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Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.
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With the simultaneously connected 26.66 billion devices worldwide, the Internet of Things (IoT) is becoming a vast field of research and helping hand to every individual. However, when IoT and Artificial Intelligence (AI) and machine learning (ML) consolidate, it results in smart applications and future revolutions that are known as Artificial Intelligent of Things (AIoT). Similarly, the unmanned aerial vehicle (UAV) domain is also developing daily, helping many unrest people in the healthcare industry. One step towards developing the healthcare industry is the use of UAV devices like drones embedded with AIoT to work autonomously in the healthcare industry. This can help the healthcare industry in many ways. This chapter proposes an algorithm to recast these UAV drones to autonomous UAV drones and use them as intelligent or smart for various healthcare purposes like COVID-19. The proposed autonomous UAV drone uses Raspberry Pi 3, a Hubney, and a bearing formula to automatically determine the direction of the UAV movement, making it work without any controller. Also, the comparative study presented in this chapter highlighted the benefits of this proposed algorithm with others present in the literature.
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The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.
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The worldwide health crisis is caused by the widespread of the Covid-19 virus. The virus is transmitted through droplet infection and it causes the common cold, coughing, sneezing, and also respiratory distress in the infected person and sometimes becomes fatal causing death. As the world battles against covid-19, the proposed approach can help to contain the clustering of covid hotspot areas for the treatment of over a million affected patients. Drones/ Unmanned Aerial Vehicles (UAVs) offer a great deal of support in this pandemic. As suggested in this research, they can also be used to get to remote places more quickly and efficiently than with conventional means. In the hospital's control room, there would be a person in command of the ambulance drone. For hotspot area detection, the drone would be equipped with FLIR camera and for detection and recognition of face the video transmission is used by raspberry pi camera. The detection of face is done by Haar cascade Classifier and recognition of the face with LBPH algorithm. This is used for identify the each individual's medical history or can be verified by Aadhar Card. Face recognition between still and video photos was compared, and the average accuracy of still and video images was 99.8 percent and 99.57 percent, respectively. To find the hotspot area is to use the CNN Crowd counting algorithm. If the threshold value is less than equal to 0.5 than it is hotspot area , if it is greater than 0.5 and less than equal to 0.75 than it is semi-normal area , if it is greater than 0.75 and less than equal to 1 than it is normal area. © 2022 IEEE.
ABSTRACT
PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.
ABSTRACT
Autonomous unmanned aerial vehicles (UAVs) have witnessed a rapid increase in their utilization in various applications and will continue to do so in the coming decades. These UAVs, also known as drones, are designed to either assist humans or perform tasks that involve people. Drones of today have grown to be faster and less expensive by integrating several technologies, supported by hybrid algorithms, and perform various tedious, challenging, filthy and hazardous tasks. The deployment of machine learning and other AI-based algorithms enhances drones' autonomous and vision capabilities. Today, part of an effort to curtail the spread of COVID-19, this research has designed, developed and built a mobile disinfectant dispenser based on autonomous quadrotor UAV. It is a 'flying dispenser', able to detect a person's hand gestures from afar, based on machine learning (ML), to fly and maneuver towards the person and finally spray disinfectant on his/her hand. In order to identify various hand motions for maneuvering, this research studies and improves the ML algorithms and carries out various experiments to improve the drones' response time and maneuvering performance, for the final objective of taking precautions to protect humans from Covid-19. © 2022 IEEE.
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The outbreak of the Covid-19 pandemic in recent years and the epidemics of infectious diseases that have occurred around the world over the years, there are problems of lack of medical supplies and difficulties in personnel scheduling. Intelligent medical transportation through modern technology is an effective means to solve this problem. AGV(Automated Guided Vehicle) transportation and UAV(Unmanned Aerial Vehicle) transportation are important ways for intelligent transportation of medical materials. This paper investigates semantic segmentation as a key technology for AGV transport and UAV transport. This paper compares other traditional semantic segmentation networks, and at the same time considers the characteristics of all-weather, all-terrain, and complex transportation of materials in medical transportation, and proposes SSMMTNet(Semantic segmentation of medical material transportation Net). Among them, we propose a Scaling Transformer Block that can extract depth features of point clouds to enrich contextual information. At the same time, the network is validated on the benchmark Semantic3D dataset, obtaining 71.5% mIoU and 90.6% OA. © 2022 IEEE.
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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.
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The main goal of this research paper is to develop an autonomous medicine delivery quadcopter and validate a simulator model for it. It is intended to use this drone in crisis of COVID-19 due to restriction of social distancing and unavailability of regular hospital facilities. A simulator is then modified and used as a pre-mission tool to predict mission outcome and after validation, it will be used to predict complex missions without actually risking the expensive drone. An efficient payload system is designed and constructed for the quadcopter to fulfill its delivery purpose. Once the drone is assembled along with the payload mechanism, its physical parameters are calculated using SolidWorks. The same parameters such as performance coefficients and moments of inertia are then updated in the simulator's quadcopter properties. The equations of motions model used is improved by including physical theoretical effects. In the end, same autonomous delivery mission tests have been done for the real quadcopter and the simulator in order to compare the results and show the effect of improved equations of motion and physical parameters. © 2022 IEEE.
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Advances in unmanned aerial vehicle technology in terms of industrial processes and communication and network technologies have led to a gradual increase in their use in civil, commercial and social applications since 2000. Global rules in most countries in the past have limited the use of drones to military applications. However, the SARS-CoV-2 pandemic is helping to expand the use of drones in many ways - including human medicine. Recent experience suggests the importance and legitimacy of using unmanned aerial vehicles to reduce or eliminate human contact to a minimum, and to transport medical supplies to hard-to-reach areas at virtually any time of day or night throughout the year. Copyright © 2021, Medakta s.r.o.. All rights reserved.
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Objective: Some infectious diseases spread very fast, viruses such as COVID-19, once infected, do great harm to human body. In order to control the spread of infectious diseases, it is necessary to collect microbial samples of infectious diseases for research, understand the nature of infectious diseases and take reasonable measures to prevent them. However, in some places where infectious diseases with great transmission power have occurred, such as hospitals, sending personnel to collect microbial samples is in danger of being infected. In order to reduce this risk, UAV (unmanned aerial vehicle) can be used to collect microbial samples of infectious diseases. Low altitude UAV has the advantages of low cost, high flexibility and easy rapid deployment. Methods: Using wireless communication technology to control the UAV cluster network is a common method of UAV wireless remote control. With its flexible flight characteristics and good channel characteristics, UAV can stay in the air for a long time, and can also be used as an air base station to provide various communication services. If an infectious disease occurs in an area, then use the aviation UAV to enter the highly dangerous infectious disease area. The UAV is equipped with corresponding sensors to identify the specific situation of the disease, and then use special tools to collect microbial samples of infectious diseases, Including exudates, secretions, tissues, various disease body fluids, etc., for researchers to analyze the nature of infectious disease samples. Results: Various infectious diseases with high infectivity, such as COVID-19, are easy to spread. For this highly infectious virus, even if people use appropriate equipment and preventive measures, they may still be infected. The collection of microbial samples of infectious diseases by aviation UAV can prevent the staff from directly contacting with the virus of infectious diseases. This way improves the safety of the staff, which is a very effective way to prevent infectious diseases. Conclusion: Taking advantage of the flexibility of aerial UAV, some microbial samples with highly infectious diseases are collected, which is not only suitable for areas with infectious diseases, but also suitable for hospital wards and other places. Infectious diseases always have certain transmission routes and conditions, infectious diseases can be transmitted in many ways. The same infectious disease can be transmitted in many different ways. Respiratory infectious diseases, such as COVID-19, are mainly transmitted through the respiratory tract. Pathogens exist in the air or form aerosols, forming an air transmission characteristic. Once inhaled into the body, healthy people may be infected. However, as long as we master the mode of transmission of diseases and pay due attention to prevention, we can eliminate the occurrence of infectious diseases. In some areas with poor sanitary conditions and poor hygiene habits, there are more cases of infectious diseases. Therefore, for the prevention of various infectious diseases, especially COVID-19 viruses, we must strengthen personal disinfection, strictly isolate the source of infection, and make reasonable arrangements in management measures to reduce the occurrence of infectious cases.
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The usage of drone technology has increased in a vast range of disciplines, including medical services. Drones can aerially deliver medical supplies and laboratory test samples during health emergencies such as the COVID-19 pandemic. It can also be used as a delivery device for an automated external defibrillator which might significantly increase the survival chances of out-of-hospital cardiac arrest victims. Significant cost savings compared with ground transportation and speed of delivery will probably drive drone implementation in various areas in the next few years. © 2022, Association of American Publishers. All rights reserved.
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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.
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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.
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Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people’s social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd’s social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions.
ABSTRACT
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 a sensing devices can be useful in detecting overcrowding events. Accordingly, in this paper, 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 (DTL). 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. IEEE
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We designed technology-assisted intercultural learning activities in this study. University students, ten from Indonesia and ten from China recorded videos about local culture and traditions, and then they exchanged videos with partners to enable cultural virtual field trips. To record cultural videos, one has to personally go to places related to local culture and traditions;however, this was not a good idea due to the COVID-19 pandemic. To implement the physical distancing measures imposed by the COVID-19 pandemic, the participants employed drones or unmanned aerial vehicles (UAVs) to record cultural videos. This study explored the affordances of UAVs for intercultural learning and whether intercultural competence of the participants will be facilitated. Through our mixed-method approach, we collected the data from questionnaires, videos, and interviews. We obtained three main findings. First, we revealed five UAV affordances such as (1) UAVs convey visual and aural feelings of being in a place;(2) UAVs provide details on a circular route beyond the human eye-level;(3) UAVs provide landscape overviews;(4) UAVs provide detailed observations at higher altitudes or make surveillance possible, and (5) UAVs enable flying to a specific area. Second, drone-assisted intercultural learning activities facilitated intercultural competence of the Chinese and Indonesian participants. Based on the results of the study, we made several suggestions and drew implications for educators and researchers. [ FROM AUTHOR] Copyright of Journal of Research on Technology in Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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We propose a new concept and architectural design for a double hybrid tailsitter unmanned aerial vehicle with vertical takeoff and landing capability. Basically, it consists of a modified flying wing with a single combustion powertrain set and a multirotor with 2 powertrain sets with electric motors. To this end, we have designed, built, and tested a prototype that spends less energy on vertical taking off and landing and also on horizontal flight, for maximizing flight endurance and distance.With electric propellers fixed at the leading wing edge, the tailsitter has two standard surfaces for elevation control and two vertical stabilizers that are used to give the necessary direction on vertical takeoff and landing. Experiments and results show the versatility of our hybrid tailsitter for operations in a restricted field. We performed several tests starting with the aircraft on the ground in vertical positioning. These tests include executing vertical takeoffs and landing, transitions from vertical to horizontal flight modes and transitions back from horizontal to vertical flight modes, and hovering, which were carried out successfully. Transition fourth and back from combustion to multirotor modes are inherent to some of those flight mode transitions, which have been performed smoothly.We also performed tests (in bench) to estimate the flight endurance. Final autonomous flight adjustments were not performed due to the Covid-19 pandemic caused by SARS-CoV-2. To this end the proposed and currently built prototype has proven to be functional as an effective hybrid UAV system. Author
ABSTRACT
Purpose: In recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery. Design/methodology/approach: The authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation. Findings: The findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible. Research limitations/implications: The methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal. Practical implications: This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery. Social implications: The proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario. Originality/value: This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery. © 2022, Emerald Publishing Limited.