1,078 research outputs found
Disaster Monitoring with Wikipedia and Online Social Networking Sites: Structured Data and Linked Data Fragments to the Rescue?
In this paper, we present the first results of our ongoing early-stage
research on a realtime disaster detection and monitoring tool. Based on
Wikipedia, it is language-agnostic and leverages user-generated multimedia
content shared on online social networking sites to help disaster responders
prioritize their efforts. We make the tool and its source code publicly
available as we make progress on it. Furthermore, we strive to publish detected
disasters and accompanying multimedia content following the Linked Data
principles to facilitate its wide consumption, redistribution, and evaluation
of its usefulness.Comment: Accepted for publication at the AAAI Spring Symposium 2015:
Structured Data for Humanitarian Technologies: Perfect fit or Overkill?
#SD4HumTech1
Configuração automática de plataforma de gestão de desempenho em ambientes NFV e SDN
Mestrado em Engenharia de Computadores e TelemáticaWith 5G set to arrive within the next three years, this next-generation
of mobile networks will transform the mobile industry with a profound
impact both on its customers as well as on the existing technologies
and network architectures. Software-Defined Networking (SDN), together
with Network Functions Virtualization (NFV), are going to play
key roles for the operators as they prepare the migration from 4G to
5G allowing them to quickly scale their networks. This dissertation will
present a research work done on this new paradigm of virtualized and
programmable networks focusing on the performance management, supervision
and monitoring domains, aiming to address Self-Organizing
Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios
being the detection and prediction of potential network and service
anomalies. The research work itself was done while participating in
a R&D project designated SELFNET (A Framework for Self-Organized
Network Management in Virtualized and Software Defined Networks)
funded by the European Commission under the H2020 5G-PPP programme,
with Altice Labs being one of the participating partners of
this project. Performance management system advancements in a 5G
scenario require aggregation, correlation and analysis of data gathered
from these virtualized and programmable network elements. Both opensource
monitoring tools and customized catalog-driven tools were either
integrated on or developed with this purpose, and the results show
that they were able to successfully address these requirements of the
SELFNET project. Current performance management platforms of the
network operators in production are designed for non virtualized (non-
NFV) and non programmable (non-SDN) networks, and the knowledge
gathered while doing this research work allowed Altice Labs to understand
how its Altaia performance management platform must evolve in
order to be prepared for the upcoming 5G next generation mobile networks.Com o 5G prestes a chegar nos próximos três anos, esta próxima geração
de redes móveis irá transformar a indústria de telecomunicações
móveis com um impacto profundo nos seus clientes assim como nas
tecnologias e arquiteturas de redes. As redes programáveis (SDN),
em conjunto com a virtualização de funções de rede (NFV), irão desempenhar
papéis vitais para as operadoras na sua migração do 4G
para o 5G, permitindo-as escalar as suas redes rapidamente. Esta
dissertação irá apresentar um trabalho de investigação realizado sobre
este novo paradigma de virtualização e programação de redes,
concentrando-se no domínio da gestão de desempenho, supervisionamento
e monitoria, abordando cenários de redes auto-organizadas
(SON) num contexto NFV/SDN, sendo um destes cenários a deteção
e predição de potenciais anomalias de redes e serviços. O trabalho de
investigação foi enquadrado num projeto de I&D designado SELFNET
(A Framework for Self-Organized Network Management in Virtualized
and Software Defined Networks) financiado pela Comissão Europeia
no âmbito do programa H2020 5G-PPP, sendo a Altice Labs um dos
parceiros participantes deste projeto. Avanços em sistemas de gestão
de desempenho em cenários 5G requerem agregação, correlação e
análise de dados recolhidos destes elementos de rede programáveis
e virtualizados. Ferramentas de monitoria open-source e ferramentas
catalog-driven foram integradas ou desenvolvidas com este propósito,
e os resultados mostram que estas preencheram os requisitos do projeto
SELFNET com sucesso. As plataformas de gestão de desempenho
das operadoras de rede atualmente em produção estão concebidas
para redes não virtualizadas (non-NFV) e não programáveis (non-
SDN), e o conhecimento adquirido durante este trabalho de investigação
permitiu à Altice Labs compreender como a sua plataforma de gestão
de desempenho (Altaia) terá que evoluir por forma a preparar-se
para a próxima geração de redes móveis 5G
Hierarchical Fish Species Detection in Real-Time Video Using YOLO
Master's thesis in Information- and communication technology (IKT590)Information gathering of aquatic life is often based on time consuming methods with a foundation in video feeds. It would be beneficial to capture more information in a cost effective manner from video feeds, and video object detection has an opportunity to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As under-water conditions can be difficult and fish species hard to discriminate, we propose the use of a hierarchical structures in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques we present YOLO Fish. YOLO Fish is a state of the art object detector on nordic fish species, with an mAP of 91.8%. For a more stable video, YOLO Fish can be used with the object tracking algorithm SORT. This results in a complete fish detector for real-time video
ITERL: A Wireless Adaptive System for Efficient Road Lighting
This work presents the development and construction of an adaptive street lighting system
that improves safety at intersections, which is the result of applying low-power Internet of Things
(IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the
Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet
protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements
are sent to a coordinator node that collects and passes them to a local controller, which then makes
decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights
are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree
of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to
maintain up-to-date information for the system, enabling preventive maintenance.Consejería de Fomento y Vivienda Junta de Andalucía G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI
Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop
Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Augmented reality system with application in physical rehabilitation
The aging phenomenon causes increased physiotherapy services requirements, with increased costs associated with long rehabilitation periods. Traditional rehabilitation methods rely on the subjective assessment of physiotherapists without supported training data. To overcome the shortcoming of traditional rehabilitation method and improve the efficiency of rehabilitation, AR (Augmented Reality) which represents a promissory technology that provides an immersive interaction with real and virtual objects is used. The AR devices may assure the capture body posture and scan the real environment that conducted to a growing number of AR applications focused on physical rehabilitation. In this MSc thesis, an AR platform used to materialize a physical rehabilitation plan for stroke patients is presented. Gait training is a significant part of physical rehabilitation for stroke patients. AR represents a promissory solution for training assessment providing information to the patients and physiotherapists about exercises to be done and the reached results. As part of MSc work an iOS application was developed in unity 3D platform. This application immersing patients in a mixed environment that combine real-world and virtual objects. The human computer interface is materialized by an iPhone as head-mounted 3D display and a set of wireless sensors for physiological and motion parameters measurement. The position and velocity of the patient are recorded by a smart carpet that includes capacitive sensors connected to a computation unit characterized by Wi-Fi communication capabilities. AR training scenario and the corresponding experimental results are part of the thesis.O envelhecimento causa um aumento das necessidades dos serviços de fisioterapia, com aumento dos custos associados a longos períodos de reabilitação. Os métodos tradicionais de reabilitação dependem da avaliação subjetiva de fisioterapeutas sem registo automatizado de dados de treino. Com o principal objetivo de superar os problemas do método tradicional e melhorar a eficiência da reabilitação, é utilizada a RA (Realidade Aumentada), que representa uma tecnologia promissora, que fornece uma interação imersiva com objetos reais e virtuais. Os dispositivos de RA são capazes de garantir uma postura correta do corpo de capturar e verificar o ambiente real, o que levou a um número crescente de aplicações de RA focados na reabilitação física. Neste projeto, é apresentada uma plataforma de RA, utilizada para materializar um plano de reabilitação física para pacientes que sofreram AVC.
O treino de marcha é uma parte significativa da reabilitação física para pacientes com AVC. A RA apresenta-se como uma solução promissora para a avaliação do treino, fornecendo informações aos pacientes e aos profissionais de fisioterapia sobre os exercícios a serem realizados e os resultados alcançados. Como parte deste projeto, uma aplicação iOS foi desenvolvida na plataforma Unity 3D. Esta aplicação fornece aos pacientes um ambiente imersivo que combina objetos reais e virtuais. A interface de RA é materializada por um iPhone montado num suporte de cabeça do utilizador, assim como um conjunto de sensores sem fios para medição de parâmetros fisiológicos e de movimento. A posição e a velocidade do paciente são registadas por um tapete inteligente que inclui sensores capacitivos conectados a uma unidade de computação, caracterizada por comunicação via Wi-Fi. O cenário de treino em RA e os resultados experimentais correspondentes fazem parte desta dissertação
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