Patent Publication Number: US-2023139521-A1

Title: Neural network validation system

Description:
INTRODUCTION 
     The present disclosure relates to validating, e.g., cross-checking, neural network output with output from multiple other neural network models. 
     Deep neural networks (DNNs) can be used to perform many image understanding tasks, including classification, segmentation, and captioning. Typically, DNNs require large amounts of training images (tens of thousands to millions). Additionally, these training images typically need to be annotated, e.g., labeled, for the purposes of training and prediction. 
     SUMMARY 
     A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receive, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, compare the output generated by the first neural network with the output generated by the second neural network, and generate an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold. 
     In other features, the processor is further programmed to receive a selection to transition between the validation mode and a feature mode. 
     In other features, the processor is further programmed to operate at least one vehicle actuator based on the output generated by the first neural network during the feature mode. 
     In other features, the selection is transmitted from a server. 
     In other features, the selection is transmitted from an electronic controller unit of a vehicle. 
     In other features, the first neural network is trained using a first dataset and the second neural network is trained using a second dataset, wherein the second dataset is different from the first dataset. 
     In other features, the processor is further programmed to prevent the output generated by the first neural network from being used to operate a vehicle during the validation mode. 
     In other features, the unlabeled sensor data comprises sensor data collected by a fleet of vehicles. 
     A vehicle includes a system. The system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receive, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, compare the output generated by the first neural network with the output generated by the second neural network, and generate an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold. 
     In other features, the processor is further programmed to receive a selection to transition between the validation mode and a feature mode. 
     In other features, the processor is further programmed to operate at least one vehicle actuator of the vehicle based on the output generated by the first neural network during the feature mode. 
     In other features, the selection is transmitted from a server. 
     In other features, the selection is transmitted from an electronic controller unit of the vehicle. 
     In other features, the first neural network is trained using a first dataset and the second neural network is trained using a second dataset, wherein the second dataset is different from the first dataset. 
     In other features, the processor is further programmed to prevent the output generated by the first neural network from being used to operate a vehicle during the validation mode. 
     In other features, the unlabeled sensor data comprises sensor data collected by a fleet of vehicles. 
     A method includes receiving, at a first neural network, unlabeled sensor data, wherein the first neural network generates output based on the unlabeled sensor data, receiving, at a second neural network, the unlabeled sensor data, wherein the second neural network generates output based on the unlabeled sensor data during a validation mode, the second neural network different from the first neural network, comparing the output generated by the first neural network with the output generated by the second neural network, and generating an alert when a difference between the output generated by the first neural network and the output generated by the second neural network is greater than a predetermined comparison threshold. 
     In other features, the method includes receiving a selection to transition between the validation mode and a feature mode. 
     In other features, the method includes operating at least one vehicle actuator based on the output generated by the first neural network during the feature mode. 
     In other features, the selection is transmitted from a server. 
     Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. 
         FIG.  1    is a block diagram of a vehicle system that includes a validation network for comparing an output generated by a first neural network with outputs generated by a plurality of neural networks; 
         FIG.  2    is a block diagram of an example server within the system; 
         FIG.  3    is a diagram of an example neural network; 
         FIG.  4    is a block diagram of an example validation network; and 
         FIG.  5    is a flow diagram illustrating an example process for validating output generated by a neural network. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. 
     Typically, standard deep neural networks (DNNs) are pre-trained with labeled training datasets. These DNNs can be validated during testing by comparing the output of the model to ground truth. However, obtaining ground truth data can be difficult in real-world testing scenarios. Additionally, testing of the DNNs may reveal that further analysis is needed to identify a root cause of incorrect DNN output. 
     The present disclosure discloses a neural network validation system in which output generated by a neural network is compared with output generated by validation neural networks. The validation neural networks can be trained on different datasets that can be partial observations with different bias from the real-world underlying distribution. For example, the validation neural networks can comprise a different architecture with respect to the architecture of the neural network of interest. 
       FIG.  1    is a block diagram of an example vehicle system  100 . The system  100  includes a vehicle  105 , which is a land vehicle such as a car, truck, etc. The vehicle  105  includes a computer  110 , vehicle sensors  115 , actuators  120  to actuate various vehicle components  125 , and a vehicle communications module  130 . Via a network  135 , the communications module  130  allows the computer  110  to communicate with a server  145 . 
     The computer  110  includes a processor and a memory. The memory includes one or more forms of computer readable media, and stores instructions executable by the computer  110  for performing various operations, including as disclosed herein. 
     The computer  110  may operate a vehicle  105  in an autonomous, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle  105  propulsion, braking, and steering are controlled by the computer  110 ; in a semi-autonomous mode the computer  110  controls one or two of vehicles  105  propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle  105  propulsion, braking, and steering. 
     The computer  110  may include programming to operate one or more of vehicle  105  brakes, propulsion (e.g., control of acceleration in the vehicle by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, climate control, interior and/or exterior lights, etc., as well as to determine whether and when the computer  110 , as opposed to a human operator, is to control such operations. Additionally, the computer  110  may be programmed to determine whether and when a human operator is to control such operations. 
     The computer  110  may include or be communicatively coupled to, e.g., via the vehicle  105  communications module  130  as described further below, more than one processor, e.g., included in electronic controller units (ECUs) or the like included in the vehicle  105  for monitoring and/or controlling various vehicle components  125 , e.g., a powertrain controller, a brake controller, a steering controller, etc. Further, the computer  110  may communicate, via the vehicle  105  communications module  130 , with a navigation system that uses the Global Position System (GPS). As an example, the computer  110  may request and receive location data of the vehicle  105 . The location data may be in a known form, e.g., geo-coordinates (latitudinal and longitudinal coordinates). 
     The computer  110  is generally arranged for communications on the vehicle  105  communications module  130  and also with a vehicle  105  internal wired and/or wireless network, e.g., a bus or the like in the vehicle  105  such as a controller area network (CAN) or the like, and/or other wired and/or wireless mechanisms. 
     Via the vehicle  105  communications network, the computer  110  may transmit messages to various devices in the vehicle  105  and/or receive messages from the various devices, e.g., vehicle sensors  115 , actuators  120 , vehicle components  125 , a human machine interface (HMI), etc. Alternatively or additionally, in cases where the computer  110  actually comprises a plurality of devices, the vehicle  105  communications network may be used for communications between devices represented as the computer  110   in this disclosure. Further, as mentioned below, various controllers and/or vehicle sensors  115  may provide data to the computer  110 . The vehicle  105  communications network can include one or more gateway modules that provide interoperability between various networks and devices within the vehicle  105 , such as protocol translators, impedance matchers, rate converters, and the like. 
     Vehicle sensors  115  may include a variety of devices such as are known to provide data to the computer  110 . For example, the vehicle sensors  115  may include Light Detection and Ranging (lidar) sensor(s)  115 , etc., disposed on a top of the vehicle  105 , behind a vehicle  105  front windshield, around the vehicle  105 , etc., that provide relative locations, sizes, and shapes of objects and/or conditions surrounding the vehicle  105 . As another example, one or more radar sensors  115  fixed to vehicle  105  bumpers may provide data to provide and range velocity of objects (possibly including second vehicles  106 ), etc., relative to the location of the vehicle  105 . The vehicle sensors  115  may further include camera sensor(s)  115 , e.g., front view, side view, rear view, etc., providing images from a field of view inside and/or outside the vehicle  105 . 
     The vehicle  105  actuators  120  are implemented via circuits, chips, motors, or other electronic and or mechanical components that can actuate various vehicle subsystems in accordance with appropriate control signals as is known. The actuators  120  may be used to control components  125 , including braking, acceleration, and steering of a vehicle  105 . 
     In the context of the present disclosure, a vehicle component  125  is one or more hardware components adapted to perform a mechanical or electro-mechanical function or operation—such as moving the vehicle  105 , slowing or stopping the vehicle  105 , steering the vehicle  105 , etc. Non-limiting examples of components  125  include a propulsion component (that includes, e.g., an internal combustion engine and/or an electric motor, etc.), a transmission component, a steering component (e.g., that may include one or more of a steering wheel, a steering rack, etc.), a brake component (as described below), a park assist component, an adaptive cruise control component, an adaptive steering component, a movable seat, etc. 
     In addition, the computer  110  may be configured for communicating via a vehicle-to-vehicle communication module or interface  130  with devices outside of the vehicle  105 , e.g., through a vehicle to vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communications to another vehicle, to (typically via the network  135 ) a remote server  145 . The module  130  could include one or more mechanisms by which the computer  110  may communicate, including any desired combination of wireless (e.g., cellular, wireless, satellite, microwave and radio frequency) communication mechanisms and any desired network topology (or topologies when a plurality of communication mechanisms are utilized). Exemplary communications provided via the module  130  include cellular, Bluetooth®, IEEE 802.11, dedicated short-range communications (DSRC), and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The network  135  can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth, Bluetooth Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as Dedicated Short-Range Communications (DSRC), etc.), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
     A computer  110  can receive and analyze data from sensors  115  substantially continuously, periodically, and/or when instructed by a server  145 , etc. Further, object classification or identification techniques can be used, e.g., in a computer  110  based on lidar sensor  115 , camera sensor  115 , etc., data, to identify a type of object, e.g., vehicle, person, rock, pothole, bicycle, motorcycle, etc., as well as physical features of objects. 
       FIG.  2    is a block diagram of an example server  145 . The server  145  includes a computer  235  and a communications module  240 . The computer  235  includes a processor and a memory. The memory includes one or more forms of computer readable media, and stores instructions executable by the computer  235  for performing various operations, including as disclosed herein. The communications module  240  allows the computer  235  to communicate with other devices, such as the vehicle  105 . 
       FIG.  3    is a diagram of an example deep neural network (DNN)  300  that may be used herein. The DNN  300  includes multiple nodes  305 , and the nodes  305  are arranged so that the DNN  300  includes an input layer, one or more hidden layers, and an output layer. Each layer of the DNN  300  can include a plurality of nodes  305 . While  FIG.  3    illustrates three (3) hidden layers, it is understood that the DNN  300  can include additional or fewer hidden layers. The input and output layers may also include more than one (1) node  305 . 
     The nodes  305  are sometimes referred to as artificial neurons  305 , because they are designed to emulate biological, e.g., human, neurons. A set of inputs (represented by the arrows) to each neuron  305  are each multiplied by respective weights. The weighted inputs can then be summed in an input function to provide, possibly adjusted by a bias, a net input. The net input can then be provided to activation function, which in turn provides a connected neuron  305  an output. The activation function can be a variety of suitable functions, typically selected based on empirical analysis. As illustrated by the arrows in  FIG.  3   , neuron  305  outputs can then be provided for inclusion in a set of inputs to one or more neurons  305  in a next layer. 
     The DNN  300  can be trained to accept data as input and generate an output based on the input. In one example, the DNN  300  can be trained with ground truth data, i.e., data about a real-world condition or state. For instance, the DNN  300  can be trained with ground truth data or updated with additional data by a processor. Weights can be initialized by using a Gaussian distribution, for example, and a bias for each node  305  can be set to zero. Training the DNN  300  can including updating weights and biases via suitable techniques such as backpropagation with optimizations. Ground truth data can include, but is not limited to, data specifying objects within an image or data specifying a physical parameter, e.g., angle, speed, distance, color, hue, or angle of object relative to another object. For example, the ground truth data may be data representing objects and object labels. 
     Machine learning services, such as those based on Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) neural networks, or Gated Recurrent Unit (GRUs) may be implemented using the DNNs  300  described in this disclosure. In one example, the service-related content or other information, such as words, sentences, images, videos, or other such content/information may be translated into a vector representation. 
       FIG.  4    is a diagram of an example validation network  400  for comparing an output generated by a neural network  405 , e.g., a first neural network, with outputs generated by one or more validation neural networks  410 , e.g., a plurality of second neural networks. For example, during a validation mode, the validation network  400  compares the output generated by the neural network  405  with output by the validation neural networks  410  using the same input data. The input data may comprise unlabeled training data. In this example, the validation neural networks  410  may be trained using training data not used to train the neural network  405 . 
     It is understood that the neural network  405  and the validation neural networks  410  may comprise any suitable deep neural network  300 . As shown, the validation network  400  includes the neural network  405 , the validation neural networks  410 , a comparison module  413 , and a selector module  415 . The validation network  400  can be a software program that can be loaded in memory and executed by a processor in the computer  110  and/or the server  145 , for example. 
     The selector module  415  can cause the validation network  400  to operate in feature mode or in validation mode. In feature mode, the neural network  405  receives sensor data from one or more sensors  115  via data path  420  and generates output via data path  425  based on the received sensor data. For example, the neural network  405  may comprise a CNN that receives images captured by one or more image sensors  115  via data path  420  and performs object classification based on the images. The output indicative of the object classification can be provided to one or more other software modules via the data path  425 , and the software modules can generate control instructions for vehicle  105  operation. For instance, based on the object classification, the software modules can generate control instructions that are provided to one or more actuators  120  to control operation of the vehicle  105 . 
     In validation mode, the selector module  415  sends control instructions via control path  430  such that the validation neural networks  410  receive the sensor data via data path  435 . The selector module  415  also sends control instructions via the data path  430  such that the output generated by the neural network  405  is received by the comparison module  413  via data path  440 . Thus, the validation neural networks  410  can generate output based on the same sensor data received by the neural network  405 , i.e., the same input. 
     The comparison module  413  compares the output generated by the validation neural networks  410  with the output generated by the neural network  405 . Based on the comparison, the comparison module  413  generates a comparison output indicative of the difference between the neural network  405  output and the validation neural network  410  output(s) via data path  445 . The comparison module  413  compares the comparison output with a predetermined comparison threshold to determine whether the comparison output is greater than the predetermined comparison threshold. The predetermined comparison threshold may be selected based on empirical analysis. 
     If the comparison output is greater than the predetermined comparison threshold, the comparison module  413  generates an alert and transmits the alert and the neural network  405  output to the server  145 . For example, the comparison module  413  can generate the alert to indicate that the comparison output is greater than the predetermined comparison threshold for further review purposes. In various implementations, the neural network  405  can operate in parallel with the validation neural networks  410 . 
     If the comparison output is less than or equal to the predetermined comparison threshold, the comparison module  413  transmits the comparison output to the server  145 . The server  145  may initiate an update for one or more neural networks  405  based on the comparison output, such as causing the neural network  405  to update corresponding weights and biases using a loss function that incorporates the comparison output. 
     In the validation mode, the neural network  405  receives unlabeled training data. For example, the unlabeled training data may comprise sensor data collected by a fleet of vehicles that has been uploaded to the server  145 . In these implementations, the ground truth data for the output generated by the neural network  405  is the output generated by the validation neural networks  410  based on the same received sensor data. As such, the neural network  405  output may not be provided to the software modules for vehicle decision making during the validation mode. 
     As discussed above, the validation neural networks  410  can comprise neural networks having a different architecture with respect to the neural network  405 . For example, the validation neural networks  410  may be trained with datasets that differ with respect to the datasets used to train the neural network  405 . 
     In some implementations, the selector module  415  can determine whether to operate the vehicle in feature mode or in validation mode based on input received via data path  450 . For example, the server  145  may transmit control instructions to the selector module  415  to cause the selector module  415  to transition between the feature mode and the validation mode. In other examples, the processor of the computer  110  may send control instructions to the selector module  415  to cause the selector module  415  to transition between the feature mode and the validation mode. 
     In various implementations, the validation network  400  may be deployed as a microservice. The computer  110  may store the validation neural networks  410  in memory and load the validation neural networks  410  when invoked by the selector module  415 . 
       FIG.  5    is a flowchart of an example process  500  for validating output of the neural network  405  during the validation mode. Blocks of the process  500  can be executed by the computer  110 . The process  500  begins at block  505  in which a determination is made whether the validation mode has been enabled. For example, the validation mode is enabled based on input received by the selector module  415 . The input may be provided by the server  145  or another ECU. 
     If the validation mode is not enabled, the neural network  405  is loaded to operate in feature mode at block  510 . In feature mode, the neural network  405  can generate output based on sensor data. This output can be used by one or more software modules to at least partially operate the vehicle  105 , i.e., control steering, acceleration, braking, etc. 
     At block  515 , the computer  110  initiates one or more communication protocols for feature mode operation. For example, the computer  110  can initiate one or more gateway modules for interoperability purposes. The gateway modules can allow data to flow between the various communication networks within the vehicle  105 , such as a sensor gateway and/or an actuator gateway. 
     At block  520 , the computer  110  operates the neural network  405  in feature mode. For example, the neural network  405  receives sensor data from the sensors  115  and generates output based on the sensor data. As discussed above, the neural network  405  can be trained for object classification in one implementation, and the neural network  405  outputs object classification data based on the sensor input. Using the object classification data, one or more software modules employed by the computer  110  can assist in vehicle operation. At block  525 , the vehicle  105  is operated based on the output from the neural network  405 . For example, one or more software modules may generate control instructions that are sent to the actuators  120  to operate one or more components  125  of the vehicle  105  based on the neural network  405  output. The process  500  then transitions back to block  505 . 
     If the validation mode is enabled, one or more vehicle  105  actuators  120  are disengaged from the neural network  405  at block  530 . For example, if the selector module  415  receives input to select the validation mode, the software modules and/or corresponding gateway modules may be disabled to prevent output from the neural network  405  from operating the vehicle  105 . 
     At block  535 , the computer  110  loads the validation neural networks  410 . For example, the computer  110  may access and load the validation neural networks  410  into memory for validating purposes. At block  540 , the computer  110  reconfigures sensor data provided to one or more neural networks  405 ,  410 . For example, depending on the type of unlabeled sensor data received for validation purposes, one or more neural network configurations may need to be modified. The computer  110  may modify the neural network configuration based on a configuration file provided by the server  145  and/or a configuration file stored in memory. 
     At block  545 , the computer  110  causes the validation network  400  to compare the output generated by the neural network  405  with output generated by one or more validation neural networks  410 . It is understood that multiple validation neural networks  410  may be used in which the output of the neural network  405  is compared with corresponding outputs from each validation neural network  410 . At block  550 , the comparison module  413  compares the output from the neural network  405  with the output from the validation neural networks  410 . At block  555 , the comparison module determines whether the comparison output is greater than the predetermined comparison threshold. If the comparison output is greater than the predetermined comparison threshold, the comparison module  413  generates transmits the alert and the comparison data to the server  145  at block  560 . The process  500  then transitions back to block  505 . If the comparison output is not greater than the predetermined comparison threshold, the comparison module  413  transmits the comparison output to the server  145  at block  565 . The process  500  then transitions back to block  505 . 
     The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure. 
     In general, the computing systems and/or devices described may employ any of a number of computer operating systems, including, but by no means limited to, versions and/or varieties of the Microsoft Automotive® operating system, the Microsoft Windows® operating system, the Unix operating system (e.g., the Solaris® operating system distributed by Oracle Corporation of Redwood Shores, California), the AIX UNIX operating system distributed by International Business Machines of Armonk, New York, the Linux operating system, the Mac OSX and iOS operating systems distributed by Apple Inc. of Cupertino, California, the BlackBerry OS distributed by Blackberry, Ltd. of Waterloo, Canada, and the Android operating system developed by Google, Inc. and the Open Handset Alliance, or the QNX® CAR Platform for Infotainment offered by QNX Software Systems. Examples of computing devices include, without limitation, an on-board vehicle computer, a computer workstation, a server, a desktop, notebook, laptop, or handheld computer, or some other computing system and/or device. 
     Computers and computing devices generally include computer executable instructions, where the instructions may be executable by one or more computing devices such as those listed above. Computer executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Matlab, Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some of these applications may be compiled and executed on a virtual machine, such as the Java Virtual Machine, the Dalvik virtual machine, or the like. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer readable media. A file in a computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random-access memory, etc. 
     Memory may include a computer readable medium (also referred to as a processor readable medium) that includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random-access memory (DRAM), which typically constitutes a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of an ECU. Common forms of computer readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     Databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store is generally included within a computing device employing a computer operating system such as one of those mentioned above, and are accessed via a network in any one or more of a variety of manners. A file system may be accessible from a computer operating system, and may include files stored in various formats. An RDBMS generally employs the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above. 
     In some examples, system elements may be implemented as computer readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.), stored on computer readable media associated therewith (e.g., disks, memories, etc.). A computer program product may comprise such instructions stored on computer readable media for carrying out the functions described herein. 
     In this application, including the definitions below, the term “module” or the term “controller” may be replaced with the term “circuit.” The term “module” may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip. 
     The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module. 
     With regard to the media, processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps may be performed simultaneously, that other steps may be added, or that certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claims. 
     Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the invention is capable of modification and variation and is limited only by the following claims. 
     All terms used in the claims are intended to be given their plain and ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.