13,511 research outputs found

    Key technologies for safe and autonomous drones

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    Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

    Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access

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    In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. In particular, the common sparsity pattern in the received pilot and data signal has been ignored in most existing studies, and auxiliary information of channel decoding has not been utilized for user activity detection. This paper endeavors to develop advanced receivers in a holistic manner for joint activity detection, channel estimation, and data decoding. In particular, a turbo receiver based on the bilinear generalized approximate message passing (BiG-AMP) algorithm is developed. In this receiver, all the received symbols will be utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, while the side information-aided receiver notably outperforms the conventional method with a relatively low complexity

    DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback

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    Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. However, such simplification may fail to meet practical needs due to the neglect of the fact that motion prediction in real applications is not an isolated ``observe then predict'' unit, but a consecutive process composed of many rounds of such unit, semi-overlapped along the entire sequence. As time goes on, the predicted part of previous round has its corresponding ground truth observable in the new round, but their deviation in-between is neither exploited nor able to be captured by existing isolated learning fashion. In this paper, we propose DeFeeNet, a simple yet effective network that can be added on existing one-off prediction models to realize deviation perception and feedback when applied to consecutive motion prediction task. At each prediction round, the deviation generated by previous unit is first encoded by our DeFeeNet, and then incorporated into the existing predictor to enable a deviation-aware prediction manner, which, for the first time, allows for information transmit across adjacent prediction units. We design two versions of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more complicated BABEL, experimental results indicate that our proposed network improves consecutive human motion prediction performance regardless of the basic model.Comment: accepted by CVPR202

    OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI

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    Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this paper, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Elite perceptions of the Victorian and Edwardian past in inter-war England

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    It is often argued by historians that members of the cultivated Elite after 1918 rejected the pre-war past. or at least subjected it to severe denigration. This thesis sets out to challenge such a view. Above all, it argues that inter-war critics of the Victorian and Edwardian past were unable to reject it even if that was what they felt inclined to do. This was because they were tied to those periods by the affective links of memory, family, and the continually unfolding consequences of the past in the present. Even the severest critics of the pre-war world, such as Lytton Strachey, were less frequently dismissive of history than ambivalent towards it. This ambivalence, it is argued, helped to keep the past alive and often to humanise it. The thesis also explores more positive estimation of Victorian and Edwardian history between the wars. It examines nostalgia for the past, as well as instances of continuity of practice and attitude. It explores the way in which inter-war society drew upon aspects of Victorian and Edwardian history both as illuminating parallels to contemporary affairs and to understand directly why the present was shaped as it was. Again, this testifies to the enduring power of the past after 1918. There are three parts to this thesis. Part One outlines the cultural context in which writers contemplated the Victorian and Edwardian past. Part Two explores some of the ways in which history was written about and used by inter-war society. Part Three examines the ways in which biographical depictions of eminent Victorians after 1918 encouraged emotional negotiation with the pas

    Nitrite and insulin lower the oxygen cost of ATP synthesis in skeletal muscle cells by pleiotropic stimulation of glycolysis

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    Dietary nitrate lowers the oxygen cost of submaximal exercise, but precise mechanistic insight into how this occurs is lacking. Research suggests that dietary nitrate may render oxidative ATP synthesis more efficient, but evidence is inconclusive at present. This thesis aimed to establish how nitrite (a reduced form of nitrate) affects the bioenergetics of cultured skeletal muscle cells. Comparison between the acute effects of nitrite and insulin, a hormonal regulator of muscle function that increases mitochondrial efficiency, was explored to assess possible mechanistic overlap. Calculation of real-time intracellular ATP synthesis rates from simultaneous oxygen consumption and medium acidification measurements revealed the effects of sodium nitrite and insulin on intact rat (L6) myoblasts and myotubes. These extracellular flux data were also used to determine how mitochondrial and glycolytic ATP supply is used to fuel ATP-demanding processes. The data presented in this thesis revealed that both nitrite and insulin acutely stimulate glycolytic ATP synthesis. This stimulation occurs without significant mitochondrial ATP supply changes, thus increasing the glycolytic index of myocytes. Consequently, nitrite and insulin lower the oxygen cost of cellular ATP supply. Notably, insulin lowers oxygen consumption linked to mitochondrial proton leak, thus increasing mitochondrial efficiency. Nitrite does not improve coupling efficiency in myoblasts or myotubes. Further investigations revealed that stimulation of glycolytic ATP supply is not secondary to increased glucose availability. In myotubes, glycolytic stimulation persists in the presence of a mitochondrial uncoupler, suggesting that glycolysis is increased directly. In myoblasts, stimulation is annulled by uncoupler, suggesting that glycolysis increases indirectly, via increased ATP consumption. The molecular targets of nitrite and insulin remain unclear, but the data exclude stimulation of protein synthesis. Together, the data demonstrate that nitrite and insulin lower the oxygen cost of ATP synthesis in skeletal muscle cells by pleiotropic stimulation of glycolysis. The data inform the ongoing debate regarding the mechanism by which dietary nitrate lowers the oxygen cost of exercise, suggesting a push toward a more glycolytic phenotype. Such mechanistic insight is crucial for achieving the full translational potential of dietary nitrate
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