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Novel Optoelectronic Sensors
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Novel Optoelectronic Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 11868

Special Issue Editor

Prof. Dr. Likarn Wang

E-Mail Website
Guest Editor
Institute of Photonics Technologies, National Tsing Hua University, Hsinchu, Taiwan
Interests: fiber optic sensors; Mach-Zehnder interferometers; Michelson interferometers; distributed sensors; erbium-doped fiber lasers; laser cavity resonators; light interference; light interferometers; optical cables; optical design techniques; pattern clustering; fiber-optic perimeter intrusion detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optoelectronic sensors are devices that produce electrical signals proportional to the amount of light incident on an active area. This Special Issue aims to provide a comprehensive overview of novel optoelectronic sensors. We seek research articles and reviews that provide insight into novel optoelectronic sensors. Topics of interest include (but are not limited to):

  • Optoelectronic sensors;
  • Optoelectronic sensing of physical/chemical/biological phenomena;
  • Pressure sensors;
  • Close tolerance measurement;
  • Quality control;
  • Laser technology;
  • Atmospheric particle measurement;
  • Security systems;
  • Optical communication and networks;
  • Optoelectronic devices and applications.

Prof. Dr. Likarn Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (4 papers)

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Research

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11 pages, 4300 KiB  
Communication
Design of a Completely Vertical, Polarization-Independent Two-Dimensional Grating Coupler with High Coupling Efficiency
by Chung-Chih Lin, Yen-Cheng Lu, Yu-Hsuan Liu, Likarn Wang and Neil Na
Sensors 2023, 23(10), 4662; https://doi.org/10.3390/s23104662 - 11 May 2023
Cited by 2 | Viewed by 2500
Abstract
An efficient optical coupler to transfer the signal between an optical fiber and a silicon waveguide is essential for realizing the applications of silicon photonic integrated circuits such as optical communication and optical sensing. In this paper, we numerically demonstrate a two-dimensional grating [...] Read more.
An efficient optical coupler to transfer the signal between an optical fiber and a silicon waveguide is essential for realizing the applications of silicon photonic integrated circuits such as optical communication and optical sensing. In this paper, we numerically demonstrate a two-dimensional grating coupler based on a silicon-on-insulator platform to obtain completely vertical and polarization-independent couplings, which potentially ease the difficulty of packaging and measurement of photonic integrated circuits. To mitigate the coupling loss induced by the second-order diffraction, two corner mirrors are respectively placed at the two orthogonal ends of the two-dimensional grating coupler to create appropriate interference conditions. Partial single-etch is assumed to form an asymmetric grating to obtain high directionalities without a bottom mirror. The two-dimensional grating coupler is optimized and verified with finite-difference time-domain simulations, achieving a high coupling efficiency of −1.53 dB and a low polarization-dependent loss of 0.015 dB when coupling to a standard single-mode fiber at approximately 1310 nm wavelength. Full article
(This article belongs to the Special Issue Novel Optoelectronic Sensors)
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15 pages, 4645 KiB  
Article
High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network
by Erfan Dejband, Yibeltal Chanie Manie, Yu-Jie Deng, Mekuanint Agegnehu Bitew, Tan-Hsu Tan and Peng-Chun Peng
Sensors 2023, 23(4), 2360; https://doi.org/10.3390/s23042360 - 20 Feb 2023
Cited by 8 | Viewed by 3304
Abstract
In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at [...] Read more.
In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance. Full article
(This article belongs to the Special Issue Novel Optoelectronic Sensors)
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9 pages, 1353 KiB  
Communication
Classification of Holograms with 3D-CNN
by Dániel Terbe, László Orzó and Ákos Zarándy
Sensors 2022, 22(21), 8366; https://doi.org/10.3390/s22218366 - 31 Oct 2022
Cited by 6 | Viewed by 2955
Abstract
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques [...] Read more.
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image. Full article
(This article belongs to the Special Issue Novel Optoelectronic Sensors)
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Review

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41 pages, 1422 KiB  
Review
Optical Correlators for Cryptosystems and Image Recognition: A Review
by Andrei Drăgulinescu
Sensors 2023, 23(2), 907; https://doi.org/10.3390/s23020907 - 12 Jan 2023
Cited by 7 | Viewed by 2527
Abstract
Optical correlators are efficient optical systems that have gained a wide range of applications both in image recognition and encryption, due to their special properties that benefit from the optoelectronic setup instead of an all-electronic one. This paper presents, to the best of [...] Read more.
Optical correlators are efficient optical systems that have gained a wide range of applications both in image recognition and encryption, due to their special properties that benefit from the optoelectronic setup instead of an all-electronic one. This paper presents, to the best of our knowledge, the most extensive review of optical correlators to date. The main types are overviewed, together with their most frequent applications in the newest contributions, ranging from security uses in cryptosystems, to medical and space applications, femtosecond pulse detection and various other image recognition proposals. The paper also includes a comparison between various optical correlators developed recently, highlighting their advantages and weaknesses, to gain a better perspective towards finding the best solutions in any specific domain where these devices might prove highly efficient and useful. Full article
(This article belongs to the Special Issue Novel Optoelectronic Sensors)
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