Prediction of Cyclic Top Events

The invention is in the field of monitoring cyclic top faults.

Cyclic top is a phenomenon that affects rail vehicles. Such vehicles typically travel along a pair of rails known as the track. Cyclic top occurs when a dip in a rail causes the suspension of a vehicle passing over it to bounce. The rail then receives an impact loading at the end of the bounce that can create a second rail dip and, as that second dip deteriorates with successive impacts, trains will then bounce a second time, creating a third dip, and so on. After time a sequence of dips and corresponding peaks can be created which may, depending on the suspension characteristics of the trains using the line, cause each successive bounce to increase to the point where the vehicle will derail. The tendency for a sequence of dips, and hence corresponding peaks, to be created depends on the natural resonance frequency of the vehicles. Therefore the dips typically have one or more characteristic wavelengths. A sequence of dips or peaks in a single rail or a track caused by cyclic top is referred to here as a cyclic top fault.

The vehicles most susceptible to cyclic top are freight wagons of various types, since passenger trains usually have extra damping on the suspension tending to act against the build-up of faults in the track. However, passenger trains are not immune to cyclic top.

Tracks are currently monitored for cyclic top, for example using a measurement vehicle, to identify what are known as cyclic top “events”, i.e. cyclic top faults that require remedial or preventive action either shortly or eventually in a longer time period. If a cyclic top event is detected, typically a speed limit for freight trains is placed in a track length including the event, to prevent the bouncing building up to the derailment point and also to reduce the rate at which the track is deteriorating. The event then is recorded for rectification within a timescale that depends on the event magnitude, with severe events needing urgent repair, for example within 36 hours.

However the current systems for monitoring cyclic top have been found to be inadequate, and unexpected derailments have occurred.

Some embodiments of the invention described below solve some of these problems. However the invention is not limited to solutions to these problems.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter.

In one aspect there is provided in the following a method of predicting cyclic top faults in a rail, the method comprising: receiving measurement data indicating variations in height of the rail along the length of the rail at different time points; analysing the measurement data to obtain peaks at points along the rail for one or more wavelengths at each of a plurality of the different time points; for each of the plurality of different time points for at least one wavelength, identifying a length of rail containing at least two amplitude peaks above a first threshold as a risk site for a cyclic top event; for each risk site, predicting an accumulated amplitude value for at least one future point in time based on the measurement data for consecutive peaks within the risk site above the first threshold.

There is also provided a computer readable medium comprising instructions which, when implemented in a processor in a computing system cause the system to perform any of the methods described here.

There is also provided a computing system comprising a processor configured to perform any of the methods described here.

Features of different aspects and embodiments of the invention may be combined as appropriate, as would be apparent to a skilled person, and may be combined with any of the aspects of the invention.

Common reference numerals are used throughout the figures to indicate similar features.

Embodiments of the present invention are described below by way of example only. These examples represent the best ways of putting the invention into practice that are currently known to the applicant although they are not the only ways in which this could be achieved.

A cyclic top event, i.e. a fault that requires remedial action, may be defined in a number of ways. To pose a serious risk of derailment it was thought that two or more evenly spaced dips (amplitude) were required. One current industry definition of a cyclic top event is where a predetermined number (or more) of consecutive peaks along a track, for a specific wavelength, are identified over a predetermined amplitude threshold. Following the “two dips” theory, a cyclic top event has been defined as 3 or more consecutive peaks, for a specific wavelength, over a threshold such as 5 mm, and satisfying an accumulated value described further below.

It has been found that defining a cyclic top event in this way is not adequate to avoid accidents caused by cyclic top.

For example in a series of five peaks, the first and last two may exceed the threshold with the middle point being below the threshold. This would not currently be identified as a cyclic top fault. However, the middle peak could increase between successive measurements to the extent that it exceeds the threshold, in which case there are five successive peaks above the threshold which could cause a derailment. This kind of event can go undetected in current systems for monitoring cyclic top. In particular current systems do not make any attempt to predict cyclic top.

It would be useful to be able to monitor cyclic top faults so that the affected length of track can be repaired before the fault becomes worse and requires more extensive repair and/or before a speed limit is required, i.e. the fault becomes an “event”.

A method according to some embodiments of the invention will now be described. This method is largely described in the context of a track comprising two rails. However, some vehicles travel on single rails and may also be susceptible to cyclic top. Therefore, instances of “track” mentioned here include not only pairs of rails but also single rails, unless otherwise stated.

1 FIG. 1 FIG. 101 101 This method may commence with receiving measurement data indicating variations in height of the rail along the length of the rail at different time points, as indicated inoperation. This measurement data may be obtained by a measurement train of the kind currently used to monitor cyclic top and to perform other track measurement functions. The measurement train will typically travel along a track length being monitored to obtain the measurement data at regular intervals, for example monthly. Some service trains such as passenger and freight trains are being equipped to obtain this kind of measurement data which may then augment or replace data obtained from a measurement train. Therefore the measurement data received at operationmay be received at intervals for example in a continuous process and the subsequent operations ofmay commence as soon as measurement data for one point in time has been received.

The measurement data will typically indicate deviations of the track from its original geometry caused by wear, including cyclic top. Thus for example the measurements may be based on a notional straight line geometry which is not necessarily horizontal depending on the rail location. Thus, “height” does not necessarily mean vertical height.

In a specific example the measurement data may comprise Track Geometry condition data from a Normalised Location Parquet File or other binary compressed file. The two track geometry parameters used to generate the cyclic top values may be 35 m Top Left (to generate cyclic top values for the left rail) and 35 m Top Right (to generate cyclic top values for the right rail). Here, “top” refers to the track vertical alignment. “Left” refers to the left hand rail of the pair of rails that make up the track, in the direction of travel (for track recordings vehicles). ‘Top Left’, therefore, refers to the vertical alignment of the left rail.

Other data formats and other track geometry data may be used for different applications of this method. The measurement time points may be every 200 mm in a typical example.

1 FIG. 2 FIG. 2 FIG. 103 This measurement data is then analysed to obtain peaks at points along the rail, i.e. spatial locations, for one or more wavelengths, as indicated inoperation. A Butterworth filter or other suitable algorithm may be used for this purpose, as is known in the art. This analysis may be performed for a plurality of the different time points represented in the measurement data.shows the result of this analysis for one wavelength, where the distance between peaks represents the wavelength. Notably the peaks are at different heights and represent peak amplitudes, i.e. the wavelength amplitude varies along the rail. Graphs similar to that shown inmay be generated for different wavelengths from the same measurement data.

The wavelengths may be predetermined and may be chosen depending on the track or rail being monitored for cyclic top. For example the wavelengths may be determined from expected weaknesses knowing the geometry and or the vehicles that commonly travel along the track. The following table shows example frequencies used in a Butterworth filter for an example track geometry where the wavelengths are harmonics of a 18 m wavelength.

TABLE 1 Wavelength 4.5 m 6 m 9 m 13 m 18 m Upper Band 5.48 7.3 10.96 15.83 22.52 Lower Band 3.82 5.1 7.64 11.03 15.7

The result of analysing the measurement data is to obtain peak amplitude values, for example for each rail for all of the wavelengths (4.5 m, 6 m, 9 m, 13.5 m and 18 m) for different track segments, i.e. lengths of track, for example between stations or cities, which may be further identified as fast/slow, up/down etc.,

3 FIG. 3 FIG. 2 FIG. 3 FIG. The resultant peak amplitudes may be displayed in a peak amplitude chart. An example peak amplitude chart for a single wavelength is shown infor 1.6 Km (a mile) of track. Here the x-axis represents the recording direction, for example the direction of travel of the measurement train.corresponds toexcept that only the peaks are shown and a longer track segment is represented. It is important to remember thatis not an analysis of the wavelengths present in the rail shape, it shows the wave peaks for a particular wavelength. The peaks are consecutive spatial peaks at different points along a rail.

105 1 FIG. The peak amplitudes may be used to identify one or more “risk sites”, in other words locations along a rail where cyclic top events exist or where there is a risk of a cyclic top event occurring. This is indicated as operationin. This may be done using the peak amplitudes for all of the predetermined wavelengths. Risk site identification may use the peak amplitudes for both rails.

A currently used definition of a cyclic top event is where 3 or more consecutive peaks along a rail, for a specific wavelength, are identified over a 5 mm threshold. One object of the method described here is to predict cyclic top events, in other words to identify where cyclic top events will occur unless remedial action is taken, for example to enable remedial action to be taken and prevent the cyclic top event occurring.

The prediction may be based on a suitable time window. For example the method may look for sites which are at risk of becoming a cyclic top event within the next 90 days. The identification of a risk site may therefore use broader criteria than the current definition of a cyclic top event and therefore present engineers with data that was not previously available to them. As with the definition of a cyclic top event a peak threshold and minimum number of peaks may be used. For example a risk site may be identified where there are, within a run of consecutive peaks, at least two peaks above a first threshold. A further requirement may be that there is at least one additional peak above a second, for example lower, threshold. Notably, in contrast to current definitions of cyclic top, the identification of risk sites may not be limited to a specific wavelength. In other words the consecutive peaks may be at any wavelength.

In a specific example, risk sites may be identified where there are, within a run of consecutive peaks, at least 2 peaks over 4.5 mm and 1 peak over 3 mm.

Once such a run of consecutive peaks has been identified, the risk site may be extended beyond these peaks to include other consecutive peaks above one of the thresholds, for example the lower threshold. Thus risk site start location may be determined by looking backwards (against the direction of recording) for the first peak below a third predetermined threshold, e.g. equal to the lower threshold of 3 mm in this example. The end location may then be identified by looking forward (in the direction of recording) for the next peak below the third predetermined threshold, e.g. equal to the lower threshold of 3 mm in this example. It has been found successful to use the lower threshold to determine the length of the risk site but the higher threshold could also be used in which case the risk site would be shorter.

An upper limit may be placed on the possible length of a risk site. So for example if no peaks are found in the forward direction below the threshold within this upper length limit then the end location of the risk site is defined at the maximum length. A new risk site may then be defined in the next length of rail. A suitable upper limit might be determined in measurement points, for example 1000 measurement points or around 200 m.

4 a b FIGS.() and () 1 5 show two peak amplitude charts in which a square indicates an example of a risk site. In each case there are only two peaks above the higher threshold and so these would not yet qualify as cyclic top events by the currently used definition. In each case the start location is just after the first peak in the recording direction below the lower threshold, peakin each chart, and the end location is just before the next peak in the recording direction below the lower threshold, peakin (a) and not shown in (b).

1 FIG. 103 105 101 103 105 101 The process of analysing the measurement data and identifying one or more risk sites is repeated for subsequent points in time, for example for each measurement run. In other words, in the process illustrated in, operationsandare performed to obtain peaks at a plurality of the different time points in the received measurement data, more usually but not necessarily at all of the different time points for which data is received at operation. This may be a continuous process with operationsandbeing performed for each point in time as the measurement data is received at operation.

The obtained peaks for each of the identified risk sites are then further analysed in order to predict cyclic top events. It should be noted here that this further analysis may be performed on all of the obtained peaks and not only those in the identified risk sites. The limitation of the analysis to the risk sites is an optional operation that reduces the amount of peak data that needs to be analysed and therefore leads to a reduction in processing power and/or time needed to perform the methods described here. Therefore while the further analysis described below refers to the use of risk sites but it should be borne in mind that the use of risk sites at this stage is not essential.

It should further be noted that obtaining the wavelength peaks and using them for the further analysis of cyclic top saves on data processing as compared for example to comparing all of the measurement data obtained run on run since this will contain many data points. In the subsequent operations following obtaining the wavelength peaks measurement data between peaks is not used, only the peak amplitude data is used. This is a large saving of data since the wavelengths are typically of the order of several metres whereas the measurement data may be spaced by only 200 mm.

It is acceptable only to use the peak amplitude data since it is the peaks which impart energy back to the vehicle as it passes a fault and may cause a derailment.

Once the peak values have been identified at a first time point they are monitored using measurement data from subsequent points in time to estimate any future degradation of the track, for example evident from increases in amplitude of one or more peaks.

107 At operationthe peaks obtained for different points in time in the identified risk sites may be aligned, for example in order to correct for any measurement inaccuracies from one measurement run to the next. This may be performed using any suitable alignment algorithm known in the art. In one example, cross correlation is used to align raw track geometry data, cyclic top calculations are performed, and the peak amplitudes may then be further aligned if required, optionally using a simpler process suitable to the granularity of the cyclic top data.

As noted above, the identified risk sites may be used to denote which data to align so that the algorithm is not required to run for all the obtained peaks. In a specific example the peak amplitudes output from the Butterworth Filter within the identified risk sites are used to align the data run on run. The output of the alignment may for example comprise values with corresponding time points, e.g. dates, for each peak which has been aligned over a number of measurement runs, for example 10 measurement runs including the most recent measurement run.

109 1 FIG. These peak values from a plurality of different time points are used to evaluate any changes in the amplitudes of the peaks and thereby predict any degradation, e.g. increase in amplitude, of the peaks over a future time period, e.g. the next 90 days. This prediction is indicated at operationin. The prediction may be performed in a number of ways, for example using a suitable computer algorithm, one of which is described below.

The input to a prediction algorithm may be the aligned peak values and measurement run dates for each peak from the normalised data for a plurality of runs, for example 10 runs, for example performed at four weekly intervals. For each row of aligned data, i.e. each peak that is aligned from at least one run to another, the algorithm interpolates the peaks to create values between the measurement run data, for example daily values. Further, missing values from a measurement run may be populated, for example using the same interpolation process. A number of suitable interpolation algorithms are available, one suitable algorithm is available from https://pandas.pydata.org/pandas-docs.

A time series model may then be applied to the peak values and measurement run dates to generate a forecast for each peak, limited to peaks within risk sites where identified. A number of time series models are available, a suitable one is a Holt Winters time series model. This may be used to generate a forecast for the next 90 days or other suitable time period as required. A Holt-Winters model may model three aspects of a time series: a typical value (average), a slope (trend) over time, and a cyclical repeating pattern (seasonality). The table below shows suitable time series model parameters that may be used.

TABLE 2 Parameter Trend Seasonality Value Additive False

111 At operation, for each identified risk site an accumulated amplitude value is predicted for at least one future point in time based on the measurement data for consecutive peaks within the risk site above the first threshold.

111 105 If the risk sites have not already been identified they may be identified at a later stage prior to operation, in the manner described with reference to operation.

109 The accumulated amplitude value calculation may be performed on the measured and forecast values for each peak determined in operation. In other words, for each peak in each risk site a amplitude value is predicted for at least one future point in time based on the measurement data for consecutive peaks within the risk site above the first threshold. Then the predicted accumulated amplitude value is determined from the predicted amplitude values for single peaks.

109 In an alternative method, for each risk site at each of the plurality of time points, an accumulated amplitude value may be determined for consecutive peaks within the risk site above the first threshold, and then the predicted accumulated amplitude value may determined from the determined accumulated amplitude values. In other words, it is not essential to predict the peaks at operationin order to predict accumulated amplitude values.

The accumulated amplitude value may comprise a sum of peak values within a risk site. This accumulated amplitude value of peak amplitude measurements is indicative of the amount of energy that will be imparted to a vehicle as it passes over a risk site and is therefore particularly useful in the prediction of a cyclic top event. In an example the accumulated amplitude value may comprise a sum of peak values above the upper threshold. In another example it may comprise a sum of only consecutive peak values above the upper threshold. Where there is more than one set of consecutive values at a risk site the largest set may be chosen.

For each consecutive peak, in the direction of recording, over 5 mm the magnitude of the peak (in mm) is multiplied by a weighting factor. A factor of 0.8 has been found to be suitable. The consecutive values for each peak over 5 mm are summed to create an accumulated amplitude value for the risk site. In a specific example, the accumulated amplitude value calculation may be determined as follows:

5 FIG. An example calculation is shown infor a risk site containing 9 peaks above the lower threshold. There are two sets of consecutive values above the higher 5 mm threshold, one containing three peaks and the other containing two peaks. The set of three peaks is chosen for the accumulated amplitude value calculation.

a) A current cyclic top category-a number of categories with different levels of severity may be defined, examples of which are presented below. b) Dates when the accumulated amplitude value, which denotes a cyclic top fault, is expected to exceed one or more category thresholds within a certain future period, e.g. the next 90 days; c) Alert limit (IL), intervention limit (IL) and immediate action limit (IAL) exceedance dates corresponding to each accumulated value; and 6 FIG. d) A Cyclic Top degradation chart, an example of which is shown in. The accumulated amplitude value may be used for one or more of the following:

Referring to item a) if, for the current recorded value (day 0 forecast), the accumulated amplitude value for consecutive peaks over the higher threshold exceeds the defined threshold for a Cyclic Top fault, as defined in NR/L2/TRK/001 Mod11, or other applicable standard, then the fault is given a severity based on the categories listed in Table 3: Cyclic Top Category Thresholds, which are dictated by the standard. For some categories there different thresholds are defined depending on whether one or both rails are affected. It will therefore be appreciated that different categories could be defined, for example to meet different standards.

TABLE 3 Cyclic Top Se- Category verity Speed band Threshold Level Cat D AL All Speeds >=18 mm and <20 mm on one rail Cat D AL All Speeds >=38 mm and <40 mm on both rails Cat C IL All Speeds >=20 mm and <23 mm on one rail Cat C IL All Speeds 40 mm >= and <43 mm on both rails Cat B IL All Speeds >=23 mm and <26 mm on one rail Cat B IL All Speeds >=43 mm and <46 mm on both rails Cat A IL All Speeds >=26 mm and <30 mm on one rail Cat A IL All Speeds >=46 mm and <50 mm on both rails Cat I IAL All Speeds >=30 mm on one rail Cat I IAL All Speeds >=50 mm on both rails

If a Cyclic Top fault is present in the Left Rail and Right Rail at the same location and for the same wavelength* then a Both Rail Cyclic Top fault is created, as well as the Left and Right Cyclic Top faults. In other words, if there is a fault on the left rail, and a fault on the right rail, then there will also be a fault attributed to both rails.

Left and Right Rail Cyclic Top faults are considered at the same location where:

S L—Left Rail Start Location E L—Left Rail End Location S R—Rail Start Location E R—Rail End Location

A Both Rail accumulated value may be calculated as the sum of the accumulated values for the right and left rails.

109 Regarding item b) If the peak accumulated value of the fault (for a single rail or both rails) is below the lowest relevant threshold for the Cyclic Top event, but is predicted to exceed it within 90 days according to operationthen the fault severity is considered ‘Pre-Alert’.

The standard NR/L2/TRK/001 Mod11 includes additional categories that are not presently considered here but may also be taken into consideration, for example categories C+T and C+A. Additionally, the Cyclic Top calculations do not currently consider line speeds so faults are not assigned low speed Category Values (LC, LB, LA and LI) but this is a possible future development.

Regarding item c), the accumulated value for the last recording run date may be used to determine the Cyclic Top Category (Severity) for the Cyclic Top risk site which may be presented in a defect summary table. The threshold exceedance dates are used derive the AL, IL and IAL prediction dates for each of the Cyclic Top risk sites in accordance with Table 3: Cyclic Top Category Thresholds.

6 FIG. shows a cyclic top degradation chart. Here, accumulated values for one risk site are shown, where the solid lines are based on measured data and the dotted lines represent forward predictions for right and left rails and the sum of right and left. The thresholds for the accumulated values applicable to different categories mentioned in table 3 are indicated.

109 As noted earlier, operationis not essential and accumulated amplitude values determined from the measurement data could be used as inputs to the prediction algorithm in order to predict the accumulated amplitude vales.

Either way it is important to note that for each measurement run, i.e. each time point in the measurement data, the individual wavelength peaks are identified and tracked, rather than the accumulated values for example. It has been found that individual peaks can grow at different rates and therefore the methods described here are better able to predict cyclic track faults than methods used to date.

A rolling standard deviation function may be used to generate a rolling standard deviation for each measure point along the recorded track or rail, for example at each 200 mm interval, which may be provided as additional information within a Cyclic Top Defect Review page, for example on a user display.

7 FIG. In one example, for each CDMS File the normalised measurements of 35 m Top Left and Top Right are used to derive the Rolling Standard Deviation information. Then for every 200 mm the Standard Deviation is calculated over a window 500 records forward and 500 records backwards from that location (200 m window). A Rolling SD Parquet file may then be generated which includes the Rolling Standard Deviation for each 200 mm normalised measure point within the ingested CDMS file. This information may then be used to generate a Rolling SD Chart as part of a Cyclic Top defect review page. An example of such a chart is shown in.

The methods described here have been found in tests to enable the prediction and hence prevention of cyclic top events of kinds that had previously gone unnoticed and in some cases led to derailment of vehicles. Using these methods, engineers may be forewarned and therefore able to take preventive action that may for example avoid the need for speed restrictions on tracks, which is costly in terms of efficient track usage.

Some operations of the methods described herein may be performed by software in machine readable form e.g. in the form of a computer program comprising computer program code. Thus some aspects of the invention provide a computer readable medium which when implemented in a computing system cause the system to perform some or all of the operations of any of the methods described here. The computer readable medium may be in transitory or tangible (or non-transitory) form such as storage media include disks, thumb drives, memory cards etc. The software can be suitable for execution on a parallel processor or a serial processor such that the method steps may be carried out in any suitable order, or simultaneously.

The methods described here may be implemented using any suitable computer, computing device or computing system.

8 FIG. 800 800 800 805 815 820 825 830 820 835 840 A suitable computing system is illustrated infor completeness. Computing systemmay comprise a single computing device or components such as a laptop, tablet, desktop or other computing device. Alternatively functions of systemmay be distributed across multiple computing devices. Computing systemmay include one or more controllers such as controllerthat may be, for example, a central processing unit processor (CPU), a chip or any suitable processor or computing or computational device, an operating system, a memorystoring executable code, storagewhich may be external to the system or embedded in memory, one or more input devicesand one or more output devices.

805 805 820 805 805 825 1 FIG. One or more processors in one or more controllers such as controllermay be configured to carry out any of the methods described here. For example, one or more processors within controllermay be connected to memorystoring software or instructions that, when executed by the one or more processors, cause the one or more processors to carry out a method according to some embodiments of the present invention. Controlleror a central processing unit within controllermay be configured, for example, using instructions stored in memory, to perform the operations shown in.

101 805 825 1 FIG. Measurement data received at operationmaybe received at a processor comprised in the controllerwhich then controls the subsequent operations ofaccording to one or more algorithms which may be stored as part of the executable code.

835 800 835 840 800 840 Input devicesmay be or may include a mouse, a keyboard, a touch screen or pad or any suitable input device. It will be recognized that any suitable number of input devices may be operatively connected to computing systemas shown by block. Output devicesmay include one or more displays, speakers and/or any other suitable output devices. It will be recognized that any suitable number of output devices may be operatively connected to computing systemas shown by block. The input and output devices may for example be used to enable a user to select information, e.g. graphs as shown here, to be displayed.

This application acknowledges that firmware and software can be valuable, separately tradable commodities. It is intended to encompass software, which runs on or controls “dumb” or standard hardware, to carry out the desired functions. It is also intended to encompass software which “describes” or defines the configuration of hardware, such as HDL (hardware description language) software, as is used for designing silicon chips, or for configuring universal programmable chips, to carry out desired functions.

The embodiments described above are largely automated. In some examples a user or operator of the system may manually instruct some steps of the method to be carried out.

It will be understood that the benefits and advantages described above may relate to one embodiment or may relate to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages.

Any reference to “an” item or “piece” refers to one or more of those items unless otherwise stated. The term “comprising” is used herein to mean including the method steps or elements identified, but that such steps or elements do not comprise an exclusive list and a method or apparatus may contain additional steps or elements.

Further, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

The figures illustrate exemplary methods. While the methods are shown and described as being a series of acts that are performed in a particular sequence, it is to be understood and appreciated that the methods are not limited by the order of the sequence. For example, some acts can occur in a different order than what is described herein. In addition, an act can occur concurrently with another act. Further, in some instances, not all acts may be required to implement a method described herein.

The order of the steps of the methods described herein is exemplary, but the steps may be carried out in any suitable order, or simultaneously where appropriate. Additionally, steps may be added or substituted in, or individual steps may be deleted from any of the methods without departing from the scope of the subject matter described herein. Aspects of any of the examples described above may be combined with aspects of any of the other examples described to form further examples.

It will be understood that the above description of a preferred embodiment is given by way of example only and that various modifications may be made by those skilled in the art. What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable modification and alteration of the above devices or methods for purposes of describing the aforementioned aspects, but one of ordinary skill in the art can recognize that many further modifications and permutations of various aspects are possible. Accordingly, the described aspects are intended to embrace all such alterations, modifications, and variations that fall within the scope of the appended claims.