Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / Esta tese consiste em uma investigação bibliográfica e documental sobre as políticas de assistência e proteção à infância no Brasil. Com relação a esta questão, pesquisei fios de nossa história desde o período colonial até o final da chamada Era Vargas. Inicio este percurso histórico num momento em que o abandono de crianças não era considerado um problema, e funcionavam engrenagens de proteção por proximidade; caminho até os anos de 1930, quando médicos e juristas se mobilizam no sentido de organizar a assistência e a proteção ao menor de idade, contribuindo para a criação de outros modelos de atendimento à infância, baseados na assistência social e na proteção de Estado. Neste ponto, faço uma breve análise da chegada de Getúlio Vargas ao governo e de sua permanência nele até 1945. Em seguida discuto as mudanças ocorridas na estrutura das relações entre capital e trabalho na sociedade capitalista européia, quando o social se configurou enquanto um novo domínio de saber que possibilitou a emergência dos chamados profissionais do social, apontando similaridades com o processo ocorrido no Brasil durante a Era Vargas. A análise se foca, então, nos movimentos de criação e implementação de ações específicas para tratar da criança dita desassistida durante o Estado Novo, implementadas a partir do funcionamento de dois órgãos: o Departamento Nacional da Criança (DNCr) criado em 1940 para coordenar todas as atividades relativas a proteção à maternidade, à infância e à adolescência e o Serviço de Assistência a Menores (S.A.M.) criado em 1941 para superintender todo o serviço de amparo a menores desvalidos e delinqüentes, buscando sempre identificar que noções de psicologia que se faziam aí presentes. À guisa de conclusão, compartilho descobertas, encontros e desencontros proporcionados pela realização deste trabalho. / This thesis consists of a bibliographical and documental investigation about the infancy assistance and protection policies in Brazil. Related to this question, I researched historical lines since the colonial period up to the end of the so called Vargas Era. I start this historical course at the moment in which the children abandonment was not considered as a problem, and mechanisms worked as protection by proximity; I go back to the 1930s, when physicians and jurists mobilize themselves towards the underaged assistance and protection organization, which contributed to the creation of other attendance models, based on social assistance and on the State protection. At his point I make a concise analysis of Getúlio Vargas arrival at government and of his stay as president until 1945. Following this, I discuss the ocurred changes in the relationship structures between capital and work in the capitalist european society, when the social was configured as a new knowledge domain which allowed the emergence of the so caleed social professionals, pointing to similarities with the ocurred process in Brazil during the Vargas Era. Then, the analysis focuses itself in the creation and implementation movements of specific actions to treat the called not assisted child during the New State, implemented from the operation of two organs: the National Child Department (NCD) created in 1940 to coordinate all activities related to the protection of motherhood, infancy and adolescence and the Underaged Assistance Servic (UAS) created in 1941 to superintend all the services to assist unprotected underaged and delinquents , always searching to identify which psychology conceptions were present in both of them. As Conclusion, I share the foundings, encounters and discordances allowed by the realization of this work.

Current airspace regulations require the remote pilot-in-command of an unmanned aircraft systems (UAS) to maintain visual line of sight with the vehicle for situational awareness. The future of UAS will not have these constraints as technology improves and regulations are changed. An operational model for the future of UAS is proposed where a remote operator will monitor remote vehicles with the capability to intervene if needed. One challenge facing this future operational concept is the ability for a flight data system to effectively communicate flight status to the remote operator. A system architecture has been developed to facilitate the implementation of such a flight data system. Utilizing the system architecture framework, a Phase I prototype was designed and built for two vehicles in the Autonomous Flight Laboratory (AFL) at Cal Poly. The project will continue to build on the success of Phase I, culminating in a fully functional command and control system for remote UAS operational testing.

The work contains a brief overview of some modern unmanned aerial systems and later their capabilities of integration into controlled airspace in Europe. It discusses the current legislation dealing with different areas of integration of UAS and the update and developing process of legislation. Another section deals with the exploration of the possibilities of using unmanned aircraft in the commercial sector and their impact on the existing air transportation. The last section is devoted to the technical aspects associated with the integration of UAS into controlled areas. It describes the current systems of the CNS and their possible development in conjunction with UAVs and also a significant part of the work is devoted to the technology of the S&A (Sense and Avoid). During the entire work is gradually evaluate the current situation of the operation of UAS and are designed and recommended necessary changes that will need to be perform to their successful integration into the controlled airspace.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.

<div>Counter Unmanned Aerial System (C-UAS) development and fielding has greatly accelerated over the last several years to protect against all classes of Unmanned Aerial System (UAS) threats. Integration of the detection and tracking systems, the engagement systems, and other portions of the kill chain including command and control (C2) is ongoing. A significant concern is that the majority of these developments are designed for defending ships at sea. Most of these technological advances cannot be used within restricted waters or in port, foreign or domestic, due to the potential of high collateral damages and the fact that they are not currently readily available for dissemination to the Fleet.</div><div>The problem addressed by this project is to determine how to defend high value units from the threat of weaponized UAVs while moored pier-side with currently in-place weapons systems. This study will take a parameter-driven approach based on existing technologies to determine if an enhanced fire-control system integrated with standard issue weaponry can increase watchstander accuracy required to safely defend a high value unit pierside.</div>

Unmanned aircraft systems (UASs) are an important future technology with early generations already being used in many areas of application encompassing both military and civilian domains. This thesis proposes a number of integration techniques for combining control-based navigation with more abstract path planning functionality for UASs. These techniques are empirically tested and validated using an RMAX helicopter platform used in the UASTechLab at Linköping University. Although the thesis focuses on helicopter platforms, the techniques are generic in nature and can be used in other robotic systems. At the control level a navigation task is executed by a set of control modes. A framework based on the abstraction of hierarchical concurrent state machines for the design and development of hybrid control systems is presented. The framework is used to specify  reactive behaviors and for sequentialisation of control modes. Selected examples of control systems deployed on UASs are presented. Collision-free paths executed at the control level are generated by path planning algorithms.We propose a path replanning framework extending the existing path planners to allow dynamic repair of flight paths when new obstacles or no-fly zones obstructing the current flight path are detected. Additionally, a novel approach to selecting the best path repair strategy based on machine learning technique is presented. A prerequisite for a safe navigation in a real-world environment is an accurate geometrical model. As a step towards building accurate 3D models onboard UASs initial work on the integration of a laser range finder with a helicopter platform is also presented. Combination of the techniques presented provides another step towards building comprehensive and robust navigation systems for future UASs.

The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors. First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings. Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.

<p dir="ltr">Efficient management of plant diseases and their spread within fields requires a system capable of early and accurate disease identification and its severity estimation. Many plant diseases have distinct visual symptoms, which can be used to correctly identify, classify, and manage them. Recent technological advancements have led to increased adoption of deep neural networks (DNN) for developing deep learning (DL)-based computer vision systems. An accurate disease identification and severity estimation system using a DL-based computer vision framework is critical for efficiently managing corn diseases under field conditions and further restricting the spread of disease. Image processing and machine learning methods for disease identification and classification have been employed in the last two decades using high-cost sensors that need frequent calibration. Researchers have used low-cost red, green, and blue (RGB) sensors to mostly identify single diseases affecting crops, whereas, in real-world applications, a single leaf can be affected by multiple diseases. This research identifies gaps in knowledge of DL applications to field crops by reviewing 70 research articles published between 1983 and 2022. It creates a much-needed disease database for corn grown under field conditions by adding custom-acquired image data to other publicly available image repositories. The image data was used to train and evaluate the performance of commonly used DL-based image classification models for differentiating single diseases on individual corn leaves under field conditions. However, many disease lesions of different shapes and sizes can simultaneously develop on infected leaves. The performance of DL-based image classification and object detection models was evaluated to accurately identify multiple simultaneous diseases with varying symptoms. Disease identification under field conditions is necessary to implement an effective disease management system. However, recent work has demonstrated poor generalization accuracies of DL models trained on lab-acquired imagery for identifying diseases in the field. Therefore, after achieving promising results for disease identification, DL generalization performance was assessed and improved using different dataset combinations with varying backgrounds. A novel neural network architecture using a hierarchical structure was also proposed, which resulted in improved generalization performance. Additionally, disease severity must be estimated to implement an effective management response. DL models were evaluated to estimate the severity of multiple corn diseases under field conditions using aerial and ground-based platforms to identify specific lesions from above and below the canopy. A progressive web application was designed to empower end users with disease recognition capabilities. Overall, this research reports findings of the performance of deep learning image processing, object detection, and segmentation models for identifying single/multiple diseases on field corn and the development of tools that can potentially be a component of production-ready disease diagnosis systems for implementing effective management practices.</p>

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