Evaluating possible uses of a Raspberry Pi in an academic library environment

Loughborough University's Library Systems Team investigated two potential uses for Raspberry Pis. The first use to be identified for investigation was using the Raspberry Pi as a replacement for the existing OPAC hardware. While it met a majority of the requirements there were issues with the responsiveness to user input at certain times. The second use for Raspberry Pis investigated was to provide a number of digital signs to display details about resource bookings and the availability of PCs in IT labs around campus. The Raspberry Pi demonstrated that it was ideally suited to this task

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

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

SNR: Software Library for Introductory Robotics

This thesis introduces SNR, a Python library for programming robotic systems in the context of introductory robotics courses. Greater demand for roboticists has pressured educational institutions to expand robotics curricula. Students are now more likely to take robotics courses earlier and with less prior programming experience. Students may be attempting to simultaneously learn a systems programming language, a library API, and robotics concepts. SNR is written purely in Python to present familiar semantics, eliminating one of these learning curves. Industry standard robotics libraries such as ROS often require additional build tools and configuration languages. Students in introductory courses frequently lack skills needed for these tools. SNR does not use any additional build tools, so students are faced with fewer compounding learning curves. SNR presents students with concepts important to robotic systems programming such as modular and event driven architectures to bridge the gap between introductory programming courses and industry standard libraries

URBAN DATA COLLECTION USING A BIKE MOBILE SYSTEM WITH A FOSS ARCHITECTURE

European community is working to improve the quality of the life in each European country, in particular to increase the quality air condition and safety in each city. The quality air is daily monitored, using several ground station, which do not consider the variation of the quality during the day, evaluating only the average level. In this case, it could be interesting to have a “smart” system to acquire distributed data in continuous, even involving the citizens. On the other hand, to improve the safety level in urban area along cycle lane, road and pedestrian path, exist a lot of algorithms for visibility and safety analysis; the crucial aspect is the 3D model considered as “input” in these algorithms, which always needs to be updated. A bike has been instrumented with two digital camera as Raspberry PI-cam. Image acquisition has been realized with a dedicated python tool, which has been implemented in the Raspberry PI system. Images have been georeferenced using a u-blox 8T, connected to Raspberry system. GNSS data has been acquired using a specific tool developed in Python, which was based on RTKLIB library. Time synchronization has been obtained with GNSS receiver. Additionally, a portable laser scanner, an air quality system and a small Inertial platform have been installed and connected with the Raspberry system. The system has been implemented and tested to acquire data (image and air quality parameter) in a district in Turin. Also a 3D model of the investigated site has been carried. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be described. different low cost sensors, in particular digital camera and laser scanner to collect easily geospatial data in urban area

Optimization of deep learning algorithms for an autonomous RC vehicle

Dissertação de mestrado em Engenharia InformáticaThis dissertation aims to evaluate and improve the performance of deep learning (DL) algorithms to autonomously drive a vehicle, using a Remo Car (an RC vehicle) as testbed. The RC vehicle was built with a 1:10 scaled remote controlled car and fitted with an embedded system and a video camera to capture and process real-time image data. Two different embedded systems were comparatively evaluated: an homogeneous system, a Raspberry Pi 4, and an heterogeneous system, a NVidia Jetson Nano. The Raspberry Pi 4 with an advanced 4-core ARM device supports multiprocessing, while the Jetson Nano, also with a 4-core ARM device, has an integrated accelerator, a 128 CUDA-core NVidia GPU. The captured video is processed with convolutional neural networks (CNNs), which interpret image data of the vehicle’s surroundings and predict critical data, such as lane view and steering angle, to provide mechanisms to drive on its own, following a predefined path. To improve the driving performance of the RC vehicle, this work analysed the programmed DL algorithms, namely different computer vision approaches for object detection and image classification, aiming to explore DL techniques and improve their performance at the inference phase. The work also analysed the computational efficiency of the control software, while running intense and complex deep learning tasks in the embedded devices, and fully explored the advanced characteristics and instructions provided by the two embedded systems in the vehicle. Different machine learning (ML) libraries and frameworks were analysed and evaluated: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN and TensorRT. They play a key role to deploy the relevant algorithms and to fully engage the hardware capabilities. The original algorithm was successfully optimized and both embedded systems could perfectly handle this workload. To understand the computational limits of both devices, an additional and heavy DL algorithm was developed that aimed to detect traffic signs. The homogeneous system, the Raspberry Pi 4, could not deliver feasible low-latency values, hence the detection of traffic signs was not possible in real-time. However, a great performance improvement was achieved using the heterogeneous system, Jetson Nano, enabling their CUDA-cores to process the additional workload.Esta dissertação tem como objetivo avaliar e melhorar o desempenho de algoritmos de deep learning (DL) orientados à condução autónoma de veículos, usando um carro controlado remotamente como ambiente de teste. O carro foi construído usando um modelo de um veículo de controlo remoto de escala 1:10, onde foi colocado um sistema embebido e uma câmera de vídeo para capturar e processar imagem em tempo real. Dois sistemas embebidos foram comparativamente avaliados: um sistema homogéneo, um Raspberry Pi 4, e um sistema heterogéneo, uma NVidia Jetson Nano. O Raspberry Pi 4 possui um processador ARM com 4 núcleos, suportando multiprocessamento. A Jetson Nano, também com um processador ARM de 4 núcleos, possui uma unidade adicional de processamento com 128 núcleos do tipo CUDA-core. O vídeo capturado e processado usando redes neuronais convolucionais (CNN), interpretando o meio envolvente do veículo e prevendo dados cruciais, como a visibilidade da linha da estrada e o angulo de direção, de forma a que o veículo consiga conduzir de forma autónoma num determinado ambiente. De forma a melhorar o desempenho da condução autónoma do veículo, diferentes algoritmos de deep learning foram analisados, nomeadamente diferentes abordagens de visão por computador para detecção e classificação de imagens, com o objetivo de explorar técnicas de CNN e melhorar o seu desempenho na fase de inferência. A dissertação também analisou a eficiência computacional do software usado para a execução de tarefas de aprendizagem profunda intensas e complexas nos dispositivos embebidos, e explorou completamente as características avançadas e as instruções fornecidas pelos dois sistemas embebidos no veículo. Diferentes bibliotecas e frameworks de machine learning foram analisadas e avaliadas: TensorFlow, TensorFlow Lite, Arm NN, PyArmNN e TensorRT. Estes desempenham um papel fulcral no provisionamento dos algoritmos de deep learning para tirar máximo partido das capacidades do hardware usado. O algoritmo original foi otimizado com sucesso e ambos os sistemas embebidos conseguiram executar os algoritmos com pouco esforço. Assim, para entender os limites computacionais de ambos os dispositivos, um algoritmo adicional mais complexo de deep learning foi desenvolvido com o objetivo de detectar sinais de transito. O sistema homogéneo, o Raspberry Pi 4, não conseguiu entregar valores viáveis de baixa latência, portanto, a detecção de sinais de trânsito não foi possível em tempo real, usando este sistema. No entanto, foi alcançada uma grande melhoria de desempenho usando o sistema heterogeneo, Jetson Nano, que usaram os seus núcleos CUDA adicionais para processar a carga computacional mais intensa