Patent Application
20220057341
This application claims priority to European Patent Application No. EP 20191722.6, filed on Aug. 19, 2020 with the European Patent Office. The contents of the aforesaid patent application are incorporated herein for all purposes.
The present invention relates to computer-implemented methods, computing devices, and computer programs for processing volumetric scanning data and analyzing the performance of structural elements represented by computed tomography scanning data.
This background section is provided for the purpose of generally describing the context of the disclosure. Work of the presently named inventor(s), to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
The analysis of faults that occur during the fabrication of parts, such as vehicular parts, is a field of research and development. Since many components are housed in a closed casing, a manual analysis of such component's faults might be performed only at the cost of destroying the casing, which may lead to additional faults that have not been present before opening the casing, and may not be applicable for spot-checks being performed during manufacturing. Therefore, in some cases, computed tomography (CT) scans are used for fault analysis, or in more general terms, spot tests during manufacturing.
CT scans usually comprise a plurality of voxels (i.e., three-dimensional pixels). A brightness of the voxels indicates a rate of absorption of the x-rays used for the CT scanning. Different components within the casing usually show different rates of absorption, so they may be distinguished in the CT scans. Based on the CT scans, the inner components of the parts may be analyzed.
There may be a desire for an improved analysis of volumetric scanning data with regards to faults in components.
This desire is addressed by the subject-matter of independent claims. Embodiments of the invention are described in the dependent claims, the following description, and the drawings.
The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description, drawings, and from the claims.
In the following description of embodiments of the invention, specific details are described in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the instant description.
Embodiments of the present disclosure are based on the finding that the analysis of volumetric scanning data, such as Computed Tomography (CT) scanning data, Functional Magnetic Resonance Imaging (FMRI scanning data) or Positron Emission Tomography scanning data, may be improved by extracting the surfaces of the respective components of a structural element, and analyzing the interrelationship of the surfaces to determine the structural integrity of the structural element. Accordingly, voxels of the volumetric scanning data that are part of the bulk (i.e., not at the surface) of a component may be discarded from the analysis. The interrelationship of the surfaces may be analyzed to determine distances between the surfaces, an alignment of surfaces etc. These structural integrity features may be important for the analysis of potential faults in structural elements, such as batteries, where an overlap of cathodes and anodes may be avoided.
Some embodiments relate to a computer-implemented method for processing volumetric scanning data. The method comprises obtaining volumetric scanning data of a structural element. The structural element comprises a plurality of instances of two or more types of components. The volumetric scanning data is represented by a plurality of voxels. The method comprises assigning the plurality of voxels to one of the two or more different types of components. The method comprises identifying, for each type of component, the voxels that are part of a surface of an instance of the component (at this point, no instantiation has taken place). The method comprises extracting the surfaces of the instances of the components using the voxels that are part of the surface of the respective type of component. The method comprises determining information on a structural integrity of the structural element based on the extracted surfaces. In general, surfaces that show irregularities may be detrimental to the structural integrity of the structural element, and may thus be analyzed to determine the structural integrity of the structural element.
For example, the structural element may be a battery. The two or more different types of components may comprise a cathode and an anode of the battery. In general, in batteries, an overlap of cathodes and anodes may be avoided, and the surfaces of the cathodes and anodes may be analyzed to detect such a potential overlap.
In general, the voxels might either be part of the surface of an instance of the component or part of a bulk of the instance of the component. The method may comprise discarding voxels that are part of the bulk of the instance of the component. The voxels of the bulk may be discarded to reduce the processing power required for analyzing the structural integrity of the structural element.
In some embodiments, the method comprises segmenting the instances of the components based on the extracted surfaces. The information on the structural integrity of the structural element may be determined based on the extracted surfaces of the instances of the components. For example, returning to the example of the structural element being a battery, each cathode and anode layer may be treated as a separate instance of the respective components. The same principle may also be applied to cylindrical batteries.
For example, the information on the structural integrity may be determined based on at least one of a distance between surfaces, an alignment of surfaces, a uniformity of a distance along an extent of two surfaces, an intersection of surfaces of the extracted surfaces, and a difference between the extracted surfaces and a three-dimensional model of the structural element. A distance and/or intersection of surfaces may reveal structural faults within the structural element. A divergence between the three-dimensional model (e.g., a CAD drawing) and the extracted surfaces may also indicate potential faults.
In some embodiments, extracting the surfaces of the instances of the components comprises performing local plane fitting for the voxels that are part of the surface of the respective type of component. Local plane fitting may be used to generate a continuous (and smooth) surface from the voxels that are part of the surface of the respective component. For example, the local plane fitting may be used as a plausibility check for the voxels that are part of the surface.
In some embodiments, the voxels are assigned to one of the two or more different types of components using a machine-learning model that is suitable for image segmentation. For example, the voxels may be assigned to one of the two or more different types of components using a machine-learning model that is based on a U-Net architecture. In general, there are various suitable machine-learning frameworks that may be used to perform the respective image segmentation.
In general and in some embodiments, it may be useful to determine the orientation of the respective surfaces as well, e.g., whether a surface is a top surface or a bottom surface of an instance of a component. The method may comprise determining an orientation of the surfaces of the instances of the components, and classifying (e.g., top of bottom) the surfaces of the instances of the components based on the orientation of the respective surfaces. The information on the structural integrity may be determined based on the classification of the respective surfaces. For example, the classification may be used to apply an advanced rule-set for the determination of the structural integrity, e.g., “the top surface of an anode may not intersect with a bottom surface of a cathode”.
For example, the orientation of the surfaces of the instances of the components may be determined based on a mean position of a bulk of the respective instances of the components (e.g. without requiring instantiation at the point). For example, if the mean position of the bulk is beneath a surface, the surface may be a top surface, and if the mean position of the bulk is above a surface, the surface may be a bottom surface. In some embodiments, only the bulk of the instance the surface is associated with may be considered. In some embodiments, however, the entire bulk of the same type of components may be taken into account.
In some embodiments, the method comprises correlating a performance of the structural component with the information on the structural integrity of the component. The correlation may be used subsequently to determine an estimated performance of a structural element based on its volumetric scanning data.
Some embodiments relate to a computer-implemented method for determining a correlation between a structural integrity of structural elements and a performance of the structural elements. The method comprises obtaining information on a structural integrity of a plurality of structural elements, the information on the structural integrity being generated using the above-referenced method. The method comprises obtaining information on a performance of the plurality of structural elements. The method comprises determining a correlation between the structural integrity of the plurality of structural elements and the performance of the plurality of structural elements. The correlation may be used to determine an estimated performance of a structural element based on its volumetric scanning data.
Some embodiments relate to a computing device comprising an interface for exchanging information, and processing circuitry configured to perform the method for processing the volumetric scanning data.
Some embodiments relate to a computing device comprising an interface for exchanging information, and processing circuitry configured to perform the method for determining the correlation. For example, the computing device may correspond to the above-referenced computing device.
Some embodiments relate to a computer program having a program code for performing the method for processing the volumetric scanning data and/or the method for determining the correlation, when the computer program is executed on a computer, a processor, or a programmable hardware component.
Other features or aspects will be described using the following non-limiting embodiments of apparatuses or methods or computer programs or computer program products by way of example only, and with reference to the accompanying FIGS.
Various example embodiments will now be described more fully with reference to the accompanying drawings in which some example embodiments are illustrated. In the figures, the thicknesses of lines, layers or regions may be exaggerated for clarity. Optional components may be illustrated using broken, dashed or dotted lines.
Accordingly, while example embodiments are capable of various modifications and alternative forms, embodiments thereof are shown by way of example in the figures and will herein be described in detail. It should be understood, however, that there is no intent to limit example embodiments to the particular forms disclosed, but to the contrary, example embodiments are to cover all modifications, equivalents, and alternatives falling within the scope of the invention. Like numbers refer to like or similar elements throughout the description of the figures. Specific references to components, process steps, and other elements are not intended to be limiting.
As used herein, the term, “or” refers to a non-exclusive or, unless otherwise indicated (e.g., “or else” or “or in the alternative”). Furthermore, as used herein, words used to describe a relationship between elements should be broadly construed to include a direct relationship or the presence of intervening elements unless otherwise indicated. For example, when an element is referred to as being “connected” or “coupled” to another element, the element may be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. Similarly, words such as “between”, “adjacent”, and the like should be interpreted in a like fashion.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components or groups thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The following description relates to the computer-implemented method of
Various exemplary aspects of the present disclosure relate to a computer-implemented method, computing device and computer program for processing volumetric scanning data (i.e., three-dimensional scanning data), such as Computer Tomography (CT) scanning data, Functional Magnetic Resonance Imaging (FMRI scanning data) or Positron Emission Tomography scanning data, of a structural element. As has been mentioned above, the teachings herein may be used for fault analysis and/or spot check, and for example for the determination of a correlation between volumetric scanning data of a structural element and the performance of the structural element. In this context, the structural element may be an object, e.g., a part or component for use in a machine, e.g., a part or component for use in a vehicle. In the context of the present disclosure, the invention is discussed with respect to a battery. In other words, the structural element may be a battery. However, the invention is also applicable to other structural components, such as multi-layer sensors and the like or any structural element comprising components that have surfaces that are arranged in a pre-defined geometry. For example, the structural element may be an electric motor, a hydraulic damper or a fuel cell, with the first two having a cylindrical buildup.
The method comprises obtaining 110 the volumetric scanning data of the structural element. For example, the volumetric scanning data may be obtained via an interface, or from a storage device. For example, the volumetric scanning data may comprise the output of a CT device (i.e., a CT scanner), the output of an FMRI scanner, or the output of a PET scanner. In general, the volumetric scanning data may comprise scanning data from a single scan of the structural element. In some cases, however, multiple scans may be taken,
The structural element comprises a plurality of instances of two or more types of components. In the context of the present disclosure, a distinction is made between a “type” and an “instance” of a component. For example, a battery comprises anodes and cathodes, which are the types of components of the battery. In other words, the two or more different types of components may comprise a cathode and an anode of the battery. Of each component, the structural element may comprise one or more instances. For example, the structural element may comprise a plurality of cathodes (i.e., a plurality of instances of the cathode) and a plurality of anodes (i.e., a plurality of instances of the anode).
In general, the volumetric scanning data is represented by a plurality of voxels. In the context of volumetric scanning data, a voxel is an image point that has a coordinate in three dimensions (in a three-dimensional raster). In CT scanning data, each voxel indicates a brightness of the structural element being scanned at a three-dimensional coordinate. The brightness, in turn, indicates an absorption of the x-rays being used to scan the structural element. In other words, each voxel is characterized by a brightness value that corresponds to the absorption (of x-rays).
The method comprises assigning 120 the plurality of voxels to one of the two or more different types of components. By assigning the plurality of voxels to the different types of components, a first step is taken towards the segmentation of the components being visible in the volumetric scanning data. In general, the voxels may be assigned to one of the two or more different types of components using a machine-learning model that is suitable for image segmentation. In other words, the volumetric scanning data, or a derivative thereof, may be provided at an input of the machine-learning model, and the assignment between the voxels and types of components may be obtained at the output of the machine-learning model. There are various frameworks that are suitable for image segmentation. In general, the machine-learning model may be suitable for image segmentation in 2D, or for image segmentation in 3D. One type of framework that has proven to be suitable is the so-called U-Net framework, which is predominately used for image segmentation in medical applications (on 2D image data). Accordingly, the voxels may be assigned to one of the two or more different types of components using a machine-learning model that is based on a U-Net architecture. More information may be found in an article by Ronneberger et al: “U-Net: Convolutional Networks for Biomedical Image Segmentation” (2015). Alternatively, a machine-learning model for segmentation of volumetric image data may be used, e.g., as used in the biomedical domain. Different types of machine-learning models for segmentation may be suitable, e.g., as long as they assign each voxel to one of a plurality of types/classes.
The method comprises identifying 130, for each type of component, the voxels that are part of a surface of an instance of the component. Once the voxels are assigned to one of the types of components, the surface voxels may be identified. In general, the voxels are either part of the surface of an instance of the component or part of a bulk of the instance of the component. The identification of the surface voxels may be done by comparing, for a given voxel, whether the adjoining voxels belong to the same type of component. If all of the adjoining voxels belong to the same type of component, the voxel may be identified to be a bulk voxel, if not, e.g., if only five of the six adjoining voxels belong to the same type of component, the voxel may be identified as a surface voxel. The method may comprise discarding 135 voxels that are part of the bulk of the instance of the component. For example, in the following, merely the surface voxels, and the surfaces being created by the surface voxels, may be considered.
The method comprises extracting 140 the surfaces of the instances of the components using the voxels that are part of the surface of the respective type of component. In other words, the surfaces of the components may be abstracted from the individual pixels, and transformed into contiguous planes for the subsequent processing. In general, the surface extraction may comprise one or more sub-tasks. For example, the assignment of the voxels to the different component types, and the identification of the voxels that are part of the surface may be considered a sub-task of the surface extraction. Additionally, extracting 140 the surfaces of the instances of the components may comprise performing 145 local plane fitting for the voxels that are part of the surface of the respective type of component. In general, the local plane may be fitted as close as possible to the local surface. This may be done by calculating the local gyration tensor for all surface vectors and identifying the eigenvector and minimum eigenvalue with the plane normal. An example for the procedure is given in connection with
In some embodiments, the method further comprises segmenting 160 the instances of the components based on the extracted surfaces. For example, segmenting 160 the instances of the components may comprise identifying contiguous instances of components that are formed by the plurality of voxels having the same type. For example, the orientation of the surface may be taken. For example, a component may be formed by all of the voxels having the same type that are directly adjacent to another voxel having the same type. Again, the information on the structural integrity of the structural element may be determined based on the extracted surfaces of the instances of the components.
Once the surfaces of the components have been extracted, and, optionally, the instances of the components have been segmented, the resulting surfaces may be used to determine the information on the structural integrity of the structural element. In other words, the method comprises determining 170 information on a structural integrity of the structural element based on the extracted surfaces. In general, the information on the structural integrity may indicate a compliance of the structural element with an idealized version of the structural element, e.g., with regard to the surfaces of the components. For example, an idealized version of the structural element, e.g., a three-dimensional model of the structural element, may be compared to the extracted surfaces to determine the information on the structural identity. Accordingly, the information on the structural integrity may indicate a deviation of the extracted surfaces from the idealized version of the structural element.
If no such model is available, or in addition to the model, the interrelationship of the surfaces may be analyzed to determine the information on the structural integrity. For example, the determination of the information on the structural integrity may comprise determining distances between the extracted surfaces. Accordingly, the information on the structural integrity may be determined based on at least one of a distance between surfaces (of the extracted surfaces), an alignment of surfaces (of the extracted surfaces), a uniformity of a distance along an extent of two surfaces (of the extracted surfaces), and an intersection of surfaces of the extracted surfaces.
In some embodiments, this information on the structural integrity may be used to determine a correlation between the information on the structural integrity, and a performance of the respective structural element. For example, each structural element may be tested in addition to the CT scanning-based analysis of the structural element. The performance that is determined by the test may be correlated with the information on the structural identity that is determined based on the volumetric scanning data. In other words, the method may comprise correlating 180 a performance of the structural component with the information on the structural integrity of the component. Accordingly, the method may comprise obtaining information on a performance of the structural element. For example, the information on the performance may relate to a structural performance of the structural element, e.g., a pliability of the structural element, or to a functionality of the structural element, e.g., whether the structural element is functional, or how high a key performance indicator, such as an electrical characteristic variable, of the structural element is. Both the determination of the information on the structural integrity and the correlation of the information on the structural integrity with the performance of the structural element may be repeated with other sets of volumetric scanning data, the resulting information on the structural integrity and the corresponding performance, to yield a correlation that is based on a plurality of structural elements (of the same model). The correlation may be suitable for determining a key performance indicator of structural elements based on information on the structural integrity of the respective elements, and by determining the information on the structural integrity from the volumetric scanning data, based on volumetric scanning data representing the structural elements. For example, the correlation may be determined using statistical methods. Alternatively, a machine-learning model may be trained using supervised training, with the information on the structural integrity being used as training input and the respective information on the performance being used as desired output of the training of the machine-learning model. The same determination of the correlation is described, detached from the determination of the information on the structural integrity, in connection with
The interface 12 may correspond to one or more inputs and/or outputs for receiving and/or transmitting information, which may be in digital (bit) values according to a specified code, within a module, between modules or between modules of different entities. For example, the interface 12 may comprise interface circuitry configured to receive and/or transmit information.
In some embodiments, the processing circuitry 14 may be implemented using one or more processing units, one or more processing devices, any means for processing, such as a processor, a computer or a programmable hardware component being operable with accordingly adapted software. In other words, the described function of the processing circuitry 14 may as well be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may comprise a general-purpose processor, a Digital Signal Processor (DSP), a micro-controller, etc.
In at least some embodiments, the computing device 10 may comprise one or more storage devices, e.g., at least one element of the group of a computer readable storage medium, such as a magnetic or optical storage medium, e.g. a hard disk drive, a flash memory, Floppy-Disk, Random Access Memory (RAM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), an Electronically Erasable Programmable Read Only Memory (EEPROM), or a network storage.
More details and aspects of the computer-implemented method, computing device and computer program for processing the volumetric scanning data are mentioned in connection with the teachings herein or one or more examples described above or below (e.g.,
As has been mentioned in connection with
The interface 22 may correspond to one or more inputs and/or outputs for receiving and/or transmitting information, which may be in digital (bit) values according to a specified code, within a module, between modules or between modules of different entities. For example, the interface 22 may comprise interface circuitry configured to receive and/or transmit information.
In some embodiments, the processing circuitry 24 may be implemented using one or more processing units, one or more processing devices, any means for processing, such as a processor, a computer or a programmable hardware component being operable with accordingly adapted software. In other words, the described function of the processing circuitry 24 may as well be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may comprise a general-purpose processor, a Digital Signal Processor (DSP), a micro-controller, etc.
In at least some embodiments, the computing device 20 may comprise one or more storage devices, e.g., at least one element of the group of a computer readable storage medium, such as an magnetic or optical storage medium, e.g. a hard disk drive, a flash memory, Floppy-Disk, Random Access Memory (RAM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), an Electronically Erasable Programmable Read Only Memory (EEPROM), or a network storage.
More details and aspects of the computer-implemented method, computing device and computer program for determining the correlation are mentioned in connection with teachings herein or one or more examples described above or below (e.g.,
Some embodiments relate to robust instance segmentation for automated extraction of key performance indicators from computer tomography scans of structural elements, such as batteries, using computer vision.
The production of batteries, e.g., for electric vehicles, is becoming ever more important. In many battery production plants, however, the relationship between production quality and product performance may not be known. Various embodiments of the present disclosure use a computer tomography system to quantify the product performance. While generally, using CT scans for quality control in production systems may be known, these known approaches may use different techniques than the approach outlined below. The described approach is generally applicable to any manufacturing process, where non-destructive analysis is to be performed. The presented approach assumes certain symmetries of the product (e.g., layered or circular). The approach may, however, also be applied to structural elements apart from batteries.
The teachings herein are part of an overall workflow trying to correlate the performance of manufactured parts with the measurements collected during production. With the insights gained from analyzing how performance changes due to production measurements, the overall production may be optimized.
The motivating application were battery packs, but the described features may be applied also to other manufacturing processes. In the following an analysis process is described that is improved or optimized to extract information about production quality from computer tomography scans of the part (i.e., structural element), e.g. battery pack, during production.
The basis for the analysis are computed tomography scans, i.e., volumetric images where each voxel is characterized by a brightness value that corresponds to the absorption of x-rays.
1. Part segmentation by type. For batteries, the relevant parts are the battery cathode 420 & anode 425. This step assigns each voxel to be one type of part. Note, that at this step, all voxels may be independent and there might be no information about which voxels form whole objects, e.g., anode sheets. The segmentation may be performed with a neural network-based image segmentation model. In an exemplary implementation, a so-called U-Net architecture was used, that was initially developed for image segmentation in the medical fields.
2. Surface extraction and classification 430. Instead of analyzing the voxels directly, only surface information might be used for increased robustness. In this step, the surface of each class may be extracted, and each voxel assigned to a type of surface, e.g., top or bottom of an anode.
3. Instance segmentation 440. In this step, all connected voxels of a single type may be assumed to be part of the same object, e.g., the top layer of a single anode.
4. Automatic KPI computation 450. Based on the detected objects, key performance indicators that quantify production quality may be calculated (i.e., the structural integrity may be determine). For the example of a battery, that could be alignment of anode sheets or comparison to CAD files that the production was based on.
In the present disclosure, the focus is on step 2. The second step may be further subdivided into the following sub-steps:
2
a: Surface extraction. Each voxel may be classified into bulk and surface. Any voxel that has the full number of direct neighbors of the same type (6 in 3D) may be classified as bulk, all other voxels may be classified as surface. For example, an anode voxel with 4 direct neighbors that are also anodes, may be classified to be a surface voxel.
2
b: Fitting of a local plane for each surface voxel. Around each surface voxel, a local plane may be fitted that tries to be as close as possible to the local surface. This may be done by computing the local gyration tensor (for all surface voxels)
and identifying the eigenvector of the minimum eigenvalue with the plane normal.
Gv=λ
min
v
This vector however is not oriented as it could point into either direction. The squares magnitude of the eigenvector projected onto the main axes may be denoted as local plane parameters. The local plane parameters may be defined as:
p
x=(exTv)2,py=(eyTv)2,pz=(ezTv)2
See also
2
c: Orientation of the plane. The correct sign of the normal may be calculated by computing the mean position of the bulk around the surface voxel. For example, if the majority of the bulk is found below the surface voxel, the local surface is the top of the object. For example, the following formulae may be used for calculating the mean bulk position:
See also
2
d: Classification of the surface type based on plane orientation, voxel position, and voxel part type. Next, each surface voxel may be classified by thresholding the local plane parameter and the mean bulk position. For example, the top of an anode sheet would be an anode surface voxel with pz>0.5 and nz>0. This approach may further be extended to non-layered geometries, e.g. for cylindrical geometries, each voxel may be classified as either normal or perpendicular to a perfectly aligned cylinder by using the radial vector as the projection target for the local plane parameters. For example, the structural element may be a structural element with a cylindrical construction, such as a cylindrical battery, an electric motor or a hydraulic damper.
The described approach is tailored, but not limited, to the analysis of volumetric scanning data of batteries and takes the layered geometry of the battery pack into explicit account. Therefore, additional robustness may be observed even for imperfect CT scans. For example, for low resolutions, other approaches to instance segmentation may lead to undesired artifacts due to overlapping sheets. Here, any such situations may be explicitly forbidden, and the corresponding voxels may be filtered out.
As already mentioned, in embodiments the respective methods may be implemented as computer programs or codes, which may be executed on a respective hardware. Hence, another embodiment is a computer program having a program code for performing at least one of the above methods, when the computer program is executed on a computer, a processor, or a programmable hardware component. A further embodiment is a computer readable storage medium storing instructions which, when executed by a computer, processor, or programmable hardware component, cause the computer to implement one of the methods described herein.
A person of skill in the art would readily recognize that steps of various above-described methods may be performed by programmed computers, for example, positions of slots may be determined or calculated. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions where said instructions perform some or all of the steps of methods described herein. The program storage devices may be, e.g., digital memories, magnetic storage media such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform said steps of methods described herein or (field) programmable logic arrays ((F)PLAs) or (field) programmable gate arrays ((F)PGAs), programmed to perform said steps of the above-described methods.
The description and drawings merely illustrate the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, Digital Signal Processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional or custom, may also be included. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention. 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 so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
Furthermore, the following claims are hereby incorporated into the detailed description, where each claim may stand on its own as a separate embodiment. While each claim may stand on its own as a separate embodiment, it is to be noted that—although a dependent claim may refer in the claims to a specific combination with one or more other claims—other embodiments may also include a combination of the dependent claim with the subject matter of each other dependent claim. Such combinations are proposed herein unless it is stated that a specific combination is not intended. Furthermore, it is intended to include also features of a claim to any other independent claim even if this claim is not directly made dependent to the independent claim.
It is further to be noted that methods disclosed in the specification or in the claims may be implemented by a device having means for performing each of the respective steps of these methods.
The invention has been described in the preceding using various exemplary embodiments. Other variations to the disclosed embodiments may be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor, module or other unit or device may fulfil the functions of several items recited in the claims.
The term “exemplary” used throughout the specification means “serving as an example, instance, or exemplification” and does not mean “preferred” or “having advantages” over other embodiments.
The mere fact that certain measures are recited in mutually different dependent claims or embodiments does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20191722.6 | Aug 2020 | EP | regional |
