<p dir="ltr">This research explored a solution to a high accident rate in remotely operating Unmanned Aerial Vehicles (UAVs) in a complex environment; it presented a new Brain-Computer Interface (BCI) enabled supervisory control system to fuse human and machine intelligence seamlessly. This study was highly motivated by the critical need to enhance the safety and reliability of UAV operations, where accidents often stemmed from human errors during manual controls. Existing BCIs confronted the challenge of trading off a fully remote control by humans and an automated control by computers. This study met such a challenge with the proposed supervisory control system to optimize human-machine collaboration, prioritizing safety, adaptability, and precision in operation.</p><p dir="ltr">The research work included designing, training, and testing BCI and the BCI-enabled control system. It was customized to control a UAV where the user’s motion intents and cognitive states were monitored to implement hybrid human and machine controls. The DJI Tello drone was used as an intelligent machine to illustrate the application of the proposed control system and evaluate its effectiveness through two case studies. The first case study was designed to train a subject and assess the confidence level for BCI in capturing and classifying the subject’s motion intents. The second case study illustrated the application of BCI in controlling the drone to fulfill its missions.</p><p dir="ltr">The proposed supervisory control system was at the forefront of cognitive state monitoring to leverage the power of an ML model. This model was innovative compared to conventional methods in that it could capture complicated patterns within raw EEG data and make decisions to adopt an ensemble learning strategy with the XGBoost. One of the key innovations was capturing the user’s intents and interpreting these into control commands using the EmotivBCI app. Despite the headset's predefined set of detectable features, the system could train the user’s mind to generate control commands for all six degrees of freedom of adapting to the quadcopter by creatively combining and extending mental commands, particularly in the context of the Yaw rotation. This strategic manipulation of commands showcased the system's flexibility in accommodating the intricate control requirements of an automated machine.</p><p dir="ltr">Another innovation of the proposed system was its real-time adaptability. The supervisory control system continuously monitors the user's cognitive state, allowing instantaneous adjustments in response to changing conditions. This innovation ensured that the control system was responsive to the user’s intent and adept at prioritizing safety through the arbitrating mechanism when necessary.</p>

Uninterrupted network connectivity is vital for real-time and mission-critical communication networks. The failure of Base Stations due to unforeseen circumstances such as natural disasters or emergencies can affect the coverage and capacity provided by terrestrial communication networks. The use of Unmanned Aerial Vehicles (UAVs) or drones in cellular networks is an upcoming area of research interest in 5G where the public sector and the communication service providers are fervently discussing it. The drones can be rapidly deployed to bridge the gaps in coverage or capacity of the network due to unforeseen circumstances. This thesis explores drone base stations' use for a simple hexagonal cell deployment scenario where the deployable base stations replace two failed macro base stations to improve the mean network capacity. Simulations show that the introduction of the deployable base stations indeed helps improve mean network capacity in case of one or multiple macro base station fail. The Genetic Algorithm is used to achieve Pareto optimality between downlink and uplink capacity of the simulated network. The simulation results show that introducing deployable nodes in a network can improve the network's capacity while also giving near-optimal transmit power values. / Oavbruten nätverksanslutning är avgörande för realtids- och missionskritiska kommunikationsnätverk. Fel på basstationer på grund av oförutsedda omständigheter som naturkatastrofer eller nödsituationer kan påverka täckningen och kapaciteten som tillhandahålls av markbundna kommunikationsnätverk. Användningen av Unmanned Aerial Vehicles (UAV) eller drönare i cellulära nätverk är ett kommande område av forskningsintresse inom 5G där den offentliga sektorn och leverantörerna av kommunikationstjänster ivrigt diskuterar det. Drönarna kan snabbt sättas in för att överbrygga klyftorna i nätverkets täckning eller kapacitet på grund av oförutsedda omständigheter. Denna avhandling utforskar drönarbasstationers användning för ett enkelt scenarie för hexagonal celldistribution där de utplacerbara basstationerna ersätter två misslyckade makrobasstationer för att förbättra den genomsnittliga nätverkskapaciteten. Simuleringar visar att introduktionen av de utplacerbara basstationerna verkligen hjälper till att förbättra den genomsnittliga nätverkskapaciteten i händelse av att en eller flera makrobasstationer misslyckas. Den genetiska algoritmen används för att uppnå Pareto-optimalitet mellan nedlänks- och upplänkkapaciteten i det simulerade nätverket. Simuleringsresultaten visar att införandet av utplacerbara noder i ett nätverk kan förbättra nätverkets kapacitet samtidigt som det ger nästan optimala värden för sändningseffekt.

The aim of this thesis is to answer the question whether the U.S. policy on targeted killings with combat drones is compatible with the legal doctrine of just war theory, applicable international law, and human rights law. Moreover, this paper intends to examine the legal issues arising from the U.S. practice of international law in relation to the justification of targeted killings. The purpose of this thesis is to determine whether the practice of targeted killings can be considered lawful and, if not, to provide knowledge about how the method violates applicable international law and the ethics of war. The focus is placed on relevant treaties and customary international law, and just war theory is used as a theoretical complement to explain the meaning and purpose of selected laws in order to determine their applicability to the research problem. Furthermore, this procedure has been conducted by using a legal method to identify the legal problem and interpret relevant sources of law in order to determine their applicability to the research problem. The thesis has determined that the U.S. policy on targeted killings with combat drones is not consistent with applicable international law and fundamental human rights law. In particular, the practice of targeted killings violates the principle of distinction.

This thesis aims to analyse the Central Intelligence Agency’s (CIA) potential violation of the right to a fair trial in placing alleged terrorists on the infamous ‘kill list’ without due process. The CIA has operated a targeted killing program using Armed Unmanned Aerial Vehicles (AUAV) over ‘areas outside active hostilities’, such as Yemen, since 2002. Due process is inherent to modern democratic societies and shapes their rule of law. The doctrinal legal analysis makes use of the key principles of due process to analyse the relevant Presidential Policy Guidance, its compliance with International Covenant on Civil and Political Rights (ICCPR) Art. 14, and a case study. These key principles and analyses will result in a clear overview of the program’s violations of the right to a fair trial. Concluding that the CIA’s AUAV program for targeted killing, particularly in Yemen, constitute extrajudicial executions.

L'extraction des paramètres de surface est une activité essentielle des Sciences de la vie et de la Terre. Ce mémoire propose une nouvelle méthodologie pour l'analyse des paramètres biophysiques et géophysiques, appelée Réalité Terrain Étendue, et qui mêle les avantages des relevés terrain et de la télédétection. Nous nous sommes en particulier attachés aux avantages de la télédétection basse altitude et d'un système micro-drone multi-caméras pour la cartographie de la dynamique de la végétation à l'échelle des individus. Cette problématique pose de nombreuses contraintes sur notre système car l'identification des arbrisseaux nécessite des capteurs innovants et une adaptation aux cycles phénologiques pour améliorer leur capacité de discrimination. La télédétection basse altitude semble être une solution intéressante en terme de résolution spatiale et de souplesse opérationnelle, et le développement des micro-drones civils permet des outils d'autant plus performants et fiables pour les missions terrain. Nous avons donc mis en place un système complet de drone avec une charge utile spécifique emportant simultanément trois appareils photographiques pour l'acquisition à la demande d'images obliques, stéréoscopiques ou multispectrales et permettant le développement de nouvelles méthodes d'identification de la végétation. Enfin, en participant à un relevé terrain du Muséum national d'Histoire naturelle, nous avons validé l'intérêt de notre système pour la cartographie de la dynamique de la végétation. Ce travail s'ouvre sur de nombreuses applications et perspectives de recherche, comme l'extraction de paramètres biophysiques par stéréo-restitution et l'agriculture de précision

Thesis (M. of Military Art and Science)--U.S. Army Command and General Staff College, 2007. / The original document contains color images.

Thesis (Master of Military Studies)-Marine Corps Command and Staff College, 2008. / Title from title page of PDF document (viewed on: Feb 11, 2010). Includes bibliographical references.

Les travaux de recherches sur la commande, le diagnostic et la tolérance aux défauts appliqués aux drones deviennent de plus en plus populaires. Il est judicieux de concevoir des lois de commande qui garantissent la stabilité et les performances du drone, non seulement dans le cas nominal, mais également en présence de fortes perturbations et de défauts.Dans cette thèse, un nouvel algorithme bio-inspiré adapté pour la recherche de solutions dans des problèmes d’optimisation est développé. Cet algorithme est utilisé pour trouver les gains des différents contrôleurs conçus pour les drones. La commande par mode glissant est utilisée pour développer deux contrôleurs passifs tolérants aux défauts pour les quadrirotors: un contrôleur par mode glissant augmentée avec un intégrateur, et un contrôleur par mode glissant implémenté en cascade. Parce que les commandes passives ont une robustesse réduite, une commande active par mode glissant est développée. Pour traiter les défauts extrêmes, un contrôleur d’urgence basé sur la conversion du quadrirotor en trirotor est développé. Les commandes actives, passives, et le contrôleur d’urgences sont ensuite intégrés pour former un contrôleur tolérant aux défauts capable de gérer un grand nombre de défaillances tout en garantissant les ressources actionneur et en limitant la charge de calcul du processeur. Finalement, des contrôleurs tolérants aux défauts, actifs et passifs, basés sur des méthodes par mode glissant du premier et deuxième ordre sont développées pour les octorotors. La commande active utilise des méthodes d’allocation de contrôles pour redistribuer les efforts sur les actionneurs sains, réduisant ainsi l’effet du défaut. / Unmanned Aerial Vehicles (UAV) are more and more popular for their civil and military applications. Classical control laws usually show weaknesses in the presence of parameter uncertainties, environmental disturbances, and actuator and sensor faults. Therefore, it is judicious to design a control law capable of stabilizing the UAV not only in the fault-free nominal cases, but also in the presence of disturbances and faults. In this thesis, a new bio-inspired search algorithm called Ecological Systems Algorithm (ESA) suitable for engineering optimization problems is developed. The algorithm is used over the thesis to find optimal gains for the fault tolerant controllers. Sliding Mode Control theory is used to develop two Passive Fault Tolerant Controllers for quadrotor UAVs: Regular and Cascaded SMC. Because Passive Controllers handle a few numbers of faults, an Active Sliding Mode Fault Tolerant Controller using Kalman Filter is developed. To overcome severe faults and failures, an emergency controller based on the Quadrotor-to-Trirotor conversion maneuver is developed. The Controllers developed so far (Passive, Active, and emergency controllers) are then integrated to form the Integrated Fault Tolerant Controller (IFTC). The IFTC is a powerful controller that is able to handle a wide number of faults, and save actuator resources as well as processor computational effort. Finally, Passive and Active Fault Tolerant Controllers are designed for octorotor UAVs based on First Order and Second Order Sliding Mode Control. The AFTC uses Dynamic and Pseudo-Inverse Control Allocation methods to redistribute the control effort among healthy actuators reducing the effect of fault.

This thesis examines the foreign policy of the 44th President of the United States of America, Barack Obama. A significant theme of Barack Obama's candidacy for presidency was "change"; more specifically change in policy from previous administration. Therefore, the thesis looks at the changes brought about by President Obama once he was elected. First, we look at foreign policy of the United States under President George W. Bush, then we look at foreign policy-related assertions of Barack Obama during his candidacy, and lastly we look at the actual policies of the new administration, with particular focus on the wars in Iraq and Afghanistan, drones and extrajudicial killing, Guantanamo detention facility and extraordinary rendition, and NSA surveillance.

Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements.