Unmanned Aerial System (UAS) is a versatile and essential tool for law enforcement, first responders, utility providers, and the general public. Integrating the UAS into the National Airspace System (NAS) poses a significant challenge to policymakers and manufacturers. A UAS Integration Safety and Security Technology Ontology (ISSTO) has been developed in the Web Ontology Language (OWL) to aid in this integration. ISSTO is a domain ontology covering aviation topics corresponding to flights, aircraft types, manufacturers, temporal/spatial, waivers and authorizations, track data, NAS facilities, air traffic control advisories, weather phenomena, surveillance and security equipment, and events, sensor types, radio frequency ranges, actions, and outcomes. As ISSTO is a domain ontology, it models the current state of UAS integration into the NAS and provides a comprehensive view of every aspect of UAS.

Northern bobwhite (Colinus virginianus) monitoring (e.g., covey-call surveys) is labor-intensive and imprecise. We evaluated the influence of bobwhite covey size and cover type on covey detectability when surveyed with a thermal camera-equipped small unmanned aerial system (sUAS). We placed bobwhite groups (3, 6, and 12 individuals/cage) among three cover types (grass, shrub, forest) on a private farm in Clay County, Mississippi (3 replicates, 27 total cages). At civil twilight, the sUAS flew over cages at 30 m, capturing photographs every 5 s. We asked 31 volunteers to evaluate 57 photographs for covey presence. Overall true positive rate was 0.551, but improved with increasing covey size. Coveys in grass had lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that thermal sUAS could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves.

No description available.

Cover crops are widely planted in the mid-Atlantic region for their environmental and agronomic benefits, but incomplete or delayed termination can lead to cover crops becoming weeds in the subsequent cash crop, known as volunteers. Studies were conducted from 2020-2022 to evaluate the effect of four commonly grown cover crop species, winter wheat (Triticum aestivum L.), cereal rye (Secale cereal L.), hairy vetch (Vicia villosa Roth), and rapeseed (Brassica napus L.), and four termination timings; 28, 14, 5, and 1 days before corn planting (DBP). Results indicated volunteerism was only an issue with rapeseed. Delaying rapeseed termination resulted in 0, 5, 12, and 22 volunteer plants m-2 at 28, 14, 5, and 1 DBP in corn. In order to manage these rapeseed volunteers, herbicide evaluations were conducted and indicated that atrazine, isoxaflutole, metribuzin, and pyroxasulfone resulted in 92-94% control when applied preemergence. Similarly, atrazine and glyphosate provided 99% rapeseed control and glufosinate resulted in 89% control when applied postemergence. Therefore, volunteers can easily be controlled with commonly used herbicides in corn. Studies were also conducted to evaluate the benefits of these cover crops, which have the potential to overcome the aforementioned risks. Results indicate that hairy vetch produced the most biomass and provided greater control of summer annual grasses, small-seeded broadleaf and large-seeded broadleaf weeds than other cover crops. Biomass accumulation and extent of weed control increased with delaying cover crop termination. Corn yield was greatest following hairy vetch and was least in rapeseed plots. Termination of cover crops 14 DBP planting increased corn yield by 12%; whereas termination at 1 DBP decreased corn yield by 15% as compared to no cover crop-no till plots. Effective termination of cover crops is an important management consideration and information on termination efficiency can help in devising management plans. In order to assist managers by evaluating cover crop termination efficiency, studies were conducted to evaluate selective and non-selective herbicides and a roller crimper for correlating vegetative indices with visible termination efficiency. Among vegetative indices, the Green Leaf Index had the highest Pearson correlation coefficient for wheat (r = -0.79, p = <0.0001) and cereal rye (r = -0.80, p = <0.0001) with visible termination efficiency. Whereas, for rapeseed, Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient (r = -0.66, p = <0.0001). However, for hairy vetch none of the vegetative indices correlated significantly with visible termination efficiency. While further research is necessary, remote sensing technologies may help in devising management plans by increasing crop scouting efficiency. / Master of Science in Life Sciences / Cover crops reduce soil erosion, leaching of soil nutrients in the water bodies, and provide benefits like weed suppression and improving the cash crop yield. Cover crops are generally planted in fall after the harvest of cash crop and are killed (terminated) before or after planting of next cash crop in the spring. Cover crop plants can also become weedy when they grow as volunteer plants in cash crops and if not terminated effectively. Therefore, effective termination of cover crops is also an important management consideration. Keeping these aspects in view, field experiments were conducted to evaluate different cover crops, winter wheat, cereal rye, hairy vetch, and rapeseed and four termination timings, 28, 14, 5, and 1 days before corn planting (DBP) for biomass accumulation, weed control, and impact on corn yield. Among cover crops, hairy vetch was found to be the best in terms of biomass production, weed control, and improving corn yield, whereas rapeseed had least biomass accumulation and reduced corn yield. Among termination timing, 1 and 5 DBP resulted in the most biomass production and weed control, but corn yield was greatest when terminated at 14 DBP. Delaying rapeseed termination from 28 DBP to 14, 5, and 1 DBP increased volunteer rapeseed in corn by 5, 12, and 22 plants m-2. Preemergence (PRE) and postemergence (POST) herbicides were evaluated for volunteer rapeseed control in corn. Among preemergence (PRE) herbicides, mesotrione, rimsulfuron and flumioxazin provided more than 95% volunteer rapeseed control, whereas atrazine, isoxaflutole, metribuzin, and pyroxasulfone provided 92-94% control. Among postemergence (POST) herbicides, atrazine and glyphosate provided 99% visible control of rapeseed, followed by glufosinate (89%). Various selective and non-selective herbicides were also evaluated for the termination of wheat, cereal rye, hairy vetch, and rapeseed. Non-selective herbicides like glyphosate, glufosinate and paraquat were found more effective for termination of cover crops as compared to non-selective herbicides. Vegetative indices (VI) were evaluated and correlated with visible termination efficiency (ground truth data) and found that VI can be used for estimating termination efficiency and these estimates can help in devising plans for management operations. Among VI, Green Leaf Index had the highest correlation coefficient for wheat and cereal rye visible termination ratings. Whereas for rapeseed, Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient value. However, for hairy vetch none of the vegetative indices correlated significantly with visible termination efficiency. Overall, hairy vetch was found to be the best cover crop for biomass accumulation, weed control and corn yield improvement. Delayed termination of rapeseed plants resulted in infestation of volunteer rapeseed in corn and reduced corn yield. However, volunteer rapeseed plants can be effectively managed by effective termination and use of proper PRE and POST herbicides in corn. Furthermore, remote sensing-based VIs have shown potential to estimate cover crop termination efficiency.

Traditional active noise control (ANC) systems rely on a physical sensor to measure the error signal at the desired location of attenuation. The error signal is then used to update an adaptive controller, which ultimately attenuates the measured response. However, it is not always practical to use traditional ANC in real-world applications. For example, as small unmanned aerial systems (UAS) become more commonly used, community noise exposure also increases, along with the desire to reduce UAS noise. Traditional ANC systems that rely on physical sensors at observer locations are impractical, since a UAS does not typically have real-time access to the response at an observer's ears, which is realistically in the far-field. Virtual error sensing (VES) can augment an ANC system using near-field measurements to estimate the response at a desired far-field location. In this way, the VES technique effectively shifts the zone of quiet from the location of the physical sensor(s) to a different "virtual" location. This thesis begins by outlining past work that used traditional ANC methods and virtual error sensing techniques. Numerical modeling results showing the predicted spatial change in SPL achieved using a virtual sensor will be presented. Experimental tests used ANC to attenuate the noise from a single UAS rotor at far-field locations using a near-field microphone and the remote microphone technique (RMT) to develop the VES. The results of the VES alone and with an ANC approach at several far-field virtual locations will be presented and discussed. / Master of Science / Small unmanned aerial systems (sUAS) are becoming increasingly common for private, military, and commercial use, and as such, community noise exposure is increasing. Reducing the noise produced by UAS could help improve community acceptance. Active noise control (ANC) might be used to attenuate noise produced by sUAS, however, traditional ANC systems would require a physical sensor in the far-field, which is not feasible. A virtual error sensor (VES) could eliminate the need for a far-field sensor. This thesis describes the proposed VES strategy, and presents numerical simulations and experimental results that highlight both the benefits and limitations of the approach. Results of the VES system with and without an ANC approach are discussed. Experimental testing focused on attenuating the tonal noise produced by one 2-bladed rotor with a tip radius of 4.7 inches. Pressure variations caused by the blade rotation were measured in the near and far-field using electret microphones and externally polarized condenser microphones, respectively. The ANC system used the filtered-x least mean squares algorithm in conjunction with the VES system to estimate the far-field response. A 2-inch diameter speaker served as the secondary source to provide the appropriate control input to the system. Experimental results show reductions between 6-13 dB at varying far-field locations and rotation rates.

This thesis investigates the possibility of using reinforcement learning (RL) techniques to create a flight controller for a quadrotor Micro Aerial Vehicle (MAV). A capable flight control system is a core requirement of any unmanned aerial vehicle. The challenging and diverse applications in which MAVs are destined to be used, mean that considerable time and effort need to be put into designing and commissioning suitable flight controllers. It is proposed that reinforcement learning, a subset of machine learning, could be used to address some of the practical difficulties. While much research has delved into RL in unmanned aerial vehicle applications, this work has tended to ignore low level motion control, or been concerned only in off-line learning regimes. This thesis addresses an area in which accessible information is scarce: the performance of RL when used for on-policy motion control. Trying out a candidate algorithm on a real MAV is a simple but expensive proposition. In place of such an approach, this research details the development of a suitable simulator environment, in which a prototype controller might be evaluated. Then inquiry then proposes a possible RL-based control system, utilising the Q-learning algorithm, with an adaptive RBF-network providing function approximation. The operation of this prototypical control system is then tested in detail, to determine both the absolute level of performance which can be expected, and the effect which tuning critical parameters of the algorithm has on the functioning of the controller. Performance is compared against a conventional PID controller to maximise the usability of the results by a wide audience. Testing considers behaviour in the presence of disturbances, and run-time changes in plant dynamics. Results show that given sufficient learning opportunity, a RL-based control system performs as well as a simple PID controller. However, unstable behaviour during learning is an issue for future analysis. Additionally, preliminary testing is performed to evaluate the feasibility of implementing RL algorithms in an embedded computing environment, as a general requirement for a MAV flight controller. Whilst the algorithm runs successfully in an embedded context, observation reveals further development would be necessary to reduce computation time to a level where a controller was able to update sufficiently quickly for a real-time motion control application. In summary, the study provides a critical assessment of the feasibility of using RL algorithms for motion control tasks, such as MAV flight control. Advantages which merit interest are exposed, though practical considerations suggest at this stage, that such a control system is not a realistic proposition. There is a discussion of avenues which may uncover possibilities to surmount these challenges. This investigation will prove useful for engineers interested in the opportunities which reinforcement learning techniques represent.

Counter Unmanned Aerial System (CUAS) security systems have unrealistic performance expectations hyped on marketing and idealistic testing environments. By developing an agent-based model to simulate these systems, an average performance metric can be obtained, thereby providing better representative values of true system performance.<br><br>Due to high cost, excessive risk, and exponentially large parameter possibilities, it is unrealistic to test a CUAS system for optimal performance in the real world. Agent-based simulation can provide the necessary variability at a low cost point and allow for numerous parametric possibilities to provide actionable output from the CUAS system. <br><br>This study describes and documents the Simulation of CUAS Networks and Sensors (SCANS) Framework in a novel attempt at developing a flexible modeling framework for CUAS systems based on device parameters. The core of the framework rests on sensor and communication device agents. These sensors, including Acoustic, Radar, Passive Radio Frequency (RF), and Camera, use input parameters, sensor specifications, and UAS specifications to calculate such values as the sound pressure level, received signal strength, and maximum viewable distance. The communication devices employ a nearest-neighbor routing protocol to pass messages from the system which are then logged by a command and control agent. <br><br>This framework allows for the flexibility of modeling nearly any CUAS system and is designed to be easily adjusted. The framework is capable of reporting true positives, true negatives, and false negatives in terms of UAS detection. For testing purposes, the SCANS Framework was deployed in AnyLogic and models were developed based on existing, published, empirical studies of sensors and detection UAS.<br>

This thesis addresses the subject of drone deliveries within healthcare to examine whether unmanned aerial vehicles can contribute with increased accessibility to medical supply. An overview of the advancements made in the field since its introduction in health care, is first presented in the report. Through a literary review on the subject, dimensions have been identified as significant for determining the possibly increased accessibility to medical equipment that the unmanned aerial system provides, and thus the utility of the system. This thesis further evaluates the system out of an accessibility perspective, which aims to meet theoretical criteria established by previous researchers in the accessibility field, and thereby adopt a holistic approach to the subject. It is concluded that the dimensions are dependent on characteristics of the geographical location and should be considered when discussing drones in medicine. However, knowledge and evaluations are lacking from regions that have implemented such a system, which contributes to uncertainties for how drone deliveries work in practice.

三維城市模型之建置需求漸趨繁多，可提供都市規劃、城市導航及虛擬實境等相關應用，過去研究多以建置LOD2城市模型為主，且較著重於屋頂結構。近年來，逐漸利用垂直影像及傾斜影像作為原始資料，提供建物牆面之建置，並且，隨著無人機系統(Unmanned Aircraft System, UAS）發展，可利用其蒐集高解析度且高重疊垂直及傾斜拍攝之建物影像，並採影像密匹配技術產製高密度點雲，進而快速取得建物包含屋頂及牆面之三維資訊，而這些資訊可進一步提供後續建置LOD3建置層級之模型，而在建置前，首先須對資料進行特徵分析，萃取特徵點、線、面，進而提供建置模型所需之資訊。 因此，本研究期望利用密匹配點雲，計算其點雲特徵，並採用Mean Shift分群法（Comaniciu and Meer, 2002）萃取建物點雲資訊，並提供一最佳分群策略。首先，本研究將以UAS為載具，設計一野外率定場率定相機，並蒐集建物高重疊UAS影像密匹配產製高密度點雲，針對單棟建物高密度點雲，實驗測試點雲疏化程度後，依據疏化成果計算點雲特徵，並以此批點雲資料實驗測試Mean shift分群法（Cheng, 1995）中之參數，後設計分群流程以分離平面點群及曲面點群，探討分群成果以決定最佳分群策略。實驗結果顯示本研究提出之分群策略，可自動區分平面點群及曲面點群，並單獨將平面點群分群至各牆面。 / Unmanned Aerial System (UAS) offer several new possibilities in a wide range of applications. One example is the 3D reconstruction of buildings. In former times this was either restricted by earthbound vehicles to the reconstruction of facades or by air-borne sensors to generate only very coarse building models. UAS are able to observe the whole 3D scene and to capture images of the object of interest from completely different perspectives. Therefore, this study will use UAS to collected images of buildings and to generate point cloud from dense image matching for modeling buildings. In the proposed approach, this method computes principal orientations by PCA and identifies clusters by Mean shift clustering. Analyze the factors which can affect the clustering methods and try to decrease the use of threshold, and this result can cluster the façade of buildings automatically and offer the after building reconstruction for LOD3.

En karta är en färskvara som är i ständigt behov av ajourhållning. Ajourhållning görs normalt med traditionella metoder: fotogrammetriska och/eller geodetiska. Men i och med att utvecklingen går framåt har intresset för en ny metod, UAS (Unmanned Aerial Systems), ökat. UAS är en relativt ny fotogrammetrisk metod där obemannade flygfarkoster används. Detta examensarbete har utvärderat vilken osäkerhet vanligt förekommande detaljer i en karta kan få i framställda "produkter" som genererats med hjälp av UAS-bilder som georefererats med naturliga stödpunkter. Produkterna som framställdes var en digital ytmodell och ett ortofoto och togs fram i datorprogrammet Agisoft Photoscan. Bilderna som bearbetades i denna studie erhölls från Swecos UAS-flygning och var tagna över deponiområdet Fågelmyra i Ornäs, Dalarnas län. I den digitala ytmodellen och i ortofotot mättes detaljer in för att sedan kontrolleras mot kontrollpunkter inmätta med nätverks-RTK (Real-Time Kinematic). Studien visade att detaljer inmätta i den digitala ytmodellen och ortofotot resulterade i en osäkerhet på 0,28 m respektive 0,08 m i plan. Varför osäkerheterna skiljer sig mellan den digitala ytmodellen och ortofotot kan ha att göra med att det är svårt att identifiera objekt i den digitala ytmodellen. Utifrån denna studie kan det konstateras att UAS och georeferering med naturliga stödpunkter lämpar sig för kartering av mindre områden. Dessutom kan det konstateras att UAS är effektiv och relativt enkel teknik. / A map is in constant need of being updated. Map updating is normally performed with traditional methods such as photogrammetric and/or geodetic. But by technical development the interest in new methods has increased, like in UAS (Unmanned Aerial Systems). UAS is a relatively new photogrammetric method using unmanned aerial vehicles (UAV). The purpose of this study is to evaluate what uncertainty common details in a map can get in "products" generated from UAS images georeferenced with natural ground control points. The products that were generated was a digital surface model and an orthophoto and was produced in the software Agisoft Photoscan. The images that were processed in this study were obtained from Sweco’s UAS flight and taken over landfill area Fågelmyra in Ornäs, Dalarna county. In the digital surface model and the orthophoto details were measured and controlled against check points surveyed with Network RTK (Real-Time Kinematic). The study has shown that the surveyed details in the digital surface model and ortohophoto resulted in a planimetric uncertainty of 0.28 m and 0.08 m, respectively. The reason for why the uncertainties for the digital surface model and orthophoto are different may be that it is difficult to identify objects in the digital surface model. Based on this study it can be concluded that UAS and georeferencing with natural ground control points is suitable for mapping of smaller areas. In addition, it can be concluded that UAS is efficient and relatively easy technique.