LAMI, Mohammed, AL‐NAEMI, Faris and ISSA, Walid (2022). Automated quantification system for vision through polymer‐dispersed liquid crystal double‐glazed windows: circuit implementation. IET Circuits, Devices & Systems.
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Abstract
Polymer‐dispersed liquid crystal automated quantification system for vision through polymer‐dispersed liquid crystal double‐glazed windows: Circuit implementation (PDLC)‐windows played an essential role in providing a visual comfort for occupants in commercial buildings recently. PDLC windows adjust the visible transparency of the glazing to control the daylight accessed to internal environments. A former study proposed an algorithm to quantify the vision through the PDLC glazing in terms of image contrast. The quantification algorithm determines the minimum level of transparency that maintains a comfortable vision through the window. This study introduced the implementation of a real‐time automated system that achieves the vision quantification process. Firstly, system on‐chip was utilised to realise the quantification algorithm, including contrast estimation. Secondly, the contrast determination action was re‐implemented using MATLAB, Cortex‐A9 microcontroller, and Cyclone V field programmable gate array field programmable gate array‐chip. The implemented systems were evaluated based on the latency, throughput, power consumption, and cost.
Item Type: | Article |
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Additional Information: | ** Article version: VoR ** From Wiley via Jisc Publications Router ** Licence for VoR version of this article: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: issn 1751-858X; issn 1751-8598 **Article IDs: publisher-id: cds212135 **History: published 27-11-2022; accepted 14-11-2022; rev-recd 09-11-2022; submitted 13-10-2022 |
Uncontrolled Keywords: | ORIGINAL RESEARCH, Cortex‐A9 microcontroller, FPGA, PDLC glazing, smart windows, system on‐chip, vision quantification |
Identification Number: | https://doi.org/10.1049/cds2.12135 |
SWORD Depositor: | Colin Knott |
Depositing User: | Colin Knott |
Date Deposited: | 29 Nov 2022 10:53 |
Last Modified: | 29 Nov 2022 10:53 |
URI: | https://shura.shu.ac.uk/id/eprint/31074 |
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