Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/17771| Title: | Detecting defects in PCB using deep learning via convolution neural networks |
| Authors: | Adibhatla, VA Shieh, JS Abbod, MF Chih, HC Hsu, CC Cheng, J |
| Keywords: | Convolution;Deep learning;Training;Inspection |
| Issue Date: | 2019 |
| Publisher: | IEEE |
| Citation: | Proceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT, 2019, 2018-October pp. 202 - 205 |
| URI: | http://bura.brunel.ac.uk/handle/2438/17771 |
| DOI: | http://dx.doi.org/10.1109/IMPACT.2018.8625828 |
| ISSN: | 2150-5934 http://dx.doi.org/10.1109/IMPACT.2018.8625828 |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Embargoed Research Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Embargoed until 1/1/2030 | 650.32 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.