Infrared Physics & Technology最新文献

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Enhancing thyroid nodule classification: A comprehensive analysis of feature selection in thermography

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-30 DOI: 10.1016/j.infrared.2025.105730

Mahnaz Etehadtavakol , Mojtaba Sirati-Amsheh , Golnaz Moallem , Eddie Yin Kwee Ng

Early detection of thyroid malignancies is crucial, yet traditional diagnostic methods are often costly and carry inherent risks. Thermography presents a non-invasive alternative, but existing studies frequently lack comprehensive methodological frameworks for broader applications. In the realm of machine learning and classification, feature selection is pivotal for enhancing model performance by reducing overfitting, shortening training times, minimizing dimensionality, improving interpretability, and focusing on the most relevant features.

This study aims to identify the most informative features and evaluate the efficacy of various feature selection techniques—both unsupervised and supervised (filter, wrapper, and embedded)—in improving the classification accuracy of thyroid nodules using thermography images. Multiple machine learning models, including Support Vector Machines, Random Forest, Decision Tree, AdaBoost, and XGBoost, were assessed as classifiers utilizing group k-fold cross-validation.

Among the feature selection methods, LASSO (supervised embedding-based feature selection) showed the best performance, achieving 86% accuracy with an AUC of 0.91 for the random forest model and 86 % accuracy with an AUC of 0.92 for the XGBoost model. This research underscores the critical role of feature selection in the classification of thyroid nodules using thermography, providing valuable insights for advancing non-invasive diagnostic methodologies in thyroid assessment.

{"title":"Enhancing thyroid nodule classification: A comprehensive analysis of feature selection in thermography","authors":"Mahnaz Etehadtavakol , Mojtaba Sirati-Amsheh , Golnaz Moallem , Eddie Yin Kwee Ng","doi":"10.1016/j.infrared.2025.105730","DOIUrl":"10.1016/j.infrared.2025.105730","url":null,"abstract":"<div><div>Early detection of thyroid malignancies is crucial, yet traditional diagnostic methods are often costly and carry inherent risks. Thermography presents a non-invasive alternative, but existing studies frequently lack comprehensive methodological frameworks for broader applications. In the realm of machine learning and classification, feature selection is pivotal for enhancing model performance by reducing overfitting, shortening training times, minimizing dimensionality, improving interpretability, and focusing on the most relevant features.</div><div>This study aims to identify the most informative features and evaluate the efficacy of various feature selection techniques—both unsupervised and supervised (filter, wrapper, and embedded)—in improving the classification accuracy of thyroid nodules using thermography images. Multiple machine learning models, including Support Vector Machines, Random Forest, Decision Tree, AdaBoost, and XGBoost, were assessed as classifiers utilizing group k-fold cross-validation.</div><div>Among the feature selection methods, LASSO (supervised embedding-based feature selection) showed the best performance, achieving 86% accuracy with an AUC of 0.91 for the random forest model and 86 % accuracy with an AUC of 0.92 for the XGBoost model. This research underscores the critical role of feature selection in the classification of thyroid nodules using thermography, providing valuable insights for advancing non-invasive diagnostic methodologies in thyroid assessment.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105730"},"PeriodicalIF":3.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An automatic identification method of high-temperature particle stray radiation sources

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-30 DOI: 10.1016/j.infrared.2025.105723

Yinan Wang , Xue Chen , Shurui Wang , Wanqin Fu , Chuang Sun

Infrared stray radiation adversely impacts the imaging performance of optical systems and affects detection accuracy. The suppression of stray radiation is regarded as essential in the design process of optical systems. Currently, most stray radiation analysis methods employ the Monte Carlo ray tracing approach, which is used to simulate the influence of stray radiation sources on the imaging process in a forward manner. However, for cases where the stray radiation source is unknown, such as high-temperature particles, forward simulation makes it challenging to accurately determine the location of the stray radiation source. To address this issue, a rapid tracing method is proposed in this study, which identifies stray radiation sources based on interference information obtained from the detection surface. This method combines the Monte Carlo approach with a genetic algorithm, enabling rapid determination of the location and size of stray radiation sources.

The Monte Carlo ray tracing method is applied to characterize the imaging process within the optical system. A genetic algorithm is employed to localize the particles, with the objective function defined based on specific parameters of the image, including the inner and outer diameters and the offset position. Parameter identification is then performed to determine the size and position of the particles. The identified parameters are validated by comparing the generated images with real images. Then the results show that the maximum radius error is approximately 3%, while the largest offset error is about 3.5%. Based on this automatic identification method, the stray radiation source caused by particles can be quickly and accurately identified, providing valuable support for subsequent stray radiation suppression and optical system design.

{"title":"An automatic identification method of high-temperature particle stray radiation sources","authors":"Yinan Wang , Xue Chen , Shurui Wang , Wanqin Fu , Chuang Sun","doi":"10.1016/j.infrared.2025.105723","DOIUrl":"10.1016/j.infrared.2025.105723","url":null,"abstract":"<div><div>Infrared stray radiation adversely impacts the imaging performance of optical systems and affects detection accuracy. The suppression of stray radiation is regarded as essential in the design process of optical systems. Currently, most stray radiation analysis methods employ the Monte Carlo ray tracing approach, which is used to simulate the influence of stray radiation sources on the imaging process in a forward manner. However, for cases where the stray radiation source is unknown, such as high-temperature particles, forward simulation makes it challenging to accurately determine the location of the stray radiation source. To address this issue, a rapid tracing method is proposed in this study, which identifies stray radiation sources based on interference information obtained from the detection surface. This method combines the Monte Carlo approach with a genetic algorithm, enabling rapid determination of the location and size of stray radiation sources.</div><div>The Monte Carlo ray tracing method is applied to characterize the imaging process within the optical system. A genetic algorithm is employed to localize the particles, with the objective function defined based on specific parameters of the image, including the inner and outer diameters and the offset position. Parameter identification is then performed to determine the size and position of the particles. The identified parameters are validated by comparing the generated images with real images. Then the results show that the maximum radius error is approximately 3%, while the largest offset error is about 3.5%. Based on this automatic identification method, the stray radiation source caused by particles can be quickly and accurately identified, providing valuable support for subsequent stray radiation suppression and optical system design.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105723"},"PeriodicalIF":3.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

1480 nm diode-pumped sub-kHz single-frequency Er-doped fiber laser at 1600.05 nm

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-30 DOI: 10.1016/j.infrared.2025.105743

Kaile Wang , Ping Wang , Zengrun Wen , Tian Cao , Hao Li , Ting Yang

This study successfully realized a single-frequency erbium-doped fiber laser operating at 1600.05 nm by harnessing fiber-based saturable absorber filtering effects. To mitigate adverse impacts of the fiber-based saturable absorber’s length on laser loss, threshold, and cost, suitable fiber components were meticulously selected, facilitating the achievement of both single-frequency laser output and the desired power level. Spectral and frequency analysis revealed that the resultant single-frequency fiber laser demonstrates a specific power output range, with a maximum output power exceeding 10 mW. The average linewidth, measured using the delayed self-heterodyne method, was approximately 679.8 Hz, validated by the perfect Lorentz linear signal. During one hour of stability monitoring, the wavelength and power fluctuations were observed to be 1.51 pm and 0.082 %, respectively. Furthermore, we meticulously observe and quantify the laser spectrum and power dynamics during the experiment, and contrast the outcomes of various linewidth signals. This approach offers a novel perspective for observing and expressing the parameters of narrow linewidth lasers, particularly those equipped with extended fiber cavities.

{"title":"1480 nm diode-pumped sub-kHz single-frequency Er-doped fiber laser at 1600.05 nm","authors":"Kaile Wang , Ping Wang , Zengrun Wen , Tian Cao , Hao Li , Ting Yang","doi":"10.1016/j.infrared.2025.105743","DOIUrl":"10.1016/j.infrared.2025.105743","url":null,"abstract":"<div><div>This study successfully realized a single-frequency erbium-doped fiber laser operating at 1600.05 nm by harnessing fiber-based saturable absorber filtering effects. To mitigate adverse impacts of the fiber-based saturable absorber’s length on laser loss, threshold, and cost, suitable fiber components were meticulously selected, facilitating the achievement of both single-frequency laser output and the desired power level. Spectral and frequency analysis revealed that the resultant single-frequency fiber laser demonstrates a specific power output range, with a maximum output power exceeding 10 mW. The average linewidth, measured using the delayed self-heterodyne method, was approximately 679.8 Hz, validated by the perfect Lorentz linear signal. During one hour of stability monitoring, the wavelength and power fluctuations were observed to be 1.51 pm and 0.082 %, respectively. Furthermore, we meticulously observe and quantify the laser spectrum and power dynamics during the experiment, and contrast the outcomes of various linewidth signals. This approach offers a novel perspective for observing and expressing the parameters of narrow linewidth lasers, particularly those equipped with extended fiber cavities.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105743"},"PeriodicalIF":3.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An ant interaction scheme based wrapper strategy for hyperspectral band selection

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-30 DOI: 10.1016/j.infrared.2025.105726

Kamal Deep , Bhisham Dev Verma , Manoj Thakur

Hyperspectral imaging acquires information in an extensive range of narrow and contiguous spectral bands. However, information redundancy in neighboring bands directly affects classification performance. Substantial work has been proposed to select pertinent but fewer spectral bands by employing metaheuristic-based band selection strategies. Despite encouraging results, slow convergence and susceptibility to local optima remain significant challenges for metaheuristic techniques. Additionally, the selection of optimal hyperparameters for the underlying classifier significantly influences the algorithm’s efficacy. The present work suggests a novel wrapper technique to address the band selection problem. Two strategies are employed to select optimal bands and hyperparameter simultaneously. Firstly, a novel power distribution-based ant interaction and pheromone modeling technique is utilized for information sharing and efficiently balancing localized and globalized exploration of the search space. Secondly, to maintain diversity in ant tours, a random initialization strategy is applied to those ant tours that fail to improve over successive iterations. The proposed approach also has the advantage of requiring fewer parameters compared to existing continuous ant colony optimization algorithms, thereby significantly reducing the computational effort needed for fine-tuning. Experimental results demonstrate that the proposed strategy selects fewer spectral bands without compromising classification accuracy and outperforms other metaheuristic techniques.

{"title":"An ant interaction scheme based wrapper strategy for hyperspectral band selection","authors":"Kamal Deep , Bhisham Dev Verma , Manoj Thakur","doi":"10.1016/j.infrared.2025.105726","DOIUrl":"10.1016/j.infrared.2025.105726","url":null,"abstract":"<div><div>Hyperspectral imaging acquires information in an extensive range of narrow and contiguous spectral bands. However, information redundancy in neighboring bands directly affects classification performance. Substantial work has been proposed to select pertinent but fewer spectral bands by employing metaheuristic-based band selection strategies. Despite encouraging results, slow convergence and susceptibility to local optima remain significant challenges for metaheuristic techniques. Additionally, the selection of optimal hyperparameters for the underlying classifier significantly influences the algorithm’s efficacy. The present work suggests a novel wrapper technique to address the band selection problem. Two strategies are employed to select optimal bands and hyperparameter simultaneously. Firstly, a novel power distribution-based ant interaction and pheromone modeling technique is utilized for information sharing and efficiently balancing localized and globalized exploration of the search space. Secondly, to maintain diversity in ant tours, a random initialization strategy is applied to those ant tours that fail to improve over successive iterations. The proposed approach also has the advantage of requiring fewer parameters compared to existing continuous ant colony optimization algorithms, thereby significantly reducing the computational effort needed for fine-tuning. Experimental results demonstrate that the proposed strategy selects fewer spectral bands without compromising classification accuracy and outperforms other metaheuristic techniques.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105726"},"PeriodicalIF":3.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DUSRNet: Deep Unfolding Sparse-Regularized Network for Infrared Small Target Detection

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-29 DOI: 10.1016/j.infrared.2025.105727

Lizhen Deng , Qi Liu , Guoxia Xu , Hu Zhu

In the field of Infrared Small Target Detection (ISTD), deep unfolding-based techniques have demonstrated significant efficacy. However, existing methods that utilize low-rank sparse models for ISTD task tend to heavily emphasize the global low-rank characteristic of infrared image and ignore local structural feature. To solve the challenges of complex background and low signal-noise ratio, we propose a Deep Unfolding Sparse-Regularized Network termed as DUSRNet. It intuitively, combines the powerful feature extraction capability of deep learning with the fine structure description capability of sparse regularization over infrared image background. To adaptively describe the low-rank and sparse characteristic between background and target, the sparse-regularized infrared small target detection model is seamlessly embedded into the deep neural network in a end-to-end manner. We customize adaptive background estimation module, sparse target extraction module and infrared image reconstruction module to unfold the proposed model. Extensive experimental results demonstrate that our DUSRNet achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST-Aug and IRSTD-1k datasets. Especially, compared with RPCANet,which also adopted deep unfolding method, on IRSTD-1k dataset with extremely high scene complexity and variability, the proposed method has an increase of 35.63%,19.05%,7.23% and

-

73.56% in mIoU, F1, Pd and Fa indexes, respectively.

{"title":"DUSRNet: Deep Unfolding Sparse-Regularized Network for Infrared Small Target Detection","authors":"Lizhen Deng , Qi Liu , Guoxia Xu , Hu Zhu","doi":"10.1016/j.infrared.2025.105727","DOIUrl":"10.1016/j.infrared.2025.105727","url":null,"abstract":"<div><div>In the field of Infrared Small Target Detection (ISTD), deep unfolding-based techniques have demonstrated significant efficacy. However, existing methods that utilize low-rank sparse models for ISTD task tend to heavily emphasize the global low-rank characteristic of infrared image and ignore local structural feature. To solve the challenges of complex background and low signal-noise ratio, we propose a Deep Unfolding Sparse-Regularized Network termed as DUSRNet. It intuitively, combines the powerful feature extraction capability of deep learning with the fine structure description capability of sparse regularization over infrared image background. To adaptively describe the low-rank and sparse characteristic between background and target, the sparse-regularized infrared small target detection model is seamlessly embedded into the deep neural network in a end-to-end manner. We customize adaptive background estimation module, sparse target extraction module and infrared image reconstruction module to unfold the proposed model. Extensive experimental results demonstrate that our DUSRNet achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST-Aug and IRSTD-1k datasets. Especially, compared with RPCANet,which also adopted deep unfolding method, on IRSTD-1k dataset with extremely high scene complexity and variability, the proposed method has an increase of 35.63%,19.05%,7.23% and <span><math><mo>−</mo></math></span>73.56% in mIoU, F1, Pd and Fa indexes, respectively.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"146 ","pages":"Article 105727"},"PeriodicalIF":3.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Tunable wavelength optical injection locked actively Q-switched random fiber laser based on RPS-FBG and EOM and analysis of multi pulse phenomenon

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-28 DOI: 10.1016/j.infrared.2025.105737

Chunqi Chen, Rupeng Li, Honggang Pan, Bin Li, Yichen Li, Zihong Zhao

In this paper, a tunable wavelength optical injection locked actively Q-switched random fiber laser based on random phase-shifted fiber Bragg grating (RPS-FBG) and electro-optic modulator (EOM) is proposed and experimentally verified. In the experiment, wavelength locking is achieved by injecting an external seed light from a wavelength tunable laser into the resonant cavity, resulting in a stable single-wavelength laser with a wavelength tuning range of approximately 37.4 nm. In the injection locked state, the wavelength drift of the laser is less than 0.04 nm and the power fluctuation is less than 0.14 dB within 1 h, which has good stability. An EOM is added to introduce actively Q-switched. When the pump power increases from 50mW to 350mW, single pulse to three pulses can be generated. The proposed multi-pulse injection-locked random fiber laser is of great significance for the research of wavelength selection and pulse coding in disordered systems.

引用次数: 0

PPGS-YOLO: A lightweight algorithms for offshore dense obstruction infrared ship detection

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-27 DOI: 10.1016/j.infrared.2025.105736

Yong Wang, Bairong Wang, Yunsheng Fan

With the increasing number of ships at sea, ship monitoring has become increasingly important. However, traditional visible light detection technologies are limited in various environments, particularly in low-light or adverse weather conditions. In contrast, infrared-based ship detection technology performs well under low light and harsh weather conditions, making it an effective alternative. However, most infrared-based ship detection methods currently focus primarily on improving detection accuracy, often at the cost of significant computational resources. To address this issue, this paper proposes a lightweight infrared ship detection algorithm, specifically designed for near-coast applications. We combine the PP-LCNet backbone network with YOLOv5, effectively reducing the model’s parameter count and computational load. Additionally, we introduce a convolution operation suitable for mobile devices, GSConv, to further enhance the algorithm’s computational efficiency, achieving higher performance without compromising accuracy. In the face of frequent ship occlusion in near-coast dense scenarios, we employ the Soft-NMS technique, significantly improving the algorithm’s target detection ability in such environments. Finally, the improved algorithm in this paper achieved a 2.4 % increase in mAP_0.50:0.95 on the dataset, while reducing Flops by 4G and parameters by 1.63 M. The effectiveness of the improved algorithm is verified through extensive experiments.

{"title":"PPGS-YOLO: A lightweight algorithms for offshore dense obstruction infrared ship detection","authors":"Yong Wang, Bairong Wang, Yunsheng Fan","doi":"10.1016/j.infrared.2025.105736","DOIUrl":"10.1016/j.infrared.2025.105736","url":null,"abstract":"<div><div>With the increasing number of ships at sea, ship monitoring has become increasingly important. However, traditional visible light detection technologies are limited in various environments, particularly in low-light or adverse weather conditions. In contrast, infrared-based ship detection technology performs well under low light and harsh weather conditions, making it an effective alternative. However, most infrared-based ship detection methods currently focus primarily on improving detection accuracy, often at the cost of significant computational resources. To address this issue, this paper proposes a lightweight infrared ship detection algorithm, specifically designed for near-coast applications. We combine the PP-LCNet backbone network with YOLOv5, effectively reducing the model’s parameter count and computational load. Additionally, we introduce a convolution operation suitable for mobile devices, GSConv, to further enhance the algorithm’s computational efficiency, achieving higher performance without compromising accuracy. In the face of frequent ship occlusion in near-coast dense scenarios, we employ the Soft-NMS technique, significantly improving the algorithm’s target detection ability in such environments. Finally, the improved algorithm in this paper achieved a 2.4 % increase in mAP<sub>0.50:0.95</sub> on the dataset, while reducing Flops by 4G and parameters by 1.63 M. The effectiveness of the improved algorithm is verified through extensive experiments.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105736"},"PeriodicalIF":3.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

High performance 640 × 512 long-wavelength superlattice infrared focal plane array detector based on InAs substrate

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-26 DOI: 10.1016/j.infrared.2025.105734

Zhi Jiang, Xu-chang Zhou, Qiu-si Peng, Xiao-hong Lei, Chun-zhang Yang, Bi-wen Duan, Hai-peng Wang, Gong-rong Deng, Yan-hui Li, Jin-cheng Kong

Long-wavelength infrared (LWIR) type-II superlattice (T2SL) photodetector with a double barrier structure and n-type absorber is developed on InAs substrates. The epi-layer of the detector with this structure has a flat surface and excellent crystal quality. The dark current density of the single-diodes remains below 1 × 10^-4 A/cm⁻² within the bias range of −0.1–−0.5 V at 77 K. Due to the low dark current density, focal plane arrays (FPAs) are fabricated and evaluated. The FPA has a format of 640 × 512 with a pixel pitch of 15 μm, and exhibit a 50 % cutoff wavelength of 9.3 μm. The noise equivalent temperature difference (NETD) and operability are 20 mK and 99.6 % respectively under an optical aperture of F/2.0 and an integration time of 700 μs at 77 K, at the same time, the response non-uniformity is 4.7 %.

引用次数: 0

Estimation models for maize leaf water content at various stages using near-infrared spectroscopy

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-24 DOI: 10.1016/j.infrared.2025.105732

Yi Ren, Wang Zhang, Huiting Wang, Zhao Zhang, Wenyi Sheng, Ruicheng Qiu, Man Zhang

The leaf water content (LWC) of maize is crucial for its photosynthesis and overall growth. Accurate and non-destructive estimation of LWC can enhance the monitoring of growth status and optimize field management practices. In this study, spectral data in the range of 900–1700 nm from maize leaves at various positions and growth stages were used to identify the optimal measurement method and develop models for quantitative estimation of LWC. Principal component analysis, competitive adaptive reweighted sampling, and successive projections algorithms were employed to extract effective wavelengths. These wavelengths were then combined with partial least squares regression and support vector regression algorithms to build quantitative models for LWC estimation and to determine the optimal measurement position on the leaves. In addition, a deep learning model based on a multilayer perceptron (MLP) and permutation importance (PI) method was developed. The results indicated that the middle of maize leaves is the optimal position for LWC estimation. The MLP-PI model using the extracted 12 wavelengths, achieved a coefficient of determination of 0.91 and a root mean square error of 1.23 %, respectively. This study demonstrates that the middle position of maize leaves, when combined with the near-infrared spectra and deep learning techniques, provides a robust approach for non-destructive LWC estimation.

{"title":"Estimation models for maize leaf water content at various stages using near-infrared spectroscopy","authors":"Yi Ren, Wang Zhang, Huiting Wang, Zhao Zhang, Wenyi Sheng, Ruicheng Qiu, Man Zhang","doi":"10.1016/j.infrared.2025.105732","DOIUrl":"10.1016/j.infrared.2025.105732","url":null,"abstract":"<div><div>The leaf water content (LWC) of maize is crucial for its photosynthesis and overall growth. Accurate and non-destructive estimation of LWC can enhance the monitoring of growth status and optimize field management practices. In this study, spectral data in the range of 900–1700 nm from maize leaves at various positions and growth stages were used to identify the optimal measurement method and develop models for quantitative estimation of LWC. Principal component analysis, competitive adaptive reweighted sampling, and successive projections algorithms were employed to extract effective wavelengths. These wavelengths were then combined with partial least squares regression and support vector regression algorithms to build quantitative models for LWC estimation and to determine the optimal measurement position on the leaves. In addition, a deep learning model based on a multilayer perceptron (MLP) and permutation importance (PI) method was developed. The results indicated that the middle of maize leaves is the optimal position for LWC estimation. The MLP-PI model using the extracted 12 wavelengths, achieved a coefficient of determination of 0.91 and a root mean square error of 1.23 %, respectively. This study demonstrates that the middle position of maize leaves, when combined with the near-infrared spectra and deep learning techniques, provides a robust approach for non-destructive LWC estimation.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105732"},"PeriodicalIF":3.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

1 × 2 MMI teardrop quantum cascade lasers with a high SMSR up to 45 dB

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-23 DOI: 10.1016/j.infrared.2025.105731

Jin-Lin Yao , Jin-Chuan Zhang , Feng-Min Cheng , Shan Niu , Yong-Qiang Sun , Ning Zhuo , Li-Jun Wang , Feng-Qi Liu

Single-mode lasing teardrop quantum cascade lasers (QCLs) at λ ∼ 8.3 μm with a 1 × 2 multimode interferometer (MMI) have been fabricated. At room temperature, the laser exhibits a peak optical power greater than 1 W from single cleaved facet operated in pulsed mode. A very high side mode suppression ratio (SMSR) of 45 dB under the condition of large driving pulse width was achieved due to the Vernier effect of the coupled cavity. Single-mode emission can be maintained when the injection current changing up to 2.4 times the threshold.

引用次数: 0

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Infrared Physics & Technology

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