Infrared Physics & Technology最新文献

Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations 打破高光谱目标检测中的维度障碍：阿特罗斯卷积与格拉米安角场表示法

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-13 DOI: 10.1016/j.infrared.2024.105623

Hongzhou Wang , Yulei Wang , Yuchao Yang , Enyu Zhao , Jian Zeng

{"title":"Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations","authors":"Hongzhou Wang , Yulei Wang , Yuchao Yang , Enyu Zhao , Jian Zeng","doi":"10.1016/j.infrared.2024.105623","DOIUrl":"10.1016/j.infrared.2024.105623","url":null,"abstract":"<div><div>Hyperspectral images contain extensive spectral bands with rich spectral information that reflects object properties. Leveraging state-of-the-art deep learning techniques has proven to be effective in hyperspectral target detection. However, compared to two-dimensional matrix data, the one-dimensional nature of spectral sequence limits the information that can be extracted, posing a challenge for deep learning-based hyperspectral target detection methodologies. To address this issue, a novel hyperspectral target detection method employing atrous convolution with gramian angular field representations is proposed in this paper. This approach breaks the barrier between one-dimensional vector and two-dimensional matrix by gramian angular field, transforming the spectral sequences from one-dimensional vectors into two-dimensional matrices, enabling the exploration of multidimensional relationships within spectral band relations through an atrous convolution-based spectral feature extraction network. The proposed model transcends the traditional one-dimensional spectral target detection limitations, offering a new perspective for spectral-based hyperspectral target detection. Experimental results on four real-world hyperspectral datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in detection performance, showcasing its potential for advancing hyperspectral target detection.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105623"},"PeriodicalIF":3.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655772","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

Multi-Scale convolutional neural network for finger vein recognition 用于手指静脉识别的多尺度卷积神经网络

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-12 DOI: 10.1016/j.infrared.2024.105624

Junbo Liu, Hui Ma, Zishuo Guo

{"title":"Multi-Scale convolutional neural network for finger vein recognition","authors":"Junbo Liu, Hui Ma, Zishuo Guo","doi":"10.1016/j.infrared.2024.105624","DOIUrl":"10.1016/j.infrared.2024.105624","url":null,"abstract":"<div><div>With the continuous advancement of science and technology, an increasing number of deep learning methods are being applied in the field of finger vein recognition to describe the structural characteristics of finger veins. However, some deep learning methods fail to adequately extract longer texture features. during the feature extraction process, resulting in a decrease in the uniqueness of extracted finger vein features. Additionally, these methods tend to extract global information while neglecting the importance of local texture information. To address the aforementioned issues, this paper introduces a multiscale convolution network (MCNet) model based on finger vein structure. On one hand, a multiscale feature extraction (MFE) model based on the rectangular and square convolution kernels are employed to extract structural information from finger veins and to simultaneously enhance the features of longer texture features. On the other hand, the paper introduces a cross-information fusion attention (CFA) block that combines spatial and channel information, in order to enhance local details information and the network’s ability to extract vein patterns. The experimental results on the public datasets FV-USM, SDUMLA-HMT, and HKPU validate the effectiveness of MCNet with the recognition rates of 99.86%, 99.11%, and 99.15% respectively.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105624"},"PeriodicalIF":3.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655769","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

Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder 使用堆叠去噪卷积自动编码器进行时间去噪和深度特征学习，以增强热成像中的缺陷检测能力

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-10 DOI: 10.1016/j.infrared.2024.105612

Naga Prasanthi Yerneni , V.S. Ghali , G.T. Vesala , Fei Wang , Ravibabu Mulaveesala

{"title":"Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder","authors":"Naga Prasanthi Yerneni , V.S. Ghali , G.T. Vesala , Fei Wang , Ravibabu Mulaveesala","doi":"10.1016/j.infrared.2024.105612","DOIUrl":"10.1016/j.infrared.2024.105612","url":null,"abstract":"<div><div>Thermal wave imaging uses the temporal temperature distribution over the object’s surface for subsurface analysis. However, the noise generated during experimentation corrupts this temporal history and hampers the detection of defect signatures. As denoising of the temporal thermal history enhances the defect detectability, this study offers a Stacked Denoising Convolution Autoencoder (SDCAE) in frequency-modulated thermal wave imaging with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features resulting in improved defect signs. Experimental results on mild steel and carbon fiber reinforced polymer specimens with different sizes of defects at various depths demonstrate that integrating temporal denoising and deep feature learning techniques into a single novel framework significantly improved defect detectability. In addition, defect signal-to-noise ratios of the denoised thermal data and latent space of the proposed model compared to conventional autoencoder and dimensionality reduction techniques recommend the superiority of the proposed method.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105612"},"PeriodicalIF":3.1,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655768","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

Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging 基于优化深度神经网络和高光谱成像的红茶发酵质量检测

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-09 DOI: 10.1016/j.infrared.2024.105625

Minghao Huang , Yu Tang , Zhiping Tan , Jinchang Ren , Yong He , Huasheng Huang

{"title":"Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging","authors":"Minghao Huang , Yu Tang , Zhiping Tan , Jinchang Ren , Yong He , Huasheng Huang","doi":"10.1016/j.infrared.2024.105625","DOIUrl":"10.1016/j.infrared.2024.105625","url":null,"abstract":"<div><div>The quality of black tea significantly relies on its fermentation process. Nevertheless, achieving precise and objective evaluations remains challenging due to the subjective nature of manual judgment involved in quality monitoring. To address this problem, hyperspectral imaging combined with the deep learning algorithms are proposed to identify the fermentation quality of black tea. Firstly, the hyperspectral data of Yinghong No. 9 black tea during five fermentation time intervals within 0–5 h are collected. Then, the Support Vector Machine (SVM), Artificial Neural Network (ANN), Partial Least Squares Discriminant Analysis (PLS-DA), and Naive Bayesian (NB) are used to construct black tea fermentation quality detection models based on full spectrum and selected spectrum data. Furthermore, deep learning algorithms including the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Swarm Optimization (PSO) optimized CNN-LSTM (PSO-CNN-LSTM) are also used to build the detection model using the spectral images. The experimental results indicate that deep learning algorithms have obvious advantages over traditional machine learning algorithms in tea fermentation quality detection. Besides, the PSO-CNN-LSTM model shows the best classification performance compared to other algorithms and achieves an accuracy of 96.78% on the test set. This study demonstrates the significant potential of combining deep learning with hyperspectral imaging for predicting black tea fermentation quality. This provides a new approach for effective monitoring of the black tea fermentation process and a useful reference for other applications in similar fields.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105625"},"PeriodicalIF":3.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655766","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

Hyperspectral and multispectral images fusion based on pyramid swin transformer 基于金字塔斯温变换器的高光谱和多光谱图像融合

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-07 DOI: 10.1016/j.infrared.2024.105617

Han Lang , Wenxing Bao , Wei Feng , Kewen Qu , Xuan Ma , Xiaowu Zhang

{"title":"Hyperspectral and multispectral images fusion based on pyramid swin transformer","authors":"Han Lang , Wenxing Bao , Wei Feng , Kewen Qu , Xuan Ma , Xiaowu Zhang","doi":"10.1016/j.infrared.2024.105617","DOIUrl":"10.1016/j.infrared.2024.105617","url":null,"abstract":"<div><div>Remote sensing image fusion aims to generate a high spatial resolution hyperspectral image (HR-HSI) by integrating a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI). While Convolutional Neural Networks (CNNs) have been widely employed in addressing the HSI-MSI fusion problem, their limited receptive field poses challenges in capturing global relationships within the feature maps. On the other hand, the computational complexity of Transformers hinders their application, especially in dealing with high-dimensional data like hyperspectral images (HSIs). To overcome this challenge, we propose an HSI-MSI fusion method based on the Pyramid Swin Transformer (PSTF). The pyramid design of the PSTF effectively extracts multi-scale information from images. The Spatial–Spectral Crossed Attention (SSCA) module, comprising the Cross Spatial Attention (CSA) and the Spectral Feature Integration (SFI) modules. The CSA module employs a cross-shaped self-attention mechanism, providing greater modeling flexibility for different spatial scales and non-local structures compared to traditional convolutional layers. Meanwhile, the SFI module introduces a global memory block (MB) to select the most relevant low-rank spectral vectors, integrating global spectral information with local spatial–spectral correlation to better extract and preserve spectral information. Additionally, the Separate Feature Extraction (SFE) module enhances the network’s ability to represent image features by independently processing positive and negative parts of shallow features, thus capturing details and structures more effectively and preventing the vanishing gradient problem. Compared with the state-of-the-art (SOTA) methods, experimental results demonstrate the effectiveness of the PSTF method.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105617"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655770","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

Small aircraft detection in infrared aerial imagery based on deep neural network 基于深度神经网络的红外航空图像中的小型飞机探测

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-04 DOI: 10.1016/j.infrared.2024.105454

Kai Zhang , Xiaotian Wang , Shaoyi Li , Bingyi Zhang

{"title":"Small aircraft detection in infrared aerial imagery based on deep neural network","authors":"Kai Zhang , Xiaotian Wang , Shaoyi Li , Bingyi Zhang","doi":"10.1016/j.infrared.2024.105454","DOIUrl":"10.1016/j.infrared.2024.105454","url":null,"abstract":"<div><div>Detection of aerial target is an important part of infrared image processing. Both neural network method and traditional method can be used in infrared object detection. Neural network method has many advantages such as high accuracy and good portability compared with traditional object detection method. Since the features extracted by neural network method can change over detection target, automatic feature extraction makes neural network based detection method more effective. In recent years deep learning method has been also found wide use for object detection in images. In this paper, an object detection model based on the deep learning network YOLO is constructed for solving the infrared aircraft detection problem. We construct the dataset used for training and testing with recognized features being iteratively learned. The task of infrared object detection is sensitive to model size and detection speed. There is a requirement of using quantization method to reduce the storage space and the computation complexity. We propose a quantized model with appropriate accuracy for infrared object detection task. To solve the detection task for multiple extremely small aircrafts, model adjustment and quantization are used in proposed model and it gets a better performance. Experimental results on the constructed dataset show that the storage space for weight after quantization shrinks to a quarter, and there is no precision loss for extremely small aircrafts compared to the original model. The optimized YOLO-based deep learning model is effective to detect the small aircraft target in infrared aerial imagery.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105454"},"PeriodicalIF":3.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655771","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

Improvement of mid-wavelength InAs/InAsSb nBn infrared detectors performance through interface control 通过接口控制提高中波长 InAs/InAsSb nBn 红外探测器的性能

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-03 DOI: 10.1016/j.infrared.2024.105619

Ye Zhang , Yifan Shan , Faran Chang , Yan Liang , Xiangyu Zhang , Guowei Wang , Donghai Wu , Dongwei Jiang , Hongyue Hao , Yingqiang Xu , Haiqiao Ni , Dan Lu , Zhichuan Niu

引用次数: 0

Optimization and effect comparison of typical gas pressure compensation model in chemical industry park 化工园区典型气体压力补偿模型的优化与效果比较

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-03 DOI: 10.1016/j.infrared.2024.105621

Fuchao Tian , Xinyu Xiang , Lejing Qin , Jiliang Huang , Bo Tan

{"title":"Optimization and effect comparison of typical gas pressure compensation model in chemical industry park","authors":"Fuchao Tian , Xinyu Xiang , Lejing Qin , Jiliang Huang , Bo Tan","doi":"10.1016/j.infrared.2024.105621","DOIUrl":"10.1016/j.infrared.2024.105621","url":null,"abstract":"<div><div>In chemical parks, the leakage of harmful gases can lead to poisoning and even explosions. Therefore, its monitoring and leakage warnings are crucial. This paper takes the harmful gases CO<sub>2</sub>, CH<sub>4</sub>, and CO as examples, and set up an infrared gas sensor pressure compensation experimental platform to carry out experiments, to solve the problem of decreased detection accuracy of gas sensors due to changes in ambient pressure during detection. The pressure compensation experimental ranges of the gas sensors are 0 ∼ 5 %, 0 ∼ 20 %, and 0 ∼ 1000 ppm, and the maximum absolute errors of the infrared gas test data obtained under different concentrations and pressures are 0.24 ∼ 0.67, 0.89 ∼ 1.12, and 45 ∼ 60 ppm, respectively. The pressure compensation model based on the least squares method was constructed, and the maximum absolute errors were obtained as 0.08 ∼ 0.19, 0.13 ∼ 0.64, and 24 ∼ 37 ppm, respectively. The pressure compensation model based on the GA-BP neural network was constructed, and the maximum absolute errors were 0.04 ∼ 0.10, 0.08 ∼ 0.10, and 0.60 ∼ 8.30 ppm, respectively. The GA-BP neural network combines the genetic algorithm and the backpropagation algorithm, which can better deal with nonlinear problems. The comparison of these two models reflects the superiority of the GA-BP neural network model in the compensation effect. The establishment of the neural network pressure compensation model optimized by the genetic algorithm can effectively improve the detection accuracy of the gas sensor, and it is expected that the results are of great practical significance to guarantee production safety and protect the environment in the enterprise chemical park.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105621"},"PeriodicalIF":3.1,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655763","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

Intermediate state between steady and breathing solitons in fiber lasers 光纤激光器中介于稳定和呼吸孤子之间的中间状态

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-02 DOI: 10.1016/j.infrared.2024.105622

Zichuan Yuan , Qiang Ling , Ke Dai , Si Luo , Chenning Tao , Lin Huang , Jiantao Dong , Zuguang Guan , Yusheng Zhang , Daru Chen , Yudong Cui

{"title":"Intermediate state between steady and breathing solitons in fiber lasers","authors":"Zichuan Yuan , Qiang Ling , Ke Dai , Si Luo , Chenning Tao , Lin Huang , Jiantao Dong , Zuguang Guan , Yusheng Zhang , Daru Chen , Yudong Cui","doi":"10.1016/j.infrared.2024.105622","DOIUrl":"10.1016/j.infrared.2024.105622","url":null,"abstract":"<div><div>Ultrafast fiber laser, a vital tool in both science and industry, exhibits two distinct pulse states: the steady soliton (SS) and the breathing soliton (BS). While these states have been extensively studied individually, understanding the complex transition between them is crucial for controlling lasing states effectively. Herein, our experimental observations reveal an intermediate state that toggles between SS and BS, enabled by the dispersive Fourier transform technique. We find that energy hop and decaying breathing processes, driven respectively by the energy quantization effect and Q-switched modulation, govern this transition. Additionally, we observe that the transition between different BS states primarily involves a pure decaying breathing process. Numerical simulations are used to generate similar transition dynamics in a model that combines equations describing the population inversion in a mode-locked laser. This study sheds light on the transition dynamics in non-equilibrium systems, offering insights for intelligently manipulating lasing states.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105622"},"PeriodicalIF":3.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572615","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

Improving the thermochromic performance of VO2 films by embedding Cu-Al nanoparticles as heterogeneous nucleation cores in the VO2/VO2 bilayer structure 通过在 VO2/VO2 双层结构中嵌入铜铝纳米粒子作为异质成核核心，提高 VO2 薄膜的热变色性能

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-11-02 DOI: 10.1016/j.infrared.2024.105620

Haitao Zong , Zhiguo Liu , Ming Li , Houchang Chen , Xinchun Tao , Yuehong Yin , Wei Wang , Cong Zhang , Wentao Qiao , Lingling Yan , Bai Sun

{"title":"Improving the thermochromic performance of VO2 films by embedding Cu-Al nanoparticles as heterogeneous nucleation cores in the VO2/VO2 bilayer structure","authors":"Haitao Zong , Zhiguo Liu , Ming Li , Houchang Chen , Xinchun Tao , Yuehong Yin , Wei Wang , Cong Zhang , Wentao Qiao , Lingling Yan , Bai Sun","doi":"10.1016/j.infrared.2024.105620","DOIUrl":"10.1016/j.infrared.2024.105620","url":null,"abstract":"<div><div>VO<sub>2</sub>-based films show great potential applications in thermochromic smart windows. However, enhancing luminous transmittance (<em>T<sub>lum</sub></em>) while maintaining high solar modulation ability (<em>ΔT<sub>sol</sub></em>) remains a formidable challenge. Here, we present a novel VO<sub>2</sub>/Cu-Al nanoparticles (NPs)/VO<sub>2</sub> composite film structure, seamlessly integrating Cu-Al bimetallic NPs within VO<sub>2</sub> films by pulsed laser deposition on alkali-free glass substrates. The content of Cu-Al NPs in the composite films is controlled by the pulse number (<em>N<sub>p</sub></em>) applied to the Cu-Al alloy target. X-ray diffraction results indicate that the crystallinity of VO<sub>2</sub> films is significantly enhanced by the incorporation of an appropriate amount of Cu-Al NPs. The SEM characterization results revealed that the particle size of VO<sub>2</sub> composite films initially increases to approximately 131 nm and subsequently decreases to around 120 nm as <em>N<sub>p</sub></em> increases, with a concurrent transition in particle shape from quasi-circular to elongated. The <em>T<sub>lum</sub></em> and <em>ΔT<sub>sol</sub></em> of the resulting composite films were dramatically improved to 71.6 % and 9.5 %, respectively, when <em>N<sub>p</sub></em> was 300. These enhanced thermochromic properties are attributed to the localized surface plasmon resonance (LSPR) of the VO<sub>2</sub> particles. This research opens up a promising avenue for the convenient production of customized high-quality VO<sub>2</sub> films tailored for smart window applications.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"143 ","pages":"Article 105620"},"PeriodicalIF":3.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593302","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