Essa dissertaÃÃo estabelece como objeto central a avaliaÃÃo do Programa de AÃÃes Integradas e Referenciais de Enfrentamento à ViolÃncia Sexual Infanto-Juvenil no TerritÃrio Brasileiro â PAIR. Esta polÃtica do governo federal executada em Fortaleza parte do desafio de articular os diversos atores locais para o enfrentamento da violÃncia sexual de crianÃas e adolescentes. Nesse sentido, tentaremos desvendar quais foram os impactos que este programa trouxe, no territÃrio do Jangurussu, um dos bairros da cidade em que o Programa foi implantado. O esforÃo deste estudo foi o de compreendermos como o fenÃmeno da violÃncia sexual de crianÃas e adolescentes se relaciona na dinÃmica da vida contemporÃnea brasileira, sobretudo num contexto de pobreza em que este bairro està inserido, percebendo os limites e as possibilidades do Estado em combatÃ-lo. Para isso, lanÃamos esforÃos para perceber como esta articulaÃÃo institucional - a qual o Programa se propÃe por meio das suas diversas instituiÃÃes, tanto governamentais, como da sociedade civil - està impactando como alternativa para o enfrentamento desta forma de violÃncia. Consideramos a noÃÃo de Estado, trabalhando a categoria gramsciana de âEstado ampliadoâ para perceber as potencialidades e as tensÃes presentes neste campo. Realizamos uma avaliaÃÃo, que se utilizou de tÃcnicas da pesquisa etnogrÃfica, para adentrar o lugar onde acontece o Programa, a fim de compreender, a partir das narrativas dos atores envolvidos, os sentidos desta polÃtica para o combate da violÃncia sexual infanto-juvenil. / This dissertation establishes as central object the evaluation of the Program of Integrated and Referential Actions to Combat Sexual Violence against Children and Adolescents in the Brazilian territory - PAIR. This federal government policy implemented in Fortaleza comes from the challenge of articulating the various local actors to combat sexual violence of children and adolescents. Accordingly, we will try to unravel what was the impact that this program has brought to the territory of Jangurussu, one of the city districts where the program was implemented. The effort of this study was to understand how the phenomenon of sexual violence against children and adolescents relates in the dynamics of Brazilian contemporary life, particularly in a context of poverty where this neighborhood is inserted, realizing the limits and possibilities of the State to combat it. To do so, we launched efforts to understand how this institutional articulation, which the program aims through its various institutions, both governmental and civil society, is impacting as an alternative to face this kind of violence. We considered the notion of State, working the Gramscian category of "extended state" to realize the potentialities and tensions presented in this field. We conducted an evaluation, which used techniques of ethnographic research, to enter the place where the program takes place in order to understand, from the narratives of the actors involved, the directions of this policy to combat sexual violence against children and adolescents.

With the emergence of Industry 4.0, there is a pronounced emphasis on the necessity for enhanced flexibility in assembly processes. In the domain of bolt-tightening, this transition is evident. Tools are now required to navigate a variety of bolts and unpredictable tightening methodologies. Each bolt, possessing distinct tightening parameters, necessitates a specific sequence to prevent issues like bolt cross-talk or unbalanced force. This thesis introduces an approach that integrates advanced computing techniques with machine learning to address these challenges in the tightening areas. The primary objective is to offer edge computation for bolt detection and tightening tools' precise localization. It is realized by leveraging visual-inertial data, all encapsulated within a System-on-Chip (SoC) Field Programmable Gate Array (FPGA). The chosen approach combines visual information and motion detection, enabling tools to quickly and precisely do the localization of the tool. All the computing is done inside the SoC FPGA. The key element for identifying different bolts is the YOLOv3-Tiny-3L model, run using the Deep-learning Processor Unit (DPU) that is implemented in the FPGA. In parallel, the thesis employs the Error-State Extended Kalman Filter (ESEKF) algorithm to fuse the visual and motion data effectively. The ESEKF is accelerated via a full implementation in Register Transfer Level (RTL) in the FPGA fabric. We examined the empirical outcomes and found that the visual-inertial localization exhibited a Root Mean Square Error (RMSE) position of 39.69 mm and a standard deviation of 9.9 mm. The precision in orientation determination yields a mean error of 4.8 degrees, offset by a standard deviation of 5.39 degrees. Notably, the entire computational process, from the initial bolt detection to its final localization, is executed in 113.1 milliseconds. This thesis articulates the feasibility of executing bolt detection and visual-inertial localization using edge computing within the SoC FPGA framework. The computation trajectory is significantly streamlined by harnessing the adaptability of programmable logic within the FPGA. This evolution signifies a step towards realizing a more adaptable and error-resistant bolt-tightening procedure in industrial areas. / Med framväxten av Industry 4.0, finns det en uttalad betoning på nödvändigheten av ökad flexibilitet i monteringsprocesser. Inom området bultåtdragning är denna övergång tydlig. Verktyg krävs nu för att navigera i en mängd olika bultar och oförutsägbara åtdragningsmetoder. Varje bult, som har distinkta åtdragningsparametrar, kräver en specifik sekvens för att förhindra problem som bultöverhörning eller obalanserad kraft. Detta examensarbete introducerar ett tillvägagångssätt som integrerar avancerade datortekniker med maskininlärning för att hantera dessa utmaningar i skärpningsområdena. Det primära målet är att erbjuda kantberäkning för bultdetektering och åtdragningsverktygs exakta lokalisering. Det realiseras genom att utnyttja visuella tröghetsdata, allt inkapslat i en System-on-Chip (SoC) Field Programmable Gate Array (FPGA). Det valda tillvägagångssättet kombinerar visuell information och rörelsedetektering, vilket gör det möjligt för verktyg att snabbt och exakt lokalisera verktyget. All beräkning sker inuti SoC FPGA. Nyckelelementet för att identifiera olika bultar är YOLOv3-Tiny-3L-modellen, som körs med hjälp av Deep-learning Processor Unit (DPU) som är implementerad i FPGA. Parallellt använder avhandlingen algoritmen Error-State Extended Kalman Filter (ESEKF) för att effektivt sammansmälta visuella data och rörelsedata. ESEKF accelereras via en fullständig implementering i Register Transfer Level (RTL) i FPGA-strukturen. Vi undersökte de empiriska resultaten och fann att den visuella tröghetslokaliseringen uppvisade en Root Mean Square Error (RMSE) position på 39,69 mm och en standardavvikelse på 9,9 mm. Precisionen i orienteringsbestämningen ger ett medelfel på 4,8 grader, kompenserat av en standardavvikelse på 5,39 grader. Noterbart är att hela beräkningsprocessen, från den första bultdetekteringen till dess slutliga lokalisering, exekveras på 113,1 millisekunder. Denna avhandling artikulerar möjligheten att utföra bultdetektering och visuell tröghetslokalisering med hjälp av kantberäkning inom SoC FPGA-ramverket. Beräkningsbanan är avsevärt effektiviserad genom att utnyttja anpassningsförmågan hos programmerbar logik inom FPGA. Denna utveckling innebär ett steg mot att förverkliga en mer anpassningsbar och felbeständig skruvdragningsprocedur i industriområden.