Towards Spatial Queries over Phenomena in Sensor Networks

Today, technology developments enable inexpensive production and deployment of tiny sensing and computing nodes. Networked through wireless radio, such senor nodes form a new platform, wireless sensor networks, which provide novel ability to monitor spatiotemporally continuous phenomena. By treating a wireless sensor network as a database system, users can pose SQL-based queries over phenomena without needing to program detailed sensor node operations. DBMS-internally, intelligent and energyefficient data collection and processing algorithms have to be implemented to support spatial query processing over sensor networks. This dissertation proposes spatial query support for two views of continuous phenomena: field-based and object-based. A field-based view of continuous phenomena depicts them as a value distribution over a geographical area. However, due to the discrete and comparatively sparse distribution of sensor nodes, estimation methods are necessary to generate a field-based query result, and it has to be computed collaboratively ‘in-the-network’ due to energy constraints. This dissertation proposes SWOP, an in-network algorithm using Gaussian Kernel estimation. The key contribution is the use of a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes, and processes the estimation result efficiently. An object-based view of continuous phenomena is interested in aspects such as the boundary of an ‘interesting region’ (e.g. toxic plume). This dissertation presents NED, which provides object boundary detection in sensor networks. NED encodes partial event estimation results based on confidence levels into optimized, variable length messages exchanged locally among neighboring sensor nodes to save communication cost. Therefore, sensor nodes detect objects and boundaries based on moving averages to eliminate noise effects and enhance detection quality. Furthermore, the dissertation proposes the SNAKE-based approach, which uses deformable curves to track the spatiotemporal changes of such objects incrementally in sensor networks. In the proposed algorithm, only neighboring nodes exchange messages to maintain the curve structures. Based on in-network tracking of deformable curves, other types of spatial and spatiotemporal properties of objects, such as area, can be provided by the sensor network. The experimental results proved that our approaches are resource friendly within the constrained sensor networks, while providing high quality query results

Detection of an anomalous cluster in a network

We consider the problem of detecting whether or not, in a given sensor network, there is a cluster of sensors which exhibit an "unusual behavior." Formally, suppose we are given a set of nodes and attach a random variable to each node. We observe a realization of this process and want to decide between the following two hypotheses: under the null, the variables are i.i.d. standard normal; under the alternative, there is a cluster of variables that are i.i.d. normal with positive mean and unit variance, while the rest are i.i.d. standard normal. We also address surveillance settings where each sensor in the network collects information over time. The resulting model is similar, now with a time series attached to each node. We again observe the process over time and want to decide between the null, where all the variables are i.i.d. standard normal, and the alternative, where there is an emerging cluster of i.i.d. normal variables with positive mean and unit variance. The growth models used to represent the emerging cluster are quite general and, in particular, include cellular automata used in modeling epidemics. In both settings, we consider classes of clusters that are quite general, for which we obtain a lower bound on their respective minimax detection rate and show that some form of scan statistic, by far the most popular method in practice, achieves that same rate to within a logarithmic factor. Our results are not limited to the normal location model, but generalize to any one-parameter exponential family when the anomalous clusters are large enough.Comment: Published in at http://dx.doi.org/10.1214/10-AOS839 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

CAREER: Data Management for Ad-Hoc Geosensor Networks

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important task of such geosensor networks is to collect, analyze and estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation. The project has a broad impact on the community involving undergraduate and graduate students in spatial database research at the University of Maine as well as being a key component of a current IGERT program in the areas of sensor materials, sensor devices and sensor. More information about this project, publications, simulation software, and empirical studies are available on the project\u27s web site (http://www.spatial.maine.edu/~nittel/career/)

SST: Integrated Fluorocarbon Microsensor System Using Catalytic Modification

Selective, sensitive, and reliable sensors are urgently needed to detect air-borne halogenated volatile organic compounds (VOCs). This broad class of compounds includes chlorine, fluorine, bromine, and iodine containing hydrocarbons used as solvents, refrigerants, herbicides, and more recently as chemical warfare agents (CWAs). It is important to be able to detect very low concentrations of halocarbon solvents and insecticides because of their acute health effects even in very low concentrations. For instance, the nerve agent sarin (isopropyl methylphosphonofluoridate), first developed as an insecticide by German chemists in 1938, is so toxic that a ten minute exposure at an airborne concentration of only 65 parts per billion (ppb) can be fatal. Sarin became a household term when religious cult members on Tokyo subway trains poisoned over 5,500 people, killing 12. Sarin and other CWAs remain a significant threat to the health and safety of the general public. The goal of this project is to design a sensor system to detect and identify the composition and concentration of fluorinated VOCs. The system should be small, robust, compatible with metal oxide semiconductor (MOS) technology, cheap, if produced in large scale, and has the potential to be versatile in terms of low power consumption, detection of other gases, and integration in a portable system. The proposed VOC sensor system has three major elements that will be integrated into a microreactor flow cell: a temperature-programmable microhotplate array/reactor system which serves as the basic sensor platform; an innovative acoustic wave sensor, which detects material removal (instead of deposition) to verify and quantify the presence of fluorine; and an intelligent method, support vector machines, that will analyze the complex and high dimensional data furnished by the sensor system. The superior and complementary aspects of the three elements will be carefully integrated to create a system which is more sensitive and selective than other CWA detection systems that are commercially available or described in the research literature. While our sensor system will be developed to detect fluorinated VOCs, it can be adapted for other applications in which a target analyte can be catalytically converted for selective detection. Therefore, this investigation will examine the relationships between individual sensor element performance and joint sensor platform performance, integrated with state-of-the-art data analysis techniques. During development of the sensor system, the investigators will consider traditional reactor design concepts such as mass transfer and residence time effects, and will apply them to the emerging field of microsystems. The proposed research will provide the fundamental basis and understanding for examining multifunctional sensor platforms designed to provide extreme selectivity to targeted molecules. The project will involve interdisciplinary researchers and students and will connect to K-12 and RET programs for underrepresented students from rural areas

3D Interaction System with Multiple Identified,Small,Wireless,Battery-less,Occlusion-free Magnetic Markers

Tohoku University北村喜

Development of an active vision system for robot inspection of complex objects

Dissertação de mestrado integrado em Engenharia Mecânica (área de especialização em Sistemas Mecatrónicos)The dissertation presented here is in the scope of the IntVis4Insp project between University of Minho and the company Neadvance. It focuses on the development of a 3D hand tracking system that must be capable of extracting the hand position and orientation to prepare a manipulator for automatic inspection of leather pieces. This work starts with a literature review about the two main methods for collecting the necessary data to perform 3D hand tracking. These divide into glove-based methods and vision-based methods. The first ones work with some kind of support mounted on the hand that holds all the necessary sensors to measure the desired parameters. While the second ones recur to one or more cameras to capture the hands and through computer vision algorithms track their position and configuration. The selected method for this work was the vision-based method Openpose. For each recorded image, this application can locate 21 hand keypoints on each hand that together form a skeleton of the hands. This application is used in the tracking system developed throughout this dissertation. Its information is used in a more complete pipeline where the location of those hand keypoints is crucial to track the hands in videos of the demonstrated movements. These videos were recorded with an RGB-D camera, the Microsoft Kinect, which provides a depth value for every RGB pixel recorded. With the depth information and the 2D location of the hand keypoints in the images, it was possible to obtain the 3D world coordinates of these points considering the pinhole camera model. To define the hand, position a point is selected among the 21 for each hand, but for the hand orientation, it was necessary to develop an auxiliary method called “Iterative Pose Estimation Method” (ITP), which estimates the complete 3D pose of the hands. This method recurs only to the 2D locations of every hand keypoint, and the complete 3D world coordinates of the wrists to estimate the right 3D world coordinates of all the remaining points on the hand. This solution solves the problems related to hand occlusions that a prone to happen due to the use of only one camera to record the inspection videos. Once the world location of all the points in the hands is accurately estimated, their orientation can be defined by selecting three points forming a plane.A dissertação aqui apresentada insere-se no âmbito do projeto IntVis4Insp entre a Universidade do Minho e a empresa Neadavance, e foca-se no desenvolvimento de um sistema para extração da posição e orientação das mãos no espaço para posterior auxílio na manipulação automática de peças de couro, com recurso a manipuladores robóticos. O trabalho inicia-se com uma revisão literária sobre os dois principais métodos existentes para efetuar a recolha de dados necessária à monitorização da posição e orientação das mãos ao longo do tempo. Estes dividem-se em métodos baseados em luvas ou visão. No caso dos primeiros, estes recorrem normalmente a algum tipo de suporte montado na mão (ex.: luva em tecido), onde estão instalados todos os sensores necessários para a medição dos parâmetros desejados. Relativamente a sistemas de visão estes recorrem a uma câmara ou conjunto delas para capturar as mãos e por via de algoritmos de visão por computador determinam a sua posição e configuração. Foi selecionado para este trabalho um algoritmo de visão por computador denominado por Openpose. Este é capaz de, em cada imagem gravada e para cada mão, localizar 21 pontos pertencentes ao seu esqueleto. Esta aplicação é inserida no sistema de monitorização desenvolvido, sendo utilizada a sua informação numa arquitetura mais completa onde é efetuada a extração da localização dos pontos chave de cada mão nos vídeos de demonstração dos movimentos de inspeção. A gravação destes vídeos é efetuada com uma câmara RGB-D, a Microsoft Kinect, que fornece um valor de profundidade para cada pixel RGB gravado. Com os dados de profundidade e a localização dos pontos chave nas imagens foi possível obter as coordenadas 3D no mundo destes pontos considerando o modelo pinhole para a câmara. No caso da posição da mão é selecionado um ponto de entre os 21 para a definir ao longo do tempo, no entanto, para o cálculo da orientação foi desenvolvido um método auxiliar para estimação da pose tridimensional da mão denominado por “Iterative Pose Estimation Method” (ITP). Este método recorre aos dados 2D do Openpose e às coordenadas 3D do pulso de cada mão para efetuar a correta estimação das coordenadas 3D dos restantes pontos da mão. Isto permite essencialmente resolver problemas com oclusões da mão, muito frequentes com o uso de uma só câmara na gravação dos vídeos. Uma vez estimada corretamente a posição 3D no mundo dos vários pontos da mão, a sua orientação pode ser definida com recurso a quaisquer três pontos que definam um plano

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms