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Public awareness of preventing pathogenic microorganisms has significantly increased. Among numerous microbial prevention methods, the deep-ultraviolet (DUV) disinfection technology has received wide attention by using the nitride-based light-emitting diode (LED). However, the light extraction efficiency of DUV LEDs and the utilization rate of emitted DUV light are relatively low at the current stage. In this study, a light distribution design (referred to as the reflective system) was explored to enhance the utilization of emitted DUV from LEDs, leading to successful and efficient surface and air disinfection. Optical power measurements and microbial inactivation tests demonstrated an approximately 79% improvement in average radiation power density achieved by the reflective system when measured at a 5 cm distance from the irradiation surface. Moreover, a statistically significant enhancement in local surface disinfection was observed with low electric power consumption. The reflective system was integrated into an air purifier and underwent air disinfection testing, effectively disinfecting a 3 m3 space within ten minutes. Additionally, a fluorine resin film at the nanolevel was developed to protect the light module from oxidation, validated through a 1200 h accelerated aging test under humid conditions. This research offers valuable guidance for efficient and energy-saving DUV disinfection applications. [ABSTRACT FROM AUTHOR]

Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7% higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels. [ABSTRACT FROM AUTHOR]

High dynamic range image (HDRI) which is combined with low dynamic range image (LDRI) needs to be mapped to a low dynamic area to display. In the process of mapping, it is impossible to determine the contribution of low dynamic image sequences in the display images, so that it results in a problem that the low dynamic images cannot be accurately selected. In this paper, for the first time, a contribution algorithm from LDRI to HDRI according to the corresponding response curve of the camera is proposed. [ABSTRACT FROM AUTHOR]

The imaging process of terahertz in-line digital holography is susceptible to environmental interference, and it is difficult to obtain high-quality images and image segmentation results. The challenge can be better handled by using the region of interest (ROI) condition to improve the image quality of the object region and reduce the interference of peripheral noise. In this paper, for two-dimensional 2.52 THz in-line digital holography, a method to add a variety of real backgrounds in the simulation scene is proposed, and through this method, a sufficient amount of close-to-real-scene images are produced to train the YOLOv5 model. The object ROI is extracted by the YOLOv5 model (YOLOv5-ROI). Based on the region, a new, to the best of our knowledge, support-domain-constrained phase retrieval algorithm (YOLOv5-ROI-SPRA) and an image segmentation method combined with the Otsu method (YOLOv5-ROI-Otsu) are proposed, respectively. The better results show that the expected purpose is achieved.