Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

DroneRFa:用于侦测低空无人机的大规模无人机射频信号数据集

为研究与发展反无人机检测识别技术,该文公开了一个名为DroneRFa的大规模无人机射频信号数据集。该数据集使用软件无线电设备探测无人机与遥控器相互通信的射频信号,包含城市户外场景下运动无人机信号9类、城市室内场景下信号15类以及背景参照信号1类。每类数据有不少于12个片段,每个片段包含1亿个以上的采样点。数据采集覆盖了3个ISM无线电频段,记录无人机多频通信的真实活动。该数据集具有详细的无人机户外飞行距离和工作频段标注,以前缀字符结合二进制编码的形式方便用户灵活访问所需数据。此外,该文提供了基于频谱可视统计特征和基于深度学习表征的两种无人机识别方案,以验证数据集的可靠和有效性。

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

基于Raspberry Pi及Drone Kit的无人机飞行控制教学实践应用

基于开源硬件Raspberry Pi,Pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用Raspberry Pi作为无人机的机载计算机与Pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

Drone Usage in Civil Engineering—A Case Study of the Pristina-Gjilan Highway

The use of drones in construction engineering has gained increasing attention in recent years due to its potential to revolutionize the industry. Drones, offer the ability to capture high-resolution aerial imagery and collect data that was previously difficult or impossible to obtain. The integration drones in construction engineering presents opportunities for accurate data collection, analysis and visualization, which can improve decision-making processes and improve project outcomes. For example, drones equipped with GIS technology can be used to capture high-resolution aerial images of construction sites, allowing engineers to monitor progress, identify potential issues, and make informed adjustments as needed. By harnessing drones, civil engineers in the civil engineering field can potentially optimize project planning, design and execution while minimizing risks and costs. The work of this topic examines the case of the use of Drones combined with GIS in construction engineering. During this study, aerial photography of a certain segment of the Pristina-Gjilan Highway was taken. The results generated by the processing of aerial photos have been compared with the project. However, further research is needed to fully understand the capabilities and limitations of these technologies in this specific context, as well as to explore any potential challenges and barriers to their widespread adoption.

基于YOLOX-drone的反无人机系统抗遮挡目标检测算法

为解决现实场景下无人机目标被部分遮挡,导致不易检测问题,本文提出了基于YOLOX-S改进的反无人机系统目标检测算法YOLOX-drone.首先,建立无人机图像数据集;其次,搭建YOLOX-S目标检测网络,在此基础上引入坐标注意力机制,来增强无人机的目标图像显著度,突出有用特征抑制无用特征;然后,再去除特征融合层中自下而上的路径增强结构,减少网络复杂度,并设计了自适应特征融合网络结构,增强有用特征的表达能力,抑制干扰,提升检测精度.在DUT-AntiUAV数据集上的测试结果表明:YOLOX-drone与YOLOX-S、YOLOv5-S和YOLOX-tiny相比,平均准确率(IoU=0.5)提升了3.2%、4.7%和10.1%;在自建的无人机图像数据集上的测试结果表明:YOLOX-drone与原YOLOX-S目标检测模型相比,在无遮挡、一般遮挡、严重遮挡情况下,平均准确率(IoU=0.5)分别提高了2.4%、2.1%和6.4%,验证了改进的算法具有良好的抗遮挡检测能力.

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

Application of pre-whitened-constrained-MUSIC in ship-borne radar

In ship-borne radar, because of the influence of interference factors such as the correlation of background array noise and the coherence of targets and so on, the performance of high-resolution algorithms such as MUSIC is degraded. In this document by pre-whitening of background array color noise, de-correlation of coherent targets, compensation of amplitude-phase mismatch, pre-whitened-constrained-MUSIC algorithm in ship-borne radar effectively resolutes ship target and first-order sea echo. Furthermore, the algorithm performance is compared with other algorithms, result shows that pre-whitened-constrained-MUSIC can be applied effectively in high-resolution processing in ship-borne radar.

A mobility compensation method for drones in SG-eloT

In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization.The long power transmission line and the complex field operation environment urge the introduction of drones into the eloT for fast power transmission line inspection,data collection from sensors for further big data analysis,adaptive control of power line voltage,etc.Additionally,drones can also act as a central communication control or relay point to serve the data exchange among sensors,drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available.However,the fast mobility of drones may affect the signal transmission and position estimation performance,which may further deteriorate the networking performance.In order to solve this problem,a mobility compensation method is proposed,which includes the steps of frequency offset estimation and relative velocity calculation.Through the Monte Carlo simulations,the proposed algorithm shows favorable gains compared with the conventional ones.

A tree detection method based on trunk point cloud section in dense plantation forest using drone Li DAR data

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone Li DAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone Li DAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with undercanopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha^(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone Li DAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m^(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy(F1-Score=0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

Loader and Tester Swarming Drones for Cellular PhoneNetwork Loading and Field Test: Non-stochasticParticle Swarm Optimization

Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.

Minor Component Analysis-based Landing Forecast System for Ship-borne Helicopter

The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. To validate the feasibility of this landing forecast system, time series for the roll, pitch and heave are generated by simulation and then forecasted based on MCA. Simulation results show that ship-borne helicopters can land safely in higher sea condition while carrying on rescue or replenishment tasks at sea in terms of the landing forecast system.

Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time applications.As several applications comprise IoD in real time environment,significant interest has been received by research communications.Since IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD environment.This article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD environment.The major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD environment.To do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible format.In addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate features.Moreover,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of FFA.The performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

Swiss Innovation Week 2019:Home of Drones,the New High Flyer of Swiss Industry

Following the successful Swiss Innovation Week(SIW)held in July 2018,the Embassy of Switzerland in China launches its 2nd edition from 12 to 14of June 2019.With Swiss drones as the brand-new theme,Switzerland’s drone ecosystem and innovation in the field of flying robots were present.According to various rankings,Switzerland is one of the most innovative countrie s in the world and also one of the most competitive co untries.

Evaluating the Use of Drones in the Area of Transportation/Construction

Drones are proving out as a valuable tool and growing quickly in the world of technological advances.The applications of these vehicles are spreading widely in the areas of remote sensing,real time monitoring,goods delivery,security,defense,surveillance,infrastructure inspection.Although,the intent behind creating this tool was remote sensing.Smart drones will be the next big innovation and modification,which would have much wider applications especially in the field of infrastructure where it can reduce risks and lower costs.Current direct evaluation techniques are tedious,and the information caught is frequently not led in a precise manner with the areas tested not being geographically correct and the resulting reports being delivered past the point of no return.These were the reasons,which have increased the demand and usage of unmanned vehicles.In this research paper,we present critical review of main advancements of Drones in the area of transportation and agriculture.We present all the research related to civil applications in those areas and challenges including traffic monitoring,Bridge condition assessment,Roadway asset detection and many other applications related to infrastructure inspection enhancement.The paper also contributes with a discussion on the opportunities,which are opened,and the challenges that need to be addressed.Findings from the case studies,it is reported that around 25%of the bridges in united states are deficient and need continuous monitoring for enhancements to prevent any hazard.Unmanned vehicles could be a great help in monitoring these bridges and other important components of transportation,which can efficiently minimize the cost as well as the time spent on inspection for each of this component,as manual inspection requires labor and time which would be subsequently reduced by incorporating the usage of drones in the area of transportation.

Efficient Remote Identification for Drone Swarms

With the advancement of unmanned aerial vehicle(UAV)technology,the market for drones and the cooperation of many drones are expanding.Drone swarms move together in multiple regions to perform their tasks.A Ground Control Server(GCS)located in each region identifies drone swarmmembers to prevent unauthorized drones from trespassing.Studies on drone identification have been actively conducted,but existing studies did not consider multiple drone identification environments.Thus,developing a secure and effective identification mechanism for drone swarms is necessary.We suggested a novel approach for the remote identification of drone swarms.For an efficient identification process between the drone swarm and the GCS,each Reader drone in the region collects the identification information of the drone swarmand submits it to the GCS for verification.The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarmsimultaneously.To prove the security and correctness of the proposed protocol,we conducted a formal security verification using ProVerif,an automatic cryptographic protocol verifier.We also implemented a non-flying drone swarmprototype usingmultiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness.We showed simulation results regarding various drone simulation scenarios.

MF2-DMTD: A Formalism and Game-Based Reasoning Framework for Optimized Drone-Type Moving Target Defense

Moving-target-defense(MTD)fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations.However,the existing naive MTD studies were conducted focusing only on wired network mutations.And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations,such as hostility,mobility,and dependency.Therefore,to solve these conceptual limitations,this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle to adaptively secure the sustainability of drone operations.In addition,this study also specifies MF2-DMTD(model-checkingbased formal framework for drone-type MTD),a formal framework that adopts model-checking and zero-sum game,for attack-defense simulation and performance evaluation of drone-type MTD.Subsequently,by applying the proposed models,the optimization of deceptive defense performance of drone-type MTD for each mutation period also additionally achieves through mixed-integer quadratic constrained programming(MIQCP)and multiobjective optimization-based Pareto frontier.As a result,the optimal mutation cycles in drone-type MTD were derived as(65,120,85)for each control-mobility,telecommunication,and payload component configured inside the drone.And the optimal MTD cycles for each swarming cluster,ground control station(GCS),and zone service provider(ZSP)deployed outside the drone were also additionally calculated as(70,60,85),respectively.To the best of these authors’knowledge,this study is the first to calculate the deceptive efficiency and functional continuity of the MTD against drones and to normalize the trade-off according to a sensitivity analysis with the optimum.

A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.