Tan, Chang Jie (2019) Development of PCB Reverse Engineering Software using Machine Vision System. Final Year Project (Bachelor), Tunku Abdul Rahman University College.
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Abstract
Without prior information or knowledge of a device, the only way to analyze it is to extract information from the device’s printed circuit board (PCB) to understand the fundamental operation of that board. This analysis is known as PCB reverse engineering (RE). The title of the project is “Development of PCB Reverse Engineering software using Machine Vision System”. The whole idea of this project is the reversion process of PCB construction from the circuit board back to its schematic circuit diagram. A program prototype is developed with a framework that can automatically analyze PCB boards for PCB reverse engineering development process. By researching and reviewing related journals, different method to extract the distinct parameters on the PCB is known. It includes several types of machine vision image processing algorithms such as Hough Circle Transformation, Harris Corner detection, finding and drawing of image contours, edge detection and so on. Each of the algorithms function accordingly in order to identify the components, determines the circuit connection or netlist of the components, and ultimately the schematic reconstruction of the particular PCB. In this dissertation, it demonstrates the complete reverse engineering process by reconstructing schematic diagram using the netlist generated from the program output. The prototype of the program is mainly developed using Microsoft Visual Studio by doing simulation, testing and debugging to produce a graphical user interface (GUI) for user to interact with functions such as capturing PCB image, extracting PCB board information and netlist generation.
Item Type: | Final Year Project |
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Subjects: | Technology > Electrical engineering. Electronics engineering |
Faculties: | Faculty of Engineering > Bachelor of Engineering (Honours) Electrical and Electronics |
Depositing User: | Library Staff |
Date Deposited: | 07 Feb 2020 09:23 |
Last Modified: | 13 Apr 2022 08:38 |
URI: | https://eprints.tarc.edu.my/id/eprint/13075 |