Real-time, full web image processing method and system for web manufacturing supervision

Abstract

A real-time, full web image processing method for analyzing formation in a web is described, the web is transported in a moving direction during a web manufacturing process, the method including the steps of acquiring a two-dimensional original image P.sub.0 of the web, the image being representable as a digital image representable by a plurality of pixel values P.sub.0,i,j with i{1; . . . ; I}, j{1; . . . ; J}; and producing a plurality of P processed images P.sub.p with p{1; . . . ; P}, each of the processed images being representable by pixel values P.sub.p,m,n with m{1; . . . ; M}, n{1; . . . ; N}, the processed images being obtained by spatial bandpass filtering of the original image, wherein a spatial different bandpass filter is used for obtaining each of the processed.

Claims

1. A method, implemented on a computer, for detecting, monitoring and/or analyzing a quality of a product being produced in a manufacturing process, said product being transported, on a conveyor belt, in a moving direction during said manufacturing process, the method comprising the steps of: a) acquiring an original image P.sub.0 of the product, said image being representable as a two-dimensional digital image having a plurality of pixels having pixel values P.sub.0,i,j with i{1; . . . ; I}, j{1; . . . ; J}; b) producing a plurality of P processed images P.sub.p with p{1; . . . ; P}, each of said processed images being representable by pixel values P.sub.p,m,n with m{1; . . . ; M}, n{1; . . . ; N}, said processed images being obtained by spatial filtering in form of spatial bandpass filtering, of the original image, wherein a different spatial filter in form of spatial bandpass filter, is used for obtaining each of the processed images, and c) combining at least two, preferably all, of the processed images P.sub.p with p{1; . . . ; P} to obtain a feature map F being representable by values F.sub.m,n with m{1; . . . ; M}, n{1; . . . ; N}, preferably m{1; . . . ; M}, n{1; . . . ; N} wherein the values F.sub.m,n of the feature map correspond to a predominant size category for m,n with F.sub.m,n=F(m,n)p.sub.max,m,n with P(p.sub.max,m,n,m,n)>P(p,m,n) with p{1; . . . ; P}\{p.sub.max,m,n}, wherein the processed images P.sub.p with p{1; . . . ; P} are thresholded and converted to binary images representable by pixel values P.sub.p,m,n{0; 1} for p{1; . . . ; P}, m{1; . . . ; M}, n{1; . . . ; N}.

2. The method according to claim 1, wherein the product is a web, in particular a paper web, and the quality being monitored and/or analyzed includes formation in said web.

3. The method according to claim 1, wherein the two-dimensional digital image by which the original image may be represented is provided as a stream of data, preferably in real time, and preferably without intermediate storage of the entire two-dimensional digital image.

4. The method according to claim 1, wherein at least one of the plurality of P processed images P.sub.p is provided as a stream of data, preferably in real time, and preferably without intermediate storage of said processed image.

5. The method according to claim 1, wherein characteristics of at least one spatial filter may be adapted, in particular by setting a filter parameter; and a) the pixel values P.sub.p,m,n are obtained successively by applying one or more of the filters successively to individual pixels or subsets of pixels representing the original image; wherein b) at least some pixel values P.sub.p,m,n that have already been obtained are used to adapt the characteristics of the at least one spatial filter prior to obtaining further pixel values.

6. The method according to claim 1, wherein the feature map F.sub.m,n is obtained according to F.sub.m,nP.sub.max,m,n with P.sub.max,m,n=max{P.sub.p,m,n|p{1; . . . ;P}}.

7. The method according to claim 6, wherein the feature map F.sub.m,n is a scalar feature map, a first component of F.sub.m,n contains values P.sub.max,m,n, while a second component contains values P.sub.min,m,n with P.sub.min,m,n=min{P.sub.p,m,n|p{1; . . . ; P}}.

8. The method according to claim 1, further comprising the step of: a) determining, from at least two, preferably all of the processed images P.sub.p with p{1; . . . ; P}, an image feature vector v=(v.sub.1, . . . , v.sub.P), wherein vector component v.sub.p of said image feature vector v is determined from processed image P.sub.p with p{1; . . . ; P}.

9. The method according to claim 8, further comprising determining the image feature vector v on the basis of the feature map F.

10. The method according to claim 8, further comprising determining a first global image feature vector v on the basis of the whole original image and a second local image feature vector on the basis of a subregion or subarea of the original image and comparing the first and second image feature vectors.

11. The method according to claim 8, further comprising the step of applying gain and/or offset correction to at least a selection of processed images P.sub.p with p{1; . . . ; P}, in particular applying individual gain and/or offset correction to a selection of processed images P.sub.p with pS{1; . . . ; P}, wherein gain correction and/or offset for processed images P.sub.p is repeatedly updated based on a deviation between a current value of a local or image feature vector component v.sub.p and a target value {circumflex over (v)}.sub.p for said feature vector component v.sub.p.

12. The method according to claim 1, wherein a) the two-dimensional original image is obtained from a raw digital image of product web, preferably obtained by means of a real-time linescan or matrix camera using fixed scan time, and b) said raw digital image is corrected by an adaptive flat line correction method.

13. The method according to claim 1, wherein a) in step b) of claim 1, a plurality of smoothed images B.sub.q with q{1; . . . ; Q} each of said smoothed images being representable by pixel values B.sub.q,m,n, with m{1; . . . ; M}, n{1; . . . ; N}, is produced, each of said smoothed images being obtained applying a spatial low pass or smoothing filter to the original image, with a different filter being used for each of the smoothed images B.sub.q,m,n; b) each of the processed images P.sub.p with p{1; . . . ; P} is produced by subtracting two smoothed images B.sub.p1,m,n, B.sub.p2,m,n with p1p2.

14. The method according to claim 1, wherein a) a standard deviation of the original image P.sub.0 is determined; b) the processed images P.sub.p with p{1; . . . ; P} are thresholded with the standard deviation or a multiple thereof.

15. The method according to claim 14, further characterized in that the feature map F is displayed as a two-dimensional digital color image, with a different color being displayed for each different value of F.sub.m,n with m{1; . . . ; M}, n{1; . . . ; N}.

16. The method according to claim 1, further comprising the step of displaying the feature map F as a two-dimensional digital image.

17. The method according to claim 1, wherein at least one bandpass filter is a two-dimensional bandpass filter having transfer characteristics for a first spatial direction which are different from transfer characteristics for a second spatial direction.

18. An optical web inspection system comprising: a) an image acquisition unit for acquiring a raw image and/or an original image P.sub.0 of a web being transported in a moving direction during a web manufacturing process, b) a digitization unit, preferably included by the image acquisition unit, for determining pixel values P.sub.0,i,j with i{1; . . . ; I}, j{1; . . . ; J} representing said original image P.sub.0, c) a processing unit configured to execute the method including the steps: a) acquiring an original image P.sub.0 of the product, said image being representable as a two-dimensional digital image having a plurality of pixels having pixel values P.sub.0,i,j with i{1; . . . ; I}, j{1; . . . ; J}; b) producing a plurality of P processed images P.sub.p with p{1; . . . ; P}, each of said processed images being representable by pixel values P.sub.p,m,n with m{1; . . . ; M}, n{1; . . . ; N}, said processed images being obtained by spatial filtering in form of spatial bandpass filtering, of the original image, wherein a different spatial filter in form of spatial bandpass filter, is used for obtaining each of the processed images, and c) combing at least two, preferably all, of the processed images P.sub.p with p{1; . . . ; P} to obtain a feature map F being representable by values F.sub.m,m with m{1; . . . ; M}, n{1; . . . ; N}, preferably m{1; . . . ; M}, n{1; . . . ; N} wherein the values F.sub.m,n of the feature map correspond to a predominant size category for m, n with F.sub.m,n=F(m,n)p.sub.max, m,n with P(p.sub.max,m,n,m,n)>P(p,m,n) with p{1; . . . ; P}\{p.sub.max,m,n}, wherein the processed images P.sub.p with p{1; . . . ; P} are thresholded and converted to binary images representable by pixel values P.sub.p,m,n{0; 1} for p{1; . . . ; P}, m{1; . . . ; M}, n{1; . . . ; N}; and d) a display unit for displaying results obtained when executing said method, in particular the feature map F.sub.mn and/or an image feature vector v.

19. The optical web inspection system according to claim 18, wherein the processing unit includes a field-programmable gate array.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The subject matter of the invention will be explained in more detail in the following text with reference to exemplary embodiments which are illustrated in the attached drawings, of which:

(2) FIG. 1 shows exemplary images of two paper types having different formation;

(3) FIG. 2 illustrates a web inspection system which may be used for applying the method in accordance with the present invention to a web manufacturing process;

(4) FIG. 3 shows an example of the parallel architecture of product imaging algorithms as implemented in the web inspection system of FIG. 1;

(5) FIG. 4 shows bandpass filter bank based formation analysis result visualization and the corresponding bar graph;

(6) FIG. 5 shows a flow diagram of an exemplary embodiment of the method in accordance with the present invention;

(7) FIG. 6 shows a flow diagram of an exemplary internal structure of video correction.

DETAILED DESCRIPTION

(8) FIG. 2 illustrates a web inspection system which may be used for applying the method in accordance with the invention to a web manufacturing process.

(9) During said web manufacturing process, a web 11 moves in a moving direction MD underneath a line scan camera 12, preferably a CMOS line scan camera, which comprises a plurality of X pixel sensors 13 (of which only four are shown for clarity) arranged in a row extending in a cross direction CD of the web perpendicular to the moving direction. In operation, the line scan camera 12 scans the web as it passes by in order to acquire an image of said web and delivers a stream of line scans. A number Y of consecutive line scans may be combined into a two-dimensional digital image of a section of the web in moving direction, said digital image having a pixel dimension of X.Math.Y pixels and comprising a plurality P=X.Math.Y pixels P.sub.i with i{1; . . . ; X.Math.Y}, each pixel having one or more pixel values representative of a local color or total intensity, hue, saturation. The pixel values may have a certain bit depth or bit resolution, and may in particular be binary values representable by a single bit, which bit depth may correspond to a bit depth of the line scan camera, or have been obtained through up- or downsampling of the bit depth. For a line scan rate f.sub.line, and a transport velocity v.sub.MD of the web in moving direction, a length of the section of the web in moving direction imaged this way is Y.Math.v.sub.MD/f.sub.line.

(10) In the exemplary setting of FIG. 2, line scan camera 12 has 40001 pixels, and is capable of scanning 80.000 lines per second. Thus, X=4000 may in particular be chosen as pixel resolution in CD.

(11) Line scan camera 12 can be directly or indirectly coupled to image-processing unit 15. Functions of the image-processing unit 15 may also be integrated with the camera, in which case the camera is a more complicated and self-contained image-processing unit. Image data output of an analog camera, for example an analog CCD or CMOS line scan camera or matrix camera, has to first be converted to digital format. Digital camera output is typically more ready for digital processing in the image-processing unit 15. The image-processing unit 15 receives from the line scan cameras 12 a digital representation of the view imaged by said cameras. The representation is in the form of a series of digital numbers. Image processing unit 15 interprets this data as an electronic image, which is elsewhere referred to as an image, on the basis of the information it has about the properties of the Line scan camera 12.

(12) The signal from the line scan camera 12 is forwarded to the next processing step, which is image analysis. This step can be done in image-processing unit 15 or in a separate computer, which may be a part of an operator station 16 of the visual inspection system 10 and it is typically common to all the cameras 13. Image analysis comprises, for example, further segmentation of the interesting areas, such as defects, in the image. After segmentation, features describing properties of the regions found by segmentation can be extracted. The features are numeric values that will be used in recognizing the areas, i.e. in classifying them.

(13) The image-processing unit 15 is a separate, typically programmable, hardware unit. It can be partially or totally integrated with the line scan camera 12, as depicted in FIG. 1. It can be also a personal computer or any other type of universal computer. One computer may take care of image data processing of one or several cameras. The method for processing image data is applied in this stage. The detection, i.e. obtaining an inspection signal that is recognized coming from a defect, is performed and by means of the method for processing image data the image of the web is divided into interesting regions. The outcome of this processing stage is a set of electronic images representing segmented parts of the web, the images being manipulated electronically to meet requirements of the application at hand.

(14) Operator station 16 contains the user interface of the visual inspection system 10. It is used for entering various tuning parameters and selecting desired displays and reports, which for example show the status of the system and the quality of the inspected products. Naturally the visual inspection system 10 requires separate means for supplying power to the system and devices for interfacing with the external systems such as the process itself. These means, which are well known to those of ordinary skill in the art, can be located in an electronic cabinet 17. In addition to operator station 16, external devices 18 can be used for alerting the operator.

(15) The image data may be stored in an image database. The image collection of the database consists of different types of digitized web defects. In addition to formation analysis, defects may be detected and their images are digitized from a running web. For classifying the defects a classifier 19 may be used. Defect classification may, in particular be based on the method as described in EP patent application EP 16180281.4, which is hereby included by reference in its entirety; or in Huotilainen, T., Laster, M., Riikonen, S., Real-time ROI Morphometrics in High Quality Web Imaging, PaperCon, 2016, which is hereby included by reference in its entirety.

(16) FIG. 3 shows an example of the parallel architecture of product imaging algorithms as implemented in the web inspection of FIG. 1, and illustrates how various inspection and monitoring methodologies, in particular for detection of discrete or strong defects, subtle or weak defects, streaks and/or dirt may interact with the formation analysis method in accordance with the present invention. In particular, as may be seen, various aspects of the invention as described herein may be combined with various aspects from the methods as described in EP patent application EP 16180281.4 or in WO 2017/198348 A1; or in Huotilainen, T., Laster, M., Riikonen, S., Real-time ROI Morphometrics in High Quality Web Imaging, PaperCon, 2016, both of which are hereby included by reference in their entirety. In particular, the processed images P.sub.p with p{1; . . . ; P} may be used starting point for these methods, allowing to also extract, e.g. shape and/or feature orientation information to be extracted for different size categories. Such information may then also be represented by means of local feature vectors and/or image feature vectors as described above, or combined with such feature vectors.

(17) The method in accordance with the present invention is carried out on the image-processing unit 15. The results, in particular feature map and feature vectors obtained, may be displayed on a display contained in the operator station 16.

(18) Bandpass filter bank based floc or blob detection results are visualized by combining 16 different floc or blob size categories, also referred to as floc or blob scales, in a feature map as illustrated in FIG. 4. Different colors are chosen and correspond to the floc or blob size categories. Additionally, the floc or blob power (unweighted area or area weighted by intensity) inside the size categories or scales are presented with a bar graph visualization of the respective image feature vector. The bar graph colors are the same as in the image representing the different size categories.

(19) In an alternative and/or supplementary description, the method in accordance with the invention comprises the following steps:

(20) 1. A raw digital image (in particular a 12 bit image) of a product web is generated by a real-time linescan (or matrix) camera using fixed scan time, and said image is corrected by an adaptive flat line correction method developed for ABB WIS system earlier.

(21) 2. The method described in WO 2017/198348 A1, which is hereby included by reference in its entirety, and related to a real-time (online) full web paper formation analysis, or other product formation or surface analysis may optionally be utilized for look through type real-time analysis.

(22) 3. The corrected image is filtered with smoothing filter bank having smoothing filters, in particular spatial low pass filters, with different spatial widths.

(23) 4. The smoothed image signals are used to generate spatial bandpass filtered results by subtracting the low pass filtered images of the neighboring smoothing filters. This can be based on different kind of spatial low pass filters. Real-time streaming video imaging sets extremely high requirements for designing spatial filters. One option is to use Difference of Gaussians (DoG) filters but also other options seems to work. A combination of two directional CD Gaussian filtering (recursive Gaussian technique) and MD IIR filtering may also be use and provide results which are correlating with DoG method.

(24) 5. The bandpass filtered images are thresholded with a standard deviation (or a multiple thereof) of the original corrected image signal to form a base for floc power (area*intensity) analysis inside different size categories.

(25) 6. Online and/or offline image and bar graph based individual or combination visualization may be formed. An example of all scale combination visualization is shown in FIG. 4. The visualization is formed based on the different scale responses by selecting the scale (and thus the corresponding color) to individual pixels based on the local computed feature value of scales. Feature value, which is used to select the scale and defines to color, can be for example scale power, orientation or some other shape feature.

(26) 7. The results may be reported and visualized also in values if desired. The statistical analysis results are related to specified measurement area.

(27) 8. A system in accordance with the invention may be equipped with the detection of formation feature defects appearing in a specific floc size category (scale). This may be done by applying a gain correction principle to the feature distribution signal and forming a reference signal. Changes in the different formation size categories may then be detected. Feature vector may include power distribution, size categorized shape features, in particular orientation.

(28) 9. Additionally, automatically adjusted threshold values are generated and applied to the different scale bandpass filtered results to form binary images. The threshold levels are generated in light and dark sides of the dynamic range by autoadjustment to achieve desired percentage of exceeded floc and void areas of the product.

(29) 10. The detected floc and/or void region shapes are analyzed. The analysis of the floc and/or void areas is based on the real-time (online) digital filtering method, which combines the neighborhood pixels and calculates the features of the floc and/or void region simultaneously as described in WO 2017/198348 A1.

(30) 11. The area averages of the detected flocs and/or voids and the corresponding intensity averages of the same floc and/or void regions are calculated and combined.

(31) 12. The calculated floc and/or void features are stored and can be visualized in an online map.

(32) FIG. 5 shows a flow diagram which illustrates an exemplary embodiment of the method as described above.

(33) The method starts (on the top left corner of FIG. 5) with raw video streamed from a line scan camera as described herein. The streamed video signal is then subjected an initial video signal correction as described herein, and exemplary shown in FIG. 6. The correction may in particular include corrections for effects due to imaging optics, in particular lenses, and illumination, in particular to ensure that response will be independent of illumination. Flat field correction may also be applied.

(34) More specifically, in video correction method, a gain value may be stored in memory for every cross direction (CD) pixel position. Gain values g(n) may be adjusted regularly by a rate defined, e.g., by an operator. Thus, the corrected video signal nagc(n) is
nagc(n)=g(n)raw(n)(1)

(35) where g(n) is adjustable gain, raw(n) is raw video signal and n is the pixel position. In AGC the target for video level in long-term is in the middle of the whole dynamic range. If the corrected video signal nagc(n) is above the target, the gain value may be reduced. Correspondingly, if the signal value is below the target the gain value may be enlarged. Hence, the gain adjusting method may be expressed by
g.sub.new(n)=g.sub.old(n)+sign(tnagc(n))rate(2)

(36) where sign is a function which returns 1 for positive and 1 for negative results, rate defines the speed of adjusting (Normal AGC Adjust Rate control parameter) and t is the target value.

(37) The signal as output from the initial video signal correction is fed back to a correction gain adjustment, which adjusts an instantaneous or current gain value applied by the initial video signal correction. For further processing, the signal as output from the initial video signal correction is subsequently subjected to corrected video dynamic range optimization, from which a corrected video signal is output, i.e. streamed.

(38) The corrected video signal is then subjected to preprocessing for formation analysis, resulting in a preprocessed video signal. Preprocessing for formation analysis may include several preprocessing stages, which may be needed for the different filtering options and supporting the control of the formation analysis method; in particular:

(39) a. small local area averaging (for example streaming average of 4 scan line pixels)

(40) b. video scan line turning and synchronization for two directional lowpass infinite impulse response (IIR) filtering

(41) c. corrected video statistical and other measures for the parallel algorithm control and for feature vector/signal i. for example local area std, larger area std, skewness, kurtosis, CD average floc size based on direct algorithm, MD average floc size based on direct algorithm, anisotropy (CD average floc size/MD average floc size), floc orientation (average floc angle), void orientation (average void angle), floc size variation, void size variation, ii. automatic threshold level options generation based on statistical measures for the control of the parallel formation analysis method

(42) d. handling of the input signal validity: control of the measurement area, control of the valid intensity range, invalid product area elimination during the streaming formation analysis process i. measurement location control ii. input signal intensity analysis/control 1. for example discrete defect detection for formation analysis masking iii. masking (based on CD locations, or video intensity range) 1. for example formation method control/handling near camera viewing area edges or product edges iv. signal replacing or measurement enabling/disabling as needed.

(43) The preprocessed video signal is then subjected to spatial bandpass filtering as described herein. The streamed signal obtained from the spatial bandpass filtering is then subjected to floc or blob size category power analysis as described herein, and feature signal generation, as described herein. In parallel to the floc or blob size category power analysis, floc or blob size category shape analysis as described in WO 2017/198348 A1 may also be carried out on the preprocessed video signal.

(44) The corrected video signal, the preprocessed video signal and/or the results from the floc or blob size category shape analysis, may also be taken into account in the feature signal generation.

(45) Further results from the feature signal generation may be fed back into the spatial bandpass filtering, in particular to adapt filter parameters as described herein, into floc or blob size category power analysis, and/or into floc or blob size category shape analysis.

(46) Preprocessing for the formation analysis, spatial bandpass filtering, size category shape analysis, size category power analysis and feature signal generation are closely related and are working closely with each other during the streaming/parallel/feedback process and thus may be considered as one functional block, which is processing streaming corrected video input and giving feature signal (vector) as output.

(47) A raw feature signal as delivered by the feature signal generation and described herein may then be used directly for formation reporting, in particular by creating a feature map as described herein, and displaying said feature map as described herein, and shown in the top part of FIG. 4. Formation reporting may also include results from formation raw feature classification, as described herein and shown exemplary in form of the bar graph in the bottom part of FIG. 4.

(48) The raw feature signal may also be used as an input to feature signal analysis and/or feature signal correction.

(49) Different approaches can be considered for the feature signal value correction i.e. the feature signal standard generation. We can use 1. different kinds of lowpass filtering methods including averaging, FIR or IIR based lowpass filtering.

(50) In the simplest case we can use gain correction or offset value based correction for standard signal generation by calculating difference of the current corrected feature signal value and the corresponding target signal value. So in this case, there are gain and offset values stored in memory for every feature vector value to be corrected. Gain values g(n) and offset values a(n) can be adjusted regularly by operator defined rate. Thus, the corrected feature signal fea(v) is
fea()=a()+g()rawfea()(3)

(51) where g(v) is adjustable gain, a(v) is adjustable offset value, rawfea(v) is raw feature signal and v is the feature value index. In feature signal correction the target for the vector value in long-term is in the desired position of the whole range. If the corrected feature signal value fea(v) is above the target the gain or offset value is reduced. Correspondingly, if the signal value is below the target the gain or offset value is enlarged. Hence, the adjusting methods can be expressed by
g.sub.new()=g.sub.old()+sign(t()fea())grate(4)
a.sub.new()=a.sub.old()+sign(t()fea())arate(5)

(52) where sign is a function which returns 1 for positive and 1 for negative results, grate and arate define the speed of adjusting and t(v) is the target value for the corresponding feature vector value. In normal cases only one adjustable correction principle is enabled at a time i.e. offset or gain correction and the other is either fixed or disabled. The chosen correction principle is depending on the basis and characteristics of the features of the feature vector. The adjustment process can be freezed after some time to keep the fixed reference or the adjustment can be slow continuous process.

(53) The corrected feature signal represent the dissimilarity between standard (reference, which can for example represent normal quality or can represent a specific case, which we are searching) and current feature signals. The next step is to detect (feature defect segmentation) feature defects (large enough dissimilarities) by setting threshold levels to selected corrected feature vector values or for some feature vector value combination sets. Combination set value can be given by calculating the vector length of the feature vector value set utilizing chosen distance measure. The most common distance measure is Euclidean distance. Segmented feature vectors (selected/segmented by thresholding) can then be used for formation feature classification by utilizing chosen classification method. Examples of applicable classification methods are 1. decision trees, 2. k-NN (k-nearest neighbors) classifier, 3. neural network classifiers (for example MLP) and 4. simple matching classifier. In matching classifier the result can be derived by vector comparison between current feature vector and the class vectors in formation feature vector library.

(54) In some special cases the formation quality can be analyzed based on straight classification of the raw feature signal.

(55) Formation feature defect classification and formation feature defect segmentation may then be carried out on the corrected feature signal as described herein.

(56) While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art and practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the term comprising does not exclude other elements or steps, and the indefinite article a or an does not exclude a plurality. The mere fact that certain features and/or measures are recited in different or separate embodiments, and/or mutually different dependent claims does not indicate that a combination of these features and/or measures may not be considered disclosed, claimed, or cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

(57) As used herein, i.e. anywhere in this document, the terms computer, and related terms, e.g., processor, processing device, central processing unit (CPU), computing device, and controller may not be limited to just those integrated circuits referred to in the art as a computer, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller (PLC), and application specific integrated circuit, and other programmable circuits, and these terms are used interchangeably herein. In the embodiments described herein, memory may include, but is not limited to, a computer-readable medium, such as a random access memory (RAM), a computer-readable non-volatile medium, such as a flash memory. Alternatively, a floppy disk, a compact discread only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD), a USB stick and/or a flash memory card (e.g. CF, SD, miniSD, microSD) may also be used.

(58) Further, as used herein, the terms software and firmware are interchangeable, and include any computer program which may be stored in memory for execution by computers as defined above, including workstations, clients, and/or servers.

(59) As used herein, the term non-transitory computer-readable media is intended to be representative of any tangible computer-based device implemented in any method of technology for short-term and/or long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in a tangible, non-transitory, computer-readable medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a computer as defined above, cause the computer to perform at least a portion of the methods described herein. Moreover, as used herein, the term non-transitory computer-readable media may include all tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including without limitation, volatile and non-volatile media, and removable and non-removable media such as firmware, physical and virtual storage, CD-ROMS, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being transitory, propagating signal.

(60) As used herein, the term real-time may refer to at least one of the time of occurrence of an associated event, a time of measurement and collection of predetermined data, a time at which data is processed, and/or a time of a system response to the events and the environment. In the embodiments described herein, these activities and events may occur substantially instantaneously, and may in particular be scheduled to occur simultaneously, in particular within one clock cycle of an involved computer, or within a limited number, in particular less than 10, 50, 100, 500, 1000, or 10000 clock cycles, or less than 10n clock cycles with n<5, 6, 7, 8, or 9.

(61) Unless stated otherwise, it shall be assumed throughout this entire document that a statement ab implies that |ab|/(|a|+|b|)<10.sup.1, preferably |ab|/(|a|+|b|)<10.sup.2, wherein a and b may represent arbitrary variables as described and/or defined anywhere in this document, or as otherwise known to a person skilled in the art. Further, a statement that a is at least approximately equal or at least approximately identical to b implies that a=b, preferably a=b. Further, unless stated otherwise, it shall be assumed throughout this entire document that a statement a>>b implies that a>10b, preferably a>100b; and statement a<<b implies that 10a<b, preferably 100a<b.

(62) It should be noted that the term comprising does not exclude other features, in particular elements or steps, and that the indefinite article a or an does not exclude the plural. Also elements described in association with different embodiments may be combined. It should also be noted that reference signs in the claims shall not be construed as limiting the scope of the claims.

(63) It will be appreciated by those skilled in the art that the present invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The presently disclosed embodiments are therefore considered in all respects to be illustrative and not restricted. The scope of the invention is indicated by the appended claims rather than the foregoing description and all changes that come within the meaning and range and equivalence thereof are intended to be embraced therein.

(64) The disclosure as provided by this entire document also may include embodiments and variants with any combination of features, in particular individual features, mentioned or shown above or subsequently in separate or different embodiments, even if such features may only be shown and/or described in connection with further features. It may also include individual features from the figures, even if such features may only be shown in connection with further features, and/or are not mentioned in the above or following text. Likewise, any such features, in particular individual features, as described above, may also be excluded from the subject matter of the invention or from the disclosed embodiments and variants. The disclosure may include embodiments which include only the features described in the claims or in the exemplary embodiments, as well as embodiments which include additional other features.

(65) Preferred embodiments of the present invention, in particular as described above, may be realized as detailed in the items listed below, advantageously in combination with one or more of the features as detailed above: