2024

• Record 61 of
  
  Title:Design of Mantis-Shrimp-Inspired Multifunctional Imaging Sensors with Simultaneous Spectrum and Polarization Detection Capability at a Wide Waveband
  
  Author(s):Wang, Tianxin(1,2); Wang, Shuai(1); Gao, Bo(1); Li, Chenxi(1); Yu, Weixing(1,2)
  Source: Sensors  Volume: 24  Issue: 5  DOI: 10.3390/s24051689  Published: March 2024  
  Abstract:The remarkable light perception abilities of the mantis shrimp, which span a broad spectrum ranging from 300 nm to 720 nm and include the detection of polarized light, serve as the inspiration for our exploration. Drawing insights from the mantis shrimp’s unique visual system, we propose the design of a multifunctional imaging sensor capable of concurrently detecting spectrum and polarization across a wide waveband. This sensor is able to show spectral imaging capability through the utilization of a 16-channel multi-waveband Fabry–Pérot (FP) resonator filter array. The design incorporates a composite thin film structure comprising metal and dielectric layers as the reflector of the resonant cavity. The resulting metal–dielectric composite film FP resonator extends the operating bandwidth to cover both visible and infrared regions, specifically spanning a broader range from 450 nm to 900 nm. Furthermore, within this operational bandwidth, the metal–dielectric composite film FP resonator demonstrates an average peak transmittance exceeding 60%, representing a notable improvement over the metallic resonator. Additionally, aluminum-based metallic grating arrays are incorporated beneath the FP filter array to capture polarization information. This innovative approach enables the simultaneous acquisition of spectrum and polarization information using a single sensor device. The outcomes of this research hold promise for advancing the development of high-performance, multifunctional optical sensors, thereby unlocking new possibilities in the field of optical information acquisition. © 2024 by the authors.
  Accession Number: 20241115750294
• Record 62 of
  
  Title:TMCFN: Text-Supervised Multidimensional Contrastive Fusion Network for Hyperspectral and LiDAR Classification
  
  Author(s):Yang, Yueguang(1); Qu, Jiahui(2); Dong, Wenqian(2,3); Zhang, Tongzhen(2); Xiao, Song(2,4); Li, Yunsong(2)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 62  Issue:   DOI: 10.1109/TGRS.2024.3374372  Published: 2024  
  Abstract:The joint classification of hyperspectral images (HSIs) and LiDAR data plays a crucial role in Earth observation missions. Most advanced methods are based on discrete label supervision. However, since discrete labels only convey limited information that a sample belongs to a single definite class and lack of prior information, it is difficult to supervise the model to capture rich inherent semantic information in complex data distributions, hindering the classification performance. To this end, we propose a text-supervised multidimensional contrastive fusion network (TMCFN), which leverages class text information to guide the learning of visual representations while establishing a semantic association of text and visual features for classification using multidimensionally incorporated contrastive learning (CL) paradigms. Specifically, TMCFN is composed of text information encoding (TIE), visual features representation (VFR), and text-visual features alignment and classification (TVFAC). TIE is employed to extract semantic information from class text extended from class names, intrinsic attributes and inter-class relationships. VFR mainly comprises a new fusion-based contrastive feature learning module (FCFLM) to extract discriminative visual features and a text-guided attention feature fusion module (TAF2M) to fuse visual features under the guidance of text information. TVFAC optimizes the learning of visual features under the supervision of text information while using a CL paradigm to align text and visual features for establishing the semantic association, and achieves the classification by directly computing the similarity between the visual features and each text feature without an additional classifier. Experiments with three standard datasets verify the effectiveness of TMCFN. © 1980-2012 IEEE.
  Accession Number: 20241115732523
• Record 63 of
  
  Title:Optical-signal token guided change detection network for heterogeneous remote sensing image
  
  Author(s):Liu, Qinsen(1); Sun, Bangyong(1,2)
  Source: National Remote Sensing Bulletin  Volume: 28  Issue: 1  DOI: 10.11834/jrs.20233067  Published: 2024  
  Abstract:Change Detection (CD) is a vital technique for identifying and analyzing changes over time in a specific area using optical signals from remote sensing images. This technique has been extensively utilized in various fields, including national defense security, environmental monitoring, and urban construction. However, some challenges in achieving accurate and reliable CD are still encountered due to inherent disparities in imaging mechanisms, spectral ranges, and spatial resolutions among heterogeneous images. These challenges lead to issues such as inadequate accuracy, missed detections, and false detections. Heterogeneous remote sensing images can be regarded as sequences of different optical signals from the channel perspective. For example, RGB and infrared images can be regarded as sequences of spectral signals from different ranges. Transformers employ a multi-head attention mechanism that can effectively handle and analyze sequence information to achieve accurate heterogeneous CD. Thus, the paper proposes an optical signal token guided CD network for heterogeneous remote sensing images. This paper presents a novel heterogeneous CD network, primarily comprising the optical-signal token transformer (OT-Former) and the cross-temporal transformer (CT-Former). The proposed method demonstrates the capacity to effectively handle diverse remote sensing images of distinct categories and attain precise CD results. Specifically, OT-Former can encode diverse heterogeneous images in channel-wise for adaptively generating the optical-signal tokens. Meanwhile, CT-Former can use the optical-signal tokens as a guide to interact with the patch token for the learning of change rules. Moreover, a Difference Amplification Module (DAM) is embedded into the network to enhance the extraction of difference information. This module utilizes a 1×2 convolutional kernel to effectively fuse difference information. Finally, the differential token is predicted by multilayer perceptron to output the CD results. Experiments were conducted on three heterogeneous datasets and one homogeneous dataset to evaluate the performance of the proposed method. Furthermore, the proposed method was compared with six typical CD methods and evaluated the performance using overall accuracy (OA), Kappa coefficient, and F1-score, among other evaluation metrics, to validate the effectiveness of the proposed network in this study. A limited number of samples were utilized for training during the experiment. Under identical experimental conditions, the proposed method demonstrated exceptional performance in homogeneous and heterogeneous CD. The results show that the proposed approach surpasses existing state-of-the-art methods in terms of qualitative and visual performance. Additionally, ablation experiments and parameter analyses were conducted to validate the effectiveness of the proposed methods, including the OT-Former, CT-Former, and DAM modules, and to assess the impact of various parameters within the network. Overall, the current study presents a novel heterogeneous CD network based on the transformer framework. Within this network, OT-Former is proposed to achieve the adaptive generation of optical-signal tokens from diverse remote sensing images. Moreover, the CT-Former utilizes these optical-signal tokens as a guide to facilitate interaction with patch tokens for the learning of change rules. Additionally, DAM modules were embedded into the network to effectively extract the difference information. An extremely limited number of samples were utilized only for training in the experiments. Remarkably, the proposed method outperformed the existing state-of-the-art methods, achieving a significantly advanced performance in heterogeneous CD. © 2024 Science Press. All rights reserved.
  Accession Number: 20241515892473
• Record 64 of
  
  Title:Rapid Solidification of Invar Alloy
  
  Author(s):He, Hanxin(1); Yao, Zhirui(2); Li, Xuyang(3); Xu, Junfeng(2)
  Source: Materials  Volume: 17  Issue: 1  DOI: 10.3390/ma17010231  Published: January 2024  
  Abstract:The Invar alloy has excellent properties, such as a low coefficient of thermal expansion, but there are few reports about the rapid solidification of this alloy. In this study, Invar alloy solidification at different undercooling (ΔT) was investigated via glass melt-flux techniques. The sample with the highest undercooling of ΔT = 231 K (recalescence height 140 K) was obtained. The thermal history curve, microstructure, hardness, grain number, and sample density of the alloy were analyzed. The results show that with the increase in solidification undercooling, the XRD peak of the sample shifted to the left, indicating that the lattice constant increased and the solid solubility increased. As the solidification of undercooling increases, the microstructure changes from large dendrites to small columnar grains and then to fine equiaxed grains. At the same time, the number of grains also increases with the increase in the undercooling. The hardness of the sample increases with increasing undercooling. If ΔT ≥ 181 K (128 K), the grain number and the hardness do not increase with undercooling. © 2023 by the authors.
  Accession Number: 20240315384601
• Record 65 of
  
  Title:Adaptive location method for film cooling holes based on the design intent of the turbine blade
  
  Author(s):Hou, Yaohua(1); Wang, Jing(1); Mei, Jiawei(2); Zhao, Hualong(1)
  Source: International Journal of Advanced Manufacturing Technology  Volume: 132  Issue: 3-4  DOI: 10.1007/s00170-024-13456-4  Published: May 2024  
  Abstract:Due to the inevitable deviation of the casting process, the dimensional error of the turbine blade is introduced. As a result, the location datum of the film cooling holes is changed, which has an impact on the machining accuracy. The majority of pertinent studies concentrate on the rigid location approach for the entire blade, which results in a modest relative position error of the blade surface but still fails to give the exact position and axial direction of the film cooling holes of the deformed blade. In this paper, the entire deformation of the blade cross-section curve is divided into a number of deformation combinations of the mean line curve based on the construction method of the blade design intent. The exact location of the film cooling holes in the turbine blade with deviation is therefore efficiently solved by a flexible deformation of the blade that optimises the position and axial direction of the holes. The verification demonstrates that the novel method can significantly reduce both the contour deviation of the blade surface and the location issue of the film cooling holes. After machining experiments, the maximum position deviation of the holes is reduced by approximately 80% compared to the rigid location method of the entire blade, and the average value and standard deviation are also decreased by about 70%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
  Accession Number: 20241215791556
• Record 66 of
  
  Title:Hyperspectral scene classification dataset based on Zhuhai-1 images
  
  Author(s):Liu, Yuan(1,2); Zheng, Xiangtao(3); Lu, Xiaoqiang(3)
  Source: National Remote Sensing Bulletin  Volume: 28  Issue: 1  DOI: 10.11834/jrs.20233283  Published: 2024  
  Abstract:Hyperspectral remote sensing is a key technology for remotely obtaining the physical parameters of ground objects and realizing fine identification. It can not only get geometrical properties of the target scenes but also obtain radiance that reflects the characteristics of ground objects. With the development of hyperspectral remote sensing data to unprecedented spatial, spectral, temporal resolution and large data volume, how to adapt to the requirements of massive data and achieve efficient and rapid processing of hyperspectral remote sensing data has become the current research focus. Researchers are introducing scene classification into hyperspectral image classification, integrating the spatial and spectral information to obtain semantic information oriented to larger observation units. However, almost all existing multispectral/hyperspectral scene classification datasets have a number of limitations, including inconsistent spectral and spatial resolutions or spatial resolutions too large to meet the needs of fine-grained classification. Based on the hyperspectral images of Xi’an taken by the "Zhuhai-1" constellation, we combine the result of unsupervised spectral clustering and Google Earth to establish a hyperspectral satellite image scene classification dataset named HSCD-ZH (Hyperspectral Scene Classification Dataset from Zhuhai-1). It consists of 737 images divided into six categories: urban, agriculture, rural, forest, water, and unused land. Each image with a size of 64 × 64 pixels consists of 32 bands covering the wavelength in the range of 400—1000 nm. In addition, we conduct spatial-based and spectral-based experiments to analyze the performance of existing datasets, and the benchmark results are reported as a valuable baseline for subsequent research. We choose false-color image for the spatial-based experiments and then use popular deep and non-deep learning scene classification techniques. In the experiments based on spectral, the spectral vectors at the pixel are directly used as local spectral features, and BoVW, IFK, and LLC are used to encode them to generate global representations for the scene. Using SVM as the classifier, the optimal overall classification achieved by the two experiments on the proposed dataset is 92.34% and 88.96%, respectively. Considering that those methods have a large amount of information loss, we cascade the features extracted by the two approaches to generate spatial-spectral features. The highest overall accuracy obtained reaches 94.64%, which is the highest improvement in overall accuracy compared to the other datasets. We construct HSCD-ZH by effectively exploiting both spectral and spatial features of hyperspectral images, selecting various scenes that either have representative spectral compositions, clear spatial textures, or both. It has the advantages of big intraclass diversity, strong scalability, and adapting to satellite hyperspectral intelligent information extraction requirements. Both dataset and experiments can provide effective data support for remote sensing scene classification research in the hyperspectral field. Meanwhile, experiments can indicate that extracting features based on spatial or spectral misses a large amount of available information, and integrating the features extracted by the two methods can compensate for this deficiency. In our future work, we aim to expand the number of categories and images of HSCD-ZH and continue to explore algorithms for integrating spatial and spectral information that can accelerate the interpretation and efficient exploitation of hyperspectral scene cubes. © 2024 Science Press. All rights reserved.
  Accession Number: 20241515892427
• Record 67 of
  
  Title:Fatigue mechanism analysis and life prediction model of piezoelectric ceramic tube based on fiber-optic nutator
  
  Author(s):Peng, Bo(1,2,3); Ruan, Ping(1,3); Han, Junfeng(1,3); Chang, Zhiyuan(1,3); Han, Jingyu(1,2,3); Wang, Jiahao(1,2,3); He, Deqiu(1,2,3)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 13068  Issue:   DOI: 10.1117/12.3016249  Published: 2024  
  Abstract:As a driving unit and core component of the acquisition, pointing and tracking (APT) system's fiber actuator, the fatigue mechanism and fatigue life analysis of the piezoelectric ceramic tube (PCT) nutator in a high-frequency dynamic state have become one of the urgent research issues in the reliability field of interstellar laser communicating key devices. This article commences by elucidating the principles of deflection and nutation of the PCT nutator. Subsequently, employing finite element simulation methodologies, an exhaustive analysis is conducted to discern the stress and strain energy density distribution within a single operational cycle under specific working parameters. The findings illuminate the principal fatigue failure mechanism of the dynamic piezoelectric ceramic tube, characterized by crack propagation and eventual rupture resulting from localized stress accumulation during dynamic processes. Furthermore, the coordinates of the "most dangerous element" are ascertained, and a fatigue life model for the PCT nutator in transient nutation is proposed based on the theory of material fatigue damage accumulation. Based on model calculations, the theoretical fatigue life of the PCT nutator can reach 2.31×106 cycles under the environmental conditions with a 500Hz bandwidth and maximum nutation radius. © 2024 SPIE.
  Accession Number: 20240715542577
• Record 68 of
  
  Title:Nanosecond pulse X-ray emission source based on ultrafast laser modulation
  
  Author(s):Li, Yun(1,2); Su, Tong(1); Sheng, Li-Zhi(1); Zhang, Rui-Li(1); Liu, Duo(3); Liu, Yong-An(1); Qiang, Peng-Fei(1); Yang, Xiang-Hui(1); Xu, Ze-Fang(1,2)
  Source: Wuli Xuebao/Acta Physica Sinica  Volume: 73  Issue: 4  DOI: 10.7498/aps.73.20231505  Published: 2024  
  Abstract:In response to the growing demand for miniaturized ultrafast pulsed X-ray sources in the fields of fundamental science and space applications, we design and develop an ultrafast pulsed X-ray generator based on a laser-modulated light source and a photoelectric cathode. This innovative technology addresses the limitations commonly encountered in traditional X-ray emission devices, such as low repetition rate, insufficient time stability, and suboptimal pulse characteristics. Our effort is to study and develop the ultrafast modulation control module for the pulsed X-ray generator. This effort results in achieving high levels of time accuracy and stability in ultrafast time-varying photon signals. Moreover, we successfully generate nanosecond pulsed X-rays by using a laser-controlled light source. Theoretically, we establish a comprehensive time response model for the pulsed X-ray generator in response to short pulses. This includes a thorough analysis of the time characteristics of the emitted pulsed X-rays in the time domain. Experimentally, we conduct a series of tests related to various time-related parameters of the laser-controlled light source. Additionally, we design and implemente an experimental test system for assessing the time characteristics of pulsed X-rays, by using an ultrafast scintillation detector. The experimental results clearly demonstrate that our pulsed X-ray generator achieves impressive capabilities, including high repetition rates (12.5 MHz), ultrafast pulses (4 ns), and exceptional time stability (400 ps) in X-ray emission. These results closely align with our established theoretical model. Compared with traditional modulation techniques, our system exhibits significant improvement in pulse time parameters, thereby greatly expanding its potential applications. This research provides a valuable insight into achieving ultra-high time stability and ultrafast pulsed X-ray emission sources. These advances will further enhance the capabilities of X-ray technology for scientific research and space applications. © 2024 Chinese Physical Society.
  Accession Number: 20241515855160
• Record 69 of
  
  Title:A Snapshot Multi-Spectral Demosaicing Method for Multi-Spectral Filter Array Images Based on Channel Attention Network
  
  Author(s):Zhang, Xuejun(1,2); Dai, Yidan(1,2); Zhang, Geng(1); Zhang, Xuemin(3); Hu, Bingliang(1)
  Source: Sensors  Volume: 24  Issue: 3  DOI: 10.3390/s24030943  Published: February 2024  
  Abstract:Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity. © 2024 by the authors.
  Accession Number: 20240715548327
• Record 70 of
  
  Title:Research progress on hyperspectral anomaly detection
  
  Author(s):Qu, Bo(1,2,3); Zheng, Xiangtao(1); Qian, Xueming(2); Lu, Xiaoqiang(1)
  Source: National Remote Sensing Bulletin  Volume: 28  Issue: 1  DOI: 10.11834/jrs.20232405  Published: 2024  
  Abstract:The applications of remote sensing images in numerous fields have been increasing with the continuous development of aerospace and remote sensing technologies. HyperSpectral Image (HSI) is a common type of remote sensing image that comprises a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reveal the reflection/radiation intensity of different wavelengths of electromagnetic waves, and each pixel of HSI corresponds to the spectral curve reflecting the spectral information in different wavelengths. Therefore, the hyperspectral remote sensing images are characterized by"spatial-spectral integration," which contains not only spectral information with strong discriminant but also rich spatial information. Therefore, the hyperspectral data have considerable application potential. Hyperspectral anomaly detection aims to detect pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without any previous knowledge of the target. Hyperspectral anomaly detection is an unsupervised process that does not require any priori information regarding the target to be measured in advance; thus, this type of detection plays a crucial role in real life. For example, anomaly target detection technology can be used to search and rescue people after a disaster, quickly determine the fire point of a forest fire, and search mineral points in mineral resource exploration. Hyperspectral anomaly detection has been a popular research direction in the area of remote sensing image processing in recent years, and a numerous researchers have conducted extensive research and achieved rich research results. However, hyperspectral anomaly detection still encounters many difficult problems. For example, the targets of the same material may exhibit various spectral characteristics due to the different imaging equipment and environment, which may interfere with the detection results and lead to the problem of"same object with different spectra."Meanwhile, the targets of different materials may also exhibit the problem of"different objects with different spectra."Then, most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Furthermore, the hyperspectral data may have numerous spectral bands that contain a considerable amount of redundant information, which increases the difficulty of data processing. Moreover, the number of publicly available hyperspectral anomaly detection datasets is insufficient and mostly old. In this paper, the main research progress of hyperspectral anomaly detection is first summarized. The existing mainstream algorithms are then classified and summarized. These algorithms are mainly divided into five categories: statistics-based anomaly detection methods, data expression-based anomaly detection methods, data decomposition-based anomaly detection methods, deep learning-based anomaly detection methods, and other methods. Through the investigation, analysis, and summary of the existing methods, three future development directions of hyperspectral anomaly detection are proposed. (1) Database expansion: new datasets with additional images and highly sophisticated remote sensing sensors are introduced. (2) Multisource data combination: the advantages of different imaging sensors and various types of remote sensing data are maximized. (3) Algorithm practicality: the anomaly detection algorithms are relayed for application on real platforms. © 2024 Science Press. All rights reserved.
  Accession Number: 20241515892466
• Record 71 of
  
  Title:Material removal and surface generation mechanisms in rotary ultrasonic vibration–assisted aspheric grinding of glass ceramics
  
  Author(s):Sun, Guoyan(1,2); Wang, Sheng(3); Zhao, Qingliang(3); Ji, Xiabin(1); Ding, Jiaoteng(1)
  Source: International Journal of Advanced Manufacturing Technology  Volume: 130  Issue: 7-8  DOI: 10.1007/s00170-023-12904-x  Published: February 2024  
  Abstract:High-efficiency precision grinding can shorten the machining cycle of aspheric optical elements by a factor of 2–10. To achieve this objective, ultrasonic vibration (UV)–assisted grinding (UVG) has been increasingly applied to manufacture aspheric optics. However, the mechanisms of material removal and surface formation in UV-assisted aspheric grinding of glass ceramics have rarely been studied. Herein, rotary UV-assisted vertical grinding (RUVG) was used to explore the machining mechanism of coaxial curved surfaces. First, RUV-assisted scratch experiments were conducted on aspheric surface of glass ceramics, which exhibited multiple benefits over conventional scratching. These include a reduction in the scratch force by 37.83–44.55% for tangential component and 3.87–28.15% for normal component, an increase in plastic removal length by 43.75%, and an increase in material removal rate by almost a factor of 2. Moreover, grinding marks on the aspheric surface in RUVG were accurately simulated and optimized by adjusting grinding parameters. RUVG experiments were performed to verify the accuracy of grinding texture simulations and investigate the UV effect. The results demonstrate that UV can improve the surface quality of aspheric grinding when compared with conventional vertical grinding. In particular, the total height of the profile of form accuracy and its root mean square were significantly improved by a factor of 3.38–4.54 and 7.15–10.82, respectively, and the surface roughness reduced by 10.03–12.10%. This study provides deeper insight into material removal and surface generation mechanisms for RUVG of aspheric surfaces, and it is thus envisaged that these results will be useful in engineering applications. © 2024, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
  Accession Number: 20240215352394
• Record 72 of
  
  Title:Influence of nutating deflection on fiber coupling efficiency for fiber optic nutator
  
  Author(s):Peng, Bo(1,2,3); Ruan, Ping(1,3); Wang, Xingfeng(1,3); Han, Junfeng(1,3); Chang, Zhiyuan(1,3); Han, Jingyu(1,2,3)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 13104  Issue:   DOI: 10.1117/12.3023648  Published: 2024  
  Abstract:In the relay optics of the space laser communication terminal's Acquisition, Pointing, and Tracking (APT) system, the Fiber Optic Nutator (FON), based on a Piezoelectric Ceramic Tube (PCT), is capable of actively achieving signal light reception and coupling through the implementation of energy feedback compensation algorithms with a lightweight design approach. Throughout the fiber nutation process, the deflection amplitude of the receiving fiber's end face significantly impacts the fiber coupling efficiency of the fiber optic nutator. To quantify this influence, the curve depicting the effect of the relative aperture (D/f) of the relay optics focusing lens on fiber coupling efficiency is initially computed. Notably, when D/f=0.213, the fiber coupling efficiency attains its theoretical maximum of 0.813. Subsequently, the composite motion of the fiber end face in three-dimensional space is deconstructed into radial and axial translations, along with rotations based on the axial direction. Through meticulous simulation calculations, it is ascertained that the fiber coupling efficiency decreases by more than 5% when the radial displacement r of the fiber end face exceeds 3.65μm, or when the axial displacement d surpasses 0.25mm, or when the angular deviation θ exceeds 0.08°. These findings offer quantifiable criteria for the dimensional selection of the PCT under varied application conditions, providing constructive guidance for determining core structural design parameters of the fiber optic nutator. © COPYRIGHT SPIE.
  Accession Number: 20241816027629