Optics and Laser Technology最新文献_第5页

Strain sensor based on terahertz spectral modulation of periodic circular hole array 基于太赫兹频谱调制周期圆孔阵列的应变传感器

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-12 DOI: 10.1016/j.optlastec.2025.113374

Ruan Jiangtao , Tan Keyu , Wang Zhiyong

{"title":"Strain sensor based on terahertz spectral modulation of periodic circular hole array","authors":"Ruan Jiangtao , Tan Keyu , Wang Zhiyong","doi":"10.1016/j.optlastec.2025.113374","DOIUrl":"10.1016/j.optlastec.2025.113374","url":null,"abstract":"<div><div>This paper proposes a simple, robust and low-cost strain sensor based on terahertz spectral modulation. The proposed sensor consists of an array of circular holes machined on a Polytetrafluoroethylene (PTFE) plate. Experimental measurements and numerical simulations validate the effectiveness of the proposed hole-array strain sensors. Firstly, the terahertz transmission spectrum of the sensors under unloaded condition was experimentally measured to determine their absorption characteristic. Further verification experiments were carried out to explore the modulation effect of strain on the terahertz transmission spectrum of the sensors. At last, using mechanics-electromagnetics coupling simulations, we modeled the modulation effect of strain on the terahertz transmission spectrum of the sensors to verify the experimental results. Consistent results from both experiments and simulations demonstrate that the periodic circular hole-array structure generates a characteristic absorption dip in the transmission spectrum, and that the applied strain causes an approximately linear shift in the position of this dip.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113374"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Snapshot spectral imager using orthogonal coding and an untrained spectrally informed decoder network 快照光谱成像仪使用正交编码和未经训练的频谱信息解码器网络

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-12 DOI: 10.1016/j.optlastec.2025.113219

Huxia Xie, Yuhua Shi, Mu Qiao

{"title":"Snapshot spectral imager using orthogonal coding and an untrained spectrally informed decoder network","authors":"Huxia Xie, Yuhua Shi, Mu Qiao","doi":"10.1016/j.optlastec.2025.113219","DOIUrl":"10.1016/j.optlastec.2025.113219","url":null,"abstract":"<div><div>Coded Aperture Snapshot Spectral Imaging (CASSI) has emerged as a powerful technology for acquiring hyperspectral images through a single compressed measurement. However, achieving high spatial and spectral resolution simultaneously remains a significant challenge. In this work, we propose a novel combination of orthogonal coding and a dedicated self-supervised reconstruction algorithm to significantly enhance hyperspectral imaging performance. The key insight behind our approach is that orthogonal coding fundamentally transforms the reconstruction problem from a compressed sensing (CS) problem to an inpainting problem, where each spectral channel is sampled by a random distinct subset of pixels rather than being multiplexed. This shift in problem formulation motivates our integration of a self-supervised reconstruction framework, which is particularly well-suited for inpainting-based restoration. Specifically, we design an orthogonal coded aperture (mask) that ensures spectral bands are modulated by mutually orthogonal patterns, effectively minimizing spectral cross-talk. To complement this encoding strategy, we develop a self-supervised reconstruction algorithm that employs an untrained convolutional decoder network with tailored structure and weight initialization, capturing both spatial and spectral priors. The synergy between our encoding and decoding strategies leads to superior imaging performance, significantly outperforming existing model-driven methods in terms of PSNR, SSIM, and SAM metrics, as demonstrated through simulations and real-world experiments.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113219"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Underwater hyperspectral imaging-based classification of manganese nodules using a multi-scale spatial-spectral residual network 基于水下高光谱成像的多尺度空间光谱残差网络锰结核分类研究

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-12 DOI: 10.1016/j.optlastec.2025.113353

Bohan Liu , Mengling Shen , Peng Zheng , Dewei Li , Jialing Tang , Haibin Qi , Zhongjun Ding , Shaojie Men

{"title":"Underwater hyperspectral imaging-based classification of manganese nodules using a multi-scale spatial-spectral residual network","authors":"Bohan Liu , Mengling Shen , Peng Zheng , Dewei Li , Jialing Tang , Haibin Qi , Zhongjun Ding , Shaojie Men","doi":"10.1016/j.optlastec.2025.113353","DOIUrl":"10.1016/j.optlastec.2025.113353","url":null,"abstract":"<div><div>Accurate classification of manganese nodules is critical for deep-sea mineral exploration and environmental assessments. This study presents a novel multi-scale spectral-spatial residual network (MSSRN) combined with underwater hyperspectral imaging (UHI) for the precise differentiation of deep-sea manganese nodules. The proposed MSSRN leverages joint spectral-spatial information extraction at multiple scales through specially designed spectral-spatial residual blocks. Experiments conducted on a custom-built UHI dataset demonstrate that MSSRN achieves outstanding classification performance, with an overall accuracy (OA) of 99.7%, average accuracy (AA) of 99.7%, and a Kappa coefficient of 99.41%, significantly outperforming established benchmark models such as 2D-CNN, 3D-CNN, ResNet, and HybridSN. These findings underscore the effectiveness of multi-scale spectral-spatial feature extraction for accurately differentiating manganese nodule types using UHI data. By providing a highly accurate method for manganese nodule classification, this research offers promising implications for advancing the deep-sea mineral resource assessment and environmental impact studies.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113353"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved shape from focus network for extended depth of field and rapid three-dimensional reconstruction biological microscopy 改进的形状从焦点网络扩展景深和快速三维重建生物显微镜

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-11 DOI: 10.1016/j.optlastec.2025.113345

Mengyao Liu , Jiale Wei , Zhibo Qiao , Shengyuan Chen , Liang Wang , Yang Cheng

{"title":"Improved shape from focus network for extended depth of field and rapid three-dimensional reconstruction biological microscopy","authors":"Mengyao Liu , Jiale Wei , Zhibo Qiao , Shengyuan Chen , Liang Wang , Yang Cheng","doi":"10.1016/j.optlastec.2025.113345","DOIUrl":"10.1016/j.optlastec.2025.113345","url":null,"abstract":"<div><div>Three-dimensional (3D) reconstruction microscopy has played an important role in advancing the elucidation of the roles and structures of biological cells, but the current mainstream optical microimaging techniques make it difficult to capture the 3D structures of dynamic organisms. Therefore, this paper proposes a fast and versatile end-to-end improved shape-from-focus (ISFF) network and enlarged selective kernel (ESK) module, which are applied to obtain microscopes with a large depth of field and high-precision 3D reconstruction capability. To characterize the feasibility of ISFF, our algorithm achieves a higher quality of the fused image compared to six prevalent deep learning image fusion algorithms. We apply our microscopy to 3D imaging of live biological samples such as bee tentacles, C.elegans, and zebrafish without fluorescent labels or anesthesia, and our experimental results show that high-resolution 3D observation of biodynamic processes can be achieved in 1.86 s. Quantitative analysis of the interface between the standard gauge blocks shows that the microscopy’s depth of field extends to 1200 μm under a 10 × objective and the relative errors of the reconstruction for the two gauge blocks are 0.61 % and 0.54 %, respectively. Our network eliminates the need to train exclusively on model organisms such as C. elegans and zebrafish, while still achieving good 3D reconstruction results. It not only expands the application range and system robustness of biomicroscopy but also provides new perspectives and tools for living model organisms at the millimeter scale.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113345"},"PeriodicalIF":4.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ghost edge imaging with untrained neural networks 用未经训练的神经网络进行鬼影边缘成像

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-11 DOI: 10.1016/j.optlastec.2025.113331

Yao Yang , Zhiyan Zhao , Le Wang , Shengmei Zhao

引用次数: 0

Homodyne detection based confocal phase diffraction method for thickness characterization of ultra-thin dielectric films coated on optical fibers 基于共焦相位衍射法的光纤超薄介质膜厚度表征

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-11 DOI: 10.1016/j.optlastec.2025.113299

Anıl Karatay, Enes Ataç

{"title":"Homodyne detection based confocal phase diffraction method for thickness characterization of ultra-thin dielectric films coated on optical fibers","authors":"Anıl Karatay, Enes Ataç","doi":"10.1016/j.optlastec.2025.113299","DOIUrl":"10.1016/j.optlastec.2025.113299","url":null,"abstract":"<div><div>Characterizing the thickness of thin dielectric films is crucial in fiber optic sensor technologies due to their significant impact on sensor performance. However, non-destructive thickness characterization of films in the range of tens of nanometers, particularly on non-planar surfaces, is often a challenging, complex, and tedious process. In addition, the measurements often need highly calibrated devices under the control of specialists. In this paper, we propose a novel, non-destructive, and practical method for characterizing the thickness of ultra-thin (<span><math><mo><</mo></math></span>100 nm) curved transparent dielectric films using homodyne detection of the confocal phase diffraction. The numerical simulations and experimental results show that suppressing stray light improves the influence of thickness information in the diffracted field. This significantly enhances the system’s sensitivity to nanometer-scale variations in dielectric film thickness, especially when integrated with a coherent detection scheme. According to the results, the film thickness can be precisely measured within a few nanometers, making it highly significant and promising for cost-effective optical metrology applications.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113299"},"PeriodicalIF":4.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-free three-dimensional cellular detection and analysis using holographic tomography and Raman tweezers spectroscopy 使用全息断层扫描和拉曼镊子光谱的无标签三维细胞检测和分析

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-11 DOI: 10.1016/j.optlastec.2025.113346

Chung-Hsuan Huang , Han-Yen Tu , Chau-Jern Cheng

引用次数: 0

Two-photon and three-photon excitation in GaN by spectrally broadband and few-cycle laser pulses 氮化镓中双光子和三光子的谱宽和少周期激光脉冲激发

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-11 DOI: 10.1016/j.optlastec.2025.113334

Meng-Yi Wang , Jie-Xiang Xiao , Miao Wang , Jue-Min Yi , Ye Song , Yu-Min Zhang , Jian-Feng Wang , Bing Cao , Christoph Lienau , Ke Xu

引用次数: 0

Laser power modulation for improving laser soldering defects via LSTM and CNN models 通过LSTM和CNN模型改善激光焊接缺陷的激光功率调制

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-10 DOI: 10.1016/j.optlastec.2025.113330

Wei Wang , Hongyun Zhao , Biao Yang , Fuyun Liu , Lianfeng Wei , Zengqiang Niu , Guojie Lu , Qiao Wang , Xiaoguo Song , Caiwang Tan

{"title":"Laser power modulation for improving laser soldering defects via LSTM and CNN models","authors":"Wei Wang , Hongyun Zhao , Biao Yang , Fuyun Liu , Lianfeng Wei , Zengqiang Niu , Guojie Lu , Qiao Wang , Xiaoguo Song , Caiwang Tan","doi":"10.1016/j.optlastec.2025.113330","DOIUrl":"10.1016/j.optlastec.2025.113330","url":null,"abstract":"<div><div>Achievement of high yields of using solder Sn-3.0Ag-0.5Cu (SAC305) in the large-scale soldering processes was still a formidable challenge for the field of electronic packaging. It was difficult to completely eliminate the defects by straightforward parameters tailoring or metallurgical adjustments. This work novelly proposed a LSTM (Long Short Term Memory) and CNN (Convolutional Neural Network) network to adjust the heat input for the processes optimization by the modulation of waveform. In this work, the seamless transition from long-term time coding to defect classification was realized by using LSTM and CNN models to predict the optimized process. The power data were obtained and fed to the LSTM network to predict the temperature curves. Subsequently, each temperature curve was transferred to a tensor and utilized to identify the defects. Finally, the range of optimized waveforms was obtained. The results demonstrated the LSTM and CNN models had the excellent performance which for LSTM, MAE, MSE, RMSE and R<sup>2</sup> were 0.03356 °C, 0.001361 °C<sup>2</sup>, 0.036892 and 0.978209, respectively; for CNN, the accuracy exceeded 89 %. Type 1 waveforms were found to consistently yield optimal joint formations by enhancing melting and wetting, albeit with a risk of substrate distortion, whereas Type 3 and Type 4 waveforms were associated with inadequate wetting. High-speed imaging analysis further revealed that waveform modulation could effectively adjust heat input at different stages, promote better wetting and reduce thermally induced defects. This work will provide an innovative method to improve the soldering of SAC305 in the actual production, widen the application of LSTM and CNN in the field of laser soldering and expand the tailoring methodologies to other fields.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113330"},"PeriodicalIF":4.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating multiple-folding-geometry freeform reflective imaging systems based on optical design model-informed neural network 基于光学设计模型的神经网络生成多折叠几何自由曲面反射成像系统

IF 4.6 2区物理与天体物理

Optics and Laser Technology Pub Date : 2025-06-10 DOI: 10.1016/j.optlastec.2025.113322

Guogen Chen , Tong Yang , Dewen Cheng , Yongtian Wang

{"title":"Generating multiple-folding-geometry freeform reflective imaging systems based on optical design model-informed neural network","authors":"Guogen Chen , Tong Yang , Dewen Cheng , Yongtian Wang","doi":"10.1016/j.optlastec.2025.113322","DOIUrl":"10.1016/j.optlastec.2025.113322","url":null,"abstract":"<div><div>Freeform systems play important roles in modern optical systems, but their design remains challenging due to the complexity of freeform surfaces and lack of efficient methods as well as reference designs. This paper presents a framework that leverages an optical design model-informed neural network (ODMINN) to automatically generate multiple-folding-geometry freeform reflective imaging systems. The network is trained by both the data-driven loss and the physics-informed loss. An automatic training dataset generation method, combined with a fast light-obstruction evaluation method based on equivalent spherical systems, is proposed for obtaining dataset containing systems with various parameters and folding geometries. The real optical design model is integrated into the training process, by directly calculating the physics-informed loss related to imaging performance and optical design constraints using differential ray tracing. Freeform systems can be generated immediately by the network based on the design requirements. Compared with previous network which can only generate systems with one specific folding geometry, multiple-folding-geometry freeform systems can be generated using the proposed framework. We demonstrate the framework by designing freeform off-axis three-mirror systems with all eight different folding geometries. Our approach can significantly reduce human involvement and dependency on existing reference systems in the design of freeform optics, while dramatically improving the design efficiency.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113322"},"PeriodicalIF":4.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0