A Cross-View Geo-Localization Algorithm Using UAV Image and Satellite Image.

Within research on the cross-view geolocation of UAVs, differences in image sources and interference from similar scenes pose huge challenges. Inspired by multimodal machine learning, in this paper, we design a single-stream pyramid transformer network (SSPT). The backbone of the model uses the self-attention mechanism to enrich its own internal features in the early stage and uses the cross-attention mechanism in the later stage to refine and interact with different features to eliminate irrelevant interference. In addition, in the post-processing part of the model, a header module is designed for upsampling to generate heat maps, and a Gaussian weight window is designed to assign label weights to make the model converge better. Together, these methods improve the positioning accuracy of UAV images in satellite images. Finally, we also use style transfer technology to simulate various environmental changes in order to expand the experimental data, further proving the environmental adaptability and robustness of the method. The final experimental results show that our method yields significant performance improvement: The relative distance score (RDS) of the SSPT-384 model on the benchmark UL14 dataset is significantly improved from 76.25% to 84.40%, while the meter-level accuracy (MA) of 3 m, 5 m, and 20 m is increased by 12%, 12%, and 10%, respectively. For the SSPT-256 model, the RDS has been increased to 82.21%, and the meter-level accuracy (MA) of 3 m, 5 m, and 20 m has increased by 5%, 5%, and 7%, respectively. It still shows strong robustness on the extended thermal infrared (TIR), nighttime, and rainy day datasets.

Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments.

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals for positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization task of the objects captured by UAVs, rather than UAV self-positioning. Furthermore, the existing UAV datasets apply discrete sampling to synthetic data, such as Google Maps, neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in practical scenarios. To address these issues, this paper presents a new dataset, DenseUAV, that is the first publicly available dataset tailored for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban areas. In total, over 27K UAV- and satellite-view images of 14 university campuses are collected and annotated. In terms of methodology, we first verify the superiority of Transformers over CNNs for the proposed task. Then we incorporate metric learning into representation learning to enhance the model's discriminative capacity and to reduce the modality discrepancy. Besides, to facilitate joint learning from both the satellite and UAV views, we introduce a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate both the retrieval and localization performance for the proposed task. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.01% and an SDM@1 score of 86.50% on DenseUAV. The dataset and code have been made publicly available on https://github.com/Dmmm1997/DenseUAV.

Absolute quantification of viable Vibrio cholerae in seawater samples using multiplex droplet digital PCR combined with propidium monoazide.

Introduction: Toxigenic Vibrio cholerae serogroup O1 and O139 are the pathogens responsible for the global cholera epidemic. V. cholerae can settle in the water and spread via the fecal-oral route. Rapid and accurate monitoring of live V. cholerae in environmental water has become an important strategy to prevent and control cholera transmission. Conventional plate counting is widely used to detect viable bacteria but requires time and effort. Methods: This study aims to develop a new assay that combines triplex droplet digital PCR (ddPCR) with propidium monoazide (PMA) treatment for quantitatively detecting live V. cholerae O1/O139 and cholera enterotoxin. Specific primers and probes were designed according to the conserved regions of gene rfb O1, rfb O139, and ctxA. The amplification procedures and PMA treatment conditions were optimized. The specificity, sensitivity, and ability of PMA-ddPCR to detect viable bacteria-derived DNA were evaluated in simulated seawater samples. Results and Discussion: The results revealed that the optimal primer concentrations of rfb O1, rfb O139, and ctxA were 1 µM, while the concentrations of the three probes were 0.25, 0.25, and 0.4 µM, respectively. The best annealing temperature was 58°C to obtain the most accurate results. The optimal strategy for distinguishing dead and live bacteria from PMA treatment was incubation at the concentration of 20 µM for 15 min, followed by exposure to a 650-W halogen lamp for 20 min. In pure culture solutions, the limit of detection (LODs) of V. cholerae O1 and O139, and ctxA were 127.91, 120.23 CFU/mL, and 1.5 copies/reaction in PMA-triplex ddPCR, respectively, while the LODs of the three targets were 150.66, 147.57 CFU/mL, and 2 copies/reaction in seawater samples. The PMA-ddPCR sensitivity was about 10 times higher than that of PMA-qPCR. When detecting spiked seawater samples with live bacterial concentrations of 1.53 × 102 and 1.53 × 105 CFU/mL, the assay presented a higher sensitivity (100%, 16/16) than qPCR (50.00%, 8/16) and a perfect specificity (100%, 9/9). These results indicate that the developed PMA-triplex ddPCR is superior to the qPCR regarding sensitivity and specificity and can be used to rapidly detect viable toxigenic V. cholerae O1 and O139 in suspicious seawater samples.

Position and attitude tracking control for a quadrotor UAV.

A synthesis control method is proposed to perform the position and attitude tracking control of the dynamical model of a small quadrotor unmanned aerial vehicle (UAV), where the dynamical model is underactuated, highly-coupled and nonlinear. Firstly, the dynamical model is divided into a fully actuated subsystem and an underactuated subsystem. Secondly, a controller of the fully actuated subsystem is designed through a novel robust terminal sliding mode control (TSMC) algorithm, which is utilized to guarantee all state variables converge to their desired values in short time, the convergence time is so small that the state variables are acted as time invariants in the underactuated subsystem, and, a controller of the underactuated subsystem is designed via sliding mode control (SMC), in addition, the stabilities of the subsystems are demonstrated by Lyapunov theory, respectively. Lastly, in order to demonstrate the robustness of the proposed control method, the aerodynamic forces and moments and air drag taken as external disturbances are taken into account, the obtained simulation results show that the synthesis control method has good performance in terms of position and attitude tracking when faced with external disturbances.

Second order sliding mode control for a quadrotor UAV.

A method based on second order sliding mode control (2-SMC) is proposed to design controllers for a small quadrotor UAV. For the switching sliding manifold design, the selection of the coefficients of the switching sliding manifold is in general a sophisticated issue because the coefficients are nonlinear. In this work, in order to perform the position and attitude tracking control of the quadrotor perfectly, the dynamical model of the quadrotor is divided into two subsystems, i.e., a fully actuated subsystem and an underactuated subsystem. For the former, a sliding manifold is defined by combining the position and velocity tracking errors of one state variable, i.e., the sliding manifold has two coefficients. For the latter, a sliding manifold is constructed via a linear combination of position and velocity tracking errors of two state variables, i.e., the sliding manifold has four coefficients. In order to further obtain the nonlinear coefficients of the sliding manifold, Hurwitz stability analysis is used to the solving process. In addition, the flight controllers are derived by using Lyapunov theory, which guarantees that all system state trajectories reach and stay on the sliding surfaces. Extensive simulation results are given to illustrate the effectiveness of the proposed control method.

Hybrid carbon source for producing nitrogen-doped polymer nanodots: one-pot hydrothermal synthesis, fluorescence enhancement and highly selective detection of Fe(III).

Polymer nanodots (PNDs) from a hybrid carbon source (glucose and glycine) which exhibit a stronger fluorescence than the PNDs from a single source (glucose or glycine) are obtained by one-pot hydrothermal treatment. It is attractive that PNDs can be used as an effective fluorescent probe for the detection of iron ions with good selectivity and sensitivity in an aqueous solution.