Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education

Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simu...

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Autores principales:	Marah Alhalabi, Mohammed Ghazal, Fasila Haneefa, Jawad Yousaf, Ayman El-Baz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smartphones and learning engineering education circuit diagrams augmented reality capsule networks deep learning Education L
Acceso en línea:	https://doaj.org/article/61e73291033e4424b7429bbd59e3d072
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id	oai:doaj.org-article:61e73291033e4424b7429bbd59e3d072
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spelling	oai:doaj.org-article:61e73291033e4424b7429bbd59e3d0722021-11-25T17:23:09ZSmartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education10.3390/educsci111106612227-7102https://doaj.org/article/61e73291033e4424b7429bbd59e3d0722021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7102/11/11/661https://doaj.org/toc/2227-7102Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations.Marah AlhalabiMohammed GhazalFasila HaneefaJawad YousafAyman El-BazMDPI AGarticlesmartphones and learningengineering educationcircuit diagramsaugmented realitycapsule networksdeep learningEducationLENEducation Sciences, Vol 11, Iss 661, p 661 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smartphones and learning engineering education circuit diagrams augmented reality capsule networks deep learning Education L
spellingShingle	smartphones and learning engineering education circuit diagrams augmented reality capsule networks deep learning Education L Marah Alhalabi Mohammed Ghazal Fasila Haneefa Jawad Yousaf Ayman El-Baz Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
description	Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations.
format	article
author	Marah Alhalabi Mohammed Ghazal Fasila Haneefa Jawad Yousaf Ayman El-Baz
author_facet	Marah Alhalabi Mohammed Ghazal Fasila Haneefa Jawad Yousaf Ayman El-Baz
author_sort	Marah Alhalabi
title	Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
title_short	Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
title_full	Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
title_fullStr	Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
title_full_unstemmed	Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
title_sort	smartphone handwritten circuits solver using augmented reality and capsule deep networks for engineering education
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/61e73291033e4424b7429bbd59e3d072
work_keys_str_mv	AT marahalhalabi smartphonehandwrittencircuitssolverusingaugmentedrealityandcapsuledeepnetworksforengineeringeducation AT mohammedghazal smartphonehandwrittencircuitssolverusingaugmentedrealityandcapsuledeepnetworksforengineeringeducation AT fasilahaneefa smartphonehandwrittencircuitssolverusingaugmentedrealityandcapsuledeepnetworksforengineeringeducation AT jawadyousaf smartphonehandwrittencircuitssolverusingaugmentedrealityandcapsuledeepnetworksforengineeringeducation AT aymanelbaz smartphonehandwrittencircuitssolverusingaugmentedrealityandcapsuledeepnetworksforengineeringeducation
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Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education

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