Edge analytics in the internet of things

High-data-rate sensors are becoming ubiquitous in the Internet of Things. GigaSight is an Internet-scale repository of crowd-sourced video content that enforces privacy preferences and access controls. The architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Visual telemetry transmission in marine environment using Robot Operating System platform

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Συστήματα Αυτοματισμού

Automatic Vehicle Detection and Identification using Visual Features

In recent decades, a vehicle has become the most popular transportation mechanism in the world. High accuracy and success rate are key factors in automatic vehicle detection and identification. As the most important label on vehicles, the license plate serves as a mean of public identification for them. However, it can be stolen and affixed to different vehicles by criminals to conceal their identities. Furthermore, in some cases, the plate numbers can be the same for two vehicles coming from different countries. In this thesis, we propose a new vehicle identification system that provides high degree of accuracy and success rates. The proposed system consists of four stages: license plate detection, license plate recognition, license plate province detection and vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and histogram of oriented gradients (HOG) as training dataset. To reach high accuracy in real-time application, a novel method is used to update the system. Meanwhile, via the proposed system, we can store the vehicles features and information in the database. Additionally, with the database, the procedure can automatically detect any discrepancy between license plate and vehicles

IoT system for the validation of conditions in shipping couriers

The growth in online activity these days has caused an increase in the number of online shopping businesses. As the shopping experience becomes less personal, some people try to abuse online businesses with return fraud. The number of IoT devices has also experienced growth due to the fourth industrial revolution, or Industry 4.0, which consists of process automation and data exchange in the industry through IoT and machine learning. This dissertation includes research questions, hypotheses, objectives, and a methodology for the development of a system that integrates IoT to solve the problem of fraudulent returns. The prototype developed monitors when packages leave the facilities and when they are delivered, in addition to allowing to check upon the packages’ integrity at these same moments. This is achieved by using a Raspberry Pi with a camera attached and an ESP32 with a motion sensor, connected via the MQTT and Node-RED protocol. To evaluate the system, several tests were created to simulate a scenario of real-world application. With the results obtained, it is possible to conclude that the development was a success and that the prototype can be used in logistics business to prevent return fraud.O aumento da atividade online nos dias de hoje tem causado um aumento no número de lojas de comércio online. À medida que a experiência de compra se torna menos pessoal, algumas pessoas tentam abusar negócios online com fraudes de devolução. O número de dispositivos IoT também tem crescido devido à quarta revolução industrial, ou Indústria 4.0, que consiste na automação de processos e troca de dados na indústria através de IoT e machine learning. Esta dissertação inclui questões de investigação, hipóteses, objetivos e uma metodologia para o desenvolvimento de um sistema que integra IoT para resolver o problema de devoluções fraudulentas. O protótipo desenvolvido monitoriza quando encomendas saem das instalações e quando são entregues, para além de permitir a verificação da integridade física das encomendas nestes mesmos momentos. Isto é alcançado com o uso de um Raspberry Pi com uma câmara associada e um ESP32 com um sensor de movimento, conectados entre si através do protocolo MQTT e Node-RED. Para avaliar o sistema, vários testes foram criados de forma a simular um cenário de aplicação no mundo real. Os resultados obtidos permitem concluir que o desenvolvimento foi um sucesso e que o protótipo pode ser usado em empresas de logística, distribuição e transporte para evitar fraudes de devolução

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System