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Lead‐free perovskite single crystals (SCs) have made great progress in the field of optoelectronics. However, the desired large‐size Cs3Bi2X9 SCs (X = I/Br/Cl) with fast growth and high quality are still a great challenge. In this study, millimeter‐sized Cs3Bi2X9 SCs) are first prepared within 3 days by synergistic nucleation and supersaturation engineered method in a low‐temperature solution of ≈ 50 °C, which owns unprecedented advantages such as easily tunable different X element and fast growth period. Combined with the dynamics and thermodynamics of crystal growth, a layered growth model of Cs3Bi2Br9 (CBI) SCs is proposed through in situ observation of CBI SCs growth. The carrier dynamic behaviors of three different Cs3Bi2X9 SCs are investigated by ultrafast femtosecond transient absorption (fs‐TA). Furthermore, the electron transport dynamic of the (001) and (100) crystal faces of Cs3Bi2Br9 (CBB) is demonstrated by density functional theory calculations, evidencing the crystal facet‐dependent photoelectrical performance. What's more, optical imaging sensor based on 36 pixels Cs3Bi2Br9 photodetector with ultrahigh on/off ratio of 5909 and optical communication compiled with Morse code is achieved. This strategy to synthesize the CBX SCs is both conceptually simple and synthetically rapid, offers great potential for the development of free‐Pb perovskite single crystals. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging is an emerging biomedical imaging technique that enables non-invasive visualization of the optical absorption properties of biological tissues in vivo. Although numerous studies have used contrast agents to achieve high-contrast imaging in deep tissues, targeting specific areas remains a challenge when using agents that are continuously activated. Recent research has focused on developing triggered contrast agents that are selectively activated in target areas. This review delves into the use of pH-triggered contrast agents in photoacoustic imaging, which take advantage of the lower pH of the tumor microenvironment compared to normal tissues. The paper discusses the mechanisms of pH-triggered contrast agents that contribute to improving depth and contrast in photoacoustic tumor imaging. In addition, the integration of functionalities, such as photothermal therapy and drug delivery monitoring, into these agents demonstrates significant potential for biomedical applications. [ABSTRACT FROM AUTHOR]

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing. [ABSTRACT FROM AUTHOR]

The Si‐based self‐powered broadband photodiode (SSBP) is prized for its ability to swiftly detect light across a wide spectrum without requiring an external voltage. However, boosting its efficiency remains challenging due to its high refractive index and limited UV light penetration. A combination of Si with ZnGa2O4, an ultra‐wide‐bandgap spinel material, can bring new opportunities to address these shortcomings of SSBP. In this study, a ZnGa2O4/p‐Si heterojunction photodiode is presented, which is capable of detecting UV to near‐infrared light autonomously. Operating without bias, this device exhibits excellent rectification and detects wavelengths from 265 to 1000 nm, achieving impressive performance metrics such as a photo‐to‐dark current ratio of 5.8 × 104, response speed of less than 3 ms, responsivity of 117 mA W−1, and specific detectivity of 5.5 × 1012 Jones while the photodiode demonstrates exceptional stability and durability under harsh conditions. The versatility of this device is demonstrated by applying it to the optical imaging sensors and physically unclonable security devices. This study provides new inspirations for the development of the energy‐efficient and emerging optical sensing technologies. [ABSTRACT FROM AUTHOR]

The article provides a comprehensive inventory and initial assessment of landslides triggered by heavy rainfall in Rio Grande do Sul, Brazil, in autumn 2024. The extreme event resulted in significant flooding and over 15,000 landslides across 130 municipalities, impacting 2 million individuals. The study highlights the increasing frequency of extreme precipitation events in southern Brazil, exacerbated by climate change, and emphasizes the need for adaptation and mitigation strategies to address the growing risk of natural disasters in the region. [Extracted from the article]

Quasi‐3D plasmonic nanostructures are in high demand for their ability to manipulate and enhance light‐matter interactions at subwavelength scales, making them promising building blocks for diverse nanophotonic devices. Despite their potential, the integration of these nanostructures with optical sensors and imaging systems on a large scale poses challenges. Here, a robust technique for the rapid, scalable, and seamless replication of quasi‐3D plasmonic nanostructures is presented straight from their production wafers using a microbubble process. This approach not only simplifies the integration of quasi‐3D plasmonic nanostructures into a wide range of standard and custom optical imaging devices and sensors but also significantly enhances their imaging and sensing performance beyond the limits of conventional methods. This study encompasses experimental, computational, and theoretical investigations, and it fully elucidates the operational mechanism. Additionally, it explores a versatile set of options for outfitting nanophotonic devices with custom‐designed plasmonic nanostructures, thereby fulfilling specific operational criteria. [ABSTRACT FROM AUTHOR]

Saffron is recognized as one of the important agricultural products in Iran, but in recent years, its yield production has significantly decreased by 3.42 kilograms per hectare. One of the factors contributing to this decrease is the time-consuming process of harvesting and separating the stigmas from the flowers, which requires approximately 180 to 204 minutes to process 2400 to 2700 flowers. Planting, maintenance, harvesting, and processing saffron stages are done manually, which not only adds value to the product but also raises concerns about microbial and fungal contamination. Various studies have been conducted to separate saffron stigmas from flowers, but so far, there has been no integration of these studies. In this study, different methods of stigma separation are classified into three stages: flower alignment methods before cutting (including human labor, robots, mechanical and airflow mechanisms), methods of cutting flower parts (including pulling out the stigmas, rotational force, and cutting blades), and stigma separation methods (including centrifugal force, friction coefficient, airflow, and porous surfaces). The review and comparison of various saffron stigma separation devices have shown that a combination of image processing techniques, optical sensors, optimized mechanical systems, and the application of airflow is the best method to achieve high precision and quality in stigma separation. These devices benefit from mechanical and suction mechanisms to more advanced systems such as image processing and optical sensors. High-precision image processing methods for identifying the cutting location and optical sensors for accurately detecting the color and shape of the stigmas can increase the speed and accuracy of separation. This combination, along with optimized mechanical systems and the use of airflow as a non-contact method to reduce damage to the stigmas, can help maintain the quality of the final product. This article will review the recent advancements reports by researchers in the field of saffron, particularly in the area of mechanized methods for separating stigmas from fresh saffron flowers. Furthermore, by comprehensively analyzing these methods, their strengths and weaknesses will be discussed, and suggestions for improving the mechanization of the stigma separation process from fresh flowers will be presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optical sensors are highly-promising for seed phenotype digitization. • Spectroscopy, digital imaging, and 3D reconstruction are primarily used digital techniques. • Optical sensors can detect both visible external and invisible internal phenotypes. • Matched optical sensors can effectively reduce resource loss in seed phenotype evaluation. • Future research should focus on phenotype equipment, platform, and data processing algorithms for automatic, integrated, and intelligent evaluation. The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping. [ABSTRACT FROM AUTHOR]

In this study, we developed and demonstrated a millimeter-wave electric field imaging system using an electro-optic crystal and a highly sensitive polarization measurement technique using a polarization image sensor, which was fabricated using a 0.35-µm standard CMOS process. The polarization image sensor was equipped with differential amplifiers that amplified the difference between the 0° and 90° pixels. With the amplifier, the signal-to-noise ratio at low incident light levels was improved. Also, an optical modulator and a semiconductor optical amplifier were used to generate an optical local oscillator (LO) signal with a high modulation accuracy and sufficient optical intensity. By combining the amplified LO signal and a highly sensitive polarization imaging system, we successfully performed millimeter-wave electric field imaging with a spatial resolution of 30 × 60 µm at a rate of 1 FPS, corresponding to 2400 pixels/s. [ABSTRACT FROM AUTHOR]

Luminescence in the second near‐infrared (NIR‐II, 1,000–1,700 nm) window is beneficial especially for deep tissue imaging and optical sensors because of intrinsic high permeability through various media. Strong electron‐acceptors with low‐lying lowest unoccupied molecular orbital (LUMO) energy levels are a crucial unit for donor–acceptor (D–A) π‐conjugated polymers (CPs) with the NIR‐II emission property, however, limited kinds of molecular skeletons are still available. Herein, D–A CPs involving fluorinated boron‐fused azobenzene complexes (BAz) with enhanced electron‐accepting properties are reported. Combination of fluorination at the azobenzene ligand and trifluoromethylation at the boron can effectively lower the LUMO energy level down to −4.42 eV, which is much lower than those of conventional strong electron‐acceptors. The synthesized series of CPs showed excellent absorption/fluorescence property in solution over a wide NIR range including NIR‐II. Furthermore, owing to the inherent solid‐state emissive property of the BAz skeleton, obvious NIR‐II fluorescence from the film (up to λFL=1213 nm) and the nanoparticle in water (λFL=1036 nm, brightness=up to 29 cm−1 M−1) were observed, proposing that our materials are applicable for developing next‐generation of NIR‐II luminescent materials. [ABSTRACT FROM AUTHOR]

In this article, researchers describe a novel method for identifying muscular tiredness in human subjects by combining surface electromyography (sur-EMG) signals with long short-term memory (LSTM) networks. To improve the quality of the sur-EMG data, the experiment begins with the shaving of unwanted leg hair and wiping the skin with medicinal alcohol. Sur-EMG sensors are placed on specific leg muscles, and the participants perform an incremental cycling protocol. The core contribution of this research is an enhanced wavelet packet verge denoising algorithm that effectively filters noise from sur-EMG signals. This algorithm uses a continuous and differentiable verge function to optimize denoising. A Shannon entropy-based method determines the optimal number of decomposition layers, ensuring the highest denoising efficacy. Feature extraction techniques, such as root mean square (RMS), integrated electromyogram (IEMG), median frequency (MF), and mean power frequency (MPF), are employed to capture the temporal and frequency domain characteristics of the sur-EMG signals. A muscle fatigue recognition model is built using long short-term memory (LSTM) networks. The sur-EMG signals are sorted into fatigued and non-fatigued categories using this model. The performance of the LSTM model is compared to support vector machine (SVM) and convolutional neural network (CNN) models. The accuracy of the proposed strategy is shown to be superior by the evaluation results compared to other models, achieving an accuracy rate of 95.27%. The study emphasizes the importance of feature selection and dataset choice in developing robust muscle fatigue recognition models. These findings have promising implications for the field of muscle fatigue assessment and sports science. [ABSTRACT FROM AUTHOR]

Remotely sensed images acquired by optical sensors are frequently affected by cloud cover, which can impede interpretation and analysis. Generative adversarial networks (GANs) have proved to be effective in removing cloud occlusions from such images. In this study, we compare the performance of two GAN-based algorithms, CycleGAN and Pix2Pix, for cloud cover removal on an open-source Remote sensing Image Cloud rEmoving (RICE) dataset. Our main objective is to evaluate the effectiveness of these models in terms of image quality metrics and training time. The obtained results show that the CycleGAN model achieved superior image quality, with a peak signal-to-noise ratio (PSNR) of 29.589 dB, mean square error (MSE) of 3.662, and structural similarity index (SSIM) of 0.907, with a training time of 5 h and 10 min. On the other hand, the Pix2Pix model, while achieving lower image quality metrics of 28.160 dB at 100 epochs, mean square error (MSE) of 46.760, and structural similarity index (SSIM) of 0.761, has a shorter training time of 2 h and 20 min, making it more suitable for applications that require faster processing. Our study provides valuable insights into the performance of deep learning models for cloud removal in remotely sensed images. The findings suggest that both CycleGAN and Pix2Pix models are promising approaches for cloud removal, with the potential for further optimization and development. [ABSTRACT FROM AUTHOR]

In metropolitan regions, the quick increase in vehicles has caused gridlock, pollutants, and problems in the transportation of products. IoT is a breakthrough advancing the development of intelligent management systems and automated processes throughout the universe. For automatic and intelligent cultures, this is a crucial contribution. Effective management and prevention of traffic congestion are reliable strategies that contribute to conserving numerous valuable resources. Autonomous vehicles and smart devices are equipped with an IoT-based ITM system of sensors that can understand, collect, and transfer information. Another method to enhance the transportation system is deep learning (DL). Traffic jams, delays, and a high death rate result from the numerous problems of conventional transportation management systems. An adaptive traffic management system (ATM) built on DL and IoT is designed and implemented in this study's efforts. The suggested system's architecture is built on the automobile, the framework, and the events. To account for all potential problems with the transportation system, the design uses a variety of situations. The proposed methodology uses a Quantum Optical Communication (QOC) system for Traffic monitoring and enhanced data communication. The suggested adaptive traffic management-based QOC model continuously modifies the timing of traffic signals based on traffic volume and anticipated movements from neighbouring junctions. By progressively allowing cars to cross green lights, it considerably reduces the duration of travel. It also reduces congestion in traffic by creating a smoother transition within 38 s, which is less than all other techniques. [ABSTRACT FROM AUTHOR]

With the continuous advancement of artificial intelligence and computer vision, the application of image recognition-based target tracking systems in the field of sports has become increasingly prevalent. In particular, a basketball goal recognition system holds immense importance for enhancing game fairness and penalty accuracy. Therefore, the objective of this study is to develop a basketball target recognition system that employs optical sensors and utilizes image feature extraction for precise target recognition. To begin with, image data from basketball matches is collected and undergoes preprocessing to ensure optimal image quality. Subsequently, an optical sensor is employed for data acquisition and transmission, enabling the capture of continuous images throughout the basketball game. These images are then subjected to image processing algorithms to analyze and extract key features. Furthermore, machine learning algorithms are implemented to train and optimize the recognition results, thereby enhancing the accuracy and stability of target recognition. By analyzing and processing real-time image data from actual game scenarios, the system successfully extracts the essential image features necessary for precisely recognizing basketball targets. Notably, the test results affirm the system's exceptional performance in terms of accuracy and real-time target recognition. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things and artificial intelligence, the functions of football training systems continue to expand to meet the needs of training. Optical imaging technology plays an important role in football training, which can monitor and analyze players' movements and techniques in real time. The aim of this study is to develop an intelligent football training system based on iot optical imaging and sensor data monitoring to improve the skill level of players. The optical imaging equipment and sensors are arranged on the football field, and the movement and technical data of the players are collected. The data are processed and analyzed by computer vision and data analysis algorithms. Finally, the analysis is fed back to the players and coaches. By using the Smart Football training system, players can get their own technical evaluation and feedback in real time, and coaches can better understand the training situation and progress of players. The results show that the intelligent football training system based on the Internet of Things optical imaging and sensor data monitoring is an effective tool to assist players and coaches to improve the training effect and enhance the technical level of players. [ABSTRACT FROM AUTHOR]

Background: Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer's, Parkinson's, and Huntington's disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose long-projecting axons are often vulnerable in neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring axonal ATP levels for axonal transport, critical for axonal function. Methods: We generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting a NAD hydrolase, sterile alpha and TIR motif-containing protein 1 (SARM1), prevented axonal deficits caused by NMNAT2 loss. This study used a combination of techniques, including genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos. Results: We provide in vivo evidence that NMNAT2 in glutamatergic neurons is required for axonal survival. Using in vivo and in vitro studies, we demonstrate that NMNAT2 maintains the NAD-redox potential to provide "on-board" ATP via glycolysis to vesicular cargos in distal axons. Exogenous NAD+ supplementation to NMNAT2 KO neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate both in vitro and in vivo that reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons. Conclusion: NMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport. [ABSTRACT FROM AUTHOR]

The satellite‐constellation passive‐microwave Brightness Temperature (TB) observations, with global coverage, and more additions from upcoming CubeSats, have been mainly used in surface precipitation retrievals. However, these observations can also be used to indicate the intensity of convective systems. This study attempts to relate Global Precipitation Mission (GPM) Microwave Imager (GMI) TBs to Geostationary Lightning Mapper (GLM) lightning flashes by using 4 years (02/2018–04/2022) of GPM Precipitation Feature (PF) database. GMI TBs are collocated to the GLM lightning counts, and to the ERA5 reanalysis 2‐m air temperature in PFs. Three Artificial Intelligent Neural Network Models (AI‐NN) are trained to classify PFs producing lightning in a 20‐min window respectively for land, ocean, or coast. The flash rate of the determined Lightning producing PFs (LPFs) is then quantified by three other AI‐NNs, each trained for one of the three regions. Though the models clearly capture the global geographical distribution of LPFs with a Probability Of Detection over 90%, high False Alarm Rates are found, ranging from 49.9% over land to 91.5% over the ocean. The importance of TB at each passive microwave channel varies regionally, corresponding to the different microphysical properties in various types of precipitation systems. The global lightning distribution is derived by applying the AI models to global PFs and is well compared to the lightning climatology from Lightning Imaging Sensor and Optical Transient Detector. This suggests that the use of passive microwave TBs can help to fill the gaps in lightning monitoring thanks to their global coverage. Plain Language Summary: We are taking advantage of the indirect relationship between brightness temperatures (TBs) (from GPM‐GMI core satellite) and lightning rate (from GOES‐GLM) (i.e., low TBs ∼ lightning vs. high TBs ∼ non‐lightning) to build a set of AI Shallow Neural Networks. The goal of the study is to find the extent to which TBs alone can determine the flash rate in a 20‐min window (10‐min before and after) surrounding the measurement of the TBs of a Precipitation Feature (PF). First, models are trained to classify Lightning PFs (LPFs) versus non‐Lightning PFs (nLPFs) and next, models are trained to quantify the flash count (regression). Models are trained in the GLM domain (American continents + surrounding oceans) and then tested globally. Results show that TBs allow a lightning determination and quantification to some extent, but produces high false alarm rates (i.e., ∼50% for land to ∼90% for ocean) due to limitations resulting from the indirect relationship between lightning and TBs (i.e., the limit between LPFs and nLPFs is not clear enough for the models to do better). Global application of the models show similar results to literature lightning climatologies, showing that AI can pick most areal information. This work fits the recent emergence of passive‐microwave only CubeSats. Key Points: Classification and regression AI shallow neural network models are built using GPM‐MI brightness temperature to predict lightningAI models can accurately determine extremes but brightness temperatures lack information to differentiate lightning in weaker systemsA global lightning climatology is derived from the models' results and shows similar results to satellite derived climatology [ABSTRACT FROM AUTHOR]

The simulation theory argues that physical execution, action observation, and imagery share similar underlying mechanisms. Accordingly, applying a high-level psychological variable (variability of practice) should have a similar effect on all three modes. To test this theory, a total of 90 right-handed students participated in this study and were randomly divided into variable versus constant groups in three practice conditions, including physical, observational, and imagery. After a pretest (10 random trials of the putting task), the participants completed 50 practice trials. The groups performed/observed/imagined the task in the variable (different distances to different goals) or constant (fixed distance and goal) practice conditions. Also, there was an extra variable group in the physical and observational conditions, deprived of watching the feedback from the action. The participants completed a retention test 24 hr after the training. The effect of practice variability was observed in physical and observational conditions, but was not seen in the imagery condition. The no-feedback groups did not perform significantly differently from the imagery groups. The reason could be the lack of actual visual feedback during imagery. [ABSTRACT FROM AUTHOR]

This paper presents an approach for achieving full optical photoacoustic imaging with enhanced resolution utilizing speckle pattern analysis. The proposed technique involves projecting patterns derived from binary masks corresponding to orthogonal functions onto the target to elicit a photoacoustic signal. The resulting signal is then recorded using a high-speed camera and analyzed using correlation analysis of the speckle motion. Our results demonstrate the feasibility of this optical approach to achieve imaging with enhanced resolution without the need for physical contact with the target, opening up new possibilities for non-invasive medical imaging and other applications. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging has emerged as a promising biomedical imaging technique that enables visualization of the optical absorption characteristics of biological tissues in vivo. Among the different photoacoustic imaging system configurations, optical-resolution photoacoustic microscopy stands out by providing high spatial resolution using a tightly focused laser beam, which is typically transmitted through optical fibers. Achieving high-quality images depends significantly on optical fluence, which is directly proportional to the signal-to-noise ratio. Hence, optimizing the laser-fiber coupling is critical. Conventional coupling systems require manual adjustment of the optical path to direct the laser beam into the fiber, which is a repetitive and time-consuming process. In this study, we propose an automated laser-fiber coupling module that optimizes laser delivery and minimizes the need for manual intervention. By incorporating a motor-mounted mirror holder and proportional derivative control, we successfully achieved efficient and robust laser delivery. The performance of the proposed system was evaluated using a leaf-skeleton phantom in vitro and a human finger in vivo, resulting in high-quality photoacoustic images. This innovation has the potential to significantly enhance the quality and efficiency of optical-resolution photoacoustic microscopy. [ABSTRACT FROM AUTHOR]

Nowadays, Predictive Maintenance is a mandatory tool to reduce the cost of production in the semiconductor industry. This paper considers as a case study a critical part of the electrochemical deposition system, namely, the four Pins that hold a wafer inside a chamber. The aim of the study is to replace the schedule of replacement of Pins presently based on fixed timing (Preventive Maintenance) with a Hardware/Software system that monitors the conditions of the Pins and signals possible conditions of failure (Predictive Maintenance). The system is composed of optical sensors endowed with an image processing methodology. The prototype built for this study includes one optical camera that simultaneously takes images of the four Pins on a roughly daily basis. Image processing includes a pre-processing phase where images taken by the camera at different times are coregistered and equalized to reduce variations in time due to movements of the system and to different lighting conditions. Then, some indicators are introduced based on statistical arguments that detect outlier conditions of each Pin. Such indicators are pixel-wise to identify small artifacts. Finally, criteria are indicated to distinguish artifacts due to normal operations in the chamber from issues prone to a failure of the Pin. An application (PINapp) with a user friendly interface has been developed that guides industry experts in monitoring the system and alerting in case of potential issues. The system has been validated on a plant at STMicroelctronics in Catania (Italy). The study allowed for understanding the mechanism that gives rise to the rupture of the Pins and to increase the time of replacement of the Pins by a factor at least 2, thus reducing downtime. [ABSTRACT FROM AUTHOR]

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is an important component of the Earth system as it can affect both climate and air quality. In this work, we use observed tropospheric column ozone derived from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) OMI-MLS, in addition to OMI ozone retrieved in discrete vertical layers, and compare it to tropospheric ozone from UM-UKCA simulations (which utilize the Unified Model, UM, coupled to UK Chemistry and Aerosol, UKCA). Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region, specifically its seasonal, interannual and decadal variability, and to understand what factors are driving such changes. The model exhibits a large positive bias (greater than 5 DU or ∼ 50 %) in the tropical upper troposphere: through sensitivity experiments, time series correlation, and comparison with the Lightning Imaging Sensor and Optical Transient Detector lightning flash dataset, the model positive bias in the tropics is attributed to shortcomings in the convection and lightning parameterizations, which overestimate lightning flashes in the tropics relative to mid-latitudes. Use of OMI data, for which vertical averaging kernels and a priori information are available, suggests that the model negative bias (6–10 DU or ∼ 20 %) at mid-latitudes, relative to OMI-MLS tropospheric column, could be the result of vertical sampling. Ozone in the North Atlantic peaks in spring and early summer, with generally good agreement between the modelled and observed seasonal cycle. Recent trends in tropospheric ozone were investigated: whilst both observational datasets indicate positive trends of ∼ 5 % and ∼ 10 % in North Atlantic ozone, the modelled ozone trends are much closer to zero and have large uncertainties. North Atlantic ozone interannual variability (IAV) in the model was found to be correlated to the IAV of ozone transported to the North Atlantic from the stratosphere (R=0.77) and emission of NO x from lightning in the tropics (R=0.72). The discrepancy between modelled and observed trends for 2005–2018 could be linked to the model underestimating lower stratospheric ozone trends and associated stratosphere to troposphere transport. Modelled tropospheric ozone IAV is driven by IAV of tropical emissions of NO x from lightning and IAV of ozone transport from the stratosphere; however, the modelled and observed IAV differ. To understand the IAV discrepancy we investigated how modelled ozone and its drivers respond to large-scale modes of variability. Using OMI height-resolved data and model idealized tracers, we were able to identify stratospheric transport of ozone into the troposphere as the main driver of the dynamical response of North Atlantic ozone to the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). Finally, we found that the modelled ozone IAV is too strongly correlated to the El Niño–Southern Oscillation (ENSO) compared to observed ozone IAV. This is again linked to shortcomings in the lightning flashes parameterization, which underestimates (overestimates) lightning flash production in the tropics during positive (negative) ENSO events. [ABSTRACT FROM AUTHOR]

Imaging-based problem-solving approaches are an exemplary way of handling problems in various scientific applications. With an increased demand for automation, artificial intelligence techniques have shown exponential growth in recent years. In this context, deep-learning-based "learned" solutions have been widely adopted in many applications and are thus slowly becoming an inevitable alternative tool. It is known that in contrast to the conventional "physics-based" approach, deep learning models are a "data-driven" approach, where the outcomes are based on data analysis and interpretation. Thus, deep learning approaches have been applied in several (optical and computational) imaging-based scientific problems such as denoising, phase retrieval, hologram reconstruction, and histopathology, to name a few. In this work, we present two deep-learning networks for 3D image denoising and off-focus voxel removal. [ABSTRACT FROM AUTHOR]

Optical imaging has been utilized in nature and technology for decades. Recently, new methods of optical imaging assisted by computational imaging techniques have been proposed and demonstrated. We describe several new methods of three-dimensional optical imaging, from Fresnel incoherent correlation holography (FINCH) to interferenceless coded aperture correlation holography (COACH). FINCH and COACH are methods for recording digital holograms of a three-dimensional scene. However, COACH can be used for other incoherent and coherent optical applications. The possible applications for these imaging methods, ranging from a new generation of fluorescence microscopes to noninvasive imaging methods through a scattering medium, are mentioned. [ABSTRACT FROM AUTHOR]

Image sensor communication (ISC), also known as optical camera communication, is a form of visible light communication that utilizes image sensors rather than a single photodiode, for data reception. ISC offers spatial separation properties and robustness to ambient noise, making it suitable for outdoor applications such as intelligent transportation systems (ITSs). This review analyzes the research trends in ISC, specifically concerning its application in ITSs. Our focus is on various ISC receivers, including rolling shutter cameras, global shutter high-speed cameras, optical communication image sensors, and event cameras. We analyze how each of these receivers is being utilized in ISC vehicular applications. In addition, we highlight the use of ISC in range estimation techniques and the ability to achieve simultaneous communication and range estimation. By examining these topics, we aim to provide a comprehensive overview of the role of ISC technology in ITSs and its potential for future development. [ABSTRACT FROM AUTHOR]

We describe the real-space imaging of the products of molecular scattering, applied to collisions of hydroxyl radicals with low-vapor-pressure-liquid surfaces. A pulsed molecular beam of OD (for technical reasons) with a mean laboratory-frame kinetic energy of 29.5 kJ mol−1 was directed at continually refreshed surfaces of the representative liquids perfluoropolyether, squalane, and squalene. Laser-induced fluorescence (LIF) was excited by pulsed laser light shaped into a planar sheet, tuned to selected rovibronic transitions in the OD A–X band. The LIF emission was imaged and intensified before being captured by an external camera. Sequences of images allowed the evolution of the incident packet and scattered plumes of OD molecules to be observed. The results confirm previous observations of the internal-state distributions of the scattered OD and its differential survival probability on different liquid surfaces. New measurements of the angular distributions found them all to be broad and approximately symmetric, independent of the angle of incidence. This is interpreted as implying a high degree of atomic-scale roughness, rather than a predominant trapping-desorption mechanism, because of the other observed signatures of impulsive scattering; these include the degree of OD rotational excitation, superthermal speeds, and the correlation of speed with scattering angle. This approach has considerable potential to be applied in related gas-surface scattering experiments. It is immune from the difficulties of some other imaging methods that involve charged-particle detection and allows a spatially extended region of the scattering plane perpendicular to the surface to be imaged. [ABSTRACT FROM AUTHOR]

Biomedical optical imaging is an important subdivision of optical imaging with the aim of understanding the anatomy and function of life. In principle, biomedical optical imaging systems form an image by manipulating the excitation light and detecting the signals originating from light and tissue interactions. Ever since the invention of the first optical microscope over 1000 years ago, biomedical optical imaging technologies have been steadily evolving to enable faster, deeper, and higher resolution imaging. These technologies have led to a more comprehensive understanding of life at the macro-, micro-, and nanoscales and have improved clinical diagnosis and treatment. This tutorial provides an overview of biomedical optical imaging techniques and their applications. Based on the imaging depth, this tutorial classifies the current optical imaging systems into two regimes: diffraction and diffusion. Within each regime, a few commonly used imaging techniques and their biological imaging applications are discussed. Finally, we provide an outlook of future biomedical optical imaging. [ABSTRACT FROM AUTHOR]

Gray matter, in the outermost layer of the cerebral cortex, plays a significant role in processing important information, such as when reasoning and planning, in addition to influencing intelligence, emotion, memory, and language. In this paper, measurements of the optical properties, such as the attenuation coefficients, scattering coefficients, scattering efficiency, and penetration depth of gray matter in the cerebral cortex were measured in the fresh brain tissue of a healthy human male at a spectral range of 400–1100 nm. Determining the optical properties of gray matter is important for developing NIR noninvasive diagnostic imaging techniques and therapy. The absorption spectra of the gray matter tissues obtained here showed clear peaks at 550 and 580 nm due to HBO2 and 970 nm due to water. The attenuation coefficient, transport length, and penetration depth for the gray matter were measured at 800 nm. The possible NIR optical imaging depth was roughly 3.8 mm, determined by the theoretical limit resulting from ballistic and snake photons. Using Beer's law and the Mie model, the structural properties, such as the density of neurons, in the gray matter of human brain tissue, were investigated for the first time. The density of neurons in the examined gray matter tissue sample was estimated as roughly 40,000 neurons/mg. [ABSTRACT FROM AUTHOR]

Due to the extreme environmental temperature variations, solutions that enable ultra-low thermal sensitivity in a mirror assembly are crucial for high-performance aerial optical imaging sensors (AOIS). Strategies such as the elimination of the coefficient of thermal expansion (CTE) mismatch and the employment of a flexure connection at the interface cannot be simply duplicated for the application involved, demanding specific design constraints. The contributions of support point number to the surface thermal sensitivity reduction and support stiffness improvement have been studied. A synthetic six-point support system that integrates equally spaced multiple ultra-low radial stiffness mirror flexure units and assembly external interface flexure units has been demonstrated on a 260 mm apertured annular mirror that involves significant CTE mismatch and demanding support stiffness constraint. The surface deformation RMS, due to the 35 °C temperature variation, is 16.7 nm. [ABSTRACT FROM AUTHOR]

• A multi-sensor fusion target localization scheme is proposed, and laser ranging combines visible and infrared cameras for localization. • A target localization method based on machine vision and coordinate transformation is modeled, which uses the pixel plane as the starting plane with higher accuracy. • The principle of fusion localization of visible sensors and infrared sensors is proposed, and the proposed method has higher accuracy compared with the traditional localization method. In response to the challenges of acquiring spatial target position information and achieving high precision in existing methods, this paper proposes a multi-dimensional high-precision positioning method for spatial targets through multi-sensor fusion. Utilizing optical detection technology, the method extracts two-dimensional positional information of spatial targets on the observation plane. By deriving a fusion positioning formula for visible light and infrared based on the Gaussian mixture TPHD, the proposed method enhances positioning accuracy by 0.2 m compared to using visible light or infrared alone. Additionally, by integrating laser ranging for distance dimension information, precise target positioning in the world coordinate system is achieved. Outdoor experiments for spatial target positioning validate the method's effectiveness, utilizing visible light and infrared cameras along with laser ranging. Comparative analysis with a binary star angular measurement-only method demonstrates 17.9 % improvement in positioning accuracy, with the proposed method achieving 0.12 m accuracy for 5 cm spatial targets at 5 km distance. [ABSTRACT FROM AUTHOR]

The article "Quantum optical sensors and IoT for image data analysis in traffic management" published in Optical & Quantum Electronics has been retracted by the Publisher due to concerns about compromised peer-review processes, inappropriate references, and being out of scope for the journal. The authors, Enzhan Zhang, Hengjie Jiang, and Xia Zhang, did not respond to correspondence regarding the retraction. Springer Nature, the Publisher, remains neutral in this matter. [Extracted from the article]

This innovative approach enables data transmission via light and transforms traditional cameras into intelligent communication devices. Acknowledgments The Guest Editor would like to thank all authors who submitted their valuable and insightful contributions to this Special Issue, addressing key challenges with respect to optical camera communication as well as its applications. Optical Camera Communication (OCC) is a groundbreaking technology that combines optical signals and image sensors for data transmission. [Extracted from the article]

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Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data. [ABSTRACT FROM AUTHOR]

Objectives: Optical impressions of implants in the edentulous arch are challenged by the absence of distinct surface morphology between multiple implant scan bodies. The purpose of this in vitro study was to evaluate a newly developed scan aid in various designs and colors to improve the accuracy of multi‐implant scans. Materials and Methods: A universal scan aid in three different designs (circular, square, irregular) and three different materials (colors: beige, gray, white) was applied to the implant scan bodies of a master model of six implants in an edentulous maxilla. Ten scans using an intra‐oral scanner of each scan aid were acquired. Reference scanning was performed using a desktop optical scanner. Alignment of scans was performed at the first scan body in a three‐dimensional modeling and inspection software and deviations for trueness and precision were calculated using the signed nearest‐neighbor method and then statistically analyzed (α =.05). Results: Overall, the beige irregular scan aid had the highest trueness and showed significant differences compared to unsplinted scans. The precision showed more heterogenous results and decreased when using the scan aid. Ease of use was observed with the irregular gray scan aid due to its increased elongation at break. Conclusions: The evaluated scan aid led to improved trueness when compared to unsplinted scans. Even though the irregular design in beige color showed highest trueness, the poor fracture strength of the tested material requires further improvement. [ABSTRACT FROM AUTHOR]

Plankton distributions are remarkably 'patchy' in the ocean. In this study, we investigated the contrasting phytoplankton-zooplankton distributions in relation to wind mixing events in waters around a biodiversity-rich island (Runde) located off the western coast of Norway. We used adaptive sampling from AUV and shipboard profiles of in-situ phytoplankton photo-physiology and particle identification (copepods, fecal pellets and the dinoflagellate Tripos spp.) and quantification using optical and imaging sensors. Additionally, traditional seawater and net sampling were collected for nutrient and in-vitro chlorophyll a concentrations and phytoplankton and meso-zooplankton abundances. Persistent strong wind conditions (~5 days) disrupted the stratification in offshore regions, while stratification and a subsurface chlorophyll maximum (SCM) were observed above the base of the mixed layer depth (MLD ~30 m) in inshore waters. Contrasting phytoplankton and zooplankton abundances were observed between inshore (with the presence of a SCM) and offshore waters (without the presence of a SCM). At the SCM, phytoplankton abundances (Tripos spp., the diatom Proboscia alata and other flagellates) were half (average of 200 cell L-1) of those observed offshore. On the contrary, meso-zooplankton counts were ~6× higher (732 ind m-3 for Calanus spp.) inshore (where a SCM was observed) compared to offshore areas. In parallel, fecal pellets and ammonium concentrations were high (>1000 ind m-3 for the upper 20 m) at the SCM, suggesting that the shallow mixed layer might have increased encounter rates and promoted strong grazing pressure. Low nutrient concentrations (< 1μM for nitrate) were found below the MLD (60 m) in offshore waters, suggesting that mixing and nutrient availability likely boosted phytoplankton abundances. The size of the absorption cross-section (σPII') and yield of photosystem II photochemistry under ambient light (φPII') changed according to depth, while the depth-related electron flow (JPII) was similar between regions, suggesting a high degree of community plasticity to changes in the light regime. Our results emphasize the importance of using multiple instrumentation, in addition to traditional seawater and net sampling for a holistic understanding of plankton distributions. [ABSTRACT FROM AUTHOR]

The article discusses significance of Australian army aviation capabilities. Topics discussed include the Defence Strategic Update released by Australian government in the year 2020 outlining the new strategy for defence, discussed three persistent challenges faced by army aviation command including organisational complexity exacerbated by geo - graphical dislocation, and discussed reliability and the performance of Chinook helicopters in domestic and operational environments.