7,594 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
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 Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
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
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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Efficient Neural Network Verification Using Branch and Bound
Neural networks have demonstrated great success in modern machine learning systems. However, they remain susceptible to incorrect corner-case behaviors, often behaving unpredictably and producing surprisingly wrong results. Therefore, it is desirable to formally guarantee their trustworthiness for certain robustness properties when applied to safety-/security-sensitive systems like autonomous vehicles and aircraft. Unfortunately, the task is extremely challenging due to the complexity of neural networks, and traditional formal methods were not efficient enough to verify practical properties. Recently, a Branch and Bound (BaB) framework is generally extended for neural network verification and shows great success in accelerating the verification.
This dissertation focuses on state-of-the-art neural network verifiers using BaB. We will first introduce two efficient neural network verifiers ReluVal and Neurify using basic BaB approaches involving two main steps: (1) They will recursively split the original verification problem into easier independent subproblems by splitting input or hidden neurons; (2) For each split subproblem, we propose an efficient and tight bound propagation method called symbolic interval analysis, producing sound estimated bounds for outputs using convex linear relaxations. Both ReluVal and Neurify are three orders of magnitude faster than previously state-of-the-art formal analysis systems on standard verification benchmarks.
However, basic BaB approaches like Neurify have to construct each subproblem into a Linear Programming (LP) problem and solve it using expensive LP solvers, significantly limiting the overall efficiency. This is because each step of BaB will introduce neuron split constraints (e.g., a ReLU neuron larger or smaller than 0), which are hard to be handled by existing efficient bound propagation methods. We propose novel designs of bound propagation method -CROWN and its improved variance -CROWN, solving the verification problem by optimizing Lagrangian multipliers and with gradient ascent without requiring to call any expensive LP solvers. They were built based on previous work CROWN, a generalized efficient bound propagation method using linear relaxation. BaB verification using -CROWN and -CROWN cannot only provide tighter output estimations than most of the bound propagation methods but also can fully leverage the accelerations by GPUs with massive parallelization.
Combining our methods with BaB empowers the state-of-the-art verifier ,-CROWN (alpha-beta-CROWN), the winning tool in the second International Verification of Neural Networks Competition (VNN-COMP 2021) with the highest total score. Our $\alpha,-CROWN can be three orders of magnitude faster than LP solver based BaB verifiers and is notably faster than all existing approaches on GPUs. Recently, we further generalize -CROWN and propose an efficient iterative approach that can tighten all intermediate layer bounds under neuron split constraints and strengthen the bound tightness without LP solvers. This new approach in BaB can greatly improve the efficiency of ,-CROWN, especially on several challenging benchmarks.
Lastly, we study verifiable training that incorporates verification properties in training procedures to enhance the verifiable robustness of trained models and scale verification to larger models and datasets. We propose two general verifiable training frameworks: (1) MixTrain that can significantly improve verifiable training efficiency and scalability and (2) adaptive verifiable training that can improve trained verifiable robustness accounting for label similarity. The combination of verifiable training and BaB based verifiers opens promising directions for more efficient and scalable neural network verification
Interference-aware Demand-based User Scheduling in Precoded High Throughput Satellite Systems
In recent years, dynamic traffic demand requisites have driven the satellite communication service providers to implement reconfigurable demand-driven features to align the delivered throughput with the temporal and geographical variations of the traffic demand. Also, in current interference-limited High Throughput Satellite (HTS) systems, the resulting inter-beam co-channel interference can be mitigated by carefully performing precoding and user scheduling. Unfortunately, the conventional user scheduling algorithms fail to provide demand satisfaction for dynamic traffic demand requisites. Hence, in this paper, we focus on the user scheduling design for precoded satellite systems where both co-channel interference and user demands are taken into account. In particular, we first classify the sectors in each beam according to the interference they may cause to neighboring beams. Next, we formulate the scheduling problem such as the activation of neighboring beam sectors is avoided while proportionally dwelling on the sectors based on their traffic demands. The supporting numerical results for different demand distribution profiles validate the effectiveness of proposed interference-aware demand-based user scheduling over conventional scheduling techniques
Despliegue de red LPWAN en entorno industrial con movilidad
[ES] La tecnología que comenzó a conectar masivamente a las personas hace décadas se ha desarrollado para
conectar dispositivos también. La red de conectividad global resultante se denomina el internet de las cosas.
Tiene aplicaciones útiles en todos los sectores de la economía y está preparado para liderar la cuarta
revolución industrial, que busca la eficiencia a través de la recopilación de datos. Para lograrlo se necesita
un número cada vez mayor de dispositivos, que deben ser eficientes energéticamente para permitir que estas
redes sean viables tanto económica como ambientalmente. Cubrir grandes espacios con la menor cantidad
posible de recursos de hardware también ayuda a reducir los costes de despliegue, y aquí es exactamente
donde entran en juego las redes LPWAN (Low-Power Wide-Area Network).
El objetivo de este proyecto es crear una herramienta que permita el despliegue rápido y sencillo de una red
LPWAN en un entorno industrial en un contexto de movilidad. El autor ha seleccionado la tecnología
LPWAN que mejor se adapta al proyecto (LoRaWAN) y una solución basada en ella, ChirpStack. Se ha
desarrollado una aplicación web funcional como candidata ideal para ser la herramienta que permita
despliegues de movilidad LPWAN.
El uso de la aplicación web desarrollada conlleva además una mayor eficiencia de costes, ya que ahorra al
usuario múltiples pasos de configuración tediosos antes de activar un nuevo nodo. Esta herramienta también
logra una mayor abstracción de la tecnología de comunicaciones que se está implementando, haciéndola
accesible a un mercado aún mayor.
Un análisis de los resultados obtenidos destaca el éxito en la consecución de dos objetivos secundarios, la
reducción del tiempo de activación del dispositivo final y la abstracción de la tecnología adyacente, además
de ser una herramienta de movilidad válida para el despliegue industrial de redes LPWAN.[EN] The technology that started massively connecting people decades ago has been developed to begin
connecting devices as well. The resulting global connectivity network is called the Internet of Things. It
has useful applications in every sector and is set to lead the fourth industrial revolution. Efficiency through
data gathering is the goal of an ever-increasing number of devices. Energy efficiency is key to make this
network scalable without skyrocketing electrical consumption. Covering big spaces with as few hardware
resources as possible also helps at reducing costs. This is exactly where Low-Power Wide-Area Networks
come into play.
The aim of this project is to create a tool that allows the fast and easy deployment of a LPWAN network
in an industrial environment in a mobility context. The author has selected the LPWAN technology that
best fits the project (LoRaWAN) and a solution based on it, ChirpStack. A functional web application has
been developed as an ideal candidate to be the tool that allows LPWAN mobility deployments.
Further cost efficiency is unlocked by the developed web application, which saves the user multiple
tedious configuration steps before activating a new end-device. This tool also achieves further abstraction
from the technology that is being implementing, making it accessible to an even greater market.
An analysis of the results obtained highlights the success in achieving both secondary goals, a reduction
in end-device activation time and an abstraction of the telecommunications technology, apart from being a
mobility tool for industrial deployment of LPWAN networks.Hernández Álvarez, R. (2022). Despliegue de red LPWAN en entorno industrial con movilidad. Universitat Politècnica de València. http://hdl.handle.net/10251/181897TFG
Cultivating Agrobiodiversity in the U.S.: Barriers and Bridges at Multiple Scales
The diversity of crops grown in the United States (U.S.) is declining, causing agricultural landscapes to become more and more simplified. This trend is concerning for the loss of important plant, insect, and animal species, as well as the pollution and degradation of our environment. Through three separate but related studies, this dissertation addresses the need to increase the diversity of these agricultural landscapes in the U.S., particularly through diversifying the type and number of crops grown. The first study uses multiple, openly accessible datasets related to agricultural land use and policies to document and visualize change over recent decades. Through this, I show that U.S. agriculture has gradually become more specialized in the crops grown, crop production is heavily concentrated in certain areas, and crop diversity is continuing to decline. Meanwhile, federal agricultural policy, while having become more influential over how U.S. agriculture operates, incentivizes this specialization. The second study uses nonlinear statistical modeling to identify and compare social, political, and ecological factors that best predict crop diversity across nine regions in the U.S. Factors of climate, prior land use, and farm inputs best predict diversity across regions, but regions show key differences in how factors are important, indicating that patterns at the regional scale constrain and enable further diversification. Finally, the third study relied on interviews with farmers and key informants in southern Idaho’s Magic Valley – a cluster of eight counties that is known to be agriculturally diverse. Interviews gauge what farmers are currently doing to manage crop diversity (the present) and how they imagine alternative landscapes (the imaginary). We found that farmers in the Magic Valley manage current diversity mainly through cover cropping and diverse crop rotations, but daily struggles and political barriers make experimenting with and imagining alternative landscapes difficult and unlikely to occur. Together, these three studies provide an integrated view of how and why U.S. agriculture landscapes simplify or diversify, as well as the barriers and bridges such pathways of diversification
Instrumentation and validation of a robotic cane for transportation and fall prevention in patients with affected mobility
Dissertação de mestrado integrado em Engenharia Física, (especialização em Dispositivos, Microssistemas e Nanotecnologias)O ato de andar é conhecido por ser a forma primitiva de locomoção do ser humano, sendo que este
traz muitos benefícios que motivam um estilo de vida saudável e ativo. No entanto, há condições de saúde
que dificultam a realização da marcha, o que por consequência pode resultar num agravamento da saúde,
e adicionalmente, levar a um maior risco de quedas. Nesse sentido, o desenvolvimento de um sistema de
deteção e prevenção de quedas, integrado num dispositivo auxiliar de marcha, seria essencial para reduzir
estes eventos de quedas e melhorar a qualidade de vida das pessoas. Para ultrapassar estas necessidades
e limitações, esta dissertação tem como objetivo validar e instrumentar uma bengala robótica, denominada
Anti-fall Robotic Cane (ARCane), concebida para incorporar um sistema de deteção de quedas e um
mecanismo de atuação que possibilite a prevenção de quedas, ao mesmo tempo que assiste a marcha.
Para esse fim, foi realizada uma revisão do estado da arte em bengalas robóticas para adquirir um
conhecimento amplo e aprofundado dos componentes, mecanismos e estratégias utilizadas, bem como os
protocolos experimentais, principais resultados, limitações e desafios em dispositivos existentes.
Numa primeira fase, foi estipulado o objetivo de: (i) adaptar a missão do produto; (ii) estudar as
necessidades do consumidor; e (iii) atualizar as especificações alvo da ARCane, continuação do trabalho de
equipa, para obter um produto com design e engenharia compatível com o mercado. Foi depois estabelecida
a arquitetura de hardware e discutidos os componentes a ser instrumentados na ARCane. Em seguida foram
realizados testes de interoperabilidade a fim de validar o funcionamento singular e coletivo dos componentes.
Relativamente ao controlo de movimento, foi desenvolvido um sistema inovador, de baixo custo e
intuitivo, capaz de detetar a intenção do movimento e de reconhecer as fases da marcha do utilizador. Esta
implementação foi validada com seis voluntários saudáveis que realizaram testes de marcha com a ARCane
para testar sua operabilidade num ambiente de contexto real. Obteve-se uma precisão de 97% e de 90% em
relação à deteção da intenção de movimento e ao reconhecimento da fase da marcha do utilizador.
Por fim, foi projetado um método de deteção de quedas e mecanismo de prevenção de quedas para
futura implementação na ARCane. Foi ainda proposta uma melhoria do método de deteção de quedas, de
modo a superar as limitações associadas, bem como a proposta de dispositivos de deteção a serem
implementados na ARCane para obter um sistema completo de deteção de quedas.The act of walking is known to be the primitive form of the human being, and it brings many benefits
that motivate a healthy and active lifestyle. However, there are health conditions that make walking difficult,
which, consequently, can result in worse health and, in addition, lead to a greater risk of falls. Thus, the
development of a fall detection and prevention system integrated with a walking aid would be essential to
reduce these fall events and improve people quality of life. To overcome these needs and limitations, this
dissertation aims to validate and instrument a cane-type robot, called Anti-fall Robotic Cane (ARCane),
designed to incorporate a fall detection system and an actuation mechanism that allow the prevention of
falls, while assisting the gait. Therefore, a State-of-the-Art review concerning robotic canes was carried out to
acquire a broad and in-depth knowledge of the used components, mechanisms and strategies, as well as
the experimental protocols, main results, limitations and challenges on existing devices.
On a first stage, it was set an objective to (i) enhance the product's mission statement; (ii) study the
consumer needs; and (iii) update the target specifications of the ARCane, extending teamwork, to obtain a
product with a market-compatible design and engineering that meets the needs and desires of the ARCane
users. It was then established the hardware architecture of the ARCane and discussed the electronic
components that will instrument the control, sensory, actuator and power units, being afterwards subjected
to interoperability tests to validate the singular and collective functioning of cane components altogether.
Regarding the motion control of robotic canes, an innovative, cost-effective and intuitive motion
control system was developed, providing user movement intention recognition, and identification of the user's
gait phases. This implementation was validated with six healthy volunteers who carried out gait trials with
the ARCane, in order to test its operability in a real context environment. An accuracy of 97% was achieved
for user motion intention recognition and 90% for user gait phase recognition, using the proposed motion
control system.
Finally, it was idealized a fall detection method and fall prevention mechanism for a future
implementation in the ARCane, based on methods applied to robotic canes in the literature. It was also
proposed an improvement of the fall detection method in order to overcome its associated limitations, as
well as detection devices to be implemented into the ARCane to achieve a complete fall detection system
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