Real-time monitoring of antibiotics in hospital and pharmaceutical wastewater using ultraviolet–visible (UV–Vis) spectroscopy is considered a promising method. Although gas chromatography–mass spectrometry (GC–MS) and other methods can detect antibiotics with quite low limits of detection (LOD), they possess various limitations. UV–Vis spectroscopy combined with chemometric methods is a promising choice for monitoring antibiotics. In this study, two immersed in situ UV–Vis sensors were used to explore the relationship between absorption spectra and antibiotics and study the influence of the optical path length on the LOD. The LODs of sensor 2 using a 10 cm optical path is up to 300 times lower than that of sensor 1 using a 0.5 mm optical path. Moreover, multiple antibiotics in the wastewater were investigated in real-time manner. The absorption spectra of 70 groups of wastewater samples containing different concentrations of tetracycline, ofloxacin, and chloramphenicol were measured. The results indicate that the nine wavelengths selected by interval partial least squares (iPLS) after the second derivative pretreatment have better predictability for ofloxacin and the six wavelengths selected by competitive adaptive reweighted sampling (CARS) after the first derivative. The multi-fold cross-validation results indicate that the model has a good predictive ability.

Mid-infrared imaging systems are widely applied in gas-leak detection. However, infrared images generally suffer from low contrast and poor quality. In this paper, an image-enhancement method based on Gaussian filtering and adaptive histogram segmentation is proposed to effectively improve the quality of infrared images. It can effectively improve the quality of infrared images, which contributes to the subsequent gas-image feature extraction. The traditional background modeling algorithm is analyzed, and the ViBe (visual background extractor) algorithm is studied in depth. Based on the advantages and disadvantages of the ViBe algorithm and the characteristics of gas-leak images, a gas-leak region detection method based on the improved ViBe algorithm is proposed. The test results show that it can quickly establish a background model, segment the gas-leak region with motion characteristics, and render the gas-leak region in color based on grayscale mapping to achieve the automatic detection and enhanced display of gas leaks.

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.

Underwater optical imaging plays a crucial role in maritime safety, enabling reliable navigation, efficient search and rescue operations, precise target recognition, and robust military reconnaissance. However, conventional underwater imaging methods often suffer from severe backscattering noise, limited detection range, and reduced image clarity—challenges that are exacerbated in turbid waters. To address these issues, Underwater Laser Range-Gated Imaging has emerged as a promising solution. By selectively capturing photons within a controlled temporal gate, this technique effectively suppresses backscattering noise-enhancing image clarity, contrast, and detection range. Nevertheless, residual noise within the imaging slice can still degrade image quality, particularly in challenging underwater conditions. In this study, we propose an enhanced U-Net neural network designed to mitigate noise interference in underwater laser range-gated images, improving target recognition performance. Built upon the U-Net architecture with added residual connections, our network combines a VGG16-based perceptual loss with Mean Squared Error (MSE) as the loss function, effectively capturing high-level semantic features while preserving critical target details during reconstruction. Trained on a semi-synthetic grayscale dataset containing synthetically degraded images paired with their reference counterparts, the proposed approach demonstrates improved performance compared to several existing underwater image restoration methods in our experimental evaluations. Through comprehensive qualitative and quantitative evaluations, underwater target detection experiments, and real-world oceanic validations, our method demonstrates significant potential for advancing maritime safety and related applications.

Terahertz antennas can greatly enhance the near fields and enable strong light–matter interactions, and thus have been widely used in applications such as terahertz sensing and detection. Here we propose a novel approach to further enhance the near fields in terahertz antennas. We show that by sandwiching hyperbolic metamaterials that are composed of InSb and SiO ${}_2$ multilayer and that are dressed with hole arrays, between a terahertz dipole antenna and the substrate, the near-field electric field intensities in the antenna can be further enhanced by more than three times. Simulations reveal that this enhancement originates from the doubly enhanced in-plane electric field component and the significantly enhanced out-of-plane electric field component. We expect this work will advance the design of terahertz antennas that are widely used in sensors and detectors.