Patent Publication Number: US-2022230535-A1

Title: Electronic device and method for navigating pedestrian

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to Chinese Patent Application No. 202110077881.7 filed on Jan. 20, 2021, in China National Intellectual Property Administration, the contents of which are incorporated by reference herein. 
     FIELD 
     The subject matter herein generally relates to navigation, and particularly to an electronic device and a method for navigating pedestrian. 
     BACKGROUND 
     Navigation technology is widely used in our daily life. Navigation of routes from an origin to a destination, users can drive or walk by navigating through a map application. However, for the visually impaired, although walking is assisted by blind tracks laid on the roads, lack of navigation when actual walking, such as the presence of obstacles on the road, may be problematic for the visually impaired. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, the emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  is a schematic view of an embodiment of an application environment of an electronic device according to the present disclosure. 
         FIG. 2  is a block diagram of an embodiment of an electronic device according to the present disclosure. 
         FIG. 3  is a block diagram of an embodiment of an image recognition system operating in an electronic device according to the present disclosure. 
         FIG. 4  is a schematic diagram of an embodiment of a movement track of a pedestrian according to the present disclosure. 
         FIG. 5  is a schematic diagram of another embodiment of the movement track of the pedestrian according to the present disclosure. 
         FIG. 6  illustrates a flowchart of an embodiment of a method of navigating pedestrian according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. Also, the description is not to be considered as limiting the scope of the embodiments described herein. The drawings are not necessarily to scale and the proportions of certain parts have been exaggerated to better illustrate details and features of the presented disclosure. 
     The presented disclosure, including the accompanying drawings, is illustrated by way of examples and not by way of limitation. Several definitions that apply throughout this disclosure will now be presented. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean “at least one.” 
     Furthermore, the term “module”, as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as Java, C, or assembly. One or more software instructions in the modules can be embedded in firmware, such as in an EPROM. The modules described herein can be implemented as either software and/or hardware modules and can be stored in any type of non-transitory computer-readable medium or another storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives. The term “comprising” means “including, but not necessarily limited to”; it in detail indicates open-ended inclusion or membership in a so-described combination, group, series, and the like. 
     Referring to  FIG. 1 , an electronic device (electronic device  1 ) is illustrated. The electronic device  1  communicates with at least one mobile device  2  through a network. The network can be a wired network or a wireless network. The wireless network can be WI-FI or a cellular network. The cellular network can be a 4G network or a 5G network. 
     In one embodiment, the electronic device  1  can be a personal computer, a server, and the like, the server can be a single server, a server cluster, or a cloud server. The mobile device  2  can be a smart phone, a tablet computer, or a smart wearable device. 
     The electronic device  1  includes, but is not limited to, a processor  10 , a storage device  20 , a computer program  30 , and a number of image capturing devices  40 . The computer program  30  may be executed by the processor  10  to implement a method for navigating pedestrian.  FIG. 1  illustrates only one example of the electronic device  1 . Other examples can include more or fewer components than as illustrated or have a different configuration of the various components in other embodiments. 
     The processor  10  can be a central processing unit (CPU), a microprocessor, or other data processor chip that performs functions in the electronic device  1 . 
     In one embodiment, the storage device  20  can include various types of non-transitory computer-readable storage mediums. For example, the storage device  20  can be an internal storage system, such as a flash memory, a random access memory (RAM) for the temporary storage of information, and/or a read-only memory (ROM) for permanent storage of information. The storage device  20  can also be an external storage system, such as a hard disk, a storage card, or a data storage medium. 
     The image capturing device  40  can be a camera device. The image capturing device  40  is arranged on the road, and captures images of a road environment. 
     As illustrated in  FIG. 2 , the electronic device  1  runs a pedestrian navigation system  100 . The pedestrian navigation system  100  at least includes a first determining module  101 , a capturing module  102 , a second determining module  103 , a recognizing module  104 , a third determining module  105 , and a prompt module  106 . The modules  101 - 106  can be collections of software instructions stored in the storage device  20  of the electronic device  1  and executed by the processor  10 . The modules  101 - 106  also can include functionality represented by hardware or integrated circuits, or by software and hardware combinations, such as a special-purpose processor or a general-purpose processor with special-purpose firmware. 
     The first determining module  101  is configured to determine the image capturing device  40  which is closest to the mobile device  2  when the electronic device  1  receives a navigation request from the mobile device  2 . 
     In one embodiment, when the electronic device  1  receives the navigation request from the mobile device  2 , the first determining module  101  determines the location information of the mobile device  2 , calculates distances between the number of image capturing devices  40  and the mobile device  2  according to the location information, and then determines the image capturing device  40  which is closest to the mobile device  2  according to the calculated distances. 
     If a distance between an image capturing device  40  and the mobile device  2  is the shortest distance of the calculated distances, the image capturing device  40  is determined to be closest to the mobile device  2 . 
     In other embodiments, when the electronic device  1  receives the navigation request from the mobile device  2 , the first determining module  101  determines the location information of the mobile device  2 , determines the road where the mobile terminal  2  is located according to the location information, determines the presence or absence of at least one image capturing device  40  arranged on the road, calculates the distance between the at least one image capturing device  40  and the mobile device  2 , and then determines the image capturing device  40  which is closest to the mobile device  2  according to the calculated distance. 
     In response to the navigation request from the mobile device  2 , the capturing module  102  is configured to capture the images of an environment of the road where the mobile device  2  is located at preset time intervals. 
     In one embodiment, the capturing module  102  controls the image capturing device  40  which is closest to the mobile device  2  to capture the images of an environment of the road where the mobile device  2  is located. In one embodiment, the preset time interval can be 0.5 seconds. In other embodiments, the preset time interval can also be set to other suitable time according to requirements. 
     The second determining module  103  is configured to determine whether at least one first obstacle exists on the road according to the captured images. 
     In one embodiment, the second determining module  103  segments each of the captured images according to a Fully Convolutional Network algorithm (FCN) and a Conditional Random Field algorithm (CRF). 
     In detail, the second determining module  103  normalizes each of the captured image, then inputs the normalized captured image into an FCN network, and obtains multiple feature values through a convolution and maximum pooling processes. The width and height of the output image are 1/32 of the width and height of the initial input image, the second determining module  103  further obtains upsampled features by upsampling the feature values, and obtains a segmented image corresponding to each of the captured images by inputting each upsampled feature into a logistic regression prediction (softmax prediction) function. Then, the second determining module  103  inputs the segmented image into a CRF model to optimize the segmented image. In one embodiment, the segmented image includes the outline of each object in the captured image. 
     In one embodiment, the second determining module  103  further determines whether the segmented image includes contours of objects other than a contours of the road. In detail, the second determining module  103  determines whether the segmented image includes the contours of objects other than the contours of the road by contour feature identification. 
     In one embodiment, when the second determining module  103  determines that the segmented image includes the contours of objects other than the contours of the road, it is determined that the first obstacle exists on the road. When the second determining module  103  determines that the segmented image does not include the contours of objects other than the contours of the road, it is determined that no first obstacles exists on the road. In one embodiment, the first obstacle may be an obviously visible obstacle. 
     The recognizing module  104  is configured to recognize a category of the first obstacle when the second determining module  103  determines that the at least one first obstacle exists on the road. 
     In one embodiment, the category can be a generic name of the first obstacle, such as street light poles, billboards, transformer boxes, bus stop sign supports, and the like. 
     The recognizing module  104  is further configured to recognize pedestrians in the captured images, and determine a movement track of each pedestrian. 
     In one embodiment, the pedestrians are the persons other than the user on the road. The recognizing module  104  recognizes the pedestrians in each image according to a target detection algorithm. In one embodiment, the target detection algorithm may be a MobileNet-SSD model, the MobileNet-SSD model may be a pre-trained model. The recognizing module  104  inputs the captured images into the MobileNet-SSD model, so that the pedestrians in each image can be recognized by the MobileNet-SSD model. In other embodiments, the target detection algorithm may also be a YOLOv3 model. 
     In one embodiment, the recognizing module  104  further marks each pedestrian in each image with the head as a reference, the position of the head of the pedestrian represents the position of the pedestrian, and generates the movement track of each pedestrian according to positional changes of the head of the pedestrian in the number of captured images. 
     The second determining module  103  is further configured to determine whether each pedestrian is walking in a single direction according to the movement track of each pedestrian. 
     Referring to  FIG. 4 , in a first embodiment, the second determining module  103  determines a preset walking path of each pedestrian according to an orientation of each pedestrian in a first image of the captured images, and sets two threshold lines located on both sides of the preset walking path and the head of the pedestrian in the captured images. The two threshold lines are set based on the head position of pedestrian, one threshold line is located near the top of the head, and the other threshold line is located near the bottom of the head. In  FIG. 4 , a box is used to indicate the head of the pedestrian, an arrow is used to indicate the preset walking path of the pedestrian, and two solid lines are used to indicate the threshold line. 
     The second determining module  103  further selects two reference points on the head of the pedestrian in the image, such as reference points A and B in  FIG. 4 . The second determining module  103  further determines whether two lines between the same reference points in any two images of the captured images intersect with at least one of the two threshold lines, that is, whether the line between two reference points A or the line between two reference points B in  FIG. 4  intersect with the threshold line. When it is determined that at least one line between the same reference points in any two images intersects the threshold line, the second determining module  103  determines that the pedestrian is deviating from the single direction of walking. When it is determined that the two lines between the same reference points in any two images do not intersect any threshold line, the second determining module  103  determines that the pedestrian is walking in the single direction. 
     Referring to  FIG. 5 , in a second embodiment, the second determining module  103  determines a preset walking path of the pedestrian according to the orientation of the pedestrian in the captured first image, and sets two threshold lines located on both sides of the preset walking path and the head of the pedestrian. The two threshold lines are set based on the head position of pedestrians, one threshold line is located near the top of the head, and the other threshold line is located near the bottom of the head. In  FIG. 5 , a box is used to indicate the head of a pedestrian, an arrow parallel to the threshold line is used to indicate the preset path of a pedestrian, and two solid lines are used to indicate the threshold line. 
     The second determining module  103  further selects two reference points on the head of the pedestrian in the image, such as reference points A and B in  FIG. 5 . The second determining module  103  further calculates a first sum of first distances between each of the two reference points and an adjacent threshold line in the first image, and calculates a second sum of distances between each of the two reference points and an adjacent threshold line in another captured image. 
     In one embodiment, equation 
     
       
         
           
             
               
                 
                   
                     2 
                     ⁢ 
                     d 
                   
                   = 
                   
                     2 
                     * 
                     
                       
                          
                         
                           
                             ax 
                             1 
                           
                           + 
                           
                             by 
                             1 
                           
                           + 
                           c 
                         
                          
                       
                       
                         
                           
                             a 
                             2 
                           
                           + 
                           
                             b 
                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ( 
                       1 
                       ) 
                     
                   
                   ) 
                 
               
             
           
         
       
     
     is used for calculating the first sum of the distances and the second sum of the distances by the second determining module  103 . In the equation, the sum of the distances 2d=d 1 +d 2 , ax+by+c=0 (equation (2)) is a straight line equation of the threshold line, and (x1, y1) is the coordinate of A or B in a coordinate system of the image. For example, d 1  is a distance between the reference point A and the upper threshold line L 1 , d 2  is a distance between the reference point B and the lower threshold line L 2 . 
     In detail, the second determining module  103  determines whether the pedestrian is moving away from or approaching the image capturing device  40  according to the image. For example, when the second determining module  103  determines that the image includes the pedestrian&#39;s face, it is determined that the pedestrian is approaching the image capturing device  40 . When the second determining module  103  determines that the image does not include the pedestrian&#39;s face, it is determined that the pedestrian is moving away from the image capturing device  40 . When it is determined that the pedestrian is moving away from the image capturing device  40 , the second determining module  103  determines whether the first sum of the distances is less than the second sum of the distances. When the first sum of distances is less than the second sum of the distances, the second determining module  103  determines that the pedestrian is walking in the single direction. When the first sum of the distances is greater than or equal to the second sum of the distances, the second determining module  103  determines that the pedestrian is deviating from the single direction. 
     When it is determined that the pedestrian is approaching the image capturing device  40 , the second determining module  103  determines whether the first sum of the distances is greater than the second sum of the distances. When the first sum of the distances is greater than the second sum of the distances, the second determining module  103  determines that the pedestrian is walking in the single direction. When the first sum of the distances is less than or equal to the second sum of the distances, the second determining module  103  determines that the pedestrian is deviating from the single direction. 
     The third determining module  105  is configured to determine that at least one second obstacle exists on the road when the pedestrian is deviating from the single direction. The third determining module  105  is further configured to determine that no second obstacle exists on the road when the pedestrian is walking in the single direction. In one embodiment, the second obstacle may be a hidden obstacle, such as a pothole or the like. 
     The prompting module  106  is configured to transmit an obstacle avoidance prompt to the mobile device  2  when it is determined that the first obstacle and/or the second obstacle exist on the road. 
     In one embodiment, the obstacle avoidance prompt can include the category of the first obstacle, and the positions of the first obstacle and/or the second obstacle relative to the mobile device  2 , that is, relative to the user. 
     Further, when it is determined that the first obstacle and/or the second obstacle exist on the road, the second determining module  103  determines whether the first obstacle and/or the second obstacle are located on the path of the user carrying the mobile device  2 . When it is determined that the first obstacle and/or the second obstacle are located on the current path of the user carrying the mobile device  2 , the obstacle avoidance prompt is transmitted to the mobile device  2 . 
       FIG. 6  illustrates a flowchart of an embodiment of a method for navigating pedestrian. The method is provided by way of example, as there are a variety of ways to carry out the method. The method described below can be carried out using the configurations illustrated in  FIGS. 1-5 , for example, and various elements of these figures are referenced in explaining the example method. Each block shown in  FIG. 6  represents one or more processes, methods, or subroutines carried out in the example method. Furthermore, the illustrated order of blocks is by example only and the order of the blocks can be changed. Additional blocks may be added or fewer blocks may be utilized, without departing from this disclosure. The example method can begin at block  601 . 
     The first determining module  101  determines an image capturing device  40  which is closest to the mobile device  2  when the electronic device  1  receives a navigation request from the mobile device  2 . 
     In one embodiment, when the electronic device  1  receives the navigation request from the mobile device  2 , the first determining module  101  determines the location information of the mobile device  2 , calculates distances between the number of image capturing devices  40  and the mobile device  2  according to the location information, and then determines the image capturing device  40  which is closest to the mobile device  2  according to the calculated distances. 
     If a distance between an image capturing device  40  and the mobile device  2  is the shortest distance of the calculated distances, the image capturing device  40  is determined to be closest to the mobile device  2 . 
     In other embodiments, when the electronic device  1  receives the navigation request from the mobile device  2 , the first determining module  101  determines the location information of the mobile device  2 , determines the road where the mobile terminal  2  is located according to the location information, determines the presence or absence of at least one image capturing device  40  arranged on the road, calculates the distance between the at least one image capturing device  40  and the mobile device  2 , and then determines the image capturing device  40  which is closest to the mobile device  2  according to the calculated distance. 
     At block  601 , the capturing module  102  captures images of an environment of the road where the mobile device  2  is located at preset time intervals, in response to the navigation request from the mobile device  2 . 
     In one embodiment, the capturing module  102  controls the image capturing device  40  which is closest to the mobile device  2  to capture the images of the road where the user is located. In one embodiment, the preset time interval can be 0.5 seconds. In other embodiments, the preset time interval can also be set to other suitable time according to requirements. 
     At block  602 , the second determining module  103  determines whether at least one first obstacle exists on the road according to the captured images. 
     In one embodiment, the second determining module  103  segments each of the captured images according to a Fully Convolutional Network algorithm (FCN) and a Conditional Random Field algorithm (CRF). 
     In detail, the second determining module  103  normalizes each of the captured image, then inputs the normalized captured image into an FCN network, and obtains multiple feature values through a convolution and maximum pooling processes. The width and height of the output image are 1/32 of the width and height of the initial input image, the second determining module  103  further obtains upsampled features by upsampling the feature values, and obtains a segmented image corresponding to each of the captured images by inputting each upsampled feature into a logistic regression prediction (softmax prediction) function. Then, the second determining module  103  inputs the segmented image into a CRF model to optimize the segmented image. In one embodiment, the segmented image includes the outline of each object in the captured image. 
     In one embodiment, the second determining module  103  further determines whether the segmented image includes contours of objects other than a contours of the road. In detail, the second determining module  103  determines whether the segmented image includes the contours of objects other than the contours of the road by contour feature identification. 
     In one embodiment, when the second determining module  103  determines that the segmented image includes the contours of objects other than the contours of the road, it is determined that the first obstacle exists on the road. When the second determining module  103  determines that the segmented image does not include the contours of objects other than the contours of the road, it is determined that no first obstacles exists on the road. In one embodiment, the first obstacle may be an obviously visible obstacle. 
     In one embodiment, the recognizing module  104  recognizes a category of the first obstacle when the second determining module  103  determines that the at least one first obstacle exists on the road. 
     In one embodiment, the category can be a generic name of the first obstacle, such as street light poles, billboards, transformer boxes, bus stop sign supports, and the like. 
     At block  603 , the recognizing module  104  recognizes pedestrians in the captured images, and determine a movement track of each pedestrian. 
     In one embodiment, the pedestrians are the persons other than the user on the road. The recognizing module  104  recognizes the pedestrians in each image according to a target detection algorithm. In one embodiment, the target detection algorithm may be a MobileNet-SSD model, the MobileNet-SSD model may be a pre-trained model. The recognizing module  104  inputs the captured images into the MobileNet-SSD model, so that the pedestrians in each image can be recognized by the MobileNet-SSD model. In other embodiments, the target detection algorithm may also be a YOLOv3 model. 
     In one embodiment, the recognizing module  104  further marks each pedestrian in each image with the head as a reference, the position of the head of the pedestrian represents the position of the pedestrian, and generates the movement track of each pedestrian according to positional changes of the head of the pedestrian in the number of captured images. 
     At block  604 , the second determining module  103  further determines whether each pedestrian is walking in a single direction according to the movement track of each pedestrian. 
     Referring to  FIG. 4 , in a first embodiment, the second determining module  103  determines a preset walking path of each pedestrian according to an orientation of each pedestrian in a first image of the captured images, and sets two threshold lines located on both sides of the preset walking path and the head of the pedestrian in the captured images. The two threshold lines are set based on the head position of pedestrian, one threshold line is located near the top of the head, and the other threshold line is located near the bottom of the head. In  FIG. 4 , a box is used to indicate the head of the pedestrian, an arrow is used to indicate the preset walking path of the pedestrian, and two solid lines are used to indicate the threshold line. 
     The second determining module  103  further selects two reference points on the head of the pedestrian in the image, such as reference points A and B in  FIG. 4 . The second determining module  103  further determines whether two lines between the same reference points in any two images of the captured images intersect with at least one of the two threshold lines, that is, whether the line between two reference points A or the line between two reference points B in  FIG. 4  intersect with the threshold line. When it is determined that at least one line between the same reference points in any two images intersects the threshold line, the second determining module  103  determines that the pedestrian is deviating from the single direction of walking. When it is determined that the two lines between the same reference points in any two images do not intersect any threshold line, the second determining module  103  determines that the pedestrian is walking in the single direction. 
     Referring to  FIG. 5 , in a second embodiment, the second determining module  103  determines a preset walking path of the pedestrian according to the orientation of the pedestrian in the captured first image, and sets two threshold lines located on both sides of the preset walking path and the head of the pedestrian. The two threshold lines are set based on the head position of pedestrians, one threshold line is located near the top of the head, and the other threshold line is located near the bottom of the head. In  FIG. 5 , a box is used to indicate the head of a pedestrian, an arrow parallel to the threshold line is used to indicate the preset path of a pedestrian, and two solid lines are used to indicate the threshold line. 
     The second determining module  103  further selects two reference points on the head of the pedestrian in the image, such as reference points A and B in  FIG. 5 . The second determining module  103  further calculates a first sum of first distances between each of the two reference points and an adjacent threshold line in the first image, and calculates a second sum of distances between each of the two reference points and an adjacent threshold line in another captured image. 
     In one embodiment, equation 
     
       
         
           
             
               
                 
                   
                     2 
                     ⁢ 
                     d 
                   
                   = 
                   
                     2 
                     * 
                     
                       
                          
                         
                           
                             ax 
                             1 
                           
                           + 
                           
                             by 
                             1 
                           
                           + 
                           c 
                         
                          
                       
                       
                         
                           
                             a 
                             2 
                           
                           + 
                           
                             b 
                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ( 
                       1 
                       ) 
                     
                   
                   ) 
                 
               
             
           
         
       
     
     is used for calculating the first sum of the distances and the second sum of the distances by the second determining module  103 . In the equation, the sum of the distances 2d=d 1 +d 2 , ax+by+c=0 (equation (2)) is a straight line equation of the threshold line, and (x1, y1) is the coordinate of A or B in a coordinate system of the image. For example, d 1  is a distance between the reference point A and the upper threshold line L 1 , d 2  is a distance between the reference point B and the lower threshold line L 2 . 
     In detail, the second determining module  103  determines whether the pedestrian is moving away from or approaching the image capturing device  40  according to the image. For example, when the second determining module  103  determines that the image includes the pedestrian&#39;s face, it is determined that the pedestrian is approaching the image capturing device  40 . When the second determining module  103  determines that the image does not include the pedestrian&#39;s face, it is determined that the pedestrian is moving away from the image capturing device  40 . When it is determined that the pedestrian is moving away from the image capturing device  40 , the second determining module  103  determines whether the first sum of the distances is less than the second sum of the distances. When the first sum of distances is less than the second sum of the distances, the second determining module  103  determines that the pedestrian is walking in the single direction. When the first sum of the distances is greater than or equal to the second sum of the distances, the second determining module  103  determines that the pedestrian is deviating from the single direction. 
     When it is determined that the pedestrian is approaching the image capturing device  40 , the second determining module  103  determines whether the first sum of the distances is greater than the second sum of the distances. When the first sum of the distances is greater than the second sum of the distances, the second determining module  103  determines that the pedestrian is walking in the single direction. When the first sum of the distances is less than or equal to the second sum of the distances, the second determining module  103  determines that the pedestrian is deviating from the single direction. 
     At block  605 , the third determining module  105  determines that there is at least one second obstacle on the road when the pedestrian is deviating from the straight and single direction. 
     The third determining module  105  further determines that there is no second obstacle on the road when the pedestrian keeps walking in the single direction. In one embodiment, the second obstacle can be a hidden obstacle, such as a pothole or the like. 
     At block  606 , the prompting module  106  transmits an obstacle avoidance prompt to the mobile device  2  when it is determined that the first obstacle and/or the second obstacle exist on the road. 
     In one embodiment, the obstacle avoidance prompt includes the category of the first obstacle, and the positions of the first obstacle and/or the second obstacle relative to the mobile device  2 , that is, relative to the user. 
     Further, when it is determined that the first obstacle and/or the second obstacle exist on the road, the second determining module  103  determines whether the first obstacle and/or the second obstacle are located on the path of the user. If it is determined that the first obstacle and/or the second obstacle are located on the current path of the user, the obstacle avoidance prompt is transmitted to the mobile device  2 . 
     It is believed that the present embodiments and their advantages will be understood from the foregoing description, and it will be apparent that various changes may be made thereto without departing from the spirit and scope of the disclosure or sacrificing all of its material advantages, the examples hereinbefore described merely being embodiments of the present disclosure.