736 research outputs found

    Object detection and localization: an application inspired by RobotAtFactory using machine learning

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    Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe evolution of artificial intelligence and digital cameras has made the transformation of the real world into its digital image version more accessible and widely used. In this way, the analysis of information can be carried out with the use of algorithms. The detection and localization of objects is a crucial task in several applications, such as surveillance, autonomous robotics, intelligent transportation systems, and others. Based on this, this work aims to implement a system that can find objects and estimate their location (distance and angle), through the acquisition and analysis of images. Having as motivation the possible problems that can be introduced in the robotics competition, RobotAtFactory Lite, in future versions. As an example, the obstruction of the path developed through the printed lines, requiring the robot to deviate, and/or the positioning of the boxes in different places of the initial warehouses, being positioned so that the robot does not know its previous location, having to find it somehow. For this, different methods were analyzed, based on machine leraning, for object detection using feature extraction and neural networks, as well as object localization, based on the Pinhole model and triangulation. By compiling these techniques through python programming in the module, based on a Raspberry Pi Model B and a Raspi Cam Rev 1.3, the goal of the work is achieved. Thus, it was possible to find the objects and obtain an estimate of their relative position. In the future, in a possible implementation together with a robot, this data can be used to find objects and perform tasks.A evolução da inteligência artificial e das câmeras digitais, tornou mais acessível e amplamente utilizada a transformação do mundo real, para sua versão em imagem digital. Dessa maneira, a análise das informações pode ser efetuada com a utilização de algoritmos. A deteção e localização de objetos é uma tarefa crucial em diversas aplicações, tais como vigilância, robótica autônoma, sistemas de transporte inteligente, entre outras. Baseado nisso, este trabalho tem como objetivo implementar um sistema que consiga encontrar objetos e estimar sua localização (distância e ângulo), através da aquisição e análise de imagens. Tendo como motivação os possíveis problemas que possam ser introduzidos na competição de robótica, Robot@Factory Lite, em versões futuras. Podendo ser citados como exemplo a obstrução do caminho desenvolvido através das linhas impressas, requerendo que o robô desvie, e/ou o posicionamento das caixas em locais diferentes dos armazéns iniciais, sendo posicionadas de modo que o robô não saiba sua localização prévia, devendo encontra-las de alguma maneira. Para isso, foram analisados diferentes métodos, baseadas em machine leraning, para deteção de objetos utilizando extração de características e redes neurais, bem como a localização de objetos, baseada no modelo de Pinhole e triangulação. Compilando essas técnicas através da programação em python, no módulo, baseado em um Raspberry Pi Model B e um Raspi Cam Rev 1.3, o objetivo do trabalho é alcançado. Assim, foi possível encontrar os objetos e obter uma estimativa da sua posição relativa. Futuramente, em uma possível implementação junta a um robô, esses dados podem ser utilizados para encontrar objetos e executar tarefas

    20th SC@RUG 2023 proceedings 2022-2023

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    20th SC@RUG 2023 proceedings 2022-2023

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    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

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    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases

    Light transport by topological confinement

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    The growth of data capacity in optical communications links, which form the critical backbone of the modern internet, is facing a slowdown due to fundamental nonlinear limitations, leading to an impending "capacity crunch" on the horizon. Current technology has already exhausted degrees of freedom such as wavelength, amplitude, phase and polarization, leaving spatial multiplexing as the last available dimension to be efficiently exploited. To minimize the significant energy requirements associated with digital signal processing, it is critical to explore the upper limit of unmixed spatial channels in an optical fiber, which necessitates ideally packing spatial channels either in real space or in momentum space. The former strategy is realized by uncoupled multi-core fibers whose channel count has already saturated due to reliability constraint limiting fiber sizes. The later strategy is realized by the unmixed multimode fiber whose high spatial efficiency suggest the possibility of high channel-count scalability but the right subset of mode ought to be selected in order to mitigate mode coupling that is ever-present due to the plethora of perturbations a fiber normally experiences. The azimuthal modes in ring-core fibers turn out to be one of the most spatially efficient in this regard, by exploiting light’s orbital angular momentum (OAM). Unmixed mode counts have reached 12 in a ~1km fiber and 24 in a ~10m fiber. However, there is a fundamental bottleneck for scalability of conventionally bound modes and their relatively high crosstalks restricts their utility to device length applications. In this thesis, we provide a fundamental solution to further fuel the unmixed-channel count in an MMF. We utilize the phenomenon of topological confinement, which is a regime of light guidance beyond conventional cutoff that has, to the best of our knowledge, never been demonstrated till publications based on the subject matter of this thesis. In this regime, light is guided by the centrifugal barrier created by light’s OAM itself rather than conventional total internal reflection arising from the index inhomogeneity of the fiber. The loss of these topologically confined modes (TCMs) decreases down to negligible levels by increasing the OAM of fiber modes, because the centrifugal barrier that keeps photons confined to a fiber core increases with the OAM value of the mode. This leads to low-loss transmission in a km-scale fiber of these cutoff modes. Crucially, the mode-dependent confinement loss of TCMs further lifts the degeneracy of wavevectors in the complex space, leading to frustration of phase-matched coupling. This thus allows further scaling the mode count that was previously hindered by degenerate mode coupling in conventionally bound fiber modes. The frustrated coupling of TCMs thus enables a record amount of unmixed OAM modes in any type of fiber that features a high index contrast, whether specially structured as a ring-core, or simply constructed as a step-index fiber. Using all these favorable attributes, we achieve up to 50 low-loss modes with record low crosstalk (approaching -45 dB/km) over a 130-nm bandwidth in a ~1km-long ring-core fiber. The TCM effect promises to be inherently scalable, suggesting that even higher modes counts can be obtained in the future using this design methodology. Hence, the use of TCMs promises breaking the record spectral efficiency, potentially making it the choice for transmission links in future Space-Division-Multiplexing systems. Apart from their chief attribute of significantly increasing the information content per photon for quantum or classical networks, we expect that this new light guidance may find other applications such as in nonlinear signal processing and light-matter interactions

    Histograms: An educational eye

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    Many high-school students are not able to draw justified conclusions from statistical data in histograms. A literature review showed that most misinterpretations of histograms are related to difficulties with two statistical key concepts: data and distribution. The review also pointed to a lack of knowledge about students’ strategies when solving histogram tasks. As the literature provided little guidance for the design of lesson materials, several studies were conducted in preparation. In a first study, five solution strategies were found through qualitative analysis of students’ gazes when solving histograms and case-value plot tasks. Quantitative analysis of several histogram tasks through a mathematical model and a machine learning algorithm confirmed these results, which implied that these strategies could reliably and automatically be identified. Literature also suggested that dotplot tasks can support students’ learning to interpret histograms. Therefore, gazes on histogram tasks were compared before and after students solved dotplot tasks. The "after" tasks contained more gazes associated with correct strategies and fewer gazes associated with incorrect strategies. Although answers did not improve significantly, students’ verbal descriptions suggest that some students changed to a correct strategy. Newly designed materials thus started with dotplot tasks. From the previous studies, we conjectured that students lacked embodied experiences with actions related to histograms. Designed from an embodied instrumentation perspective, the tested materials provide starting points for scaling up. Together, the studies address the knowledge gaps identified in the literature. The studies contribute to knowledge about learning histograms and use in statistics education of eye-tracking research, interpretable models and machine learning algorithms, and embodied instrumentation design

    Interdisciplinarity in the Age of the Triple Helix: a Film Practitioner's Perspective

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    This integrative chapter contextualises my research including articles I have published as well as one of the creative artefacts developed from it, the feature film The Knife That Killed Me. I review my work considering the ways in which technology, industry methods and academic practice have evolved as well as how attitudes to interdisciplinarity have changed, linking these to Etzkowitz and Leydesdorff’s ‘Triple Helix’ model (1995). I explore my own experiences and observations of opportunities and challenges that have been posed by the intersection of different stakeholder needs and expectations, both from industry and academic perspectives, and argue that my work provides novel examples of the applicability of the ‘Triple Helix’ to the creative industries. The chapter concludes with a reflection on the evolution and direction of my work, the relevance of the ‘Triple Helix’ to creative practice, and ways in which this relationship could be investigated further
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