405 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    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

    The study of renal function and toxicity using zebrafish (Danio rerio) larvae as a vertebrate model

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    Zebrafish (Danio rerio) is a powerful model in biomedical and pharmaceutical sciences. The zebrafish model was introduced to toxicological sciences in 1960, followed by its use in biomedical sciences to investigate vertebrate gene functions. As a consequence of many research projects in this field, the study of human genetic diseases became instantly feasible. Consequently, zebrafish have been intensively used in developmental biology and associated disciplines. Due to the simple administration of medicines and the high number of offspring, zebrafish larvae became widely more popular in pharmacological studies in the following years. In the past decade, zebrafish larvae were further established as a vertebrate model in the field of pharmacokinetics and nanomedicines. In this PhD thesis, zebrafish larvae were investigated as an earlystage in vivo vertebrate model to study renal function, toxicity, and were applied in drug-targeting projects using nanomedicines. The first part focused on the characterization of the renal function of three-to four-dayold zebrafish larvae. Non-renal elimination processes were additionally described. Moreover, injection techniques, imaging parameters, and post-image processing scripts were established to serve as a toolbox for follow-up projects. The second part analyzed the impact of gentamicin (a nephrotoxin) on the morphology of the pronephros of zebrafish larvae. Imaging methodologies such as fluorescent-based laser scanning microscopy and X-ray-based microtomography were applied. A profound comparison study of specimens acquired with different laboratory X-ray-based microtomography devices and a radiation facility was done to promote the use of X-ray-based microtomography for broader biomedical applications. In the third part, the toxicity of nephrotoxins on mitochondria in renal epithelial cells of proximal tubules was assessed using the zebrafish larva model. Findings were compared with other teleost models such as isolated renal tubules of killifish (Fundulus heteroclitus). In view of the usefulness and high predictability of the zebrafish model, it was applied to study the pharmacokinetics of novel nanoparticles in the fourth part. Various in vivo pharmacokinetic parameters such as drug release, transfection of mRNA/pDNA plasmids, macrophage clearance, and the characterization of novel drug carriers that were manipulated with ultrasound were assessed in multiple collaborative projects. Altogether, the presented zebrafish model showed to be a reliable in vivo vertebrate model to assess renal function, toxicity, and pharmacokinetics of nanoparticles. The application of the presented model will hopefully encourage others to reduce animal experiments in preliminary studies by fostering the use of zebrafish larvae

    Rethinking FPGA Architectures for Deep Neural Network applications

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    The prominence of machine learning-powered solutions instituted an unprecedented trend of integration into virtually all applications with a broad range of deployment constraints from tiny embedded systems to large-scale warehouse computing machines. While recent research confirms the edges of using contemporary FPGAs to deploy or accelerate machine learning applications, especially where the latency and energy consumption are strictly limited, their pre-machine learning optimised architectures remain a barrier to the overall efficiency and performance. Realizing this shortcoming, this thesis demonstrates an architectural study aiming at solutions that enable hidden potentials in the FPGA technology, primarily for machine learning algorithms. Particularly, it shows how slight alterations to the state-of-the-art architectures could significantly enhance the FPGAs toward becoming more machine learning-friendly while maintaining the near-promised performance for the rest of the applications. Eventually, it presents a novel systematic approach to deriving new block architectures guided by designing limitations and machine learning algorithm characteristics through benchmarking. First, through three modifications to Xilinx DSP48E2 blocks, an enhanced digital signal processing (DSP) block for important computations in embedded deep neural network (DNN) accelerators is described. Then, two tiers of modifications to FPGA logic cell architecture are explained that deliver a variety of performance and utilisation benefits with only minor area overheads. Eventually, with the goal of exploring this new design space in a methodical manner, a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations is first proposed. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then suggested together with a family of new embedded blocks, called MLBlocks

    Roadmap for optical tweezers

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    Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMOptical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space explorationEuropean Commission (Horizon 2020, Project No. 812780
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