Control remoto de presentaciones mediante Raspberry PI y Android

Este proyecto fin de carrera trata de mejorar los sistemas actuales de control en la visualización de diapositivas. La solución adoptada constará de un sistema con modelo cliente-servidor. El servidor formado por un mini ordenador, en este caso una Raspberry Pi, que estará conectado al proyector de video. Este servidor se mantendrá a la espera de recibir una conexión entrante vía Bluetooth. Una vez se realice la conexión interpretará los comandos mandados por el cliente a través de una API con formato JSON y realizará las acciones indicadas para el control de la presentación. El cliente será una aplicación móvil para dispositivos Android. A través de ella el profesor accederá al servidor escaneando un código QR que será proyectado y una vez conectado enviará los comandos de control de la presentación, tales como abrir una presentación, avanzar y retroceder diapositiva, etc. La solución final deberá ser eficiente, sencilla de utilizar y con un bajo coste para resultar atractiva y ser así útil en el mundo real. Para ello se contará con valores añadidos como el poder iniciar la presentación desde el dispositivo móvil, el mostrar las notas de la diapositiva actual o contar con un temporizador para permitir un mejor control sobre el tiempo disponible para la presentación. ABSTRACT. This final project pursues the improvement of the current presentation control systems. The solution it provides is based on a server-client architecture. The server will be a mini PC, a Raspberry Pi model in this case, that will be connected to a video projector or a screen monitor. This server will remain idle waiting for an incoming Bluetooth connection. Once the connection is accepted the server will parse the commands sent by the client through a JSON API and will execute them accordingly to control the system. The client we decided to develop is an Android application. The speaker will be able to connect with the server by scanning a QR code that will be generated and displayed into the projector or screen monitor. Once the connection is accepted the client will sent the commands to control the slides, such as opening a presentation, move forward and backwards, etc. The adopted solution must be efficient, easy to use and with low cost to be appealing and useful to the real world. To accomplish the task this project will count with improvements over the current systems, such as the possibility to open a presentation from the smartphone, the visualization of the current slide notes from the mobile phone and a countdown timer to have a better control over the available time for the presentation.

WINDOWS 10 IoT SU RASPBERRY PI 2: MULTI-PARADIGM PROGRAMMING TRA JAVA E C#

Questa tesi ha come obiettivo la sperimentazione del nuovo sistema operativo Windows 10 IoT Core su tecnologia Raspberry Pi 2, verificandone la compatibilita con alcuni sensori in commercio. Tale studio viene poi applicato in un contesto di Home Intelligence al fine di creare un agente per la gestione di luci LED, in prospettiva della sua integrazione nel sistema prototipale Home Manager.

Implementazione di sistemi per visible light communication su Raspberry Pi

Grazie all'enorme sviluppo dei LED, le comunicazioni tramite luce visibile stanno acquisendo sempre maggiore importanza. Obiettivo di questa tesi è implementare su schede a basso costo (come Rasperry Pi) un sistema di trasmissione e ricezione basato appunto sulla visible light communication. Dopo un primo tentativo di trasferire il codice OpenVLC, sviluppato dal centro di ricerca spagnolo IMDEA Network, su Rasperry Pi, si è deciso di intraprendere una nuova strada e si è implementato un trasmettitore VLC in Simulink di Matlab e una prima bozza di ricevitore che sfrutta la SPI (serial-parallel interface). I primi risultati mostrano il corretto funzionamento del sistema anche se con data rate molto basse. Sviluppi futuri prevederanno l'ottimizzazione del sistema.

Application of the fault-tolerant deadline mechanism to a satellite on-board computer system /

"June 1980."

Co-simulación HW/SW en Raspberry Pi

En el mundo de la simulación existen varios tipos de sistemas reales, entre los que se encuentran los sistemas de eventos discretos. Para poder simular estos sistemas se pueden utilizar, entre otras, herramientas basadas en el formalismo DEVS (Discrete EVents system Specification), como la utilizada en este proyecto: xDEVS. La simulación posee una importancia muy elevada en campos como la educación y la ciencia, y en ocasiones es necesario incluir datos del medio físico o sacar información al exterior del simulador. Por ello es necesario contar con herramientas que puedan realizar simulaciones utilizando sensores, actuadores, circuitos externos, etc., o lo que es lo mismo, que puedan realizar co-simulaciones entre software y hardware. De esta forma se puede facilitar el desarrollo de sistemas por medio de modelado y simulación, pudiendo extraer el hardware gradualmente y analizar los resultados en cada etapa. Este proyecto es de carácter incremental, y trata de extender la funcionalidad de la plataforma xDEVS para poder realizar co-simulaciones entre hardware y software sobre una Raspberry Pi. Para ello se van a utilizar circuitos lógicos como hardware externo y se enlazarán al simulador a través de ficheros de dispositivo, gestionados por módulos del kernel de Linux. Como caso de estudio se desarrolla la co-simulación entre hardware y software completa de un ascensor de siete plantas para mostrar el uso y funcionamiento en xDEVS, extrayendo los circuitos integrados de uno en uno.

Raspberry Pi-pohjainen dataloggeri

Dataloggerit ovat tärkeitä mittaustekniikassa käytettäviä mittalaitteita, joiden tarkoituksena on kerätä talteen mittausdataa pitkiltä aikaväleiltä. Dataloggereita voidaan käyttää esimerkiksi teollista prosessia osana olevien toimilaitteiden tai kotitalouden energiajärjestelmän seurannassa. Teollisen luokan dataloggerit ovat yleensä hinnaltaan satojen tai tuhansien eurojen luokkaa. Työssä pyrittiin löytämään teollisen luokan laitteille halpa ja helppokäyttöinen vaihtoehto, joka on kuitenkin riittävän tehokas ja toimiva. Työssä suunniteltiin ja toteutettiin dataloggeri Raspberry Pi-alustalle ja testattiin sitä oikeaa teollista ympäristöä vastaavissa olosuhteissa. Kirjallisuudesta ja internet artikkeleista etsittiin samankaltaisia laite- ja ohjelmistoratkaisuja ja niitä käytettiin dataloggausjärjestelmän pohjana. Raspberry Pi-alustalle koodattiin yksinkertainen Python-kielinen data-loggausohjelma, joka käyttää Modbus-tiedonsiirtoprotokollaa. Testien perusteella voidaan todeta, että toteutettu dataloggeri on toimiva ja kykenee kaupallisten dataloggereiden tasoiseen mittaukseen ainakin pienillä näytteistystaajuuksilla. Toteutettu dataloggeri on myös huomattavasti kaupallisia dataloggereita halvempi. Helppokäyttöisyyden näkökulmasta dataloggerissa havaittiin puutteita, joita käydään läpi jatkokehitysideoiden muodossa.

Sistemi operativi real-time su dispositivi embedded - Caso di studio : FreeRTOS su Raspberry Pi

La tesi si concentra sullo studio dell'architettura di un sistema operativo real-time e tratta approfonditamente il dispositivo embedded Raspberry Pi. Successivamente,si procede con l'installazione di BitThunder(un RTOS basato su FreeRTOS) su tale sistema embedded e si attua un test pratico per verificarne il funzionamento.

Monitoraggio attivo di un cluster di Raspberry Pi

In questa tesi viene trattata la selezione ed implementazione di un software che permetta di effettuare il monitoraggio e l'invio di segnalazioni automatiche di allerta per un cluster composto da dispositivi aventi capacità computazionali ridotte.

Cost-effective and lightweight mobile units for MixAR: A comparative trial among different setups

Cultural heritage has arousing the interest of the general public (e.g. tourists), resulting in the increasing number of visitations to archaeological sites. However, many buildings and monuments are severely damaged or completely destroyed, which doesn’t allow to get a full experience of “travelling in time”. Over the years, several Augmented Reality (AR) approaches were proposed to overcome these issues by providing three-dimensional visualization of reconstructed ancient structures in situ. However, most of these systems were made available through heavy and expensive technological bundles. Alternatively, MixAR intends to be a lightweight and cost-effective Mixed Reality system which aims to provide the visualization of virtual ancient buildings reconstructions in situ, properly superimposed and aligned with real-world ruins. This paper proposes and compares different AR mobile units setups to be used in the MixAR system, with low-cost and lightweight requirements in mind, providing different levels of immersion. It was propounded four different mobile units, based on: a laptop computer, a single-board computer (SBC), a tablet and a smartphone, which underwent a set of tests to evaluate their performances. The results show that mobile units based on laptop computer and SBC reached a good overall performance while mobile units based on tablet and smartphone did not meet such a satisfactory result even though they are acceptable for the intended use.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Sviluppo di una piattaforma software per applicazioni di monitoraggio di parametri vitali basate su tecnologie wearable e mobile

L'obiettivo della tesi è quello di sviluppare una piattaforma software a supporto della programmazione di applicazioni mobile per la rilevazione di parametri vitali. Questo caso di studio offre una ampia discussione su wearable computing, healthcare e prototipazione del wearable. La tesi va a descrivere tutte le fasi di analisi, modellazione e progettazione del sistema, evidenziando problematiche e soluzioni adottate.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Spacecraft Collision Probability Calculation from Conjunction Data Message and High Precision Orbit Propagator

The increasing number of Resident Space Objects (RSOs) is a threat to spaceflight operations. Conjunction Data Messages (CDMs) are sent to satellite operators to warn for possible future collision and their probabilities. The research project described herein pushed forward an algorithm that is able to update the collision probability directly on-board starting from CDMs and the state vector of the hosting satellite which is constantly updated thanks to an onboard GNSS receiver. A large set of methods for computing the collision probability was analyzed in order to find the best ones for this application. The selected algorithm was then tested to assess and improve its performance. Finally, parts of the algorithm and external software were implemented on a Raspberry Pi 3B+ board to demonstrate the compatibility of this approach with computational resources similar to those typically available onboard modern spacecraft.