Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment
Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/47 |
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| author | Brian Pamukti Shofuro Afifah Shien-Kuei Liaw Jiun-Yu Sung Daping Chu |
| author_facet | Brian Pamukti Shofuro Afifah Shien-Kuei Liaw Jiun-Yu Sung Daping Chu |
| author_sort | Brian Pamukti |
| collection | DOAJ |
| description | Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands. Additionally, the backscattering method requires the signal to travel twice the normal distance, which can be inefficient. We propose an innovative interferometric sensing approach utilizing a Mach–Zehnder interferometer (MZI) combined with a time forest neural network (TFNN) for intrusion detection based on signal patterns. This method leverages advanced sensor characterization techniques and deep learning to improve accuracy and efficiency. Compared to the conventional one-dimensional convolutional neural network (1D-CNN), our proposed approach achieves an 8.43% higher accuracy, demonstrating the significant potential for real-time signal processing applications in smart environments. |
| format | Article |
| id | doaj-art-c27c6f37d01b4b539165d556f9a0efe0 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c27c6f37d01b4b539165d556f9a0efe02025-01-10T13:20:41ZengMDPI AGSensors1424-82202024-12-012514710.3390/s25010047Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart EnvironmentBrian Pamukti0Shofuro Afifah1Shien-Kuei Liaw2Jiun-Yu Sung3Daping Chu4Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanCentre for Photonic Devices and Sensors, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UKDistributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands. Additionally, the backscattering method requires the signal to travel twice the normal distance, which can be inefficient. We propose an innovative interferometric sensing approach utilizing a Mach–Zehnder interferometer (MZI) combined with a time forest neural network (TFNN) for intrusion detection based on signal patterns. This method leverages advanced sensor characterization techniques and deep learning to improve accuracy and efficiency. Compared to the conventional one-dimensional convolutional neural network (1D-CNN), our proposed approach achieves an 8.43% higher accuracy, demonstrating the significant potential for real-time signal processing applications in smart environments.https://www.mdpi.com/1424-8220/25/1/47pattern recognitiondeep learningsmart environment |
| spellingShingle | Brian Pamukti Shofuro Afifah Shien-Kuei Liaw Jiun-Yu Sung Daping Chu Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment Sensors pattern recognition deep learning smart environment |
| title | Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment |
| title_full | Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment |
| title_fullStr | Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment |
| title_full_unstemmed | Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment |
| title_short | Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment |
| title_sort | intelligent pattern recognition using distributed fiber optic sensors for smart environment |
| topic | pattern recognition deep learning smart environment |
| url | https://www.mdpi.com/1424-8220/25/1/47 |
| work_keys_str_mv | AT brianpamukti intelligentpatternrecognitionusingdistributedfiberopticsensorsforsmartenvironment AT shofuroafifah intelligentpatternrecognitionusingdistributedfiberopticsensorsforsmartenvironment AT shienkueiliaw intelligentpatternrecognitionusingdistributedfiberopticsensorsforsmartenvironment AT jiunyusung intelligentpatternrecognitionusingdistributedfiberopticsensorsforsmartenvironment AT dapingchu intelligentpatternrecognitionusingdistributedfiberopticsensorsforsmartenvironment |