Publication Type:

Journal Article

Source:

International Journal of Intelligent Systems Technologies and Applications, Inderscience Enterprises Ltd., Volume 16, Number 4, p.342-358 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034833215&partnerID=40&md5=33465cbd4ec2401938f23cf8f6786f19

Keywords:

Artificial intelligence, Connected component analysis, De-noising, Electric network analysis, Electronic product, Image processing, Image processing technique, Innovative solutions, Learning algorithms, Learning systems, Mobile applications, Netlist, Network components, Product design, Thinning, Timing circuits

Abstract:

<p>Building an electronic product is complex and needs many steps, guidelines to be followed. From initial phase, till testing the product complexities are numerous. Most of the time in building a product is spent towards simulating the design to check if the proposed design is successful. People with expertise in using simulators only are deployed for this purpose. Although simulation and coding skills are secondary which may not contribute to the core value in the product, it is forced that everyone should learn them. To avoid this, we have come up with an innovative solution through an application in mobile phone that involves machine learning and image processing. Our proposed application enables the user to draw the circuit which he wishes to, once drawn with freehand, the circuit would be recognised and segregation happens where wires and components are split appropriately. Then, each component will be identified through the machine learning algorithms and netlist will be generated, all in seconds with high accuracy. © 2017 Inderscience Enterprises Ltd.</p>

Notes:

cited By 0

Cite this Research Publication

S. K. Vasudevan, Venkatachalam, K., and Sundaram, R. M. D., “A novel mobile application for circuit component identification and recognition through machine learning and image processing techniques”, International Journal of Intelligent Systems Technologies and Applications, vol. 16, pp. 342-358, 2017.

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