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Copyright of Iraqi Journal of Physics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Infrared detection technology has important applications in laser ranging, imaging, night vision, and other fields. Furthermore, recent studies have proven that hot carriers which are generated by surface plasmon decay can be exploited for photodetection to get beyond semiconductors' bandgap restriction. In this study, silicon nanopillars (NPs) and gold film at the top and bottom of silicon nanopillars were designed to generate surface plasmon resonance and Fabry–Perot resonance to achieve perfect absorption. The absorption was calculated using the Finite Difference Time Domain (FDTD) method, and factors' effects on resonance wavelength and absorption were examined. Here we demonstrate how this perfect absorber can be used to achieve near-unity optical absorption using ultrathin plasmonic nanostructures with thicknesses of 15 nm, smaller than the hot electron diffusion length. Further study revealed that the resonance wavelength can be redshifted to the mid-infrared band (e.g., 3.75 μm) by increasing the value of the structure parameters. These results demonstrate a success in the study of polarization insensitivity, detection band adjustable, and efficient perfect absorption infrared photodetectors. [ABSTRACT FROM AUTHOR]

Infrared camouflage technology has received considerable attention for escaping the tracking by an infrared detector. The phase-changing material Ge2Sb2Te5 (GST) has widely been utilized to design dynamic spectral regulation. Herein, we propose a planar selective emitter based on the GST with different phases to simultaneously realize infrared camouflage and radiative heat dissipation. The fabricated emitter demonstrates a low emissivity of 32.6% inside the atmospheric windows and a high emissivity of 67.8% outside the atmospheric windows due to the formed quasi-optical cavity of the GST and the metal Ni layer in the structure. Moreover, it also has the potential to be used in different conditions by controlling the emission peak position. The facile process technique allows scalable production and large-scale deployment of the proposed emitter. [ABSTRACT FROM AUTHOR]

Infrared small-target detection is a key technology for the infrared search and track system (IRST), but some problems still exist, such as false detections in complex backgrounds and clutter. To solve these problems, a novel image patch tensor (IPT) model for infrared small-target detection is proposed. First, to better estimate the background component, we utilize the Laplace operator to approximate the background tensor rank. Secondly, we combined local gradient features and highlighted area indicators to model the local targets prior, which can effectively suppress the complex background clutter. The proposed model was solved by the alternating direction method of multipliers (ADMM). The experimental results on various scenes show that our model achieves an excellent performance in suppressing strong edge clutter and estimating small targets. [ABSTRACT FROM AUTHOR]

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the semiconductor films, titanium nitride (TiN) film is a promising candidate for infrared stealth technology. This study investigated the effect of substrate bias voltage on the infrared characteristics of TiN film. TiN films were prepared using direct current reactive magnetron sputtering with various substrate bias voltages ranging from 0 V to −150 V. It was revealed that when substrate bias voltage increased from 0 V to −120 V, the resistivity and infrared emissivity of TiN films increased first and then decreased. As substrate bias voltage continued to increase from −120 V to −150 V, the resistivity and infrared emissivity increased. The infrared emissivity of TiN films showed the lowest value of 0.48 at substrate bias voltage of −120 V. [ABSTRACT FROM AUTHOR]

This work reports on recent advancements in the development of the Hg1−xCdxTe linear-mode, electron-initiated avalanche photodiode at Leonardo DRS. These detectors exhibit single-photon sensitivity from the visible to mid-wave infrared and have enabled several new NASA space LiDAR applications. In this paper, we first report on the issue of mitigating dark counts due to "ROIC glow." By introducing a revised light-barrier in our detector design, we demonstrate a near factor of 4 reduction in glow-induced dark counts. We further present direct measurements of excess noise factor and describe post-analysis models that accurately extract this quantity from focal plane array performance data. All methods consistently reveal excess noise factors well below the McIntyre limit. Gain measurements are conducted on a smaller-format avalanche photodiode array and reveal gains of ~6100 (measured at a reverse bias of 14.9 V, operating temperature of 84 K and using a 1.55 µm illumination source), which is a new record for the mid-wave infrared technology at Leonardo DRS. Additional performance metrics such as false-event rate, photon-detection efficiency and timing jitter are presented for larger-format arrays (i.e. arrays with a greater number of pixels). We conclude with an examination of a non-fundamental, injection contact-induced breakdown phenomenon. Experimental data and modeling reveal that this breakdown is dependent on the gain and the photon signal level, and unrelated to classical avalanche breakdown. [ABSTRACT FROM AUTHOR]

Within the theoretical framework of computational prediction, this study introduces a novel tetrapodal Cd 0.9 Zn 0.1 S/NiB (CZS/NiB) Schottky heterojunction material, which demonstrates exceptional photothermal effects and remarkable photocatalytic properties. Notably, under visible light irradiation for 3 h, the photocatalytic hydrogen evolution activity of CZS/NiB-3 % surpasses that of pure CZS by a factor of 10.52. This outstanding photocatalytic hydrogen production performance stems from the successful construction of the CZS/NiB-3 % Schottky heterojunction with promoted the separation and transfer of photogenerated charge carriers and excellent photothermal effect. Additionally, the synthesized CZS/NiB exhibits high photostability and cycling stability. The favorable photothermal effect of the synthesized photocatalyst was experimentally validated through infrared thermography technology. Through in-depth computational investigations using density functional theory, the pathways of charge transfer in the Schottky heterojunction are validated. This study provides a feasible strategy for research on photocatalytic water splitting for hydrogen evolution. • The photocatalytic hydrogen evolution rate reached 143.84 mmol g−1 h−1. • Schottky heterojunction was confirmed by calculation and experimental. • The synergetic effects of photothermal and heterojunction were investigated. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. Currently, the medical diagnosis of SLE mainly relies on the clinical experience of physicians, and there is no universally accepted objective method for diagnosing SLE. Therefore, there is an urgent need to design an intelligent approach to accurately diagnose SLE to assist physicians in formulating appropriate treatment plans. With the rapid development of intelligent medical diagnostic technology, medical data is becoming increasingly multimodal. Multimodal data fusion can provide richer information than single-modal data, and the fusion of multiple modalities can effectively enhance the richness of data features to improve modeling performance. In this paper, a cross-modal specific transfer fusion technique based on infrared spectra and metabolomics is proposed to effectively integrate infrared spectra and metabolomics by fully exploiting the intrinsic relationships between features across different modalities, thus achieving the diagnosis of SLE. In this research, a Decision Level Fusion module is also proposed to fuse the representations of two specific transfers further, obtaining the final prediction scores. Comprehensive experimental results demonstrate that the proposed method significantly improves the performance of SLE prediction, with accuracy and Area Under Curve (AUC) reaching 94.98 % and 97.13 %, respectively, outperforming existing methods. Our framework effectively integrates infrared spectra and metabolomics to achieve a more accurate prediction of SLE. Our research indicates that prediction methods based on different modalities outperform those using single-modality data. The Cross-modal Specific Transfer Fusion module effectively captures the complex relationships within each single modality and models the complex relationships between different modalities. [Display omitted] • This study propose a cross-modal specific transfer fusion technique. • This study integrates infrared spectra and metabolomics to successfully diagnose SLE. • Propose a Fusion module to fuse representations of two specific transfers further. [ABSTRACT FROM AUTHOR]

[Display omitted] • The tank sludge measurement experiment system is designed and built. • Measurement of sludge deposits in storage tanks. • Prediction method of sludge based on Three-Dimensional Residue Recovery Algorithm. • Identification of sludge boundary in threshold segmentation method. • The error of the proposed method is within 5% in the experiments. Non-contact sludge measurement methods for storage tanks can address the challenge of measuring the volume of sedimented sludge during long-term storage. While infrared thermography technology can address the issue of liquid level detection, its measurement accuracy for the undulating interface of sludge is insufficient. This study designed and constructed experimental setups for measuring sludge in storage tanks. In this study, infrared images taken by an infrared camera were used to record the temperature distribution of the outer wall of the storage tank. The threshold segmentation method is used to determine the accurate sludge boundary line in image processing. Finally, the Three-Dimensional Tank Residue Recovery Algorithm (3D-TRRA) was applied to fit the 3D distribution of the sludge and calculate accurate sludge volumes. The results indicate that the best segmentation is achieved with a threshold of 170. The measurement error for sludge volume is less than 5%. Accurate visual positioning and recognition of sludge are achieved. [ABSTRACT FROM AUTHOR]

• Thermal imaging (TI) is emerging in the postharvest of fruit and vegetables (FV). • Proper temperature monitoring tools improve product quality and reduce food waste. • TI is essential in temperature monitoring of preharvest and postharvest of FV. • TI, combined with other technologies, aids in evaluating quality attributes of FV. Fruit and vegetables (FV) are susceptible to adverse physiological and biochemical changes due to temperature abuse during preharvest and postharvest handling. Rapid, reliable, and non-invasive technologies such as infrared (IR) cameras are new, cost-effective tools that can record temperature profiles of FV in real-time. In this article, the basic principles of thermal imaging (TI) technology and its recent applications in preharvest and postharvest handling of FV as a temperature monitoring tool are concisely highlighted. Detection of preharvest and postharvest quality of FV (wetness, immaturity, bruise, etc.) based on thermal images are also summarized. Additionally, different modeling and simulation methods, including statistical modeling, computer simulation, machine learning (MLR), and deep learning (DLR), are briefly introduced into the preharvest and postharvest handling of FV, and applications of TI technique as a temperature validation tool in these methods are also discussed. This review article summarized that non-contact TI technique exhibits great potential to monitor temperature profiles on the field and during the postharvest handling of FV in real-time. Quality attributes, such as sunburn, immaturity, and bruises in FV, can be detected by using thermal images. Therefore, yields can be increased in the field, and food losses reduced during the postharvest handling of FV using TI technology. Furthermore, TI technology is an effective validation tool for simulation studies of foods. Future research should focus on developing low-cost TI systems and integrating multiple imaging techniques to facilitate the decision-making process in the FV industry. [ABSTRACT FROM AUTHOR]

• We conducted channel selection for the retrieval of CH 4 profile using Atmospheric Sounder Spectrometer by Infrared Spectral Technology, demonstrating the instrument's capability to detect CH 4 profile. • The method of information-weighted channel selection is proposed, which quantifies the impact of interfering factors such as environment and instrument noise. • We evaluated the anti-interference ability of the selected channels using simulated spectra under clear sky conditions. Methane (CH 4) is the second-largest greenhouse gas contributing to global warming, surpassed only by CO 2 , has a large difference in its vertical concentration distribution, and closely affects the global environment and climate change. The variations in the vertical concentrations of CH 4 need to be monitored. Ground-based infrared hyperspectrometers can measure the fine variations of the CH 4 concentrations in the vertical distribution within the planetary boundary layer (PBL). However, different detection channels are easily affected by instrumental noise and other environmental factors, leading to differences in the channel spectral characteristics and thereby affecting the accuracy of the CH 4 profile retrieval. In this study, an information-weighted channel selection method is proposed for the CH 4 profile retrieval using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) to address the differences in the channel characteristics from the different interference factors. This method leverages the information content of CH 4 and its environmental interference factors in each channel to derive the weighting factors, and a comprehensive weighting approach is subsequently applied to ascertain the effective information content of CH 4. The method then establishes the threshold for the effective CH 4 information content, considering the influence of noise, to select the optimal channels. We employ this method in our study, and 22 channels are selected as the optimal channels for the CH 4 profile retrieval. We also evaluate the retrieval capability of the CH 4 profile and the anti-interference ability of the selected channels using simulated spectra under clear-sky conditions. When retrieving the CH 4 profile using the 1200–1390 cm−1 band (394 channels in total), the CH 4 profile is mainly affected by temperature, water vapor, aerosol optical depth (AOD) and N 2 O. In addition, the mean absolute error (MAE) and the root mean square error (RMSE) for the CH 4 profile retrieval using the selected channels are substantially reduced. [ABSTRACT FROM AUTHOR]

We employed Fourier infrared spectroscopy, laser infrared technology, and stable isotope analysis to investigate the relationships between characteristics of ingested plastics and size-related feeding ecology of a mesopelagic predator, longnose lancetfish Alepisaurus ferox. Plastics were detected in 81.48 % of specimens, up to 8.81 ± 8.29 items/individual and 0.59 ± 0.66 items/g wet weight of intestine, and were sized 20.00 μm to 6.50 cm. The majority were granules and fragments in shape, and polystyrene and acrylate copolymer in polymer type. The diversity indices exhibited a correlation with trophic position and body size groups, emphasizing that the ontogenetic dietary shift of A. ferox may influence the diversity of plastics ingested. This study provides new insights into the plastic pathways linking epipelagic and mesopelagic food webs and demonstrates that biochemical ecological tracers can effectively indicate the bioavailability of plastic correlated with growth in mesopelagic predator. • Plastics are highly bioavailable to mesopelagic predator Alepisaurus ferox. • The diversity of plastics ingested was associated with dietary change. • New insights into plastic ingestion from the perspective of feeding ecology [ABSTRACT FROM AUTHOR]

Location of doping atoms in C 60 fullerene influences the desired fullerene applications. Doped C 60 fullerene with Beryllium and Lithium atoms is used in variable energy applications such as Lithium batteries, hydrogen storage and solar cells as well as they are produced in the interstellar medium. So far, the infrared (IR) spectra and vibrational modes of Li- and Be-doped fullerenes are still unknown, although IR spectroscopy can gather information about geometrical structure and assess the purity of a compound. Therefore, the IR spectra and vibrational modes of C 59 X and C 60 X fullerenes (X = Li, Be) are performed using density functional theory (DFT) and implementing 6-311G++(d) basis set within G09W program. The results show that IR spectroscopy exhibits a remarkable dependence on types of dopant atoms (Li, Be), types of doping (substituted doping, endohedral doping, exohedral doping) and types of bonds (C P -C h and C h -C h bonds). Remarkably, the endohedral and exohedral fullerenes C 60 X can be distinguished by IR spectra which are found to be mainly attributed to the range of 1400–1600 cm−1 and 500 cm−1-1000 cm−1, respectively. Whereas the substituted C 60 X fullerene is attributed to range of 0–500 cm−1 and 1000–1400 cm−1 for the dopant Li and Be atoms, respectively. Therefore, infrared spectroscopy technology can be effectively adopted to identify the types of bonds and dopant atoms as well as the location of dopant atoms. Finally, the obtained infrared spectroscopic data can be also applied to correlate unidentified infrared bands with interstellar spectroscopic data. [Display omitted] [ABSTRACT FROM AUTHOR]

• CO concentration was detected with WMS-TDLAS technology. • The CNN-LSTM-Attention concentration inversion model was established. • The model input increased the mean data of segments in the crest interval. • Moving average filter is employed to reduce the collected data noise. In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle. [ABSTRACT FROM AUTHOR]

• The applications of FTIR in asphalt materials research are reviewed. • The mechanisms of different ageing method are discussed from FTIR perspective. • The performance prediction models based on FTIR and chemometrics is discussed. • Multispectral data fusion approach is envisaged for future research. Fourier-transform infrared spectroscopy (FTIR) has become a crucial method for both qualitative and quantitative analysis in characterizing asphalt materials. The present work aims to provide a comprehensive review of recent uses of FTIR in this field, that ranged from material quality control, material identification, to the analysis of modification mechanism, including ageing mechanisms, as well as for macroscopic performance prediction. The narrative unfolds within a broader context, with the basic principles, and insightful comparisons with ultraviolet–visible (UV–vis) spectroscopy, Raman spectroscopy, and fluorescence spectroscopy. The instrumentations employed in FTIR research are inspected in detail, delving into testing modes, sample preparation, and data analysis methods, with a particular emphasis on its ability to identify asphalt components at the molecular level. Furthermore, FTIR analysis provides useful indications for various applications: i) quality identification; ii) asphalt materials modifications and ageing states identification according to molecular changes; iii) prediction for the physical and rheological properties of asphalt binder based on chemometric method. The prospects in asphalt research are proposed stressing the relevance of combining FTIR analysis to high-throughput experimental methods and other spectroscopy technologies, and coupling to machine learning (ML) methods. This paper provides a reference for researchers who intend to explore the quality control and to analyze asphalt binders from a microscale perspective, fostering further research on relationships between microscopic compositions and macroscopic properties. [ABSTRACT FROM AUTHOR]

Thermal radiation and texture data from two different sensor types are usually combined in the fusion of infrared and visible images for generating a single image. In recent years, convolutional neural network (CNN) based on deep learning has become the mainstream technology for many infrared and visible image fusion methods, which often extracts shallow features and ignores the role of long-range dependencies in the fusion task. However, due to its local perception characteristics, CNN can only obtain global contextual information by continuously stacking convolutional layers, which leads to low network efficiency and difficulty in optimization. To address this issue, we proposed a global context fusion network (GCFN) to model context using a global attention pool, which adopts a two-stage strategy. First, a GCFN-based autoencoder network is trained for extracting multi-scale local and global contextual features. To effectively incorporate the complementary information of the input image, a dual branch fusion network combining CNN and transformer is designed in the second step. Experimental results on a publicly available dataset demonstrate that the proposed method outperforms nine advanced methods in fusion performance on both subjective and objective metrics. [ABSTRACT FROM AUTHOR]

[Display omitted] • A real-time grading method of roasted tobacco quality based on near infrared spectroscopy. • A key indicators selection method based on information gain and Fisher criterion was proposed. • A chemical indicator grading table was designed for reference. • Chemical indicator grading based on near infrared spectroscopy is a convenient, rapid and promising grading method. Traditional tobacco quality grading methods have limitations such as high human resource consumption and poor consistency, resulting in the grading accuracy being affected. Near infrared spectroscopy (NIRS) technology, as a rapid and non-destructive analytical method, has been widely used for online inspection. This study aims to accomplish rapid, accurate and reliable real-time grading of tobacco quality by employing online near-infrared spectroscopy to detect key chemical indicators. A key indicators selection method based on information gain and Fisher criterion (IFRI) was proposed in this paper by associating tobacco chemical indicators with traditional quality grades. The key chemical indicators were selected from many indicators by two classical feature selection functions, and a chemical indicator grading table was designed based on a large number of statistical data on tobacco chemical indicators, and the table was applied to grade the tobacco quality of real data from different years. The results show that the IFRI algorithm is an effective method for selecting key indicators, and the selected reducing sugars-to-total nitrogen ratio and nicotine are the key indicators for tobacco quality grading. Moreover, we demonstrated that the designed grading table is reasonable and feasible. Compared with traditional grading, chemical indicator grading based on near infrared spectroscopy can not only be realized quickly by using an online near infrared instrument, but also the tobacco of the same grade is less discrete, better consistency and more representative after grading, and it is applicable to tobacco samples of different years, which is a convenient, rapid and promising grading method that can provide a reference for tobacco quality grading. Furthermore, the formulation design by utilizing different grades after grading can greatly improve the utilization rate of tobacco leaves. Notably, the method can also be applied to other fields, such as quality control of agricultural products, monitoring of food processing processes, etc. [ABSTRACT FROM AUTHOR]

As the rapid developing in infrared detection technology, infrared camouflage has attracted increasing attention in militarily. The two-dimensional transition metal carbide/nitride (MXene)-based film have been developed in IR stealth due to the low IR emissivity. However, the adjusting thermal camouflage is still a great challenge. In this study, we successfully fabricated a sandwich-structured MXene-ANF-MXene nanocomposite film by the vacuum-assisted filtration process. The composite film displayed remarkable infrared stealth performance of large reduction in radiation temperature. The lowest IR emissivity was 0.21. Additionally, adjusting infrared disguise and temperature modulation of the film were achieved by a stable and reliable Joule heating capability. The resulting composite film also exhibited multifunctionality including an impressively high electromagneticinterference(EMI) shielding efficiency of up to 56.2 dB, excellent mechanical properties, flame retardancy, and hydrophobicity. This work offers a simple avenue for preparing multifunctional sandwich-structured composite film by vacuum-assisted filtration method, demonstrating the great promise of MXene film for multifunctional application. [ABSTRACT FROM AUTHOR]

• An appropriate emissivity for IR camouflage in a specific scenario is demonstrated through outdoor experiments. • Quantitative analysis on the influence of emissivity on IR camouflage performance is conducted based on the heat transfer model. • A general method for determining the optimal emissivity of infrared camouflage is proposed. • An optimal emissivity calculator for IR camouflage is accessible. The rapid development of infrared detection technology has generated an urgent demand for infrared camouflage, sparking widespread interest in low-emissivity materials. Novel material designs and advanced micro/nanofabrication technologies make it possible to realize materials with extremely low emissivity. However, a lower infrared emissivity does not always mean a better camouflage performance. There is a lack of sufficient discussion on how to determine an appropriate emissivity for a specific working condition to achieve effective infrared camouflage. Here, through outdoor experiments, we demonstrated that for a specific scenario, an appropriate emissivity always exists that can make the infrared characteristics of the target effectively blend into its background, and deviations from the emissivity result in deteriorated camouflage performance. Further, we established a heat transfer model to conduct quantitative analysis on the influence of emissivity on infrared camouflage performance in terms of surface temperature and radiative temperature in various conditions. In addition, we proposed a general method for determining the optimal emissivity of infrared camouflage, defined as the emissivity value at which the radiative temperatures of the target and the background are equal. To facilitate practical application of this method, we developed a user-friendly MATLAB app named "Optimal Emissivity Calculator" to calculate the optimal emissivity. It was found that for a vehicle's engine compartment surface at approximately 340.0 K, the optimal emissivity is 0.4 with a background temperature of 300.0 K. This work highlights the significance of selecting appropriate emissivity for infrared camouflage and provides a reference for designing the emissivity of infrared camouflage materials. [ABSTRACT FROM AUTHOR]

The healthcare supply chain is a network made up of various systems, processes, and elements that function and interact seamlessly to offer healthcare services and products. Food safety is an essential component of the healthcare supply chain. In the cattle industry, the healthcare supply chain contributes positively to the global economy through providing high-quality products such as milk and meat. Stress in cattle is one of main factor that cause low quality meat called "dark meat". Numerous studies have been conducted on the development of different non-invasive methods based on Infrared Thermography Technology (IRT) to enhance the meat quality by detecting stress in cattle pre-slaughtering. These studies have the following issues: lack of automating in detecting body temperature of cattle and ignoring detecting stress with prediction dark meat incidence. The present study endeavors a new fully automated system for detecting stress, and dark meat incidence, incorporating the following new approaches: Multiview face detecting, Automatic eye localisation for detecting body temperature automatically employing computer vision and image processing, respectively. Furthermore , machine learning algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT) have been developed specifically for stress detection and the prediction of dark meat. To develop automated system, two forms of data were collected: statistical temperature and infrared thermal images. Infrared thermal images are used to develop Multiview face detection and Automatic eye localisation. Temperature data used to develop the machine learning model. Results reveal that Multiview face detection better than the current methods in term of Precision 0.99, Recall 0.91, F-score 0.95 with high True positive rate 0.90 and zero False-positive rate. Automatic eye localisation has high accuracy, with the following values: sensitivity 0.9780, precision 0.7212, F measure of 0.8024, and misclassification 0.0455. Lastly, results elaborate that the decision tree model can attain a notable level of accuracy in terms of specificity, recall, F-measure, and Area Under the Curve (AUC), all at an optimal rate of 98%. [ABSTRACT FROM AUTHOR]

The intensification of total phenolic compound (TPC) extraction from blood orange peels was optimized using a novel green infrared-assisted extraction technique (IRAE, Ired-Irrad®) and compared to the conventional extraction using a water bath (WB). Response surface methodology (RSM) allowed for the optimization of ethanol concentration (E), time (t), and temperature (T) in terms of extracted TPC and their antiradical activity, for both WB extraction and IRAE. Using WB extraction, the multiple response optimums as obtained after 4 h at 73 °C and using 79% ethanol/water were 1.67 g GAE/100 g for TPC and 59% as DPPH inhibition percentage. IRAE increased the extraction of TPC by 18% using 52% ethanol/water after less than 1 h at 79 °C. This novel technology has the advantage of being easily scalable for industrial usage. HPLC analysis showed that IRAE enhanced the recovery of gallic acid, resveratrol, quercetin, caffeic acid, and hesperidin. IR extracts exhibited high bioactivity by inhibiting the production of Aflatoxin B1 by 98.9%. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The longwave infrared (LWIR) region of the spectrum spans 8 to 14 μm and enables high-performance sensing and imaging for detection, ranging, and monitoring. Chip-scale LWIR photonics has enormous potential for real-time environmental monitoring, explosive detection, and biomedicine. However, realizing technologies such as precision sensors and broadband frequency combs requires ultra low-loss and low-dispersion components, which have so far remained elusive in this regime. Here, we use native germanium to demonstrate the first high-quality microresonators in the LWIR. These microresonators are coupled to partially-suspended Ge waveguides on a separate glass chip, allowing for the first unambiguous measurements of isolated linewidths. At 8 μm, we measured losses of 0.5 dB/cm and intrinsic quality (Q) factors of 2.5 × 105, nearly two orders of magnitude higher than prior LWIR resonators. Our work portends the development of novel sensing and nonlinear photonics in the LWIR regime. Developing longwave infrared technology hide intrinsic challenges but at the same time is important to develop sensing and imaging for detection, ranging, and monitoring systems. Here the authors demonstrate the fabrication of high-quality microresonators in the LWIR with the simple use of native germanium. [ABSTRACT FROM AUTHOR]

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods. • A reliable multi-scale decomposition method for CNN is proposed. • MSCNN extracts fault information over multiple scales with better feature extraction ability. • The proposed method yields best diagnostic ability by comparing with other five methods. [ABSTRACT FROM AUTHOR]

Featured Application: A new algorithm for rapid determination of SBS content in asphalt based on infrared spectroscopy was provided and Upgraded method for asphalt sample preparation in FTIR testing in this research. Infrared spectroscopy SBS asphalt-modifier content detection technology has been applied in practical projects, which can effectively ensure the quality of pavement engineering. If the composition ratio of modified asphalt is not clear, the accuracy of the test results will be affected, which is the disadvantage of conventional infrared spectroscopy detection methods. In this paper, the influence of different kinds of additives on the accuracy of the infrared spectrogram and SBS content test was analyzed, and the microscopic mechanism of the effect of additives on the accuracy of SBS modified asphalt content testing was uncovered. Meanwhile, the sample preparation technique was improved and the testing technique was optimized. Results demonstrate that the improved standard curve algorithm eliminates the effect of additives on test accuracy, and the effect of additives on test accuracy was eliminated by improving the standard curve algorithm. Without the addition of additives, the content of the modifier is accurately measured by simplifying the boundary conditions of the test technique. The preparation of samples by the dissolution method has more advantages than the tableting method and can be used as a new sample preparation technology. Improvements in sample preparation techniques and standard curve algorithms improve the safety, convenience, applicability, and accuracy of detection techniques. [ABSTRACT FROM AUTHOR]

In the recent years, the infrared technology has significantly improved the development of science and technology, the military, industry and medical care. It also overcomes many failures in traditional visible light images. However, the output response error of the sensor is caused by the defective hardware of the infrared sensor and the lack of manufacturing technology, which affects the image quality. In this paper, the common infrared image problem is aimed to solve with a correction on the gray-scale dynamic range. This study also proposes a new high-performance compensation algorithm. It uses a redesigned linear conversion architecture to enhance image details through a segmented method, and improve compensation. In addition, the hardware design and bit size of the multiplier is improved. Compared to traditional histogram equalization, the use of hardware resources is significantly reduced. Especially, the compensation part is integrated into the combined logic circuit for implementation and the pipeline technology should be included to reduce time-consuming. The hardware architecture is described in Verilog hardware description language (HDL) via field-programmable gate array (FPGA). The chip, used to perform and verify functions, is Xilinx Kintex-7, while its operating frequency and the hardware resource size used is 105 MHz and 458 LUTS, respectively. [ABSTRACT FROM AUTHOR]

Control technology for infrared characteristic has been a hot topic for decades. Herein, a hybrid structure for controlling infrared characteristic combining a wavelength‐selective emitter, microchannel heat exchanger, and thermal barrier coatings (TBC) is proposed to tune the surface infrared signature. First, the optimization algorithm is used to optimize the design to realize the spectral selective emission of the surface. The optimized 1D photonic crystal (PC) has high emittance in the 5–8 μm nonatmospheric window (ε5−8 = 0.723) for radiative cooling and low emittance in the 3–5 and 8–14 μm atmospheric window (ε3−5 = 0.480 and ε8−14 = 0.067) for infrared camouflage. Further control of surface temperature is achieved by combining the nanofluid‐cooled microchannel heat exchanger and TBCs. When the Reynolds number is 1000 and the wall heating temperature is 1473.15 K, the IR signal intensity can be reduced by 35.3 dB. This scheme may provide a new idea for infrared characteristic signal control. [ABSTRACT FROM AUTHOR]

Panting score (PS) is a common research tool used to assess the physiological state of cows exposed to heat stress, but it is subjective. Infrared temperature (IRT), measured by either infrared thermometers or cameras, may be a more objective and reliable alternative. Very few studies thus far have evaluated the associations between PS, IRT, and milk production. We investigated the applicability of IRT compared with PS as a means of assessing heat stress and milk yield reduction in dairy cows in tropical smallholder dairy farms (SDF). In autumn 2017, SDF located across 4 typical dairy regions of Vietnam were each visited once to collect farm (n = 32) and individual cow data (n = 344). For each SDF, heat load index (HLI) inside the cowsheds, an indicator of environmental heat load calculated from ambient temperature, humidity, and wind speed, was measured. For each cow, PS (0 indicates a cow breathing normally, not panting; 4.5 indicates an extremely heat-stressed cow with excessive panting, tongue fully extended, and excessive drooling), IRT of the cow's body, single-day energy-corrected milk yield (ECM), body weight, and body condition score were measured. Cow genotype, age, lactation number, and days in milk were recorded. The IRT of the cows' inner vulval lip (IVuT) were measured with an infrared thermometer; and the IRT of the cows' vulval surface (OVuT), inner tail base surface (ITBT), ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, fore hoof, and hind hoof were also measured with an infrared camera. Multivariate mixed-effects models were used to assess the associations between HLI with PS and IRT, and associations between PS and IRT with ECM while accounting for the effects of other cow variables. All IRT correlated positively with PS (Pearson correlation, r = 0.23–0.50). Each unit increase in HLI was associated with increases of 0.07 units in PS and 0.09 to 0.23°C in IRT. Each degree (°C) increase in IVuT, OVuT, and ITBT was associated with decreases of 0.75, 0.87, and 0.70 kg/cow per day in ECM, respectively, whereas PS and other IRT were not significantly associated with ECM. Thus, all IRT showed potential to assess the heat stress level of cows; and IVuT, OVuT, and ITBT, but not PS and other IRT, showed potential to predict ECM reduction in cows during heat stress. First cross (F1) Holstein Brown Swiss and F1 Holstein Jersey showed lower PS and yielded higher ECM than the third backcross (B3) Holstein Zebu (7/8 Holstein + 1/8 Zebu) and pure Holstein. Thus, F1 Holstein Brown Swiss and F1 Holstein Jersey could be more suitable for tropical SDF than B3 Holstein Zebu and pure Holstein. [ABSTRACT FROM AUTHOR]

In a specially designed infrared oven equipped with a two‐way infrared heating and vacuum pump systems, the effects of drying parameters (100–300 W infrared powers and 100–250 mmHg vacuum pressures) on drying kinetics and quality properties of pear slices were comprehensively studied. The samples dried at 300 W had high HMF values (12.73–95.68 mg/kg d.m). The highest sensory scores were observed in pear slices dried at 300 W and 250 mmHg. Six different mathematic models were taken into account to analyze the drying characteristics of pear slices, with the Wang and Singh model (R2 = 0.9996, χ2 = 0.0004, and RSME = 0.0062) presenting the best prediction of the drying behavior. The effective moisture diffusivity values were 1.1829–4.6684 × 10−8. The activation energies for 100 and 250 mmHg vacuum applications were 44.21 and 43.89 kJ/mol, respectively. It was concluded that a vacuum‐combined infrared dryer (300 W, 250 mmHg) could be a fast and economic method for drying pear slices. Novelty impact statement: A combination of 300 W infrared power and 250 mmHg vacuum pressure was the fastest combination for the drying of pear slices and gave the highest score in sensory evaluation. The Wang & Singh model exhibited the best results in fitting the experimental data. As a result, the vacuum‐combined infrared technology was improved the drying efficiency and quality of dried pear. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the traditional infrared polarization imaging device, such as large volume, complex structure, difficult registration, and inability to recognize moving targets, this paper proposes an infrared polarization imaging method based on a micropolarizer. The long refractive wave infrared polarization optical system is designed to achieve the imaging detection of a 0.25 m target at 1 km; for this, a design of a micropolarizer array suitable for the long-wave infrared band is proposed. In the simulation analysis, the effects of the grating substrate and line grating material, grating period, duty cycle, and grating slot depth on the polarization performance of grating are discussed, respectively. Through infrared polarization imaging experiments on typical targets, the unique advantages of infrared polarization technology in distinguishing metal and non-metal, natural and artificial objects, and high-temperature object recognition are verified, which provides practical support for physical evidence searches and camouflage target recognition. [ABSTRACT FROM AUTHOR]

The electrical equipment of intelligent substation cannot deal with the problem of load mutation, and the load adjustment is not timely, which leads to the poor performance of thermal fault identification of electrical equipment. Therefore, a study on thermal fault identification of the electrical equipment of intelligent substation based on infrared technology is proposed. First, under the background of infrared technology, according to the thermal fault feature extraction model of substation electrical equipment, the principle of double station cross location is proposed; combined with the sampling results of thermal fault characteristic parameters of electrical equipment in intelligent substation, the thermal fault characteristics are analyzed; through the process of thermal fault identification of electrical equipment in intelligent substation, the preliminary classification structure of thermal fault is obtained and the thermal fault identification is completed. The experimental results show that the designed method has good performance of thermal fault identification, high output stability, better identification effect after optimization, and highly sensitive identification ability. [ABSTRACT FROM AUTHOR]

The real-time detecting and monitoring of ethylene gas molecules could benefit the agricultural, horticultural and healthcare industries. In this regard, we comprehensively review the current state-of-the-art ethylene gas sensors and detecting technologies, covering from preconcentrator-equipped gas chromatographic systems, Fourier transform infrared technology, photonic crystal fiber-enhanced Raman spectroscopy, surface acoustic wave and photoacoustic sensors, printable optically colorimetric sensor arrays to a wide range of nanostructured chemiresistive gas sensors (including the potentiometric and amperometric-type FET-, CNT- and metal oxide-based sensors). The nanofabrication approaches, working conditions and sensing performance of these sensors/technologies are carefully discussed, and a possible roadmap for the development of ethylene detection in the near future is proposed. [ABSTRACT FROM AUTHOR]

Informatization is an inevitable trend of livestock breeding industry development. The effective monitoring of environmental and growth information in the breeding process is helpful to realizing the production process with low cost, high efficiency, and comfortable income. In commercial farms, the intelligent information perception technology could auto-complete monitoring of massive information and provide technical support for liberating productivity, reducing cost, and increasing efficiency. This paper describes the main research methods of information perception technology, including sensor technology, image processing technology, audio processing technology, thermal infrared technology, for environmental monitoring, critical growth information perception of livestock, and disease warning in the livestock breeding process. The accuracy, validity, and generalization of current mainstream methods in this field were analyzed. The future research emphases of information sensing technology in the livestock breeding industry were summarized, and the future development direction was prospected. [ABSTRACT FROM AUTHOR]

RedTacton is a Human Area Networking (HAN) device that uses the surface of the human body to transmit data faster. It utilizes the tiny electric field that is emitted on the surface of the human body setting it apart completely from wireless and infrared technologies. When a human body comes into contact with a RedTacton transceiver, a transmission path is generated. It is possible to communicate using any part of the body, including the face, legs, arms, feet, hands, and fingers. The RedTacton chip has a transmitter and a receiver for accepting and sending digital data. This paper discusses RedTacton's working principles, and features. This paper describes a healthcare monitoring system that monitors patients' body temperatures, blood pressure, heart rates, and glucose levels without the use of wires, Bluetooth, Local Area Network, Radio Frequency (RF), or other external equipment. Consequently, the capital and investment cost is reduced. [ABSTRACT FROM AUTHOR]

Context. Thermal imaging has been shown to be a valuable technique for detecting a range of terrestrial mammals across different environments. The limited studies looking at its effectiveness in detecting arboreal mammals in rainforest ecosystems have had mixed success due to the complexity of the environment and limitations of the technology itself. Aims. We investigated whether using a hand-held thermal imaging device would detect more individuals of six species of nocturnal arboreal mammal in tropical rainforests than the most-used detection method of spotlighting. We determined whether environmental variables effecting either equipment operation or mammal behaviour would influence these results. Methods. We surveyed eight transects across the Wet Tropics of northern Queensland for six species of arboreal mammals using both a hand-held thermal imager and a spotlight. We used a measure of underestimation to compare counts of individual species, and then modelled total mammal counts with detection method and environmental variables to find the best approximating model. Key results. Spotlighting underestimated the total number of each species by between 33 and 100% when compared with thermal imaging. Detection method alone without any environmental interaction term provided the best approximating model (AICc = 275.58, marginal pseudo R2 = 0.286), with thermal imaging technology detecting almost double the number of our target individuals (12.3 ± 1.76) compared with spotlighting (6.7 ± 1.02). Conclusions. Despite recorded operational limitations, thermal imaging technology greatly improved our ability to locate both small and large nocturnal, arboreal mammals, including a species that is rarely observed in the wild. Implications. The potential to not only improve detection of nocturnal, arboreal mammals but also improve cryptic species distribution and abundance measures suggests thermal imaging technology is an important tool for use globally across rainforests environments. [ABSTRACT FROM AUTHOR]

An ecofriendly extraction technology using infrared (IR) irradiation Ired-Irrad® was applied to purple corn cobs to enhance polyphenol recovery for the first time. The IR extraction efficiency was compared to that of the water bath (WB) method. Response surface methodology (RSM) using a central composite design was conducted to determine the effect of the experimental conditions (extraction time and treatment temperature) and their interactions on the total polyphenol and anthocyanin yields. Optimal extraction of total phenolic compounds (37 mg GAE/g DM) and total monomeric anthocyanins (14 mg C3G/g DM) were obtained at 63 °C for 77 min using IR as an extraction technique and water as a solvent. HPLC revealed that the recovery of peonidin 3-O-glucoside and cyanidin 3-O-glucoside was enhanced by 26% and 34%, respectively, when using IR. Finally, purple corn cobs' spray-dried extract was proven to be an important natural colorant of pickled turnip. It offers great potential for use as a healthy alternative to the carcinogenic rhodamine B synthetic dye, which was banned. [ABSTRACT FROM AUTHOR]

With the development of smart grid, infrared recognition technology has been widely used in substations, and gradually become a hot technology for power equipment defect detection. However, from the perspective of application effect, there are still some limitations. Based on this, this paper, on the basis of deep learning theory, designs an infrared image classification algorithm for defective power equipment, and tests it through experiment. The final result confirms the feasibility of the classification algorithm and can better detect the defect types of power equipment. The purpose of this study is to promote the application of infrared image classification algorithm for defect type recognition of power equipment, reduce the cost of operation and maintenance of power equipment, and make a positive contribution to the development of smart substation. [ABSTRACT FROM AUTHOR]

Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture. [ABSTRACT FROM AUTHOR]

A high prevalence of postural deviations is confirmed by the literature and considering the risk factors that can manifest in adolescents, an efficient method of intervention, that can be used on every type of patient, is important to be available to the therapist, taking into account the lack of a pro-active behaviour dominant in today society. The purpose of this study is to check if the therapeutic intervention carried out through manual techniques can improve the body alignment at the level of the spine. The subjects of this study were 20 14 to 16 years old high school students, from which 8 females and 12 males. Materials and method: The participants were evaluate using the KINEOD posturograph that utilizes infrared acquisition technology to provide a complete 3D postural analysis. An intervention program based on osteopathic manual technics, made by 10 sessions, was applied over a course of 3 months, and then the participants were reevaluated. The data received from the software of the KINEOD posturograph was analyzed and interpreted statistically, and the results were presented as tables and graphs. Conclusions: Results indicate that the intervention program used generated a numerical positive outcome in all four parameters evaluated, and in 2 of them the difference is statistically significative ("Shoulder imbalance" - P=0.007, "C7 deviation - Barré Vertical" - P= 0.049). From this we can conclude that manual technics can be used successfully in correction of the posture at spinal level. [ABSTRACT FROM AUTHOR]

With the development of superframe high-dynamic-range infrared imaging technology that extends the dynamic range of thermal imaging systems, a key issue that has arisen is how to choose different integration times to obtain an HDR fusion image that contains more information. This paper proposes a multi-integration time adaptive method, in order to address the lack of objective evaluation methods for the selection of superframe infrared images, consisting of the following steps: image evaluation indicators are used to obtain the best global exposure image (the optimal integration time); images are segmented by region-growing point to obtain the ambient/high-temperature regions, selecting the local optimum images with grayscale closest to the medium grayscale of the IR imaging system for the two respective regions (lowest and highest integration time); finally, the three images above are fused and enhanced to achieve HDR infrared imaging. By comparing this method with some existing integration time selection methods and applying the proposed method to some typical fusion methods, via subjective and objective evaluation, the proposed method is shown to have obvious advantages over existing algorithms, and it can optimally select the images from different integration time series images to form the best combination that contains full image information, expanding the dynamic range of the IR imaging system. [ABSTRACT FROM AUTHOR]

Quantum dots (QDs) have been explored for many photonic applications, both as emitters and absorbers. Thanks to the bandgap tunability and ease of processing, they are prominent candidates to disrupt the field of imaging. This review article illustrates the state of technology for infrared image sensors based on colloidal QD absorbers. Up to now, this wavelength range has been dominated by III–V and II–VI imagers realized using flip-chip bonding. Monolithic integration of QDs with the readout chip promises to make short-wave infrared (SWIR) imaging accessible to applications that could previously not even consider this modality. Furthermore, QD sensors show already state-of-the-art figures of merit, such as sub-2- $\mu \text{m}$ pixel pitch and multimegapixel resolution. External quantum efficiencies already exceed 60% at 1400 nm. With the potential to increase the spectrum into extended SWIR and even mid-wave infrared, QD imagers are a very interesting and dynamic technology segment. [ABSTRACT FROM AUTHOR]

In an effort to overcome the difficulty of real-time early warning via traditional infrared imaging technology caused by the complex working environment in coal mines, this paper proposes a mine early warning method based on uncooled infrared focal plane technology. The infrared thermal spectrogram of the detected object was visually displayed in a pseudo-color image with high resolution and high sensitivity, which can realize the real-time detection and early warning of personnel safety in modern mines. The multipoint compression correction algorithm based on human visual characteristics divided the response units of all acquisition units into gray intervals according to a threshold value, then the corresponding parameters were set in different intervals, and finally, each interval was compressed using a two-point correction algorithm. The volume of stored data was the sum of the calibration curve and the data from an encode table corrected by a MATLAB simulation, and the number of CPU cycles was run by a CCS 3.3 clock calculation algorithm. The results showed that when the temperature of the blackbody reached 115 °C, the nonuniformity before correction was 6.32%, and the nonuniformity after the multipoint correction of human eyes was 2.99%, which implied that the algorithm proposed in this paper had good denoising ability. The number of CPU cycles occupied by this algorithm was 18,257,363 cycles/frame with a frequency of 29.97 Hz. The sharpness of the compressed infrared images was obviously improved, and the uniformity was better. The method proposed in this paper can meet the need for modern mine personnel search and rescue, equipment supervision and dangerous area detection and other early warning requirements so as to achieve the goal of developing smart mines and ensuring safety in coal mine production. [ABSTRACT FROM AUTHOR]