{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:16:01Z","timestamp":1775229361310,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC 112- 2221-E-027-076-MY2"],"award-info":[{"award-number":["NSTC 112- 2221-E-027-076-MY2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and time domain information, enabling a comprehensive feature extraction which enhances the prediction accuracy and reliability performance. To investigate the IFFT-DNN\u2019s performance, we propose a multichannel water level sensing system based on Free Space Optics (FSO) to measure the water level at multiple points in remote areas. The experimental results demonstrate the system\u2019s high precision, with a Mean Absolute Error (MAE) of 0.07 cm, even in complex conditions. Hence, this system provides a cost-effective and reliable remote water level sensing solution, highlighting its practical applicability in various industrial settings.<\/jats:p>","DOI":"10.3390\/s24154903","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T12:27:43Z","timestamp":1722256063000},"page":"4903","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhancing Multichannel Fiber Optic Sensing Systems with IFFT-DNN for Remote Water Level Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0620-4536","authenticated-orcid":false,"given":"Erfan","family":"Dejband","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tan-Hsu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"},{"name":"Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3031-6407","authenticated-orcid":false,"given":"Cheng-Kai","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"En-Ming","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng-Chun","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102295","DOI":"10.1016\/j.flowmeasinst.2022.102295","article-title":"Development of Liquid Level Measurement Technology: A Review","volume":"89","author":"Mohindru","year":"2023","journal-title":"Flow Meas. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012020","DOI":"10.1088\/1755-1315\/1142\/1\/012020","article-title":"Study on Automatic Water Level Detection Process Using Ultrasonic Sensor","volume":"1142","author":"Djalilov","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.sna.2019.07.021","article-title":"Plastic Fiber Optic Sensor for Continuous Liquid Level Monitoring","volume":"296","author":"Rajamani","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.measurement.2016.04.019","article-title":"Water Level Sensing: State of the Art Review and Performance Evaluation of a Low-Cost Measurement System","volume":"89","author":"Loizou","year":"2016","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103090","DOI":"10.1016\/j.yofte.2022.103090","article-title":"Self-Healing Integration of Fiber\/FSO Communication and Sensor Network for Improving Survivability","volume":"74","author":"Hayle","year":"2022","journal-title":"Opt. Fiber Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"386","DOI":"10.18196\/jrc.v3i3.14590","article-title":"A Free Space Optic\/Optical Wireless Communication: A Survey","volume":"3","author":"Mahdi","year":"2022","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.12928\/telkomnika.v21i1.24255","article-title":"Design a Remote Sensing of Multi-BOTDR Fiber Optic Sensors for Fuel Pipeline Monitoring","volume":"21","author":"Shabaneh","year":"2023","journal-title":"Telkomnika (Telecommun. Comput. Electron. Control)"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sharma, D., Tripathi, A., and Kumari, M. (2022). FSO Systems for next Generation Networks: A Review, Techniques and Challenges. J. Opt. Commun.","DOI":"10.1515\/joc-2022-0288"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1109\/ACCESS.2020.3048049","article-title":"A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks","volume":"9","author":"Salem","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100697","DOI":"10.1016\/j.osn.2022.100697","article-title":"Hybrid FSO\/RF Networks: A Review of Practical Constraints, Applications and Challenges","volume":"47","author":"Mohsan","year":"2023","journal-title":"Opt. Switch. Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13729","DOI":"10.1109\/ACCESS.2024.3357396","article-title":"Performance Enhancement of FSO Communication System Under Rainy Weather Environment Using a Novel Encryption Technique","volume":"12","author":"Mohamed","year":"2024","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dubey, A., and Singh, H. (2021). Performance Enhancement of MIMO Configurations in FSO System Under Different Weather Conditions. Recent Innovations in Computing, Proceedings of the ICRIC 2020, Jammu, India, 2\u20133 January 2020, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-15-8297-4_7"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5870","DOI":"10.1109\/JLT.2023.3270864","article-title":"Bidirectional Free Space Optics Communication for Long-Distance Sensor System","volume":"41","author":"Dehnaw","year":"2023","journal-title":"J. Light. Technol."},{"key":"ref_14","first-page":"289","article-title":"Gain Flatness and Noise Figure Optimization of C-Band EDFA in 16-Channels WDM System Using FBG and GFF","volume":"7","author":"Mounia","year":"2017","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kumar, S., Kumar, N., and Singh, J. (2022, January 8\u201310). Design and Analysis of Oil Pipeline Leakage Detection Model Using WDM FBG Sensors through Simulation of Temperature and Strain Effects. Proceedings of the 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON, Raigarh, India.","DOI":"10.1109\/OTCON56053.2023.10113984"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Manie, Y.C., Li, J.W., Peng, P.C., Shiu, R.K., Chen, Y.Y., and Hsu, Y.T. (2020). Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network. Sensors, 20.","DOI":"10.3390\/s20041070"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5000204","DOI":"10.1109\/LSENS.2022.3156544","article-title":"Multiplexed Active Optical Fiber Bragg Grating Sensor Array Enabled by Femtosecond Laser Induced Nanoscattering Centers","volume":"6","author":"Zhao","year":"2022","journal-title":"IEEE Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, H.K., Choo, J., Shin, G., and Kim, J. (2020). Long-Reach DWDM-Passive Optical Fiber Sensor Network for Water Level Monitoring of Spent Fuel Pool in Nuclear Power Plant. Sensors, 20.","DOI":"10.3390\/s20154218"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pereira, K., Coimbra, W., Lazaro, R., Frizera-Neto, A., Marques, C., and Leal-Junior, A.G. (2021). FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest. Sensors, 21.","DOI":"10.3390\/s21134568"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102612","DOI":"10.1016\/j.yofte.2021.102612","article-title":"Machine Learning Techniques for Liquid Level Estimation Using FBG Temperature Sensor Array","volume":"65","author":"Nascimento","year":"2021","journal-title":"Opt. Fiber Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.4302\/plp.v15i3.1207","article-title":"The Application of Fiber Optic Sensors Using Machine Learning Algorithms for Temperature Measurement of Lithium-Ion Batteries","volume":"15","author":"Cierpiak","year":"2023","journal-title":"Photonics Lett. Pol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1515\/teme-2022-0098","article-title":"A Collection of Machine Learning Assisted Distributed Fiber Optic Sensors for Infrastructure Monitoring","volume":"90","author":"Karapanagiotis","year":"2023","journal-title":"Tech. Mess."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2100067","DOI":"10.1002\/aisy.202100067","article-title":"Recent Advances in Machine Learning for Fiber Optic Sensor Applications","volume":"4","author":"Venketeswaran","year":"2022","journal-title":"Adv. Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, F., and Liu, B. (2022). Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing. Sensors, 22.","DOI":"10.3390\/s22020413"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, S., Zuo, X., Li, Z., and Wang, H. (2020). Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection. Sensors, 20.","DOI":"10.3390\/s20030911"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.3390\/s22103714","article-title":"A Water Level Measurement Approach Based on YOLOv5s","volume":"22","author":"Qiao","year":"2022","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/JLT.2020.2971240","article-title":"Enhancement of the Multiplexing Capacity and Measurement Accuracy of FBG Sensor System Using IWDM Technique and Deep Learning Algorithm","volume":"38","author":"Manie","year":"2020","journal-title":"J. Light. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104771","DOI":"10.1016\/j.autcon.2023.104771","article-title":"Hybrid DNN Training Using Both Synthetic and Real Construction Images to Overcome Training Data Shortage","volume":"149","author":"Kim","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"28162","DOI":"10.1109\/ACCESS.2023.3259107","article-title":"Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification","volume":"11","author":"Khan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.optcom.2018.11.060","article-title":"Bidirectional Free Space Optical Communication (FSO) in WDM Access Network with 1000-m Supportable Free Space Link","volume":"435","author":"Yeh","year":"2019","journal-title":"Opt. Commun."},{"key":"ref_31","first-page":"5732","article-title":"Investigation of Single Beam Near-Infrared Free Space Optical Communication under Different Weather Anomalies","volume":"11","author":"Shah","year":"2016","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.1109\/JLT.2022.3186895","article-title":"Hybrid of Free Space Optics Communication and Sensor System Using IWDM Technique","volume":"40","author":"Hayle","year":"2022","journal-title":"J. Light. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yao, C.K., Manie, Y.C., and Peng, P.C. (2023, January 24\u201328). Free Space Optical Communication for Vibration Sensing of Multiple-Channel Fiber Bragg Grating Array. Proceedings of the Photonic Networks and Devices. Optica Publishing Group, Washington, DC, USA.","DOI":"10.1364\/IPRSN.2023.JTu4A.8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"060901","DOI":"10.1117\/1.OE.59.6.060901","article-title":"Fiber Bragg Grating Sensors for Monitoring of Physical Parameters: A Comprehensive Review","volume":"59","author":"Sahota","year":"2020","journal-title":"Opt. Eng."},{"key":"ref_35","unstructured":"Cortes, C., Mohri, M., and Rostamizadeh, A. (2009, January 18\u201321). L2 Regularization for Learning Kernels. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI, Montreal, QC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Salehin, I., and Kang, D.K. (2023). A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain. Electronics, 12.","DOI":"10.3390\/electronics12143106"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dejband, E., Manie, Y.C., Deng, Y.J., Bitew, M.A., Tan, T.H., and Peng, P.C. (2023). High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network. Sensors, 23.","DOI":"10.3390\/s23042360"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4903\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:25:34Z","timestamp":1760109934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":37,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24154903"],"URL":"https:\/\/doi.org\/10.3390\/s24154903","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]}}}