configured to determine caiorimetric vales 318 from one of the resized first and second images. In one embodiment, the CVDM 326 derives one or more RGB pixel values associated with each pixel of the resized first and the resized second image and determines a plurality of device dependent pixel values of the resized first and the resized second images based on each of the one or more derived RGB pixel values.

The CVDM 326 determines the plurality of device dependent pixel values of the resized first and the resized second images by normalizing and applying gamma correction technique. In one aspect, the CVDM 326 normalizes the one or more derived RGB pixel values based on predetermined reference pixel values for example, 100 pixels, to obtain one or more normalized RGB pixel values(i? _c G _c B _c ). Upon normalization, the CVDM 326 calculates intensity value (c) of each of the one or more derived RGB pixel values based a predetermined gamma correction factor represented in terms of a predetermined intensity value. In one example, the predetermined gamma correction factor is within the range of 1.7 to 2.7. Gamma correction factor represented in terms of predetermined intensity value enables low number of computations during the conversion of device dependent pixel values into device independent pixel values. Based on the intensity value of each of the one or more derived RGB pixel values thus calculated, the CVDM 326 determines one or more linearized gamma corrected device dependent RGB pixel values (R _\ G _{ B ) corresponding to each of the one or more normalized RGB pixel values (R _c G _c B _c ),

In one example, the CVDM 326 determines the linearized gamma corrected device dependent RGB pixel values (βι G _L - B _t ) ^" for each the one or more normalized RGB pixel values from the reference set (R _c G _c B _c ) obtained.Upon determining the linearized gamma corrected device dependent RGB pixel values (Ri G _; the CVDM 326 converts the plurality of linearized gamma corrected device dependent pixel values into a corresponding device independent pixel values (X Y Z) associated with the resized first and the resized second images.

At block 710, evaluate physiological parameters based on determined calorimetric values. In one embodiment, the PPEM 330 determines the one or more physiological parameters 320 based on the respective calibration curves and associated difference or change in luminosity values. The LCDM 328 determines the change in luminosity based on the first and the second calorimetric values 318. In one embodiment, the LCDM 328 generates a calibration curve corresponding to each of the first and the second calorimetric values 318 and determines a corresponding first and a second luminosity values. The PPEM 330 determines the one or more physiological parameters 320 based on the respective calibration curves, associated difference in luminosity values and predetermined calorimetric values determined at the time of manufacturing. Thus, the calorimetric device 101 determines the physiological parameter associated with the test fluid with low number of computations, involving less time and memory.

As described above, the modules 310, amongst other things, include routines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The modules 310may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulate signals based on operational instructions. Further, the modules 310can be implemented by one or more hardware components, by computer -readable instructions executed by a processing unit, or by a combination thereof.

The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term "computer-readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

It is intended that the disclosure and examples be considered as exemplar}' only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.