Ten Quick Tips for Using a Raspberry Pi

Much of biology (and, indeed, all of science) is becoming increasingly computational. We tend to think of this in regards to algorithmic approaches and software tools, as well as increased computing power. There has also been a shift towards slicker, packaged solutions--which mirrors everyday life, from smart phones to smart homes. As a result, it's all too easy to be detached from the fundamental elements that power these changes, and to see solutions as "black boxes". The major goal of this piece is to use the example of the Raspberry Pi--a small, general-purpose computer--as the central component in a highly developed ecosystem that brings together elements like external hardware, sensors and controllers, state-of-the-art programming practices, and basic electronics and physics, all in an approachable and useful way. External devices and inputs are easily connected to the Pi, and it can, in turn, control attached devices very simply. So whether you want to use it to manage laboratory equipment, sample the environment, teach bioinformatics, control your home security or make a model lunar lander, it's all built from the same basic principles. To quote Richard Feynman, "What I cannot create, I do not understand".Comment: 12 pages, 2 figure

Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

Evaluating XMPP Communication in IEC 61499-based Distributed Energy Applications

The IEC 61499 reference model provides an international standard developed specifically for supporting the creation of distributed event-based automation systems. Functionality is abstracted into function blocks which can be coded graphically as well as via a text-based method. As one of the design goals was the ability to support distributed control applications, communication plays a central role in the IEC 61499 specification. In order to enable the deployment of functionality to distributed platforms, these platforms need to exchange data in a variety of protocols. IEC 61499 realizes the support of these protocols via "Service Interface Function Blocks" (SIFBs). In the context of smart grids and energy applications, IEC 61499 could play an important role, as these applications require coordinating several distributed control logics. Yet, the support of grid-related protocols is a pre-condition for a wide-spread utilization of IEC 61499. The eXtensible Messaging and Presence Protocol (XMPP) on the other hand is a well-established protocol for messaging, which has recently been adopted for smart grid communication. Thus, SIFBs for XMPP facilitate distributed control applications, which use XMPP for exchanging all control relevant data, being realized with the help of IEC 61499. This paper introduces the idea of integrating XMPP into SIFBs, demonstrates the prototypical implementation in an open source IEC 61499 platform and provides an evaluation of the feasibility of the result.Comment: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA

The use of Sensor Networks to create smart environments

Internet of Things is taking the world in order to be the next big thing since the Internet, with almost every object being connected to gather data and allow control through mobile and web devices. But this revolution has some barriers with the lack of standardization in communications or sensors. In this dissertation we present a proposal of a system dedicated to creating smart environments using sensor networks, with a practical application developed to achieve automation, efficiency and versatility, allowing real-time monitoring and remote control of any object or environment improving user experience, tasks efficiency and leading to costs reduction. The developed system, that includes software and hardware, is based on adaptive and Artificial Intelligence algorithms and low cost IoT devices, taking advantage of the best communication protocols, allowing the developed system to be suited and easily adapted to any specification by any person. We evaluate the best communication and devices for the desired implementa tion and demonstrate how to create all the network nodes, including the build of a custom IoT Gateway and Sensor Node. We also demonstrate the efficiency of the developed system in real case scenarios. The main contributions of our study are the design and implementation of a novel architecture for adaptive IoT projects focus on environment efficiency, with practical demonstration, as well as comparison study for the best suited communication protocols for low cost IoT devices.A Internet of Things está a atingir o mundo de modo a tornar-se a próxima grande revolução depois da Internet, com quase todos os objectos a estarem ligados para recolher dados e permitir o controlo através de dispositivos móveis. Mas esta revolução depara-se com vários desafios devido à falta de standards no que toca a comunicações ou sensores. Nesta dissertação apresentamos uma proposta para um sistema dedicado a criar ambientes inteligentes usando redes de sensores, com uma aplicação prática desenvolvida para oferecer automação, eficiência e versatilidade, permitindo uma monitorização e controlo remoto seguro em tempo real de qualquer objecto ou ambiente, melhorando assim a experiência do utilizador e a eficiência das tarefas evando a redução de custos. O sistema desenvolvido, que inclui software e hard ware, usa algoritmos adaptáveis com Inteligência Artificial e dispositivos IoT de baixo custo, utilizando os melhores protocolos de comunicação, permitindo que o mesmo seja apropriado e facilmente adaptado para qualquer especificação por qualquer pessoa. Avaliamos os melhores métodos de comunicação e dispositivos necessários para a implementação e demonstramos como criar todos os nós da rede, incluindo a construção de IoT Gateway e Sensor Node personalizados. Demonstramos também a eficácia do sistema desenvolvido através da aplicação do mesmo em casos reais. As principais contribuições do nosso estudo passam pelo desenho e implemen tação de uma nova arquitectura para projectos adaptáveis de IoT com foco na eficiência do objecto, incluindo a demonstração pratica, tal como um estudo com parativo sobre os melhores protocolos de comunicação para dispositivos IoT de baixo custo

Block-Based Development of Mobile Learning Experiences for the Internet of Things

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund

Design and implementation of an IoT gateway to create smart environments

The paper presents a proposal of a practical implementation for an IoT gateway dedicated to real-time monitoring and remote control of a swimming pool. Based on a Raspberry Pi, the gateway allows bidirectional communication and data exchange between the user and the sensor network implemented on the environment using an Arduino.info:eu-repo/semantics/publishedVersio

Object detection and localization: an application inspired by RobotAtFactory using machine learning

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe evolution of artificial intelligence and digital cameras has made the transformation of the real world into its digital image version more accessible and widely used. In this way, the analysis of information can be carried out with the use of algorithms. The detection and localization of objects is a crucial task in several applications, such as surveillance, autonomous robotics, intelligent transportation systems, and others. Based on this, this work aims to implement a system that can find objects and estimate their location (distance and angle), through the acquisition and analysis of images. Having as motivation the possible problems that can be introduced in the robotics competition, RobotAtFactory Lite, in future versions. As an example, the obstruction of the path developed through the printed lines, requiring the robot to deviate, and/or the positioning of the boxes in different places of the initial warehouses, being positioned so that the robot does not know its previous location, having to find it somehow. For this, different methods were analyzed, based on machine leraning, for object detection using feature extraction and neural networks, as well as object localization, based on the Pinhole model and triangulation. By compiling these techniques through python programming in the module, based on a Raspberry Pi Model B and a Raspi Cam Rev 1.3, the goal of the work is achieved. Thus, it was possible to find the objects and obtain an estimate of their relative position. In the future, in a possible implementation together with a robot, this data can be used to find objects and perform tasks.A evolução da inteligência artificial e das câmeras digitais, tornou mais acessível e amplamente utilizada a transformação do mundo real, para sua versão em imagem digital. Dessa maneira, a análise das informações pode ser efetuada com a utilização de algoritmos. A deteção e localização de objetos é uma tarefa crucial em diversas aplicações, tais como vigilância, robótica autônoma, sistemas de transporte inteligente, entre outras. Baseado nisso, este trabalho tem como objetivo implementar um sistema que consiga encontrar objetos e estimar sua localização (distância e ângulo), através da aquisição e análise de imagens. Tendo como motivação os possíveis problemas que possam ser introduzidos na competição de robótica, Robot@Factory Lite, em versões futuras. Podendo ser citados como exemplo a obstrução do caminho desenvolvido através das linhas impressas, requerendo que o robô desvie, e/ou o posicionamento das caixas em locais diferentes dos armazéns iniciais, sendo posicionadas de modo que o robô não saiba sua localização prévia, devendo encontra-las de alguma maneira. Para isso, foram analisados diferentes métodos, baseadas em machine leraning, para deteção de objetos utilizando extração de características e redes neurais, bem como a localização de objetos, baseada no modelo de Pinhole e triangulação. Compilando essas técnicas através da programação em python, no módulo, baseado em um Raspberry Pi Model B e um Raspi Cam Rev 1.3, o objetivo do trabalho é alcançado. Assim, foi possível encontrar os objetos e obter uma estimativa da sua posição relativa. Futuramente, em uma possível implementação junta a um robô, esses dados podem ser utilizados para encontrar objetos e executar tarefas