Hardening digital systems with distributed functionality: robust networks

Collaborative hardening and hardware redundancy are nowadays the most interesting solutions in terms of fault tolerance achieved and low extra cost imposed to the project budget. Thanks to the powerful and cheap digital devices that are available in the market, extra processing capabilities can be used for redundant tasks, not only in early data processing (sensed data) but also in routing and interfacing1

Estimación de variables dasométricas a partir de datos LiDAR y obtención de modelos de referencia para las distribuciones de alturas y diámetros del arbolado

La Gestión Forestal Sostenible se define como “la administración y uso de los bosques y tierras forestales de forma e intensidad tales que mantengan su biodiversidad, productividad, capacidad de regeneración, vitalidad y su potencial para atender, ahora y en el futuro, las funciones ecológicas, económicas y sociales relevantes a escala local, nacional y global, y que no causan daño a otros ecosistemas” (MCPFE Conference, 1993). Dentro del proceso los procesos de planificación, en cualquier escala, es necesario establecer cuál será la situación a la que se quiere llegar mediante la gestión. Igualmente, será necesario conocer la situación actual, pues marcará la situación de partida y condicionará el tipo de actuaciones a realizar para alcanzar los objetivos fijados. Dado que, los Proyectos de Ordenación de Montes y sus respectivas revisiones son herramientas de planificación, durante la redacción de los mismos, será necesario establecer una serie de objetivos cuya consecución pueda verificarse de forma objetiva y disponer de una caracterización de la masa forestal que permita conocer la situación de partida. Esta tesis se centra en problemas prácticos, propios de una escala de planificación local o de Proyecto de Ordenación de Montes. El primer objetivo de la tesis es determinar distribuciones diamétricas y de alturas de referencia para masas regulares por bosquetes, empleando para ello el modelo conceptual propuesto por García-Abril et al., (1999) y datos procedentes de las Tablas de producción de Rojo y Montero (1996). Las distribuciones de referencia obtenidas permitirán guiar la gestión de masas irregulares y regulares por bosquetes. Ambos tipos de masas aparecen como una alternativa deseable en aquellos casos en los que se quiere potenciar la biodiversidad, la estabilidad, la multifuncionalidad del bosque y/o como alternativa productiva, especialmente indicada para la producción de madera de calidad. El segundo objetivo de la Tesis está relacionado con la necesidad de disponer de una caracterización adecuada de la masa forestal durante la redacción de los Proyectos de Ordenación de Montes y de sus respectivas revisiones. Con el fin de obtener estimaciones de variables forestales en distintas unidades territoriales de potencial interés para la Ordenación de Montes, así como medidas de la incertidumbre en asociada dichas estimaciones, se extienden ciertos resultados de la literatura de Estimación en Áreas Pequeñas. Mediante un caso de estudio, se demuestra el potencial de aplicación de estas técnicas en inventario forestales asistidos con información auxiliar procedente de sensores láser aerotransportados (ALS). Los casos de estudio se realizan empleando datos ALS similares a los recopilados en el marco del Plan Nacional de Ortofotografía Aérea (PNOA). Los resultados obtenidos muestran que es posible aumentar la eficiencia de los inventarios forestales tradicionales a escala de proyecto de Ordenación de Montes, mediante la aplicación de estimadores EBLUP (Empirical Best Linear Unbiased Predictor) con modelos a nivel de elemento poblacional e información auxiliar ALS similar a la recopilada por el PNOA. ABSTRACT According to MCPFE (1993) Sustainable Forest Management is “the stewardship and use of forests and forest lands in a way, and at a rate, that maintains their biodiversity, productivity, regeneration capacity, vitality and their potential to fulfill, now and in the future, relevant ecological, economic and social functions, at local, national, and global levels, and that does not cause damage to other ecosystems”. For forest management planning, at any scale, we must determine what situation is hoped to be achieved through management. It is also necessary to know the current situation, as this will mark the starting point and condition the type of actions to be performed in order to meet the desired objectives. Forest management at a local scale is no exception. This Thesis focuses on typical problems of forest management planning at a local scale. The first objective of this Thesis is to determine management objectives for group shelterwood management systems in terms of tree height and tree diameter reference distributions. For this purpose, the conceptual model proposed by García-Abril et al., (1999) is applied to the yield tables for Pinus sylvestris in Sierra de Guadrrama (Rojo y Montero, 1996). The resulting reference distributions will act as a guide in the management of forests treated under the group shelterwood management systems or as an approximated reference for the management of uneven aged forests. Both types of management systems are desirable in those cases where forest biodiversity, stability and multifunctionality are pursued goals. These management systems are also recommended as alternatives for the production of high quality wood. The second objective focuses on the need to adequately characterize the forest during the decision process that leads to local management. In order to obtain estimates of forest variables for different management units of potential interest for forest planning, as well as the associated measures of uncertainty in these estimates, certain results from Small Area Estimation Literature are extended to accommodate for the need of estimates and reliability measures in very small subpopulations containing a reduced number of pixels. A case study shows the potential of Small Area Estimation (SAE) techniques in forest inventories assisted with remotely sensed auxiliary information. The influence of the laser pulse density in the quality of estimates in different aggregation levels is analyzed. This study considers low laser pulse densities (0.5 returns/m2) similar to, those provided by large-scale Airborne Laser Scanner (ALS) surveys, such as the one conducted by the Spanish National Geographic Institute for about 80% of the Spanish territory. The results obtained show that it is possible to improve the efficiency of traditional forest inventories at local scale using EBLUP (Empirical Best Linear Unbiased Predictor) estimators based on unit level models and low density ALS auxiliary information.

Closed-Loop Deep Brain Stimulation: Bidirectional Neuroprosthetics for Tremor and BCI

Thesis (Ph.D.)--University of Washington, 2016-06

Towards sea surface pollution detection from visible band images

This paper presents a novel approach to water pollution detection from remotely sensed low-platform mounted visible band camera images. We examine the feasibility of unsupervised segmentation for slick (oily spills on the water surface) region labelling. Adaptive and non adaptive filtering is combined with density modeling of the obtained textural features. A particular effort is concentrated on the textural feature extraction from raw intensity images using filter banks and adaptive feature extraction from the obtained output coefficients. Segmentation in the extracted feature space is achieved using Gaussian mixture models (GMM).

Remote sensing applications to flood hydrology in Belize

The research compares the usefullness of four remote sensing information sources, these being LANDSAT photographic prints, LANDSAT computer compatible tapes, Metric Camera and SIR-A photographic prints. These sources provide evaluations of the catchment characteristics of the Belize and Sibun river basins in Central America. Map evaluations at 1:250,000 scale are compared to the results of the same scale, remotely sensed information sources. The values of catchment characteristics for both maps and LANDSAT prints are used in multiple regression analysis, providing flood flow formulae, after investigations to provide a suitable dependent variable discharge series are made for short term records. The use of all remotely sensed information sources in providing evaluations of catchment characteristics is discussed. LANDSAT prints and computer compatible tapes of a post flood scene are used to estimate flood distributions and volumes. These are compared to values obtained from unit hydrograph analysis, using the dependent discharge series and evaluate the probable losses from the Belize river to the floodplain, thereby assessing the accuracy of LANDSAT estimates. Information relating to flood behaviour is discussed in terms of basic image presentation as well as image processing. A cost analysis of the purchase and use of all materials is provided. Conclusions of the research indicate that LANDSAT print material may provide information suitable for regression analysis at levels of accuracy as great as those of topographic maps, that the differing information sources are uniquely applicable and that accurate estimates of flood volumes may be determined even by post flood imagery.

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-West European continental shelf

In 2006, a large and prolonged bloom of the dinoflagellate Karenia mikimotoi occurred in Scottish coastal waters, causing extensive mortalities of benthic organisms including annelids and molluscs and some species of fish ( Davidson et al., 2009). A coupled hydrodynamic-algal transport model was developed to track the progression of the bloom around the Scottish coast during June–September 2006 and hence investigate the processes controlling the bloom dynamics. Within this individual-based model, cells were capable of growth, mortality and phototaxis and were transported by physical processes of advection and turbulent diffusion, using current velocities extracted from operational simulations of the MRCS ocean circulation model of the North-west European continental shelf. Vertical and horizontal turbulent diffusion of cells are treated using a random walk approach. Comparison of model output with remotely sensed chlorophyll concentrations and cell counts from coastal monitoring stations indicated that it was necessary to include multiple spatially distinct seed populations of K. mikimotoi at separate locations on the shelf edge to capture the qualitative pattern of bloom transport and development. We interpret this as indicating that the source population was being transported northwards by the Hebridean slope current from where colonies of K. mikimotoi were injected onto the continental shelf by eddies or other transient exchange processes. The model was used to investigate the effects on simulated K. mikimotoi transport and dispersal of: (1) the distribution of the initial seed population; (2) algal growth and mortality; (3) water temperature; (4) the vertical movement of particles by diurnal migration and eddy diffusion; (5) the relative role of the shelf edge and coastal currents; (6) the role of wind forcing. The numerical experiments emphasized the requirement for a physiologically based biological model and indicated that improved modelling of future blooms will potentially benefit from better parameterisation of temperature dependence of both growth and mortality and finer spatial and temporal hydrodynamic resolution.

Individual-based modelling of the development and transport of a Karenia mikimotoi bloom on the North-West European continental shelf

In 2006, a large and prolonged bloom of the dinoflagellate Karenia mikimotoi occurred in Scottish coastal waters, causing extensive mortalities of benthic organisms including annelids and molluscs and some species of fish ( Davidson et al., 2009). A coupled hydrodynamic-algal transport model was developed to track the progression of the bloom around the Scottish coast during June–September 2006 and hence investigate the processes controlling the bloom dynamics. Within this individual-based model, cells were capable of growth, mortality and phototaxis and were transported by physical processes of advection and turbulent diffusion, using current velocities extracted from operational simulations of the MRCS ocean circulation model of the North-west European continental shelf. Vertical and horizontal turbulent diffusion of cells are treated using a random walk approach. Comparison of model output with remotely sensed chlorophyll concentrations and cell counts from coastal monitoring stations indicated that it was necessary to include multiple spatially distinct seed populations of K. mikimotoi at separate locations on the shelf edge to capture the qualitative pattern of bloom transport and development. We interpret this as indicating that the source population was being transported northwards by the Hebridean slope current from where colonies of K. mikimotoi were injected onto the continental shelf by eddies or other transient exchange processes. The model was used to investigate the effects on simulated K. mikimotoi transport and dispersal of: (1) the distribution of the initial seed population; (2) algal growth and mortality; (3) water temperature; (4) the vertical movement of particles by diurnal migration and eddy diffusion; (5) the relative role of the shelf edge and coastal currents; (6) the role of wind forcing. The numerical experiments emphasized the requirement for a physiologically based biological model and indicated that improved modelling of future blooms will potentially benefit from better parameterisation of temperature dependence of both growth and mortality and finer spatial and temporal hydrodynamic resolution.

Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

A GEOBIA methodology for fragmented agricultural landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System.

Smart cards for transit : multi-use remotely interrogated stored-data cards for fare and toll payment /

"FTA-MA-26-0020-95-1."

Dynamically managing sensed context data

This thesis reports on an investigation of the feasibility and usefulness of incorporating dynamic management facilities for managing sensed context data in a distributed contextaware mobile application. The investigation focuses on reducing the work required to integrate new sensed context streams in an existing context aware architecture. Current architectures require integration work for new streams and new contexts that are encountered. This means of operation is acceptable for current fixed architectures. However, as systems become more mobile the number of discoverable streams increases. Without the ability to discover and use these new streams the functionality of any given device will be limited to the streams that it knows how to decode. The integration of new streams requires that the sensed context data be understood by the current application. If the new source provides data of a type that an application currently requires then the new source should be connected to the application without any prior knowledge of the new source. If the type is similar and can be converted then this stream too should be appropriated by the application. Such applications are based on portable devices (phones, PDAs) for semi-autonomous services that use data from sensors connected to the devices, plus data exchanged with other such devices and remote servers. Such applications must handle input from a variety of sensors, refining the data locally and managing its communication from the device in volatile and unpredictable network conditions. The choice to focus on locally connected sensory input allows for the introduction of privacy and access controls. This local control can determine how the information is communicated to others. This investigation focuses on the evaluation of three approaches to sensor data management. The first system is characterised by its static management based on the pre-pended metadata. This was the reference system. Developed for a mobile system, the data was processed based on the attached metadata. The code that performed the processing was static. The second system was developed to move away from the static processing and introduce a greater freedom of handling for the data stream, this resulted in a heavy weight approach. The approach focused on pushing the processing of the data into a number of networked nodes rather than the monolithic design of the previous system. By creating a separate communication channel for the metadata it is possible to be more flexible with the amount and type of data transmitted. The final system pulled the benefits of the other systems together. By providing a small management class that would load a separate handler based on the incoming data, Dynamism was maximised whilst maintaining ease of code understanding. The three systems were then compared to highlight their ability to dynamically manage new sensed context. The evaluation took two approaches, the first is a quantitative analysis of the code to understand the complexity of the relative three systems. This was done by evaluating what changes to the system were involved for the new context. The second approach takes a qualitative view of the work required by the software engineer to reconfigure the systems to provide support for a new data stream. The evaluation highlights the various scenarios in which the three systems are most suited. There is always a trade-o↵ in the development of a system. The three approaches highlight this fact. The creation of a statically bound system can be quick to develop but may need to be completely re-written if the requirements move too far. Alternatively a highly dynamic system may be able to cope with new requirements but the developer time to create such a system may be greater than the creation of several simpler systems.

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with a low overhead. In this article, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.

RMAC-M: Extending the R-MAC Protocol for an Energy Efficient, Delay Tolerant Underwater Acoustic Sensor Network application with a mobile data mule node

One of the major applications of underwater acoustic sensor networks (UWASN) is ocean environment monitoring. Employing data mules is an energy efficient way of data collection from the underwater sensor nodes in such a network. A data mule node such as an autonomous underwater vehicle (AUV) periodically visits the stationary nodes to download data. By conserving the power required for data transmission over long distances to a remote data sink, this approach extends the network life time. In this paper we propose a new MAC protocol to support a single mobile data mule node to collect the data sensed by the sensor nodes in periodic runs through the network. In this approach, the nodes need to perform only short distance, single hop transmission to the data mule. The protocol design discussed in this paper is motivated to support such an application. The proposed protocol is a hybrid protocol, which employs a combination of schedule based access among the stationary nodes along with handshake based access to support mobile data mules. The new protocol, RMAC-M is developed as an extension to the energy efficient MAC protocol R-MAC by extending the slot time of R-MAC to include a contention part for a hand shake based data transfer. The mobile node makes use of a beacon to signal its presence to all the nearby nodes, which can then hand-shake with the mobile node for data transfer. Simulation results show that the new protocol provides efficient support for a mobile data mule node while preserving the advantages of R-MAC such as energy efficiency and fairness.

Understanding the indoor environment through mining sensory data - A case study

A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.