One of the principal challenges facing the structural health monitoring community is taking large, heterogeneous sets of data collected from sensors, and extracting information that allows the estimation of the damage condition of a structure. Another important challenge is to collect relevant data from a structure in a manner that is cost-effective, and respects the size, weight, cost, energy consumption and bandwidth limitations placed on the system. In this work, we established the suitability of compressed sensing to address both challenges. A digital version of a compressed sensor is implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is tested in a surrogate SHM application using acceleration measurements. Currently, the prototype compressed sensor is capable of collecting compressed coefficients from measurements and sending them to an off-board processor for signal reconstruction using ' 1 norm minimization. A compressed version of the matched filter known as the smashed filter has also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.

Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driverassistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximizing the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of finetuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driverassistance systems as well as training and simulation environments.Experiment 3 Finally, we implement the same domain-adversarial approach to study how the network, trained only on the Brain4Cars data, adapts to our new dataset, in which the drivers and the driving set-up -e.g. position of the mirrors, windows, and the camera-differ from the Brain4Cars dataset.The results confirm that, without adaptation, the model is not able to predict manoeuvres from observations in which the features have very different marginal distributions compared to the training set. We conclude discussing the potential applications of the domain-adversarial approach to apply domain 4

This article introduces the use of emerging augmented reality technology to enable the next generation of structural infrastructure inspection and awareness. This work is driven by the prevalence of visual structural inspection. It is known that current visual inspection techniques have multiple sources of variance that should be reduced in order to achieve less ambiguous visual inspections. Emerging augmented reality tools feature a variety of sensors, computation, and communication resources that can enable relevant structural inspection data to be collected at very high resolution in an unambiguous manner. This work shows how emerging augmented reality tools can be used to greatly enhance our ability to capture comprehensive, high-resolution, three-dimensional measurements of critical infrastructure. This work also provides detailed information on the software architecture for augmented reality structural inspection applications that helps meet the goals of the framework. The fact that the framework is designed to accommodate the considerations associated with high-consequence infrastructure implies that it is also comprehensive enough to be applied to less hazardous but still high-value infrastructure such as bridges, dams, and tunnels. Augmented reality has great potential to enable the next generation of smart infrastructure, and this work focuses on addressing how augmented reality can be leveraged to enable the next generation of structural awareness for high-consequence, long-lifespan structures.

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This work presents a novel, comprehensive framework that leverages emerging augmented reality headset technology to enable smart nuclear industrial infrastructure that a human can easily interact with to improve their performance in terms of safety, security, and productivity. Nuclear industrial operations require some of the most complicated infrastructure that must be managed today. Nuclear infrastructure and their associated industrial operations typically features stringent requirements associated with seismic, personnel management (e.g., access control, equipment access), safety (e.g., radiation, criticality, mechanical, electrical, spark, and chemical hazards), security (cyber/physical), and sometimes international treaties for nuclear non-proliferation. Furthermore, a wide variety of manufacturing and maintenance operations take place within these facilities further complicating their management. Nuclear facilities require very thorough and stringent documentation of the operations occurring within these facilities as well as maintaining a tight chain-of-custody for the materials being stored within the facility. The emergence of augmented reality and a variety of Internet of Things (IoT) devices offers a possible solution to help mitigate these challenges. This work provides a demonstration of a prototype smart nuclear infrastructure system that leverages augmented reality to illustrate the advantages of this system. It will also present example augmented reality tools that can be leveraged to create the next generation of smart nuclear infrastructure. The discussion will layout future directions of research for this class of work.

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