Search Results (245)

As one of the payloads on board the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) spacecraft, the ultraviolet imager (UVI) aims to capture N2 Lyman–Birge–Hopfield (LBH) aurora continuously on a high-eccentricity orbit. The UVI instrument includes an intensified charge-coupled device (ICCD) for far ultraviolet (FUV) wavelength. It comprises a sealed image intensifier, a relay lens system, a CCD, and a mechanical housing. ICCD’s performance characteristics are evaluated before integrating with the optical system of the UVI, including the quantum efficiency, radiant gain, background characteristics, excess noise factor, image quality, and signal-to-noise ratio (SNR). The testing procedure and results are presented and discussed. The results demonstrate that the comprehensive performance of the detector is good, and provide critical technical support for quantitative applications. Full article

The Yangtze River Basin, one of China’s most vital watersheds, sustains both ecological balance and human livelihoods through its extensive lake systems. However, since the 1980s, these lakes have experienced significant ecological degradation, particularly in terms of aquatic vegetation decline. To acquire reliable aquatic vegetation data during the peak growing season (July–September), when clear-sky conditions are scarce, we employed Chinese domestic satellite imagery—Gaofen-1/6 (GF-1/6) Wide Field of View (WFV) and Huanjing-2A/B (HJ-2A/B) Charge-Coupled Device (CCD)—with approximately one-day revisit frequency after constellation networking, 16 m spatial resolution, and excellent spectral consistency, in combination with deep learning algorithms, to monitor aquatic vegetation across the basin. Comparative experiments identified the near-infrared, red, and green bands as the most informative input features, with an optimal input size of 256 × 256. Through visual interpretation and dataset augmentation, we generated a total of 5016 labeled image pairs of this size. The U-Net++ model, equipped with an EfficientNet-B5 backbone, achieved robust performance with an mIoU of 90.16% and an mPA of 95.27% on the validation dataset. On independent test data, the model reached an mIoU of 79.10% and an mPA of 86.42%. Field-based assessment yielded an overall accuracy (OA) of 75.25%, confirming the reliability of the model. As a case study, the proposed model was applied to satellite imagery of Lake Taihu captured during the peak growing season of aquatic vegetation (July–September) from 2020 to 2025. Overall, this study introduces an automated classification approach for aquatic vegetation using 16 m resolution Chinese domestic satellite imagery and deep learning, providing a reliable framework for large-scale monitoring of aquatic vegetation across lakes in the Yangtze River Basin during their peak growth period. Full article

In this work, a quantitative analysis of Li in natural brines was carried out by laser-induced breakdown spectroscopy (LIBS) assisted by the τ–algorithm for detailed analysis of the experimental line shapes (τLIBS). Brine samples were collected from different salars located in the Puna plateau (Northwest Argentina) and analyzed by LIBS in the form of solid pressed pellets. The emission intensities of Li I, H_α, and Mg I–II lines were measured and spatially integrated along the line of sight with temporal resolution by using a high-spectral-resolution spectrometer equipped with an intensified charge-coupled device (iCCD) detector. The plasma was characterized through the determination of the electron density and the temperature. The τ–algorithm calculated the optical thicknesses of the Li I lines to generate synthetic intensity profiles that were subsequently fitted to the experimental spectra. By applying the developed τLIBS approach, valuable spectroscopic insight was recovered about the physical processes occurring in the plasma, such as self-absorption. The analytical process involved an univariate external calibration process using the resonant Li I line at 6707.7 Å measured from a series of Li standard samples. Self-absorption effects were evaluated and subsequently compensated. The final LIBS results, with an enhanced accuracy of 15%, were validated by crosschecking them against those obtained with the standard AAS method. Full article

The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors. Full article

When using optical remote sensing methods for Kelvin wake imaging, the imaging is affected by sea-surface stochastic fluctuation, imaging noise, and weak reflectivity contrast, resulting in weak wake image signals. In order to better obtain wake optical remote sensing images, this article proposes a multi-objective parameter stochastic optimization method for a time-delayed integration optical remote sensing imaging system. By constructing the wake imaging mechanism framework integrating a hydrodynamic model, rough sea surface probability and statistics model, and Time-Delay Integration Charge-Coupled Device (TDI-CCD) imaging link model, a stochastic multi-objective optimization model with constraints is established. The multi-objective function of this model is specifically defined as follows: maximizing the digital number difference between the crest and trough of Kelvin wakes in imaging results, maximizing the F number, minimizing the integration stages, and minimizing the quantization bits. Meanwhile, a two-stage solution method based on sample average approximation (SAA), branch and bound method (B&B), and the complex method is designed. The model can be used to obtain optimized design results for remote sensing imaging parameters, providing theoretical and methodological support for the design of remote sensing imaging systems. Numerical simulation results show that the optimized parameter combination can achieve clear imaging of the Kelvin wake, and the core indicators meet the design requirements. Full article

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

Accurate river flow velocity estimation is critical for flood risk management and sediment transport modeling. This study proposes an artificial intelligence (AI)-based framework that integrates optical flow analysis and deep learning to estimate flow velocity from charge-coupled device (CCD) camera videos. The approach was tested on a field dataset from Yufeng No. 2 stream (torrent), consisting of 3263 ten min 4 K videos recorded over two months, paired with Doppler radar measurements as the ground truth. Video preprocessing included frame resizing to 224 × 224 pixels, day/night classification, and exclusion of sequences with missing frames. Two deep learning architectures—a convolutional neural network combined with long short-term memory (CNN+LSTM) and a three-dimensional convolutional neural network (3D CNN)—were evaluated under different input configurations: red–green–blue (RGB) frames, optical flow, and combined RGB with optical flow. Performance was assessed using Nash–Sutcliffe Efficiency (NSE) and the index of agreement (d statistic). Results show that optical flow combined with a 3D CNN achieved the best accuracy (NSE > 0.5), outperforming CNN+LSTM and RGB-based inputs. Increasing the training set beyond approximately 100 videos provided no significant improvement, while nighttime videos degraded performance due to poor image quality and frame loss. These findings highlight the potential of combining optical flow and deep learning for cost-effective and scalable flow monitoring in small rivers. Future work will address nighttime video enhancement, broader velocity ranges, and real-time implementation. By improving the timeliness and accuracy of river flow monitoring, the proposed approach supports early warning systems, flood risk reduction, and sustainable water resource management. When integrated with turbidity measurements, it enables more accurate estimation of sediment loads transported into downstream reservoirs, helping to predict siltation rates and safeguard long-term water supply capacity. These outcomes contribute to the Sustainable Development Goals, particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by enhancing disaster preparedness, protecting communities, and promoting climate-resilient water management practices. Full article

Sea surface roughness (SSR) retrieval is a frontier topic in the field of ocean remote sensing, and SSR retrieval based on multi angle, passive, visible spectrum remote sensing images has been proven to have potential applications. Traditional multi angle retrieval models ignored the nonlinear relationship between radiation and digital signals, resulting in low accuracy in SSR retrieval using visible spectrum remote sensing images. Therefore, we analyze the transmission characteristics of signals and random noise in sea surface imaging, establish signals and noise transmission models for typical sea surface imaging visible spectrum remote sensing systems using Complementary Metal Oxide Semiconductor (CMOS) and Time Delay Integration-Charge Coupled Device (TDI-CCD) sensors, and propose a model for SSR retrieval using multi angle passive visible spectrum remote sensing images. The proposed model can effectively suppress the noise behavior in the imaging link and improve the accuracy of SSR retrieval. Simulation experiments show that when simulating the retrieval of multi angle visible spectrum images obtained using CMOS or TDI-CCD imaging systems with four SSR levels of 0.02, 0.03, 0.04, and 0.05, the proposed model relative errors using two angles are decreased by 4.0%, 2.7%, 2.3%, and 2.0% and 6.5%, 4.3%, 3.7%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 7.0%, 6.7%, 7.8%, and 9.0% and 9.5%, 8.3%, 9.0%, and 10.2%. When using more fitting data, the relative errors of the model were decreased by 5.0%, 2.7%, 2.5%, and 2.0% and 7.0%, 5.0%, 4.3%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 8.5%, 7.0%, 8.0%, and 9.4%, and 10.0%, 8.7%, 9.3%, and 10.0%. Full article

This paper suggests a perspective-controlled solution for an integrated Infrared micro-/nanoplastic spectroscopy/photometry-based detection, from the diffraction up to the geometry etendue, with the aim of yielding a universal spectrometer/photometer. Spectrophotometry, unlike spectroscopy that shows the interaction between matter and radiated energy, is a specific form of photometry that measures light parameters in a particular range as a function of wavelength. The solution, meant for diffraction grating and geometry etendue of the display unit, is provided by a controller that tunes the grating pitch to accommodate any emitted/transmitted wavelength from a sample made of microplastics, their degraded forms and their potential retention, and ensures that all the diffracted wavelengths are concentrated on the required etendue. The purpose is not only to go below the current Infrared limit of $20\mathsf{\mu }\mathrm{m}$ microplastic size, or to suggest an Infrared spectrophotometry geometry capable of detecting micro- and nanoplastics in the range of ($1\mathrm{nm}$–$20\mathsf{\mu }\mathrm{m}$) for integrated nano- and micro-scales, but also to transform most of the pivotal components to be directly wavelength-independent. The related controlled geometry solutions, from the controlled grating slit-width up to the controlled display unit etendue functions, are suggested for a wider generic range integration. The results from image-size characterization show that the following charge-coupled devices, nanopixel CCDs, and/or micropixel CCDs of less than $100\mathrm{nm}$ are required on the display unit, justifying the Infrared micro- and nanoplastic-integrated spectrophotometry, and the investigation conducted with other electromagnetic spectrum ranges that suggests a possible universal spectrometer/photometer. Full article

The scope of this study was to evaluate the response of Ce-doped gadolinium aluminum gallium garnet (GAGG:Ce) crystalline scintillator under medical X-ray irradiation for medical imaging applications. A 10 × 10 × 10 mm³ crystal was irradiated at X-ray tube voltages ranging from 50 kVp to 150 kVp. The crystal’s compatibility with several commercially available optical photon detectors was evaluated using the spectral matching factor (SMF) along with the absolute efficiency (AE) and the effective efficiency (EE). In addition, the energy-absorption efficiency (EAE), the quantum-detection efficiency (QDE) as well as the zero-frequency detective quantum detection efficiency DQE(0) were determined. The crystal demonstrated satisfactory AE values as high as 26.3 E.U. (where 1 E.U. = 1 μW∙m⁻²/(mR∙s⁻¹)) at 150 kVp, similar, or in some cases, even superior to other cerium-doped scintillator materials. It also exhibits adequate DQE(0) performance ranging from 0.99 to 0.95 across all the examined X-ray tube voltages. Moreover, it showed high spectral compatibility with commonly used photoreceptors in modern day such as complementary metal–oxide–semiconductors (CMOS) and charge-coupled-devices (CCD) with SMF values of 0.95 for CCD with broadband anti-reflection coating and 0.99 for hybrid CMOS blue. The aforementioned properties of this scintillator material were indicative of its superior efficiency in the examined medical energy range, compared to other commonly used scintillators. Full article

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

Automated and robotic welding have become standard practices in manufacturing, requiring precise control to maintain weld quality without relying on skilled welders. In Tungsten Inert Gas (TIG) welding, monitoring the weld pool is crucial for ensuring the necessary weld penetration, which is vital for maintaining weld integrity. Real-time observation is essential to prevent defects and improve weld quality. Various sensing technologies have been developed to address this need, with vision-based systems showing particular effectiveness in enhancing welding quality and productivity within the framework of Industry 4.0. This review looks at the latest technologies for monitoring weld pools and bead shapes. It covers methods like using Complementary Metal-Oxide Semiconductors (CMOS) to take clear images of the melt pool for better process identification, Active Appearance Model (AAM) to capture 3D images of the weld pool for accurate penetration measurement, and Charge-Coupled Devices (CCD) and Laser-Induced Breakdown Spectroscopy (LIBS) to analyze plasma spectra and create material composition graphs. Full article

Wear particle analysis, which identifies failure modes caused by the wear of various machine components, is an essential technique for monitoring machinery conditions. This analysis plays a vital role in predictive maintenance by revealing component degradation in machinery. This study proposes an automated framework to classify four standard wear particle textures—rough, striated, pitted, and fatigued—using artificial neural networks (ANNs) combined with advanced image processing techniques. Images acquired via Charged-Coupled Device (CCD) microscopy were preprocessed using sharpening, histogram stretching, and four edge detection algorithms: Sobel, Laplacian, Boie–Cox, and Canny. The Laplacian and Canny methods yielded the highest classification accuracies of 97.9% and 98.9%, respectively. By minimizing human subjectivity, this automated approach enhances diagnostic consistency and represents a scalable solution for industrial condition monitoring. Full article

In this paper, we present the analysis of functional alloy samples containing metals aluminum (Al), copper (Cu), lead (Pb), silicon (Si), tin (Sn), and zinc (Zn) using a Q-switched Nd laser operating at a wavelength of 532 nm with a pulse duration of 5 ns. Nine pelletized alloy samples were prepared, each containing varying chemical concentrations (wt.%) of Al, Cu, Pb, Si, Sn, and Zn—elements commonly used in electrotechnical and thermal functional materials. The laser beam is focused on the target surface, and the resulting emission spectrum is captured within the temperature interval of $9.0\times {10}^3$ to $1.1\times {10}^4$ K using a set of compact Avantes spectrometers. Each spectrometer is equipped with a linear charged-coupled device (CCD) array set at a 2 μs gate delay for spectrum recording. The quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under the assumptions of optically thin plasma and self-absorption-free conditions, as well as local thermodynamic equilibrium (LTE). The net normalized integrated intensities of the selected emission lines were utilized for the analysis. The intensities were normalized by dividing the net integrated intensity of each line by that of the aluminum emission line (Al II) at 281.62 nm. The results obtained using CF-LIBS were compared with those from the laser ablation time-of-flight mass spectrometer (LA-TOF-MS), showing good agreement between the two techniques. Furthermore, a random forest technique (RFT) was employed using LIBS spectral data for sample classification. The RFT technique achieves the highest accuracy of ~98.89% using out-of-bag (OOB) estimation for grouping, while a 10-fold cross-validation technique, implemented for comparison, yields a mean accuracy of ~99.12%. The integrated use of LIBS, LA-TOF-MS, and machine learning (e.g., RFT) enables fast, preparation-free analysis and classification of functional metallic materials, highlighting the synergy between quantitative techniques and data-driven methods. Full article

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article