11,997 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

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    Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

    Machine learning and mixed reality for smart aviation: applications and challenges

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    The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

    Sputter deposition on composites : interplay between film and substrate properties

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    The Development and Performance of the First BICEP Array Receiver at 30 and 40 GHz for Measuring the Polarized Synchrotron Foreground

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    The existence of the CMB marks a big success of the lambda cold dark matter standard model, which describes the universe’s evolution with six free parameters. The inflationary theory was added to the picture in the ’80s to explain the initial conditions of the universe. Scalar perturbations from inflation seeded the formation of the large-scale structure and produced the curl-free E-mode polarization pattern in the CMB. On the other hand, tensor fluctuations sourced primordial gravitational waves (PGW), which could leave unique imprints in the CMB polarization: the gradient-free B-mode pattern. The amplitude of B modes is directly related to the tensor-to-scalar ratio r of the primordial fluctuations, which indicates the energy scale of inflation. The detection of the primordial B modes will be strong supporting evidence of inflation and give us opportunities to study physics at energy scales far beyond what can ever be accessed in laboratory experiments on the Earth. Currently, the main challenge for the B-mode experiments is to separate the primordial B modes from those sourced by matter between us and the last scattering surface: the galactic foregrounds and the gravitational lensing effect. The two most important foregrounds are thermal dust and synchrotron, which have very different spectral properties from the CMB. Thus the key to foreground cleaning is the high sensitivity data at multiple frequency bands and the accurate modeling of the foregrounds in data analyses and simulations. In this dissertation, I present my work on ISM and dust property studies which enriched our understanding of the foregrounds. The BICEP/Keck (BK) experiments build a series of polarization-sensitive microwave telescopes targeting degree-scale B-modes from the early universe. The latest publication from the collaboration with data taken through 2018 reported tensor-to-scalar ratio r0.05 &#60; 0.036 at 95% C.L., providing the tightest constraint on the primordial tensor mode. BICEP Array is the latest generation of the series experiments. The final configuration of the BICEP Array has four BICEP3-class receivers spanning six frequency bands, aiming to achieve σ(r) ≾ 0.003. The first receiver of the BICEP Array is at 30 and 40 GHz, constraining the synchrotron foregrounds. In this dissertation, I cover the development of this new receiver focusing on the design and performance of the detectors. I report on the characterizing and diagnosing tests for the receiver during its first few observing seasons.</p

    ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images

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    BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM

    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

    Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems

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    In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the GE Signa integrated PET/MR. The methods were divided into three closely related categories: NEMA performance measurements, system modelling and evaluation of the image quality of the state-of-the-art of clinical PET scanners. NEMA performance measurements for characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and scatter corrections, and noise equivalent count rate (NECR) were performed using clinically relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT scanner to investigate whether and to what extent noise can be reduced. The outcome of this thesis will allow clinicians to reduce the PET dose which is especially relevant for young patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will allow physicists and engineers to better understand and design integrated PET/MR systems

    Diagnosis of Pneumonia Using Deep Learning

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    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification

    Optimisation of Triboelectric Nanogenerator performance in vertical contact-separation mode

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    Triboelectric nanogenerator (TENG) is one of the most promising energy harvesters – a technology that uses repeated or reciprocating contact of suitably chosen materials to generate charge via the triboelectric effect (TE) and utilizes this as usable voltage and current. TENGs are attractive as they can continuously generate charge over a wide range of operating conditions and have several valuable advantages such as light weight, simple structure, low cost and high efficiency. Therefore, TENGs have been explored in a wide range of applications, including self-powered wearable electronics, powering electronics and even for harvesting ocean wave/wind energy. One of the major limitations of TENGs is their low power output (usually <500 W/m2). This thesis focuses of a few specific approaches to optimising TENG output performance. This thesis begins by presenting a solution to this challenge by optimizing a low permittivity substrate beneath the tribo-contact layer. The open circuit voltage is found to increase by a factor of 1.3 in moving from PET to the lower permittivity PTFE. TENG performance is also believed to depend on contact force, but the origin of the dependence had not previously been explored. Herein, we show that this behaviour results from a contact force dependent real contact area Ar as governed by surface roughness. The open circuit voltage Voc, short circuit current Isc and Ar for a TENG were found to increase with contact force/pressure. Critically, Voc and Isc saturate at the same contact pressure as Ar suggesting that electrical output follows the same evolution as Ar. Assuming that tribo charges can only transfer across the interface at areas of real contact, it follows that an increasing Ar with contact pressure should produce a corresponding increase in the electrical output. These results underline the importance of accounting for real contact area in TENG design, as well as the distinction between real and nominal contact area in tribo-charge density definition. High-performance ferroelectricassisted TENGs (Fe-TENGs) are developed using electrospun fibrous surfaces based on P(VDFTrFE) with dispersed BaTiO3 (BTO) nanofillers in either cubic (CBTO) or tetragonal (TBTO) form in this thesis. TENGs with three types of tribo-negative surface were investigated and output increased progressively. Critically, P(VDF-TrFE)/TBTO produced higher output than P(VDFTrFE)/ CBTO even though permittivity is nearly identical. Thus, it is shown that BTO fillers boost output, not just by increasing permittivity, but also by enhancing the crystallinity and amount of the β-phase (as TBTO produced a more crystalline β-phase present in greater amounts)
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