Patent Publication Number: US-2023162480-A1

Title: Frequency-based feature constraint for a neural network

Description:
INTRODUCTION 
     The present disclosure relates to training a neural network with a loss function that includes a frequency-based feature consistency constraint. 
     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 neural network, frequency filtered spatial domain data, compare an output generated by the neural network to a loss function including a frequency-based feature consistency constraint, and update at least one weight of the neural network according to the loss function. 
     In other features, the processor is further programmed to transform data from a spatial domain to a frequency domain using a Fourier transform process. 
     In other features, the processor is further programmed to filter features from the transformed data based on a predetermined frequency. 
     In other features, the processor is further programmed to transform the filtered transformed data from the frequency domain to the spatial domain to generate the frequency filtered spatial domain data. 
     In other features, the processor is further programmed to filter the features based on at least one of a high-pass frequency or a low-pass frequency. 
     In other features, the Fourier transform process comprises a Fast Fourier transform process. 
     In other features, the output generated by the neural network comprises a latent representation of the frequency filtered spatial domain data. 
     In other features, the neural network comprises a convolutional neural network. 
     In other features, the frequency filtered spatial domain data corresponds to an image captured within a field-of-view of a vehicle camera. 
     In other features, the image comprises a Red-Green-Blue image. 
     A method includes receiving, at a first neural network, receiving, at a neural network, frequency filtered spatial domain data, comparing an output generated by the neural network to a loss function including a frequency-based feature consistency constraint, and updating at least one weight of the neural network according to the loss function. 
     In other features, the method includes transforming data from a spatial domain to a frequency domain using a Fourier transform process. 
     In other features, the method includes filtering features from the transformed data based on a predetermined frequency. 
     In other features, the method includes transforming the filtered transformed data from the frequency domain to the spatial domain to generate the frequency filtered spatial domain data. 
     In other features, the method includes filtering the features based on at least one of a high-pass frequency or a low-pass frequency. 
     In other features, the Fourier transform process comprises a Fast Fourier transform process. 
     In other features, the output generated by the neural network comprises a latent representation of the frequency filtered spatial domain data. 
     In other features, the neural network comprises a convolutional neural network. 
     In other features, the frequency filtered spatial domain data corresponds to an image captured within a field-of-view of a vehicle camera. 
     In other features, the image comprises a Red-Green-Blue image. 
     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 an example system including a vehicle; 
         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 frequency feature extraction system; 
         FIG.  5    is a block diagram of an example convolutional neural network; 
         FIGS.  6 A through  6 C  is a block diagram illustrating an example process for training a neural network; 
         FIG.  7    is a block diagram of an example domain adaptation network; 
         FIG.  8    is a flow diagram illustrating an example process for controlling a vehicle; and 
         FIG.  9    is a flow diagram illustrating an example process for training 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. 
     Domain adaptation is directed to generalizing a model from source domain to a target domain. Typically, the source domain has a large amount of training data while data in the target domain can be scarce. For instance, the availability of back-up camera lane data can be constrained due to camera-supplier constraints, rewiring problems, lack of relevant applications, and the like. However, there may be a number of datasets that include forward looking camera images that include lanes. 
     The present disclosure discloses systems and methods that train a neural network using a loss function that includes a frequency-based feature consistency constraint. The trained neural network can receive data in a source domain and generate data in a target domain. For example, the trained neural network can receive images including one or more features captured in daylight and generate images including the features such that the image appears to have been captured at night. 
       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 frequency feature extraction system  400 . The frequency feature extraction system  400  can be a software program that can be loaded in memory and executed by a processor in the vehicle  105  and/or the server  145 , for example. As shown, the frequency feature extraction system  400  can include a transform module  405  and an inverse transform module  410 . In various implementations, the transform module  405  and the inverse transform module  410  perform suitable Fourier transform processes, such as Fast Fourier transform processes, on the received data. For example, the transform module  405  converts input data  415  from the spatial domain to the frequency domain. The input data  415  can comprise image data, audio data, or the like. 
     The transform module  405  provides the transformed data, i.e., data represented in the frequency domain, to a high-pass filter  420  and to a low-pass filter  425 . The high-pass filter  420  passes data having a frequency higher than a predetermined high-pass cutoff frequency, and the low-pass filter  425  passes data having a frequency lower than a predetermined low-pass cutoff frequency. The high-pass filter  420  provides the filtered data to the inverse transform module  410  that converts the filtered data from the frequency domain to the spatial domain, and the low-pass filter  425  provides the filtered data to the inverse transform module  410  that converts the filtered data from the frequency domain to the spatial domain. For instance, the inverse transform module  410  can generate an altered image based on the respective filtering from the high-pass filter  420  or the low-pass filter  425 . 
     As described in greater detail below, the filtered spatial domain data, such as the altered images, can be provided to a DNN  300  to apply feature constraints to input data based on the frequency feature. For example, the DNN  300  can be trained using a loss function including a frequency-based feature consistency constraint to allow the DNN  300  to learn domain independent features. As such, labeled features can be maintained in an image within the source domain. 
       FIG.  5    is a block diagram illustrating an example DNN  300 . In the implementation illustrated in  FIG.  5   , the DNN  300  is a convolutional neural network  500 . The convolutional neural network  500  may include multiple different types of layers based on connectivity and weight sharing. As shown, the convolutional neural network  500  includes convolution blocks  505 A,  505 B. Each of the convolution blocks  505 A,  505 B may be configured with a convolution layer (CONV)  510 , a normalization layer (LNorm)  515 , and a max pooling layer (MAX POOL)  520 . 
     The convolution layers  510  may include one or more convolutional filters, which are be applied to the input data  545  to generate an output  540 . While  FIG.  5    illustrates only two convolution blocks  505 A,  505 B, the present disclosure may include any number of the convolution blocks  505 A,  505 B. The normalization layer  515  may normalize the output of the convolution filters. For example, the normalization layer  515  may provide whitening or lateral inhibition. The max pooling layer  520  may provide down sampling aggregation over space for local invariance and dimensionality reduction. 
     The deep convolutional network  500  may also include one or more fully connected layers  525  (FC1 and FC2). The deep convolutional network  500  may further include a logistic regression (LR) layer  530 . Between each layer  510 ,  515 ,  520 ,  525 ,  530  of the deep convolutional network  500  are weights that can be updated. The output of each of the layers (e.g.,  510 ,  515 ,  520 ,  525 ,  530 ) may serve as an input of a succeeding one of the layers (e.g.,  510 ,  515 ,  520 ,  525 ,  530 ) in the convolutional neural network  500  to learn features from input data  540 , e.g., images, audio, video, sensor data and/or other input data provided at the first of the convolution blocks  505 A. The output  535  can represent a latent representation of one or more features based on the input data. For example, the output  535  can comprise latent features of an input image within a first domain sourced from a real data distribution, such as a Red-Green-Blue (RGB) image captured during daylight. The output  535  can be converted, via a decoder, to a synthetic image within a second domain, such as a synthetic RGB image that illustrates features from the input image during night. 
       FIGS.  6 A and  6 B  illustrate an example process for training the DNN  300  in accordance with one or more implementations of the present disclosure. As shown in  FIG.  6 A , during an initial training phase, a DNN  300  receives a set of labeled training data  605  and training labels  610 . The training data  605  can comprise transformed frequency filtered spatial domain data in accordance with the process described above in reference to  FIG.  4   . For example, the filtered spatial domain data can comprise one or more images depicting objects within a field-of-view (FOV) of a vehicle camera. The training labels  610  may comprise object labels, object type labels, domain type, and/or distance of the object with respect to the source of the image. 
     After the initial training phase, at a supervised training phase, a set of N training data  615  is input to the DNN  300 . The DNN  300  generates outputs translated data for each of the N training data  615  inputs. For example, the DNN  300  can generate a synthetic image that includes the features within the training data in the second domain.  FIG.  6 B  illustrates an example of generating output based on training data  615 , e.g., non-labeled training images, of the N training data  615 . Based on the initial training, the DNN  300  outputs a vector representation  620  of the output data, e.g., latent representations of the training data. The vector representation  620  is compared to ground-truth data  625 . The ground-truth data  625  can include a frequency-based feature consistency constraint. For example, the frequency-based feature consistency constraint may comprise a portion of a loss function such that features within the data corresponding to low frequency are consistent across the domains and features within the data corresponding to high frequency are mitigated or reduced across the domains. 
     The DNN  300  updates network parameters based on the comparison to the ground-truth data  625 . For example, the network parameters, e.g., weights associated with the neurons, may be updated via backpropagation. The DNN  300  may be trained at the server  145  and provided to the vehicle  105  via the communication network  135 . The vehicle  105  may also provide data captured by the vehicle  105  systems to the server  145  for further training purposes. 
       FIG.  7    is a diagram of an example domain adaptation network  700  that can convert data within the source domain to the source domain to data within the target domain. The domain adaptation network  700  can be a software program that can be loaded in memory and executed by a processor in the vehicle  105  and/or the server  145 , for example. In an example implementation, the domain adaptation network  700  can receive a sequence of images in the source domain, e.g., daytime, and output a sequence of images in the target domain, e.g., nighttime. 
     As shown, the domain adaptation network  700  comprises an autoencoder that includes an encoder  705  and a decoder  710 . In an example implementation, the encoder  705  can comprise the trained DNN  300  as described above with respect to  FIGS.  6 A and  6 B . In various implementations, the decoder  710  is symmetrical to the encoder  705 . The encoder  705  receives input data from the source domain and generates a latent representation  715  of the input data, and the decoder  710  reconstructs output data in the source domain based on the latent representation  715  of the input data in the target domain. 
       FIG.  8    is a flowchart of an example process  800  for controlling the vehicle  105  based on the determined output of a neural network trained according to the processes described herein. Blocks of the process  800  can be executed by the computer  110 . The process  800  begins at block  805 , in which the computer  110  determines whether to actuate the vehicle  105  based on the determined output. The computer  110  can include a lookup table that establishes a relationship between a determined output and a vehicle actuation action. For example, based on an image captured by one or more sensors  115  of the vehicle  105 , the computer  110  may cause the vehicle  105  to perform a specified action, e.g., initiate a vehicle  105  turn, adjust vehicle  105  direction, adjust vehicle  105  speed, etc. In another example, based on the determined distance between the vehicle  105  and an object, the computer  110  may cause the vehicle  105  to perform a specified action, e.g., initiate a vehicle  105  turn, initiate an external alert, adjust vehicle  105  speed, etc. 
     If the computer determines that no actuation is to occur, the process  800  returns to block  805 . Otherwise, at block  810 , the computer  110  causes the vehicle  105  to actuate according to the specified action. For example, the computer  110  transmits the appropriate control signals to the corresponding actuators  120 . 
       FIG.  9    is a flowchart of an example process  900  for training the DNN  300 . Blocks of the process  900  can be executed by the computer  235 . The process  900  begins in a block  905 , in which the computer  235  trains the DNN  300 . For example, the DNN  300  may be trained with transformed filtered spatial domain data as described in greater detail above. 
     At block  910 , the computer  235  transmits the trained DNN  300  to the vehicle  105 . The computer  235  determines whether additional data has been received at block  815 . For example, the data may be sensor data that the computer  110  has uploaded. If no additional sensor data has been uploaded, the process  900  returns to block  915 . If additional sensor data has been uploaded, the process  900  returns to block  905  so that the DNN  300  can be trained with transformed filtered spatial domain data based on the uploaded sensor data. 
     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.