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Dr. Cetin's research interests are in the areas of inverse problems, biomedical image processing, computational camera design, ambient assisted living sensors and systems, agricultural systems, computer vision, and environmental monitoring systems.
Cyber-Physical Environment Monitoring Systems: I have been developing Cyber-Physical Systems (CPS) for environment monitoring. My students and I developed a computer vision-based wildfire detection system using ordinary visible range Pan-Tilt-Zoom (PTZ) cameras placed on forest-watch towers in 2008 and 2009. Since smoke rises above the crowns of trees immediately after the wildfire starts it is possible to use ordinary cameras to spot the smoke. We modeled the smoke using (i) wavelet domain texture parameters, (ii) the covariance matrix of slow-moving regions, and (iii) gray color information and developed an adaptive machine vision algorithm capable of detecting smoke in the video in real-time. The resulting system is a low-cost system because it does not use the expensive infrared (IR) technology. We received funding from the Ministry of Environment in Turkey and the European Commission (FIRESENSE: https://cordis.europa.eu/result/rcn/143051_en.html). We received the best paper award describing our work from a conference organized by UNESCO and the European Union in 2012. The Turkish General Directorate of Forestry installed the wildfire detection system to more than 100 forest look-out towers in Mediterranean Turkey. We currently have wildfire detection systems in many countries including the US, Cyprus, Greece, Korea, and Singapore. As a part of the “FIRESENSE” project, we also developed differential infrared flame detection sensors for fire-sensitive areas and historic buildings. We deployed a wireless camera and sensor network in the ancient city of Rhodiapolis by the Mediterranean [14,15,20]. I plan to develop a tethered balloon (or drone)-based wildfire detection system using regular and infrared cameras. Currently, my students and I are working on computationally efficient deep learning methods for wildfire detection, and we are converting our classical computer vision-based algorithm to a deep-learning-based approach. We developed a novel neural network based on a multiplication-free operator related to the l_1 norm. I am currently collaborating with a start-up (Volant-Aerial) to develop wildfire detection software from a tethered balloon. Our work is funded by an NSF SBIR grant. We were invited to present our work in the “Tactical Fire Remote Sensing Advisory Committee Meeting” organized by USDA and NASA in December 2021.
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Xin Zhu,Hongyi Pan,Yury Velichko, Adam B. Murphy,Ashley Ross, Baris Turkbey,Ahmet Enis Cetin,Ulas Bagci
CoRR (2024)
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arXiv (Cornell University) (2023)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.9151-9155, (2023)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023): 2160-2164
2023 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)pp.113-118, (2023)
CoRR (2023): 26891-26903
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