Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

Isolíneas meteorológicas dinámicas transitorias generadas a partir del análisis IoT de temperatura y humedad relativa utilizando el NodeMCU ESP8266 en Bogotá

Context: This article presents the real-time estimation of temperature (°C) and relative humidity ( %) (interval of 16 seconds) for the city of Bogota DC via the Internet of Things (IoT). Method: This prototype is based on the Arduino ESP8266 NodeMCU module and the DHT11 sensor, as well as on a server-client HTPP communication protocol viaWi-Fi, with remote access to information. 16 sensors were installed in Bogota DC. These sensors send the observed data to the MATLAB storage cloud (ThingSpeak) via theWi-Fi module and can be downloaded in real-time. The temperature (°C) and relative humidity ( %) values were calibrated based on measurements made by the TTH002-certified digital thermo-hygrometer. Results: Based on the average temperature and relative humidity obtained, two maps were elaborated by implementing QGis: one with the isotherms and another one with isohumes. The inverse distance weighting (IDW) interpolation algorithm was used. Conclusions: The use of monitoring devices based on the IoT significantly contributes to automating meteorological data and structuring and utilizing robust databases in the field of Civil Engineering. Thus, the real-time transmission of temperature and relative humidity data allows for the online analysis of variables. Finally, the term adaptive dynamic cartography is proposed, which is associated with the generation of maps via the IoT, through which changes in the observed variables are displayed in real time, which allows monitoring the variables making adjustments based on an interpolation algorithm, as well as automatically and instantaneously generating isolines, which significantly reduces the uncertainty implied by the spatial-temporal resolution of current cartography.Contexto: Este articulo presenta la estimación de la temperatura (°C) y la humedad relativa ( %) en tiempo real (intervalo de 4 segundos) para la ciudad de Bogotá DC a través del Internet de las Cosas (IoT). Metodo: Este prototipo se basa en el módulo NodeMCU ESP8266 de Arduino y el sensor DHT11, así como en un protocolo servidor-cliente de comunicación HTPP vía Wifi, con acceso remoto a la informacion. Se instalaron 16 sensores en Bogotá DC. Estos sensores envían los datos observados a la nube de almacenamiento de MATLAB (ThingSpeak) a través del módulo Wifi y pueden ser descargados en tiempo real. Los valores de temperatura (°C) y humedad relativa ( %) fueron calibrados a partir de mediciones realizadas por el termohigrómetro digital certificado TTH002. Resultados: A partir de la temperatura medias y la humedad relativa obtenidas, se realizaron dos mapas implementando QGis: uno de isotermas y otro de isohumas. Se utilizo el algoritmo de interpolación por ponderación de distancia inversa (IDW). Conclusiones: La utilización de dispositivos de monitoreo a partir del Internet de las cosas (IoT) contribuye de manera significativa a la automatización de datos meteorológicos y al uso y estructuración de bases de datos robustas en el campo de la Ingeniería Civil. Así, la transmisión en tiempo real de los datos de temperatura y humedad relativa permiten analizar variables en línea. Finalmente, se propone el termino cartografía dinámica adaptativa, asociado a la generación de mapas a partir del IoT, mediante el cual se visualizan cambios en la variable observada en tiempo real, lo que permite monitorear la variable y hacer ajustes a partir de un algoritmo de interpolación, así como la generación automática e instantánea de isolíneas, lo cual reduce de forma significativa la incertidumbre existente en la resolución temporal y espacial de la cartografía actual

Large-Scale Textured 3D Scene Reconstruction

Die Erstellung dreidimensionaler Umgebungsmodelle ist eine fundamentale Aufgabe im Bereich des maschinellen Sehens. Rekonstruktionen sind für eine Reihe von Anwendungen von Nutzen, wie bei der Vermessung, dem Erhalt von Kulturgütern oder der Erstellung virtueller Welten in der Unterhaltungsindustrie. Im Bereich des automatischen Fahrens helfen sie bei der Bewältigung einer Vielzahl an Herausforderungen. Dazu gehören Lokalisierung, das Annotieren großer Datensätze oder die vollautomatische Erstellung von Simulationsszenarien. Die Herausforderung bei der 3D Rekonstruktion ist die gemeinsame Schätzung von Sensorposen und einem Umgebunsmodell. Redundante und potenziell fehlerbehaftete Messungen verschiedener Sensoren müssen in eine gemeinsame Repräsentation der Welt integriert werden, um ein metrisch und photometrisch korrektes Modell zu erhalten. Gleichzeitig muss die Methode effizient Ressourcen nutzen, um Laufzeiten zu erreichen, welche die praktische Nutzung ermöglichen. In dieser Arbeit stellen wir ein Verfahren zur Rekonstruktion vor, das fähig ist, photorealistische 3D Rekonstruktionen großer Areale zu erstellen, die sich über mehrere Kilometer erstrecken. Entfernungsmessungen aus Laserscannern und Stereokamerasystemen werden zusammen mit Hilfe eines volumetrischen Rekonstruktionsverfahrens fusioniert. Ringschlüsse werden erkannt und als zusätzliche Bedingungen eingebracht, um eine global konsistente Karte zu erhalten. Das resultierende Gitternetz wird aus Kamerabildern texturiert, wobei die einzelnen Beobachtungen mit ihrer Güte gewichtet werden. Für eine nahtlose Erscheinung werden die unbekannten Belichtungszeiten und Parameter des optischen Systems mitgeschätzt und die Bilder entsprechend korrigiert. Wir evaluieren unsere Methode auf synthetischen Daten, realen Sensordaten unseres Versuchsfahrzeugs und öffentlich verfügbaren Datensätzen. Wir zeigen qualitative Ergebnisse großer innerstädtischer Bereiche, sowie quantitative Auswertungen der Fahrzeugtrajektorie und der Rekonstruktionsqualität. Zuletzt präsentieren wir mehrere Anwendungen und zeigen somit den Nutzen unserer Methode für Anwendungen im Bereich des automatischen Fahrens

Conceptual design of an airborne laser Doppler velocimeter system for studying wind fields associated with severe local storms

An airborne laser Doppler velocimeter was evaluated for diagnostics of the wind field associated with an isolated severe thunderstorm. Two scanning configurations were identified, one a long-range (out to 10-20 km) roughly horizontal plane mode intended to allow probing of the velocity field around the storm at the higher altitudes (4-10 km). The other is a shorter range (out to 1-3 km) mode in which a vertical or horizontal plane is scanned for velocity (and possibly turbulence), and is intended for diagnostics of the lower altitude region below the storm and in the out-flow region. It was concluded that aircraft flight velocities are high enough and severe storm lifetimes are long enough that a single airborne Doppler system, operating at a range of less than about 20 km, can view the storm area from two or more different aspects before the storm characteristics change appreciably

Webcam Configurations for Ground Texture Visual Servo

Singapor

Distributed Adaptive Learning of Graph Signals

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.Comment: To appear in IEEE Transactions on Signal Processing, 201

Non-linear Kalman filters for calibration in radio interferometry

We present a new calibration scheme based on a non-linear version of Kalman filter that aims at estimating the physical terms appearing in the Radio Interferometry Measurement Equation (RIME). We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. We show using simulations that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly cheap algorithm that we believe to be robust, especially in low signal-to-noise regime. Potentially the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visibilities are understood and analytically stable. Recursive algorithms are particularly well adapted for pre-calibration and sky model estimate in a streaming way. This may be useful for the SKA-type instruments that produce huge amounts of data that have to be calibrated before being averaged