Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network.

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Impact of hybrid teaching methodology during COVID-19 pandemic on Operating systems course

Since 2019 the COVID-19 pandemic has caused huge changes in our lives. The government's health policies restricted everyday life, especially in schools. In the school year 2021/2022 we had to teach smaller groups face-to-face and later in the semester switch to distance learning. This article focuses on the Operating systems course teaching techniques used during the school year 2021/2022. Operating systems are one of the fundamentals needed for the development of complex UAV systems. © 2022 IEEE.

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.

A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle

Autonomous flight of an unmanned aerial vehicle (UAV) or its weaponized variant named unmanned combat aerial vehicle (UCAV) requires a route or path determined carefully by considering the optimization objectives about the enemy threats and fuel consumption of the system being operated. Immune Plasma algorithm (IP algorithm or IPA) is one of the most recent optimization techniques and directly models the fundamental steps of a medical method also used for the COVID-19 disease and known as convalescent or immune plasma treatment. In this study, IP algorithm for which a promising performance has already been validated with a single population was first extended to a multi-population domain supported by a migration schema. Moreover, the usage of the donor as a source of plasma for the treatment operations of a receiver was remodeled. The new variant of the IPA empowered with the multi-population and modified donor usage approach was called Multi-IP algorithm or MULIPA. For investigating the solving capabilities of the MULIPA as a UCAV path planner, different battlefield scenarios and algorithm specific parameter configurations were used. The results obtained by the MULIPA were compared with the results of other meta-heuristic based path planners. The comparative studies between MULIPA and other techniques showed that newly proposed IPA variant is capable of finding more secure and fuel efficient paths for a UCAV system. © 2022 Elsevier Ltd

Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications IEEE

Monte Carlo Tree Search improved Genetic Algorithm for unmanned vehicle routing problem with path flexibility

With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.

METHODS FOR FORECASTING DEMAND IN REPAIRING AN AIRLINE'S REPAIRABLE LINE REPLACEABLE UNIT PARTS

Verspreide mislukkingsfrekwensie, veranderlike en komplekse bei'nvloedende faktore, en 'n lae akkuraatheid in die voorspelling van voorraadaanvraag is kenmerke van lynvervangbare eenheid (LRU) onderdele. Sommige duur herstelbare LRU (HR-LRU) onderdele het 'n aansienlike impak op die koste van vliegtuigonderdele. Baie lugrederye stel baie belang om die vraag na HR-LRU-onderdele te voorspel. Hierdie studie bied prosedures aan om die optimale model vir die voorspelling van die vraag na HR-LRU-onderdele te identifiseer. Eerstens is 'n tradisionele voorspellingsmodel, sewe enkelmetingsmodelle en vier gekombineerde modelle gekies en gebruik om mislukkingsdata te voorspel. Vervolgens is evalueringsindekse vir assessering gekies om die optimale model te verkry. Laastens het ons die werklike en voorspelde waardes vergelyk om die gevolgtrekkings wat tydens die vorige evalueringstap gemaak is, te verifieer. Die resultate het aangedui dat, onder die enkelmodelle, die negatiewe binomiale regressiemodel en die Holt-Winters model die mees geskikte was vir HR-LRU dele. Die SSE en MAE van die negatiewe binomiale regressie was die laagste op 118.4114 en 1.97352 onderskeidelik, en die Holt-Winters model se MAE was die laagste op 1. 13270. Die IOWA operateur voorspellingsmodel en die fout wederkerige veranderlike gewig kombinasie metode het voorspellings opgelewer wat die naaste aan die werklike waardes was onder die gekombineerde modelle. Die voorspellingsfoute van die negatiewe binomiale regressiemodel en die IOWA-operateurmodel was slegs 0,169 3 en 1,411 3 in 2018. Benewens die samestelling van 'n stel prosesse om die vraag na HR-LRU-onderdele te voorspel, bespreek ons ook die graad van passing van verskillende metodes, die redes vir die verandering in die gewaarborgde koers van HR-LRU-onderdele, en die redes vir die voorkoms van spesiale jare. Ons vergelyk ook die ooreenkomste en verskille tussen hierdie artikel en ander navorsingsartikels.Alternate :Scattered failure frequency, variable and complex influencing factors, and a low accuracy in predicting inventory demand are characteristics of line replaceable unit (LRU) parts. Some high-priced repairable LRU (HR-LRU) parts have a considerable impact on the cost of aircraft spare parts.This study presents procedures to identify the optimal model for forecasting the demand for HR-LRU parts. First, a traditional prediction model, seven single measurement models, and four combined models were selected and used to predict failure data. Subsequently, evaluating indexes were selected for assessment to obtain the optimal model. Finally, we compared the actual and predicted values to verify the conclusions drawn during the previous evaluation step. The results indicated that, among the single models, the negative binomial regression model and the Holt-Winters model were most suitable for HRLRU parts. The SSE (sum of squares error) and MAE (mean absolute error) of the negative binomial regression were the lowest at 118.4114 and 1.97352 respectively, and the Holt-Winters model's MAE was the lowest at 1. 13270. The IOWA operator prediction model and the error reciprocal variable weight combination method produced predictions closest to the actual values among the combined models. In addition to constructing a set of processes to prediction, we also discuss the fit of different methods, the reasons for the change in the guaranteed rate, and the reasons for the occurrence of special years. We also compare the similarities and differences between this article and other papers.

UAV Fuzzy Inference System Design for Covid-19 Infection Risk Assessment

By emerging of the Covid-19 in 2020, the use of masks in indoor and outdoor areas has turned into a daily routine. In order to prevent the spread of the virus, many states supported and made it mandatory to wear masks. After the emergence of the virus, many studies have analyzed that wearing a mask reduces the risk of transmission, and even the ambient temperature is effective in spreading. In addition to the virus transmission risk studies in the literature, this study presents the evaluation of the images taken from UAVs in the fuzzy inference system by developing a model in machine learning for the control of mask use and the analysis of the virus spread environment. Ambient images and temperature information are provided from UAVs. With the machine learning model developed in the Python environment, the image file is processed and it detects whether a person is wearing mask or not as percentage data. The main contribution of the study is to evaluate the environmental risk according to the rules written in the fuzzy inferential system, mask usage information, and temperature level. © 2022 IEEE.

Work in Progress: Development of UAS Module in Laboratory Class for a Senior Engineering Core Course

Designing a senior-level course that involves problem-based learning, including project completion task, is laborious and challenging. A well-designed project motivates the students to be self-learners and prepares them for future industrial or academic endeavors. The COVID-19 pandemic brought many challenges when instructions were forced to move either online or to a remote teaching/learning environment. Due to this rapid transition, delivery modes in teaching and learning modalities faced disruption making course design more difficult. The senior level Flight Controls course AME - 4513 is designed with Unmanned Aerial Systems (UAS) related projects for the students to have a better understanding of UAS usage on various applications in support of Advanced Technological Education (ATE) program. The purpose of this paper is to present the UAS lab modules in a junior level robotics lab, AME - 4802, which preceded the Flight Controls course in the school of Aerospace and Mechanical Engineering at the University of Oklahoma. Successfully completing the course project requires independent research and involves numerical simulations of UAS. The Robotics Lab course focuses on hands-on projects of robotic systems with an emphasis on semi-autonomous mobile robots, including an UAS introduction module. • The UAS module in the Robotics Lab class is introduced in Spring 2020. Therefore, most of the students enrolled in the Spring 2020 Robotics Lab course have introductory knowledge about the UAS system when taking the Fall 2020 Flight Control course. In addition, Spring 2020 Robotics Lab was affected due to COVID-19. • The UAS module was not introduced in 2019 Spring Robotics lab. Thus, the students enrolled in Fall 2019 Flight Controls course did not have prior knowledge on the UAS system. • We thus present the implementation of UAS module in a junior level robotics lab which preceded the senior level Flight Controls course in following Fall semester, when the same instructor taught the course. © American Society for Engineering Education, 2022.

Wireless Communication System and Its Application in Big Data Remote Monitoring and Decision-Making

The development of computer technology has promoted the widespread application of unmanned technology. Remote monitoring of wireless devices is an application of unmanned technology. To improve the remote monitoring of wireless devices, this study establishes a remote monitoring and decision-making framework based on wireless communication systems. With the wireless communication system, signals that characterize the operating status of devices can be obtained in real-time. Based on the collected signals, the remote monitoring system can identify the current health status of wireless devices, thereby providing auxiliary decision-making for device operation. In the case study, the main engine of an unmanned surface vehicle is used as the study object. The results show that most of the relative errors corresponding to the state identification results of the established remote monitoring framework are within 5%. Moreover, the results present that the linear correlation coefficients between the predicted and real results are greater than 0.95. Therefore, the established remote monitoring framework based on the wireless communication system has good reliability in the state identification of wireless devices.

Research on logistics UAV task assignment based on improved ant colony algorithm

Logistics UAV delivery has been well developed in the fight against COVID-19 pneumonia, and attracts more and more scholars to research. Ant Colony Optimization (ACO) is one of the effective solutions to solve the UAV task assignment problem. The algorithm adopts the principle of positive feedback to speed up the evolution process. However, the algorithm has some defects, such as long search time, easy to fall into local optimum and so on. Aiming at the defects of ACO, we put forward two improvements in this paper: On the one hand, differential distribution of initial pheromone is proposed to avoid blind search in the initial stage and improve the convergence speed. On the other hand, we will reduce the number of candidate nodes in the dynamic strategy, and ants choose the next node in the dynamic candidate list to reduce the calculation of local exploitation. Simulation results show that the improved ACO can significantly improve the convergence speed and has a good effect on solving the task assignment problem of logistics UAV. © 2022 SPIE.

Resolving the Urban Dilemma of Two Adjacent Rivers through a Dialogue between GIS and Augmented Reality (AR) of Fabrics

Keelung Harbor, which is the most important center of sea freight in northern Taiwan, suffers from deteriorating urban development due to limited land supply. A dilemma arose from the Asahikawa River and the Tianliao River fronts, which evolved from cultural landscapes to buried and truncated rivers. This research was aimed at resolving the urban dilemma of the two adjacent rivers through a dialogue between the physical and augmented interaction of fabrics in three scenarios: GIS to AR, AR to GIS, and both. The physical dynamics were used to trace development chronologically by the area and length assessed from historical maps of hydrogeography, architecture, and the railroad. The augmented dynamics involved AR-based simulations and comparisons in terms of skyline overlay, fabric substitution, and fabric disposition. The dynamics involved AR models made by UAV images and 3D drawings. The assessments and simulations determined the key event in Keelung history when the Asahikawa River was leveled up. The dilemma verified from the augmented dynamics facilitated comprehension of the evolvement of the physical dynamics. With the assistance of AR and GIS, we concluded that the specific instance of riverfront reconstruction was an important landmark of meta-relationship.

Virtual Training System for Unmanned Aerial Vehicle Control Teaching–Learning Processes

The present work is focused on the development of a Virtual Environment as a test system for new advanced control algorithms for an Unmanned Aerial Vehicles. The virtualized environment allows us to visualize the behavior of the UAV by including the mathematical model of it. The mathematical structure of the kinematic and dynamic models is represented in a matrix form in order to be used in different control algorithms proposals. For the dynamic model, the constants are obtained experimentally, using a DJI Matrice 600 Pro UAV. All of this is conducted with the purpose of using the virtualized environment in educational processes in which, due to the excessive cost of the materials, it is not possible to acquire physical equipment;moreover, is it desired to avoid damaging them. Finally, the stability and robustness of the proposed controllers are determined to ensure analytically the compliance with the control criteria and its correct operation.

Formal Modeling of IoT-Based Drone Network for Combating COVID-19 Pandemic

Coronavirus biologically named COVID-19 is a disease that is circulating throughout the world due to its viral nature. The interaction of people is a source of spreading of coronavirus. Millions of people have been affected by this virus, and most of them have lost their lives. At present, this viral disease has grown into a worldwide pandemic which is a troubling spot for the whole world. Few technologies are supporting to manage and solve the COVID-19 crisis. In this paper, unified modeling language (UML) will be used to describe requirements and behavior of the proposed system. Unmanned aerial vehicle (UAV) drones are flying mechanical devices without any human pilot that is efficient to reduce the spreading rate of COVID-19. In the proposed IoT-based model, a cluster-based drones’ network will be used to monitor and perform required actions to tackle the violations of standard operating procedures (SOPs). The drones will gather all data through embedded cameras and sensors and will communicate with the control room to operate the actions as required. In this model, a well-maintained and collision-free network of drones will be designed using graph theory. Drones’ network will observe the violation of SOPs in the targeted area and make decisions such as produce alarm sound to alert persons and through communications by sending people warning messages on their smartphones. Further, the persons having COVID symptoms such as high temperature and unbalance respiratory rates will be identified using wearable sensors that are deployed to the targeted area and will send information to the control room to perform required actions. Drones will be able to provide medical kits to the patients’ residences that are identified using wearable sensors to reduce interaction of people. The model will be specified using Vienna Development Method-Specification language (VDM-SL) and validated through the VDM-SL toolbox.

Agile roadmap for application-driven Multi-UAV networks: The case of COVID-19

Drones, also known as Unmanned Aerial Vehicles (UAVs), are about to bring drastic transformations to our world and daily lives. News thinking and efficient deployment are required to boost the adoption of UAV-augmented commercial/civil applications. Yet, network service providers are still facing several design challenges of UAV-assisted application, due to lack of a roadmap allowing to meet the target service level agreement requirements. In this paper, we propose a complete framework for the UAV as a service paradigm, integrating all the actors/stakeholders contributing to the UAV-augmented service, and draw their interactions using data/service/money flows. Next, we instantiate our framework on the COVID-19-like pandemics, and discuss how to use it force social distancing, spray disinfectants, broadcast messages, deliver medical supplies and enhance surveillance. Computer simulation provide insights on how to set the multi-UAV network to combat COVID-19.

Bharat Electronics Ltd marches forward

Some of the major orders executed during the year included Long Range Surface to Air Missile;Akash Weapon System;Fire Control System;Integrated Air Command & Control System;Advanced Composite Communication System;Integrated Electronic Warfare Suite and Coastal Surveillance System. Some of the new products/systems introduced during the year included Laser Fence System, IR Jammer for Active Tank Protection System, Gimbal for Tethered UAV, Drainage Intrusion Detection System, Solid State Power Controller Cards for Akash NG / QRSAM, S-Band 150W Power Amplifier, GNSS Receiver, Managed Ethernet Switch -12 Port, IP EPABX System, Navigation Complex System, C BAND GaN PA & C BAND GaAs MMICs and Oxygen Concentrator 5LPM & 10 LPM and Dialysis Machines. Signifi cant among these orders included avionics package for LCA, Advanced Electronic Warfare (EW) suite for fighter aircraft, Instrumented Electronic Warfare Range (IEWR), CDR TI cum Day Light Sights for T-90 Tanks, Electronic Voting Machine & VVPAT, RWR & MAWS for C-295 aircraft, Gun Electronic Upgrade, Electronic Warfare Systems for Ships, Weapon Locating Radar and Integrated Observation Equipment.

An UAV Assisted Need Assessment and Prediction System for COVID-19 Vaccination

Health monitoring by government in rural and Urban areas become very much challenging task as they require huge amount of technicians, doctors and funds to complete. In the time of COVID-19 pandemic, it is difficult to allow doctors to visit rural areas for monitoring the health of public, rather than allocate their duties in COVID-19 hospitals to save critical patients. But, it is also necessary to monitor health of public to vaccinate them priority wise in the scarcity of COVID-19 vaccines. In this paper we have proposed a novel UAV (Unmanned Aerial Vehicle) assisted health monitoring system which can be operated in any remote location to get required data about the health condition of the people. After collecting the desired data from the user, system saves them in memory. In the control room, UAV uploads the collected data to the server for analysis. From the analysed data the system can decide whom need to be vaccinated immediately. UAV system will analyse the data with respect to different parameters like age, co-morbidity, blood pressure and other attributes. From this analysed data using machine learning algorithm, system also predicts how many days might be taken to complete the whole vaccination process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Physical and Augmented Dynamics of a Cultural Event

The Taiwan Lantern Festival (TLF) is a specific cultural tradition that has evolved over many years. It is a large-scale festival as determined by the large number of installations and visitors—that is, 20 million visitors in a period of two weeks. The aim of this study is to combine the TLF-related physical dynamics of land use and lantern installations with the augmented dynamics of lantern installations at reallocated sites. We compared five cities in Taiwan with regard to land alterations between 2016 and 2020. The TLF land assessment identified 34 cross-referred types of land use between aerial imagery and GIS surveys in a small area of 2 km × 2 km, in total. The change in land use by year varied between 2% and 499%, up to three times. The complexity of physical dynamics was re-experienced by a more sustainable dynamic of augmented reality (AR) using a scan-to-AR approach to reactivate the installations and fabrics at redeployed sites. The installations of the 2016 TLF were applied. We found that the land use, 3D scan, and AR reshaped the spatio-temporal festivalscape by both types of dynamics. The simulation demonstrated that the fabric retrieved by heterogeneous technologies had equal importance in assessing the host city and in enabling a reactivation for more diversified scales and characters, even with a smartphone AR.