No abstract

The study of the catalytic activity and activation mechanism of asymmetric uranyl‐salophens with α, β‐unsaturated aldehydes or α, β‐unsaturated ketones, is a research hotspot. In this paper, the complexes of the uranyl–salophen(U‐S) modified by unilateral benzene, coordinated with cyclohexenone, cyclopentenone and acrolein, were investigated using density functional theory calculations at the level of B3LYP/6‐311G(d, p) basis set. The results showed that the uranyl‐salophen(U‐S) weakened the large π bond between C = C and C = O of the α, β‐unsaturated aldehydes and ketones, making the unsaturated aldehydes and ketones activated. In addition, the molecular‐recognition selectivity of the asymmetrical uranyl‐salophen for cyclohexenone and cyclopentenone were much higher than for acrolein.

It is a challenging task for unmanned aerial vehicles (UAVs) without a positioning system to locate targets by using images. Matching drone and satellite images is one of the key steps in this task. Due to the large angle and scale gap between drone and satellite views, it is very important to extract finegrained features with strong characterization ability. Most of the published methods are based on the CNN structure, but a lot of information will be lost when using such methods. This is caused by the limitations of the convolution operation (e.g. limited receptive field and downsampling operation). To make up for this shortcoming, a transformer-based network is proposed to extract more contextual information. The network promotes feature alignment through semantic guidance module (SGM). SGM aligns the same semantic parts in the two images by classifying each pixel in the images based on the attention of pixels. In addition, this method can be easily combined with existing methods. The proposed method has been implemented with the newest UAV-based geo-localization dataset. Compared with the existing state-of-the-art (SOTA) method, the proposed method achieves almost 8% improvement in accuracy. INDEX TERMSCross-view image matching, geo-localization, UAV image localization, deep neural network.

Unmanned Aerial Vehicle (UAV) localization capability is critical in a Global Navigation Satellite System (GNSS) denial environment. The aim of this paper is to investigate the problem of locating the UAV itself through a purely visual approach. This task mainly refers to: matching the corresponding geo-tagged satellite images through the images acquired by the camera when the UAV does not acquire GNSS signals, where the satellite images are the bridge between the UAV images and the location information. However, the sampling points of previous cross-view datasets based on UAVs are discrete in spatial distribution and the inter-class relationships are not established. In the actual process of UAV-localization, the inter-class feature similarity of the proximity position distribution should be small due to the continuity of UAV movement in space. In view of this, this paper have reformulated an intensive dataset for UAV positioning tasks, which is named DenseUAV, aiming to solve the problems caused by spatial distance and scale transformation in practical application scenarios, so as to achieve high-precision UAV-localization in GNSS denial environment. In addition, a new continuum-type evaluation metric named SDM is designed to evaluate the accuracy of model matching by exploiting the continuum of UAVs in space. Specifically, with the ideas of siamese networks and metric learning, a transformer-based baseline was constructed to enhance the capture of spatially subtle features. Ultimately, a neighbor-search post-processing strategy was proposed to solve the problem of large distance localisation bias.