This paper discusses my thesis piece Dear...,. It describes how the piece was put together and the deeper symbolism of aspects of the music.

Thèse doctorat : Traitement du signal et télécommunications : Rennes 1 : 2008. / Titre provenant de la page du titre du document électronique. Bibliogr. p. 245-258.

The United States use of Unmanned Combat Aerial Vehicles (UCAV) or drones for targeted killings of terrorists has been on the rise in recent years and the method has become the core element of president Obamas strategy in the war against terror. This study examines the deterrent effect of targeted killings on terrorism using UCAV/drones as a method of counterterrorism. Building on the literature on counterterrorism, UCAV, targeted killings, deterrence theory and statistics on terrorism the study provides a case study of the CIA drone operations in Pakistan between the years 2004-2010. The goal has been to analyze drone operations and the extent of terrorism from the beginning of the drone campaign until 2010. This as a means of identifying possible trends in terrorism activity due to the occurrence of drone strikes. Findings suggest that the possibility of a deterrent effect is far from evident and that there are some indications of increased terrorism.

Thèse (M.Sc.)--Université Laval, 2008. / Titre de l'écran-titre (visionné le 25 mars 2009). Bibliogr.

In this diploma thesis we are proposing solution for the search and track problem an evading ground object by an air pursuer in a 3D environment. In addition to static obstacles, there may be dynamic obstacles in the environment which are not plotted on the map provided to the drone. In order to avoid a collision, a safety method has been proposed which is based on processing the image from the input camera by a neural network. The tracking subtask is solved by a reactive algorithm adapted for movement in a built-up area. The probabilistic search algorithm is based on solving Art Gallery and Orienteering problems using nature-inspired algorithms. All algorithms and procedures are evaluated in a simulated environment on randomly generated maps.

This paper gives a thοrοugh lοοk in the use οf Unmanned Aerial Vehicles (UAV) in search and rescue missiοns. The οbjective is tο identify, thrοugh a literature review, all types οf UAV, the available remοte sensοrs that can be mοunted in a UAV and what type οf data each sensοr prοvides, the methοdοlοgy οn flight planning and what is the regulatοry framewοrk, guidelines and limitatiοns, οf UAV flight in sοme οf the biggest cοuntries wοrldwide and in specific fοr the Eurοpean Uniοn and Sweden. Alsο, a brief research in the field οf machine learning was dοne in οrder tο identify the pοssibilities given in use οf οbject recοgnitiοn and avοidance techniques. In the case οf Sweden, we have alsο identified the framewοrk fοr the Search and Rescue οperatiοns accοrding tο the “Civil Prοtectiοn Act. Gοvernment Bill 2002/03:119. Refοrmed rescue services legislatiοn”, in whοse hands falls the respοnsibility tο use and implement UAVs in such type οf missiοns. The main οbjectives fοr future develοpment are the full autοnοmy οf a UAV, the energy limitatiοns in the οperatiοnal phase, the fast prοcessing οf data οnbοard and the acquisitiοn οf the data thrοugh cοmmunicatiοn links withοut the need οf a physical cοnnectiοn between the UAV and the grοund cοntrοl statiοn (GCS). Accοrding tο οur research the οptimal cοnfiguratiοn οf UAVs in a search and rescue scenariο is a fleet οf fully autοnοmοus heterοgeneοus UAVs (fixed-wing UAVs and quadcοpters) with sοlar panels attached tο the fixed-wing UAVs, a high-resοlutiοn camera, a thermal camera and a Synthetic Aperture Radar (SAR). As the field οf UAVs is vastly grοwing the research οn the subject was cοnducted until April 1st, 2018 due tο new papers and research cοnducted in the subject regularly.

Nosema ceranae is a recently described, widespread microsporidian parasite of Apis mellifera that has raised concerns as to whether it is contributing to increased colony losses. To better understand this parasite, investigations were made into the seasonality of infections, alternative transmission mechanisms, and potential control approaches. All studies used real-time PCR with specific primers and probes for N. ceranae, as well as traditional spore analysis. Monthly colony monitoring in Virginia showed that N. ceranae was present yearlong with the highest levels observed in April-June and lower levels through the fall and winter. There was no difference in infection levels among bees sampled from different areas of the hive regardless of the time of year. Additionally, N. ceranae infects all castes of the colony. Drones of different ages, including pupae, in-hive, and flying drones, were found to be infected at low levels with infections most prevalent during peak annual levels in April-June. Approximately 5% of flying drones had moderate to high levels of infection indicating that flying drones, which would be the most likely age group to drift, could assist in the horizontal transmission of N. ceranae both within and between apiaries. Immature and mated queens were also found to be infected at low levels. Infection in the ovaries and spermathecae suggests the possibility for vertical transmission. Finally, control of N. ceranae is thought to improve the health of bees and to reduce colony losses. Fall fumagillin treatments and winter stimulative pollen feeding were compared. Neither treatment significantly lowered N. ceranae levels in colonies sampled 3-6 months later, nor did they significantly improve colony survival. Due to the high cost of treatment and the time required, we do not recommend either treatment for N. ceranae infections during the fall. Colony winter losses due solely to N. ceranae seem unlikely because levels of N. ceranae were low. Impacts from N. ceranae infections were also minimal during the summer as productive colonies had some of the highest levels of infection. Although N. ceranae is prevalent throughout hives, it does not seem to be a major cause of colony losses. / Ph. D.

Drones are unmanned aerial vehicles (UAVs) operated remotely with the help of cameras, GPS, and on-device SD cards. These are used for many applications including civilian as well as military. On the other hand, drone swarms are a fleet of drones that work together to achieve a special goal through swarm intelligence approaches. These provide a lot of advantages such as better coverage, accuracy, increased safety, and improved flexibility when compared to a single drone. However, the deployment of such swarms in an adversarial environment poses significant challenges. This work provides an overview of the current state of research on drone swarms in adversarial environments including algorithms for swarming formation of robotic attack drones with their strengths and weaknesses as well as the attack strategies used by attackers. This work also outlines the common adversarial counter-attack methods to disrupt drone attacks consisting of detection and destruction of drone swarms along with their drawbacks, a counter UAV defense system, and splitting large-scale drones into unconnected clusters. After identifying several challenges, an optimized algorithm is proposed to split the large-scale drone swarms more efficiently.

Tracking small unmanned aircraft systems (SUAS) becomes increasingly important with their increased use in populated areas. We propose an effective tracking solution for SUAS using a networked system of one-dimensional scanning radars. Tracking SUAS with a network of radar systems requires extrinsic calibration of each radar to a common frame. We provide a calibration solution using an orthogonal Procrustes formulation that associates radar measurements from the ground-mounted radar to real-time-kinematic Global Positioning System (RTK-GPS) data. Two variations of the calibration are presented, an online and batch processing method. Outdoor hardware experiments validate the capabilities of the radar network in tracking SUASs. The advantages and disadvantages of the two calibration methods are also illustrated with simulation and hardware tests.

The introduction of electric autonomous Unmanned Arial Vehicles (UAVs) in cities is considered the ultimate disruptive sustainable technological solution due to the promised speed, affordability, and significant greenhouse gas (GHG) emission reductions. The integration of UAVs into the future smart city fabric offers a wide range of applications. In particular, UAVs are ideal for last-mile operation, which is expected to reduce delivery costs, GHG emissions, and delivery time compared to light trucks and other traditional delivery methods. As UAVs operate in the city airspace, and with the current generation of older cities, several technological challenges arise with the anticipated proliferation of heterogeneous UAV fleets in low-altitude airspace of dense urban areas. Being a fairly new disruptive technology with no real-world operation data, the literature only considers a few of the system design parameters and often disregards the impact of other essential parameters such as Kinematics and airspace policies. This leads to significant uncertainty in the estimated UAV energy consumption, ranges, and emissions yielding inaccurate conclusions regarding the full system design predilections. Therefore, an effective UAV system design should strive to understand the broad spectrum of parameters’ impacts to optimize the integration and operation. Towards that end, this research aims at investigating the different UAV system design parameters and their intertwined impacts on operation efficiency to obtain accurate system optimization results. The research utilized several datasets for the delivery demand and digital-twin city model data of Toronto, Ontario, Canada. The research employed a state-of-the-art flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory along with several proposed novel airspace discretization, trajectory optimization, and charging infrastructure allocation optimization models. In this respect, this dissertation quantified the impact of airspace policies, discretization, and trajectory generation on the energy consumption of UAVs. Furthermore, it unveiled the operation uncertainties and their implications on the cost, emissions, and allocated charging infrastructure demand. Unlike the UAV literature, our research included all system design parameters and their impact on the performance metrics. The dissertation also proposes a novel combined airspace discretization and trajectory generation algorithm for optimal UAV energy consumption, airspace capacity maximization, airspace traffic control, and off-grid solar charging station allocation. For instance, it is found that UAV deployment with carefully tailored airspace policies in delivery could reduce GHG emissions in the freight sector by up to 35% compared to EVs. Furthermore, the research highlighted how building integrated photovoltaic BIPV upgrades with associated buildings can eliminate GHG emissions and significantly reduce the decarbonization price through associated savings and excess generated electricity. Overall, this research presents a unique contribution to the knowledge of UAV research for practitioners, policymakers, and academia. / Thesis / Doctor of Philosophy (PhD)