Patent Publication Number: US-11645998-B2

Title: System and method of controlling brightness on digital displays for optimum and visibility and power consumption

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a bypass continuation application of International Application No. PCT/KR2022/001549, filed on Jan. 28, 2022, which is based on and claims priority under 35 U.S.C. § 119 to Indian Application No. 202111022216, filed on May 18, 2021, in the Indian Intellectual Property Office, the disclosures of which are incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     1. Field 
     The present disclosure relates to a system and a method for adjusting brightness on digital displays for optimum visibility and power consumption. In particular, the present disclosure relates to a system and a method of optimum brightness calculation for displays with a hindrance object thereupon based on reflectance and transmittance through a hindrance surface as measured based on a Recurrent Neural Network (RNN) learning model. 
     2. Description of Related Art 
     A digital surface may include various features, such as displaying a background for digital trace or tracing type of applications. Tracing is an effective tool to understand linear perspective, anatomy and structures. Further, trace drawing is utilized for learning complex art pieces and mode of learning. For example, as shown in  FIG.  1 A , “Foreshortening” may be performed to visualize how an object looks when projected or extended in space. For example, if you were drawing a figure with an arm stretched out toward you, it would appear “foreshortened”. Typically, a user struggles with foreshortening because an outer shape of the object may not appear as expected. Further, as shown in  FIG.  1 B , existing methods require an additional device to enable trace drawing, use maximum battery for optimal display performance, and/or require using another hand to effectively perform trace drawing or adjust brightness related issues to optimize the brightness. Furthermore, as shown in  FIG.  1 C , a digital surface utilized as a digital trace drawing requires either to manually adjust brightness which makes the method of digital trace drawing cumbersome and not user-friendly, and thus, the digital trace drawing is less popular among professionals and amateurs. In addition, a handheld device usually requires to be set at maximum brightness in order to ensure the visibility beyond the object like paper on the surface. The user manually adjusting the brightness of a display of the device may drive the display performance at a maximum level, thereby draining the power considerably. 
     Further, there is a number of applications available which are used for digital tracing. The current state of the art provides, for example, a) calligraphy books for children where calligraphy books provide extensive practice to young kids to learn and develop correct writing methods; b) LED Light Boxes including a light box specially made for tracing. However, it merely provides a light emitting diode (LED) light background for tracing one paper to another. There is no method to control/manage optimal brightness; c) SketchAR where the augmented reality (AR)-based application helps in drawing/sketching. Here, the user has to manually navigate between the AR application and paper while sketching; d) Tracing Paper where the application helps in digitally tracing an image without using any paper. The Tracing Paper application lacks any method to assist in paper sketching or calligraphy. However, none of the arts are widely used due to lack of user-friendliness. In particular, the above-discussed applications do not involve placing paper over a mobile phone for paper tracing. Further, optimal brightness determining appropriate screen brightness is not considered which impacts higher battery energy consumption of the device. 
     Therefore, there is a need for a mechanism to provide a methodology that can efficiently overcome the aforementioned issues. 
     SUMMARY 
     Additional aspects will be set forth in part in the description which follows, and in part, will be apparent from the description, or may be learned by practice of the presented embodiments. 
     In accordance with an aspect of the disclosure, there is provided a system for adjusting brightness of a displayed content in a device. The system includes: an imaging device for receiving a camera feed based on a visible-light; a light sensor for measuring a light sensor value indicating an intensity of the visible-light; and a brightness calibration module configured to: detect a translucent surface over a display screen based on capturing a plurality of visible-light parameters from the camera feed and the light sensor value; predict a reflectance value of the translucent surface using an artificial neural network model based on the plurality of visible-light parameters and an input brightness level associated with the display screen; determine a proportionality of a change in at least one of a camera feed value and the light sensor value with respect to the input brightness level; verifying the reflectance value through a statistical function based on the determined proportionality; and determining an output brightness level for the display screen based on the reflectance value. 
     The artificial neural network model further includes: a plurality of convolution layers configured to: convolute raw image data captured by the imaging device to determine a reflected light and a transmitted light for the input brightness level based on the plurality of visible-light parameters; and predict the reflectance value of the translucent surface based on the reflected light; a plurality of logic gates module configured to iteratively fetch and store historical data pertaining to the plurality of visible-light parameters captured by the imaging device and the light sensor, and a plurality of brightness levels; and a feedback-and-cost function module configured to verify the predicted reflectance value based on a cost function acting as the statistical function. 
     At least one condition of the cost function is determined according to at least one of: a combination of the reflected light and the transmitted light resulting back into a raw image; and a ratio of a change in the input brightness level that is proportional to a ratio of a change in the reflected light. 
     The cost function is further determined based on: calculating a delta through an adder based on an execution of the at least one condition; and using the artificial neural network model to iteratively predict a revised reflectance of the translucent surface for minimizing the delta computed by the adder. 
     The brightness calibration module is further configured to: calculate a transmittance value of the translucent surface based on the reflectance value; determine an optimal brightness level for the display screen as the output brightness level based on at least one of the transmittance value and the reflectance value; and adjust display properties of the display screen based on the optimal brightness level. 
     The system further includes a co-ordinate selection module configured to: detect a movement of the translucent surface on the display screen; calculate an offset value due to the movement of the translucent surface with respect to the display screen; and adjust the displayed content on the display screen based on the offset value. 
     The co-ordinate selection module further includes: an image scalar module configured to: receive a paper size and an aspect ratio of a digital image to be traced; and scale the digital image based on a predefined aspect ratio of the display screen; an origin and alignment selection module configured to: notify a user for placing the display screen at a corner of the translucent surface; select a coordinate as an origin based on a position of the corner of the translucent surface; select one or more coordinates as alignment coordinates based on the movement of the translucent surface; and an alignment confirmation module configured to: notify the user to adjust an alignment of the display screen based on the movement of the translucent surface with respect to the display screen, wherein the digital image to be traced is adjusted with respect to the display screen based on the offset value. 
     In accordance with an aspect of the disclosure, there is provided a method for adjusting brightness of a displayed content in a device. The method includes: receiving a camera feed based on a visible-light; measuring a light sensor value indicating an intensity of the visible-light; detecting a translucent surface over a display screen based on capturing a plurality of visible-light parameters from the camera feed and the light sensor value; predicting a reflectance value of the translucent surface using an artificial neural network model based on the plurality of visible-light parameters and an input brightness level associated with the display screen; determining a proportionality of a change in at least one of a camera feed value and the light sensor value with respect to the input brightness level; verifying the reflectance value through a statistical function based on the determined proportionality; and determining an output brightness level for the display screen based on the reflectance value. 
     The predicting the reflectance value of the translucent surface using the artificial neural network model includes: convoluting raw image data captured by an imaging device to determine a reflected light and a transmitted light for the input brightness level based on the plurality of visible-light parameters; and predicting the reflectance value of the translucent surface based on the reflected light; iteratively fetching and store historical data pertaining to the plurality of visible-light parameters captured by the imaging device and the light sensor and a plurality of brightness levels; and verifying the predicted reflectance value based on a cost function acting as the statistical function. 
     At least one condition of the cost function is determined according to at least one of: a combination of the reflected light and the transmitted light resulting back into raw image; and a ratio of a change in the input brightness level that is proportional to a ratio of a change in the reflected light. 
     The cost function is determined based on: calculating a delta through an adder based on an execution of the at least one condition; and using the artificial neural network model to iteratively predict a revised reflectance of the translucent surface for minimizing the delta computed by the adder. 
     The method further includes: calculating a transmittance value of the translucent surface based on the reflectance value; determining an optimal brightness level for the display screen as the output brightness level based on at least one of the transmittance value and the reflectance value; and adjusting display properties of the display screen based on the optimal brightness level. 
     The method further includes: detecting a movement of the translucent surface on the display screen; calculating an offset value due to the movement of the translucent surface with respect to the display screen; and adjusting the displayed content on the display screen based on the offset value. 
     The method further includes: receiving a paper size and an aspect ratio of a digital image to be traced; scaling the digital image based on a predefined aspect ratio of the display screen; notifying a user for placing the display screen at a corner of the translucent surface; selecting a coordinate as an origin based on a position of the corner of the translucent surface; selecting one or more coordinates as alignment coordinates based on the movement of the translucent surface; and notifying the user to adjust alignment of the display screen based on the movement of the translucent surface with respect to the display screen, wherein the digital image to be traced is adjusted with respect to the display screen based on the offset value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other features, aspects, and advantages of certain embodiments of the present disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which: 
         FIGS.  1 A,  1 B, and  1 C  illustrate diagrams of trace drawing in the related art s; 
         FIG.  2    is a diagram illustrating a relationship between a variation in brightness of a screen display with respect to a light reflected and transmitted when a translucent object is placed over a screen, according to an embodiment; 
         FIG.  3    is a flowchart illustrating a method for adjusting brightness of displayed content in a device according to an embodiment; 
         FIG.  4    is a flow diagram illustrating a detailed implementation of a method of  FIG.  3   , according to an embodiment; 
         FIG.  5    is a diagram illustrating a system architecture according to an embodiment; 
         FIG.  6    illustrates an example of training an RNN module, according to an embodiment; 
         FIGS.  7 A,  7 B, and  7     c  illustrate an example operation of a coordinate selection module, according to an embodiment; 
         FIGS.  8 A to  11    illustrate various example images, according to an embodiment; 
         FIG.  12    illustrates another system architecture, according to an embodiment; and 
         FIG.  13    illustrates a device according to an embodiment. 
     
    
    
     A person skilled in the art will appreciate that elements illustrated in the accompanying drawings are illustrated for simplicity and may not be drawn to scale. For example, the flowcharts illustrate one or more methods in terms of prominent steps to help understanding of various aspects of the present disclosure. Furthermore, in terms of a device and a system, one or more components of a device may be shown, however, the accompanying drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the understanding of the embodiments. 
     DETAILED DESCRIPTION 
     It should be understood that, although illustrative implementations of the embodiments of the present disclosure are described below, the embodiments of the present disclosure may be implemented using any number of techniques that are currently known or in existence. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques described below, including the exemplary design and implementation illustrated and described herein, but may be variously modified within the scope of the appended claims. 
     The terminology and structure described herein is for describing one or more embodiments, and specific features and elements should not be construed as limiting the scope of the disclosure. 
     More specifically, any terms used herein such as, but not limited to, “include,” “comprise,” “has/have,” “consist,” and grammatical variants thereof may not specify an exact limitation or restriction and certainly do not exclude possible variations of one or more features or elements, unless indicated otherwise. Moreover, the one or more embodiments should not be construed as excluding the possible removal of one or more of the listed features and elements, unless indicated otherwise. 
     Whether or not a certain feature or element was limited to being used only once, either way it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless indicated otherwise. 
     Unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having an ordinary skill in the art. 
     The present disclosure provides a system and method for adjusting brightness on digital displays for optimum visibility and power consumption. In particular, the present disclosure relates to a system and a method of optimum brightness calculation for displays with hindrance objects thereupon based on reflectance and transmittance through the hindrance surface as measured based on RNN learning model. Also, the present disclosure allows display content adjustment with respect to a device moment in order to perform techniques such as trace drawing. 
     Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. 
     According to one aspect of the present disclosure, there exists a relationship between a variation in brightness of screen display with respect to a light reflected and transmitted when a translucent object, like a piece of paper, is placed over a screen as shown in  FIG.  2   . This relationship may be used to determine an optimal screen brightness level and overall savings in energy consumption by the device. In particular, as shown in  FIG.  2   , when environmental light falls on the surface of the display screen, some part of the light is transmitted that may be calculated as transmittance and some other part of the light is reflected that may be calculated as reflectance. Thus, by calculating the transmittance and the reflectance, an optimal brightness level can be calculated. The implementation details of the brightness calibration will be discussed in detail below. 
       FIG.  3    is a flowchart illustrating a method for adjusting brightness of displayed content in a device according to an embodiment. 
     In an implementation as depicted in  FIG.  3   , the present subject matter refers to a method for adjusting the brightness of the displayed content in a device. 
     The method  300  includes receiving, from an imaging device, a camera feed based on visible light (operation  301 ). As an example, the imaging device may be a device capable of capturing an image like in cameras that are in-built in a handheld device. For example, the handheld device can be a mobile phone (e.g., a cellular phone, a phone running on a local network, or any other telephone handset), a wired telephone (e.g., a phone attached by a wire), a personal digital assistant (PDA), a video game player, a video game controller, navigation device, a mobile internet device (MID), a personal navigation device (PND), a digital still camera, a digital video a camera, a binoculars, a telephoto lens, a portable music, video, or a media player, a remote control, or another handheld device, or a combination of one or more of these devices. In addition, the camera feed may be due to environment light or may be due to phone brightness that is incident upon a surface of a display screen of the imaging device/handheld device. 
     In operation  303 , a light sensor measures the visible-light intensity to provide a light sensor value. In operation  305  the brightness calibration module detects the presence of a translucent surface over the display screen based on capturing a plurality of visible light parameters from the camera feed and light sensor value. As an example, the visible light parameters may be a reflected light as reflectance, transmitted light as a transmittance, and the like. In operation  307 , the brightness calibration module predicts a reflectance value of the translucent surface using an artificial neural network model based on the plurality of visible light parameters and an input brightness level associated with the display screen. 
     In operation  309 , after predicting the reflectance value at step  307  by the artificial neural network, the brightness calibration module determines a proportionality of change in at least one of the camera feed values and the light sensor values with respect to the input brightness level. In operation  311 , the brightness calibration module verifies the determined reflectance value through a statistical function based on the determined proportionality. 
     In an embodiment, the brightness calibration module includes the artificial neural network. The artificial neural network includes one or more convolution layers. The prediction of the reflectance value of the translucent surface using the artificial neural network of operation  307 , includes convoluting, by the one or more convolution layers, raw image data captured by the imaging device by resolving the plurality of visible light parameters into reflected light and transmitted light for the input brightness level. The one or more convolution layers predict the reflectance value of the translucent surface based on the reflected light. In another embodiment, the artificial neural network includes a plurality of logic gates module to iteratively fetch and store historical data pertaining to the plurality of visible light parameters captured by the imaging device and the light sensor, and a plurality of brightness level. The artificial neural network further includes a feedback-and-cost function module. The feedback-and-cost function module verifies the predicted reflectance value based on satisfaction of a cost function acting as the statistical function. 
     In an embodiment, the cost function may be determined according to at least one of a) a combination of the reflected light and the transmitted light of the raw image; or b) a ratio of a change in the input brightness level that is proportional to a ratio of a change in the reflected light. 
     According an embodiment, the cost function may be derived by calculating a delta through an adder module based on the execution of at least one condition and then using the neural network to iteratively predict a revised reflectance of the translucent surface for minimizing the delta computed by the adder. 
     In operation  313 , the brightness calibration module determines an output brightness level for the display screen based on the verified reflectance value. 
     In addition, the method  300  calculates, by the brightness calibration module, a transmittance value of the translucent surface based on the determined reflectance value. The brightness calibration module determines an optimal brightness level for the display screen as the output brightness level based on at least one of the calculated transmittance values and the determined reflectance value. Thus, the brightness calibration module adjusts the display properties of the display screen based on the determined optimal brightness level. 
     The method  300  further includes detecting, by a co-ordinate selection module, a movement of the translucent surface on the display screen. The co-ordinate selection module calculates an offset value based on the movement of the translucent surface with respect to the display screen and adjusting a displayed content at the display screen based on the calculated offset value. 
     The method  300  further includes receiving a paper size and an aspect ratio of a digital image to be traced. The reception of the paper size and the aspect ratio of a digital image to be traced is performed by an image scalar module included in the co-ordinate selection module. Thereafter, the digital image is scaled based on a predefined aspect ratio of the display screen. 
     The method  300  further includes notifying, by an origin and alignment selection module that is included in the co-ordinate selection module, a user to adjust the display screen based on a corner of the translucent surface. Thus, upon notification the origin and alignment selection module selects one or more coordinates as an origin based on the position of the corner and selects one or more coordinates as alignment coordinates based on the movement of the translucent surface. Thereafter, an alignment confirmation module which is included in the co-ordinate selection module notifies the user to adjust the alignment of the display screen based on the movement of the translucent surface with respect to the display screen. Thus, the digital image which is to be traced is adjusted with respect to the display screen based on the calculated offset. 
     Referring to  FIGS.  4  to  6   , the method  300  of  FIG.  3    will be described in more detail below.  FIG.  4    is a flow diagram illustrating a detailed implementation of the method  300  of  FIG.  3   , according to an embodiment. Referring to  FIG.  4   , the method  300  may be implemented in a three major block of implementation. Block  1  represents a corresponding operation flow of Brightness Calibration Module  401 , block  2  represents a corresponding operation flow of Co-ordinate selection module  403 , and block  3  represents a corresponding operation flow of Display Selection module. The implementation details of each of the modules is shown in a system architecture in  FIGS.  5  and  6   . Similar reference numerals denoting corresponding features are consistently used throughout the figures for the sake of brevity in the embodiments. 
     Referring to  FIG.  4   , oeprations  407  and  409  correspond to the operations  301  and  303  of  FIG.  3   . That is, the camera receives the camera feed based on the visible light and the light sensor measures the visible-light intensity to provide a light sensor value. The camera feed value  601  and the light sensor value  603  (as shown in  FIG.  6   ) are provided to the Brightness Calibration Module  401  for brightness calibration at operation  409 . Additionally, some amount of brightness value  603  is also considered as an input to the Brightness Calibration Module  401 . 
     According to an embodiment, the Brightness Calibration Module  401  includes the artificial neural network. In particular, the Brightness Calibration Module  401  is a Recurrent Neural Network (RNN) based artificial neural network module. The Brightness Calibration Module  401  includes one or more convolution layers  501 , Remember and Forget Gates  503 , and Feedback and Cost function  505 . The Brightness Calibration Module  401  performs operations  305  to  313  of  FIG.  3    operation. Accordingly, the descriptions provided above with respect to operations  305  to  313  will be omitted. 
     In an implementation, the RNN based Brightness Calibration Module  401  predicts the optimum brightness required for visibility after a tracing paper is placed (as described in operation  305  of  FIG.  3   ) on top of the handheld device. In particular, the brightness calibration module  401  uses the RNN  607  to separate the FOV into a reflected vector and transmitted vector based on the reflectance of the medium being calculated (as described in operation  307   FIG.  3   ). It uses multiple iterations of light sensor and camera FOV data to calculate the proportionality of change in the camera feed value and the light sensor value with respect to the input brightness level. That is Brightness Calibration Module  401  determines proportionality constant of ratio of increase in reflected light from the handheld device with an increase in brightness of device (as described in operation  309  of  FIG.  3   ). 
     Accordingly, the convolution layer  501  is a preprocessing layer of the model that decreases the complexity by convoluting the raw image data provided as input. In addition, there can be one or more convolution layers  501 . In an implementation, the convolution of the raw image data captured by the imaging device may be performed by resolving the plurality of visible light parameters into reflected light and a transmitted light for the input brightness level and thereafter, predicting the reflectance value of the translucent surface based on the reflected light. 
     Subsequent to the prediction of the reflectance value, the Remember and Forget Gates  503  which are logic section of the Brightness Calibration Module  401  undergoes through multiple records of light sensor and FOV data with respect to brightness level. For any specific iteration, model remembers the value which predicts the data better and forgets the value for which there is a data mismatch. In particular, the Remember and Forget Gates  503  may be configured to iteratively fetch and store historical data pertaining to the plurality of visible light parameters captured by the imaging device and the light sensor and a plurality of brightness level. That is, during the iteration processing, the Remember and Forget Gates  503  may maintain data that is greater than or equal to a predetermined threshold and uses the maintained data to predict the brightness level for the next iteration. The Remember and Forget Gates  503  may discard or not use data that is less than the predetermined threshold for the next iteration. 
     Subsequently, the Feedback and cost function module  505  at operation  411  of  FIG.  4    may be configured to verify (as described in operation  311  of  FIG.  3   ) the predicted reflectance value based on satisfaction of a cost function acting as the statistical function. In particular, to satisfy the objective of the cost function following conditions are checked: 
     a) A combination of the reflected light and the transmitted light to result back into the raw image. In particular, the vectors separated as transmitted and reflected light must combine to form the input image: Cost function for this condition will be the difference in the sum of separated vectors with respect to the input vector as defined in equation 1. 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                     
                   
                     
                       ( 
                       
                         Ii 
                         - 
                         
                           ( 
                           
                             
                               A 
                               i 
                             
                             + 
                             Bi 
                           
                           ) 
                         
                       
                       ) 
                     
                     2 
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Where, A i =input reflected light and B i =input transmitted light 
     b) Ascertaining if a ratio of change in the input brightness level is proportionate to a ratio of change in the reflected light. In particular, the ratio of change in reflected light should be proportional to the change in brightness: The cost function for this condition is the difference between the ratio of change in brightness to change in reflected light with a constant ‘K’ as defined in the equation 2. 
     
       
         
           
             
               
                 
                   
                     1 
                     N 
                   
                   ⁢ 
                   
                     
                       ∑ 
                       … 
                     
                     
                       
                         ( 
                         
                           
                             
                               B 
                               ⁡ 
                               ( 
                               
                                 
                                   i 
                                   + 
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                                 , 
                                 j 
                               
                               ) 
                             
                             
                               B 
                               ⁡ 
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                               ) 
                             
                           
                           - 
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                         ) 
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     Where, B=input brightness level, 
     K=Proportionality constant 
     Thereby, determining (as described in operation  313  of  FIG.  3   ) an output brightness level for the display screen based on the verified reflectance value. 
       FIG.  6    illustrates an example of training the RNN module, according to an embodiment. Accordingly, the RNN determines the reflectance of the paper by repeatedly matching the relation of change in the camera feed and light sensor values with respect to change in the brightness level of an electronic device. Since the paper with similar reflectance will behave similarly, the RNN remembers the significant patterns to determine the reflectance of the paper. The RNN tries to separate camera feed to external transmitted light and internally reflected values for multiple brightness levels in the device. To verify the same, the sum of those values is checked against the original image. 
     It is assumed that the total light being received by the camera be P, then P=t 1 +r 2 , where t 1  is the light transmitted from the external environment and r 2  is light reflected from the screen of the electronic device given some external brightness Bi, and the transmittance+reflectance=1. Here, more reflectance means more light to the camera feed. Also, the input to camera feed and light sensor will be proportional to the brightness level of the display, and since more brightness means more light from the electronic device, the light in the camera feed and light sensor values will increase based on the reflectance of the paper. 
     For the same change brightness level and environment light, the paper with more reflectance will more light being reflected and hence more light in the camera feed and light sensor. Using this relation, the RNN based model can be trained based on input the light sensor value, camera feed, and brightness level of the device. During the training phase, RNN takes multiple iterations of camera feed and light sensor data for every brightness level. The optimization function for the RNN is made to remember the patterns for a change in light with respect to brightness and based on that determine the reflectance of the electronic device. Then, based on environment light and reflectance, the optimal brightness level is calculated and set to the device. 
     Thus, from the above is known that that reflected light from paper is proportional to the brightness. as defined in equation 3.
 
Bk/Bi=K*(Rk/Ri)  (3)
 
     Accordingly, the cost function may be defined by calculating delta through an adder module  609  based on the execution of at least one condition at equation 1 and 2 and collaboratively defined as below in equation 4: 
     Cost function (delta) 
                     =           ∑     i   =   1     N         (     Ii   -     (       A   i     +     B   ⁢   i       )       )     2       +       1   N     ⁢       ∑   …         (         B   ⁡   (       i   +   1     ,   j     )       B   ⁡   (     i   ,   j     )       -   K     )     2           =       ,           (   4   )               
where
 
     Additionally, calculation of ‘K’ from light sensor data is given by equation 5 as below: 
     
       
         
           
             
               
                 
                   
                     
                       ( 
                       
                         
                           
                             B 
                             
                               i 
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                             B 
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                         = 
                         
                           k 
                           ⁢ 
                           
                             
                               R 
                               
                                 
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                               R 
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                     where 
                   
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                         - 
                         
                           L 
                           i 
                         
                       
                       
                         
                           ( 
                           
                             
                               2 
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                               k 
                             
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                         ⁢ 
                         
                           B 
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
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     Accordingly, to summarize, the RNN predicts “a reflectance of paper” that may be iteratively calculated as output. This also satisfies the “cost function criteria” (based on proportion k). Once the reflectance of the paper is iteratively predicted, the optimal brightness level may be calculated as the final output. 
     In a further implementation, the Brightness Calibration Module  401  calculates a transmittance value of the translucent surface based on the determined reflectance value as described with respect to  FIGS.  4  to  6   . The Brightness Calibration Module  401  determines an optimal brightness level for the display screen as the output brightness level based on at least one of the calculated transmittance value and the determined reflectance value. In particular, the transmittance is actually the light that has passed through the surface and so can be calculated based on reducing reflectance calculated in the previous step from the total light. Different hindrance objects may change this factor differently based on their characteristics. Thus, adjusting display properties of the display screen based on the determined optimal brightness level. Thus, calculating the optimal brightness level helps in reducing the battery consumption by determining an optimal brightness level instead of a fixed brightness level. 
     Referring back to  FIG.  4   , block  2  represents the corresponding operation flow of co-ordinate selection module  403 . According to an embodiment, the coordinate selection module  403  includes an image scalar  507 , an origin selector  509 , and alignment confirmation  511 . The co-ordinate selection module  403  provides functionality of a selection of an appropriate co-ordinate system and origin based on which the position and alignment of the device is calculated. This module also constantly monitors the displacement and rotation of the phone and confirms if it matches with the original alignment to aid in precise tracing. 
     In an implementation, the co-ordinate selection module  403  prompts or notifies the user to provide the paper size and aspect ratio of the digital image being traced and choose a starting location and an alignment of the device before tracing starts. This module takes scales the mentioned image and chooses an origin (generally starting point of the device) and a co-ordinate system to describe the position and orientation of the device. It also prompts the user to realign if alignment mismatches during tracing. 
     Referring to the  FIG.  4   , at operations  419  and  421 , the image scaler module  507  receives a paper size and an aspect ratio of the digital image as input, and a paper size and an aspect ratio of the image to be traced. Based on the received paper sizes and the aspect ratios, the image scaler module  507  scales the digital image to the intended aspect ratio and size as shown in  FIG.  7 A . 
     The Origin selection module  509  prompts or notifies the user to place the electronic device on one corner as shown in  FIG.  7 B  and remembers the selected position as origin and alignment as co-ordinate system. The selected position is later used in describing the position and alignment of the device by a display the selector module. 
     At operation  415 , the Alignment confirmation module  511  monitors the movement of the device and prompts user to fix alignment if a mismatch occurs for precise tracing (operation  417 ). To detect mismatch, a given percentage of the display section should overlap in two successive positions and alignment of the electronic device (operation  417 ). Upon confirmation of the fix alignment the Origin selection module  509  provides a coordinate of the selected origin to a display selection  405 . 
     In an implementation, the display selection  405  includes sensor data  513 , Kalman filter  515 , and fragment selector  517 . Accordingly, the display selection  405  calculates the position and orientation of the device using sensors data of accelerometer, gyroscope, or camera feed and based on that it selects the segment of the image to be displayed on the screen. 
     The new coordinates data for the display may be calculated based on the below equation and as shown in  FIG.  7 C : 
     Translation:
 
 X new= X prev+ X ′(0 if  X new&lt;0, else min(Paper Length,  X new))  (6)
 
 Y new= Y prev+ Y ′(0 if  Y new&lt;0, else min(Paper height,  Y new))  (7)
 
     Rotation:
 
 X 1 new   =X 1 prev   +H *sin(ø) (0 if  X 1 new &lt;0, else min(Paper Length,  X   new ))  (8)
 
 Y 1 new   =Y 1 prev   +H (1−cos(Ø) (0 if  Y   new &lt;0, else min(Paper height,  Y   new ))  (9)
 
 X 2 new   =X 1 new   +L *cos(Ø) (0 if  X 2 new &lt;0, else min(Paper Length,  X 2 new )  (10)
 
 Y 2 new   =Y 2 prev   +L *sin(Ø) (0 if  Y   new &lt;0, else min(Paper height,  Y   new ))  (11)
 
 X 3 new   =X 3 prev   (12)
 
 Y 3 new   =Y 3 prev   (13)
 
 X 4 new   =X 3 new   +L *cos(Ø) (0 if  X   new &lt;0, else min(Paper Length,  X   new )  (14)
 
 Y 4 new   =Y 3 prev   +L *sin(Ø) (0 if  Y   new &lt;0, else min(Paper height,  Y   new ))   (15)
 
     Further, the mathematical derivation is given as below:
 
 {dot over (A)}={dot over (A)}x+{dot over (A)}y+{dot over (A)}z (components of accleration vector)
 
     Integrating acceleration vector will give us the velocity vector
 
∫ {dot over (A)} dt=∫( {dot over (A)}x+{dot over (A)}y+{dot over (A)}z )dT
 
∫ {dot over (A)} dT= {dot over (Y)}x+{dot over (Y)}y+{dot over (Y)}z +constant ( {dot over (Y)}k  are components of the velocity vector)
 
     Integrating velocity vector will give us the displacement vector
 
∫   Y   dt=∫( {dot over (Y)}x+{dot over (Y)}y+{dot over (Y)}z )dT
 
∫ {dot over (Y)} dT= {dot over (S)}x+{dot over (S)}y+{dot over (S)}z +constant ( {dot over (S)}k  are conponents of displacement vector)
 
 {dot over (S)}={dot over (S)}x+{dot over (S)}y+{dot over (S)}z  (Final displacement vector)
 
     Now, various use cases for implementing mechanisms as disclosed above will be disclosed.  FIG.  8 A  illustrates a digital image obtained by using the related art in which a sheet is placed over a digital display in order to trace or draw a digital image. In the related art, it would require a high brightness device which could lead to drain a huge amount of battery, and thus, would not be efficient. In contrast, referring to  FIG.  8 B , a digital image obtained according to the one or more embodiments of the disclosure, a device can learn on its own to adjust the display brightness with respect to the sheet thickness used in order to make the digital display image clearly visible over the sheet and enable the user to efficiently do the trace drawings. 
     In another example, as shown in  FIGS.  9 A and  9 B , by implementing the disclosed techniques, a user (e.g., kids) can clearly visualize and learn very easily by simply putting the drawing sheet over the display device and start tracing. For example, the user can learn different sizes and angles of objects on an existing sketch by tracing them understanding the different sizes of different objects. As another example, students can learn engineering drawing very easily by just putting the drawing sheet over the display device and start tracing. It is very easy to learn the different perspective of an engineering drawing (top, front, back view). 
     In another example as shown in  FIGS.  10 A and  10 B , by implementing the one or more embodiments of the disclosure, a user (e.g., kids) can learn calligraphy very easily by just putting the drawing sheet over the display device and start tracing. The user can learn different form/styles/fonts of letters by using the disclosed techniques. 
     In another example shown in  FIG.  11   , by implementing the one or more embodiments of the disclosure, fashion designers can very easily draw each portion of a dress with some inspiration from other sources. For Example, a beautiful flower in a modern art could be easily drawn on the portion of a dress using the disclosed techniques. 
       FIG.  12    illustrates a system architecture  2400  to provide tools and development environment described herein for a technical-realization of the implementation server, controller and nodes in the mesh network through a computing device.  FIG.  13    is a non-limiting example of an electronic device, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The architecture may be executing on hardware such as a computing machine  2400  of  FIG.  13    that includes, among other things, processors, memory, and various application-specific hardware components. 
     The architecture  2400  may include an operating-system, libraries, frameworks or middleware. The operating system may manage hardware resources and provide common services. The operating system may include, for example, a kernel, services, and drivers defining a hardware interface layer. The drivers may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration. 
     A hardware interface layer includes libraries which may include system libraries such as file-system (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries may include API libraries such as audio-visual media libraries (e.g., multimedia data libraries to support presentation and manipulation of various media format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g. WebKit that may provide web browsing functionality), and the like. 
     A middleware may provide a higher-level common infrastructure such as various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The middleware may provide a broad spectrum of other APIs that may be utilized by the applications or other software components/modules, some of which may be specific to a particular operating system or platform. 
     The term “module” used in this disclosure may refer to a certain unit that includes one of hardware, software and firmware or any combination thereof. The module may be interchangeably used with unit, logic, logical block, component, or circuit, for example. The module may be the minimum unit, or part thereof, which performs one or more particular functions. The module may be formed mechanically or electronically. For example, the module disclosed herein may include at least one of ASIC (Application-Specific Integrated Circuit) chip, FPGAs (Field-Programmable Gate Arrays), and programmable-logic device, which have been known or are to be developed. 
     Further, the system architecture  2400  depicts an aggregation of audio/video processing device based mechanisms and machine learning/natural language processing (ML/NLP) based mechanism in accordance with an embodiment. A user-interface defined as input and interaction  2401  refers to overall input. It can include one or more of the following—touch screen, microphone, camera etc. A first hardware module  2402  depicts specialized hardware for ML/NLP based mechanisms. In an example, the first hardware module  2402  may include one or more of neural processors, FPGA, DSP, GPU etc. 
     A second hardware module  2412  depicts specialized hardware for executing the data splitting and transfer. ML/NLP based frameworks and APIs  2404  correspond to the hardware interface layer for executing the ML/NLP logic  2406  based on the underlying hardware. In an example, the frameworks may be one or more or the following—Tensorflow, Café, NLTK, GenSim, ARM Compute etc. Simulation frameworks and APIs  2414  may include one or more of—Audio Core, Audio Kit, Unity, Unreal etc. 
     A database  2408  depicts a pre-trained database. The database  2408  may be remotely accessible through cloud by the ML/NLP logic  2406 . In other example, the database  2408  may partly reside on cloud and partly on-device based on usage statistics. 
     Another database  2418  may be an object database including a memory. The database  2418  may be remotely accessible through cloud. In other example, the database  2418  may partly reside on the cloud and partly on-device based on usage statistics. 
     A rendering module  2405  is provided for rendering audio output and trigger further utility operations. The rendering module  2405  may be manifested as a display cum touch screen, monitor, speaker, projection screen, etc. 
     A general-purpose hardware and driver module  2403  may be the computing device  2500  as shown in  FIG.  13   , and instantiates drivers for the general purpose hardware units as well as the application-specific units (e.g., the first hardware module  2402  and the second hardware module  2412 ). 
     In an example, the ML mechanism underlying the system architecture  2400  may be remotely accessible and cloud-based, thereby being remotely accessible through a network connection. An audio/video processing device may be configured for remotely accessing the NLP/ML modules and simulation modules may comprise skeleton elements such as a microphone, a camera a screen/monitor, a speaker etc. 
     Further, at-least one of the plurality of modules of mesh network may be implemented through AI based on an ML/NLP logic  2406 . A function associated with AI may be performed through the non-volatile memory, the volatile memory, and the processor constituting the first hardware module  2402  i.e. specialized hardware for ML/NLP based mechanisms. The processor may include one or a plurality of processors. One or a plurality of processors may be a general purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor such as a neural processing unit (NPU). The aforesaid processors collectively correspond to the processor  2502  of  FIG.  13   . 
     The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning. 
     Here, being provided through learning means that, by applying a learning logic/technique to a plurality of learning data, a predefined operating rule or AI model of a desired characteristic is made. “Obtained by training” means that a predefined operation rule or artificial intelligence model configured to perform a desired feature (or purpose) is obtained by training a basic artificial intelligence model with multiple pieces of training data by a training technique. The learning may be performed in a device (i.e. the architecture  2400  or the device  2500 ) itself in which AI according to an embodiment is performed, and/or may be implemented through a separate server/system. 
     The AI model may include a plurality of neural network layers. Each layer has a plurality of weight values, and performs a neural network layer operation through calculation between a result of computation of a previous-layer and an operation of a plurality of weights. Examples of neural-networks include, but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q-networks. 
     The ML/NLP logic  2406  is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. 
       FIG.  13    illustrates an electronic device according to an embodiment. For example, the computer system  2500  may be one of the hardware configurations of the system architecture  2400 . The computer system  2500  may include a memory that stores a set of instructions that can be executed by at least one processor to cause the computer system  2500  to perform any one or more of the embodiments described above. The computer system  2500  may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices. 
     In a networked deployment, the computer system  2500  may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system  2500  can also be implemented as or incorporated across various devices, such as a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single computer system  2500  is illustrated, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions. 
     The computer system  2500  may include a processor  2502  e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor  2502  may be a component in a variety of systems. For example, the processor  2502  may be part of a standard personal computer or a workstation. The processor  2502  may be one or more general processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor  2502  may implement a software program, such as code generated manually (e.g., programmed). 
     The computer system  2500  may include a memory  2504 , such as a memory  2504  that can communicate via a bus  2508 . The memory  2504  may include, but is not limited to computer-readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one example, memory  2504  includes a cache or random access memory for the processor  2502 . In alternative examples, the memory  2504  is separate from the processor  2502 , such as a cache memory of a processor, the system memory, or other memory. The memory  2504  may be an external storage device or database for storing data. The memory  2504  is operable to store instructions executable by the processor  2502 . The functions, acts or tasks illustrated in the figures or described may be performed by the programmed processor  2502  for executing the instructions stored in the memory  2504 . The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like. 
     As shown, the computer system  2500  may or may not further include a display unit  2510 , such as a liquid crystal display (LCD), an organic light-emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display  2510  may act as an interface for the user to see the functioning of the processor  2502 , or specifically as an interface with the software stored in the memory  2504  or the drive unit  2516 . 
     Additionally, the computer system  2500  may include an input device  2512  configured to allow a user to interact with any of the components of system  2500 . The computer system  2500  may also include a disk or optical drive unit  2516 . The disk drive unit  2516  may include a computer-readable medium  2522  in which one or more sets of instructions  2524 , e.g. software, can be embedded. Further, the instructions  2524  may embody one or more of the methods or logic as described above. In a particular example, the instructions  2524  may reside completely, or at least partially, within the memory  2504  or within the processor  2502  during execution by the computer system  2500 . 
     The present disclosure contemplates a computer-readable medium that includes instructions  2524  or receives and executes instructions  2524  responsive to a propagated signal so that a device connected to a network  2526  can communicate voice, video, audio, images, or any other data over the network  2526 . Further, the instructions  2524  may be transmitted or received over the network  2526  via a communication port or interface  2520  or using a bus  2508 . The communication port or interface  2520  may be a part of the processor  2502  or maybe a separate component. The communication port  2520  may be created in software or maybe a physical connection in hardware. The communication port  2520  may be configured to connect with a network  2526 , external media, the display  2510 , or any other components in system  2500 , or combinations thereof. The connection with the network  2526  may be a physical connection, such as a wired Ethernet connection or may be established wirelessly as discussed later. Likewise, the additional connections with other components of the system  2500  may be physical or may be established wirelessly. The network  2526  may alternatively be directly connected to the bus  2508 . 
     The network  2526  may include wired networks, wireless networks, Ethernet AVB networks, or combinations thereof. The wireless network may be a cellular telephone network, an 802.11, 802.16, 802.20, 802.1Q or WiMax network. Further, the network  826  may be a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols. The system is not limited to operation with any particular standards and protocols. For example, standards for Internet and other packet-switched network transmissions (e.g., TCP/IP, UDP/IP, HTML, and HTTP) may be used. 
     While specific terms have been used to describe the embodiments of the disclosure, the embodiments of the disclosure should not be construed as being limited to the aforementioned embodiments. It should be understood that any modification, replacement and/or equivalents thereof may be construed as falling within the inventive concept of the present disclosure. 
     The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. 
     Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims. 
     The above-described embodiments are specific embodiments of the disclosure. It should be understood that a person of ordinary skill in the art may make improvements and modifications without departing from the scope of the present disclosure, and these improvements and modifications should be construed as falling within the protection scope of the present disclosure.