362 research outputs found

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Imaging cultural heritage at different scales : part I, the micro-scale (manufacts)

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    Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties.peer-reviewe

    Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems

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    224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil.Translated by Beverly Victoria Young and Karl Stephan Mokross

    Engineering a Low-Cost Remote Sensing Capability for Deep-Space Applications

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    Systems engineering (SE) has been a useful tool for providing objective processes to breaking down complex technical problems to simpler tasks, while concurrently generating metrics to provide assurance that the solution is fit-for-purpose. Tailored forms of SE have also been used by cubesat mission designers to assist in reducing risk by providing iterative feedback and key artifacts to provide managers with the evidence to adjust resources and tasking for success. Cubesat-sized spacecraft are being planned, built and in some cases, flown to provide a lower-cost entry point for deep-space exploration. This is particularly important for agencies and countries with lower space exploration budgets, where specific mission objectives can be used to develop tailored payloads within tighter constraints, while also returning useful scientific results or engineering data. In this work, a tailored SE tradespace approach was used to help determine how a 6 unit (6U) cubesat could be built from commercial-off-the-shelf (COTS)-based components and undertake remote sensing missions near Mars or near-Earth Asteroids. The primary purpose of these missions is to carry a hyperspectral sensor sensitive to 600-800nm wavelengths (hereafter defined as “red-edge”), that will investigate mineralogy characteristics commonly associated with oxidizing and hydrating environments in red-edge. Minerals of this type remain of high interest for indicators of present or past habitability for life, or active geologic processes. Implications of operating in a deep-space environment were considered as part of engineering constraints of the design, including potential reduction of available solar energy, changes in thermal environment and background radiation, and vastly increased communications distances. The engineering tradespace analysis identified realistic COTS options that could satisfy mission objectives for the 6U cubesat bus while also accommodating a reasonable degree of risk. The exception was the communication subsystem, in which case suitable capability was restricted to one particular option. This analysis was used to support an additional trade investigation into the type of sensors that would be most suitable for building the red-edge hyperspectral payload. This was in part constrained by ensuring not only that readily available COTS sensors were used, but that affordability, particularly during a geopolitical environment that was affecting component supply surety and access to manufacturing facilities, was optimized. It was found that a number of sensor options were available for designing a useful instrument, although the rapid development and life-of-type issues with COTS sensors restricted the ability to obtain useful metrics on their performance in the space environment. Additional engineering testing was conducted by constructing hyperspectral sensors using sensors popular in science, technology, engineering and mathematics (STEM) contexts. Engineering and performance metrics of the payload containing the sensors was conducted; and performance of these sensors in relevant analogous environments. A selection of materials exhibiting spectral phenomenology in the red-edge portion of the spectrum was used to produce metrics on the performance of the sensors. It was found that low-cost cameras were able to distinguish between most minerals, although they required a wider spectral range to do so. Additionally, while Raspberry Pi cameras have been popular with scientific applications, a low-cost camera without a Bayer filter markedly improved spectral sensitivity. Consideration for space-environment testing was also trialed in additional experiments using high-altitude balloons to reach the near-space environment. The sensor payloads experienced conditions approximating the surface of Mars, and results were compared with Landsat 7, a heritage Earth sensing satellite, using a popular vegetation index. The selected Raspberry Pi cameras were able to provide useful results from near-space that could be compared with space imagery. Further testing incorporated comparative analysis of custom-built sensors using readily available Raspberry Pi and astronomy cameras, and results from Mastcam and Mastcam/z instruments currently on the surface of Mars. Two sensor designs were trialed in field settings possessing Mars-analogue materials, and a subset of these materials were analysed using a laboratory-grade spectro-radiometer. Results showed the Raspberry Pi multispectral camera would be best suited for broad-scale indications of mineralogy that could be targeted by the pushbroom sensor. This sensor was found to possess a narrower spectral range than the Mastcam and Mastcam/z but was sensitive to a greater number of bands within this range. The pushbroom sensor returned data on spectral phenomenology associated with attributes of Minerals of the type found on Mars. The actual performance of the payload in appropriate conditions was important to provide critical information used to risk reduce future designs. Additionally, the successful outcomes of the trials reduced risk for their application in a deep space environment. The SE and practical performance testing conducted in this thesis could be developed further to design, build and fly a hyperspectral sensor, sensitive to red-edge wavelengths, on a deep-space cubesat mission. Such a mission could be flown at reasonable cost yet return useful scientific and engineering data

    mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

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    Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.Comment: This paper will appear in PNAS Nexu

    6th International Meeting on Retouching of Cultural Heritage, RECH6

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    RECH Biennial Meeting is one of the largest educational and scientific events in Retouching field, an ideal venue for conservators and scientists to present their research results about retouching. The main focus will be to promote the exchange of ideas, concepts, terminology, methods, techniques and materials applied during the retouching process in different areas of conservation: mural painting, easel painting, sculpture, graphic documentation, architecture, plasterwork, photography, contemporary art, among others. This Meeting aims to address retouching by encouraging papers that contribute to a deeper understanding of this final task of the conservation and restoration intervention. The main theme embraces the concepts of retouching, the criteria and limits in the retouching process, the bad retouching impact on heritage and their technical and scientific developments.This Meeting will discuss real-life approaches on retouching, focusing on practical solutions and on sharing experiencesColomina Subiela, A.; Doménech García, B.; Bailão, A. (2023). 6th International Meeting on Retouching of Cultural Heritage, RECH6. Editorial Universitat Politècnica de València. https://doi.org/10.4995/RECH6.2021.1601

    ATHENA Research Book, Volume 2

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    ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers
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