An Augmented Reality Visualization System for Simulated Multirotor Aerial Vehicles.

Multirotors Aerial Vehicles are special class of Unmanned Aerial Vehicles with many practical applications. The growing demand for this class of aircraft requires tools that speed up their development. Simulated environments have gained increasing importance, as they facilitate testing and prototyping solutions, where virtual environments allow real-time interaction with simulated models, with similar behavior to real systems. More recently, the use of Augmented Reality has allowed an increasing experience of immersion and integration between the virtual world and a real scenario. This work proposes the use of Augmented Reality technology and a simulated model of a multirotor to create an interactive flight environment, aiming to improve the user experience in the analysis of simulated models. For this purpose, a smartphone was adopted as a hardware platform, a game engine is used as a basis for the development of the Augmented Reality application, that represents a numerical simulation of the flight dynamics and the control system of a multirotor, and a game controller is adopted for user interaction. The resulting system demonstrates that Augmented Reality is a viable technology that can be used to increase the possibilities of evaluating simulated systems.

Biofuel blending reduces particle emissions from aircraft engines at cruise conditions.

Aviation-related aerosol emissions contribute to the formation of contrail cirrus clouds that can alter upper tropospheric radiation and water budgets, and therefore climate. The magnitude of air-traffic-related aerosol-cloud interactions and the ways in which these interactions might change in the future remain uncertain. Modelling studies of the present and future effects of aviation on climate require detailed information about the number of aerosol particles emitted per kilogram of fuel burned and the microphysical properties of those aerosols that are relevant for cloud formation. However, previous observational data at cruise altitudes are sparse for engines burning conventional fuels, and no data have previously been reported for biofuel use in-flight. Here we report observations from research aircraft that sampled the exhaust of engines onboard a NASA DC-8 aircraft as they burned conventional Jet A fuel and a 50:50 (by volume) blend of Jet A fuel and a biofuel derived from Camelina oil. We show that, compared to using conventional fuels, biofuel blending reduces particle number and mass emissions immediately behind the aircraft by 50 to 70 per cent. Our observations quantify the impact of biofuel blending on aerosol emissions at cruise conditions and provide key microphysical parameters, which will be useful to assess the potential of biofuel use in aviation as a viable strategy to mitigate climate change.

A Comparative Study of Visual Identification Methods for Highly Similar Engine Tubes in Aircraft Maintenance, Repair and Overhaul.

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark.

Inflight Polymerase Chain Reaction of samples with drones.

A system devised to conduct Polymerase Chain Reaction (PCR) in-flight on drones that uses the spatial displacement of capillary tubes on thermal blocks kept at 94 °C, 58 °C and 72 °C corresponding to cycling temperatures for denaturation, annealing and extension is demonstrated here. The use of acetal as the thermal block material reduced heat loss and the input power (within 18.5 W) needed to maintain the required temperatures. Tests showed that concentrations of samples down to 1.16 × 106 DNA copies/µL could be significantly and consistently detected above the background emission of the fluorescence signal intensity.

Human Mirror Neuron System Based Alarms in the Cockpit: A Neuroergonomic Evaluation.

Controlled Flight Into Terrain (CFIT) events still remain among the deadliest accidents in aviation. When facing the possible occurrence of such an event, pilots have to immediately react to the ground proximity alarm ("Pull Up" alarm) in order to avoid the impending collision. However, the pilots' reaction to this alarm is not always optimal. This may be at least partly due to the low visual saliency of the current alarm and the deleterious effects of stress that alleviate the pilot's reactions. In the present study, two experiments (in a laboratory and in a flight simulator) were conducted to (1) investigate whether hand gesture videos (a hand pulling back the sidestick) can trigger brainwave frequencies related to the mirror neuron system; (2) determine whether enhancing the visual characteristics of the "Pull Up" alarm could improve pilots' response times. Electrophysiological results suggest that hand gesture videos attracted more participants' attention (greater alpha desynchronization in the parieto-occipital area) and possibly triggered greater activity of the mirror neuron system (greater mu and beta desynchronizations at central electrodes). Results obtained in the flight simulator revealed that enhancing the visual characteristics of the original "Pull Up" alarm improved the pilots' reaction times. However, no significant difference in reaction times between an enlarged "Pull Up" inscription and the hand gesture video was found. Further work is needed to determine whether mirror neuron system based alarms could bring benefits for flight safety, in particular, these alarms should be assessed during a high stress context.

Identification of Swimmers in Distress Using Unmanned Aerial Vehicles: Experience at the Mont-Tremblant IRONMAN Triathlon.

Background: This preliminary report describes our experience using unmanned aerial vehicles (UAVs) to identify swimmers in distress at the 2018 Mont-Tremblant IRONMAN triathlon (Quebec, Canada). Methods: In a prospective pilot study, we sought to determine whether UAV surveillance could identify swimmers showing signs of distress quicker than conventional methods (i.e., lifeguards on the ground and on watercraft). In addition, we investigated the feasibility of using UAVs for medical surveillance at a triathlon event in terms of operations, costs, safety, legal parameters, and added value. Prior to the race, we screened participants for medical conditions that could elevate their risk of injury during the swim portion of the triathlon. Athletes deemed to be at increased risk were given a yellow swimming cap to enhance their surveillance by trained observers watching a live video feed from the UAVs. Results: On race day, a total of 3 UAVs (2 mobile, 1 tethered) were launched over Lake Tremblant and provided 3 observers with live video of the swimmers. Of the 2,473 race participants, there were 25 athletes with pre-identified medical conditions who wore a yellow cap during the swim. We did not detect any signs of distress among swimmers wearing yellow caps. Among the remaining 2,448 athletes, there were 5 swimmers who demonstrated signs of distress and required mobilization of water rescue boats; UAV surveillance identified 1 of these 5 distress events before it was seen by lifeguards on rescue boats. None of the athletes in the IRONMAN suffered an adverse event while swimming. Several technical and safety issues related to UAV surveillance arose including poor visibility, equipment loss, and flight autonomy. Conclusion: While our preliminary findings suggest that using UAVs to identify distressed swimmers during an IRONMAN race is feasible and safe, more research is necessary to determine how to optimize UAV surveillance at mass sporting events and integrate this technology within the existing emergency response teams.

Feasibility of bystander-administered naloxone delivered by drone to opioid overdose victims.

BACKGROUND: Currently, ≤5% of bystanders witnessing an opioid overdose (OD) in the US administer antidote to the victim. A possible model to mitigate this crisis would be a system that enables 9-1-1 dispatchers to both rapidly deliver naloxone by drone to bystanders at a suspected opioid OD and direct them to administer it while awaiting EMS arrival. METHODS: A simulated 9-1-1 dispatcher directed thirty subjects via 2-way radio to retrieve naloxone nasal spray from atop a drone located outside the simulation building and then administer it using scripted instructions. The primary outcome measure was time from first contact with the dispatcher to administration of the medication. RESULTS: All subjects administered the medication successfully. The mean time interval from 9 -1-1 contact until antidote administration was 122 [95%CI 109-134] sec. There was a significant reduction in time interval if subjects had prior medical training (p = 0.045) or had prior experience with use of a nasal spray device (p = 0.030). Five subjects had difficulty using the nasal spray and four subjects had minor physical impairments, but these barriers did not result in a significant difference in time to administration (p = 0.467, p = 0.30). A significant number of subjects (29/30 [97%], p = 0.044) indicated that they felt confident they could administer intranasal naloxone to an opioid OD victim after participating in the simulation. CONCLUSIONS: Our results suggest that bystanders can carry out 9-1-1 dispatcher instructions to fetch drone-delivered naloxone and potentially decrease the time interval to intranasal administration which supports further development and testing of a such a system.

Comparison of univariate and multivariate anthropometric design requirements methods for flight deck design application.

Designing aircraft cockpits to accommodate the wide range of body sizes and shapes existing in the world population has always been a difficult problem for crew station engineers. There is no consensus on the best method for obtaining measurements for body forms that statistically represent the variation within a population. The aim of this research is to compare the two most commonly used anthropometric approaches for dimension specification and flight deck design: the boundary cases multivariate and the percentile univariate. The multivariate approach captured more subjects than the percentile approach (p < .05) for all accommodation assessments using Brazilian Air Force pilots' anthropometry, but was not as effective as had been suggested in the literature. This study showed that the Boundary Cases Multivariate Method was better at evaluating design criteria for cockpit accommodation than the Percentile Univariate Method for accommodation of the central 90% envelope for the Brazilian Air Force crew application. Practitioner summary: The findings show that the Multivariate Boundary Cases approach can better provide anthropometric limits for the desired accommodation level when multiple body dimensions need to be simultaneously considered in a design. It will help researchers, designers, and engineers to solve complex design situations, make improved judgement and take right decisions. Abbreviations: FAR: federal aviation regulation; CS: certification specification; FAA: federal aviation administration; EASA: European union aviation safety agency; EMB: embraer; FAB: Brazilian Air Force; CAD: computer-aided design; MAM: multivariate anthropometric method; USAF: United States Air Force; PCA: principal component analysis; PC: principal component; JSF: joint strike fighter; NATO: North atlantic treaty organization; ISO: International Organization for Standardization; BPAD: Brazilian pilots anthropometric database; RD: radial distance; Error = A-E: error = achieved - expected; SPSS: statistical package for the social sciences; IBM Corp.: International business machines corporation; Acr. Ht, st: acromion height, sitting; But-kn lgt: buttock-knee length; Eye Ht, sit: eye height, sitting; Knee Ht, sit: knee height, sitting; Sitting Ht: sitting height; Thumbtip rch: thumbtip reach; Accom %: accommodated percentage; Af, Am, …, Zf, Zm: cases A-D and W-Z ("f" for female subjects and "m" for male subjects); T: trainer; A: atack; KC: anker and cargo; F: fighter; NG-BR: new generation - Brasil; PPE: personal protective equipment.

Eyes in the Sky: Assessing the Feasibility of Low-Cost, Ready-to-Use Unmanned Aerial Vehicles to Monitor Primate Populations Directly.

Primates face many climate and land use change threats, making long-term population monitoring critical to prioritizing conservation efforts. Ground-based line transects are typically conducted to estimate and monitor primate populations. However, transects may be costly and logistically challenging. We sought to test whether low-cost (<5,000 USD), ready-to-use unmanned aerial vehicles (UAVs) could effectively monitor primate populations in north-eastern Madagascar. Critically Endangered [A3cd] golden-crowned sifakas (Propithecus tattersalli) are medium-sized, white lemurs whose creamy colour contrasts against defoliated tree canopies, making them an ideal study species for aerial counts. Quad-copter UAV flights over sifaka groups did not elicit antipredator responses. Photographs demonstrated the ability of UAVs to capture viable imagery of sifakas from approximately 20 m above ground level. Unfortunately, crashes resulting from an inability to programme automated flights over hilly forests cut this pilot study short, highlighting several challenges that remain to implementing UAVs in remote field studies. This study demonstrates that while UAVs offer considerable promise in the future of primate research and conservation, high start-up costs and remote field conditions provide challenging obstacles to first time users hoping to use this exciting new technology. Furthermore, we strongly recommend that thermal cameras be used for direct primate counts with UAVs.

Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat (Triticum aestivum L.).

KEY MESSAGE: Genome-wide association on winter survival was conducted using data from image-based phenotyping method. Nine QTL were observed and three of them with candidate gene identified. Winter survival is an essential trait of winter wheat (Triticum aestivum L.) grown in regions with high risk of winterkill. We characterized a diversity panel of 450 Canadian wheat varieties that included mostly winter-growth habit wheats to identify key genetic factors that contribute to higher winter survival under field conditions. To more accurately quantify winter survival differences among varieties, image-based phenotyping methods, captured by unmanned aerial vehicle (UAV) and on ground level, were used to estimate the winter survival of each varieties. Winter survival index was developed to correct for emergence when evaluating winter survival. Winter survival measurement estimated by visual estimation, UAV imagery and ground imagery showed strong correlation with each other and had comparable broad-sense heritability. Genome-wide association studies resulted in the identification of seven quantitative trait loci (QTL) for winter survival including Vrn-A1. By using the recently released annotated sequence of the wheat genome and the available RNA-Seq data, two putative candidate genes underlying the QTL for winter survival were identified. However, our study showed that certain QTL was unique to specific winter survival measurement. Collectively, our study demonstrated the feasibility of using UAV-based imagery for the identification of loci associated with winter survival in wheat. The complexity of in-field condition make our result a valuable complement to indoor frost-tolerance studies in the identification of genetic factors not directly linked to freezing tolerance.

Remote Scene Size-up Using an Unmanned Aerial Vehicle in a Simulated Mass Casualty Incident.

INTRODUCTION: The scene-size-up is a crucial first step in the response to a mass casualty incident (MCI). Unmanned aerial vehicles (UAV) may potentially enhance the scene-size-up with real-time visual feedback during chaotic, evolving or inaccessible events. We performed this study to test the feasibility of paramedics using UAV video from a simulated MCI to identify scene hazards, initiate patient triage, and designate key operational locations. METHODS: We simulated an MCI, including 15 patients plus 4 hazards, on a college campus. A UAV surveyed the scene, capturing video of all patients, hazards, surrounding buildings and streets. We invited attendees of a provincial paramedic meeting to participate. Participants received a lecture on Sort-Assess-Lifesaving Interventions-Treatment/Transport (SALT) Triage and MCI scene management principles. Next, they watched the UAV video footage. We directed participants to sort patients according to SALT Triage Step One, identify injuries, and to localize the patients within the campus. Additionally, we asked them to select a start point for SALT Triage Step Two, identify and locate hazards, and designate locations for an Incident Command Post, Treatment Area, Transport Area and Access/Egress routes. The primary outcome was the number of correctly allocated triage scores. RESULTS: Ninety-six individuals participated. Mean age was 35 years (SD 11); 46% (44) were female and 49% (47) were Primary Care Paramedics. Most participants (79; 82%) correctly sorted at least 12 of 15 patients. Increased age was associated with decreased triage accuracy [-0.04(-0.07, -0.01); p = 0.031]. Fifty-two (54%) correctly localized 12 or more patients to a 27 × 20m grid area. Advanced paramedic certification, and local residency were associated with improved patient localization [2.47(0.23,4.72); p = 0.031], [3.36(1.10,5.61); p = 0.004]. The majority of participants (70; 81%) chose an acceptable location to start SALT Triage Step Two and 75 (78%) identified at least 3 of 4 hazards. Approximately half (53; 56%) of participants appropriately designated 4 or more of 5 key operational areas. CONCLUSION: This study demonstrates the ability of UAV technology to remotely facilitate the scene size-up in an MCI. Additional research is required to further investigate optimal strategies to deploy UAVs in this context.

Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm.

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs' task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments.

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10-5° and 2.01 × 10-5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient's locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.

Cyber Attacks on Healthcare Devices Using Unmanned Aerial Vehicles.

The growing use of wireless technology in healthcare systems and devices makes these systems particularly open to cyber-based attacks, including denial of service and information theft via sniffing (eaves-dropping) and phishing attacks. Evolving technology enables wireless healthcare systems to communicate over longer ranges, which opens them up to greater numbers of possible threats. Unmanned aerial vehicles (UAV) or drones present a new and evolving attack surface for compromising wireless healthcare systems. An enumeration of the types of wireless attacks capable via drones are presented, including two new types of cyber threats: a stepping stone attack and a cloud-enabled attack. A real UAV is developed to test and demonstrate the vulnerabilities of healthcare systems to this new threat vector. The UAV successfully attacked a simulated smart hospital environment and also a small collection of wearable healthcare sensors. Compromise of wearable or implanted medical devices can lead to increased morbidity and mortality.

A socio-technical analysis of functional properties in a joint cognitive system: a case study in an aircraft cockpit.

In a socio-technical work domain, humans, device interfaces and artefacts all affect transformations of information flow. Such transformations, which may involve a change of auditory to visual information & vice versa or alter semantic approximations into spatial proximities from instruments readings, are generally not restricted to solely human cognition. This paper applies a joint cognitive system approach to explore a socio-technical system. A systems ergonomics perspective is achieved by applying a multi-layered division to transformations of information between, and within, human and technical agents. The approach uses the Functional Resonance Analysis Method (FRAM), but abandons the traditional boundary between medium and agent in favour of accepting aircraft systems and artefacts as agents, with their own functional properties and relationships. The joint cognitive system perspective in developing the FRAM model allows an understanding of the effects of task and information propagation, and eventual distributed criticalities, taking advantage of the functional properties of the system, as described in a case study related to the cockpit environment of a DC-9 aircraft. Practitioner Summary: This research presents the application of one systemic method to understand work systems and performance variability in relation to the transformation of information within a flight deck for a specific phase of flight. By using a joint cognitive systems approach both retrospective and prospective investigation of cockpit challenges will be better understood. Abbreviations: ATC: air traffic control; ATCO: air traffic controller; ATM: air traffic management; CSE: cognitive systems engineering; DSA: distributed situation awareness; FMS: flight management system; FMV: FRAM model visualize; FRAM: functional resonance analysis method; GF: generalised function; GW: gross weight; HFACS: human factors analysis and classification system; JCS: joint cognitive systems; PF: pilot flying; PNF: pilot not flying; SA: situation awareness; SME: subject matter expert; STAMP: systems theoretic accident model and processes; VBA: visual basic for applications; WAD: work-as-done; WAI: work-as-imagined; ZFW: zero fuel weight.

Detecting automation failures in a simulated supervisory control environment.

The goal of this research was to determine how individuals perform and allocate their visual attention when monitoring multiple automated displays that differ in automation reliability. Ninety-six participants completed a simulated supervisory control task where each automated display had a different level of reliability (namely 70%, 85% and 95%). In addition, participants completed a high and low workload condition. The performance data revealed that (1) participants' failed to detect automation misses approximately 2.5 times more than automation false alarms, (2) participants' had worse automation failure detection in the high workload condition and (3) participant automation failure detection remained mostly static across reliability. The eye tracking data revealed that participants spread their attention relatively equally across all three of the automated displays for the duration of the experiment. Together, these data support a system-wide trust approach as the default position of an individual monitoring multiple automated displays. Practitioner Summary: Given the rapid growth of automation throughout the workforce, there is an immediate need to better understand how humans monitor multiple automated displays concurrently. The data in this experiment support a system-wide trust approach as the default position of an individual monitoring multiple automated displays. Abbreviations: DoD: Department of Defense; UA: unmanned aircraft; SCOUT: Supervisory Control Operations User Testbed; UAV: unmanned aerial vehicle; AOI: areas of interest.

Use of Highways in the Sky and a virtual pad for landing Head Up Display symbology to enable improved helicopter pilots situation awareness and workload in degraded visual conditions.

Flight within degraded visual conditions is a great challenge to pilots of rotary-wing craft. Environmental cues typically used to guide interpretation of speed, location and approach can become obscured, forcing the pilots to rely on data available from in-cockpit instrumentation. To ease the task of flight during degraded visual conditions, pilots require easy access to flight critical information. The current study examined the effect of 'Highways in the Sky' symbology and a conformal virtual pad for landing presented using a Head Up Display (HUD) on pilots' workload and situation awareness for both clear and degraded conditions across a series of simulated rotary-wing approach and landings. Results suggest that access to the HUD lead to significant improvements to pilots' situation awareness, especially within degraded visual conditions. Importantly, access to the HUD facilitated pilot awareness in all conditions. Results are discussed in terms of future HUD development. Practitioner Summary: This paper explores the use of a novel Heads Up Display, to facilitate rotary-wing pilots' situation awareness and workload for simulated flights in both clear and degraded visual conditions. Results suggest that access to HUD facilitated pilots' situation awareness, especially when flying in degraded conditions.

The potential use of unmanned aircraft systems (drones) in mountain search and rescue operations.

OBJECTIVE: This study explores the potential use of drones in searching for and locating victims and of motorized transportation of search and rescue providers in a mountain environment using a simulation model. METHODS: This prospective randomized simulation study was performed in order to compare two different search and rescue techniques in searching for an unconscious victim on snow-covered ground. In the control arm, the Classical Line Search Technique (CLT) was used, in which the search is performed on foot and the victim is reached on foot. In the intervention arm, the Drone-snowmobile Technique (DST) was used, the search being performed by drone and the victim reached by snowmobile. The primary outcome of the study was the comparison of the two search and rescue techniques in terms of first human contact time. RESULTS: Twenty search and rescue operations were conducted in this study. Median time to arrival at the mannequin was 57.3min for CLT, compared to 8.9min for DST. The median value of the total searched area was 88,322.0m2 for CLT and 228,613.0m2 for DST. The median area searched per minute was 1489.6m2 for CLT and 32,979.9m2 for DST (p<0.01 for all comparisons). CONCLUSIONS: In conclusion, a wider area can be searched faster by drone using DST compared to the classical technique, and the victim can be located faster and reached earlier with rescuers transported by snowmobile.