The Wright fishhook cactus (Sclerocactus wrightiae L.D. Benson) is an endangered cactus species endemic to south-central Utah. Since its listing in 1979 by the U.S. Fish and Wildife Service, the potential impacts of soil disturbance by cattle have become a central focus of management policies and monitoring efforts. However, little to no empirical data has been collected to substantiate the hypothesis that soil disturbance by cattle has direct or indirect negative effects on Wright fishhook cactus growth or reproduction. Over the years, the Bureau of Land Mangement (BLM) and Capitol Reef National Park (CRNP) have invested significant resources documenting cactus populations including several attributes of individual cacti: GPS location, diameter, number of flowers, fruits, or buds, number of stems, and the presence or absence of a cow track within 15 cm of the cactus. While these efforts have been commendable, due to the defining phenological characteristics of this species (flower and filament color) and its short flowering period (April-May) it remains difficult to study and much basic biological information including a range wide population estimate and defined critical habitat remain unknown. Our research had two primary objectives, 1) evalutate the effects of soil disturbance by cattle on reproduction and diameter of the Wright fishhook cactus (Chapters 1 and 2), and 2) explore the use of drones and GIS to define critical habitat and obtain an accurate range wide population estimate (Chapters 3 and 4). In Chapter 1, we analyzed cactus attribute data collected by the BLM at 30 macro-plots representing different levels of soil related cattle disturbance (high, moderate, and low) from 2011-2017. We found no significant association between level of cattle disturbance and flower density or cactus diameter. We did find a significant negative association between flower frequency and increased disturbance. In Chapter 2, we conducted an experimental study where tracks were simulated within 15 cm of cacti at various levels (Ctrl, 1-Track, 2-Track, 4-Tracks, and 4-Tracks Doubled). No significant association was observed between the number of tracks and response in diameter, flower production, fruit production, or seed set. In Chapter 3, we conducted drone flights over 14 macro-plots at three different altitudes above ground level (10 m, 15 m, and 20 m) and found that while the 10 m flights provided the best remotely sensed survey results, drones are not a suitable replacement for ground censuses. In Chapter 4, we used Resource Selection Function to define critical habitat for the Wright fishhook cactus. Our modeling suggests that geology, elevation, and slope are significant factors in defining cactus habitat. Based on the results of our research we conclude that soil disturbance by cattle may not have a significant influence on Wright fishhook cactus populations or dynamics, and that accurate range wide population estimates may be best obtained through ground surveys within the predicted critical habitat.

The Wright fishhook cactus (Sclerocactus wrightiae L.D. Benson) is an endangered cactus species endemic to south-central Utah. Since its listing in 1979 by the U.S. Fish and Wildife Service, the potential impacts of soil disturbance by cattle have become a central focus of management policies and monitoring efforts. However, little to no empirical data has been collected to substantiate the hypothesis that soil disturbance by cattle has direct or indirect negative effects on Wright fishhook cactus growth or reproduction. Over the years, the Bureau of Land Mangement (BLM) and Capitol Reef National Park (CRNP) have invested significant resources documenting cactus populations including several attributes of individual cacti: GPS location, diameter, number of flowers, fruits, or buds, number of stems, and the presence or absence of a cow track within 15 cm of the cactus. While these efforts have been commendable, due to the defining phenological characteristics of this species (flower and filament color) and its short flowering period (April-May) it remains difficult to study and much basic biological information including a range wide population estimate and defined critical habitat remain unknown. Our research had two primary objectives, 1) evalutate the effects of soil disturbance by cattle on reproduction and diameter of the Wright fishhook cactus (Chapters 1 and 2), and 2) explore the use of drones and GIS to define critical habitat and obtain an accurate range wide population estimate (Chapters 3 and 4). In Chapter 1, we analyzed cactus attribute data collected by the BLM at 30 macro-plots representing different levels of soil related cattle disturbance (high, moderate, and low) from 2011-2017. We found no significant association between level of cattle disturbance and flower density or cactus diameter. We did find a significant negative association between flower frequency and increased disturbance. In Chapter 2, we conducted an experimental study where tracks were simulated within 15 cm of cacti at various levels (Ctrl, 1-Track, 2-Track, 4-Tracks, and 4-Tracks Doubled). No significant association was observed between the number of tracks and response in diameter, flower production, fruit production, or seed set. In Chapter 3, we conducted drone flights over 14 macro-plots at three different altitudes above ground level (10 m, 15 m, and 20 m) and found that while the 10 m flights provided the best remotely sensed survey results, drones are not a suitable replacement for ground censuses. In Chapter 4, we used Resource Selection Function to define critical habitat for the Wright fishhook cactus. Our modeling suggests that geology, elevation, and slope are significant factors in defining cactus habitat. Based on the results of our research we conclude that soil disturbance by cattle may not have a significant influence on Wright fishhook cactus populations or dynamics, and that accurate range wide population estimates may be best obtained through ground surveys within the predicted critical habitat.

The use of drones in conflicts is under development and is increasing rapidly. The first real drone warfare was seen in Pakistan in the War on Terrorism in 2004. Between officials and scientists, there are divided opinions about whether the drone strikes can increase terrorism or cause the collapse of organizations. The purpose of this thesis is to examine how the effectiveness of drones, in terms of precision and lethality, can be seen to escalate or de-escalate a conflict. The aim is to explore whether two existing theories about air power can explain the case of killing the Iranian general, Qasem Soleimani, in 2020. As a qualitative case study, the approach results in favor of Pape´s theory over Warden´s. Pape says that decapitation is more likely to escalate a conflict, which confirms this case. Other explanations in his theory are the absence of unexpected political effects and the overthrow of government. Though Wardens theory shows some aspects of de-escalation, the conclusion is that Soleimani was not the center of gravity that was meant to cause system collapse. Further research is required since the findings cannot identify any escalating or de-escalating effects within the Quds Force.

Cette thèse aborde le potentiel de la télédétection en tant qu'outil pour la détection automatique de la Flavescence dorée (FD) de la vigne. L'approche repose sur l'analyse des variables (bandes spectrales, indices de végétation et paramètres biophysiques) calculables à partir des images multispectrales à très haute résolution (10 cm) acquises par drone pendant la période d'expression maximal des symptômes. L'analyse de la performance de discrimination des variables est réalisée à partir d'une méthode supervisée basée sur la courbe ROC. Les zones d'entrainement et de validation utilisées dans cette étude ont été acquises sur 14 parcelles situés dans le sud de la France. La performance des variables a été testée sur trois échelles d'analyse (par parcelle, par cépage et par couleur) et pour deux niveaux d'analyse. Le premier niveau d'analyse repose sur le potentiel des variables utilisées dans la détection des zones symptomatiques de la Flavescence dorée des zones asymptomatiques. Le deuxième niveau d'analyse consiste à tester la performance des variables dans la discrimination spécifique la Flavescence dorée (cépages noirs) en faisant une distinction avec les maladies du bois. À l'issue de ces expériences, dans un point de vue méthodologique les résultats ont permis de mettre en évidence (1) une plus faible performance de discrimination pour la discrimination des zones symptomatiques de FD des zones symptomatiques des maladies du bois plus particulièrement à l'échelle par couleur ; (2) la présence des pixels mixtes mal classés plus particulièrement dans les bords des rangs de vigne et (3) une faible discrimination des zones symptomatiques (FD ou MB) avec une proportion du feuillage symptomatique faible (niveau d'infection). Dans un point de vue thématique les résultats obtenus ont mis en évidence les différences dans l'intensité de la coloration anormale des feuilles atteintes de Flavescence dorée en fonction de l'année et leur lien avec la teneur en chlorophylles et anthocyanes des feuilles. Les perspectives ouvertes par ces travaux concernent la création d'un indice spécifique à la Flavescence dorée en fonction de la couleur du cépage (noir ou blanc) ou l'intensité dans la coloration des feuilles (faible ou forte) identifiés à partir des données hyperspectrales et l'amélioration du masquage des pixels mixtes à partir des algorithmes complexes qui prennent en compte la répartition spatiale des pixels dans le feuillage de vigne. / This work investigates the potential of remote sensing as a tool for the automatic detection of Flavescence dorée (FD) grapevine disease. The approach is based on the analysis of variables (spectral bands, vegetation indices, and biophysical parameters) computed from high resolution (10 cm) multispectral images and acquired by Unmanned Aerial Vehicle (UAV) during the period of maximum expression of symptom. The analysis of the variables discrimination performance is evaluated by a supervised method based on the Receiver Operating Characteristic curve (ROC curve). The training and validation areas used in this study were acquired from 14 vineyards located in southern France. The performance of the variables was tested on three different scales of analysis (one by plot, by cultivar and by berry color). Two levels of analysis have been implemented. The first level involves the potential of variables to discriminate Flavescence dorée symptomatic vines areas from asymptomatic ones. The second level of analysis is related to test the performance of the variables for the specific discrimination of Flavescence dorée vines (for the red cultivars) and the discrimination from Grapevine Trunk Diseases (GTD). The results obtained showed (1) a lower discrimination performance for discrimination of FD symptomatic vines areas from GTD symptomatic ones, more pronounced on the color level; (2) the presence of misclassified mixed pixels especially in the edges of the rows of vines and (3) a low discrimination of symptomatic vines areas (FD or MB) with a low proportion of symptomatic foliage (level of infection). From a thematic point of view, the results obtained showed the differences in the intensity of leaf discoloration affected by Flavescence dorée by year and their link with the chlorophylls and anthocyanins content of the leaves. Future prospects for this work concern the creation of a specific Flavescence dorée index depending on the color of the cultivars (red or white) and the intensity of leaf discoloration (attenuated or marked), identified from the hyperspectral data and improving the masking of mixed pixels from complex algorithms that consider the spatial distribution of pixels in the vine foliage.

There is an ever growing need for environmentally sustainable alternatives in today's society due to the looming threat of greenhouse gasses. One field where the need for new environmentally friendly solutions is needed is the aviation industry. The problem the industry is facing is due to the weight and space constraints that exist in aerial vehicles. In this bachelor project a solution for unmanned drones is proposed where it is powered by a hybrid solution consisting of batteries working together with fuel cells. The batteries compliment each other where the fuel cell is a lightweight energy source while the battery is used to combat the changing power demand. This project was done in collaboration with the Green Raven project to evaluate the optimal setup to power the energy system for an hour. The work was done theoretically in Matlab and Simulink to find the optimal system. From these simulations, data was collected to calculate the optimal configuration between batteries and amount of hydrogen stored in the Hydrogen tank. It was concluded that the best option to store the hydrogen was in a 2 liter tank at 300 bar together with 2 additional batteries with the capacity of 4000 mAh. This setup was concluded as the best option as it used up all hydrogen and landed with less charge in the battery than at the start point. / I takt med den globala uppvärmningen så växer behovet av klimatmedvetna hållbara lösningar. Ett område i stort behov av innovation är flygindustrin som länge varit en av de största klimatbovarna. Flygindustrin stora problem är att dess fordon både har begränsad volym och vikt. I detta kandidatexamensarbete kommer vi diskutera en hybridlösning där obemannade drönare drivs av en hybridlösning där batterier tillsammans med bränsleceller driver drönaren. Batterierna och bränslecellerna komplimenterar varandra då bränslecellerna är är lättviktiga och tillför en stabil produktion av ström till drönaren medan batterierna agerar komplement och hjälper till när det behövs extra kraft. Projektet som i samarbete med The Green Raven project utfördes för att utvärdera det optimala systemet för att förse drönaren nog med kraft i en timme. Projektet har utförts teoretiskt i Matlab och Simulink för att hitta den optimala balansen mellan batterier och bränsleceller. Från dessa simuleringar samlades data in för att optimera konfigurationen mellan bränslecellerna och batterierna. Från resultaten drogs slutsatsen att 2 batterier med en kapacitet på 4000 mAh som tillsammans med vätgas som förvarades i en 2 liter tank med ett tryck på 300 bar var den bästa konfigurationen. Denna lösning ansågs som den bästa då all vätgas förbrukades under simulation och att batteriet vid stopp hade en lägre laddning än vid flygstart.

Unmanned Aerial Vehicles (UAVs) can navigate in indoor environments and through environments that are hazardous or hard to reach for humans. This makes them suitable for use in search and rescue missions and by emergency response and law enforcement to increase situational awareness. However, even for an experienced UAV tele-operator controlling the UAV in these situations without colliding into obstacles is a demanding and difficult task. This thesis presents a human-UAV interface along with a collision avoidance method, both optimized for a human tele-operator. The objective is to simplify the task of navigating a UAV in indoor environments. Evaluation of the system is done by testing it against a number of use cases and a user study. The results of this thesis is a collision avoidance method that is successful in protecting the UAV from obstacles while at the same time acknowledges the operator’s intentions. / Obemannad luftfarkoster (UAV:er) kan navigera i inomhusmiljöer och genom miljöer som är farliga eller svåra att nå för människor. Detta gör dem lämpliga för användning i sök- och räddningsuppdrag och av akutmottagning och rättsväsende genom ökad situationsmedvetenhet. Dock är det även för en erfaren UAV-teleoperatör krävande och svårt att kontrollera en UAV i dessa situationer utan att kollidera med hinder. Denna avhandling presenterar ett människa-UAV-gränssnitt tillsammans med en kollisionsundvikande metod, båda optimerade för en mänsklig teleoperatör. Målet är att förenkla uppgiften att navigera en UAV i inomhusmiljöer. Utvärdering av systemet görs genom att testa det mot ett antal användningsfall och en användarstudie. Resultatet av denna avhandling är en kollisionsundvikande metod som lyckas skydda UAV från hinder och samtidigt tar hänsyn till operatörens avsikter.

Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously and to deal with their limited field of view. The results show that the system is able to avoid obstacles in 3D. / Hinderundvikande är ett utforskat område, dock för det mesta har forskningen fokuserat på 2D-miljöer. Eftersom drönare kan röra sig i tre dimensioner är det intressant att utveckla ett system som garanterar säker rörelse i 3D. Hinderundvikande är viktigt för drönare om de ska arbeta autonomt runt människor, eftersom drönare ofta är ömtåliga och har snabba propellrar som kan skada människor. Det här projektet är baserat på Hinderrestriktionsmetoden (ORM), och använder OctoMap för att använda information från många sensorer samtidigt och för att hantera deras begränsade synfält. Resultatet visar att systemet kan undvika hinder i 3D.

As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a safety architecture for resource constrained autonomous vehicles that enables the development of safer and more efficient nano-drone systems. The insight for the proposed safety architecture is that the regular structure of data-driven models used to control drones can be exploited to efficiently compress and identify key events that should be conserved in the EDR subsystem. We describe an implementation of the architecture, including hardware and software support, and quantify the benefits of the approach. We show that the proposed techniques can increase the amount of recorded flight time by over 10x and reduce energy usage by over 10x for high-resolution systems.

Rogue drones have become a significant concern in recent years due to their potential to cause harm to people and property and disrupt critical infrastructure and public safety. As a result, there has been a growing need for effective methods to detect and mitigate the risks posed by these drones. The proposed study aims to address the task by using a Radio Frequency (RF) based approach. Also, ensemble Machine Learning (ML) methods, as well as Deep Learning (DL) techniques were utilized as classification algorithms. Three levels of classification were defined for the task which includes drone detection, identification, and characterization based on operation mode. For the three levels, Deep-Complex Convolutional Neural Network performed the best and achieved an average accuracy of 99.82%, 94.20%, and 90.25%, respectively.

No description available.