Integrated Architecture for Industrial Robot Programming and Control

As robot control systems are traditionally closed, it is difficult to add supplementary intelligence. Accordingly, as based on a new notion of user views, a layered system architecture is proposed. Bearing in mind such industrial demands as computing efficiency and simple factory-floor operation, the control layers are parameterized by means of functional operators consisting of pieces of compiled code that can be passed as parameters between the layers. The required interplay between application-specific programs and built-in motion control is thereby efficiently accomplished. The results from experimental evaluation and several case studies suggest the architecture to be very useful also in an industrial context

A visual sensor network for object recognition: Testbed realization

This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy

Ship Multimodel 3D Reconstruction and Corrosion Detection

3D reconstruction has been an area of increased interest due to the current higher demand in applications, such as virtual realities, 3D mapping, medical imaging, and many others. Although, there are still many problems associated with reconstructing a real-life object, such as capturing occluded zones, noise, and processing time. Furthermore, as deep learning technologies advance, there has been a growing interest in using such methods to replace human-driven tasks, namely corrosion inspection, as it decreases the risk of injury of the inspector, it is more efficient due to less time taken, and is cost-saving. This dissertation proposes a method for reconstructing a 3D model of ships using aerial RGB images and terrestrial RGB-D images, along with a system capable of detecting the corroded parts of the ship and highlighting them in the model. Using two different sensors in two different ground planes mitigates some of the occlusion problems and increases the final model’s accuracy. The current dissertation also aims to pick the methods that have the best trade-off between accuracy and computational speed. The final model can be advantageous for corrosion inspectors, as they will have the model of the ship, as well as the corroded zones which, with that information, can choose the steps to take next without the need to manually inspect the ship or even be in the same site as the ship. The final model is a fusion of three different 3D models. The model obtained from RGB images exploits Structure from Motion algorithm which recovers the 3D aspect of the ship from 2D images. As for the remaining models, RGB-D images were used in conjunction with the Open3D library to create 3D structures from both sides of the ship. The corrosion classifier model was trained in Google Colab and achieved an accuracy of 97.44 % on the test dataset. The images used to create the SfM 3D model were each divided into a total of 40 regions and fed into the classifier to simulate a less concise image detection algorithm instead of an image classification algorithm. The results were encoded into the 3D model, highlighting the corroded zones.A reconstrução 3D tem sido uma área com crescente interesse devido à maior demanda em aplicações como realidade virtual, mapeamento 3D, imagens médicas e muitos outros. Embora, existem ainda muitos problemas associados à reconstrução 3D de um objeto real. Exemplos desses são a captura de zonas oclusas, o ruído e o tempo de processamento necessário para efetuar a reconstrução. Adicionalmente, com o avanço das tecnologias de deep learning, tem havido um acrescido interesse em usar ditos métodos para substituir tarefas realizadas por humanos como, por exemplo, a inspeção de corrosão, pois diminui o risco de lesões ao inspetor, tem maior eficiência devido a um menor tempo gasto, e economiza os custos. Esta dissertação propõe um método de reconstrução de um modelo 3D de navios, utilizando imagens RGB aéreas e imagens RGB-D terrestres, juntamente com um sistema capaz de detetar as zonas com corrosão no navio e destacá-las no modelo. O uso de dois sensores diferentes em dois meios diferentes atenuará alguns dos problemas de oclusão e aumentará a precisão do modelo final. A presente dissertação também visa escolher os métodos que apresentam o melhor compromisso entre precisão e velocidade de processamento. O modelo final poderá ser vantajoso para os inspetores de corrosão, pois terão o modelo do navio, bem como as zonas com corrosão que, com essa informação, poderão escolher quais os passos a seguir, sem a necessidade de inspecionar manualmente o navio ou mesmo deslocar-se para o local do navio. O modelo final é uma fusão de três modelos 3D diferentes. O modelo obtido a partir de imagens RGB tirou partido do algoritmo Structure from Motion, que recupera o aspeto 3D do navio a partir de imagens 2D. Quanto aos modelos restantes, as imagens RGB-D foram utilizadas em conjunto com a biblioteca Open3D para criar estruturas 3D de ambos os lados do navio. O modelo de classificação de corrosão foi treinado em ambiente Google Colab e alcançou uma exatidão de 97.44% no dataset de teste. As imagens usadas para criar o modelo SfM 3D foram, cada uma, fracionadas num total de 40 regiões e dadas ao modelo de classificação com o intuito de simularum modelo de deteção de imagem menos conciso em vez de um modelo de classificação de imagem. Os resultados foram codificados no modelo 3D, destacando as zonas com corrosão

Digital Preservation Services : State of the Art Analysis

Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe

Roadmap of optical communications

© 2016 IOP Publishing Ltd. Lightwave communications is a necessity for the information age. Optical links provide enormous bandwidth, and the optical fiber is the only medium that can meet the modern society's needs for transporting massive amounts of data over long distances. Applications range from global high-capacity networks, which constitute the backbone of the internet, to the massively parallel interconnects that provide data connectivity inside datacenters and supercomputers. Optical communications is a diverse and rapidly changing field, where experts in photonics, communications, electronics, and signal processing work side by side to meet the ever-increasing demands for higher capacity, lower cost, and lower energy consumption, while adapting the system design to novel services and technologies. Due to the interdisciplinary nature of this rich research field, Journal of Optics has invited 16 researchers, each a world-leading expert in their respective subfields, to contribute a section to this invited review article, summarizing their views on state-of-the-art and future developments in optical communications

Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations

Modular product platform design

Modular product platforms, sets of common modules that are shared among a product family, can bring cost savings and enable introduction of multiple product variants quicker than without platforms. This thesis describes the current state of modular platform design and identifies gaps in the current state. The gaps were identified through application of three existing methods and by testing their usability and reliability on engineers and engineering students. Existing platform or modular design methods either are meant for (a) single products, (b) identify only module "cores" leaving the final module boundary definition to the designer, and (c) use only a limited set of evaluation criteria. I introduce a clustering algorithm for common module identification that takes into account possible degrees of commonality. This new algorithm can be applied both at physical and functional domains and at any, and even mixed, levels of hierarchy. Furthermore, the algorithm is not limited to a single measure for commonality analysis. To select the candidate modules for the algorithm, a key discriminator is how difficult the interfaces become. I developed an interface complexity metric based on minimizing redesign in case of a design change. The metric is based on multiple expert interviews during two case studies. The new approach was to look at the interface complexity as described by the material, energy, and information flows flowing through the interface. Finally, I introduce a multi criteria platform scorecard for improved evaluation of modular platforms. It helps a company focus on their strategy and benchmark one's own platform to the competitors'. These tools add to the modular platform development process by filling in the gaps identified. The tools are described in the context of the entire platform design process, and the validity of the methods and applicability to platform design is shown through industrial case studies and examples.reviewe