Human Action Recognition and Monitoring in Ambient Assisted Living Environments

Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user. In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical. This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments. The former topic is addressed through different approaches based on data extracted from RGB-D sensors. A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions. Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques. The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released. Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts

A decision-making framework for school infrastructure improvement programs

School infrastructure affects the quality of education and the performance of children and youth. Natural hazards such as earthquakes, hurricanes, floods, and landslides, threaten critical infrastructure such as school facilities. Additionally, problems related to the functionality of these facilities are common in the region, such as an inadequate number of classrooms, poor lighting, and insufficient ventilation, among others. At a national level, the decision-making process to prioritize schools’ interventions becomes even more challenging due to limited resources and lack of information. Furthermore, there is a lack of a systematic approach to address the need of improving existing infrastructure taking into consideration limited resources. Considering this, a novel decision-making framework is proposed that prioritizes school infrastructure investment with limited budgets, using clustering procedures, a multi-criteria utility function, and an optimization component. This framework allows better public policy decisions and benefits students in terms of buildings quality with a multi-criteria perspective, improving both safety and functional conditions. The framework is illustrated with a case study applied to the public-school infrastructure in the Dominican Republic

Mejora eficiente para la estimación de la energía libre superficial del ligante asfáltico mediante herramientas de Machine Learning

The Surface Free Energy (SFE) of a material is defined as the energy needed to create a new surface unit under vacuum conditions. This property is directly related to the resistance to fracture and recovery of material and the ability to create strong adhesion with other materials. This value can be used as a complementary parameter for the selection and optimal combination of materials for asphalt mixtures, as well as in the micromechanical modeling of fracture and recovery processes of said mixtures. This document describes the results of the implementation of the use of machine learning and Random Forest prediction techniques for the estimation of surface free energy based on data from previous studies. The experimental samples were twenty-three asphalt binders used in a Strategic Highway Research Program (SHRP). A decrease of 54% and 82% in the mean absolute error (MAE) and the mean square error (MSE), respectively was found for the new model built. While the model fits better with a 12% improvement, according to the adjusted determination coefficient, the accuracy and the score of the model also increases notably in 2% and 55%, respectively.La energía libre de superficie de un material se define como la energía necesaria para crear una nueva unidad de superficie en condiciones de vacío. Esta propiedad está directamente relacionada con la resistencia a la fractura y recuperación de un material y con la capacidad de crear una fuerte adhesión con otros materiales. Este valor puede ser utilizado como parámetro complementario para la selección y combinación óptima de materiales para mezclas asfálticas, así como en el modelado micromecánico de procesos de fractura y recuperación de dichas mezclas. Este documento describe los resultados de la implementación del uso del aprendizaje automático y las técnicas de predicción de bosque aleatorio para la estimación de la energía libre superficial basada en datos de estudios anteriores. Las muestras experimentales fueron veintitrés ligantes de asfalto usados en un Programa de Investigación Estratégica de Carreteras (SHRP). Podemos destacar una disminución de 54% y 82% en el error medio absoluto (MAE) y el error cuadrático medio (MSE), respectivamente. Si bien el modelo encaja mejor con una mejora del 12%, según el coeficiente de determinación ajustado, la precisión y la puntuación del modelo también aumentan notablemente en un 2% y 55% respectivamente

Convex Optimization of PV-Battery System Sizing and Operation with Non-Linear Loss Models

In the literature, when optimizing the sizing and operation of a residential PV system in combination with a battery energy storage system, the efficiency of the battery and the converter is generally assumed constant, which corresponds to a linear loss model that can be readily integrated in an optimization model. However, this assumption does not always represent the impact of the losses accurately. For this reason, an approach is presented that includes non-linear converter and battery loss models by applying convex relaxations to the non-linear constraints. The relaxed convex formulation is equivalent to the original non-linear formulation and can be solved more efficiently. The difference between the optimization model with non-linear loss models and linear loss models is illustrated for a residential DC-coupled PV-battery system. The linear loss model is shown to result in an underestimation of the battery size and cost as well as a lower utilization of the battery. The proposed method is useful to accurately model the impact of losses on the optimal sizing and operation in exchange for a slightly higher computational time compared to linear loss models, though far below that of solving the non-relaxed non-linear problem.Comment: submitted to Applied Energ

The Baryon Oscillation Spectroscopic Survey of SDSS-III

The Baryon Oscillation Spectroscopic Survey (BOSS) is designed to measure the scale of baryon acoustic oscillations (BAO) in the clustering of matter over a larger volume than the combined efforts of all previous spectroscopic surveys of large scale structure. BOSS uses 1.5 million luminous galaxies as faint as i=19.9 over 10,000 square degrees to measure BAO to redshifts z<0.7. Observations of neutral hydrogen in the Lyman alpha forest in more than 150,000 quasar spectra (g<22) will constrain BAO over the redshift range 2.15<z<3.5. Early results from BOSS include the first detection of the large-scale three-dimensional clustering of the Lyman alpha forest and a strong detection from the Data Release 9 data set of the BAO in the clustering of massive galaxies at an effective redshift z = 0.57. We project that BOSS will yield measurements of the angular diameter distance D_A to an accuracy of 1.0% at redshifts z=0.3 and z=0.57 and measurements of H(z) to 1.8% and 1.7% at the same redshifts. Forecasts for Lyman alpha forest constraints predict a measurement of an overall dilation factor that scales the highly degenerate D_A(z) and H^{-1}(z) parameters to an accuracy of 1.9% at z~2.5 when the survey is complete. Here, we provide an overview of the selection of spectroscopic targets, planning of observations, and analysis of data and data quality of BOSS.Comment: 49 pages, 16 figures, accepted by A

Status report on the NCRIS eResearch capability summary

Preface The period 2006 to 2014 has seen an approach to the national support of eResearch infrastructure by the Australian Government which is unprecedented. Not only has investment been at a significantly greater scale than previously, but the intent and approach has been highly innovative, shaped by a strategic approach to research support in which the critical element, the catchword, has been collaboration. The innovative directions shaped by this strategy, under the banner of the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), have led to significant and creative initiatives and activity, seminal to new research and fields of discovery. Origin This document is a Technical Report on the Status of the NCRIS eResearch Capability. It was commissioned by the Australian Government Department of Education and Training in the second half of 2014 to examine a range of questions and issues concerning the development of this infrastructure over the period 2006-2014. The infrastructure has been built and implemented over this period following investments made by the Australian Government amounting to over $430 million, under a number of funding initiatives

Interacção multimodal : contribuições para simplificar o desenvolvimento de aplicações

Doutoramento em Engenharia InformáticaA forma como interagimos com os dispositivos que nos rodeiam, no nosso diaa- dia, está a mudar constantemente, consequência do aparecimento de novas tecnologias e métodos que proporcionam melhores e mais aliciantes formas de interagir com as aplicações. No entanto, a integração destas tecnologias, para possibilitar a sua utilização alargada, coloca desafios significativos e requer, da parte de quem desenvolve, um conhecimento alargado das tecnologias envolvidas. Apesar de a literatura mais recente apresentar alguns avanços no suporte ao desenho e desenvolvimento de sistemas interactivos multimodais, vários aspectos chave têm ainda de ser resolvidos para que se atinja o seu real potencial. Entre estes aspectos, um exemplo relevante é o da dificuldade em desenvolver e integrar múltiplas modalidades de interacção. Neste trabalho, propomos, desenhamos e implementamos uma framework que permite um mais fácil desenvolvimento de interacção multimodal. A nossa proposta mantém as modalidades de interacção completamente separadas da aplicação, permitindo um desenvolvimento, independente de cada uma das partes. A framework proposta já inclui um conjunto de modalidades genéricas e módulos que podem ser usados em novas aplicações. De entre as modalidades genéricas, a modalidade de voz mereceu particular atenção, tendo em conta a relevância crescente da interacção por voz, por exemplo em cenários como AAL, e a complexidade associada ao seu desenvolvimento. Adicionalmente, a nossa proposta contempla ainda o suporte à gestão de aplicações multi-dispositivo e inclui um método e respectivo módulo para criar fusão entre eventos. O desenvolvimento da arquitectura e da framework ocorreu num contexto de I&D diversificado, incluindo vários projectos, cenários de aplicação e parceiros internacionais. A framework permitiu o desenho e desenvolvimento de um conjunto alargado de aplicações multimodais, sendo um exemplo digno de nota o assistente pessoal AALFred, do projecto PaeLife. Estas aplicações, por sua vez, serviram um contínuo melhoramento da framework, suportando a recolha iterativa de novos requisitos, e permitido demonstrar a sua versatilidade e capacidades.The way we interact with the devices around us, in everyday life, is constantly changing, boosted by emerging technologies and methods, providing better and more engaging ways to interact with applications. Nevertheless, the integration with these technologies, to enable their widespread use in current systems, presents a notable challenge and requires considerable knowhow from developers. While the recent literature has made some advances in supporting the design and development of multimodal interactive systems, several key aspects have yet to be addressed to enable its full potential. Among these, a relevant example is the difficulty to develop and integrate multiple interaction modalities. In this work, we propose, design and implement a framework enabling easier development of multimodal interaction. Our proposal fully decouples the interaction modalities from the application, allowing the separate development of each part. The proposed framework already includes a set of generic modalities and modules ready to be used in novel applications. Among the proposed generic modalities, the speech modality deserved particular attention, attending to the increasing relevance of speech interaction, for example in scenarios such as AAL, and the complexity behind its development. Additionally, our proposal also tackles the support for managing multi-device applications and includes a method and corresponding module to create fusion of events. The development of the architecture and framework profited from a rich R&D context including several projects, scenarios, and international partners. The framework successfully supported the design and development of a wide set of multimodal applications, a notable example being AALFred, the personal assistant of project PaeLife. These applications, in turn, served the continuous improvement of the framework by supporting the iterative collection of novel requirements, enabling the proposed framework to show its versatility and potential

Throw Me a Lifeline: A Comparison of Port Cities with Antithetical Adaptation Strategies to Sea-Level Rise

Sea-level rise (SLR) is a manifestation of climate change that is particularly hazardous to port cities that must remain on the waterfront to function, yet are increasingly battered and flooded by encroaching storms, and sinking into the rising saltwater. Despite sharing a common high level of risk, port cities are choosing antithetical adaptation strategies that range from hard-engineered structural flood protection, to behavioral modifications, to innovative soft-engineered measures, to doing nothing at all. Why is this? Are transnational city networks, such as C40 Cities, a lifeline to drowning cities? Do differences in governance structure, financial capacity, risk tolerance to the hazard, or the influence of special interest groups matter? These factors and the interplay of civil, public, and corporate actors in the context of changing environmental conditions are examined in this cross-disciplinary qualitative study to understand their effects on adaptation decision-making processes over time. Four at-risk global port cities—Venice, Rotterdam, Guangzhou, and Miami—were selected for comparison based on their antithetical adaptation strategies of retreating, climate proofing, innovating, and denying. The Panarchy model of nested four-stage adaptive renewal cycles frames the ongoing and cross-scalar interaction of stakeholders and special interest groups at the city, national, transnational, and international levels. This methodology enables the identification of patterns, power distributions, and path dependencies that contribute to appropriate or maladaptive adaptation. As is characteristic of complex adaptive systems, this study finds that decisions cannot be correlated with a single factor. For those cities that display key characteristics of resilience, SLR is a catalyst for proactive and appropriate adaptation. For others, socio-economic and socio-political factors trump environmental factors in deciding whether, when, and how a city decreased its risk to SLR hazard

Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

The electricity consumption forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption forecasting. A method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF), quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting, but also provide more precise results