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Item Identification of brush species and herbicide effect assessment in Southern Texas using an Unoccupied Aerial System (UAS)(2023-06-21) Shen, Xiaoqing; Clayton, Megan K.; Starek, Michael J.; Chang, Anjin; Jessup, Russell W.; Foster, Jamie L.Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.]Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment.Item Designing UAV swarm experiments: A simulator selection and experiment design process(2023-08-23) Phadke, Abhishek; Medrano, F. Antonio; Sekharam, Chandra N.; Chu, TianxingThe rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.Item Navigating the skies: Examining the FAA’s remote identification rule for unmanned aircraft systems(2023-01) Phadke, Abhishek; Boyd, Josh; Medrano, F. Antonio; Starek, MichaelAs technology and innovations in unmanned aerial vehicles progress, so does the need for regulations in place to create safe and controlled flying scenarios. The Federal Aviation Administration (FAA) is a governing body under the United States Department of Transportation that is responsible for a wide range of regulatory activities related to the United States airspace. In a recently published final rule, the FAA addresses several concerns such as the need for a system to identify all aircrafts flying in national airspace, as well as the implementation of a separate system from the prevalent Automatic Dependent Surveillance– Broadcast system to prevent interference with manned aircrafts. Their solution to these concerns is the deployment of remote identification (RID) on all unmanned aircraft systems (UAS) flying under its implied jurisdiction. While US governing agencies retain the use of the word UAS for now, the International Civil Aviation Organization terminology is remotely piloted aircraft systems. The FAA describes the RID implementation as a “Digital license plate” for all UAS flying in the United States airspace. They outline additional policies including several options for compliance, operating rules, and design and production guidelines for manufacturers. As the September 2023 deadline for compliance draws near, this article highlights possible deployment applications and challenges.Item Optimization of the fluted force-feed seeder meter with the helical roller using the discrete element method and response surface analysis(2023-07-14) Wang, Jianxiao; Sun, Wei; Simionescu, Petru Aurelian; Ju, YuanjinThe seed metering process of a fluted force-feed seeder was simulated using the Discrete Element Method and its parameters optimized using the Box–Behnken Design of Experiments and the Response Surface Method. The rotational speed of the feed roller, the lead (helix) angle of the flutes, and the number of flutes were the independent variables, while the response value was the seeding uniformity index. Two regression models were investigated, and the following conclusions drawn. For the flute lead angle between 0 and 10 degrees, and the number of flutes between 10 and 14, it was found that the number of flutes and the lead angle influenced the seeding performance the most, with the order of importance being the (i) number of flutes, (ii) lead angle and (iii) roller speed. For the flute lead angle between 5 and 15 degrees, and the number of flutes between 12 and 16, it was found that the roller speed and the number of flutes influenced the seeding performance the most, with the order of importance being the (i) roller speed, (ii) number of flutes and (iii) flute lead angle. The two regression models were then minimized for the seeding uniformity index and the corresponding optima verified experimentally on a conveyor belt test stand fitted with an image recognition system.Item Aggregating XAI methods for insights into geoscience models with correlated and high-dimensional rasters(2023-05-13) Krell, Evan; Kamangir, Hamid; Collins, Waylon; King, Scott A; Tissot, PhilippeGeoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.Item Topic 13: Design patterns(2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand the concept of design patterns, Evaluate various design patterns by category, Analyze the advantages of the design patterns in question, Understand how some significant patterns are usedItem Topic 12: Exception handling(2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand the usages of exception handling, Recognize the differences between errors and exceptions, Understand the usage of try-catch-finally to achieve exception handling, Program using Java’s exception-handling syntax, Understand how assertions are usedItem Topic 11: Thread synchronization(2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand the need for thread synchronization, Recognize basic constructs of thread synchronization, Implement synchronized solutions to problemsItem Topic 10: Concurrency basics[63](2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand concurrency and relevant terminology, Recognize various thread states, Recognize various problems that may arise due to incorrect organization of threads, Program using Runnable objectsItem Topic 9: Graphical user interfaces(2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand the concept of a graphical user interface, Build graphical user interfaces and handle various events, Recognize different components such as buttons, text fields, framesItem Topic 8: Interfaces(2023-03-02) Hadimlioglu, AlihanObjectives for this topic: Understand the concept of interfaces, Recognize the requirements of interfaces, Evaluate the differences between other polymorphic concepts and interfaces, Program using Java’s interface syntaxItem Topic 7: Polymorphism(2023-03-02) Hadimlioglu, AlihanObjectives of this topic: Understand the concept of polymorphism, Recognize the advantages of polymorphic development, Evaluate the differences between abstract and concrete classes, Use overridden methods to add more specificity to children, Program a polymorphic implementationItem Topic 6: Inheritance(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Inheritance, Subclasses and Superclasses, Protected Access, Method OverridingItem Topic 5.3: Multidimensional arrays and arraylists(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Multidimensional Arrays, Variable-length argument list, ArraylistItem Topic 5.2: Constants and configuration files(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Constants, Configuration FilesItem Topic 5.1: Arrays(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Single Dimension Arrays, Arrays of Primitive Types, Arrays of Reference TypesItem Topic 4.5: Static and finals(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Static Class Attributes, Static Methods, Final Class AttributesItem Topic 4.4: Class operations(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Data Hiding / Information Hiding, Getter (Query or Accessor) methods, Setter (Mutator) methods, Predicate methods, Helper/Utility methodsItem Topic 4.3: Useful Java classes(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Useful Java Classes, Object Class, String Class, Math Class, Integer Class, SystemItem Topic 4.2: Access modifiers(2023-03-02) Hadimlioglu, AlihanIn this module, you will learn about: Access Modifiers, Public Access, Private Access, Protected Access, Default Access