1. A target identification method based on radar high-resolution range profile is characterized in that a radar target identification model is adopted for identification, the radar target identification model comprises a clustering module, a region decomposition module and an attention mechanism module, and the method comprises the following steps:

acquiring an HRRP training sample data set of airplanes of various categories; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

inputting the training samples into a clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers;

inputting the training samples into a region decomposition module to obtain characteristic data of each clustering center of the training samples;

distributing weights to the feature data based on the attention mechanism module, and determining a prediction result of the training sample according to the feature data after the weights are distributed;

determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster;

updating parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model;

acquiring HRRP data of a target airplane to be identified;

and inputting the HRRP data into a trained radar target identification model to obtain a class identification result of the target airplane.

2. The method of claim 1, wherein the determining the second loss value of the cluster comprises:

determining a second loss value of the cluster by a KL divergence algorithm.

3. The method of claim 2, wherein the updating the parameters of the radar target recognition model according to the first loss value and the second loss value comprises:

determining a third loss value according to the sum of the first loss value and the second loss value;

and updating the parameters of the radar target recognition model according to the third loss value to obtain a trained radar target recognition model.

4. The method for target recognition based on radar high-resolution range profile according to any one of claims 1-3, wherein the determining the first loss value predicted by the radar target recognition model according to the prediction result and the classification label comprises:

and determining a first loss value predicted by the radar target recognition model through a cross entropy loss function according to the prediction result and the classification label.

5. The method for identifying a target based on a radar high-resolution range profile as claimed in claim 1, wherein the step of updating the parameters of the radar target identification model to obtain a trained radar target identification model comprises the following steps:

and if the difference value of the prediction results is smaller than a preset threshold value before and after the parameters are updated, stopping updating the parameters of the radar target identification model to obtain the trained radar target identification model.

6. A target recognition device based on radar high-resolution range profile is characterized in that the device adopts a radar target recognition model for recognition, the radar target recognition model comprises a clustering module, a region decomposition module and an attention mechanism module, and the device comprises:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring an HRRP training sample data set of airplanes of various categories; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

the input module is used for inputting the training samples into the clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

the clustering module is used for clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers;

the processing module is used for inputting the training samples into the region decomposition module to obtain the characteristic data of each clustering center of the training samples;

the prediction module is used for distributing weight to the feature data based on the attention mechanism module and determining the prediction result of the training sample according to the feature data after the weight is distributed;

the calculation module is used for determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster;

the updating module is used for updating the parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model;

the second acquisition module is used for acquiring HRRP data of the target airplane to be identified;

and the identification module is used for inputting the HRRP data into a trained radar target identification model to obtain a class identification result of the target airplane.

7. A target recognition device based on radar high-resolution range profile, comprising:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-5.

8. A computer-readable storage medium in which a program executable by a processor is stored, characterized in that: the processor executable program is for implementing the method of any one of claims 1-5 when executed by a processor.

Background

In recent years, due to high speed real-time performance and easy calculation processing performance of signal processing, radar HRRP has attracted extensive attention in the field of rarr. High Resolution Range Profile (HRRP) is a one-dimensional representation of the response echo of a scattering point of a target to a high resolution radar pulse along the radar line of sight (LOS) direction, which may reflect the scattering structure, geometry and attitude of the target and thus may be used to perform rarr. Another method for implementing the rarr is to classify based on Inverse Synthetic Aperture Radar (ISAR) images, however, due to the motion compensation phase of non-cooperative target ISAR imaging, there are great difficulties, such as inability to accurately measure motion state and trajectory, thereby degrading the imaging quality. Thus, HRRP-RATR has the significant advantage that HRRP data can be processed directly during the recognition process without the need to prepare an explicit image.

However, the original HRRP data is generally high-dimensional and contains redundant information for target identification, which results in a decrease in processing efficiency and identification performance, and in the related art, when the HRRP data is analyzed and identified by a neural network technology, there are problems of insufficient feature discrimination and poor identification effect. In view of the above, there is a need to solve the technical problems in the related art.

The noun explains:

HRRP, High Resolution Range Profile, HRRP.

RNN, Recurrent Neural Network, RNN.

RATR, Radar Automatic Target Recognition, RaTR.

Disclosure of Invention

The present application aims to solve at least one of the technical problems in the related art to some extent.

Therefore, an object of the embodiments of the present application is to provide a target identification method based on a radar high-resolution range profile, which can effectively improve accuracy of aircraft class identification and improve identification performance of a trained model.

Another object of the embodiments of the present application is to provide a target recognition apparatus based on radar high-resolution range profile.

In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:

in a first aspect, an embodiment of the present application provides a target identification method based on a radar high-resolution range profile, where the method identifies by using a radar target identification model, where the radar target identification model includes a clustering module, a region decomposition module, and an attention mechanism module, and the method includes the following steps:

acquiring an HRRP training sample data set of airplanes of various categories; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

inputting the training samples into a clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers;

inputting the training samples into a region decomposition module to obtain characteristic data of each clustering center of the training samples;

distributing weights to the feature data based on the attention mechanism module, and determining a prediction result of the training sample according to the feature data after the weights are distributed;

determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster;

updating parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model;

acquiring HRRP data of a target airplane to be identified;

and inputting the HRRP data into a trained radar target identification model to obtain a class identification result of the target airplane.

In addition, the target identification method based on the radar high-resolution range profile according to the above embodiment of the present application may further have the following additional technical features:

further, in an embodiment of the present application, the determining the second loss value of the cluster includes:

determining a second loss value of the cluster by a KL divergence algorithm.

Further, in an embodiment of the present application, the updating the parameters of the radar target recognition model according to the first loss value and the second loss value includes:

determining a third loss value according to the sum of the first loss value and the second loss value;

and updating the parameters of the radar target recognition model according to the third loss value to obtain a trained radar target recognition model.

Further, in an embodiment of the present application, the determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label includes:

and determining a first loss value predicted by the radar target recognition model through a cross entropy loss function according to the prediction result and the classification label.

Further, in an embodiment of the present application, the updating parameters of the radar target recognition model to obtain a trained radar target recognition model includes:

and if the difference value of the prediction results is smaller than a preset threshold value before and after the parameters are updated, stopping updating the parameters of the radar target identification model to obtain the trained radar target identification model.

In a second aspect, an embodiment of the present application provides a target identification apparatus based on a radar high-resolution range profile, the apparatus employs a radar target identification model for identification, the radar target identification model includes a clustering module, a region decomposition module, and an attention mechanism module, the apparatus includes:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring an HRRP training sample data set of airplanes of various categories; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

the input module is used for inputting the training samples into the clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

the clustering module is used for clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers;

the processing module is used for inputting the training samples into the region decomposition module to obtain the characteristic data of each clustering center of the training samples;

the prediction module is used for distributing weight to the feature data based on the attention mechanism module and determining the prediction result of the training sample according to the feature data after the weight is distributed;

the calculation module is used for determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster;

the updating module is used for updating the parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model;

the second acquisition module is used for acquiring HRRP data of the target airplane to be identified;

and the identification module is used for inputting the HRRP data into a trained radar target identification model to obtain a class identification result of the target airplane.

In a third aspect, an embodiment of the present application provides a target identification device based on a radar high-resolution range profile, including:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the object recognition method of the first aspect.

In a fourth aspect, this application further provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, the program is used to implement the object recognition method of the first aspect.

Advantages and benefits of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application:

according to the target identification method based on the radar high-resolution range profile, the HRRP training sample data sets of airplanes in various categories are obtained; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane; inputting the training samples into a clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space; clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers; inputting the training samples into a region decomposition module to obtain characteristic data of each clustering center of the training samples; distributing weights to the feature data based on the attention mechanism module, and determining a prediction result of the training sample according to the feature data after the weights are distributed; determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster; and updating the parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model, so as to recognize the target airplane. The method can effectively improve the accuracy of airplane category identification and improve the identification performance of the model obtained by training.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for training a radar target recognition model according to the present disclosure;

FIG. 2 is a schematic diagram of HRRP data clustering in an embodiment of a training method for a radar target recognition model according to the present application;

FIG. 3 is a schematic flowchart of an embodiment of a target identification method based on a radar high-resolution range profile according to the present application;

FIG. 4 is a schematic structural diagram of an embodiment of a target identification device based on a radar high-resolution range profile according to the present application;

fig. 5 is a schematic structural diagram of another embodiment of a training apparatus for a radar target recognition model according to the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

The identification method in the embodiment of the application can be applied to a terminal, a server, software running in the terminal or the server, and the like. The terminal may be, but is not limited to, a tablet computer, a notebook computer, a desktop computer, and the like. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and a big data and artificial intelligence platform. Referring to fig. 1, the method mainly comprises the following steps:

step 110, acquiring HRRP training sample data sets of airplanes of various categories; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

in the embodiment of the application, when training data of a radar target recognition model are obtained, HRRP data of airplanes of multiple categories are collected, then the HRRP data corresponding to each airplane are divided into multiple data sections, an HRRP sequence is obtained to serve as a training sample, and the airplane category corresponding to the training sample is marked to serve as a classification label. Specifically, when the training data set is obtained, a plurality of training samples and their corresponding classification labels are obtained, for example, the obtained training data may be denoted as X ═ { X (1), X (2),.., X (n) }, where X (n) denotes HRRP data of an nth training sample, and HRRP data of the nth training sample is divided into sequences, so that the training data set may be denoted as { X (1), X (2),₁(n),x₂(n),...,x_T(n), the classification label set corresponding to the training data set may be denoted as Y ═ Y (1), Y (2),.., Y (n). It should be noted that the categories of the airplanes in the embodiment of the present application may be distinguished according to the models of the airplanes, and airplanes with different external shapes may be considered to belong to different categories.

Step 120, inputting the training samples into a clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

in the embodiment of the application, the radar target recognition model comprises a clustering module. Specifically, the clustering module may be configured to perform the following steps: firstly, the obtained training sample, i.e. the HRRP data, is subjected to nonlinear mapping transformation, so as to obtain nonlinear mapping transformation data of the training sample in a feature space. The formula for the clustering module to perform the non-linear transformation can be expressed as:

f_φ:x_i→z_i；

wherein x is_iIs HRRP data, phi is a parameter that the model needs to learn,is in feature space relative to x_iIs embedded (i.e. non-linear mapping data), f_φParameter learning may be performed by a neural network.

Step 130, clustering the nonlinear mapping transformation data in the feature space to obtain clustering data of a plurality of clustering centers;

in the embodiment of the application, the clustering module is used for clustering the HRRP sequences to different signal regions and obtaining corresponding region vectors, namely clustering data of a clustering center. In the embodiment of the present application, in the feature space, K clustering centers may be learned simultaneously, referring to fig. 2, generally, K may take 3, that is, 3 typical signal regions when HRRP is clustered are respectively a noise region, a rising edge region, and a falling edge region. For the clustering module, the clustering loss L can be defined_cTo measure the accuracy of clustering, specifically, the clustering loss L can be determined by KL divergence algorithm_cAnd is recorded as a second loss value.

Step 140, inputting the training samples into a region decomposition module to obtain feature data of each clustering center of the training samples;

step 150, distributing weights to the feature data based on the attention mechanism module, and determining a prediction result of the training sample according to the feature data after the weights are distributed;

in the embodiment of the application, the training samples may be input into the RNN of the region decomposition module, the feature data is extracted by using the time correlation between the distance units in each HRRP data, and the attention degree to the clustering center region is improved through the attention mechanism module. The goal of the attention mechanism is to help the model focus on the more discriminative sequence features of the target region by assigning greater weight to the output of the target region (cluster center region). And according to the extracted feature data, the radar target recognition model can predict and obtain a corresponding class output result, and the class output result is recorded as a prediction result.

Step 160, determining a first loss value predicted by the radar target recognition model according to the prediction result and the classification label, and determining a second loss value of the cluster;

according to the embodiment of the application, after the prediction result output by the radar target recognition model is obtained, the accuracy of model prediction can be evaluated according to the prediction result and the classification label. For a model, the accuracy of the prediction result can be measured by a Loss Function (Loss Function), which is defined on a single training sample and is used for measuring the prediction error of the training sample, specifically, the Loss value of the training sample is determined according to the classification label of the single training sample and the prediction result of the model on the training sample. In actual training, a training sample set has many training samples, so a Cost Function (Cost Function) is generally adopted to measure the overall error of the training sample set, and the Cost Function is defined on the whole training sample set and is used for calculating the average value of the prediction errors of all the training samples, so that the prediction effect of the neural network model can be measured better. For a general model, based on the cost function, and a regularization term for measuring the complexity of the model, the general model can be used as a training objective function, and based on the objective function, the loss value of the whole training sample set can be obtained. There are many kinds of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, etc. all of which can be used as the loss function of the machine learning model, and are not described in detail herein. In the embodiment of the present application, a cross entropy loss function may be selected to determine the first loss value.

And step 170, updating parameters of the radar target recognition model according to the first loss value and the second loss value to obtain a trained radar target recognition model.

Specifically, when parameters of the radar target identification model are updated in the embodiment of the present application, a Gradient Descent method (GD) may be used, which is a common iterative algorithm for solving a global minimum for a target function, and the types of the Gradient Descent method are many, such as a Batch Gradient Descent method (BGD), a Stochastic Gradient Descent method (SGD), a small-Batch Gradient Descent method (mbi-Batch Gradient Descent, MBGD), and the like. In the embodiment of the application, a random gradient descent method can be selected, the learning rate is high, and the effect is very good. In the embodiment of the application, the convergence condition of the model training may be set to meet the iteration number or the prediction accuracy of the verification data set meets the requirement, for example, when the difference value of the prediction result is smaller than a preset threshold before and after a certain parameter update, the performance of the model may be considered to meet the requirement, and at this time, the parameter update of the radar target recognition model is stopped, so that the current model is used as the trained radar target recognition model. In addition, optionally, in this embodiment of the application, when determining the loss value, the aforementioned second loss value may also be considered, the first loss value and the second loss value are summed to obtain a third loss value, and then the model is trained according to the third loss value, and the specific training process is similar and is not described herein again.

Referring to fig. 3, in the embodiment of the present application, a radar target identification method is further provided, and similarly, the method may also be applied to a terminal, a server, and software in the terminal or the server, so as to implement a part of software functions. Fig. 3 is an alternative flowchart of a radar target identification method provided in the embodiment of the present application, and the method in fig. 3 includes steps 310 to 320.

Step 310, acquiring HRRP data of a target airplane to be identified;

and step 320, inputting the HRRP data into the radar target recognition model obtained by training the training method of the radar target recognition model shown in fig. 1 to obtain the class recognition result of the target airplane.

In the embodiment of the application, the target airplane to be identified can be input into the trained radar target identification model by collecting the HRRP data of the target airplane, so that a corresponding category identification result is obtained. In the embodiment of the application, when the Model obtained by training by the method is predicted, the accuracy can reach 92.85%, and the prediction precision is higher than that obtained by methods such as Maximum Correlation Coefficient (MCC), Adaptive Gaussian Classifier (AGC), High Markov Model (HMM), and Full Connected Network (FCN) in the prior art, so that the recognition effect in the embodiment of the application is better, and the Model performance is better.

The following describes in detail a target recognition apparatus based on a radar high-resolution range profile according to an embodiment of the present application with reference to the drawings.

Referring to fig. 4, in the target identification apparatus based on a radar high-resolution range profile provided in the embodiment of the present application, the apparatus identifies by using a radar target identification model, where the radar target identification model includes a clustering module, a region decomposition module, and an attention mechanism module, the apparatus includes:

a first obtaining module 101, configured to obtain an HRRP training sample data set including multiple categories of airplanes; each training sample in the HRRP training sample data set is provided with a classification label, and the classification label is used for marking the class information of the airplane;

the input module 102 is configured to input the training samples into the clustering module to obtain nonlinear mapping transformation data of the training samples in a feature space;

a clustering module 103, configured to cluster the nonlinear mapping transformation data in the feature space to obtain clustering data of multiple clustering centers;

the processing module 104 is configured to input the training sample into the area decomposition module to obtain feature data of each clustering center of the training sample;

the prediction module 105 is configured to assign a weight to the feature data based on the attention mechanism module, and determine a prediction result of the training sample according to the feature data to which the weight is assigned;

a calculating module 106, configured to determine, according to the prediction result and the classification label, a first loss value predicted by the radar target recognition model, and determine a second loss value of a cluster;

an updating module 107, configured to update parameters of the radar target recognition model according to the first loss value and the second loss value, so as to obtain a trained radar target recognition model;

a second obtaining module 108, configured to obtain HRRP data of a target aircraft to be identified;

and the identification module 109 is configured to input the HRRP data into a trained radar target identification model to obtain a category identification result of the target aircraft.

It is to be understood that the contents of the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as the advantageous effects achieved by the above method embodiments.

Referring to fig. 5, an embodiment of the present application provides another target identification apparatus based on a radar high-resolution range profile, including:

at least one processor 201;

at least one memory 202 for storing at least one program;

the at least one program, when executed by the at least one processor 201, causes the at least one processor 201 to implement a method of training a radar target recognition model.

Similarly, the contents of the method embodiments are all applicable to the apparatus embodiments, the functions specifically implemented by the apparatus embodiments are the same as the method embodiments, and the beneficial effects achieved by the apparatus embodiments are also the same as the beneficial effects achieved by the method embodiments.

The embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored, and the program executable by the processor 201 is configured to perform the above-mentioned training method of the radar target recognition model or the radar target recognition method when executed by the processor 201.

Similarly, the contents in the above method embodiments are all applicable to the computer-readable storage medium embodiments, the functions specifically implemented by the computer-readable storage medium embodiments are the same as those in the above method embodiments, and the beneficial effects achieved by the computer-readable storage medium embodiments are also the same as those achieved by the above method embodiments.

In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.

Furthermore, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion regarding the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the present application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the application, which is defined by the appended claims and their full scope of equivalents.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.

While the present application has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.