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Advanced Optical and Optomechanical Sensors

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

Deadline for manuscript submissions: 30 October 2024 | Viewed by 11620

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Guest Editor
Electronics and Computer Science (ECS), University of Southampton, Southampton, UK
Interests: sensor technology; microsystems
School of Engineering and Materials Science, Queen Mary University of London, London, UK
Interests: optical sensors; imaging; instrumentation; photonic devices; machine learning
School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Interests: smart batteries; battery instrumentation; fibre optics; in situ monitoring; operando monitoring; electronics; state estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, optical sensors have been one of the most rapidly growing sensor areas because of their simplicity, low cost, and quick response. Many well-established technologies, including free-space optics, integrated photonics, fiber optics approaches, and distributed fiber optic sensors have been developed to fabricate and develop increasingly efficient optical sensors. Optomechanical sensors are devices in which a certain aspect of light propagation is modified (modulated) by a mechanical variable. The precise nature of the modulation can take many different forms. Optomechanical sensors have found various applications not only in fundamental science, e.g., in quantum fields and fluid dynamics, but also in biological research, medical diagnosis, and environmental monitoring.

This Special Issue, therefore, aims to collect original research and review articles on the recent advances, technologies, solutions, applications, and new challenges in the field of optical sensors and optomechanical sensors.

Potential topics include, but are not limited to, the following:

  • Optical sensor technology and applications;
  • Optical sensors platform fabrication;
  • Optomechanical sensors technology and applications;
  • Optomechanical platforms fabrication;
  • Optomechanical sensing schemes;
  • Distributed fiber optic sensors.

Dr. Jize Yan
Dr. Lei Su
Dr. Yifei Yu
Guest Editors

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Published Papers (11 papers)

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27 pages, 14228 KiB  
Article
High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep
by Tianyi Zhang, Kohei Shimasaki, Idaku Ishii and Akio Namiki
Sensors 2024, 24(12), 4019; https://doi.org/10.3390/s24124019 - 20 Jun 2024
Viewed by 561
Abstract
Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce [...] Read more.
Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Overview of the proposed high-magnification object tracking system: The combination of (1) C-AF based on dynamic-range focal sweep and (2) 500 fps frame-by-frame object tracking achieves high-speed and precise high-magnification tracking of small objects moving in a wide scene.</p>
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<p>From time <math display="inline"><semantics> <msub> <mi>t</mi> <mn>0</mn> </msub> </semantics></math> to <math display="inline"><semantics> <msub> <mi>t</mi> <mi>k</mi> </msub> </semantics></math>, the focus of the camera system changes with the variation of the diopter of the liquid lens. During each period, the high-speed camera captures multiple images with different focuses. Using the focus measure algorithm, the image with the best focus can be extracted. Simultaneously, through the correlation between the focal length and the distance of the subject plane, the object’s depth can be calculated.</p>
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<p>Schematic of depth measurement with focal sweep.</p>
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<p>Diagram of the adjustment of the dynamic-range focal sweep: The first forward focal sweep measures the object’s depth. Then, at the backward focal sweep, the range of the focal sweep can be adjusted. At the second forward focal sweep, the system can finish the range adjustment and measure the object’s depth again.</p>
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<p>Diagram of the adjustment of the view direction: (<b>a</b>) Two Galvano mirrors are used to adjust the horizontal and vertical viewpoints. (<b>b</b>) Schematic representation of the horizontal viewpoint adjustment.</p>
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<p>The Pipeline of the object tracking method in our system: The pipeline consists of three main threads: (1) Thread 1: 500 fps Focus Measure, (2) Thread 2: Object Main-Color Updating, and (3) Thread 3: 500 fps Frame-by-Frame Object Tracking. The focus measure algorithm is implemented to extract 50 fps well-focused images and to determine the object’s depth, adjusting the focal sweep range. The object detection algorithm operates at 25 fps, providing color updating at the same rate. Meanwhile, the object tracking algorithm achieves 500 fps frame-by-frame object tracking.</p>
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<p>System configuration of proposed high-magnification autofocus tracking system.</p>
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<p>The environment of Experiment 1.</p>
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<p>The focal sweep adjustment and the depth measurement results using the proposed C-AF with butterfly model’s movements.</p>
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<p>The focal sweep adjustment and the depth measurement results using the proposed C-AF with qr code’s movements.</p>
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<p>The focal sweep adjustment and the depth measurement results using the proposed C-AF with screw’s movements.</p>
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<p>Variation of size with butterfly model’s movements.</p>
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<p>Variation of size with QR code’s movements.</p>
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<p>Variation of size with screw’s movements.</p>
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<p>Output images’ detection results with object movements at 3 m/s.</p>
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<p>Results of proposed HFR object tracking method for multiple objects at different distances: These figures were captured at 500 fps sequentially during one process of the focal sweep. The first, the second, the third columns shows the original images, color-filtered maps, and the results.</p>
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<p>Environment of Experiment 4.</p>
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<p>The focal sweep adjustment and the depth measurement results using the proposed C-AF with the object’s movements.</p>
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<p>Viewpoint’s variation during the object movement.</p>
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<p>Some results of HFR high-magnification object tracking.</p>
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22 pages, 5660 KiB  
Article
Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion
by Zhigang Hu, Chengwu Zhang, Xinzheng Wang and Aoru Ge
Sensors 2024, 24(10), 3021; https://doi.org/10.3390/s24103021 - 10 May 2024
Viewed by 794
Abstract
The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions [...] Read more.
The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>BlazePose neural network structure: fusion of heat maps and regression methods.</p>
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<p>Schematic diagram of data acquisition.</p>
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<p>Rehabilitation action design. (<b>a</b>) Represents the gait balance function rehabilitation action. (<b>b</b>) Represents weight-shifting rehabilitation movements.</p>
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<p>Rehabilitation action design. (<b>a</b>) Represents the gait balance function rehabilitation action. (<b>b</b>) Represents weight-shifting rehabilitation movements.</p>
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<p>Key point jitter experiment. (<b>a</b>) Represents the results of gait balance function rehabilitation training. (<b>b</b>) Represents the results of weight-shifting rehabilitation training.</p>
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<p>Right knee joint position curve during gait balance function rehabilitation training (key point jitter experiment). (<b>a</b>) Represents the x-position curve. (<b>b</b>) Represents the y-position curve.</p>
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<p>Right knee joint position curve during weight transfer rehabilitation training (key point jitter experiment). (<b>a</b>) Represents the x-position curve. (<b>b</b>) Represents the y-position curve.</p>
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<p>Light adaptation experiment. (<b>a</b>) Represents the results of gait balance function rehabilitation training. (<b>b</b>) Represents the results of weight-shifting rehabilitation training.</p>
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<p>Right knee joint position curve during gait balance function rehabilitation training (lighting adaptation experiment). (<b>a</b>) Represents the x-position curve. (<b>b</b>) Represents the y-position curve.</p>
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<p>Right knee joint position curve during weight transfer rehabilitation training (lighting adaptation experiment). (<b>a</b>) Represents the x-position curve. (<b>b</b>) Represents the y-position curve.</p>
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<p>Experiments under actual bad lighting. (<b>a</b>) Results of rehabilitation training for gait balance function. (<b>b</b>) Rehabilitation training results of the center of gravity transfer.</p>
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<p>Comprehensive comparison of various methods. (<b>a</b>) Represents the results of gait balance function rehabilitation training. (<b>b</b>) Represents the results of weight-shifting rehabilitation training.</p>
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<p>Right knee position and state estimation during gait balance function rehabilitation training. (<b>a</b>) x-position state estimation. (<b>b</b>) y-position state estimation.</p>
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<p>Estimation of right knee position and state during weight transfer rehabilitation training. (<b>a</b>) x-position state estimation. (<b>b</b>) y-position state estimation.</p>
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<p>Light verification experiment of gait balance function rehabilitation training. (<b>a</b>) Histogram of real-light conditions. (<b>b</b>) Histogram of simulated-light conditions.</p>
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<p>Light verification experiment of center of gravity transfer rehabilitation training. (<b>a</b>) Histogram of real-light conditions. (<b>b</b>) Histogram of simulated-light conditions.</p>
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14 pages, 5787 KiB  
Article
A 19-Bit Small Absolute Matrix Encoder
by Liming Geng, Guohua Cao, Chunmin Shang and Hongchang Ding
Sensors 2024, 24(5), 1400; https://doi.org/10.3390/s24051400 - 22 Feb 2024
Viewed by 797
Abstract
With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding [...] Read more.
With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding by engraving two circles of matrix code, and has the advantages of fewer circles of code disk engraving and higher measurement accuracy. This article mainly focuses on the design of a new matrix code disk, encoding and decoding methods, decoding circuit design, Matlab simulation analysis, and experimental error analysis. The experimental results show that the encoder designed in this paper achieves ultra-small volume Φ30 mm × 20 mm, and the angle measurement accuracy is 2.57”. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Explosion diagram of matrix encoder structure.</p>
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<p>Detection circuit of phototransistor.</p>
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<p>Code track distribution of matrix encoder.</p>
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<p>Planar expansion of matrix code tracks.</p>
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<p>Changes of a-ring detector in one reading cycle.</p>
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<p>MATLAB simulation principle flowchart.</p>
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<p>Decoding a-ring matrix code into a traditional Gray code circuit.</p>
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<p>Simulation results of the decoding module for a-ring code track.</p>
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<p>Schematic diagram of converting Gray code to binary code.</p>
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<p>Simulation results of converting Gray code to binary code.</p>
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<p>Three-dimensional model of optoelectronic encoder error detection platform.</p>
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<p>Optoelectronic encoder error detection platform.</p>
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14 pages, 5047 KiB  
Article
Optimization of Thermal Control Design for Aerial Reflective Opto-Mechanical Structure
by Huilin Wang, Yun Zhou, Xiaocun Jiang, Xiaozhou Zuo and Ming Chen
Sensors 2024, 24(4), 1194; https://doi.org/10.3390/s24041194 - 12 Feb 2024
Viewed by 789
Abstract
To improve the adaptability of aerial reflective opto-mechanical structures (mainly including the primary mirror and secondary mirror) to low-temperature environments, typically below −40 °C, an optimized thermal control design, which includes passive insulation and temperature-negative feedback-variable power zone active heating, is proposed. Firstly, [...] Read more.
To improve the adaptability of aerial reflective opto-mechanical structures (mainly including the primary mirror and secondary mirror) to low-temperature environments, typically below −40 °C, an optimized thermal control design, which includes passive insulation and temperature-negative feedback-variable power zone active heating, is proposed. Firstly, the relationship between conventional heating methods and the axial/radial temperature differences of mirrors with different shapes is analyzed. Based on the heat transfer analyses, it is pointed out that optimized thermal control design is necessary to ensure the temperature uniformity of the fused silica mirror, taking into account the temperature level when the aerial electro-optics system is working in low-temperature environments. By adjusting the input voltage based on the measured temperature, the heating power of the subregion is changed accordingly, so as to locally increase or decrease the temperature of the mirrors. The thermal control scheme ensures that the average temperature of the mirror fluctuates slowly and slightly around 20 °C. At the same time, the temperature differences within a mirror and between the primary mirror and the secondary mirror can be controlled within 5 °C. Thereby, the resolution of EO decreases by no more than 11.4%. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Structure of reflective opto-mechanical structure.</p>
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<p>Image quality in low-temperature environment.</p>
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<p>Spherical mirror.</p>
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<p>Shell thermal control.</p>
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<p>Shell zonal thermal control.</p>
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<p>Optimized primary mirror heating.</p>
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<p>Primary mirror thermal control scheme.</p>
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<p>Secondary mirror temperature control scheme.</p>
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<p>Locations of temperature measurement points.</p>
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<p>Comparison of measured values and simulation values.</p>
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<p>Time–temperature curve (ground test).</p>
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<p>Resolution test.</p>
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<p>Time–temperature curve (flight conditions).</p>
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13 pages, 18121 KiB  
Article
Demonstration of a Transportable Fabry–Pérot Refractometer by a Ring-Type Comparison of Dead-Weight Pressure Balances at Four European National Metrology Institutes
by Clayton Forssén, Isak Silander, Johan Zakrisson, Eynas Amer, David Szabo, Thomas Bock, André Kussike, Tom Rubin, Domenico Mari, Stefano Pasqualin, Zaccaria Silvestri, Djilali Bentouati, Ove Axner and Martin Zelan
Sensors 2024, 24(1), 7; https://doi.org/10.3390/s24010007 - 19 Dec 2023
Viewed by 853
Abstract
Fabry–Pérot-based refractometry has demonstrated the ability to assess gas pressure with high accuracy and has been prophesized to be able to realize the SI unit for pressure, the pascal, based on quantum calculations of the molar polarizabilities of gases. So far, the technology [...] Read more.
Fabry–Pérot-based refractometry has demonstrated the ability to assess gas pressure with high accuracy and has been prophesized to be able to realize the SI unit for pressure, the pascal, based on quantum calculations of the molar polarizabilities of gases. So far, the technology has mostly been limited to well-controlled laboratories. However, recently, an easy-to-use transportable refractometer has been constructed. Although its performance has previously been assessed under well-controlled laboratory conditions, to assess its ability to serve as an actually transportable system, a ring-type comparison addressing various well-characterized pressure balances in the 10–90 kPa range at several European national metrology institutes is presented in this work. It was found that the transportable refractometer is capable of being transported and swiftly set up to be operational with retained performance in a variety of environments. The system could also verify that the pressure balances used within the ring-type comparison agree with each other. These results constitute an important step toward broadening the application areas of FP-based refractometry technology and bringing it within reach of various types of stakeholders, not least within industry. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Pictures of the TOP from the front (<b>left</b>) and back (<b>right</b>). On top of the rack sits a temperature-regulated aluminum breadboard with an isolating enclosure, within which the DFPC is located. The rack contains seven modules, denoted A–G. (A) The gas inlet system consisting of a mass flow controller and an electronic pressure controller. (B) Optics, passive fiber optical components (e.g., circulators and isolators), and opto-electronics (EOMs and AOMs). (C) Frequency counter and vacuum gauge controllers. (D) Power supplies and control unit for the heating. (E) A 230 V power distribution unit. (F) Two Er-doped fiber lasers. (G) Two digital locking modules. Reproduced with permission from Forssén et al. [<a href="#B22-sensors-24-00007" class="html-bibr">22</a>].</p>
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<p>The first measurement series at RISE (RISE1). The red markers represent measurement data points taken by the TOP, and the solid lines and curves represent polynomial fits to them, all as a function of the pressure set by the pressure balance, <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> </msub> </semantics></math>. Panel (<b>a</b>) shows, by the individual markers, the pressure assessed by the TOP evaluated by the standard expression for refractivity with the deformation parameter set to zero, <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> </msub> </semantics></math>, in kPa. The solid curve represents the fit, <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> <mrow> <mi>f</mi> <mi>i</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>. Panel (<b>b</b>) displays the non-linear components of panel (<b>a</b>) given by, for the individual data markers, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> </msub> <mo>−</mo> <mi>b</mi> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math> and, for the fit, <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>+</mo> <mi>c</mi> <msubsup> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </semantics></math>, respectively, in Pa. Panel (<b>c</b>) illustrates the residuals of the fit from panel (<b>a</b>,<b>b</b>) in relative units, i.e., <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> </msub> <mo>−</mo> <msubsup> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> <mrow> <mi>f</mi> <mi>i</mi> <mi>t</mi> </mrow> </msubsup> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> </msub> </mrow> </semantics></math>, in parts per million (ppm), which also represent the relative deviations in the pressure assessed by the characterized TOP, <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> <mrow> <mi>C</mi> <mi>h</mi> </mrow> </msubsup> </semantics></math>, from the pressure set by the pressure balance, <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> </msub> </semantics></math>.</p>
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<p>Pictures of the four different pressure balances used in the ring comparison. (<b>Top left</b>): RISE Ruska 2465A-754, (<b>top right</b>): PTB Fluke 2465A-754, (<b>bottom left</b>): INRiM DHI-Fluke PG7601, (<b>bottom right</b>): LNE DHI-Fluke PG7607.</p>
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<p>Colored circles: deviations between the pressures assessed by the TOP refractometer, <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>T</mi> <mi>O</mi> <mi>P</mi> </mrow> <mrow> <mi>C</mi> <mi>h</mi> </mrow> </msubsup> </semantics></math>, and the set pressures of the pressure balances, <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>P</mi> <mi>B</mi> </mrow> <mi>i</mi> </msubsup> </semantics></math>, from the measurements performed at the various NMIs (i.e., with <span class="html-italic">i</span> being <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>I</mi> <mi>S</mi> <mi>E</mi> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>T</mi> <mi>B</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>N</mi> <mi>R</mi> <mi>i</mi> <mi>M</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>N</mi> <mi>E</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>I</mi> <mi>S</mi> <mi>E</mi> <mn>2</mn> </mrow> </semantics></math>, respectively). Black horizontal lines: polynomial fits of the initial characterization. The dashed curves represent the uncertainty values for the pressure balance used at the corresponding NMI. The first panel, denoted as RISE1, contains the same data as in <a href="#sensors-24-00007-f002" class="html-fig">Figure 2</a>c.</p>
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<p>The two measurement series from RISE (RISE1 and RISE2, respectively). The shaded areas indicate the (<span class="html-italic">k</span> = 2) expanded uncertainty originating from the finite resolution of the temperature-measuring instrument.</p>
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19 pages, 21258 KiB  
Article
Research on the Design and Alignment Method of the Optic-Mechanical System of an Ultra-Compact Fully Freeform Space Camera
by Yunfeng Li, Zongxuan Li, Tiancong Wang, Shuping Tao, Defu Zhang, Shuhui Ren, Bin Ma and Changhao Zhang
Sensors 2023, 23(23), 9399; https://doi.org/10.3390/s23239399 - 25 Nov 2023
Cited by 1 | Viewed by 1164
Abstract
As space resources become increasingly constrained, the major space-faring nations are establishing large space target monitoring systems. There is a demand for both the number and the detection capability of space-based optical monitoring equipment. The detection range (i.e., field of view) and parasitic [...] Read more.
As space resources become increasingly constrained, the major space-faring nations are establishing large space target monitoring systems. There is a demand for both the number and the detection capability of space-based optical monitoring equipment. The detection range (i.e., field of view) and parasitic capability (lightweight and small size) of a single optical payload will largely reduce the scale and cost of the monitoring system. Therefore, in this paper, the optic-mechanical system of an ultra-lightweight and ultra-compact space camera and the optical alignment method are investigated around a fully freeform off-axis triple-reversal large field of view (FOV) optical system. The optic-mechanical system optimisation design is completed by adopting the optic-mechanical integration analysis method, and the weight of the whole camera is less than 10 kg. In addition, to address the mounting problems caused by the special characteristics of the freeform surface optical system, a dual CGH coreference alignment method is innovatively proposed. The feasibility of the method is verified by the mounting and testing test, and the test results show that the system wavefront difference is better than 1/10 λ. The imaging test of the space camera and the magnitude test results meet the design requirements of the optical system. The optic-mechanical system design method and alignment method proposed in this paper are instructive for the design and engineering of large field of view full freeform optical loads. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Schematic diagram of an off-axis triplex optical system: (<b>a</b>) conventional off-axis three-reflector system structure form; (<b>b</b>) tilt-biased fully freeform off-axis triple-reflector system structural form designed in this paper.</p>
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<p>Design results of the framed structure.</p>
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<p>Structural layout of the optic-mechanical system.</p>
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<p>Analysis and optimisation process for the opto-mechanical integration of reflector assemblies.</p>
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<p>Initial optimisation model and optimisable parameters for the primary mirror assembly.</p>
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<p>The process of optic-mechanical system optical-mechanical-thermal analyses.</p>
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<p>Optical circuit diagram of face shape accuracy detection with CGH as a compensator.</p>
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<p>The surface shape detection results of three freeform mirrors in the system, where (<b>a</b>–<b>c</b>) are the surface shape accuracy detection results of the primary, secondary, and tertiary mirrors, respectively.</p>
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<p>Conventional TMA off-axis SLR system main mirror, three mirrors CGH common reference of the mounting, and adjustment of the optical circuit diagram.</p>
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<p>Schematic diagram of the double CGH common reference tuning scheme.</p>
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<p>Primary and secondary mirror positioning inspection chart.</p>
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<p>This is the results of two interferometer tests, where (<b>a</b>) is the test results of interferometer 1 and (<b>b</b>) is the test results of interferometer 2.</p>
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<p>Optical path diagram for the system mounting.</p>
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<p>This is the picture and imaging test results of the space camera after completing the integration of the whole camera, where (<b>a</b>) is the photo of the whole camera, and (<b>b</b>) is the results of the magnitude test.</p>
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16 pages, 6827 KiB  
Article
Dynamic Characterization of Optical Coherence-Based Displacement-Type Weight Sensor
by Zhengchuang Lai, Zhongjie Ouyang, Shuncong Zhong, Wei Liang, Xiaoxiang Yang, Jiewen Lin, Qiukun Zhang and Jinlin Li
Sensors 2023, 23(21), 8911; https://doi.org/10.3390/s23218911 - 2 Nov 2023
Viewed by 1076
Abstract
Dynamic characteristics play a crucial role in evaluating the performance of weight sensors and are essential for achieving fast and accurate weight measurements. This study focuses on a weight sensor based on optical coherence displacement. Using finite element analysis, the sensor was numerically [...] Read more.
Dynamic characteristics play a crucial role in evaluating the performance of weight sensors and are essential for achieving fast and accurate weight measurements. This study focuses on a weight sensor based on optical coherence displacement. Using finite element analysis, the sensor was numerically simulated. Frequency domain and time domain dynamic response characteristics were explored through harmonic response analysis and transient dynamic analysis. The superior dynamic performance and reduced conditioning time of the non-contact optical coherence-based displacement weight sensor were confirmed via a negative step response experiment that compared the proposed sensing method to strain sensing. Moreover, dynamic performance metrics for the optical coherence displacement-type weight sensor were determined. Ultimately, the sensor’s dynamic performance was enhanced using the pole-zero placement method, decreasing the overshoot to 4.72% and reducing the response time to 0.0132 s. These enhancements broaden the sensor’s operational bandwidth and amplify its dynamic response capabilities. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Schematic structure of the double-beam elastomer.</p>
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<p>Comparison of elastomer deformation under load.</p>
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<p>Optical coherence-based displacement-type weight sensing model.</p>
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<p>Mode shapes from modal analysis of the weight sensor.</p>
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<p>Simulation curve of frequency domain response characteristics.</p>
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<p>Simulation curve of negative step response of the weight sensor.</p>
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<p>Schematic diagram of dynamic calibration principle of weight sensor.</p>
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<p>Setup of experimental platform to determine dynamic characteristics.</p>
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<p>Steps for dynamic characteristics testing of weight sensors.</p>
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<p>Sensor output comparison of different sensing principles. (<b>a</b>) Output comparison under 0.5 kg load. (<b>b</b>) Output comparison under 1.0 kg load. (<b>c</b>) Output comparison under 2.0 kg load.</p>
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<p>Comparison of step responses between dynamic experiment and finite element simulation.</p>
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<p>Frequency domain response curves obtained via system identification.</p>
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<p>Comparison of model outputs before and after dynamic compensation.</p>
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<p>Comparison of frequency–response curves before and after weight sensor compensation.</p>
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12 pages, 3987 KiB  
Article
The Design and Fabrication of Large-Area Under-Screen Fingerprint Sensors with Optimized Aperture and Microlens Structures
by Chih-Chieh Yeh, Teng-Wei Huang, You-Ren Lin and Guo-Dung Su
Sensors 2023, 23(21), 8731; https://doi.org/10.3390/s23218731 - 26 Oct 2023
Cited by 2 | Viewed by 1310
Abstract
In this paper, we designed and fabricated an optical filter structure applied to the FoD (Fingerprint on Display) technology of the smartphone, which contains the microlens array, black matrix, and photodetector to recognize the fingerprint on a full touchscreen. First, we used optical [...] Read more.
In this paper, we designed and fabricated an optical filter structure applied to the FoD (Fingerprint on Display) technology of the smartphone, which contains the microlens array, black matrix, and photodetector to recognize the fingerprint on a full touchscreen. First, we used optical ray tracing software, ZEMAX, to simulate a smartphone with FoD and a touching finger. We then further discussed how the aperture and microlens influence the fingerprint image in this design. Through numerical analysis and process constraint adjustment to optimize the structural design, we determined that a modulation transfer function (MTF) of 60.8% can be obtained when the thickness of the black matrix is 4 μm, allowing successful manufacturing using photolithography process technology. Finally, we used this filter element to take fingerprint images. After image processing, a clearly visible fingerprint pattern was successfully captured. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>The simulation structure. (<b>a</b>) The structure of mobile phone fingerprint recognition. (<b>b</b>) The detailed simulation structures of FoD.</p>
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<p>Ray tracing. (<b>a</b>) 2D structure viewing. (<b>b</b>) Detector image sensing a small focusing spot.</p>
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<p>Processing data. (<b>a</b>) Ray tracing image in Zemax, (<b>b</b>) first process, i.e., “sum up all intensity data in the same horizontal line”, turned into a 2D diagram; the vertical axis represents the length (μm), and the horizontal axis represents the intensity (A.U.).</p>
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<p>Data processing of summing up the peaks.</p>
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<p>Schematic diagram showing R and V.</p>
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<p>RV and MTF cross-comparison. (<b>a</b>) High RV but low MTF. (<b>b</b>) High MTF but low RV. (<b>c</b>) RV and MTF are high.</p>
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<p>The process flow chart of the first layer of the black matrix.</p>
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<p>Flow chart of microlens array manufacturing using the thermal reflow method.</p>
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<p>Fingerprint photo experiment: (A) micro-optical structure, (B) objective lens, (C) tube lens, and (D) CCD camera.</p>
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<p>(<b>a</b>) The image of the fingerprint captured by the smartphone, (<b>b</b>) the image processed in grayscale, and (<b>c</b>) the thinning the fingerprint texture by a computer program.</p>
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17 pages, 2527 KiB  
Article
Nonlinearities in Fringe-Counting Compact Michelson Interferometers
by Jiri Smetana, Chiara Di Fronzo, Anthony Amorosi and Denis Martynov
Sensors 2023, 23(17), 7526; https://doi.org/10.3390/s23177526 - 30 Aug 2023
Cited by 3 | Viewed by 1120
Abstract
Compact Michelson interferometers are well positioned to replace existing displacement sensors in the readout of seismometers and suspension systems, such as those used in contemporary gravitational-wave detectors. Here, we continue our previous investigation of a customised compact displacement sensor built by SmarAct that [...] Read more.
Compact Michelson interferometers are well positioned to replace existing displacement sensors in the readout of seismometers and suspension systems, such as those used in contemporary gravitational-wave detectors. Here, we continue our previous investigation of a customised compact displacement sensor built by SmarAct that operates on the principle of deep frequency modulation. The focus of this paper is the linearity of this device and its subsequent impact on sensitivity. We show the three primary sources of nonlinearity that arise in the sensor: residual ellipticity, intrinsic distortion of the Lissajous figure, and distortion caused by exceeding the velocity limit imposed by the demodulation algorithm. We verify the theoretical models through an experimental demonstration, where we show the detrimental impact that these nonlinear effects have on device sensitivity. Finally, we simulate the effect that these nonlinearities are likely to have if implemented in the readout of the Advanced LIGO suspensions and show that the noise from nonlinearities should not dominate across the key sub-10 Hz frequency band. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Schematic (<b>a</b>) and photo (<b>b</b>) of the custom-designed sensing head manufactured by SmarAct that is used throughout this work. The sensing head is embedded within the same readout structure as shown in Figure 1 of Ref. [<a href="#B8-sensors-23-07526" class="html-bibr">8</a>].</p>
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<p>Effect of elliptical error on the estimation of the arm phase (test mass displacement). (<b>a</b>) An incorrectly circularised Lissajous figure with a 50% residual elliptical error in comparison to a properly circularised Lissajous figure. Panel (<b>b</b>) Corresponding periodic nonlinear error caused by this residual ellipticity. In comparison, the perfectly circularised Lissajous figure leads to a perfectly linear estimator. The 50% error was chosen for visual clarity only and is significantly larger than what is typically observed in our system.</p>
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<p>Nonlinear deviation of sensor readout from the ‘true’ inferred displacement. The x axis is calibrated for displacement by taking the displacement time series and fitting to the slow variations (not due to sensor nonlinearity) by a ninth-order polynomial.</p>
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<p>Adjusted layout of the experimental setup originally shown in Figure 1 of Ref. [<a href="#B8-sensors-23-07526" class="html-bibr">8</a>]. (<b>a</b>) Arrangement of the sensor, mirror, and actuator, as well as the effective springs formed by the foam blocks and rubber pads. (<b>b</b>) Photo of the setup inside an acoustically isolating box.</p>
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<p>Comparison between measured and simulated nonlinearities during a controlled and tightly band-limited injection of displacement noise using a coil-magnet actuator. The simulated spectra show good agreement with measurement for high values of elliptical error, which supports the analysis present in <a href="#sec2dot3-sensors-23-07526" class="html-sec">Section 2.3</a>. At the smallest value of elliptical error, the measured nonlinear noise is within a factor of three above the simulation, which can be attributed to the presence of other nonlinearities, particularly the nonelliptical nonlinearity laid out in <a href="#sec2dot4-sensors-23-07526" class="html-sec">Section 2.4</a> and the residual nonlinearity of the coil-magnet drive. The baseline noise curve shows the sum of all measured noises in the quiet (‘zero-displacement’) state and represents the absolute noise floor of the sensor. This sensitivity is significantly above the noise performance we demonstrated in Ref. [<a href="#B8-sensors-23-07526" class="html-bibr">8</a>], but this can be explained by three main factors: (i) the setup only includes a single sensor, meaning there is no frequency stabilisation; (ii) the box is not as well insulated with packing foam, meaning air currents are not as suppressed; and (iii) the rubber pad necessary to enable differential driving of the mirror and sensor also breaks the common mode rejection of seismic noise present in the original null measurement.</p>
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<p>Comparison between measured and simulated nonlinearities during a controlled and tightly band-limited injection of displacement noise using a coil-magnet actuator. The simulated spectra show good agreement with measurement for high values of elliptical error, which supports the analysis present in <a href="#sec2dot3-sensors-23-07526" class="html-sec">Section 2.3</a>. At the smallest value of elliptical error, the measured nonlinear noise is within a factor of three above the simulation, which can be attributed to the presence of other nonlinearities, particularly the nonelliptical nonlinearity laid out in <a href="#sec2dot4-sensors-23-07526" class="html-sec">Section 2.4</a> and the residual nonlinearity of the coil-magnet drive. The baseline noise curve shows the sum of all measured noises in the quiet (‘zero-displacement’) state and represents the absolute noise floor of the sensor. This sensitivity is significantly above the noise performance we demonstrated in Ref. [<a href="#B8-sensors-23-07526" class="html-bibr">8</a>], but this can be explained by three main factors: (i) the setup only includes a single sensor, meaning there is no frequency stabilisation; (ii) the box is not as well insulated with packing foam, meaning air currents are not as suppressed; and (iii) the rubber pad necessary to enable differential driving of the mirror and sensor also breaks the common mode rejection of seismic noise present in the original null measurement.</p>
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<p>Simulated readout of the top stage of the LIGO quadruple suspension with the SmarAct sensor, together with the corresponding residual nonlinear error. The ISI motion shows the inertial displacement of the ISI, and the QUAD TOP readout represents the displacement of the top quadruple suspension stage as measured by an ideal sensor rigidly attached to the ISI (i.e., the relative displacement between the top stage and ISI). While the residual shows that nonlinear noise significantly exceeds the nominal noise floor of the device, at no point in the spectrum does the nonlinear residual exceed the measured signal. The SmarAct sensitivity derived from a null measurement is added for comparison.</p>
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14 pages, 6540 KiB  
Article
Reconfiguration Error Correction Model for an FBG Shape Sensor Based on the Sparrow Search Algorithm
by Qiufeng Shang and Feng Liu
Sensors 2023, 23(16), 7052; https://doi.org/10.3390/s23167052 - 9 Aug 2023
Viewed by 1113
Abstract
A reconfiguration error correction model for an FBG shape sensor (FSS) is proposed. The model includes curvature, bending direction error correction, and the self-correction of the FBG placement angle and calibration error based on an improved sparrow search algorithm (SSA). SSA could automatically [...] Read more.
A reconfiguration error correction model for an FBG shape sensor (FSS) is proposed. The model includes curvature, bending direction error correction, and the self-correction of the FBG placement angle and calibration error based on an improved sparrow search algorithm (SSA). SSA could automatically correct the placement angle and calibration direction of the FBG, and then use the corrected placement angle and calibration direction to correct the curvature and bending direction of the FSS, thereby improving the accuracy of shape reconfiguration. After error correction, the tail point reconfiguration errors of different shapes were reduced from 2.56% and 4.96% to 1.12% and 2.45%, respectively. This paper provides a new reconfiguration error correction method for FSS that does not require a complicated experimental calibration process, is simpler, more efficient, and more operable than traditional methods, and has great potential in FSS application scenarios. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>FSS error delivery model. The bending curvature <span class="html-italic">k</span> and bending direction <span class="html-italic">β</span> at the detection point can be obtained according to the curvature in each core <span class="html-italic">k<sub>i</sub></span>, and the discrete local <span class="html-italic">k</span> and <span class="html-italic">β</span> are converted into the curvature and torsion functions <span class="html-italic">k</span>(s) and <span class="html-italic">τ</span>(s) through interpolation.</p>
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<p>(<b>a</b>) Illustration of cross-section at the detection point. (<b>b</b>) Placement angle deviation diagram.</p>
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<p>Schematic diagram of calibration direction deviation.</p>
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<p>(<b>a</b>) Shape sensor simulation model. (<b>b</b>) Simulation model cross-section. (<b>c</b>) FBG placement diagram.</p>
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<p>Calculation of curvature and bending direction obtained by different methods. (<b>a</b>) Bending direction calculation results. (<b>b</b>) Bending curvature calculation results.</p>
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<p>Shape reconfiguration results of different data groups.</p>
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<p>Experimental sensing system.</p>
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<p>(<b>a</b>) System diagram for the calibration experiment. (<b>b</b>) FBG fixtures. (<b>c</b>) Calibration tools.</p>
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<p>Calibration result.</p>
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<p>Flowchart of the optimization model.</p>
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<p>Iterative curves of different optimization algorithms.</p>
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<p>Experimental diagram for shape reconfigurations. (<b>a</b>) Arc shape reconfiguration. (<b>b</b>) Spiral shape reconfiguration.</p>
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<p>(<b>a</b>) Arc reconfiguration results. (<b>b</b>) Spiral reconfiguration results.</p>
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Review

Jump to: Research

27 pages, 8579 KiB  
Review
LED Junction Temperature Measurement: From Steady State to Transient State
by Xinyu Zhao, Honglin Gong, Lihong Zhu, Zhenyao Zheng and Yijun Lu
Sensors 2024, 24(10), 2974; https://doi.org/10.3390/s24102974 - 8 May 2024
Viewed by 1293
Abstract
In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology [...] Read more.
In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology and underscore the criticality of junction temperature detection. Subsequently, we delve into two key facets of LED junction temperature assessment: steady-state and transient measurements. Beginning with an examination of innovations in steady-state junction temperature detection, we cover a spectrum of approaches ranging from traditional one-dimensional methods to more advanced three-dimensional techniques. These include micro-thermocouple, liquid crystal thermography (LCT), temperature sensitive optical parameters (TSOPs), and infrared (IR) thermography methods. We provide a comprehensive summary of the contributions made by researchers in this domain, while also elucidating the merits and demerits of each method. Transitioning to transient detection, we offer a detailed overview of various techniques such as the improved T3ster method, an enhanced one-dimensional continuous rectangular wave method (CRWM), and thermal reflection imaging. Additionally, we introduce novel methods leveraging high-speed camera technology and reflected light intensity (h-SCRLI), as well as micro high-speed transient imaging based on reflected light (μ_HSTI). Finally, we provide a critical appraisal of the advantages and limitations inherent in several transient detection methods and offer prognostications on future developments in this burgeoning field. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Schematic diagram of the structure of the article.</p>
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<p>Schematic diagram of the experimental arrangement of liquid crystal thermal imaging technology. The experimental setup usually consists of a polarized laser beam, a charge-coupled camera with a color filter, and a liquid crystal covering the surface of the LED [<a href="#B56-sensors-24-02974" class="html-bibr">56</a>].</p>
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<p>Lateral infrared distribution image of flip LEDs prepared by photolithography and dry reaction etching techniques. The schematic of the chip is shown on the left, while the captured infrared radiation is shown on the right [<a href="#B66-sensors-24-02974" class="html-bibr">66</a>].</p>
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<p>μ-HSI was utilized to measure the two-dimensional temperature distribution of blue (<b>a</b>), green (<b>b</b>), and red (<b>c</b>) LEDs at a heat sink temperature of 75 °C (348.15 K). Information regarding the color, material system, and driving current of each LED is provided at the top, while the junction temperature (T<sub>j</sub>) measured by both micro-thermocouple and μ-HSI is presented at the bottom. The average standard deviation of T<sub>j</sub> (μ-TC) and T<sub>j</sub> (μ-HSI) was recorded as 0.9 °C (0.9 K) [<a href="#B84-sensors-24-02974" class="html-bibr">84</a>].</p>
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<p>Frequency domain thermoreflectance imaging of a 40 μm heater with ‘cyclic phase lag’ heterodyne locking (the frequency shown is the thermal frequency) [<a href="#B89-sensors-24-02974" class="html-bibr">89</a>].</p>
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<p>T3ster junction temperature measurement system.</p>
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<p>Simulation using Flotherm to drive dynamic junction temperature peaks of LEDs with pulse trains.</p>
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<p>(<b>a</b>) Continuous rectangular-wave current. (<b>b</b>) Different voltage response waveforms of LED [<a href="#B39-sensors-24-02974" class="html-bibr">39</a>].</p>
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<p>Voltage waveform of the LED driven by rectangular-wave current [<a href="#B94-sensors-24-02974" class="html-bibr">94</a>].</p>
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<p>Schematic diagram of time-resolved micro-Raman thermal imaging experimental device [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>The variation of the center temperature of the mesa isolated AlGaN/GaN device grown on (<b>a</b>) SiC substrate and (<b>b</b>) sapphire substrate with time [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>(<b>a</b>) The variation of the center temperature of a 20 μm wide ungated AlGaN/GaN device on SiC substrate with time, and the simulated temperature evolution at different depths (z) in the device and SiC substrate. (<b>b</b>) The measured temperature evolution at different depths in the SiC substrate. The device operates at 25 μs long 40 V (159 mA) square bias pulse and a 50% duty cycle [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>Schematic diagram of the heat reflection imaging experimental device [<a href="#B104-sensors-24-02974" class="html-bibr">104</a>].</p>
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<p>(<b>a</b>) |μ R/R| is a function of time, pixel offset to 12 V 10 ms; (<b>b</b>) Timing diagram: the timing of the LED pulse relative to the pixel pulse changes; four function generators are used to generate various pulses, with LED pulses of 1 ms [<a href="#B104-sensors-24-02974" class="html-bibr">104</a>].</p>
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<p>(<b>a</b>) The setup used in the experiment to measure the dynamic two-dimensional temperature distribution. The measurement setup mainly contains highspeed camera (2), microscope with a high-pass filter (3), blue LUT (5), incident red LED (7), Heat sink (6) with temperature controller (8) (9), electrical source meter (10) (11) for blue LUT and incident red LED. (<b>b</b>) An illustration detailing the drive current waveform of the LUT, the acquisition waveform of the camera, and the processed transient temperature waveform [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>Transient two-dimensional temperature distribution of the rising edge of the blue LUT at 300 mA at (<b>a</b>) 0 ms, (<b>b</b>) 75 ms, (<b>c</b>) 95 ms, and (<b>d</b>) 125 ms, and (<b>e</b>) the comparison of the transient response of the h-SCRLI method and the thermal reflection imaging method [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>The evolving two-dimensional temperature profile of the blue LUT, propelled by a 300 mA current, is depicted at the descent of (<b>a</b>) 0 ms, (<b>b</b>) 5 ms, (<b>c</b>) 10 ms, and (<b>d</b>) 145 ms. Additionally, (<b>e</b>) presents a comparative analysis of the transient responses between the h-SCRLI method and the thermal reflection imaging method [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>(<b>a</b>) The pulse signal diagram of driving the LUT (heating process); (<b>b</b>) Dropping sampling pulse setting (cooling process) [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>Schematic diagram of the experimental device [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>The transient two-dimensional temperature distribution of LUT driven by different pulse widths during the heating process of (<b>a</b>) 10 ns, (<b>b</b>) 500 ns, (<b>c</b>) 100 μs, and (<b>d</b>) 1 ms. (<b>e</b>) The time–response curve in the logarithmic coordinate of the average transient junction temperature in the red box [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>The transient two-dimensional temperature distribution of LUT driven by different pulse widths during the cooling process of (<b>a</b>) 0 μs, (<b>b</b>) 50 μs, (<b>c</b>) 100 μs, and (<b>d</b>) 200 μs. (<b>e</b>) The logarithmic coordinate time–response curve of the average transient junction temperature in the red box [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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