The increase in connectivity and use of “smart” devices offers companies new possibilities to improve their efficiency by using digitalization. For example, booking of meeting rooms have gone from using a paper calendar to electronic booking. To enable both digital remote booking, as well as being able to book a room directly (as with a paper calendar) this project has developed a room manager system. The room manager is a device that provides a quick and intuitive way for employees to handle conference room booking. The project was started on behalf of the company ÅF, who would like to optimize their use of conference rooms.  The result was a fully functional touchscreen device built using a Raspberry Pi. The room manager integrates successfully with the existing calendar system used at ÅF and meets all the requirements set by ÅF. The device will be used to determine if a room manager system is worth investing in and may be used as a foundation for continued development.

The purpose of this thesis is to create an simple graphic library in the programming C language, through which it will be possible to draw and simulate the basic functions of a car backlight, and subseguently to create the graphic user interface.v The first part of the thesis is based on the study of the given issue, briefly it describes the differences between raster and vector graphics,most used formats, describes diverse color models and the area of their use, ,basic graphic adapters, video memorry and its control in the operating system Linux. In the second part there is stated the practical realization of the basic graphic algorithms needed for drawing the algorithm primitives. An mathematical aparatus described in detail and well-founded with formulas. Also there are the advantages and disatvantages of each used algorithm and their realization in the programming C language. The 3rd and the last part of the thesis is dedicated to the creation of the graphic user interface in the FreePascal programming language and further to the describtion of his main elements.

<p>Bilaga: 1 CD.</p>

The BlackPearl in the Computer Engineering Department is an Automotive Demonstrator, which has a variety of sensors, and users can control these via the server. The server is responsible for the remote interaction, the Smart Queue, and the Raspberry Pi display for human interaction. The Automotive Demonstrator consists of four components, which are installed on the CE-Box: Main QML Application, Main Server, Live Stream, and Smart Queue. All of these servers are running on three single-board computers (Raspberry Pi 3B): Main, BlackPearl, and Camera servers. The Automotive Demonstrator is built with the latest version from both Qt and NodeJS, and the components can access, store and exchange the data in JSON format. The BlackPearl will be controlled via four types of interaction methods: Web server, Voice commands (Sparrow), Pi Display, and Gamepad. The outcome of this thesis is a configurable and adaptive User Interface for Automotive Demonstrator, and this can be easily updated, customized, and accessible for new applications without the need to update or rebuild the program.

Orientador: Aparecido Augusto de Carvalho / Resumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approach... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

A utilização de Veículos Aéreos Não Tripulados (VANTs), também chamados UAV (Unmanned Aerial Vehicle) vem ganhando espaço nas mais diversas áreas, tais como inspeções em linhas de transmissão e em torres de fracionamento de refinarias, entre outros. Já na construção civil, estudos recentes estão sendo focados na utilização dos VANTs para inspeção de pontes, viadutos e estradas. O presente trabalho visa fornecer uma análise para o uso de VANT na área civil, na detecção de manifestações patológicas em revestimentos de argamassa, de forma a auxiliar na procura por fissuras em fachadas, sobretudo aqueles que a visualização esteja prejudicada, seja pela distância ou pela acessibilidade difícil ao local. Este trabalho analisa possíveis implementações de dois algoritmos de processamentos de imagens desenvolvidos a partir da ferramenta MATLAB para a indicação de presença de fissuras na alvenaria, obtendo o desempenho destes diferentes algoritmos quando executados em software em plataforma que possibilite a embarcação em VANTs. Utilizando a geração automática de código em C a partir do ambiente MATLAB, é realizada uma análise temporal em plataforma ARM e RISC dos algoritmos propostos, demonstrando a oportunidade de utilização de dispositivos na tarefa de processamento de imagem para a aplicação proposta. Esta análise possibilita a previsão do comportamento na utilização de um VANT, uma vez que isto pode impactar na velocidade durante a aplicação e consequentemente sua autonomia. / The use of Unmanned Aerial Vehicles (UAVs), has been gaining space in several areas, such as inspections of transmission lines, refineries fractionation towers among others. In the construction, recent studies have been focused on the use of UAVs for inspection of bridges, viaducts and roads. The present study aims to provide an analysis using UAVs in the civil construction area, in the detection of pathologic manifestations mortar coatings in order to aid in the search for cracks in the facades especially those that visualization is impaired, or may be the distance the accessibility difficult to spot. This paper provides an analysis of two algorithms of image processing developed from the Matlab tool for indicating the presence of cracks in masonry, getting the performance of these different algorithms when implemented in software platform that enables the vessel in UAV. Using the automatic generation of C/C++ code from the MATLAB environment is performed the temporal analysis on ARM and RISC plataform of the proposed algorithms demonstrates the opportunity to use devices in the image processing task for the proposed application. This analysis allows the prediction of the behavior of a UAV using one since it can impact velocity during application, and therefore their autonomy.

This report describes an approach of creating a system identifying fruit and vegetables in the retail market using images captured with a video camera at- tached to the system. The system helps the customers to label desired fruits and vegetables with a price according to its weight. The purpose of the sys- tem is to minimize the number of human computer interactions, speed up the identification process and improve the usability of the graphical user interface compared to existing systems. To accomplish creating a system improving these properties, an idea of implementing machine learning to identify the products aroused. Instead of assigning the responsibility to the user, who usually iden- tify the products manually, the responsibility is given to a computer. To classify an object, different convolutional neural networks have been tested and retrained. The networks have been retrained on data sets collected from ImageNet. To improve the accuracy, the networks have also been retrained on images where the background environment is similar to the environment the networks are supposed to perform in. The networks tested in this report are MobileNet and Inception. The networks have different propagation time and varies in accuracy. MobileNet performs the classification about seven times faster than Inception, but Inception gives more accurate results. To improve the systems further, usability testing has been performed on the graphical user interface of existing system and resulted system. To test the usability, a heuristic evaluation has been performed in combination of a second test produced by the authors. The tests concluded that the resulted system was more user friendly compared to existing systems. The hardware of the system constitutes of a Raspberry Pi, camera, display, load cell and a case. The software includes Python-code to label an image, a graphical user interface to interact with the user and a server created with Node.js. The graphical user interface has been programmed with JavaScript supplemented with the React library. To conclude, implementing convolutional neural networks to classify images and developing a new user interface resulted in a faster identification process together with fewer usability flaws.

Submitted by Renato de Macedo Giovanini null (renato81243@aluno.feis.unesp.br) on 2017-09-25T14:52:54Z No. of bitstreams: 1 dissertacao_renato_de_macedo_giovanini_2017_final.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-27T20:24:55Z (GMT) No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) / Made available in DSpace on 2017-09-27T20:24:55Z (GMT). No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5) Previous issue date: 2017-08-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world controlling devices without the use of muscles or peripheral nerves, using only his decoded brain activity. To achieve this, there is a need to develop robust pattern recognition systems, that must be able to detect the user’s intention through electroencephalography (EEG) signals and activate the corresponding output with reliable accuracy and within the shortest possible processing time. In this work, different EEG signal processing techniques were studied, and it is presented the development of a EEG under visual stimulation (Steady-State Visual Evoked Potentials - SSVEP) pattern recognition system. Using only Open Source tools and Python programming language, modules to manage datasets, reduce noise, extract features and perform classification of EEG signals were developed, and a comparative study of different techniques was performed, using filter banks and Discrete Wavelet Transforms (DWT) as feature extraction approaches, and the classifiers K-Nearest Neighbors, Multilayer Perceptron and Random Forests. Using DWT approach with Random Forest and Multilayer Perceptron classifiers, high accuracy rates up to 92 % were achieved in deeper decomposition levels. Then, the small-size microcomputer Raspberry Pi was used to perform time processing evaluation, obtaining short processing times for every classifiers. This work is a preliminary study of BCIs at the Laboratório de Instrumentação e Engenharia Biomédica, and, in the future, the system here presented may be part of a complete SSVEP-BCI system. / Existem, atualmente, cerca de 110 milhões de pessoas no mundo que vivem com algum tipo de deficiência motora severa. Especificamente no Brasil, é estimado que cerca de 2.2% da população conviva com alguma condição que dificulte a locomoção. Com o intuito de auxiliar tais pessoas, uma grande variedade de dispositivos, técnicas e serviços são atualmente desenvolvidos. Dentre elas, uma das técnicas mais complexas e desafiadoras é o estudo e o desenvolvimento de Interfaces Cérebro-Computador (ICMs). As ICMs são sistemas que permitem ao usuário comunicar-se com o mundo externo, controlando dispositivos sem o uso de músculos ou nervos periféricos, utilizando apenas sua atividade cerebral decodificada. Para alcançar isso, existe a necessidade de desenvolvimento de sistemas robustos de reconhecimento de padrões, que devem ser capazes de detectar as intenções do usuáro através dos sinais de eletroencefalografia (EEG) e ativar a saída correspondente com acurácia confiável e o menor tempo de processamento possível. Nesse trabalho foi realizado um estudo de diferentes técnicas de processamento de sinais de EEG, e o desenvolvimento de um sistema de reconhecimento de padrões de sinais de EEG sob estimulação visual (Potenciais Evocados Visuais de Regime Permanente - PEVRP). Utilizando apenas técnicas de código aberto e a linguagem Python de programação, foram desenvolvidos módulos para realizar o gerenciamento de datasets, redução de ruído, extração de características e classificação de sinais de EEG, e um estudo comparativo de diferentes técnicas foi realizado, utilizando-se bancos de filtros e a Transformada Wavelet Discreta (DWT) como abordagens de extração de características, e os classificadores K-Nearest Neighbors, Perceptron Multicamadas e Random Forests. Utilizando-se a DWT juntamente com Random Forests e Perceptron Multicamadas, altas taxas de acurácia de até 92 % foram obtidas nos níveis mais profundos de decomposição. Então, o computador Raspberry Pi, de pequenas dimensões, foi utilizado para realizar a avaliação do tempo de processamento, obtendo um baixo tempo de processamento para todos os classificadores. Este trabalho é um estudo preliminar em ICMs no Laboratório de Instrumentação e Engenharia Biomédica e, no futuro, pode ser parte de um sistema ICM completo.

A utilização de Veículos Aéreos Não Tripulados (VANTs), também chamados UAV (Unmanned Aerial Vehicle) vem ganhando espaço nas mais diversas áreas, tais como inspeções em linhas de transmissão e em torres de fracionamento de refinarias, entre outros. Já na construção civil, estudos recentes estão sendo focados na utilização dos VANTs para inspeção de pontes, viadutos e estradas. O presente trabalho visa fornecer uma análise para o uso de VANT na área civil, na detecção de manifestações patológicas em revestimentos de argamassa, de forma a auxiliar na procura por fissuras em fachadas, sobretudo aqueles que a visualização esteja prejudicada, seja pela distância ou pela acessibilidade difícil ao local. Este trabalho analisa possíveis implementações de dois algoritmos de processamentos de imagens desenvolvidos a partir da ferramenta MATLAB para a indicação de presença de fissuras na alvenaria, obtendo o desempenho destes diferentes algoritmos quando executados em software em plataforma que possibilite a embarcação em VANTs. Utilizando a geração automática de código em C a partir do ambiente MATLAB, é realizada uma análise temporal em plataforma ARM e RISC dos algoritmos propostos, demonstrando a oportunidade de utilização de dispositivos na tarefa de processamento de imagem para a aplicação proposta. Esta análise possibilita a previsão do comportamento na utilização de um VANT, uma vez que isto pode impactar na velocidade durante a aplicação e consequentemente sua autonomia. / The use of Unmanned Aerial Vehicles (UAVs), has been gaining space in several areas, such as inspections of transmission lines, refineries fractionation towers among others. In the construction, recent studies have been focused on the use of UAVs for inspection of bridges, viaducts and roads. The present study aims to provide an analysis using UAVs in the civil construction area, in the detection of pathologic manifestations mortar coatings in order to aid in the search for cracks in the facades especially those that visualization is impaired, or may be the distance the accessibility difficult to spot. This paper provides an analysis of two algorithms of image processing developed from the Matlab tool for indicating the presence of cracks in masonry, getting the performance of these different algorithms when implemented in software platform that enables the vessel in UAV. Using the automatic generation of C/C++ code from the MATLAB environment is performed the temporal analysis on ARM and RISC plataform of the proposed algorithms demonstrates the opportunity to use devices in the image processing task for the proposed application. This analysis allows the prediction of the behavior of a UAV using one since it can impact velocity during application, and therefore their autonomy.

A utilização de Veículos Aéreos Não Tripulados (VANTs), também chamados UAV (Unmanned Aerial Vehicle) vem ganhando espaço nas mais diversas áreas, tais como inspeções em linhas de transmissão e em torres de fracionamento de refinarias, entre outros. Já na construção civil, estudos recentes estão sendo focados na utilização dos VANTs para inspeção de pontes, viadutos e estradas. O presente trabalho visa fornecer uma análise para o uso de VANT na área civil, na detecção de manifestações patológicas em revestimentos de argamassa, de forma a auxiliar na procura por fissuras em fachadas, sobretudo aqueles que a visualização esteja prejudicada, seja pela distância ou pela acessibilidade difícil ao local. Este trabalho analisa possíveis implementações de dois algoritmos de processamentos de imagens desenvolvidos a partir da ferramenta MATLAB para a indicação de presença de fissuras na alvenaria, obtendo o desempenho destes diferentes algoritmos quando executados em software em plataforma que possibilite a embarcação em VANTs. Utilizando a geração automática de código em C a partir do ambiente MATLAB, é realizada uma análise temporal em plataforma ARM e RISC dos algoritmos propostos, demonstrando a oportunidade de utilização de dispositivos na tarefa de processamento de imagem para a aplicação proposta. Esta análise possibilita a previsão do comportamento na utilização de um VANT, uma vez que isto pode impactar na velocidade durante a aplicação e consequentemente sua autonomia. / The use of Unmanned Aerial Vehicles (UAVs), has been gaining space in several areas, such as inspections of transmission lines, refineries fractionation towers among others. In the construction, recent studies have been focused on the use of UAVs for inspection of bridges, viaducts and roads. The present study aims to provide an analysis using UAVs in the civil construction area, in the detection of pathologic manifestations mortar coatings in order to aid in the search for cracks in the facades especially those that visualization is impaired, or may be the distance the accessibility difficult to spot. This paper provides an analysis of two algorithms of image processing developed from the Matlab tool for indicating the presence of cracks in masonry, getting the performance of these different algorithms when implemented in software platform that enables the vessel in UAV. Using the automatic generation of C/C++ code from the MATLAB environment is performed the temporal analysis on ARM and RISC plataform of the proposed algorithms demonstrates the opportunity to use devices in the image processing task for the proposed application. This analysis allows the prediction of the behavior of a UAV using one since it can impact velocity during application, and therefore their autonomy.