Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 2Citation - Scopus: 2Subwavelength Thickness Characterization of Curved Dielectric Films Exploiting Spatially Structured Entangled Photons(Optica Publishing Group, 2023) Ataç, Enes; Dinleyici, Mehmet SalihPrecise determination of thin dielectric film optical properties is a critical issue for fiber optic sensor technologies. However, conventional methods for the optical characterization of these films not only are generally complex and tedious processes on curved surfaces but also require well-calibrated and overly sophisticated devices. We, on the other hand, propose a novel and practical quantum-based phase diffraction scheme to characterize the thickness of ultra-thin transparent dielectric films coated on an optical fiber beyond the classical diffraction limits in this paper. The approach is implemented by evaluating the effect of thickness variations on the highly visible two-photon diffraction pattern's zero crossings and amplitudes. The mathematical model and numerical simulations con-tribute to a better understanding of how the spatially structured entangled photons improve thickness precision with the help of intensity correlations and a confocal aperture. To prove the impact of the proposed system, it is compared with the classical phase diffraction method in the literature via simulations. According to the results, the thickness of the transparent dielectric films can be accurately estimated below one-twentieth of the wavelength of interest. & COPY; 2023 Optica Publishing GroupArticle Citation - WoS: 9Citation - Scopus: 12Intensity and Phase Stacked Analysis of a 40-Otdr System Using Deep Transfer Learning and Recurrent Neural Networks(Optica Publishing Group, 2023) Kayan, Ceyhun Efe; Yüksel Aldoğan, Kıvılcım; Gümüş, AbdurrahmanDistributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phasesensitive optical time domain reflectometer (40-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the 40-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications. (c) 2023 Optica Publishing Group