Automated tablet quality assurance and identification for hospital pharmacies

Chaczko, Z; Kale, A

Automated tablet quality assurance and identification for hospital pharmacies

Chaczko, Z

Kale, A

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Publication Type:: Journal Article
Citation:: International Journal of Electronics and Telecommunications, 2011, 57 (2), pp. 153 - 158
Issue Date:: 2011-06-01

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Field	Value	Language
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Kale, A	en_US
dc.date.issued	2011-06-01	en_US
dc.identifier.citation	International Journal of Electronics and Telecommunications, 2011, 57 (2), pp. 153 - 158	en_US
dc.identifier.issn	2081-8491	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18282
dc.description.abstract	The tablet quality checking and identification in hospital pharmacies is done manually and does not use any automated solution. Manual sorting and handling makes this activity laborious and error-prone. This paper describes a low cost solution that is characterised by a small size of the infrastructure involved. Discussed are design and implementation details of Tablet Inspection System based on Machine Vision. The described process uses a dedicated sequence of operation to perform dispensing, scanning and sorting using mini factory setup. Machine Vision System uses a novel Genetic Evolution algorithm. The algorithm provides robust and scalable output. Due to its versatile nature and easy shape recognition ability the approach can be easily adapted to a large variety of medical tablets. The proposed solution attempts to follow the concept of single objective with multiple optima in GA that is designed to scan multiple number of tablets in one cycle of operation.	en_US
dc.relation.ispartof	International Journal of Electronics and Telecommunications	en_US
dc.relation.isbasedon	10.2478/v10177-011-0022-y	en_US
dc.title	Automated tablet quality assurance and identification for hospital pharmacies	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	57	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

The tablet quality checking and identification in hospital pharmacies is done manually and does not use any automated solution. Manual sorting and handling makes this activity laborious and error-prone. This paper describes a low cost solution that is characterised by a small size of the infrastructure involved. Discussed are design and implementation details of Tablet Inspection System based on Machine Vision. The described process uses a dedicated sequence of operation to perform dispensing, scanning and sorting using mini factory setup. Machine Vision System uses a novel Genetic Evolution algorithm. The algorithm provides robust and scalable output. Due to its versatile nature and easy shape recognition ability the approach can be easily adapted to a large variety of medical tablets. The proposed solution attempts to follow the concept of single objective with multiple optima in GA that is designed to scan multiple number of tablets in one cycle of operation.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/18282