Non-line-of-sight imaging over 1.43 km

Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial-temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial-temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.https://www.pnas.org/content/pnas/118/10/e2024468118.full.pdfPublished versio

Physicochemical Properties of Water-Based Copolymer and Zeolite Composite Sustained-Release Membrane Materials

A nitrogen fertilizer slow-release membrane was proposed using polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), epoxy resin, and zeolite as raw materials. The effects of the water-based copolymer (PVA:PVP) solution ratio A (A1–A4) and zeolite amount B (B1–B4) on the water absorption rate (XS), water permeability (TS), fertilizer permeability (TF), tensile strength (KL), elongation at break (DSL), and viscosity (ND) of the membrane were explored using the swelling method, a self-made device, and a universal testing machine. The optimal combination of the water-based copolymer and zeolite amount was determined by the coefficient-of-variation method. The results show that the effects of the decrease in A on KL and the increase in B on KL and DSL are promoted first and then inhibited. DSL and ND showed a negative response to the A decrease, whereas XS, TS, and TF showed a positive response. The effect of increasing B on ND, TS, and TF showed a zigzag fluctuation. In the condition of A1–A3, XS showed a negative response to the B increase, whereas in the condition of A4, XS was promoted first and then inhibited. Adding PVP and zeolite caused the hydroxyl stretching vibration peak of PVA at 3300 cm−1 to widen; the former caused the vibration peak to move to low frequencies, and the latter caused it to move to high frequencies. The XRD pattern shows that the highest peak of zeolite is located at 2θ = 7.18° and the crystallization peak of the composite membrane increases with the rise in the proportion of zeolite. Adding PVP made the surface of the membrane smooth and flat, and adding a small amount of zeolite improved the mechanical properties of the membrane and exhibited good compatibility with water-based copolymers. In the evaluation model of the physicochemical properties of sustained-release membrane materials, the weight of all indicators was in the following order: TF > ND > TS > KL > XL > DSL. The optimal membrane material for comprehensive performance was determined to be A2B3

An Unknown Hidden Target Localization Method Based on Data Decoupling in Complex Scattering Media

Due to the effect of the complex scattering medium, the photons carrying target information will be attenuated when passing through scattering media, and target localization is difficult. The resolution of the target-position information from scattered images is crucial for achieving accurate target localization in environments such as dense fog in military applications. In this paper, a target localization network incorporating an attention mechanism was designed based on the robust feature resolution ability of neural networks and the characteristics of scattering formation. A training dataset with basic elements was constructed to achieve data decoupling, and then realize the position estimation of targets in different domains in complex scattering environments. Experimental validation showed that the target was accurately localized in speckle images with different domain data by the above method. The results will provide ideas for future research on the localization of typical targets in natural scattering environments

An Unknown Hidden Target Localization Method Based on Data Decoupling in Complex Scattering Media

Due to the effect of the complex scattering medium, the photons carrying target information will be attenuated when passing through scattering media, and target localization is difficult. The resolution of the target-position information from scattered images is crucial for achieving accurate target localization in environments such as dense fog in military applications. In this paper, a target localization network incorporating an attention mechanism was designed based on the robust feature resolution ability of neural networks and the characteristics of scattering formation. A training dataset with basic elements was constructed to achieve data decoupling, and then realize the position estimation of targets in different domains in complex scattering environments. Experimental validation showed that the target was accurately localized in speckle images with different domain data by the above method. The results will provide ideas for future research on the localization of typical targets in natural scattering environments

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%

Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement

Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder–decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics