N
Neil W. Bergmann
Researcher at University of Queensland
Publications - 193
Citations - 2989
Neil W. Bergmann is an academic researcher from University of Queensland. The author has contributed to research in topics: Field-programmable gate array & System on a chip. The author has an hindex of 25, co-authored 193 publications receiving 2652 citations. Previous affiliations of Neil W. Bergmann include Flinders University & Queensland University of Technology.
Papers
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Journal ArticleDOI
IoT Privacy and Security Challenges for Smart Home Environments
Huichen Lin,Neil W. Bergmann +1 more
TL;DR: After surveying existing solutions for enhancing IoT security, key future requirements for trusted Smart Home systems are identified and a gateway architecture is selected as the most appropriate for resource-constrained devices, and for high system availability.
Journal ArticleDOI
Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis
Liqun Hou,Neil W. Bergmann +1 more
TL;DR: Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%.
Journal ArticleDOI
An Arabic optical character recognition system using recognition-based segmentation
TL;DR: An Arabic OCR system is proposed, which uses a recognition-based segmentation technique to overcome the classical segmentation problems and shows a 90% recognition accuracy with a 20 chars/s recognition rate.
Embedded Linux as a Platform for Dynamically Self-Reconfiguring Systems-On-Chip
John Williams,Neil W. Bergmann +1 more
TL;DR: This paper presents examples and demonstrations that show how complex operations such as obtaining partial bit streams from remote servers and initiating reconfiguration are achieved with a single line of Linux shell script.
Proceedings ArticleDOI
An automatic image quality assessment technique incorporating higher level perceptual factors
TL;DR: An objective image quality assessment technique which is based on the properties of the human visual system and consists of an early vision model and a visual attention model which indicates regions of interest in a scene through the use of importance maps.