252 research outputs found

    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

    Speciation and sex-biased gene expression in the scarce swallowtails

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    Speciation is the process by which closely related populations of organisms differentiate following reductions in the effective rate of genetic exchange between them over time. For most speciation events, population genetic data is the only available information about how reproductive isolation has arisen. We have a poor understanding of how evolutionary forces and genomic features contribute to reproductive isolation, primarily due to the difficulty of inferring barriers to gene flow. In particular, it is unclear what role genes that are sex-biased in expression and/or sex-linked play in speciation. In my thesis, I aim to infer the locations of putative barriers to gene flow to understand to what extent different genomic features, in particular fast-evolving sex-biased genes, contribute to reproductive isolation between a sister species pair of scarce swallowtail (Iphiclides) butterflies. In my first research project, I estimate core population genetic parameters across all sister species pairs of European butterflies and fit simple models of divergence to ask how well classic phylogeographic hypotheses fit recent diversification events in this taxonomic group. In my second research project, I infer explicit models of the speciation process and model effective migration rates along the genome to locate putative barriers to gene flow. I ask whether these barriers to long-term gene flow are associated with areas of the genome that show a reduction in recent introgression across a hybrid zone. In my third and final research project, I extend the demographic modeling of speciation in the Iphiclides species pair to the Z chromosome and ask whether barrier regions are associated with sex-biased genes, as a result of their faster rate of evolution. In summary, my findings suggest that fast-evolving male-biased genes likely contribute to extensive sex-linked reproductive isolation, as well as paving the way for future research on the population genetics of European butterflies and the evolutionary genomics of speciation

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Exploring Social Hierarchy Computationally to Further Our Understanding of Social Organizations Within Their Environments

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    Hierarchy is ever-present across countless human societies, a seemingly inescapable reality of small organizations and national governments. However, there is a lot about hierarchy we don’t understand, and if we want to make better organizations and better society, it is crucial we learn more about it. This dissertation investigates three questions: 1) “What is hierarchy?” 2) “How is hierarchy useful?” 3) “How does hierarchy vary?” I find that social scientists do not all mean the same thing by hierarchy, even within the same fields; yet, they do consistently write of hierarchy as control (like boss-employee relations), hierarchy as rank (like social class relations), and hierarchy as nested structure (like cities in states), so future scholars can and should be clear in what they mean. Next, I use a computer simulation to show that control hierarchy can be useful in changing environments where workers see local views of change and managers see the big picture—a tension that is unavoidable in such environments. Hierarchy can make this tension useful if and only if the workers have autonomy to weigh the manager’s information about the environment in their decisions; if they must obey the manager no matter what, then they do very poorly in nearly all types of changing environments. Lastly, I use workforce data from US federal agencies to look at organizational structure and control hierarchy in agencies from 2004 to 2021. I find that hierarchy is similar across most agencies, suggesting that overall, hierarchy relates more to scale than function. However, agencies with offices spread across the nation are different from the others, with more and broader management at higher levels. I also find that agencies vary in their organizational structure in other ways, such as the number of distinct occupations they have, and the number of formal rules they must follow, in patterns that are predictable based on their mission statements and agency type; form does follow function. Overall, this dissertation shows that the use of computational techniques in the study of hierarchy can provide great insight, and help us understand organizations more generally

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    University of Maine Undergraduate Catalog, 2022-2023

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    The University of Maine undergraduate catalog for the 2022-2023 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture

    Point cloud data compression

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    The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic
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