228 research outputs found
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
Seguimento de pessoas com drones em espaços inteligentes
Recent technological progress made over the last decades in the field
of Computer Vision has introduced new methods and algorithms with
ever increasing performance results. Particularly, the emergence of
machine learning algorithms enabled class based object detection on
live video feeds. Alongside these advances, Unmanned Aerial Vehicles (more commonly known as drones), have also experienced advancements in both hardware miniaturization and software optimization. Thanks to these improvements, drones have emerged from their
military usage based background and are now both used by the general
public and the scientific community for applications as distinct as aerial
photography and environmental monitoring.
This dissertation aims to take advantage of these recent technological
advancements and apply state of the art machine learning algorithms
in order to create a Unmanned Aerial Vehicle (UAV) based network
architecture capable of performing real time people tracking through
image detection.
To perform object detection, two distinct machine learning algorithms
are presented. The first one uses an SVM based approach, while the
second one uses an Convolutional Neural Network (CNN) based architecture. Both methods will be evaluated using an image dataset
created for the purposes of this dissertation’s work.
The evaluations performed regarding the object detectors performance
showed that the method using a CNN based architecture was the best
both in terms of processing time required and detection accuracy, and
therefore, the most suitable method for our implementation.
The developed network architecture was tested in a live scenario context, with the results showing that the system is capable of performing
people tracking at average walking speeds.O recente progresso tecnológico registado nas últimas décadas no
campo da Visão por Computador introduziu novos métodos e algoritmos com um desempenho cada vez mais elevado. Particularmente,
a criação de algoritmos de aprendizagem automática tornou possível
a detecção de objetos aplicada a feeds de vídeo capturadas em tempo
real. Paralelo com este progresso, a tecnologia relativa a veículos aéreos
não tripulados, ou drones, também beneficiaram de avanços tanto na
miniaturização dos seus componentes de hardware assim como na optimização do software. Graças a essas melhorias, os drones emergiram
do seu passado militar e são agora usados tanto pelo público em geral
como pela comunidade científica para aplicações tão distintas como
fotografia e monitorização ambiental.
O objectivo da presente dissertação pretende tirar proveito destes recentes avanços tecnológicos e aplicar algoritmos de aprendizagem automática de última geração para criar um sistema capaz de realizar
seguimento automático de pessoas com drones através de visão por
computador.
Para realizar a detecção de objetos, dois algoritmos distintos de aprendizagem automática são apresentados. O primeiro é dotado de uma
abordagem baseada em Support Vector Machine (SVM), enquanto o
segundo é caracterizado por uma arquitetura baseada em Redes Neuronais Convolucionais. Ambos os métodos serão avaliados usando uma
base de dados de imagens criada para os propósitos da presente dissertação.
As avaliações realizadas relativas ao desempenho dos algoritmos de detecção de objectos demonstraram que o método baseado numa arquitetura de Redes Neuronais Covolucionais foi o melhor tanto em termos
de tempo de processamento médio assim como na precisão das detecções, revelando-se portanto, como sendo o método mais adequado
de acordo com os objectivos pretendidos.
O sistema desenvolvido foi testado num contexto real, com os resultados obtidos a demonstrarem que o sistema é capaz de realizar o
seguimento de pessoas a velocidades comparáveis a um ritmo normal
humano de caminhada.Mestrado em Engenharia Eletrónica e Telecomunicaçõe
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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