UAV image target localization method based on outlier filter and frame buffer

With rapid development of UAV technology,research on UAV image analysis has gained attention.As the existing techniques of UAV target localization often rely on additional equipment,a method of UAV target localization based on depth estimation has been proposed.However,the unique perspective of UAVs poses challenges such as the significant field of view variations and the presence of dynamic objects in the scene.As a result,the existing methods of depth estimation and scale recovery cannot be directly applied to UAV perspectives.Additionally,there is a scarcity of depth estimation datasets tailored for UAV perspectives,which makes supervised algorithms impractical.To address these issues,an outlier filter is introduced to enhance the applicability of depth estimation networks to target localization.A frame buffer method is proposed to achieve more accurate scale recovery,so as to handle complex scene textures in UAV images.The proposed method demonstrates a 14.29%improvement over the baseline.Compared with the average recovery results from UAV perspectives,the difference is only 0.88%,approaching the performance of scale recovery using ground truth labels.Furthermore,to overcome the limited availability of traditional UAV depth datasets,a method for generating depth labels from video sequences is proposed.Compared to state-of-the-art methods,the proposed approach achieves higher accuracy in depth estimation and stands for the first attempt at target localization using image sequences.Proposed algorithm and dataset are available at https://github.com/uav-tan/uav-object-localization.

蛋白化学合成中的片段增溶策略

蛋白质在多种生物过程和生物医学研究中起到关键作用,获取高度均一性的蛋白质样品是这类生化研究的重要一环。相较于重组表达法,蛋白质化学合成能够更为稳健地获取精准修饰的,甚至是人为设计的蛋白质。而一些可作为药物靶点的重要蛋白(如人源白细胞介素-2、K^(+)通道蛋白Kir5.1等)在化学合成过程中面临多肽片段溶解度不佳的问题,为后续的纯化、表征、连接反应等操作带来困难。这类问题的主要原因可能是这些目标蛋白的多肽片段之间易通过疏水相互作用、氢键等作用模式自组装形成二级结构,进而使得片段溶解度降低。增溶标签策略是这类问题的解决途径之一,本文介绍了在多肽片段主链、侧链和骨架上安装增溶标签的策略,选取膜蛋白FCER1G、共伴侣蛋白GroES等蛋白作为目标展示,并对增溶标签策略未来的发展方向作出展望。

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is designed.First,the original input images are segmented into several overlapped patches.Second,each patch is separately fed into an SSD to detect the objects.Third,the patches are merged together through two stages.In the first stage,the truncated object of the sub-layer detection result is spliced.In the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers.The boxes that are not detected in the main-layer are retained.In addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this issue.The deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case study.Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.

Optimization of bits allocation and path planning with trajectory constraint in UAV-enabled mobile edge computing system

In this paper,an Unmanned Aerial Vehicle(UAV)enabled Mobile Edge Computing(MEC)system is studied,in which UAV acts as server to offer computing offloading service to the Mobile Users(MUs)with limited computing capability and energy budget.We aim to minimize the total energy consumption of MUs by jointly optimizing the bit allocation for uplink,computing at the UAV and downlink,along with the UAV trajectory in a unified framework.To this end,a trajectory constraint model is employed to avoid sudden changes of velocity and acceleration during flying.Due to high-order information in use,we lead to a more reasonable nonconvex optimization problem than prior arts.An Alternating Direction Method of Multipliers(ADMM)method is introduced to solve the optimization problem,which is decomposed into a set of easy subproblems,to meet the requirement on the efficiency in edge computing.Numerical results demonstrate that our approach leads a smoother UAV trajectory,significantly save the energy consumption for UAV during flying.

Chemical synthesis and racemic crystallization of rat C5a-desArg

The deletion of the C-terminal arginine of the anaphylatoxin protein C5a reduces it receptor binding affinity.Understanding how C-terminal arginine affects the structure and bioactivity of C5a is important for the development of C5a C-terminal mimics as drug candidates.Herein,we report the total chemical synthesis of rat C5a and its D-enantiomer with its C-terminal arginine deleted,namely L-rC5a-desArg and D-rC5a-desArg.The structure of rC5a-desArg was then determined by racemic crystallography for the first time.The C-terminal residues of rC5a-Arg were found to expand from the fourth helix in a continuous helical confo rmation.This C-terminal conformation is significantly different from that of the previously reported full-length of C5a,indicating that the deletion of C-terminal arginine residue could result in the destruction of a positively charged surface formed by two adjacent Arg residues in C5a.