WorldWideScience

Sample records for large sensor datasets

  1. Querying Large Biological Network Datasets

    Science.gov (United States)

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  2. Random Coefficient Logit Model for Large Datasets

    NARCIS (Netherlands)

    C. Hernández-Mireles (Carlos); D. Fok (Dennis)

    2010-01-01

    textabstractWe present an approach for analyzing market shares and products price elasticities based on large datasets containing aggregate sales data for many products, several markets and for relatively long time periods. We consider the recently proposed Bayesian approach of Jiang et al [Jiang,

  3. Dataset: Multi Sensor-Orientation Movement Data of Goats

    NARCIS (Netherlands)

    Kamminga, Jacob Wilhelm

    2018-01-01

    This is a labeled dataset. Motion data were collected from six sensor nodes that were fixed with different orientations to a collar around the neck of goats. These six sensor nodes simultaneously, with different orientations, recorded various activities performed by the goat. We recorded the

  4. The Amateurs' Love Affair with Large Datasets

    Science.gov (United States)

    Price, Aaron; Jacoby, S. H.; Henden, A.

    2006-12-01

    Amateur astronomers are professionals in other areas. They bring expertise from such varied and technical careers as computer science, mathematics, engineering, and marketing. These skills, coupled with an enthusiasm for astronomy, can be used to help manage the large data sets coming online in the next decade. We will show specific examples where teams of amateurs have been involved in mining large, online data sets and have authored and published their own papers in peer-reviewed astronomical journals. Using the proposed LSST database as an example, we will outline a framework for involving amateurs in data analysis and education with large astronomical surveys.

  5. Large datasets: Segmentation, feature extraction, and compression

    Energy Technology Data Exchange (ETDEWEB)

    Downing, D.J.; Fedorov, V.; Lawkins, W.F.; Morris, M.D.; Ostrouchov, G.

    1996-07-01

    Large data sets with more than several mission multivariate observations (tens of megabytes or gigabytes of stored information) are difficult or impossible to analyze with traditional software. The amount of output which must be scanned quickly dilutes the ability of the investigator to confidently identify all the meaningful patterns and trends which may be present. The purpose of this project is to develop both a theoretical foundation and a collection of tools for automated feature extraction that can be easily customized to specific applications. Cluster analysis techniques are applied as a final step in the feature extraction process, which helps make data surveying simple and effective.

  6. Really big data: Processing and analysis of large datasets

    Science.gov (United States)

    Modern animal breeding datasets are large and getting larger, due in part to the recent availability of DNA data for many animals. Computational methods for efficiently storing and analyzing those data are under development. The amount of storage space required for such datasets is increasing rapidl...

  7. Augmented Reality Prototype for Visualizing Large Sensors’ Datasets

    Directory of Open Access Journals (Sweden)

    Folorunso Olufemi A.

    2011-04-01

    Full Text Available This paper addressed the development of an augmented reality (AR based scientific visualization system prototype that supports identification, localisation, and 3D visualisation of oil leakages sensors datasets. Sensors generates significant amount of multivariate datasets during normal and leak situations which made data exploration and visualisation daunting tasks. Therefore a model to manage such data and enhance computational support needed for effective explorations are developed in this paper. A challenge of this approach is to reduce the data inefficiency. This paper presented a model for computing information gain for each data attributes and determine a lead attribute.The computed lead attribute is then used for the development of an AR-based scientific visualization interface which automatically identifies, localises and visualizes all necessary data relevant to a particularly selected region of interest (ROI on the network. Necessary architectural system supports and the interface requirements for such visualizations are also presented.

  8. Large area CMOS image sensors

    International Nuclear Information System (INIS)

    Turchetta, R; Guerrini, N; Sedgwick, I

    2011-01-01

    CMOS image sensors, also known as CMOS Active Pixel Sensors (APS) or Monolithic Active Pixel Sensors (MAPS), are today the dominant imaging devices. They are omnipresent in our daily life, as image sensors in cellular phones, web cams, digital cameras, ... In these applications, the pixels can be very small, in the micron range, and the sensors themselves tend to be limited in size. However, many scientific applications, like particle or X-ray detection, require large format, often with large pixels, as well as other specific performance, like low noise, radiation hardness or very fast readout. The sensors are also required to be sensitive to a broad spectrum of radiation: photons from the silicon cut-off in the IR down to UV and X- and gamma-rays through the visible spectrum as well as charged particles. This requirement calls for modifications to the substrate to be introduced to provide optimized sensitivity. This paper will review existing CMOS image sensors, whose size can be as large as a single CMOS wafer, and analyse the technical requirements and specific challenges of large format CMOS image sensors.

  9. Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

    OpenAIRE

    Cury , Claire; Glaunès , Joan Alexis; Colliot , Olivier

    2014-01-01

    International audience; A common approach for analysis of anatomical variability relies on the stimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This paper presents an iterative method which quickly provides a centroid of the population in the shape space. This centr...

  10. Multiresolution persistent homology for excessively large biomolecular datasets

    Energy Technology Data Exchange (ETDEWEB)

    Xia, Kelin; Zhao, Zhixiong [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Wei, Guo-Wei, E-mail: wei@math.msu.edu [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824 (United States)

    2015-10-07

    Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

  11. Image segmentation evaluation for very-large datasets

    Science.gov (United States)

    Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

    2016-03-01

    With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

  12. FTSPlot: fast time series visualization for large datasets.

    Directory of Open Access Journals (Sweden)

    Michael Riss

    Full Text Available The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N; the visualization itself can be done with a complexity of O(1 and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64 bytes, on the x86_64 architecture currently up to 2(48 bytes are supported, and benchmarks have been conducted with 2(40 bytes/1 TiB or 1.3 x 10(11 double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.

  13. Multiresolution comparison of precipitation datasets for large-scale models

    Science.gov (United States)

    Chun, K. P.; Sapriza Azuri, G.; Davison, B.; DeBeer, C. M.; Wheater, H. S.

    2014-12-01

    Gridded precipitation datasets are crucial for driving large-scale models which are related to weather forecast and climate research. However, the quality of precipitation products is usually validated individually. Comparisons between gridded precipitation products along with ground observations provide another avenue for investigating how the precipitation uncertainty would affect the performance of large-scale models. In this study, using data from a set of precipitation gauges over British Columbia and Alberta, we evaluate several widely used North America gridded products including the Canadian Gridded Precipitation Anomalies (CANGRD), the National Center for Environmental Prediction (NCEP) reanalysis, the Water and Global Change (WATCH) project, the thin plate spline smoothing algorithms (ANUSPLIN) and Canadian Precipitation Analysis (CaPA). Based on verification criteria for various temporal and spatial scales, results provide an assessment of possible applications for various precipitation datasets. For long-term climate variation studies (~100 years), CANGRD, NCEP, WATCH and ANUSPLIN have different comparative advantages in terms of their resolution and accuracy. For synoptic and mesoscale precipitation patterns, CaPA provides appealing performance of spatial coherence. In addition to the products comparison, various downscaling methods are also surveyed to explore new verification and bias-reduction methods for improving gridded precipitation outputs for large-scale models.

  14. Orthology detection combining clustering and synteny for very large datasets.

    Science.gov (United States)

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K; Prohaska, Sonja J; Stadler, Peter F

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  15. Orthology detection combining clustering and synteny for very large datasets.

    Directory of Open Access Journals (Sweden)

    Marcus Lechner

    Full Text Available The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

  16. Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

    Directory of Open Access Journals (Sweden)

    Sai Kiranmayee Samudrala

    2015-01-01

    Full Text Available Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

  17. [Parallel virtual reality visualization of extreme large medical datasets].

    Science.gov (United States)

    Tang, Min

    2010-04-01

    On the basis of a brief description of grid computing, the essence and critical techniques of parallel visualization of extreme large medical datasets are discussed in connection with Intranet and common-configuration computers of hospitals. In this paper are introduced several kernel techniques, including the hardware structure, software framework, load balance and virtual reality visualization. The Maximum Intensity Projection algorithm is realized in parallel using common PC cluster. In virtual reality world, three-dimensional models can be rotated, zoomed, translated and cut interactively and conveniently through the control panel built on virtual reality modeling language (VRML). Experimental results demonstrate that this method provides promising and real-time results for playing the role in of a good assistant in making clinical diagnosis.

  18. Statistically and Computationally Efficient Estimating Equations for Large Spatial Datasets

    KAUST Repository

    Sun, Ying

    2014-11-07

    For Gaussian process models, likelihood based methods are often difficult to use with large irregularly spaced spatial datasets, because exact calculations of the likelihood for n observations require O(n3) operations and O(n2) memory. Various approximation methods have been developed to address the computational difficulties. In this paper, we propose new unbiased estimating equations based on score equation approximations that are both computationally and statistically efficient. We replace the inverse covariance matrix that appears in the score equations by a sparse matrix to approximate the quadratic forms, then set the resulting quadratic forms equal to their expected values to obtain unbiased estimating equations. The sparse matrix is constructed by a sparse inverse Cholesky approach to approximate the inverse covariance matrix. The statistical efficiency of the resulting unbiased estimating equations are evaluated both in theory and by numerical studies. Our methods are applied to nearly 90,000 satellite-based measurements of water vapor levels over a region in the Southeast Pacific Ocean.

  19. Statistically and Computationally Efficient Estimating Equations for Large Spatial Datasets

    KAUST Repository

    Sun, Ying; Stein, Michael L.

    2014-01-01

    For Gaussian process models, likelihood based methods are often difficult to use with large irregularly spaced spatial datasets, because exact calculations of the likelihood for n observations require O(n3) operations and O(n2) memory. Various approximation methods have been developed to address the computational difficulties. In this paper, we propose new unbiased estimating equations based on score equation approximations that are both computationally and statistically efficient. We replace the inverse covariance matrix that appears in the score equations by a sparse matrix to approximate the quadratic forms, then set the resulting quadratic forms equal to their expected values to obtain unbiased estimating equations. The sparse matrix is constructed by a sparse inverse Cholesky approach to approximate the inverse covariance matrix. The statistical efficiency of the resulting unbiased estimating equations are evaluated both in theory and by numerical studies. Our methods are applied to nearly 90,000 satellite-based measurements of water vapor levels over a region in the Southeast Pacific Ocean.

  20. Privacy-preserving record linkage on large real world datasets.

    Science.gov (United States)

    Randall, Sean M; Ferrante, Anna M; Boyd, James H; Bauer, Jacqueline K; Semmens, James B

    2014-08-01

    Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. A Large-Scale 3D Object Recognition dataset

    DEFF Research Database (Denmark)

    Sølund, Thomas; Glent Buch, Anders; Krüger, Norbert

    2016-01-01

    geometric groups; concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching...... performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat...

  2. Scalable and portable visualization of large atomistic datasets

    Science.gov (United States)

    Sharma, Ashish; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya

    2004-10-01

    A scalable and portable code named Atomsviewer has been developed to interactively visualize a large atomistic dataset consisting of up to a billion atoms. The code uses a hierarchical view frustum-culling algorithm based on the octree data structure to efficiently remove atoms outside of the user's field-of-view. Probabilistic and depth-based occlusion-culling algorithms then select atoms, which have a high probability of being visible. Finally a multiresolution algorithm is used to render the selected subset of visible atoms at varying levels of detail. Atomsviewer is written in C++ and OpenGL, and it has been tested on a number of architectures including Windows, Macintosh, and SGI. Atomsviewer has been used to visualize tens of millions of atoms on a standard desktop computer and, in its parallel version, up to a billion atoms. Program summaryTitle of program: Atomsviewer Catalogue identifier: ADUM Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADUM Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: 2.4 GHz Pentium 4/Xeon processor, professional graphics card; Apple G4 (867 MHz)/G5, professional graphics card Operating systems under which the program has been tested: Windows 2000/XP, Mac OS 10.2/10.3, SGI IRIX 6.5 Programming languages used: C++, C and OpenGL Memory required to execute with typical data: 1 gigabyte of RAM High speed storage required: 60 gigabytes No. of lines in the distributed program including test data, etc.: 550 241 No. of bytes in the distributed program including test data, etc.: 6 258 245 Number of bits in a word: Arbitrary Number of processors used: 1 Has the code been vectorized or parallelized: No Distribution format: tar gzip file Nature of physical problem: Scientific visualization of atomic systems Method of solution: Rendering of atoms using computer graphic techniques, culling algorithms for data

  3. Dataset on photonic crystal fiber based chemical sensor.

    Science.gov (United States)

    Ahmed, Kawsar; Paul, Bikash Kumar; Chowdhury, Sawrab; Islam, Md Shadidul; Sen, Shuvo; Islam, Md Ibadul; Asaduzzaman, Sayed; Bahar, Ali Newaz; Miah, Mohammad Badrul Alam

    2017-06-01

    This article represents the data set of micro porous core photonic crystal fiber based chemical sensor. The suggested structure is folded cladding porous shaped with circular air hole. Here is investigated four distinctive parameters including relative sensitivity, confinement loss, numerical aperture (NA), and effective area ( A eff). The numerical outcomes are computed over the E+S+C+L+U communication band. The useable sensed chemicals are methanol, ethanol, propanol, butanol, and pentanol whose are lies in the alcohol series (Paul et al., 2017) [1]. Furthermore, V -parameter ( V ), Marcuse spot size (MSS), and beam divergence (BD) are also investigated rigorously. All examined results have been obtained using finite element method based simulation software COMSOL Multiphysics 4.2 versions with anisotropic circular perfectly matched layer (A-CPML). The proposed PCF shows the high NA from 0.35 to 0.36; the low CL from ~10 -11 to ~10 -7  dB/m; the high A eff from 5.50 to 5.66 µm 2 ; the MSS from 1.0 to 1.08 µm; the BD from 0.43 to 0.46 rad at the controlling wavelength λ = 1.55 µm for employing alcohol series respectively.

  4. Dataset on photonic crystal fiber based chemical sensor

    Directory of Open Access Journals (Sweden)

    Kawsar Ahmed

    2017-06-01

    Full Text Available This article represents the data set of micro porous core photonic crystal fiber based chemical sensor. The suggested structure is folded cladding porous shaped with circular air hole. Here is investigated four distinctive parameters including relative sensitivity, confinement loss, numerical aperture (NA, and effective area (Aeff. The numerical outcomes are computed over the E+S+C+L+U communication band. The useable sensed chemicals are methanol, ethanol, propanol, butanol, and pentanol whose are lies in the alcohol series (Paul et al., 2017 [1]. Furthermore, V-parameter (V, Marcuse spot size (MSS, and beam divergence (BD are also investigated rigorously. All examined results have been obtained using finite element method based simulation software COMSOL Multiphysics 4.2 versions with anisotropic circular perfectly matched layer (A-CPML. The proposed PCF shows the high NA from 0.35 to 0.36; the low CL from ~10–11 to ~10−7 dB/m; the high Aeff from 5.50 to 5.66 µm2; the MSS from 1.0 to 1.08 µm; the BD from 0.43 to 0.46 rad at the controlling wavelength λ = 1.55 µm for employing alcohol series respectively.

  5. The Path from Large Earth Science Datasets to Information

    Science.gov (United States)

    Vicente, G. A.

    2013-12-01

    The NASA Goddard Earth Sciences Data (GES) and Information Services Center (DISC) is one of the major Science Mission Directorate (SMD) for archiving and distribution of Earth Science remote sensing data, products and services. This virtual portal provides convenient access to Atmospheric Composition and Dynamics, Hydrology, Precipitation, Ozone, and model derived datasets (generated by GSFC's Global Modeling and Assimilation Office), the North American Land Data Assimilation System (NLDAS) and the Global Land Data Assimilation System (GLDAS) data products (both generated by GSFC's Hydrological Sciences Branch). This presentation demonstrates various tools and computational technologies developed in the GES DISC to manage the huge volume of data and products acquired from various missions and programs over the years. It explores approaches to archive, document, distribute, access and analyze Earth Science data and information as well as addresses the technical and scientific issues, governance and user support problem faced by scientists in need of multi-disciplinary datasets. It also discusses data and product metrics, user distribution profiles and lessons learned through interactions with the science communities around the world. Finally it demonstrates some of the most used data and product visualization and analyses tools developed and maintained by the GES DISC.

  6. Benchmarking Deep Learning Models on Large Healthcare Datasets.

    Science.gov (United States)

    Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan

    2018-06-04

    Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

  7. Comprehensive comparison of large-scale tissue expression datasets

    DEFF Research Database (Denmark)

    Santos Delgado, Alberto; Tsafou, Kalliopi; Stolte, Christian

    2015-01-01

    a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between......For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present......://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface....

  8. Topic modeling for cluster analysis of large biological and medical datasets.

    Science.gov (United States)

    Zhao, Weizhong; Zou, Wen; Chen, James J

    2014-01-01

    The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

  9. Orthology detection combining clustering and synteny for very large datasets

    OpenAIRE

    Lechner, Marcus; Hernandez-Rosales, Maribel; Doerr, Daniel; Wieseke, Nicolas; Thévenin, Annelyse; Stoye, Jens; Hartmann, Roland K.; Prohaska, Sonja J.; Stadler, Peter F.

    2014-01-01

    The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the ...

  10. Sparse kernel orthonormalized PLS for feature extraction in large datasets

    DEFF Research Database (Denmark)

    Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

    2006-01-01

    In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm...... is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...

  11. Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

    Litvinenko, Alexander

    2017-11-01

    The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\H$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.

  12. Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

    Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.

    2017-01-01

    The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\H$-) matrix format with computational cost $\\mathcal{O}(k^2n \\log^2 n/p)$ and storage $\\mathcal{O}(kn \\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.

  13. Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

    Science.gov (United States)

    Maskey, M.; Ramachandran, R.; Miller, J.

    2017-12-01

    Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

  14. Ultrafast superpixel segmentation of large 3D medical datasets

    Science.gov (United States)

    Leblond, Antoine; Kauffmann, Claude

    2016-03-01

    Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

  15. Large Survey Database: A Distributed Framework for Storage and Analysis of Large Datasets

    Science.gov (United States)

    Juric, Mario

    2011-01-01

    The Large Survey Database (LSD) is a Python framework and DBMS for distributed storage, cross-matching and querying of large survey catalogs (>10^9 rows, >1 TB). The primary driver behind its development is the analysis of Pan-STARRS PS1 data. It is specifically optimized for fast queries and parallel sweeps of positionally and temporally indexed datasets. It transparently scales to more than >10^2 nodes, and can be made to function in "shared nothing" architectures. An LSD database consists of a set of vertically and horizontally partitioned tables, physically stored as compressed HDF5 files. Vertically, we partition the tables into groups of related columns ('column groups'), storing together logically related data (e.g., astrometry, photometry). Horizontally, the tables are partitioned into partially overlapping ``cells'' by position in space (lon, lat) and time (t). This organization allows for fast lookups based on spatial and temporal coordinates, as well as data and task distribution. The design was inspired by the success of Google BigTable (Chang et al., 2006). Our programming model is a pipelined extension of MapReduce (Dean and Ghemawat, 2004). An SQL-like query language is used to access data. For complex tasks, map-reduce ``kernels'' that operate on query results on a per-cell basis can be written, with the framework taking care of scheduling and execution. The combination leverages users' familiarity with SQL, while offering a fully distributed computing environment. LSD adds little overhead compared to direct Python file I/O. In tests, we sweeped through 1.1 Grows of PanSTARRS+SDSS data (220GB) less than 15 minutes on a dual CPU machine. In a cluster environment, we achieved bandwidths of 17Gbits/sec (I/O limited). Based on current experience, we believe LSD should scale to be useful for analysis and storage of LSST-scale datasets. It can be downloaded from http://mwscience.net/lsd.

  16. Unified Access Architecture for Large-Scale Scientific Datasets

    Science.gov (United States)

    Karna, Risav

    2014-05-01

    Data-intensive sciences have to deploy diverse large scale database technologies for data analytics as scientists have now been dealing with much larger volume than ever before. While array databases have bridged many gaps between the needs of data-intensive research fields and DBMS technologies (Zhang 2011), invocation of other big data tools accompanying these databases is still manual and separate the database management's interface. We identify this as an architectural challenge that will increasingly complicate the user's work flow owing to the growing number of useful but isolated and niche database tools. Such use of data analysis tools in effect leaves the burden on the user's end to synchronize the results from other data manipulation analysis tools with the database management system. To this end, we propose a unified access interface for using big data tools within large scale scientific array database using the database queries themselves to embed foreign routines belonging to the big data tools. Such an invocation of foreign data manipulation routines inside a query into a database can be made possible through a user-defined function (UDF). UDFs that allow such levels of freedom as to call modules from another language and interface back and forth between the query body and the side-loaded functions would be needed for this purpose. For the purpose of this research we attempt coupling of four widely used tools Hadoop (hadoop1), Matlab (matlab1), R (r1) and ScaLAPACK (scalapack1) with UDF feature of rasdaman (Baumann 98), an array-based data manager, for investigating this concept. The native array data model used by an array-based data manager provides compact data storage and high performance operations on ordered data such as spatial data, temporal data, and matrix-based data for linear algebra operations (scidbusr1). Performances issues arising due to coupling of tools with different paradigms, niche functionalities, separate processes and output

  17. Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    KAUST Repository

    Zhang, Bohai; Sang, Huiyan; Huang, Jianhua Z.

    2014-01-01

    of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov

  18. TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

    KAUST Repository

    Mü ller, Matthias; Bibi, Adel Aamer; Giancola, Silvio; Al-Subaihi, Salman; Ghanem, Bernard

    2018-01-01

    Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers fine-tuned on a fraction of our dataset improve their performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.

  19. TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

    KAUST Repository

    Müller, Matthias

    2018-03-28

    Despite the numerous developments in object tracking, further development of current tracking algorithms is limited by small and mostly saturated datasets. As a matter of fact, data-hungry trackers based on deep-learning currently rely on object detection datasets due to the scarcity of dedicated large-scale tracking datasets. In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild. We provide more than 30K videos with more than 14 million dense bounding box annotations. Our dataset covers a wide selection of object classes in broad and diverse context. By releasing such a large-scale dataset, we expect deep trackers to further improve and generalize. In addition, we introduce a new benchmark composed of 500 novel videos, modeled with a distribution similar to our training dataset. By sequestering the annotation of the test set and providing an online evaluation server, we provide a fair benchmark for future development of object trackers. Deep trackers fine-tuned on a fraction of our dataset improve their performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We provide an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Our results suggest that object tracking in the wild is far from being solved.

  20. Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

    Science.gov (United States)

    Xu, Y.; Sun, Z.; Boerner, R.; Koch, T.; Hoegner, L.; Stilla, U.

    2018-04-01

    In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

  1. Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor

    Directory of Open Access Journals (Sweden)

    Chang Xu

    2018-05-01

    Full Text Available This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs. Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

  2. Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

    Science.gov (United States)

    Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

    2018-05-24

    This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

  3. RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

    DEFF Research Database (Denmark)

    Jensen, Tue Vissing; Pinson, Pierre

    2017-01-01

    , we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven...... to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecastingof renewable power generation....

  4. Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets

    Directory of Open Access Journals (Sweden)

    Gan Guojun

    2017-12-01

    Full Text Available Metamodeling techniques have recently been proposed to address the computational issues related to the valuation of large portfolios of variable annuity contracts. However, it is extremely diffcult, if not impossible, for researchers to obtain real datasets frominsurance companies in order to test their metamodeling techniques on such real datasets and publish the results in academic journals. To facilitate the development and dissemination of research related to the effcient valuation of large variable annuity portfolios, this paper creates a large synthetic portfolio of variable annuity contracts based on the properties of real portfolios of variable annuities and implements a simple Monte Carlo simulation engine for valuing the synthetic portfolio. In addition, this paper presents fair market values and Greeks for the synthetic portfolio of variable annuity contracts that are important quantities for managing the financial risks associated with variable annuities. The resulting datasets can be used by researchers to test and compare the performance of various metamodeling techniques.

  5. Spatially-explicit estimation of geographical representation in large-scale species distribution datasets.

    Science.gov (United States)

    Kalwij, Jesse M; Robertson, Mark P; Ronk, Argo; Zobel, Martin; Pärtel, Meelis

    2014-01-01

    Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution

  6. Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

    Science.gov (United States)

    Ohmichi, Yuya

    2017-07-01

    In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.

  7. Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

    Science.gov (United States)

    Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

    2017-12-01

    Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

  8. A method for generating large datasets of organ geometries for radiotherapy treatment planning studies

    International Nuclear Information System (INIS)

    Hu, Nan; Cerviño, Laura; Segars, Paul; Lewis, John; Shan, Jinlu; Jiang, Steve; Zheng, Xiaolin; Wang, Ge

    2014-01-01

    With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy. Given a training dataset of organ shapes derived from daily cone-beam CT, we align them into a common coordinate frame and select one of the training surfaces as reference surface. A statistical shape model of organs was constructed, based on the establishment of point correspondence between surfaces and non-uniform rational B-spline (NURBS) representation. A principal component analysis is performed on the sampled surface points to capture the major variation modes of each organ. A set of principal components and their respective coefficients, which represent organ surface deformation, were obtained, and a statistical analysis of the coefficients was performed. New sets of statistically equivalent coefficients can be constructed and assigned to the principal components, resulting in a larger geometry dataset for the patient’s organs. These generated organ geometries are realistic and statistically representative

  9. Extraction of drainage networks from large terrain datasets using high throughput computing

    Science.gov (United States)

    Gong, Jianya; Xie, Jibo

    2009-02-01

    Advanced digital photogrammetry and remote sensing technology produces large terrain datasets (LTD). How to process and use these LTD has become a big challenge for GIS users. Extracting drainage networks, which are basic for hydrological applications, from LTD is one of the typical applications of digital terrain analysis (DTA) in geographical information applications. Existing serial drainage algorithms cannot deal with large data volumes in a timely fashion, and few GIS platforms can process LTD beyond the GB size. High throughput computing (HTC), a distributed parallel computing mode, is proposed to improve the efficiency of drainage networks extraction from LTD. Drainage network extraction using HTC involves two key issues: (1) how to decompose the large DEM datasets into independent computing units and (2) how to merge the separate outputs into a final result. A new decomposition method is presented in which the large datasets are partitioned into independent computing units using natural watershed boundaries instead of using regular 1-dimensional (strip-wise) and 2-dimensional (block-wise) decomposition. Because the distribution of drainage networks is strongly related to watershed boundaries, the new decomposition method is more effective and natural. The method to extract natural watershed boundaries was improved by using multi-scale DEMs instead of single-scale DEMs. A HTC environment is employed to test the proposed methods with real datasets.

  10. The role of metadata in managing large environmental science datasets. Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    Melton, R.B.; DeVaney, D.M. [eds.] [Pacific Northwest Lab., Richland, WA (United States); French, J. C. [Univ. of Virginia, (United States)

    1995-06-01

    The purpose of this workshop was to bring together computer science researchers and environmental sciences data management practitioners to consider the role of metadata in managing large environmental sciences datasets. The objectives included: establishing a common definition of metadata; identifying categories of metadata; defining problems in managing metadata; and defining problems related to linking metadata with primary data.

  11. Information contained within the large scale gas injection test (Lasgit) dataset exposed using a bespoke data analysis tool-kit

    International Nuclear Information System (INIS)

    Bennett, D.P.; Thomas, H.R.; Cuss, R.J.; Harrington, J.F.; Vardon, P.J.

    2012-01-01

    Document available in extended abstract form only. The Large Scale Gas Injection Test (Lasgit) is a field scale experiment run by the British Geological Survey (BGS) and is located approximately 420 m underground at SKB's Aespoe Hard Rock Laboratory (HRL) in Sweden. It has been designed to study the impact on safety of gas build up within a KBS-3V concept high level radioactive waste repository. Lasgit has been in almost continuous operation for approximately seven years and is still underway. An analysis of the dataset arising from the Lasgit experiment with particular attention to the smaller scale features and phenomenon recorded has been undertaken in parallel to the macro scale analysis performed by the BGS. Lasgit is a highly instrumented, frequently sampled and long-lived experiment leading to a substantial dataset containing in excess of 14.7 million datum points. The data is anticipated to include a wealth of information, including information regarding overall processes as well as smaller scale or 'second order' features. Due to the size of the dataset coupled with the detailed analysis of the dataset required and the reduction in subjectivity associated with measurement compared to observation, computational analysis is essential. Moreover, due to the length of operation and complexity of experimental activity, the Lasgit dataset is not typically suited to 'out of the box' time series analysis algorithms. In particular, the features that are not suited to standard algorithms include non-uniformities due to (deliberate) changes in sample rate at various points in the experimental history and missing data due to hardware malfunction/failure causing interruption of logging cycles. To address these features a computational tool-kit capable of performing an Exploratory Data Analysis (EDA) on long-term, large-scale datasets with non-uniformities has been developed. Particular tool-kit abilities include: the parameterization of signal variation in the dataset

  12. Megastudies, crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments.

    Science.gov (United States)

    Keuleers, Emmanuel; Balota, David A

    2015-01-01

    This paper introduces and summarizes the special issue on megastudies, crowdsourcing, and large datasets in psycholinguistics. We provide a brief historical overview and show how the papers in this issue have extended the field by compiling new databases and making important theoretical contributions. In addition, we discuss several studies that use text corpora to build distributional semantic models to tackle various interesting problems in psycholinguistics. Finally, as is the case across the papers, we highlight some methodological issues that are brought forth via the analyses of such datasets.

  13. REM-3D Reference Datasets: Reconciling large and diverse compilations of travel-time observations

    Science.gov (United States)

    Moulik, P.; Lekic, V.; Romanowicz, B. A.

    2017-12-01

    A three-dimensional Reference Earth model (REM-3D) should ideally represent the consensus view of long-wavelength heterogeneity in the Earth's mantle through the joint modeling of large and diverse seismological datasets. This requires reconciliation of datasets obtained using various methodologies and identification of consistent features. The goal of REM-3D datasets is to provide a quality-controlled and comprehensive set of seismic observations that would not only enable construction of REM-3D, but also allow identification of outliers and assist in more detailed studies of heterogeneity. The community response to data solicitation has been enthusiastic with several groups across the world contributing recent measurements of normal modes, (fundamental mode and overtone) surface waves, and body waves. We present results from ongoing work with body and surface wave datasets analyzed in consultation with a Reference Dataset Working Group. We have formulated procedures for reconciling travel-time datasets that include: (1) quality control for salvaging missing metadata; (2) identification of and reasons for discrepant measurements; (3) homogenization of coverage through the construction of summary rays; and (4) inversions of structure at various wavelengths to evaluate inter-dataset consistency. In consultation with the Reference Dataset Working Group, we retrieved the station and earthquake metadata in several legacy compilations and codified several guidelines that would facilitate easy storage and reproducibility. We find strong agreement between the dispersion measurements of fundamental-mode Rayleigh waves, particularly when made using supervised techniques. The agreement deteriorates substantially in surface-wave overtones, for which discrepancies vary with frequency and overtone number. A half-cycle band of discrepancies is attributed to reversed instrument polarities at a limited number of stations, which are not reflected in the instrument response history

  14. Immersive Interaction, Manipulation and Analysis of Large 3D Datasets for Planetary and Earth Sciences

    Science.gov (United States)

    Pariser, O.; Calef, F.; Manning, E. M.; Ardulov, V.

    2017-12-01

    We will present implementation and study of several use-cases of utilizing Virtual Reality (VR) for immersive display, interaction and analysis of large and complex 3D datasets. These datasets have been acquired by the instruments across several Earth, Planetary and Solar Space Robotics Missions. First, we will describe the architecture of the common application framework that was developed to input data, interface with VR display devices and program input controllers in various computing environments. Tethered and portable VR technologies will be contrasted and advantages of each highlighted. We'll proceed to presenting experimental immersive analytics visual constructs that enable augmentation of 3D datasets with 2D ones such as images and statistical and abstract data. We will conclude by presenting comparative analysis with traditional visualization applications and share the feedback provided by our users: scientists and engineers.

  15. RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

    Science.gov (United States)

    Jensen, Tue V.; Pinson, Pierre

    2017-11-01

    Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

  16. RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

    Science.gov (United States)

    Jensen, Tue V; Pinson, Pierre

    2017-11-28

    Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

  17. Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    KAUST Repository

    Zhang, Bohai

    2014-01-01

    Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

  18. Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    2018-03-01

    Full Text Available Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN based scheme, namely P a l m R CNN (short for palmprint/palmvein recognition using CNNs. The effectiveness and efficiency of P a l m R CNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.

  19. Large scale validation of the M5L lung CAD on heterogeneous CT datasets

    Energy Technology Data Exchange (ETDEWEB)

    Lopez Torres, E., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [CEADEN, Havana 11300, Cuba and INFN, Sezione di Torino, Torino 10125 (Italy); Fiorina, E.; Pennazio, F.; Peroni, C. [Department of Physics, University of Torino, Torino 10125, Italy and INFN, Sezione di Torino, Torino 10125 (Italy); Saletta, M.; Cerello, P., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [INFN, Sezione di Torino, Torino 10125 (Italy); Camarlinghi, N.; Fantacci, M. E. [Department of Physics, University of Pisa, Pisa 56127, Italy and INFN, Sezione di Pisa, Pisa 56127 (Italy)

    2015-04-15

    Purpose: M5L, a fully automated computer-aided detection (CAD) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image datasets. Methods: M5L is the combination of two independent subsystems, based on the Channeler Ant Model as a segmentation tool [lung channeler ant model (lungCAM)] and on the voxel-based neural approach. The lungCAM was upgraded with a scan equalization module and a new procedure to recover the nodules connected to other lung structures; its classification module, which makes use of a feed-forward neural network, is based of a small number of features (13), so as to minimize the risk of lacking generalization, which could be possible given the large difference between the size of the training and testing datasets, which contain 94 and 1019 CTs, respectively. The lungCAM (standalone) and M5L (combined) performance was extensively tested on 1043 CT scans from three independent datasets, including a detailed analysis of the full Lung Image Database Consortium/Image Database Resource Initiative database, which is not yet found in literature. Results: The lungCAM and M5L performance is consistent across the databases, with a sensitivity of about 70% and 80%, respectively, at eight false positive findings per scan, despite the variable annotation criteria and acquisition and reconstruction conditions. A reduced sensitivity is found for subtle nodules and ground glass opacities (GGO) structures. A comparison with other CAD systems is also presented. Conclusions: The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large

  20. MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB.

    Science.gov (United States)

    Cockfield, Jeremy; Su, Kyungmin; Robbins, Kay A

    2013-01-01

    Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets. The considerable variability of responses within and among subjects in more realistic behavioral scenarios requires experiments to assess many more subjects over longer periods of time. This explosion of data requires better computational infrastructure to more systematically explore and process these collections. MOBBED is a lightweight, easy-to-use, extensible toolkit that allows users to incorporate a computational database into their normal MATLAB workflow. Although capable of storing quite general types of annotated data, MOBBED is particularly oriented to multichannel time series such as EEG that have event streams overlaid with sensor data. MOBBED directly supports access to individual events, data frames, and time-stamped feature vectors, allowing users to ask questions such as what types of events or features co-occur under various experimental conditions. A database provides several advantages not available to users who process one dataset at a time from the local file system. In addition to archiving primary data in a central place to save space and avoid inconsistencies, such a database allows users to manage, search, and retrieve events across multiple datasets without reading the entire dataset. The database also provides infrastructure for handling more complex event patterns that include environmental and contextual conditions. The database can also be used as a cache for expensive intermediate results that are reused in such activities as cross-validation of machine learning algorithms. MOBBED is implemented over PostgreSQL, a widely used open source database, and is freely available under the GNU general public license at http://visual.cs.utsa.edu/mobbed. Source and issue reports for MOBBED

  1. A large-scale dataset of solar event reports from automated feature recognition modules

    Science.gov (United States)

    Schuh, Michael A.; Angryk, Rafal A.; Martens, Petrus C.

    2016-05-01

    The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.

  2. A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

    KAUST Repository

    Xu, Ganggang

    2015-01-01

    When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

  3. Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis.

    Science.gov (United States)

    Llamas, César; González, Manuel A; Hernández, Carmen; Vegas, Jesús

    2016-10-01

    Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

    Science.gov (United States)

    Porwal, A.; Carranza, J.; Hale, M.

    2004-12-01

    A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

  5. Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.

    Science.gov (United States)

    Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

    2014-01-01

    As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  6. Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

    KAUST Repository

    Huang, Huang

    2017-07-16

    This thesis focuses on two topics, computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data. The first topic is motivated by the prohibitive computational burden in fitting Gaussian process models to large and irregularly spaced spatial datasets. Various approximation methods have been introduced to reduce the computational cost, but many rely on unrealistic assumptions about the process and retaining statistical efficiency remains an issue. We propose a new scheme to approximate the maximum likelihood estimator and the kriging predictor when the exact computation is infeasible. The proposed method provides different types of hierarchical low-rank approximations that are both computationally and statistically efficient. We explore the improvement of the approximation theoretically and investigate the performance by simulations. For real applications, we analyze a soil moisture dataset with 2 million measurements with the hierarchical low-rank approximation and apply the proposed fast kriging to fill gaps for satellite images. The second topic is motivated by rank-based outlier detection methods for functional data. Compared to magnitude outliers, it is more challenging to detect shape outliers as they are often masked among samples. We develop a new notion of functional data depth by taking the integration of a univariate depth function. Having a form of the integrated depth, it shares many desirable features. Furthermore, the novel formation leads to a useful decomposition for detecting both shape and magnitude outliers. Our simulation studies show the proposed outlier detection procedure outperforms competitors in various outlier models. We also illustrate our methodology using real datasets of curves, images, and video frames. Finally, we introduce the functional data ranking technique to spatio-temporal statistics for visualizing and assessing covariance properties, such as

  7. Genetic architecture of vitamin B12 and folate levels uncovered applying deeply sequenced large datasets

    DEFF Research Database (Denmark)

    Grarup, Niels; Sulem, Patrick; Sandholt, Camilla H

    2013-01-01

    of the underlying biology of human traits and diseases. Here, we used a large Icelandic whole genome sequence dataset combined with Danish exome sequence data to gain insight into the genetic architecture of serum levels of vitamin B12 (B12) and folate. Up to 22.9 million sequence variants were analyzed in combined...... in serum B12 or folate levels do not modify the risk of developing these conditions. Yet, the study demonstrates the value of combining whole genome and exome sequencing approaches to ascertain the genetic and molecular architectures underlying quantitative trait associations....

  8. A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

    KAUST Repository

    Tagle, Felipe

    2017-12-06

    Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.

  9. Study of the Integration of LIDAR and Photogrammetric Datasets by in Situ Camera Calibration and Integrated Sensor Orientation

    Science.gov (United States)

    Mitishita, E.; Costa, F.; Martins, M.

    2017-05-01

    Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

  10. STUDY OF THE INTEGRATION OF LIDAR AND PHOTOGRAMMETRIC DATASETS BY IN SITU CAMERA CALIBRATION AND INTEGRATED SENSOR ORIENTATION

    Directory of Open Access Journals (Sweden)

    E. Mitishita

    2017-05-01

    Full Text Available Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO approach was performed using Interior Orientation Parameter (IOP values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

  11. Large Capacitance Measurement by Multiple Uses of MBL Charge Sensor

    Science.gov (United States)

    Lee, Jung Sook; Chae, Min; Kim, Jung Bog

    2010-01-01

    A recent article by Morse described interesting electrostatics experiments using an MBL charge sensor. In this application, the charge sensor has a large capacitance compared to the charged test object, so nearly all charges can be transferred to the sensor capacitor from the capacitor to be measured. However, the typical capacitance of commercial…

  12. A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology—The ADAPT Study Data-Set

    Directory of Open Access Journals (Sweden)

    Alan Kevin Bourke

    2017-03-01

    Full Text Available Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps, both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86. A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.

  13. Extended data analysis strategies for high resolution imaging MS : new methods to deal with extremely large image hyperspectral datasets

    NARCIS (Netherlands)

    Klerk, L.A.; Broersen, A.; Fletcher, I.W.; Liere, van R.; Heeren, R.M.A.

    2007-01-01

    The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable

  14. Spectral methods in machine learning and new strategies for very large datasets

    Science.gov (United States)

    Belabbas, Mohamed-Ali; Wolfe, Patrick J.

    2009-01-01

    Spectral methods are of fundamental importance in statistics and machine learning, because they underlie algorithms from classical principal components analysis to more recent approaches that exploit manifold structure. In most cases, the core technical problem can be reduced to computing a low-rank approximation to a positive-definite kernel. For the growing number of applications dealing with very large or high-dimensional datasets, however, the optimal approximation afforded by an exact spectral decomposition is too costly, because its complexity scales as the cube of either the number of training examples or their dimensionality. Motivated by such applications, we present here 2 new algorithms for the approximation of positive-semidefinite kernels, together with error bounds that improve on results in the literature. We approach this problem by seeking to determine, in an efficient manner, the most informative subset of our data relative to the kernel approximation task at hand. This leads to two new strategies based on the Nyström method that are directly applicable to massive datasets. The first of these—based on sampling—leads to a randomized algorithm whereupon the kernel induces a probability distribution on its set of partitions, whereas the latter approach—based on sorting—provides for the selection of a partition in a deterministic way. We detail their numerical implementation and provide simulation results for a variety of representative problems in statistical data analysis, each of which demonstrates the improved performance of our approach relative to existing methods. PMID:19129490

  15. VisIVO: A Library and Integrated Tools for Large Astrophysical Dataset Exploration

    Science.gov (United States)

    Becciani, U.; Costa, A.; Ersotelos, N.; Krokos, M.; Massimino, P.; Petta, C.; Vitello, F.

    2012-09-01

    VisIVO provides an integrated suite of tools and services that can be used in many scientific fields. VisIVO development starts in the Virtual Observatory framework. VisIVO allows users to visualize meaningfully highly-complex, large-scale datasets and create movies of these visualizations based on distributed infrastructures. VisIVO supports high-performance, multi-dimensional visualization of large-scale astrophysical datasets. Users can rapidly obtain meaningful visualizations while preserving full and intuitive control of the relevant parameters. VisIVO consists of VisIVO Desktop - a stand-alone application for interactive visualization on standard PCs, VisIVO Server - a platform for high performance visualization, VisIVO Web - a custom designed web portal, VisIVOSmartphone - an application to exploit the VisIVO Server functionality and the latest VisIVO features: VisIVO Library allows a job running on a computational system (grid, HPC, etc.) to produce movies directly with the code internal data arrays without the need to produce intermediate files. This is particularly important when running on large computational facilities, where the user wants to have a look at the results during the data production phase. For example, in grid computing facilities, images can be produced directly in the grid catalogue while the user code is running in a system that cannot be directly accessed by the user (a worker node). The deployment of VisIVO on the DG and gLite is carried out with the support of EDGI and EGI-Inspire projects. Depending on the structure and size of datasets under consideration, the data exploration process could take several hours of CPU for creating customized views and the production of movies could potentially last several days. For this reason an MPI parallel version of VisIVO could play a fundamental role in increasing performance, e.g. it could be automatically deployed on nodes that are MPI aware. A central concept in our development is thus to

  16. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

    Science.gov (United States)

    Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian; hide

    2015-01-01

    Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex

  17. Image-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space.

    KAUST Repository

    Binyahib, Roba S.

    2013-05-13

    With an increase in processing power, more complex simulations have resulted in larger data size, with higher resolution and more variables. Many techniques have been developed to help the user to visualize and analyze data from such simulations. However, dealing with a large amount of multivariate data is challenging, time- consuming and often requires high-end clusters. Consequently, novel visualization techniques are needed to explore such data. Many users would like to visually explore their data and change certain visual aspects without the need to use special clusters or having to load a large amount of data. This is the idea behind explorable images (EI). Explorable images are a novel approach that provides limited interactive visualization without the need to re-render from the original data [40]. In this work, the concept of EI has been used to create a workflow that deals with explorable iso-surfaces for scalar fields in a multivariate, time-varying dataset. As a pre-processing step, a set of iso-values for each scalar field is inferred and extracted from a user-assisted sampling technique in time-parameter space. These iso-values are then used to generate iso- surfaces that are then pre-rendered (from a fixed viewpoint) along with additional buffers (i.e. normals, depth, values of other fields, etc.) to provide a compressed representation of iso-surfaces in the dataset. We present a tool that at run-time allows the user to interactively browse and calculate a combination of iso-surfaces superimposed on each other. The result is the same as calculating multiple iso- surfaces from the original data but without the memory and processing overhead. Our tool also allows the user to change the (scalar) values superimposed on each of the surfaces, modify their color map, and interactively re-light the surfaces. We demonstrate the effectiveness of our approach over a multi-terabyte combustion dataset. We also illustrate the efficiency and accuracy of our

  18. Mapsembler, targeted and micro assembly of large NGS datasets on a desktop computer

    Directory of Open Access Journals (Sweden)

    Peterlongo Pierre

    2012-03-01

    Full Text Available Abstract Background The analysis of next-generation sequencing data from large genomes is a timely research topic. Sequencers are producing billions of short sequence fragments from newly sequenced organisms. Computational methods for reconstructing whole genomes/transcriptomes (de novo assemblers are typically employed to process such data. However, these methods require large memory resources and computation time. Many basic biological questions could be answered targeting specific information in the reads, thus avoiding complete assembly. Results We present Mapsembler, an iterative micro and targeted assembler which processes large datasets of reads on commodity hardware. Mapsembler checks for the presence of given regions of interest that can be constructed from reads and builds a short assembly around it, either as a plain sequence or as a graph, showing contextual structure. We introduce new algorithms to retrieve approximate occurrences of a sequence from reads and construct an extension graph. Among other results presented in this paper, Mapsembler enabled to retrieve previously described human breast cancer candidate fusion genes, and to detect new ones not previously known. Conclusions Mapsembler is the first software that enables de novo discovery around a region of interest of repeats, SNPs, exon skipping, gene fusion, as well as other structural events, directly from raw sequencing reads. As indexing is localized, the memory footprint of Mapsembler is negligible. Mapsembler is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/mapsembler/.

  19. Autonomous Sensors for Large Scale Data Collection

    Science.gov (United States)

    Noto, J.; Kerr, R.; Riccobono, J.; Kapali, S.; Migliozzi, M. A.; Goenka, C.

    2017-12-01

    Presented here is a novel implementation of a "Doppler imager" which remotely measures winds and temperatures of the neutral background atmosphere at ionospheric altitudes of 87-300Km and possibly above. Incorporating both recent optical manufacturing developments, modern network awareness and the application of machine learning techniques for intelligent self-monitoring and data classification. This system achieves cost savings in manufacturing, deployment and lifetime operating costs. Deployed in both ground and space-based modalities, this cost-disruptive technology will allow computer models of, ionospheric variability and other space weather models to operate with higher precision. Other sensors can be folded into the data collection and analysis architecture easily creating autonomous virtual observatories. A prototype version of this sensor has recently been deployed in Trivandrum India for the Indian Government. This Doppler imager is capable of operation, even within the restricted CubeSat environment. The CubeSat bus offers a very challenging environment, even for small instruments. The lack of SWaP and the challenging thermal environment demand development of a new generation of instruments; the Doppler imager presented is well suited to this environment. Concurrent with this CubeSat development is the development and construction of ground based arrays of inexpensive sensors using the proposed technology. This instrument could be flown inexpensively on one or more CubeSats to provide valuable data to space weather forecasters and ionospheric scientists. Arrays of magnetometers have been deployed for the last 20 years [Alabi, 2005]. Other examples of ground based arrays include an array of white-light all sky imagers (THEMIS) deployed across Canada [Donovan et al., 2006], oceans sensors on buoys [McPhaden et al., 2010], and arrays of seismic sensors [Schweitzer et al., 2002]. A comparable array of Doppler imagers can be constructed and deployed on the

  20. Knowledge discovery in large model datasets in the marine environment: the THREDDS Data Server example

    Directory of Open Access Journals (Sweden)

    A. Bergamasco

    2012-06-01

    Full Text Available In order to monitor, describe and understand the marine environment, many research institutions are involved in the acquisition and distribution of ocean data, both from observations and models. Scientists from these institutions are spending too much time looking for, accessing, and reformatting data: they need better tools and procedures to make the science they do more efficient. The U.S. Integrated Ocean Observing System (US-IOOS is working on making large amounts of distributed data usable in an easy and efficient way. It is essentially a network of scientists, technicians and technologies designed to acquire, collect and disseminate observational and modelled data resulting from coastal and oceanic marine regions investigations to researchers, stakeholders and policy makers. In order to be successful, this effort requires standard data protocols, web services and standards-based tools. Starting from the US-IOOS approach, which is being adopted throughout much of the oceanographic and meteorological sectors, we describe here the CNR-ISMAR Venice experience in the direction of setting up a national Italian IOOS framework using the THREDDS (THematic Real-time Environmental Distributed Data Services Data Server (TDS, a middleware designed to fill the gap between data providers and data users. The TDS provides services that allow data users to find the data sets pertaining to their scientific needs, to access, to visualize and to use them in an easy way, without downloading files to the local workspace. In order to achieve this, it is necessary that the data providers make their data available in a standard form that the TDS understands, and with sufficient metadata to allow the data to be read and searched in a standard way. The core idea is then to utilize a Common Data Model (CDM, a unified conceptual model that describes different datatypes within each dataset. More specifically, Unidata (www.unidata.ucar.edu has developed CDM

  1. Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

    Energy Technology Data Exchange (ETDEWEB)

    Sreepathi, Sarat [ORNL; Kumar, Jitendra [ORNL; Mills, Richard T. [Argonne National Laboratory; Hoffman, Forrest M. [ORNL; Sripathi, Vamsi [Intel Corporation; Hargrove, William Walter [United States Department of Agriculture (USDA), United States Forest Service (USFS)

    2017-09-01

    A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

  2. FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web.

    Science.gov (United States)

    Probst, Daniel; Reymond, Jean-Louis

    2018-04-15

    During the past decade, big data have become a major tool in scientific endeavors. Although statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib and plot.ly, do not support interactive visualizations of datasets exceeding 100 000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations. However, recent hardware developments, especially advancements in graphical processing units, allow for the rendering of millions of data points on a wide range of consumer hardware such as laptops, tablets and mobile phones. Similar to the challenges and opportunities brought to virtually every scientific field by big data, both the visualization of and interaction with copious amounts of data are both demanding and hold great promise. Here we present FUn, a framework consisting of a client (Faerun) and server (Underdark) module, facilitating the creation of web-based, interactive 3D visualizations of large datasets, enabling record level visual inspection. We also introduce a reference implementation providing access to SureChEMBL, a database containing patent information on more than 17 million chemical compounds. The source code and the most recent builds of Faerun and Underdark, Lore.js and the data preprocessing toolchain used in the reference implementation, are available on the project website (http://doc.gdb.tools/fun/). daniel.probst@dcb.unibe.ch or jean-louis.reymond@dcb.unibe.ch.

  3. Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

    Directory of Open Access Journals (Sweden)

    Ranjith Gopalakrishnan

    2015-08-01

    Full Text Available Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects. A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots from the Forest Inventory and Analysis (FIA program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85. We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R2 = 0.74, RMSE = 3.0 m, n = 1755. We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2 significantly reduces the RMSE of our model from 3.0–2.4 m (~20% reduction. We conclude that when all these elements are factored into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

  4. Measurement and genetics of human subcortical and hippocampal asymmetries in large datasets.

    Science.gov (United States)

    Guadalupe, Tulio; Zwiers, Marcel P; Teumer, Alexander; Wittfeld, Katharina; Vasquez, Alejandro Arias; Hoogman, Martine; Hagoort, Peter; Fernandez, Guillen; Buitelaar, Jan; Hegenscheid, Katrin; Völzke, Henry; Franke, Barbara; Fisher, Simon E; Grabe, Hans J; Francks, Clyde

    2014-07-01

    Functional and anatomical asymmetries are prevalent features of the human brain, linked to gender, handedness, and cognition. However, little is known about the neurodevelopmental processes involved. In zebrafish, asymmetries arise in the diencephalon before extending within the central nervous system. We aimed to identify genes involved in the development of subtle, left-right volumetric asymmetries of human subcortical structures using large datasets. We first tested the feasibility of measuring left-right volume differences in such large-scale samples, as assessed by two automated methods of subcortical segmentation (FSL|FIRST and FreeSurfer), using data from 235 subjects who had undergone MRI twice. We tested the agreement between the first and second scan, and the agreement between the segmentation methods, for measures of bilateral volumes of six subcortical structures and the hippocampus, and their volumetric asymmetries. We also tested whether there were biases introduced by left-right differences in the regional atlases used by the methods, by analyzing left-right flipped images. While many bilateral volumes were measured well (scan-rescan r = 0.6-0.8), most asymmetries, with the exception of the caudate nucleus, showed lower repeatabilites. We meta-analyzed genome-wide association scan results for caudate nucleus asymmetry in a combined sample of 3,028 adult subjects but did not detect associations at genome-wide significance (P left-right patterning of the viscera. Our results provide important information for researchers who are currently aiming to carry out large-scale genome-wide studies of subcortical and hippocampal volumes, and their asymmetries. Copyright © 2013 Wiley Periodicals, Inc.

  5. Managing Large Multidimensional Array Hydrologic Datasets : A Case Study Comparing NetCDF and SciDB

    NARCIS (Netherlands)

    Liu, H.; van Oosterom, P.J.M.; Hu, C.; Wang, Wen

    2016-01-01

    Management of large hydrologic datasets including storage, structuring, indexing and query is one of the crucial challenges in the era of big data. This research originates from a specific data query problem: time series extraction at specific locations takes a long time when a large

  6. CoVennTree: A new method for the comparative analysis of large datasets

    Directory of Open Access Journals (Sweden)

    Steffen C. Lott

    2015-02-01

    Full Text Available The visualization of massive datasets, such as those resulting from comparative metatranscriptome analyses or the analysis of microbial population structures using ribosomal RNA sequences, is a challenging task. We developed a new method called CoVennTree (Comparative weighted Venn Tree that simultaneously compares up to three multifarious datasets by aggregating and propagating information from the bottom to the top level and produces a graphical output in Cytoscape. With the introduction of weighted Venn structures, the contents and relationships of various datasets can be correlated and simultaneously aggregated without losing information. We demonstrate the suitability of this approach using a dataset of 16S rDNA sequences obtained from microbial populations at three different depths of the Gulf of Aqaba in the Red Sea. CoVennTree has been integrated into the Galaxy ToolShed and can be directly downloaded and integrated into the user instance.

  7. Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

    KAUST Repository

    Huang, Huang

    2017-01-01

    that are both computationally and statistically efficient. We explore the improvement of the approximation theoretically and investigate the performance by simulations. For real applications, we analyze a soil moisture dataset with 2 million measurements

  8. TIMPs of parasitic helminths - a large-scale analysis of high-throughput sequence datasets.

    Science.gov (United States)

    Cantacessi, Cinzia; Hofmann, Andreas; Pickering, Darren; Navarro, Severine; Mitreva, Makedonka; Loukas, Alex

    2013-05-30

    Tissue inhibitors of metalloproteases (TIMPs) are a multifunctional family of proteins that orchestrate extracellular matrix turnover, tissue remodelling and other cellular processes. In parasitic helminths, such as hookworms, TIMPs have been proposed to play key roles in the host-parasite interplay, including invasion of and establishment in the vertebrate animal hosts. Currently, knowledge of helminth TIMPs is limited to a small number of studies on canine hookworms, whereas no information is available on the occurrence of TIMPs in other parasitic helminths causing neglected diseases. In the present study, we conducted a large-scale investigation of TIMP proteins of a range of neglected human parasites including the hookworm Necator americanus, the roundworm Ascaris suum, the liver flukes Clonorchis sinensis and Opisthorchis viverrini, as well as the schistosome blood flukes. This entailed mining available transcriptomic and/or genomic sequence datasets for the presence of homologues of known TIMPs, predicting secondary structures of defined protein sequences, systematic phylogenetic analyses and assessment of differential expression of genes encoding putative TIMPs in the developmental stages of A. suum, N. americanus and Schistosoma haematobium which infect the mammalian hosts. A total of 15 protein sequences with high homology to known eukaryotic TIMPs were predicted from the complement of sequence data available for parasitic helminths and subjected to in-depth bioinformatic analyses. Supported by the availability of gene manipulation technologies such as RNA interference and/or transgenesis, this work provides a basis for future functional explorations of helminth TIMPs and, in particular, of their role/s in fundamental biological pathways linked to long-term establishment in the vertebrate hosts, with a view towards the development of novel approaches for the control of neglected helminthiases.

  9. EEGVIS: A MATLAB toolbox for browsing, exploring, and viewing large datasets

    Directory of Open Access Journals (Sweden)

    Kay A Robbins

    2012-05-01

    Full Text Available Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments. Researchers also do not have a convenient method of method of visually assessing the effects of applying any stage in a processing pipeline. EEGVIS is a MATLAB toolbox that allows users to quickly explore multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. Customizable summary views reveal potentially interesting sections of data, which users can explore further by clicking to examine using detailed viewing components. The viewer and a companion browser are built on our MoBBED framework, which has a library of modular viewing components that can be mixed and matched to best reveal structure. Users can easily create new viewers for their specific data without any programming during the exploration process. These viewers automatically support pan, zoom, resizing of individual components, and cursor exploration. The toolbox can be used directly in MATLAB at any stage in a processing pipeline, as a plug in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license at http://visual.cs.utsa.edu/ eegvis.

  10. EEGVIS: A MATLAB Toolbox for Browsing, Exploring, and Viewing Large Datasets.

    Science.gov (United States)

    Robbins, Kay A

    2012-01-01

    Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments. Researchers also do not have a convenient method of method of visually assessing the effects of applying any stage in a processing pipeline. EEGVIS is a MATLAB toolbox that allows users to quickly explore multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. Customizable summary views reveal potentially interesting sections of data, which users can explore further by clicking to examine using detailed viewing components. The viewer and a companion browser are built on our MoBBED framework, which has a library of modular viewing components that can be mixed and matched to best reveal structure. Users can easily create new viewers for their specific data without any programming during the exploration process. These viewers automatically support pan, zoom, resizing of individual components, and cursor exploration. The toolbox can be used directly in MATLAB at any stage in a processing pipeline, as a plug-in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license at http://visual.cs.utsa.edu/eegvis.

  11. Active self-testing noise measurement sensors for large-scale environmental sensor networks.

    Science.gov (United States)

    Domínguez, Federico; Cuong, Nguyen The; Reinoso, Felipe; Touhafi, Abdellah; Steenhaut, Kris

    2013-12-13

    Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphone's frequency response over time. This paper presents our noise sensor's hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10.

  12. Using large hydrological datasets to create a robust, physically based, spatially distributed model for Great Britain

    Science.gov (United States)

    Lewis, Elizabeth; Kilsby, Chris; Fowler, Hayley

    2014-05-01

    The impact of climate change on hydrological systems requires further quantification in order to inform water management. This study intends to conduct such analysis using hydrological models. Such models are of varying forms, of which conceptual, lumped parameter models and physically-based models are two important types. The majority of hydrological studies use conceptual models calibrated against measured river flow time series in order to represent catchment behaviour. This method often shows impressive results for specific problems in gauged catchments. However, the results may not be robust under non-stationary conditions such as climate change, as physical processes and relationships amenable to change are not accounted for explicitly. Moreover, conceptual models are less readily applicable to ungauged catchments, in which hydrological predictions are also required. As such, the physically based, spatially distributed model SHETRAN is used in this study to develop a robust and reliable framework for modelling historic and future behaviour of gauged and ungauged catchments across the whole of Great Britain. In order to achieve this, a large array of data completely covering Great Britain for the period 1960-2006 has been collated and efficiently stored ready for model input. The data processed include a DEM, rainfall, PE and maps of geology, soil and land cover. A desire to make the modelling system easy for others to work with led to the development of a user-friendly graphical interface. This allows non-experts to set up and run a catchment model in a few seconds, a process that can normally take weeks or months. The quality and reliability of the extensive dataset for modelling hydrological processes has also been evaluated. One aspect of this has been an assessment of error and uncertainty in rainfall input data, as well as the effects of temporal resolution in precipitation inputs on model calibration. SHETRAN has been updated to accept gridded rainfall

  13. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset | Office of Cancer Genomics

    Science.gov (United States)

    Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset.

  14. MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets.

    Science.gov (United States)

    Lemieux, Sebastien; Sargeant, Tobias; Laperrière, David; Ismail, Houssam; Boucher, Geneviève; Rozendaal, Marieke; Lavallée, Vincent-Philippe; Ashton-Beaucage, Dariel; Wilhelm, Brian; Hébert, Josée; Hilton, Douglas J; Mader, Sylvie; Sauvageau, Guy

    2017-07-27

    Genome-wide transcriptome profiling has enabled non-supervised classification of tumours, revealing different sub-groups characterized by specific gene expression features. However, the biological significance of these subtypes remains for the most part unclear. We describe herein an interactive platform, Minimum Spanning Trees Inferred Clustering (MiSTIC), that integrates the direct visualization and comparison of the gene correlation structure between datasets, the analysis of the molecular causes underlying co-variations in gene expression in cancer samples, and the clinical annotation of tumour sets defined by the combined expression of selected biomarkers. We have used MiSTIC to highlight the roles of specific transcription factors in breast cancer subtype specification, to compare the aspects of tumour heterogeneity targeted by different prognostic signatures, and to highlight biomarker interactions in AML. A version of MiSTIC preloaded with datasets described herein can be accessed through a public web server (http://mistic.iric.ca); in addition, the MiSTIC software package can be obtained (github.com/iric-soft/MiSTIC) for local use with personalized datasets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Microwave Readout Techniques for Very Large Arrays of Nuclear Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Ullom, Joel [Univ. of Colorado, Boulder, CO (United States). Dept. of Physics

    2017-05-17

    During this project, we transformed the use of microwave readout techniques for nuclear sensors from a speculative idea to reality. The core of the project consisted of the development of a set of microwave electronics able to generate and process large numbers of microwave tones. The tones can be used to probe a circuit containing a series of electrical resonances whose frequency locations and widths depend on the state of a network of sensors, with one sensor per resonance. The amplitude and phase of the tones emerging from the circuit are processed by the same electronics and are reduced to the sensor signals after two demodulation steps. This approach allows a large number of sensors to be interrogated using a single pair of coaxial cables. We successfully developed hardware, firmware, and software to complete a scalable implementation of these microwave control electronics and demonstrated their use in two areas. First, we showed that the electronics can be used at room temperature to read out a network of diverse sensor types relevant to safeguards or process monitoring. Second, we showed that the electronics can be used to measure large numbers of ultrasensitive cryogenic sensors such as gamma-ray microcalorimeters. In particular, we demonstrated the undegraded readout of up to 128 channels and established a path to even higher multiplexing factors. These results have transformed the prospects for gamma-ray spectrometers based on cryogenic microcalorimeter arrays by enabling spectrometers whose collecting areas and count rates can be competitive with high purity germanium but with 10x better spectral resolution.

  16. Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm

    DEFF Research Database (Denmark)

    Grotkjær, Thomas; Winther, Ole; Regenberg, Birgitte

    2006-01-01

    Motivation: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods...... analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset...

  17. Argo_CUDA: Exhaustive GPU based approach for motif discovery in large DNA datasets.

    Science.gov (United States)

    Vishnevsky, Oleg V; Bocharnikov, Andrey V; Kolchanov, Nikolay A

    2018-02-01

    The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top "peak" ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.

  18. Learning visual balance from large-scale datasets of aesthetically highly rated images

    Science.gov (United States)

    Jahanian, Ali; Vishwanathan, S. V. N.; Allebach, Jan P.

    2015-03-01

    The concept of visual balance is innate for humans, and influences how we perceive visual aesthetics and cognize harmony. Although visual balance is a vital principle of design and taught in schools of designs, it is barely quantified. On the other hand, with emergence of automantic/semi-automatic visual designs for self-publishing, learning visual balance and computationally modeling it, may escalate aesthetics of such designs. In this paper, we present how questing for understanding visual balance inspired us to revisit one of the well-known theories in visual arts, the so called theory of "visual rightness", elucidated by Arnheim. We define Arnheim's hypothesis as a design mining problem with the goal of learning visual balance from work of professionals. We collected a dataset of 120K images that are aesthetically highly rated, from a professional photography website. We then computed factors that contribute to visual balance based on the notion of visual saliency. We fitted a mixture of Gaussians to the saliency maps of the images, and obtained the hotspots of the images. Our inferred Gaussians align with Arnheim's hotspots, and confirm his theory. Moreover, the results support the viability of the center of mass, symmetry, as well as the Rule of Thirds in our dataset.

  19. Open and scalable analytics of large Earth observation datasets: From scenes to multidimensional arrays using SciDB and GDAL

    Science.gov (United States)

    Appel, Marius; Lahn, Florian; Buytaert, Wouter; Pebesma, Edzer

    2018-04-01

    Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth's surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend the geospatial data abstraction library (GDAL) and the array-based data management and analytics system SciDB. We present an approach to automatically convert collections of scenes to multidimensional arrays and use SciDB to scale computationally intensive analytics. We evaluate the approach in three study cases on national scale land use change monitoring with Landsat imagery, global empirical orthogonal function analysis of daily precipitation, and combining historical climate model projections with satellite-based observations. Results indicate that the approach can be used to represent various EO datasets and that analyses in SciDB scale well with available computational resources. To simplify analyses of higher-dimensional datasets as from climate model output, however, a generalization of the GDAL data model might be needed. All parts of this work have been implemented as open-source software and we discuss how this may facilitate open and reproducible EO analyses.

  20. Autonomous sensor particle for parameter tracking in large vessels

    International Nuclear Information System (INIS)

    Thiele, Sebastian; Da Silva, Marco Jose; Hampel, Uwe

    2010-01-01

    A self-powered and neutrally buoyant sensor particle has been developed for the long-term measurement of spatially distributed process parameters in the chemically harsh environments of large vessels. One intended application is the measurement of flow parameters in stirred fermentation biogas reactors. The prototype sensor particle is a robust and neutrally buoyant capsule, which allows free movement with the flow. It contains measurement devices that log the temperature, absolute pressure (immersion depth) and 3D-acceleration data. A careful calibration including an uncertainty analysis has been performed. Furthermore, autonomous operation of the developed prototype was successfully proven in a flow experiment in a stirred reactor model. It showed that the sensor particle is feasible for future application in fermentation reactors and other industrial processes

  1. Validating the Use of Deep Learning Neural Networks for Correction of Large Hydrometric Datasets

    Science.gov (United States)

    Frazier, N.; Ogden, F. L.; Regina, J. A.; Cheng, Y.

    2017-12-01

    Collection and validation of Earth systems data can be time consuming and labor intensive. In particular, high resolution hydrometric data, including rainfall and streamflow measurements, are difficult to obtain due to a multitude of complicating factors. Measurement equipment is subject to clogs, environmental disturbances, and sensor drift. Manual intervention is typically required to identify, correct, and validate these data. Weirs can become clogged and the pressure transducer may float or drift over time. We typically employ a graphical tool called Time Series Editor to manually remove clogs and sensor drift from the data. However, this process is highly subjective and requires hydrological expertise. Two different people may produce two different data sets. To use this data for scientific discovery and model validation, a more consistent method is needed to processes this field data. Deep learning neural networks have proved to be excellent mechanisms for recognizing patterns in data. We explore the use of Recurrent Neural Networks (RNN) to capture the patterns in the data over time using various gating mechanisms (LSTM and GRU), network architectures, and hyper-parameters to build an automated data correction model. We also explore the required amount of manually corrected training data required to train the network for reasonable accuracy. The benefits of this approach are that the time to process a data set is significantly reduced, and the results are 100% reproducible after training is complete. Additionally, we train the RNN and calibrate a physically-based hydrological model against the same portion of data. Both the RNN and the model are applied to the remaining data using a split-sample methodology. Performance of the machine learning is evaluated for plausibility by comparing with the output of the hydrological model, and this analysis identifies potential periods where additional investigation is warranted.

  2. HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE

    Directory of Open Access Journals (Sweden)

    R. Oliveira

    2016-06-01

    Full Text Available Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.

  3. Television food advertising to children in Slovenia: analyses using a large 12-month advertising dataset.

    Science.gov (United States)

    Korošec, Živa; Pravst, Igor

    2016-12-01

    The marketing of energy-dense foods is recognised as a probable causal factor in children's overweight and obesity. To stimulate policymakers to start using nutrient profiling to restrict food marketing, a harmonised model was recently proposed by the WHO. Our objective is to evaluate the television advertising of foods in Slovenia using the above-mentioned model. An analysis is performed using a representative dataset of 93,902 food-related advertisements broadcast in Slovenia in year 2013. The advertisements are linked to specific foods, which are then subject to categorisation according to the WHO and UK nutrient profile model. Advertising of chocolate and confectionery represented 37 % of food-related advertising in all viewing times, and 77 % in children's (4-9 years) viewing hours. During these hours, 96 % of the food advertisements did not pass the criteria for permitted advertising according to the WHO profile model. Evidence from Slovenia shows that, in the absence of efficient regulatory marketing restrictions, television advertising of food to children is almost exclusively linked to energy-dense foods. Minor modifications of the proposed WHO nutrient profile model are suggested.

  4. A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets.

    Science.gov (United States)

    Ritchie, Scott C; Watts, Stephen; Fearnley, Liam G; Holt, Kathryn E; Abraham, Gad; Inouye, Michael

    2016-07-01

    Network modules-topologically distinct groups of edges and nodes-that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  5. A large fiber sensor network for an acoustic neutrino telescope

    Directory of Open Access Journals (Sweden)

    Buis Ernst-Jan

    2017-01-01

    Full Text Available The scientific prospects of detecting neutrinos with an energy close or even higher than the GKZ cut-off energy has been discussed extensively in literature. It is clear that due to their expected low flux, the detection of these ultra-high energy neutrinos (Ev > 1018 eV requires a telescope larger than 100 km3. Acoustic detection may provide a way to observe these ultra-high energy cosmic neutrinos, as sound that they induce in the deep sea when neutrinos lose their energy travels undisturbed for many kilometers. To realize a large scale acoustic neutrino telescope, dedicated technology must be developed that allows for a deep sea sensor network. Fiber optic hydrophone technology provides a promising means to establish a large scale sensor network [1] with the proper sensitivity to detect the small signals from the neutrino interactions.

  6. Sensor-based automated docking of large waste canisters

    International Nuclear Information System (INIS)

    Drotning, W.D.

    1990-01-01

    Sensor-based programmable robots have the potential to speed up remote manipulation operations while protecting operators from exposure to radiation. Conventional master/slave manipulators have proven to be very slow in performing precision remote operations. In addition, inadvertent collisions of remotely manipulated objects with their environment increase the hazards associated with remote handling. This paper describes the development of a robotic system for the sensor-based automated remote manipulation and precision docking of large payloads. Computer vision and proximity sensing are used to control the precision docking of a large object with a passive target cavity. Specifically, a container of nuclear spent fuel on a transport vehicle is mated with an emplacement door on a vertical storage borehole at a waste repository

  7. A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

    KAUST Repository

    Tagle, Felipe; Castruccio, Stefano; Genton, Marc G.

    2017-01-01

    recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article

  8. Interactive graphics on large datasets drives remote condition monitoring on a cloud

    International Nuclear Information System (INIS)

    Hickinbotham, Simon; Austin, James; McAvoy, John

    2012-01-01

    We demonstrate a new system for condition monitoring using the cloud. The system combines state of the art pattern search capability with youShare, a platform that allows people to run compute-intensive research in an ordered manner over the internet. Data from sensors distributed across one or more assets at one or more sites are uploaded to the cloud compute resource. The uploading triggers the deployment of a range of pattern search services, and is capable of rapidly detecting novel patterns in the data. The outputs of these processes are archived as a matter of course, but are also sent to a further service which processes the data for remote visualisation on a web browser. The system is built in Java, using GWT and RaphaelGWT for graphics rendering. The design of these systems must satisfy conflicting requirements of data currency and data throughput. We present an evaluation of our system that involves processing data at a range of frequencies and bandwidths that are commensurate with commercial requirements. We show that our system has the potential to satisfy a range of processing requirements with minimal latency, and that the user experience is easily sufficient for rapid interpretation of complex condition monitoring data.

  9. A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour

    Directory of Open Access Journals (Sweden)

    Peter E. Land

    2018-05-01

    Full Text Available Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.

  10. Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

    NARCIS (Netherlands)

    Shamir, L.; Carol Yerby, C.; Simpson, R.; Benda-Beckmann, A.M. von; Tyack, P.; Samarra, F.; Miller, P.; Wallin, J.

    2014-01-01

    Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds

  11. Functional Neuroimaging Distinguishes Posttraumatic Stress Disorder from Traumatic Brain Injury in Focused and Large Community Datasets

    OpenAIRE

    Amen, Daniel G.; Raji, Cyrus A.; Willeumier, Kristen; Taylor, Derek; Tarzwell, Robert; Newberg, Andrew; Henderson, Theodore A.

    2015-01-01

    Background Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are highly heterogeneous and often present with overlapping symptomology, providing challenges in reliable classification and treatment. Single photon emission computed tomography (SPECT) may be advantageous in the diagnostic separation of these disorders when comorbid or clinically indistinct. Methods Subjects were selected from a multisite database, where rest and on-task SPECT scans were obtained on a large gr...

  12. Climate Trend Detection using Sea-Surface Temperature Data-sets from the (A)ATSR and AVHRR Space Sensors.

    Science.gov (United States)

    Llewellyn-Jones, D. T.; Corlett, G. K.; Remedios, J. J.; Noyes, E. J.; Good, S. A.

    2007-05-01

    Sea-Surface Temperature (SST) is an important indicator of global change, designated by GCOS as an essential Climate Variable (ECV). The detection of trends in Global SST requires rigorous measurements that are not only global, but also highly accurate and consistent. Space instruments can provide the means to achieve these required attributes in SST data. This paper presents an analysis of 15 years of SST data from two independent data sets, generated from the (A)ATSR and AVHRR series of sensors respectively. The analyses reveal trends of increasing global temperature between 0.13°C to 0.18 °C, per decade, closely matching that expected from some current predictions. A high level of consistency in the results from the two independent observing systems is seen, which gives increased confidence in data from both systems and also enables comparative analyses of the accuracy and stability of both data sets to be carried out. The conclusion is that these satellite SST data-sets provide important means to quantify and explore the processes of climate change. An analysis based upon singular value decomposition, allowing the removal of gross transitory disturbances, notably the El Niño, in order to examine regional areas of change other than the tropical Pacific, is also presented. Interestingly, although El Niño events clearly affect SST globally, they are found to have a non- significant (within error) effect on the calculated trends, which changed by only 0.01 K/decade when the pattern of El Niño and the associated variations was removed from the SST record. Although similar global trends were calculated for these two independent data sets, larger regional differences are noted. Evidence of decreased temperatures after the eruption of Mount Pinatubo in 1991 was also observed. The methodology demonstrated here can be applied to other data-sets, which cover long time-series observations of geophysical observations in order to characterise long-term change.

  13. Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements

    Directory of Open Access Journals (Sweden)

    Mohammad Choker

    2017-01-01

    Full Text Available The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model “IEM”, Oh, Dubois, and Advanced Integral Equation Model “AIEM” using a wide dataset of SAR (Synthetic Aperture Radar data and experimental soil measurements. These forward models reproduce the radar backscattering coefficients ( σ 0 from soil surface characteristics (dielectric constant, roughness and SAR sensor parameters (radar wavelength, incidence angle, polarization. The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness. Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB and better correlation with real data in VV polarization (RMSE = 2.9 dB. The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM. Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B is the most adequate to estimate soil moisture and roughness from SAR data.

  14. MilxXplore: a web-based system to explore large imaging datasets.

    Science.gov (United States)

    Bourgeat, P; Dore, V; Villemagne, V L; Rowe, C C; Salvado, O; Fripp, J

    2013-01-01

    As large-scale medical imaging studies are becoming more common, there is an increasing reliance on automated software to extract quantitative information from these images. As the size of the cohorts keeps increasing with large studies, there is a also a need for tools that allow results from automated image processing and analysis to be presented in a way that enables fast and efficient quality checking, tagging and reporting on cases in which automatic processing failed or was problematic. MilxXplore is an open source visualization platform, which provides an interface to navigate and explore imaging data in a web browser, giving the end user the opportunity to perform quality control and reporting in a user friendly, collaborative and efficient way. Compared to existing software solutions that often provide an overview of the results at the subject's level, MilxXplore pools the results of individual subjects and time points together, allowing easy and efficient navigation and browsing through the different acquisitions of a subject over time, and comparing the results against the rest of the population. MilxXplore is fast, flexible and allows remote quality checks of processed imaging data, facilitating data sharing and collaboration across multiple locations, and can be easily integrated into a cloud computing pipeline. With the growing trend of open data and open science, such a tool will become increasingly important to share and publish results of imaging analysis.

  15. A highly efficient multi-core algorithm for clustering extremely large datasets

    Directory of Open Access Journals (Sweden)

    Kraus Johann M

    2010-04-01

    Full Text Available Abstract Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.

  16. Discovery of Protein–lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets

    International Nuclear Information System (INIS)

    Li, Jun-Hao; Liu, Shun; Zheng, Ling-Ling; Wu, Jie; Sun, Wen-Ju; Wang, Ze-Lin; Zhou, Hui; Qu, Liang-Hu; Yang, Jian-Hua

    2015-01-01

    Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.

  17. Discovery of Protein–lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jun-Hao; Liu, Shun; Zheng, Ling-Ling; Wu, Jie; Sun, Wen-Ju; Wang, Ze-Lin; Zhou, Hui; Qu, Liang-Hu, E-mail: lssqlh@mail.sysu.edu.cn; Yang, Jian-Hua, E-mail: lssqlh@mail.sysu.edu.cn [RNA Information Center, Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou (China)

    2015-01-14

    Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.

  18. Calculating p-values and their significances with the Energy Test for large datasets

    Science.gov (United States)

    Barter, W.; Burr, C.; Parkes, C.

    2018-04-01

    The energy test method is a multi-dimensional test of whether two samples are consistent with arising from the same underlying population, through the calculation of a single test statistic (called the T-value). The method has recently been used in particle physics to search for samples that differ due to CP violation. The generalised extreme value function has previously been used to describe the distribution of T-values under the null hypothesis that the two samples are drawn from the same underlying population. We show that, in a simple test case, the distribution is not sufficiently well described by the generalised extreme value function. We present a new method, where the distribution of T-values under the null hypothesis when comparing two large samples can be found by scaling the distribution found when comparing small samples drawn from the same population. This method can then be used to quickly calculate the p-values associated with the results of the test.

  19. GEMINI: a computationally-efficient search engine for large gene expression datasets.

    Science.gov (United States)

    DeFreitas, Timothy; Saddiki, Hachem; Flaherty, Patrick

    2016-02-24

    Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an [Formula: see text] expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.

  20. BigWig and BigBed: enabling browsing of large distributed datasets.

    Science.gov (United States)

    Kent, W J; Zweig, A S; Barber, G; Hinrichs, A S; Karolchik, D

    2010-09-01

    BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser. The visualization is implemented using a multi-layered software approach that takes advantage of specific capabilities of web-based protocols and Linux and UNIX operating systems files, R trees and various indexing and compression tricks. As a result, only the data needed to support the current browser view is transmitted rather than the entire file, enabling fast remote access to large distributed data sets. Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/. Source code for the creation and visualization software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. The UCSC Genome Browser is available at http://genome.ucsc.edu.

  1. DnaSAM: Software to perform neutrality testing for large datasets with complex null models.

    Science.gov (United States)

    Eckert, Andrew J; Liechty, John D; Tearse, Brandon R; Pande, Barnaly; Neale, David B

    2010-05-01

    Patterns of DNA sequence polymorphisms can be used to understand the processes of demography and adaptation within natural populations. High-throughput generation of DNA sequence data has historically been the bottleneck with respect to data processing and experimental inference. Advances in marker technologies have largely solved this problem. Currently, the limiting step is computational, with most molecular population genetic software allowing a gene-by-gene analysis through a graphical user interface. An easy-to-use analysis program that allows both high-throughput processing of multiple sequence alignments along with the flexibility to simulate data under complex demographic scenarios is currently lacking. We introduce a new program, named DnaSAM, which allows high-throughput estimation of DNA sequence diversity and neutrality statistics from experimental data along with the ability to test those statistics via Monte Carlo coalescent simulations. These simulations are conducted using the ms program, which is able to incorporate several genetic parameters (e.g. recombination) and demographic scenarios (e.g. population bottlenecks). The output is a set of diversity and neutrality statistics with associated probability values under a user-specified null model that are stored in easy to manipulate text file. © 2009 Blackwell Publishing Ltd.

  2. Functional Neuroimaging Distinguishes Posttraumatic Stress Disorder from Traumatic Brain Injury in Focused and Large Community Datasets.

    Science.gov (United States)

    Amen, Daniel G; Raji, Cyrus A; Willeumier, Kristen; Taylor, Derek; Tarzwell, Robert; Newberg, Andrew; Henderson, Theodore A

    2015-01-01

    Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are highly heterogeneous and often present with overlapping symptomology, providing challenges in reliable classification and treatment. Single photon emission computed tomography (SPECT) may be advantageous in the diagnostic separation of these disorders when comorbid or clinically indistinct. Subjects were selected from a multisite database, where rest and on-task SPECT scans were obtained on a large group of neuropsychiatric patients. Two groups were analyzed: Group 1 with TBI (n=104), PTSD (n=104) or both (n=73) closely matched for demographics and comorbidity, compared to each other and healthy controls (N=116); Group 2 with TBI (n=7,505), PTSD (n=1,077) or both (n=1,017) compared to n=11,147 without either. ROIs and visual readings (VRs) were analyzed using a binary logistic regression model with predicted probabilities inputted into a Receiver Operating Characteristic analysis to identify sensitivity, specificity, and accuracy. One-way ANOVA identified the most diagnostically significant regions of increased perfusion in PTSD compared to TBI. Analysis included a 10-fold cross validation of the protocol in the larger community sample (Group 2). For Group 1, baseline and on-task ROIs and VRs showed a high level of accuracy in differentiating PTSD, TBI and PTSD+TBI conditions. This carefully matched group separated with 100% sensitivity, specificity and accuracy for the ROI analysis and at 89% or above for VRs. Group 2 had lower sensitivity, specificity and accuracy, but still in a clinically relevant range. Compared to subjects with TBI, PTSD showed increases in the limbic regions, cingulum, basal ganglia, insula, thalamus, prefrontal cortex and temporal lobes. This study demonstrates the ability to separate PTSD and TBI from healthy controls, from each other, and detect their co-occurrence, even in highly comorbid samples, using SPECT. This modality may offer a clinical option for aiding

  3. Functional Neuroimaging Distinguishes Posttraumatic Stress Disorder from Traumatic Brain Injury in Focused and Large Community Datasets.

    Directory of Open Access Journals (Sweden)

    Daniel G Amen

    Full Text Available Traumatic brain injury (TBI and posttraumatic stress disorder (PTSD are highly heterogeneous and often present with overlapping symptomology, providing challenges in reliable classification and treatment. Single photon emission computed tomography (SPECT may be advantageous in the diagnostic separation of these disorders when comorbid or clinically indistinct.Subjects were selected from a multisite database, where rest and on-task SPECT scans were obtained on a large group of neuropsychiatric patients. Two groups were analyzed: Group 1 with TBI (n=104, PTSD (n=104 or both (n=73 closely matched for demographics and comorbidity, compared to each other and healthy controls (N=116; Group 2 with TBI (n=7,505, PTSD (n=1,077 or both (n=1,017 compared to n=11,147 without either. ROIs and visual readings (VRs were analyzed using a binary logistic regression model with predicted probabilities inputted into a Receiver Operating Characteristic analysis to identify sensitivity, specificity, and accuracy. One-way ANOVA identified the most diagnostically significant regions of increased perfusion in PTSD compared to TBI. Analysis included a 10-fold cross validation of the protocol in the larger community sample (Group 2.For Group 1, baseline and on-task ROIs and VRs showed a high level of accuracy in differentiating PTSD, TBI and PTSD+TBI conditions. This carefully matched group separated with 100% sensitivity, specificity and accuracy for the ROI analysis and at 89% or above for VRs. Group 2 had lower sensitivity, specificity and accuracy, but still in a clinically relevant range. Compared to subjects with TBI, PTSD showed increases in the limbic regions, cingulum, basal ganglia, insula, thalamus, prefrontal cortex and temporal lobes.This study demonstrates the ability to separate PTSD and TBI from healthy controls, from each other, and detect their co-occurrence, even in highly comorbid samples, using SPECT. This modality may offer a clinical option for

  4. COINS: An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets

    Directory of Open Access Journals (Sweden)

    Adam eScott

    2011-12-01

    Full Text Available The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies' implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting fMRI, diffusion tensor imaging, and structural imaging the potential of pooling data across studies continues to gain momentum.At the Mind Research Network (MRN, we have developed the COllaborative Informatics and Neuroimaging Suite (COINS; http://coins.mrn.org to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data and other assessments. The system currently hosts data from 9 institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data-sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.

  5. A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source.

    Science.gov (United States)

    Atwood, Robert C; Bodey, Andrew J; Price, Stephen W T; Basham, Mark; Drakopoulos, Michael

    2015-06-13

    Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu, a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an 'orthogonal' fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and 'facility-independent': it can run on standard cluster infrastructure at any institution.

  6. Fabric strain sensor integrated with CNPECs for repeated large deformation

    Science.gov (United States)

    Yi, Weijing

    Flexible and soft strain sensors that can be used in smart textiles for wearable applications are much desired. They should meet the requirements of low modulus, large working range and good fatigue resistance as well as good sensing performances. However, there were no commercial products available and the objective of the thesis is to investigate fabric strain sensors based on carbon nanoparticle (CNP) filled elastomer composites (CNPECs) for potential wearing applications. Conductive CNPECs were fabricated and investigated. The introduction of silicone oil (SO) significantly decreased modulus of the composites to less than 1 MPa without affecting their deformability and they showed good stability after heat treatment. With increase of CNP concentration, a percolation appeared in electrical resistivity and the composites can be divided into three ranges. I-V curves and impedance spectra together with electro-mechanical studies demonstrated a balance between sensitivity and working range for the composites with CNP concentrations in post percolation range, and were preferred for sensing applications only if the fatigue life was improved. Due to the good elasticity and failure resist property of knitted fabric under repeated extension, it was adopted as substrate to increase the fatigue life of the conductive composites. After optimization of processing parameters, the conductive fabric with CNP concentration of 9.0CNP showed linear I-V curves when voltage is in the range of -1 V/mm and 1 V/mm and negligible capacitive behavior when frequency below 103 Hz even with strain of 60%. It showed higher sensitivity due to the combination of nonlinear resistance-strain behavior of the CNPECs and non-even strain distribution of knitted fabric under extension. The fatigue life of the conductive fabric was greatly improved. Extended on the studies of CNPECs and the coated conductive fabrics, a fabric strain sensor was designed, fabricated and packaged. The Young's modulus of

  7. Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked?

    KAUST Repository

    Sun, Ying

    2012-10-01

    © 2012 John Wiley & Sons, Ltd. Band depth is an important nonparametric measure that generalizes order statistics and makes univariate methods based on order statistics possible for functional data. However, the computational burden of band depth limits its applicability when large functional or image datasets are considered. This paper proposes an exact fast method to speed up the band depth computation when bands are defined by two curves. Remarkable computational gains are demonstrated through simulation studies comparing our proposal with the original computation and one existing approximate method. For example, we report an experiment where our method can rank one million curves, evaluated at fifty time points each, in 12.4 seconds with Matlab.

  8. Geostatistics for Large Datasets

    KAUST Repository

    Sun, Ying

    2011-10-31

    Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear onlineat www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstractcommand for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstractcommand if the abstract is also to appear in the printed version of the book.

  9. Geostatistics for Large Datasets

    KAUST Repository

    Sun, Ying; Li, Bo; Genton, Marc G.

    2011-01-01

    Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear onlineat www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstractcommand for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstractcommand if the abstract is also to appear in the printed version of the book.

  10. Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2013-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

  11. CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets.

    Science.gov (United States)

    Flobak, Åsmund; Vazquez, Miguel; Lægreid, Astrid; Valencia, Alfonso

    2017-08-01

    Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required. CImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships. CImbinator is written in Ruby and R. It uses the R package drc for advanced drug response modeling. CImbinator is available at http://cimbinator.bioinfo.cnio.es , the source-code is open and available at https://github.com/Rbbt-Workflows/combination_index . A Docker image is also available at https://hub.docker.com/r/mikisvaz/rbbt-ci_mbinator/ . asmund.flobak@ntnu.no or miguel.vazquez@cnio.es. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  12. Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

    Directory of Open Access Journals (Sweden)

    E. H. Sutanudjaja

    2011-09-01

    Full Text Available The current generation of large-scale hydrological models does not include a groundwater flow component. Large-scale groundwater models, involving aquifers and basins of multiple countries, are still rare mainly due to a lack of hydro-geological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Results are promising despite the fact that we still use an offline procedure to couple the land surface and MODFLOW groundwater models (i.e. the simulations of both models are separately performed. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydro-geological parameter settings, we observe that the model can reasonably well reproduce the observed groundwater head time series. However, we note that there are still some limitations in the current approach, specifically because the offline-coupling technique simplifies the dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

  13. A Large-Scale Study of Fingerprint Matching Systems for Sensor Interoperability Problem

    Directory of Open Access Journals (Sweden)

    Helala AlShehri

    2018-03-01

    Full Text Available The fingerprint is a commonly used biometric modality that is widely employed for authentication by law enforcement agencies and commercial applications. The designs of existing fingerprint matching methods are based on the hypothesis that the same sensor is used to capture fingerprints during enrollment and verification. Advances in fingerprint sensor technology have raised the question about the usability of current methods when different sensors are employed for enrollment and verification; this is a fingerprint sensor interoperability problem. To provide insight into this problem and assess the status of state-of-the-art matching methods to tackle this problem, we first analyze the characteristics of fingerprints captured with different sensors, which makes cross-sensor matching a challenging problem. We demonstrate the importance of fingerprint enhancement methods for cross-sensor matching. Finally, we conduct a comparative study of state-of-the-art fingerprint recognition methods and provide insight into their abilities to address this problem. We performed experiments using a public database (FingerPass that contains nine datasets captured with different sensors. We analyzed the effects of different sensors and found that cross-sensor matching performance deteriorates when different sensors are used for enrollment and verification. In view of our analysis, we propose future research directions for this problem.

  14. A Large-Scale Study of Fingerprint Matching Systems for Sensor Interoperability Problem.

    Science.gov (United States)

    AlShehri, Helala; Hussain, Muhammad; AboAlSamh, Hatim; AlZuair, Mansour

    2018-03-28

    The fingerprint is a commonly used biometric modality that is widely employed for authentication by law enforcement agencies and commercial applications. The designs of existing fingerprint matching methods are based on the hypothesis that the same sensor is used to capture fingerprints during enrollment and verification. Advances in fingerprint sensor technology have raised the question about the usability of current methods when different sensors are employed for enrollment and verification; this is a fingerprint sensor interoperability problem. To provide insight into this problem and assess the status of state-of-the-art matching methods to tackle this problem, we first analyze the characteristics of fingerprints captured with different sensors, which makes cross-sensor matching a challenging problem. We demonstrate the importance of fingerprint enhancement methods for cross-sensor matching. Finally, we conduct a comparative study of state-of-the-art fingerprint recognition methods and provide insight into their abilities to address this problem. We performed experiments using a public database (FingerPass) that contains nine datasets captured with different sensors. We analyzed the effects of different sensors and found that cross-sensor matching performance deteriorates when different sensors are used for enrollment and verification. In view of our analysis, we propose future research directions for this problem.

  15. A figure control sensor for the Large Deployable Reflector (LDR)

    Science.gov (United States)

    Bartman, R.; Dubovitsky, S.

    1988-01-01

    A sensing and control system is required to maintain high optical figure quality in a segmented reflector. Upon detecting a deviation of the segmented surface from its ideal form, the system drives segment mounted actuators to realign the individual segments and thereby return the surface to its intended figure. When the reflector is in use, a set of figure sensors will determine positions of a number of points on the back surface of each of the reflector's segments, each sensor being assigned to a single point. By measuring the positional deviations of these points from previously established nominal values, the figure sensors provide the control system with the information required to maintain the reflector's optical figure. The optical lever, multiple wavelength interferometer, and electronic capacitive sensor, the most promising technologies for the development of the figure sensor, are illustrated. It is concluded that to select a particular implementation of the figure sensors, performance requirement will be refined and relevant technologies investigated further.

  16. Large-Scale, Parallel, Multi-Sensor Data Fusion in the Cloud

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.

    2012-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To efficiently assemble such decade-scale datasets in a timely manner, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. "SciReduce" is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, in which simple tuples (keys & values) are passed between the map and reduce functions, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Thus, SciReduce uses the native datatypes (geolocated grids, swaths, and points) that geo-scientists are familiar with. We are deploying within Sci

  17. Sensor equipment for quantification of spatial heterogeneity in large bioreactor

    DEFF Research Database (Denmark)

    Nørregaard, Anders; Formenti, Luca Riccardo; Stocks, Stuart M.

    of sensors and in order to apply more sensor equipment the bioreactor has to be modified which is both costly and results in production downtime. The presence of three phases (gas, liquid, and solid), and the opaque nature of the fermentation broth together with the necessity of heat sterilization further...... increases the requirements to the sensor equipment. In order to address these issues this study aims to make an investigation into freely floating, battery driven sensor particles that can follow the liquid movement in the reactor and make measurements while being distributed in the whole volume...

  18. Design of a Large-scale Three-dimensional Flexible Arrayed Tactile Sensor

    Directory of Open Access Journals (Sweden)

    Junxiang Ding

    2011-01-01

    Full Text Available This paper proposes a new type of large-scale three-dimensional flexible arrayed tactile sensor based on conductive rubber. It can be used to detect three-dimensional force information on the continuous surface of the sensor, which realizes a true skin type tactile sensor. The widely used method of liquid rubber injection molding (LIMS method is used for "the overall injection molding" sample preparation. The structure details of staggered nodes and a new decoupling algorithm of force analysis are given. Simulation results show that the sensor based on this structure can achieve flexible measurement of large-scale 3-D tactile sensor arrays.

  19. Solving the challenges of data preprocessing, uploading, archiving, retrieval, analysis and visualization for large heterogeneous paleo- and rock magnetic datasets

    Science.gov (United States)

    Minnett, R.; Koppers, A. A.; Tauxe, L.; Constable, C.; Jarboe, N. A.

    2011-12-01

    The Magnetics Information Consortium (MagIC) provides an archive for the wealth of rock- and paleomagnetic data and interpretations from studies on natural and synthetic samples. As with many fields, most peer-reviewed paleo- and rock magnetic publications only include high level results. However, access to the raw data from which these results were derived is critical for compilation studies and when updating results based on new interpretation and analysis methods. MagIC provides a detailed metadata model with places for everything from raw measurements to their interpretations. Prior to MagIC, these raw data were extremely cumbersome to collect because they mostly existed in a lab's proprietary format on investigator's personal computers or undigitized in field notebooks. MagIC has developed a suite of offline and online tools to enable the paleomagnetic, rock magnetic, and affiliated scientific communities to easily contribute both their previously published data and data supporting an article undergoing peer-review, to retrieve well-annotated published interpretations and raw data, and to analyze and visualize large collections of published data online. Here we present the technology we chose (including VBA in Excel spreadsheets, Python libraries, FastCGI JSON webservices, Oracle procedures, and jQuery user interfaces) and how we implemented it in order to serve the scientific community as seamlessly as possible. These tools are now in use in labs worldwide, have helped archive many valuable legacy studies and datasets, and routinely enable new contributions to the MagIC Database (http://earthref.org/MAGIC/).

  20. Insights into SCP/TAPS proteins of liver flukes based on large-scale bioinformatic analyses of sequence datasets.

    Directory of Open Access Journals (Sweden)

    Cinzia Cantacessi

    Full Text Available BACKGROUND: SCP/TAPS proteins of parasitic helminths have been proposed to play key roles in fundamental biological processes linked to the invasion of and establishment in their mammalian host animals, such as the transition from free-living to parasitic stages and the modulation of host immune responses. Despite the evidence that SCP/TAPS proteins of parasitic nematodes are involved in host-parasite interactions, there is a paucity of information on this protein family for parasitic trematodes of socio-economic importance. METHODOLOGY/PRINCIPAL FINDINGS: We conducted the first large-scale study of SCP/TAPS proteins of a range of parasitic trematodes of both human and veterinary importance (including the liver flukes Clonorchis sinensis, Opisthorchis viverrini, Fasciola hepatica and F. gigantica as well as the blood flukes Schistosoma mansoni, S. japonicum and S. haematobium. We mined all current transcriptomic and/or genomic sequence datasets from public databases, predicted secondary structures of full-length protein sequences, undertook systematic phylogenetic analyses and investigated the differential transcription of SCP/TAPS genes in O. viverrini and F. hepatica, with an emphasis on those that are up-regulated in the developmental stages infecting the mammalian host. CONCLUSIONS: This work, which sheds new light on SCP/TAPS proteins, guides future structural and functional explorations of key SCP/TAPS molecules associated with diseases caused by flatworms. Future fundamental investigations of these molecules in parasites and the integration of structural and functional data could lead to new approaches for the control of parasitic diseases.

  1. Transparent Fingerprint Sensor System for Large Flat Panel Display

    Directory of Open Access Journals (Sweden)

    Wonkuk Seo

    2018-01-01

    Full Text Available In this paper, we introduce a transparent fingerprint sensing system using a thin film transistor (TFT sensor panel, based on a self-capacitive sensing scheme. An armorphousindium gallium zinc oxide (a-IGZO TFT sensor array and associated custom Read-Out IC (ROIC are implemented for the system. The sensor panel has a 200 × 200 pixel array and each pixel size is as small as 50 μm × 50 μm. The ROIC uses only eight analog front-end (AFE amplifier stages along with a successive approximation analog-to-digital converter (SAR ADC. To get the fingerprint image data from the sensor array, the ROIC senses a capacitance, which is formed by a cover glass material between a human finger and an electrode of each pixel of the sensor array. Three methods are reviewed for estimating the self-capacitance. The measurement result demonstrates that the transparent fingerprint sensor system has an ability to differentiate a human finger’s ridges and valleys through the fingerprint sensor array.

  2. Transparent Fingerprint Sensor System for Large Flat Panel Display.

    Science.gov (United States)

    Seo, Wonkuk; Pi, Jae-Eun; Cho, Sung Haeung; Kang, Seung-Youl; Ahn, Seong-Deok; Hwang, Chi-Sun; Jeon, Ho-Sik; Kim, Jong-Uk; Lee, Myunghee

    2018-01-19

    In this paper, we introduce a transparent fingerprint sensing system using a thin film transistor (TFT) sensor panel, based on a self-capacitive sensing scheme. An armorphousindium gallium zinc oxide (a-IGZO) TFT sensor array and associated custom Read-Out IC (ROIC) are implemented for the system. The sensor panel has a 200 × 200 pixel array and each pixel size is as small as 50 μm × 50 μm. The ROIC uses only eight analog front-end (AFE) amplifier stages along with a successive approximation analog-to-digital converter (SAR ADC). To get the fingerprint image data from the sensor array, the ROIC senses a capacitance, which is formed by a cover glass material between a human finger and an electrode of each pixel of the sensor array. Three methods are reviewed for estimating the self-capacitance. The measurement result demonstrates that the transparent fingerprint sensor system has an ability to differentiate a human finger's ridges and valleys through the fingerprint sensor array.

  3. Range-Free Localization Schemes for Large Scale Sensor Networks

    National Research Council Canada - National Science Library

    He, Tian; Huang, Chengdu; Blum, Brain M; Stankovic, John A; Abdelzaher, Tarek

    2003-01-01

    .... Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches...

  4. Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2015-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be

  5. Mining microarray datasets in nutrition: expression of the GPR120 (n-3 fatty acid receptor/sensor) gene is down-regulated in human adipocytes by macrophage secretions.

    Science.gov (United States)

    Trayhurn, Paul; Denyer, Gareth

    2012-01-01

    Microarray datasets are a rich source of information in nutritional investigation. Targeted mining of microarray data following initial, non-biased bioinformatic analysis can provide key insight into specific genes and metabolic processes of interest. Microarrays from human adipocytes were examined to explore the effects of macrophage secretions on the expression of the G-protein-coupled receptor (GPR) genes that encode fatty acid receptors/sensors. Exposure of the adipocytes to macrophage-conditioned medium for 4 or 24 h had no effect on GPR40 and GPR43 expression, but there was a marked stimulation of GPR84 expression (receptor for medium-chain fatty acids), the mRNA level increasing 13·5-fold at 24 h relative to unconditioned medium. Importantly, expression of GPR120, which encodes an n-3 PUFA receptor/sensor, was strongly inhibited by the conditioned medium (15-fold decrease in mRNA at 24 h). Macrophage secretions have major effects on the expression of fatty acid receptor/sensor genes in human adipocytes, which may lead to an augmentation of the inflammatory response in adipose tissue in obesity.

  6. Assessment of radiation damage behaviour in a large collection of empirically optimized datasets highlights the importance of unmeasured complicating effects

    International Nuclear Information System (INIS)

    Krojer, Tobias; Delft, Frank von

    2011-01-01

    A retrospective analysis of radiation damage behaviour in a statistically significant number of real-life datasets is presented, in order to gauge the importance of the complications not yet measured or rigorously evaluated in current experiments, and the challenges that remain before radiation damage can be considered a problem solved in practice. The radiation damage behaviour in 43 datasets of 34 different proteins collected over a year was examined, in order to gauge the reliability of decay metrics in practical situations, and to assess how these datasets, optimized only empirically for decay, would have benefited from the precise and automatic prediction of decay now possible with the programs RADDOSE [Murray, Garman & Ravelli (2004 ▶). J. Appl. Cryst.37, 513–522] and BEST [Bourenkov & Popov (2010 ▶). Acta Cryst. D66, 409–419]. The results indicate that in routine practice the diffraction experiment is not yet characterized well enough to support such precise predictions, as these depend fundamentally on three interrelated variables which cannot yet be determined robustly and practically: the flux density distribution of the beam; the exact crystal volume; the sensitivity of the crystal to dose. The former two are not satisfactorily approximated from typical beamline information such as nominal beam size and transmission, or two-dimensional images of the beam and crystal; the discrepancies are particularly marked when using microfocus beams (<20 µm). Empirically monitoring decay with the dataset scaling B factor (Bourenkov & Popov, 2010 ▶) appears more robust but is complicated by anisotropic and/or low-resolution diffraction. These observations serve to delineate the challenges, scientific and logistic, that remain to be addressed if tools for managing radiation damage in practical data collection are to be conveniently robust enough to be useful in real time

  7. Lunar Mapping and Modeling On-the-Go: A mobile framework for viewing and interacting with large geospatial datasets

    Science.gov (United States)

    Chang, G.; Kim, R.; Bui, B.; Sadaqathullah, S.; Law, E.; Malhotra, S.

    2012-12-01

    bookmark those layers for quick access in subsequent sessions. A search tool is also provided to allow users to quickly find points of interests on the moon and to view the auxiliary data associated with that feature. More advanced features include the ability to interact with the data. Using the services provided by the portal, users will be able to log in and access the same scientific analysis tools provided on the web site including measuring between two points, generating subsets, and running other analysis tools, all by using a customized touch interface that are immediately familiar to users of these smart mobile devices. Users can also access their own storage on the portal and view or send the data to other users. Finally, there are features that will utilize functionality that can only be enabled by mobile devices. This includes the use of the gyroscopes and motion sensors to provide a haptic interface visualize lunar data in 3D, on the device as well as potentially on a large screen. The mobile framework that we have developed for LMMP provides a glimpse of what is possible in visualizing and manipulating large geospatial data on small portable devices. While the framework is currently tuned to our portal, we hope that we can generalize the tool to use data sources from any type of GIS services.

  8. Towards Slow-Moving Landslide Monitoring by Integrating Multi-Sensor InSAR Time Series Datasets: The Zhouqu Case Study, China

    Directory of Open Access Journals (Sweden)

    Qian Sun

    2016-11-01

    Full Text Available Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS and 18 descending images from the Environmental Satellite (ENVISAT have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface–parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu.

  9. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks.

    Science.gov (United States)

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-08-16

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.

  10. Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

    NARCIS (Netherlands)

    Hu, L.; Chen, Y.; Scanlon, W.G.

    2011-01-01

    The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that

  11. Comparison of silicon strip tracker module size using large sensors from 6 inch wafers

    CERN Multimedia

    Honma, Alan

    1999-01-01

    Two large silicon strip sensor made from 6 inch wafers are placed next to each other to simulate the size of a CMS outer silicon tracker module. On the left is a prototype 2 sensor CMS inner endcap silicon tracker module made from 4 inch wafers.

  12. Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions.

    Science.gov (United States)

    Tao, Lu-Qi; Wang, Dan-Yang; Tian, He; Ju, Zhen-Yi; Liu, Ying; Pang, Yu; Chen, Yuan-Quan; Yang, Yi; Ren, Tian-Ling

    2017-06-22

    Conventional strain sensors rarely have both a high gauge factor and a large strain range simultaneously, so they can only be used in specific situations where only a high sensitivity or a large strain range is required. However, for detecting human motions that include both subtle and large motions, these strain sensors can't meet the diverse demands simultaneously. Here, we come up with laser patterned graphene strain sensors with self-adapted and tunable performance for the first time. A series of strain sensors with either an ultrahigh gauge factor or a preferable strain range can be fabricated simultaneously via one-step laser patterning, and are suitable for detecting all human motions. The strain sensors have a GF of up to 457 with a strain range of 35%, or have a strain range of up to 100% with a GF of 268. Most importantly, the performance of the strain sensors can be easily tuned by adjusting the patterns of the graphene, so that the sensors can meet diverse demands in both subtle and large motion situations. The graphene strain sensors show significant potential in applications such as wearable electronics, health monitoring and intelligent robots. Furthermore, the facile, fast and low-cost fabrication method will make them possible and practical to be used for commercial applications in the future.

  13. 3D-Printed Disposable Wireless Sensors with Integrated Microelectronics for Large Area Environmental Monitoring

    KAUST Repository

    Farooqui, Muhammad Fahad; Karimi, Muhammad Akram; Salama, Khaled N.; Shamim, Atif

    2017-01-01

    disposable, compact, dispersible 3D-printed wireless sensor nodes with integrated microelectronics which can be dispersed in the environment and work in conjunction with few fixed nodes for large area monitoring applications. As a proof of concept

  14. Large-Area All-Textile Pressure Sensors for Monitoring Human Motion and Physiological Signals.

    Science.gov (United States)

    Liu, Mengmeng; Pu, Xiong; Jiang, Chunyan; Liu, Ting; Huang, Xin; Chen, Libo; Du, Chunhua; Sun, Jiangman; Hu, Weiguo; Wang, Zhong Lin

    2017-11-01

    Wearable pressure sensors, which can perceive and respond to environmental stimuli, are essential components of smart textiles. Here, large-area all-textile-based pressure-sensor arrays are successfully realized on common fabric substrates. The textile sensor unit achieves high sensitivity (14.4 kPa -1 ), low detection limit (2 Pa), fast response (≈24 ms), low power consumption (sensor is demonstrated to be able to recognize finger movement, hand gestures, acoustic vibrations, and real-time pulse wave. Furthermore, large-area sensor arrays are successfully fabricated on one textile substrate to spatially map tactile stimuli and can be directly incorporated into a fabric garment for stylish designs without sacrifice of comfort, suggesting great potential in smart textiles or wearable electronics. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2016-08-01

    Full Text Available In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors’ local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.

  16. Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems.

    Science.gov (United States)

    Semnani, Samaneh Hosseini; Basir, Otman A

    2015-01-01

    The ability of sensors to self-organize is an important asset in surveillance sensor networks. Self-organize implies self-control at the sensor level and coordination at the network level. Biologically inspired approaches have recently gained significant attention as a tool to address the issue of sensor control and coordination in sensor networks. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous robust dynamic area coverage and target coverage. These two coverage performance objectives are inherently conflicting. This paper presents Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. This allows the algorithm to strike balance between robust area coverage and target coverage. Such balance is facilitated via flock-sensor coordination. The performance of the proposed Semi-Flocking algorithm is examined and compared with other two flocking-based algorithms once using randomly moving targets and once using a standard walking pedestrian dataset. The results of both experiments show that the Semi-Flocking algorithm outperforms both the Flocking algorithm and the Anti-Flocking algorithm with respect to the area of coverage and the target coverage objectives. Furthermore, the results show that the proposed algorithm demonstrates shorter target detection time and fewer undetected targets than the other two flocking-based algorithms.

  17. Proteomics dataset

    DEFF Research Database (Denmark)

    Bennike, Tue Bjerg; Carlsen, Thomas Gelsing; Ellingsen, Torkell

    2017-01-01

    The datasets presented in this article are related to the research articles entitled “Neutrophil Extracellular Traps in Ulcerative Colitis: A Proteome Analysis of Intestinal Biopsies” (Bennike et al., 2015 [1]), and “Proteome Analysis of Rheumatoid Arthritis Gut Mucosa” (Bennike et al., 2017 [2])...... been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD001608 for ulcerative colitis and control samples, and PXD003082 for rheumatoid arthritis samples....

  18. 3D-Printed Disposable Wireless Sensors with Integrated Microelectronics for Large Area Environmental Monitoring

    KAUST Repository

    Farooqui, Muhammad Fahad

    2017-05-19

    Large area environmental monitoring can play a crucial role in dealing with crisis situations. However, it is challenging as implementing a fixed sensor network infrastructure over large remote area is economically unfeasible. This work proposes disposable, compact, dispersible 3D-printed wireless sensor nodes with integrated microelectronics which can be dispersed in the environment and work in conjunction with few fixed nodes for large area monitoring applications. As a proof of concept, the wireless sensing of temperature, humidity, and H2S levels are shown which are important for two critical environmental conditions namely forest fires and industrial leaks. These inkjet-printed sensors and an antenna are realized on the walls of a 3D-printed cubic package which encloses the microelectronics developed on a 3D-printed circuit board. Hence, 3D printing and inkjet printing are uniquely combined in order to realize a low-cost, fully integrated wireless sensor node.

  19. Epidermis Microstructure Inspired Graphene Pressure Sensor with Random Distributed Spinosum for High Sensitivity and Large Linearity.

    Science.gov (United States)

    Pang, Yu; Zhang, Kunning; Yang, Zhen; Jiang, Song; Ju, Zhenyi; Li, Yuxing; Wang, Xuefeng; Wang, Danyang; Jian, Muqiang; Zhang, Yingying; Liang, Renrong; Tian, He; Yang, Yi; Ren, Tian-Ling

    2018-03-27

    Recently, wearable pressure sensors have attracted tremendous attention because of their potential applications in monitoring physiological signals for human healthcare. Sensitivity and linearity are the two most essential parameters for pressure sensors. Although various designed micro/nanostructure morphologies have been introduced, the trade-off between sensitivity and linearity has not been well balanced. Human skin, which contains force receptors in a reticular layer, has a high sensitivity even for large external stimuli. Herein, inspired by the skin epidermis with high-performance force sensing, we have proposed a special surface morphology with spinosum microstructure of random distribution via the combination of an abrasive paper template and reduced graphene oxide. The sensitivity of the graphene pressure sensor with random distribution spinosum (RDS) microstructure is as high as 25.1 kPa -1 in a wide linearity range of 0-2.6 kPa. Our pressure sensor exhibits superior comprehensive properties compared with previous surface-modified pressure sensors. According to simulation and mechanism analyses, the spinosum microstructure and random distribution contribute to the high sensitivity and large linearity range, respectively. In addition, the pressure sensor shows promising potential in detecting human physiological signals, such as heartbeat, respiration, phonation, and human motions of a pushup, arm bending, and walking. The wearable pressure sensor array was further used to detect gait states of supination, neutral, and pronation. The RDS microstructure provides an alternative strategy to improve the performance of pressure sensors and extend their potential applications in monitoring human activities.

  20. Large-scale groundwater modeling using global datasets: A test case for the Rhine-Meuse basin

    NARCIS (Netherlands)

    Sutanudjaja, E.H.; Beek, L.P.H. van; Jong, S.M. de; Geer, F.C. van; Bierkens, M.F.P.

    2011-01-01

    Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global

  1. Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

    NARCIS (Netherlands)

    Sutanudjaja, E.H.; Beek, L.P.H. van; Jong, S.M. de; Geer, F.C. van; Bierkens, M.F.P.

    2011-01-01

    The current generation of large-scale hydrological models does not include a groundwater flow component. Large-scale groundwater models, involving aquifers and basins of multiple countries, are still rare mainly due to a lack of hydro-geological data which are usually only available in

  2. Large-scale groundwater modeling using global datasets: A test case for the Rhine-Meuse basin

    NARCIS (Netherlands)

    Sutanudjaja, E.H.; Beek, L.P.H. van; Jong, S.M. de; Geer, F.C. van; Bierkens, M.F.P.

    2011-01-01

    The current generation of large-scale hydrological models does not include a groundwater flow component. Large-scale groundwater models, involving aquifers and basins of multiple countries, are still rare mainly due to a lack of hydro-geological data which are usually only available in developed

  3. Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset

    Directory of Open Access Journals (Sweden)

    Eisenhaber Frank

    2010-02-01

    Full Text Available Abstract Background Algorithms designed to predict protein disorder play an important role in structural and functional genomics, as disordered regions have been reported to participate in important cellular processes. Consequently, several methods with different underlying principles for disorder prediction have been independently developed by various groups. For assessing their usability in automated workflows, we are interested in identifying parameter settings and threshold selections, under which the performance of these predictors becomes directly comparable. Results First, we derived a new benchmark set that accounts for different flavours of disorder complemented with a similar amount of order annotation derived for the same protein set. We show that, using the recommended default parameters, the programs tested are producing a wide range of predictions at different levels of specificity and sensitivity. We identify settings, in which the different predictors have the same false positive rate. We assess conditions when sets of predictors can be run together to derive consensus or complementary predictions. This is useful in the framework of proteome-wide applications where high specificity is required such as in our in-house sequence analysis pipeline and the ANNIE webserver. Conclusions This work identifies parameter settings and thresholds for a selection of disorder predictors to produce comparable results at a desired level of specificity over a newly derived benchmark dataset that accounts equally for ordered and disordered regions of different lengths.

  4. Implementation of large area CMOS image sensor module using the precision align inspection

    International Nuclear Information System (INIS)

    Kim, Byoung Wook; Kim, Toung Ju; Ryu, Cheol Woo; Lee, Kyung Yong; Kim, Jin Soo; Kim, Myung Soo; Cho, Gyu Seong

    2014-01-01

    This paper describes a large area CMOS image sensor module Implementation using the precision align inspection program. This work is needed because wafer cutting system does not always have high precision. The program check more than 8 point of sensor edges and align sensors with moving table. The size of a 2×1 butted CMOS image sensor module which except for the size of PCB is 170 mm×170 mm. And the pixel size is 55 μm×55 μm and the number of pixels is 3,072×3,072. The gap between the two CMOS image sensor module was arranged in less than one pixel size

  5. Implementation of large area CMOS image sensor module using the precision align inspection

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byoung Wook; Kim, Toung Ju; Ryu, Cheol Woo [Radiation Imaging Technology Center, JBTP, Iksan (Korea, Republic of); Lee, Kyung Yong; Kim, Jin Soo [Nano Sol-Tech INC., Iksan (Korea, Republic of); Kim, Myung Soo; Cho, Gyu Seong [Dept. of Nuclear and Quantum Engineering, KAIST, Daejeon (Korea, Republic of)

    2014-12-15

    This paper describes a large area CMOS image sensor module Implementation using the precision align inspection program. This work is needed because wafer cutting system does not always have high precision. The program check more than 8 point of sensor edges and align sensors with moving table. The size of a 2×1 butted CMOS image sensor module which except for the size of PCB is 170 mm×170 mm. And the pixel size is 55 μm×55 μm and the number of pixels is 3,072×3,072. The gap between the two CMOS image sensor module was arranged in less than one pixel size.

  6. An Accurate Method for Inferring Relatedness in Large Datasets of Unphased Genotypes via an Embedded Likelihood-Ratio Test

    KAUST Repository

    Rodriguez, Jesse M.; Batzoglou, Serafim; Bercovici, Sivan

    2013-01-01

    , accurate and efficient detection of hidden relatedness becomes a challenge. To enable disease-mapping studies of increasingly large cohorts, a fast and accurate method to detect IBD segments is required. We present PARENTE, a novel method for detecting

  7. Modification of input datasets for the Ensemble Streamflow Prediction based on large scale climatic indices and weather generator

    Czech Academy of Sciences Publication Activity Database

    Šípek, Václav; Daňhelka, J.

    2015-01-01

    Roč. 528, September (2015), s. 720-733 ISSN 0022-1694 Institutional support: RVO:67985874 Keywords : seasonal forecasting * ESP * large-scale climate * weather generator Subject RIV: DA - Hydrology ; Limnology Impact factor: 3.043, year: 2015

  8. Facing the Challenges of Accessing, Managing, and Integrating Large Observational Datasets in Ecology: Enabling and Enriching the Use of NEON's Observational Data

    Science.gov (United States)

    Thibault, K. M.

    2013-12-01

    As the construction of NEON and its transition to operations progresses, more and more data will become available to the scientific community, both from NEON directly and from the concomitant growth of existing data repositories. Many of these datasets include ecological observations of a diversity of taxa in both aquatic and terrestrial environments. Although observational data have been collected and used throughout the history of organismal biology, the field has not yet fully developed a culture of data management, documentation, standardization, sharing and discoverability to facilitate the integration and synthesis of datasets. Moreover, the tools required to accomplish these goals, namely database design, implementation, and management, and automation and parallelization of analytical tasks through computational techniques, have not historically been included in biology curricula, at either the undergraduate or graduate levels. To ensure the success of data-generating projects like NEON in advancing organismal ecology and to increase transparency and reproducibility of scientific analyses, an acceleration of the cultural shift to open science practices, the development and adoption of data standards, such as the DarwinCore standard for taxonomic data, and increased training in computational approaches for biologists need to be realized. Here I highlight several initiatives that are intended to increase access to and discoverability of publicly available datasets and equip biologists and other scientists with the skills that are need to manage, integrate, and analyze data from multiple large-scale projects. The EcoData Retriever (ecodataretriever.org) is a tool that downloads publicly available datasets, re-formats the data into an efficient relational database structure, and then automatically imports the data tables onto a user's local drive into the database tool of the user's choice. The automation of these tasks results in nearly instantaneous execution

  9. Fabrication and evaluation of hybrid silica/polymer optical fiber sensors for large strain measurement

    Science.gov (United States)

    Huang, Haiying

    2007-04-01

    Silica-based optical fiber sensors are widely used in structural health monitoring systems for strain and deflection measurement. One drawback of silica-based optical fiber sensors is their low strain toughness. In general, silica-based optical fiber sensors can only reliably measure strains up to 2%. Recently, polymer optical fiber sensors have been employed to measure large strain and deflection. Due to their high optical losses, the length of the polymer optical fibers is limited to 100 meters. In this paper, we present a novel economical technique to fabricate hybrid silica/polymer optical fiber strain sensors for large strain measurement. First, stress analysis of a surface-mounted optical fiber sensor is performed to understand the load distribution between the host structure and the optical fiber in relation to their mechanical properties. Next, the procedure of fabricating a polymer sensing element between two optical fibers is explained. The experimental set-up and the components used in the fabrication process are described in details. Mechanical testing results of the fabricated silica/polymer optical fiber strain sensor are presented.

  10. Impacts of a lengthening open water season on Alaskan coastal communities: deriving locally relevant indices from large-scale datasets and community observations

    Science.gov (United States)

    Rolph, Rebecca J.; Mahoney, Andrew R.; Walsh, John; Loring, Philip A.

    2018-05-01

    Using thresholds of physical climate variables developed from community observations, together with two large-scale datasets, we have produced local indices directly relevant to the impacts of a reduced sea ice cover on Alaska coastal communities. The indices include the number of false freeze-ups defined by transient exceedances of ice concentration prior to a corresponding exceedance that persists, false break-ups, timing of freeze-up and break-up, length of the open water duration, number of days when the winds preclude hunting via boat (wind speed threshold exceedances), the number of wind events conducive to geomorphological work or damage to infrastructure from ocean waves, and the number of these wind events with on- and along-shore components promoting water setup along the coastline. We demonstrate how community observations can inform use of large-scale datasets to derive these locally relevant indices. The two primary large-scale datasets are the Historical Sea Ice Atlas for Alaska and the atmospheric output from a regional climate model used to downscale the ERA-Interim atmospheric reanalysis. We illustrate the variability and trends of these indices by application to the rural Alaska communities of Kotzebue, Shishmaref, and Utqiaġvik (previously Barrow), although the same procedure and metrics can be applied to other coastal communities. Over the 1979-2014 time period, there has been a marked increase in the number of combined false freeze-ups and false break-ups as well as the number of days too windy for hunting via boat for all three communities, especially Utqiaġvik. At Utqiaġvik, there has been an approximate tripling of the number of wind events conducive to coastline erosion from 1979 to 2014. We have also found a delay in freeze-up and earlier break-up, leading to a lengthened open water period for all of the communities examined.

  11. Impacts of a lengthening open water season on Alaskan coastal communities: deriving locally relevant indices from large-scale datasets and community observations

    Directory of Open Access Journals (Sweden)

    R. J. Rolph

    2018-05-01

    Full Text Available Using thresholds of physical climate variables developed from community observations, together with two large-scale datasets, we have produced local indices directly relevant to the impacts of a reduced sea ice cover on Alaska coastal communities. The indices include the number of false freeze-ups defined by transient exceedances of ice concentration prior to a corresponding exceedance that persists, false break-ups, timing of freeze-up and break-up, length of the open water duration, number of days when the winds preclude hunting via boat (wind speed threshold exceedances, the number of wind events conducive to geomorphological work or damage to infrastructure from ocean waves, and the number of these wind events with on- and along-shore components promoting water setup along the coastline. We demonstrate how community observations can inform use of large-scale datasets to derive these locally relevant indices. The two primary large-scale datasets are the Historical Sea Ice Atlas for Alaska and the atmospheric output from a regional climate model used to downscale the ERA-Interim atmospheric reanalysis. We illustrate the variability and trends of these indices by application to the rural Alaska communities of Kotzebue, Shishmaref, and Utqiaġvik (previously Barrow, although the same procedure and metrics can be applied to other coastal communities. Over the 1979–2014 time period, there has been a marked increase in the number of combined false freeze-ups and false break-ups as well as the number of days too windy for hunting via boat for all three communities, especially Utqiaġvik. At Utqiaġvik, there has been an approximate tripling of the number of wind events conducive to coastline erosion from 1979 to 2014. We have also found a delay in freeze-up and earlier break-up, leading to a lengthened open water period for all of the communities examined.

  12. Modification of input datasets for the Ensemble Streamflow Prediction based on large scale climatic indices and weather generator

    Czech Academy of Sciences Publication Activity Database

    Šípek, Václav; Daňhelka, J.

    2015-01-01

    Roč. 528, September (2015), s. 720-733 ISSN 0022-1694 Institutional support: RVO:67985874 Keywords : sea sonal forecasting * ESP * large-scale climate * weather generator Subject RIV: DA - Hydrology ; Limnology Impact factor: 3.043, year: 2015

  13. A frequency-domain implementation of a sliding-window traffic sign detector for large scale panoramic datasets

    NARCIS (Netherlands)

    Creusen, I.M.; Hazelhoff, L.; With, de P.H.N.

    2013-01-01

    In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detection stage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which is employed for traffic sign

  14. Retrospective analysis of cohort database: Phenotypic variability in a large dataset of patients confirmed to have homozygous familial hypercholesterolemia

    NARCIS (Netherlands)

    Raal, Frederick J.; Sjouke, Barbara; Hovingh, G. Kees; Isaac, Barton F.

    2016-01-01

    These data describe the phenotypic variability in a large cohort of patients confirmed to have homozygous familial hypercholesterolemia. Herein, we describe the observed relationship of treated low-density lipoprotein cholesterol with age. We also overlay the low-density lipoprotein receptor gene

  15. Modelling aggregation on the large scale and regularity on the small scale in spatial point pattern datasets

    DEFF Research Database (Denmark)

    Lavancier, Frédéric; Møller, Jesper

    We consider a dependent thinning of a regular point process with the aim of obtaining aggregation on the large scale and regularity on the small scale in the resulting target point process of retained points. Various parametric models for the underlying processes are suggested and the properties...

  16. Evaluating privacy-preserving record linkage using cryptographic long-term keys and multibit trees on large medical datasets.

    Science.gov (United States)

    Brown, Adrian P; Borgs, Christian; Randall, Sean M; Schnell, Rainer

    2017-06-08

    Integrating medical data using databases from different sources by record linkage is a powerful technique increasingly used in medical research. Under many jurisdictions, unique personal identifiers needed for linking the records are unavailable. Since sensitive attributes, such as names, have to be used instead, privacy regulations usually demand encrypting these identifiers. The corresponding set of techniques for privacy-preserving record linkage (PPRL) has received widespread attention. One recent method is based on Bloom filters. Due to superior resilience against cryptographic attacks, composite Bloom filters (cryptographic long-term keys, CLKs) are considered best practice for privacy in PPRL. Real-world performance of these techniques using large-scale data is unknown up to now. Using a large subset of Australian hospital admission data, we tested the performance of an innovative PPRL technique (CLKs using multibit trees) against a gold-standard derived from clear-text probabilistic record linkage. Linkage time and linkage quality (recall, precision and F-measure) were evaluated. Clear text probabilistic linkage resulted in marginally higher precision and recall than CLKs. PPRL required more computing time but 5 million records could still be de-duplicated within one day. However, the PPRL approach required fine tuning of parameters. We argue that increased privacy of PPRL comes with the price of small losses in precision and recall and a large increase in computational burden and setup time. These costs seem to be acceptable in most applied settings, but they have to be considered in the decision to apply PPRL. Further research on the optimal automatic choice of parameters is needed.

  17. An Accurate Method for Inferring Relatedness in Large Datasets of Unphased Genotypes via an Embedded Likelihood-Ratio Test

    KAUST Repository

    Rodriguez, Jesse M.

    2013-01-01

    Studies that map disease genes rely on accurate annotations that indicate whether individuals in the studied cohorts are related to each other or not. For example, in genome-wide association studies, the cohort members are assumed to be unrelated to one another. Investigators can correct for individuals in a cohort with previously-unknown shared familial descent by detecting genomic segments that are shared between them, which are considered to be identical by descent (IBD). Alternatively, elevated frequencies of IBD segments near a particular locus among affected individuals can be indicative of a disease-associated gene. As genotyping studies grow to use increasingly large sample sizes and meta-analyses begin to include many data sets, accurate and efficient detection of hidden relatedness becomes a challenge. To enable disease-mapping studies of increasingly large cohorts, a fast and accurate method to detect IBD segments is required. We present PARENTE, a novel method for detecting related pairs of individuals and shared haplotypic segments within these pairs. PARENTE is a computationally-efficient method based on an embedded likelihood ratio test. As demonstrated by the results of our simulations, our method exhibits better accuracy than the current state of the art, and can be used for the analysis of large genotyped cohorts. PARENTE\\'s higher accuracy becomes even more significant in more challenging scenarios, such as detecting shorter IBD segments or when an extremely low false-positive rate is required. PARENTE is publicly and freely available at http://parente.stanford.edu/. © 2013 Springer-Verlag.

  18. Retrospective analysis of cohort database: Phenotypic variability in a large dataset of patients confirmed to have homozygous familial hypercholesterolemia

    Directory of Open Access Journals (Sweden)

    Frederick J. Raal

    2016-06-01

    Full Text Available These data describe the phenotypic variability in a large cohort of patients confirmed to have homozygous familial hypercholesterolemia. Herein, we describe the observed relationship of treated low-density lipoprotein cholesterol with age. We also overlay the low-density lipoprotein receptor gene (LDLR functional status with these phenotypic data. A full description of these data is available in our recent study published in Atherosclerosis, “Phenotype Diversity Among Patients With Homozygous Familial Hypercholesterolemia: A Cohort Study” (Raal et al., 2016 [1].

  19. The Transcriptome Analysis and Comparison Explorer--T-ACE: a platform-independent, graphical tool to process large RNAseq datasets of non-model organisms.

    Science.gov (United States)

    Philipp, E E R; Kraemer, L; Mountfort, D; Schilhabel, M; Schreiber, S; Rosenstiel, P

    2012-03-15

    Next generation sequencing (NGS) technologies allow a rapid and cost-effective compilation of large RNA sequence datasets in model and non-model organisms. However, the storage and analysis of transcriptome information from different NGS platforms is still a significant bottleneck, leading to a delay in data dissemination and subsequent biological understanding. Especially database interfaces with transcriptome analysis modules going beyond mere read counts are missing. Here, we present the Transcriptome Analysis and Comparison Explorer (T-ACE), a tool designed for the organization and analysis of large sequence datasets, and especially suited for transcriptome projects of non-model organisms with little or no a priori sequence information. T-ACE offers a TCL-based interface, which accesses a PostgreSQL database via a php-script. Within T-ACE, information belonging to single sequences or contigs, such as annotation or read coverage, is linked to the respective sequence and immediately accessible. Sequences and assigned information can be searched via keyword- or BLAST-search. Additionally, T-ACE provides within and between transcriptome analysis modules on the level of expression, GO terms, KEGG pathways and protein domains. Results are visualized and can be easily exported for external analysis. We developed T-ACE for laboratory environments, which have only a limited amount of bioinformatics support, and for collaborative projects in which different partners work on the same dataset from different locations or platforms (Windows/Linux/MacOS). For laboratories with some experience in bioinformatics and programming, the low complexity of the database structure and open-source code provides a framework that can be customized according to the different needs of the user and transcriptome project.

  20. Development of N+ in P pixel sensors for a high-luminosity large hadron collider

    Science.gov (United States)

    Kamada, Shintaro; Yamamura, Kazuhisa; Unno, Yoshinobu; Ikegami, Yoichi

    2014-11-01

    Hamamatsu Photonics K. K. is developing an N+ in a p planar pixel sensor with high radiation tolerance for the high-luminosity large hadron collider (HL-LHC). The N+ in the p planar pixel sensor is a candidate for the HL-LHC and offers the advantages of high radiation tolerance at a reasonable price compared with the N+ in an n planar sensor, the three-dimensional sensor, and the diamond sensor. However, the N+ in the p planar pixel sensor still presents some problems that need to be solved, such as its slim edge and the danger of sparks between the sensor and readout integrated circuit. We are now attempting to solve these problems with wafer-level processes, which is important for mass production. To date, we have obtained a 250-μm edge with an applied bias voltage of 1000 V. To protect against high-voltage sparks from the edge, we suggest some possible designs for the N+ edge.

  1. Development of N+ in P pixel sensors for a high-luminosity large hadron collider

    International Nuclear Information System (INIS)

    Kamada, Shintaro; Yamamura, Kazuhisa; Unno, Yoshinobu; Ikegami, Yoichi

    2014-01-01

    Hamamatsu Photonics K. K. is developing an N+ in a p planar pixel sensor with high radiation tolerance for the high-luminosity large hadron collider (HL-LHC). The N+ in the p planar pixel sensor is a candidate for the HL-LHC and offers the advantages of high radiation tolerance at a reasonable price compared with the N+ in an n planar sensor, the three-dimensional sensor, and the diamond sensor. However, the N+ in the p planar pixel sensor still presents some problems that need to be solved, such as its slim edge and the danger of sparks between the sensor and readout integrated circuit. We are now attempting to solve these problems with wafer-level processes, which is important for mass production. To date, we have obtained a 250-μm edge with an applied bias voltage of 1000 V. To protect against high-voltage sparks from the edge, we suggest some possible designs for the N+ edge. - Highlights: • We achieved a tolerance of 1000 V with a 250-μm edge by Al2O3 side wall passivation. • Above is a wafer process and suitable for mass production. • For edge-spark protection, we suggest N+ edge with an isolation

  2. Large Scale Triboelectric Nanogenerator and Self-Powered Flexible Sensor for Human Sleep Monitoring

    Directory of Open Access Journals (Sweden)

    Xiaoheng Ding

    2018-05-01

    Full Text Available The triboelectric nanogenerator (TENG and its application as a sensor is a popular research subject. There is demand for self-powered, flexible sensors with high sensitivity and high power-output for the next generation of consumer electronics. In this study, a 300 mm × 300 mm carbon nanotube (CNT-doped porous PDMS film was successfully fabricated wherein the CNT influenced the micropore structure. A self-powered TENG tactile sensor was established according to triboelectric theory. The CNT-doped porous TENG showed a voltage output seven times higher than undoped porous TENG and 16 times higher than TENG with pure PDMS, respectively. The TENG successfully acquired human motion signals, breath signals, and heartbeat signals during a sleep monitoring experiment. The results presented here may provide an effective approach for fabricating large-scale and low-cost flexible TENG sensors.

  3. An energy-efficient data gathering protocol in large wireless sensor network

    Science.gov (United States)

    Wang, Yamin; Zhang, Ruihua; Tao, Shizhong

    2006-11-01

    Wireless sensor network consisting of a large number of small sensors with low-power transceiver can be an effective tool for gathering data in a variety of environment. The collected data must be transmitted to the base station for further processing. Since a network consists of sensors with limited battery energy, the method for data gathering and routing must be energy efficient in order to prolong the lifetime of the network. In this paper, we presented an energy-efficient data gathering protocol in wireless sensor network. The new protocol used data fusion technology clusters nodes into groups and builds a chain among the cluster heads according to a hybrid of the residual energy and distance to the base station. Results in stochastic geometry are used to derive the optimum parameter of our algorithm that minimizes the total energy spent in the network. Simulation results show performance superiority of the new protocol.

  4. Large-strain Soft Sensors Using Elastomers Blended with Exfoliated/Fragmented Graphite Particles

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sungmin; Nam, Gyungmok; Kim, Jonghun; Yoon, Sang-Hee [Inha Univ., Incheon (Korea, Republic of)

    2016-09-15

    An elastic polymer (e.g., PDMS) blended with EFG particles is a promising conductive composite for fabricating soft sensors that can detect an object's deformation up to or more than 50 %. Here, we develop large-strain, sprayable soft sensors using a mixture of PDMS and EFG particles, which are used as a host elastomer and electrically conductive particles, respectively. A solution for a conductive composite mixture is prepared by the microwave-assisted graphite exfoliation, followed by ultrasonication-induced fragmentation of the exfoliated graphite and ultrasonic blending of PDMS and EFG. Using the prepared solutions for composite and pure PDMS, 1-, 2-, and 3-axis soft sensors are fabricated by airbrush stencil technique where composite mixture and pure PDMS are materials for sensing and insulating layers, respectively. We characterize the soft strain sensors after investigating the effect of PDMS/EFG wt % on mechanical compliance and electrical conductance of the conductive composite.

  5. Flat-Cladding Fiber Bragg Grating Sensors for Large Strain Amplitude Fatigue Tests

    Directory of Open Access Journals (Sweden)

    Xijia Gu

    2010-08-01

    Full Text Available We have successfully developed a flat-cladding fiber Bragg grating sensor for large cyclic strain amplitude tests of up to ±8,000 με. The increased contact area between the flat-cladding fiber and substrate, together with the application of a new bonding process, has significantly increased the bonding strength. In the push-pull fatigue tests of an aluminum alloy, the plastic strain amplitudes measured by three optical fiber sensors differ only by 0.43% at a cyclic strain amplitude of ±7,000 με and 1.9% at a cyclic strain amplitude of ±8,000 με. We also applied the sensor on an extruded magnesium alloy for evaluating the peculiar asymmetric hysteresis loops. The results obtained were in good agreement with those measured from the extensometer, a further validation of the sensor.

  6. Miniature large range multi-axis force-torque sensor for biomechanical applications

    International Nuclear Information System (INIS)

    Brookhuis, R A; Sanders, R G P; Ma, K; Lammerink, T S J; De Boer, M J; Krijnen, G J M; Wiegerink, R J

    2015-01-01

    A miniature force sensor for the measurement of forces and moments at a human fingertip is designed and realized. Thin silicon pillars inside the sensor provide in-plane guidance for shear force measurement and provide the spring constant in normal direction. A corrugated silicon ring around the force sensitive area provides the spring constant in shear direction and seals the interior of the sensor. To detect all load components, capacitive read-out is used. A novel electrode pattern results in a large shear force sensitivity. The fingertip force sensor has a wide force range of up to 60 N in normal direction, ± 30 N in shear direction and a torque range of ± 25 N mm. (paper)

  7. Proteomics dataset

    DEFF Research Database (Denmark)

    Bennike, Tue Bjerg; Carlsen, Thomas Gelsing; Ellingsen, Torkell

    2017-01-01

    patients (Morgan et al., 2012; Abraham and Medzhitov, 2011; Bennike, 2014) [8–10. Therefore, we characterized the proteome of colon mucosa biopsies from 10 inflammatory bowel disease ulcerative colitis (UC) patients, 11 gastrointestinal healthy rheumatoid arthritis (RA) patients, and 10 controls. We...... been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD001608 for ulcerative colitis and control samples, and PXD003082 for rheumatoid arthritis samples....

  8. ShakeNet: a portable wireless sensor network for instrumenting large civil structures

    Science.gov (United States)

    Kohler, Monica D.; Hao, Shuai; Mishra, Nilesh; Govindan, Ramesh; Nigbor, Robert

    2015-08-03

    We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software. 

  9. Towards large-scale mapping of urban three-dimensional structure using Landsat imagery and global elevation datasets

    Science.gov (United States)

    Wang, P.; Huang, C.

    2017-12-01

    The three-dimensional (3D) structure of buildings and infrastructures is fundamental to understanding and modelling of the impacts and challenges of urbanization in terms of energy use, carbon emissions, and earthquake vulnerabilities. However, spatially detailed maps of urban 3D structure have been scarce, particularly in fast-changing developing countries. We present here a novel methodology to map the volume of buildings and infrastructures at 30 meter resolution using a synergy of Landsat imagery and openly available global digital surface models (DSMs), including the Shuttle Radar Topography Mission (SRTM), ASTER Global Digital Elevation Map (GDEM), ALOS World 3D - 30m (AW3D30), and the recently released global DSM from the TanDEM-X mission. Our method builds on the concept of object-based height profile to extract height metrics from the DSMs and use a machine learning algorithm to predict height and volume from the height metrics. We have tested this algorithm in the entire England and assessed our result using Lidar measurements in 25 England cities. Our initial assessments achieved a RMSE of 1.4 m (R2 = 0.72) for building height and a RMSE of 1208.7 m3 (R2 = 0.69) for building volume, demonstrating the potential of large-scale applications and fully automated mapping of urban structure.

  10. Towards development of nanofibrous large strain flexible strain sensors with programmable shape memory properties

    Science.gov (United States)

    Khalili, N.; Asif, H.; Naguib, H. E.

    2018-05-01

    Electrospun polymeric fibers can be used as strain sensors due to their large surface to weight/volume ratio, high porosity and pore interconnectivity. Large strain flexible strain sensors are used in numerous applications including rehabilitation, health monitoring, and sports performance monitoring where large strain detection should be accommodated by the sensor. This has boosted the demand for a stretchable, flexible and highly sensitive sensor able to detect a wide range of mechanically induced deformations. Herein, a physically cross-linked polylactic acid (PLA) and thermoplastic polyurethane (TPU) blend is made into nanofiber networks via electrospinning. The PLA/TPU weight ratio is optimized to obtain a maximum attainable strain of 100% while maintaining its mechanical integrity. The TPU/PLA fibers also allowed for their thermally activated recovery due to shape memory properties of the substrate. This novel feature enhances the sensor’s performance as it is no longer limited by its plastic deformation. Using spray coating method, a homogeneous layer of single-walled carbon nanotube is deposited onto the as-spun fiber mat to induce electrical conductivity to the surface of the fibers. It is shown that stretching and bending the sensor result in a highly sensitive and linear response with a maximum gauge factor of 33.

  11. Automatic reduction of large X-ray fluorescence data-sets applied to XAS and mapping experiments

    International Nuclear Information System (INIS)

    Martin Montoya, Ligia Andrea

    2017-02-01

    In this thesis two automatic methods for the reduction of large fluorescence data sets are presented. The first method is proposed in the framework of BioXAS experiments. The challenge of this experiment is to deal with samples in ultra dilute concentrations where the signal-to-background ratio is low. The experiment is performed in fluorescence mode X-ray absorption spectroscopy with a 100 pixel high-purity Ge detector. The first step consists on reducing 100 fluorescence spectra into one. In this step, outliers are identified by means of the shot noise. Furthermore, a fitting routine which model includes Gaussian functions for the fluorescence lines and exponentially modified Gaussian (EMG) functions for the scattering lines (with long tails at lower energies) is proposed to extract the line of interest from the fluorescence spectrum. Additionally, the fitting model has an EMG function for each scattering line (elastic and inelastic) at incident energies where they start to be discerned. At these energies, the data reduction is done per detector column to include the angular dependence of scattering. In the second part of this thesis, an automatic method for texts separation on palimpsests is presented. Scanning X-ray fluorescence is performed on the parchment, where a spectrum per scanned point is collected. Within this method, each spectrum is treated as a vector forming a basis which is to be transformed so that the basis vectors are the spectra of each ink. Principal Component Analysis is employed as an initial guess of the seek basis. This basis is further transformed by means of an optimization routine that maximizes the contrast and minimizes the non-negative entries in the spectra. The method is tested on original and self made palimpsests.

  12. A wireless sensor network design and evaluation for large structural strain field monitoring

    International Nuclear Information System (INIS)

    Qiu, Zixue; Wu, Jian; Yuan, Shenfang

    2011-01-01

    Structural strain changes under external environmental or mechanical loads are the main monitoring parameters in structural health monitoring or mechanical property tests. This paper presents a wireless sensor network designed for monitoring large structural strain field variation. First of all, a precision strain sensor node is designed for multi-channel strain gauge signal conditioning and wireless monitoring. In order to establish a synchronous strain data acquisition network, the cluster-star network synchronization method is designed in detail. To verify the functionality of the designed wireless network for strain field monitoring capability, a multi-point network evaluation system is developed for an experimental aluminum plate structure for load variation monitoring. Based on the precision wireless strain nodes, the wireless data acquisition network is deployed to synchronously gather, process and transmit strain gauge signals and monitor results under concentrated loads. This paper shows the efficiency of the wireless sensor network for large structural strain field monitoring

  13. Towards a Versatile Problem Diagnosis Infrastructure for LargeWireless Sensor Networks

    NARCIS (Netherlands)

    Iwanicki, Konrad; Steen, van Maarten

    2007-01-01

    In this position paper, we address the issue of durable maintenance of a wireless sensor network, which will be crucial if the vision of large, long-lived sensornets is to become reality. Durable maintenance requires tools for diagnosing and fixing occurring problems, which can range from

  14. Node localization algorithm of wireless sensor networks for large electrical equipment monitoring application

    DEFF Research Database (Denmark)

    Chen, Qinyin; Hu, Y.; Chen, Zhe

    2016-01-01

    Node localization technology is an important technology for the Wireless Sensor Networks (WSNs) applications. An improved 3D node localization algorithm is proposed in this paper, which is based on a Multi-dimensional Scaling (MDS) node localization algorithm for large electrical equipment monito...

  15. Large area thinned planar sensors for future high-luminosity-LHC upgrades

    International Nuclear Information System (INIS)

    Wittig, T.; Lawerenz, A.; Röder, R.

    2016-01-01

    Planar hybrid silicon sensors are a well proven technology for past and current particle tracking detectors in HEP experiments. However, the future high-luminosity upgrades of the inner trackers at the LHC experiments pose big challenges to the detectors. A first challenge is an expected radiation damage level of up to 2⋅ 10 16 n eq /cm 2 . For planar sensors, one way to counteract the charge loss and thus increase the radiation hardness is to decrease the thickness of their active area. A second challenge is the large detector area which has to be built as cost-efficient as possible. The CiS research institute has accomplished a proof-of-principle run with n-in-p ATLAS-Pixel sensors in which a cavity is etched to the sensor's back side to reduce its thickness. One advantage of this technology is the fact that thick frames remain at the sensor edges and guarantee mechanical stability on wafer level while the sensor is left on the resulting thin membrane. For this cavity etching technique, no handling wafers are required which represents a benefit in terms of process effort and cost savings. The membranes with areas of up to ∼ 4 × 4 cm 2 and thicknesses of 100 and 150 μm feature a sufficiently good homogeneity across the whole wafer area. The processed pixel sensors show good electrical behaviour with an excellent yield for a suchlike prototype run. First sensors with electroless Ni- and Pt-UBM are already successfully assembled with read-out chips.

  16. Large area thinned planar sensors for future high-luminosity-LHC upgrades

    Science.gov (United States)

    Wittig, T.; Lawerenz, A.; Röder, R.

    2016-12-01

    Planar hybrid silicon sensors are a well proven technology for past and current particle tracking detectors in HEP experiments. However, the future high-luminosity upgrades of the inner trackers at the LHC experiments pose big challenges to the detectors. A first challenge is an expected radiation damage level of up to 2ṡ 1016 neq/cm2. For planar sensors, one way to counteract the charge loss and thus increase the radiation hardness is to decrease the thickness of their active area. A second challenge is the large detector area which has to be built as cost-efficient as possible. The CiS research institute has accomplished a proof-of-principle run with n-in-p ATLAS-Pixel sensors in which a cavity is etched to the sensor's back side to reduce its thickness. One advantage of this technology is the fact that thick frames remain at the sensor edges and guarantee mechanical stability on wafer level while the sensor is left on the resulting thin membrane. For this cavity etching technique, no handling wafers are required which represents a benefit in terms of process effort and cost savings. The membranes with areas of up to ~ 4 × 4 cm2 and thicknesses of 100 and 150 μm feature a sufficiently good homogeneity across the whole wafer area. The processed pixel sensors show good electrical behaviour with an excellent yield for a suchlike prototype run. First sensors with electroless Ni- and Pt-UBM are already successfully assembled with read-out chips.

  17. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xing Li

    2015-07-01

    Full Text Available With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people’s lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs. Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs, this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

  18. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.

    Science.gov (United States)

    Li, Xing; Chen, Dexin; Li, Chunyan; Wang, Liangmin

    2015-07-03

    With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

  19. Precisely Controlled Ultrathin Conjugated Polymer Films for Large Area Transparent Transistors and Highly Sensitive Chemical Sensors.

    Science.gov (United States)

    Khim, Dongyoon; Ryu, Gi-Seong; Park, Won-Tae; Kim, Hyunchul; Lee, Myungwon; Noh, Yong-Young

    2016-04-13

    A uniform ultrathin polymer film is deposited over a large area with molecularlevel precision by the simple wire-wound bar-coating method. The bar-coated ultrathin films not only exhibit high transparency of up to 90% in the visible wavelength range but also high charge carrier mobility with a high degree of percolation through the uniformly covered polymer nanofibrils. They are capable of realizing highly sensitive multigas sensors and represent the first successful report of ethylene detection using a sensor based on organic field-effect transistors. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Extracting Prior Distributions from a Large Dataset of In-Situ Measurements to Support SWOT-based Estimation of River Discharge

    Science.gov (United States)

    Hagemann, M.; Gleason, C. J.

    2017-12-01

    The upcoming (2021) Surface Water and Ocean Topography (SWOT) NASA satellite mission aims, in part, to estimate discharge on major rivers worldwide using reach-scale measurements of stream width, slope, and height. Current formalizations of channel and floodplain hydraulics are insufficient to fully constrain this problem mathematically, resulting in an infinitely large solution set for any set of satellite observations. Recent work has reformulated this problem in a Bayesian statistical setting, in which the likelihood distributions derive directly from hydraulic flow-law equations. When coupled with prior distributions on unknown flow-law parameters, this formulation probabilistically constrains the parameter space, and results in a computationally tractable description of discharge. Using a curated dataset of over 200,000 in-situ acoustic Doppler current profiler (ADCP) discharge measurements from over 10,000 USGS gaging stations throughout the United States, we developed empirical prior distributions for flow-law parameters that are not observable by SWOT, but that are required in order to estimate discharge. This analysis quantified prior uncertainties on quantities including cross-sectional area, at-a-station hydraulic geometry width exponent, and discharge variability, that are dependent on SWOT-observable variables including reach-scale statistics of width and height. When compared against discharge estimation approaches that do not use this prior information, the Bayesian approach using ADCP-derived priors demonstrated consistently improved performance across a range of performance metrics. This Bayesian approach formally transfers information from in-situ gaging stations to remote-sensed estimation of discharge, in which the desired quantities are not directly observable. Further investigation using large in-situ datasets is therefore a promising way forward in improving satellite-based estimates of river discharge.

  1. Large dynamic range pressure sensor based on two semicircle-holes microstructured fiber.

    Science.gov (United States)

    Liu, Zhengyong; Htein, Lin; Lee, Kang-Kuen; Lau, Kin-Tak; Tam, Hwa-Yaw

    2018-01-08

    This paper presents a sensitive and large dynamic range pressure sensor based on a novel birefringence microstructured optical fiber (MOF) deployed in a Sagnac interferometer configuration. The MOF has two large semicircle holes in the cladding and a rectangular strut with germanium-doped core in the center. The fiber structure permits surrounding pressure to induce large effective index difference between the two polarized modes. The calculated and measured group birefringence of the fiber are 1.49 × 10 -4 , 1.23 × 10 -4 , respectively, at the wavelength of 1550 nm. Experimental results shown that the pressure sensitivity of the sensor varied from 45,000 pm/MPa to 50,000 pm/MPa, and minimum detectable pressure of 80 Pa and dynamic range of better than 116 dB could be achieved with the novel fiber sensor. The proposed sensor could be used in harsh environment and is an ideal candidate for downhole applications where high pressure measurement at elevated temperature up to 250 °C is needed.

  2. Processing large sensor data sets for safeguards : the knowledge generation system.

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, Maikel A.; Smartt, Heidi Anne; Matthews, Robert F.

    2012-04-01

    Modern nuclear facilities, such as reprocessing plants, present inspectors with significant challenges due in part to the sheer amount of equipment that must be safeguarded. The Sandia-developed and patented Knowledge Generation system was designed to automatically analyze large amounts of safeguards data to identify anomalous events of interest by comparing sensor readings with those expected from a process of interest and operator declarations. This paper describes a demonstration of the Knowledge Generation system using simulated accountability tank sensor data to represent part of a reprocessing plant. The demonstration indicated that Knowledge Generation has the potential to address several problems critical to the future of safeguards. It could be extended to facilitate remote inspections and trigger random inspections. Knowledge Generation could analyze data to establish trust hierarchies, to facilitate safeguards use of operator-owned sensors.

  3. Fault Detection for Large-Scale Railway Maintenance Equipment Base on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Junfu Yu

    2014-04-01

    Full Text Available Focusing on the fault detection application for large-scale railway maintenance equipment with the specialties of low-cost, energy efficiency, collecting data of the function units. This paper proposed energy efficiency, convenient installation fault detection application using Sigsbee wireless sensor networks, which Sigsbee is the most widely used protocol based on IEEE 802.15.4. This paper proposed a systematic application from hardware design using STM32F103 chips as processer, to software system. Fault detection application is the basic part of the fault diagnose system, wireless sensor nodes of the fault detection application with different kinds of sensors for verities function units communication by Sigsbee to collecting and sending basic working status data to the home gateway, then data will be sent to the fault diagnose system.

  4. Contribution of Road Grade to the Energy Use of Modern Automobiles Across Large Datasets of Real-World Drive Cycles: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wood, E.; Burton, E.; Duran, A.; Gonder, J.

    2014-01-01

    Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation to inform the intelligent design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models. Aggregate results of this study suggest that road grade could be responsible for 1% to 3% of fuel use in light-duty automobiles.

  5. Building and calibrating a large-extent and high resolution coupled groundwater-land surface model using globally available data-sets

    Science.gov (United States)

    Sutanudjaja, E. H.; Van Beek, L. P.; de Jong, S. M.; van Geer, F.; Bierkens, M. F.

    2012-12-01

    The current generation of large-scale hydrological models generally lacks a groundwater model component simulating lateral groundwater flow. Large-scale groundwater models are rare due to a lack of hydro-geological data required for their parameterization and a lack of groundwater head data required for their calibration. In this study, we propose an approach to develop a large-extent fully-coupled land surface-groundwater model by using globally available datasets and calibrate it using a combination of discharge observations and remotely-sensed soil moisture data. The underlying objective is to devise a collection of methods that enables one to build and parameterize large-scale groundwater models in data-poor regions. The model used, PCR-GLOBWB-MOD, has a spatial resolution of 1 km x 1 km and operates on a daily basis. It consists of a single-layer MODFLOW groundwater model that is dynamically coupled to the PCR-GLOBWB land surface model. This fully-coupled model accommodates two-way interactions between surface water levels and groundwater head dynamics, as well as between upper soil moisture states and groundwater levels, including a capillary rise mechanism to sustain upper soil storage and thus to fulfill high evaporation demands (during dry conditions). As a test bed, we used the Rhine-Meuse basin, where more than 4000 groundwater head time series have been collected for validation purposes. The model was parameterized using globally available data-sets on surface elevation, drainage direction, land-cover, soil and lithology. Next, the model was calibrated using a brute force approach and massive parallel computing, i.e. by running the coupled groundwater-land surface model for more than 3000 different parameter sets. Here, we varied minimal soil moisture storage and saturated conductivities of the soil layers as well as aquifer transmissivities. Using different regularization strategies and calibration criteria we compared three calibration scenarios

  6. RADIOMETRIC NORMALIZATION OF LARGE AIRBORNE IMAGE DATA SETS ACQUIRED BY DIFFERENT SENSOR TYPES

    Directory of Open Access Journals (Sweden)

    S. Gehrke

    2016-06-01

    Full Text Available Generating seamless mosaics of aerial images is a particularly challenging task when the mosaic comprises a large number of im-ages, collected over longer periods of time and with different sensors under varying imaging conditions. Such large mosaics typically consist of very heterogeneous image data, both spatially (different terrain types and atmosphere and temporally (unstable atmo-spheric properties and even changes in land coverage. We present a new radiometric normalization or, respectively, radiometric aerial triangulation approach that takes advantage of our knowledge about each sensor’s properties. The current implementation supports medium and large format airborne imaging sensors of the Leica Geosystems family, namely the ADS line-scanner as well as DMC and RCD frame sensors. A hierarchical modelling – with parameters for the overall mosaic, the sensor type, different flight sessions, strips and individual images – allows for adaptation to each sensor’s geometric and radiometric properties. Additional parameters at different hierarchy levels can compensate radiome-tric differences of various origins to compensate for shortcomings of the preceding radiometric sensor calibration as well as BRDF and atmospheric corrections. The final, relative normalization is based on radiometric tie points in overlapping images, absolute radiometric control points and image statistics. It is computed in a global least squares adjustment for the entire mosaic by altering each image’s histogram using a location-dependent mathematical model. This model involves contrast and brightness corrections at radiometric fix points with bilinear interpolation for corrections in-between. The distribution of the radiometry fixes is adaptive to each image and generally increases with image size, hence enabling optimal local adaptation even for very long image strips as typi-cally captured by a line-scanner sensor. The normalization approach is implemented in

  7. High accuracy injection circuit for the calibration of a large pixel sensor matrix

    International Nuclear Information System (INIS)

    Quartieri, E.; Comotti, D.; Manghisoni, M.

    2013-01-01

    Semiconductor pixel detectors, for particle tracking and vertexing in high energy physics experiments as well as for X-ray imaging, in particular for synchrotron light sources and XFELs, require a large area sensor matrix. This work will discuss the design and the characterization of a high-linearity, low dispersion injection circuit to be used for pixel-level calibration of detector readout electronics in a large pixel sensor matrix. The circuit provides a useful tool for the characterization of the readout electronics of the pixel cell unit for both monolithic active pixel sensors and hybrid pixel detectors. In the latter case, the circuit allows for precise analogue test of the readout channel already at the chip level, when no sensor is connected. Moreover, it provides a simple means for calibration of readout electronics once the detector has been connected to the chip. Two injection techniques can be provided by the circuit: one for a charge sensitive amplification and the other for a transresistance readout channel. The aim of the paper is to describe the architecture and the design guidelines of the calibration circuit, which has been implemented in a 130 nm CMOS technology. Moreover, experimental results of the proposed injection circuit will be presented in terms of linearity and dispersion

  8. The development of the Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS): a large-scale data sharing initiative.

    Science.gov (United States)

    Lutomski, Jennifer E; Baars, Maria A E; Schalk, Bianca W M; Boter, Han; Buurman, Bianca M; den Elzen, Wendy P J; Jansen, Aaltje P D; Kempen, Gertrudis I J M; Steunenberg, Bas; Steyerberg, Ewout W; Olde Rikkert, Marcel G M; Melis, René J F

    2013-01-01

    In 2008, the Ministry of Health, Welfare and Sport commissioned the National Care for the Elderly Programme. While numerous research projects in older persons' health care were to be conducted under this national agenda, the Programme further advocated the development of The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS) which would be integrated into all funded research protocols. In this context, we describe TOPICS data sharing initiative (www.topics-mds.eu). A working group drafted TOPICS-MDS prototype, which was subsequently approved by a multidisciplinary panel. Using instruments validated for older populations, information was collected on demographics, morbidity, quality of life, functional limitations, mental health, social functioning and health service utilisation. For informal caregivers, information was collected on demographics, hours of informal care and quality of life (including subjective care-related burden). Between 2010 and 2013, a total of 41 research projects contributed data to TOPICS-MDS, resulting in preliminary data available for 32,310 older persons and 3,940 informal caregivers. The majority of studies sampled were from primary care settings and inclusion criteria differed across studies. TOPICS-MDS is a public data repository which contains essential data to better understand health challenges experienced by older persons and informal caregivers. Such findings are relevant for countries where increasing health-related expenditure has necessitated the evaluation of contemporary health care delivery. Although open sharing of data can be difficult to achieve in practice, proactively addressing issues of data protection, conflicting data analysis requests and funding limitations during TOPICS-MDS developmental phase has fostered a data sharing culture. To date, TOPICS-MDS has been successfully incorporated into 41 research projects, thus supporting the feasibility of constructing a large (>30,000 observations

  9. An Autonomous Sensor Tasking Approach for Large Scale Space Object Cataloging

    Science.gov (United States)

    Linares, R.; Furfaro, R.

    The field of Space Situational Awareness (SSA) has progressed over the last few decades with new sensors coming online, the development of new approaches for making observations, and new algorithms for processing them. Although there has been success in the development of new approaches, a missing piece is the translation of SSA goals to sensors and resource allocation; otherwise known as the Sensor Management Problem (SMP). This work solves the SMP using an artificial intelligence approach called Deep Reinforcement Learning (DRL). Stable methods for training DRL approaches based on neural networks exist, but most of these approaches are not suitable for high dimensional systems. The Asynchronous Advantage Actor-Critic (A3C) method is a recently developed and effective approach for high dimensional systems, and this work leverages these results and applies this approach to decision making in SSA. The decision space for the SSA problems can be high dimensional, even for tasking of a single telescope. Since the number of SOs in space is relatively high, each sensor will have a large number of possible actions at a given time. Therefore, efficient DRL approaches are required when solving the SMP for SSA. This work develops a A3C based method for DRL applied to SSA sensor tasking. One of the key benefits of DRL approaches is the ability to handle high dimensional data. For example DRL methods have been applied to image processing for the autonomous car application. For example, a 256x256 RGB image has 196608 parameters (256*256*3=196608) which is very high dimensional, and deep learning approaches routinely take images like this as inputs. Therefore, when applied to the whole catalog the DRL approach offers the ability to solve this high dimensional problem. This work has the potential to, for the first time, solve the non-myopic sensor tasking problem for the whole SO catalog (over 22,000 objects) providing a truly revolutionary result.

  10. Meteor Film Recording with Digital Film Cameras with large CMOS Sensors

    Science.gov (United States)

    Slansky, P. C.

    2016-12-01

    In this article the author combines his professional know-how about cameras for film and television production with his amateur astronomy activities. Professional digital film cameras with high sensitivity are still quite rare in astronomy. One reason for this may be their costs of up to 20 000 and more (camera body only). In the interim, however,consumer photo cameras with film mode and very high sensitivity have come to the market for about 2 000 EUR. In addition, ultra-high sensitive professional film cameras, that are very interesting for meteor observation, have been introduced to the market. The particular benefits of digital film cameras with large CMOS sensors, including photo cameras with film recording function, for meteor recording are presented by three examples: a 2014 Camelopardalid, shot with a Canon EOS C 300, an exploding 2014 Aurigid, shot with a Sony alpha7S, and the 2016 Perseids, shot with a Canon ME20F-SH. All three cameras use large CMOS sensors; "large" meaning Super-35 mm, the classic 35 mm film format (24x13.5 mm, similar to APS-C size), or full format (36x24 mm), the classic 135 photo camera format. Comparisons are made to the widely used cameras with small CCD sensors, such as Mintron or Watec; "small" meaning 12" (6.4x4.8 mm) or less. Additionally, special photographic image processing of meteor film recordings is discussed.

  11. Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

    Energy Technology Data Exchange (ETDEWEB)

    Cree, Johnathan Vee [Washington State Univ., Pullman, WA (United States); Delgado-Frias, Jose [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-03-01

    Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configure the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.

  12. Implementation of Cyberinfrastructure and Data Management Workflow for a Large-Scale Sensor Network

    Science.gov (United States)

    Jones, A. S.; Horsburgh, J. S.

    2014-12-01

    Monitoring with in situ environmental sensors and other forms of field-based observation presents many challenges for data management, particularly for large-scale networks consisting of multiple sites, sensors, and personnel. The availability and utility of these data in addressing scientific questions relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into functional data products. It also depends on the ability of researchers to share and access the data in useable formats. In addition to addressing the challenges presented by the quantity of data, monitoring networks need practices to ensure high data quality, including procedures and tools for post processing. Data quality is further enhanced if practitioners are able to track equipment, deployments, calibrations, and other events related to site maintenance and associate these details with observational data. In this presentation we will describe the overall workflow that we have developed for research groups and sites conducting long term monitoring using in situ sensors. Features of the workflow include: software tools to automate the transfer of data from field sites to databases, a Python-based program for data quality control post-processing, a web-based application for online discovery and visualization of data, and a data model and web interface for managing physical infrastructure. By automating the data management workflow, the time from collection to analysis is reduced and sharing and publication is facilitated. The incorporation of metadata standards and descriptions and the use of open-source tools enhances the sustainability and reusability of the data. We will describe the workflow and tools that we have developed in the context of the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) monitoring network. The iUTAH network consists of aquatic and climate sensors deployed in three watersheds to monitor Gradients Along Mountain to Urban

  13. Integrated calibration of a 3D attitude sensor in large-scale metrology

    International Nuclear Information System (INIS)

    Gao, Yang; Lin, Jiarui; Yang, Linghui; Zhu, Jigui; Muelaner, Jody; Keogh, Patrick

    2017-01-01

    A novel calibration method is presented for a multi-sensor fusion system in large-scale metrology, which improves the calibration efficiency and reliability. The attitude sensor is composed of a pinhole prism, a converging lens, an area-array camera and a biaxial inclinometer. A mathematical model is established to determine its 3D attitude relative to a cooperative total station by using two vector observations from the imaging system and the inclinometer. There are two areas of unknown parameters in the measurement model that should be calibrated: the intrinsic parameters of the imaging model, and the transformation matrix between the camera and the inclinometer. An integrated calibration method using a three-axis rotary table and a total station is proposed. A single mounting position of the attitude sensor on the rotary table is sufficient to solve for all parameters of the measurement model. A correction technique for the reference laser beam of the total station is also presented to remove the need for accurate positioning of the sensor on the rotary table. Experimental verification has proved the practicality and accuracy of this calibration method. Results show that the mean deviations of attitude angles using the proposed method are less than 0.01°. (paper)

  14. Fractional Modeling of the AC Large-Signal Frequency Response in Magnetoresistive Current Sensors

    Directory of Open Access Journals (Sweden)

    Sergio Iván Ravelo Arias

    2013-12-01

    Full Text Available Fractional calculus is considered when derivatives and integrals of non-integer order are applied over a specific function. In the electrical and electronic domain, the transfer function dependence of a fractional filter not only by the filter order n, but additionally, of the fractional order α is an example of a great number of systems where its input-output behavior could be more exactly modeled by a fractional behavior. Following this aim, the present work shows the experimental ac large-signal frequency response of a family of electrical current sensors based in different spintronic conduction mechanisms. Using an ac characterization set-up the sensor transimpedance function  is obtained considering it as the relationship between sensor output voltage and input sensing current,[PLEASE CHECK FORMULA IN THE PDF]. The study has been extended to various magnetoresistance sensors based in different technologies like anisotropic magnetoresistance (AMR, giant magnetoresistance (GMR, spin-valve (GMR-SV and tunnel magnetoresistance (TMR. The resulting modeling shows two predominant behaviors, the low-pass and the inverse low-pass with fractional index different from the classical integer response. The TMR technology with internal magnetization offers the best dynamic and sensitivity properties opening the way to develop actual industrial applications.

  15. Optical system design of CCD star sensor with large aperture and wide field of view

    Science.gov (United States)

    Wang, Chao; Jiang, Lun; Li, Ying-chao; Liu, Zhuang

    2017-10-01

    The star sensor is one of the sensors which are used to determine the spatial attitude of the space vehicle. An optical system of star sensor with large aperture and wide field of view was designed in this paper. The effective focal length of the optics was 16mm, and the F-number is 1.2, the field of view of the optical system is 20°.The working spectrum is 500 to 800 nanometer. The lens system selects a similar complicated Petzval structure and special glass-couple, and get a high imaging quality in the whole spectrum range. For each field-of-view point, the values of the modulation transfer function at 50 cycles/mm is higher than 0.3. On the detecting plane, the encircled energy in a circle of 14μm diameter could be up to 80% of the total energy. In the whole range of the field of view, the dispersion spot diameter in the imaging plane is no larger than 13μm. The full field distortion was less than 0.1%, which was helpful to obtain the accurate location of the reference star through the picture gotten by the star sensor. The lateral chromatic aberration is less than 2μm in the whole spectrum range.

  16. Simulation of Smart Home Activity Datasets

    Directory of Open Access Journals (Sweden)

    Jonathan Synnott

    2015-06-01

    Full Text Available A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

  17. Simulation of Smart Home Activity Datasets.

    Science.gov (United States)

    Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

    2015-06-16

    A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

  18. An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Choi Jeonghee

    2008-01-01

    Full Text Available Abstract So far, various addressing and routing algorithms have been extensively studied for wireless sensor networks (WSNs, but many of them were limited to cover less than hundreds of sensor nodes. It is largely due to stringent requirements for fully distributed coordination among sensor nodes, leading to the wasteful use of available address space. As there is a growing need for a large-scale WSN, it will be extremely challenging to support more than thousands of nodes, using existing standard bodies. Moreover, it is highly unlikely to change the existing standards, primarily due to backward compatibility issue. In response, we propose an elegant addressing scheme and its routing algorithm. While maintaining the existing address scheme, it tackles the wastage problem and achieves no additional memory storage during a routing. We also present an adaptive routing algorithm for location-aware applications, using our addressing scheme. Through a series of simulations, we prove that our approach can achieve two times lesser routing time than the existing standard in a ZigBee network.

  19. Fabrication of a Horizontal and a Vertical Large Surface Area Nanogap Electrochemical Sensor

    Directory of Open Access Journals (Sweden)

    Jules L. Hammond

    2016-12-01

    Full Text Available Nanogap sensors have a wide range of applications as they can provide accurate direct detection of biomolecules through impedimetric or amperometric signals. Signal response from nanogap sensors is dependent on both the electrode spacing and surface area. However, creating large surface area nanogap sensors presents several challenges during fabrication. We show two different approaches to achieve both horizontal and vertical coplanar nanogap geometries. In the first method we use electron-beam lithography (EBL to pattern an 11 mm long serpentine nanogap (215 nm between two electrodes. For the second method we use inductively-coupled plasma (ICP reactive ion etching (RIE to create a channel in a silicon substrate, optically pattern a buried 1.0 mm × 1.5 mm electrode before anodically bonding a second identical electrode, patterned on glass, directly above. The devices have a wide range of applicability in different sensing techniques with the large area nanogaps presenting advantages over other devices of the same family. As a case study we explore the detection of peptide nucleic acid (PNA−DNA binding events using dielectric spectroscopy with the horizontal coplanar device.

  20. An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Yongwan Park

    2008-12-01

    Full Text Available So far, various addressing and routing algorithms have been extensively studied for wireless sensor networks (WSNs, but many of them were limited to cover less than hundreds of sensor nodes. It is largely due to stringent requirements for fully distributed coordination among sensor nodes, leading to the wasteful use of available address space. As there is a growing need for a large-scale WSN, it will be extremely challenging to support more than thousands of nodes, using existing standard bodies. Moreover, it is highly unlikely to change the existing standards, primarily due to backward compatibility issue. In response, we propose an elegant addressing scheme and its routing algorithm. While maintaining the existing address scheme, it tackles the wastage problem and achieves no additional memory storage during a routing. We also present an adaptive routing algorithm for location-aware applications, using our addressing scheme. Through a series of simulations, we prove that our approach can achieve two times lesser routing time than the existing standard in a ZigBee network.

  1. Assessment of the effects and limitations of the 1998 to 2008 Abbreviated Injury Scale map using a large population-based dataset

    Directory of Open Access Journals (Sweden)

    Franklyn Melanie

    2011-01-01

    Full Text Available Abstract Background Trauma systems should consistently monitor a given trauma population over a period of time. The Abbreviated Injury Scale (AIS and derived scores such as the Injury Severity Score (ISS are commonly used to quantify injury severities in trauma registries. To reflect contemporary trauma management and treatment, the most recent version of the AIS (AIS08 contains many codes which differ in severity from their equivalents in the earlier 1998 version (AIS98. Consequently, the adoption of AIS08 may impede comparisons between data coded using different AIS versions. It may also affect the number of patients classified as major trauma. Methods The entire AIS98-coded injury dataset of a large population based trauma registry was retrieved and mapped to AIS08 using the currently available AIS98-AIS08 dictionary map. The percentage of codes which had increased or decreased in severity, or could not be mapped, was examined in conjunction with the effect of these changes to the calculated ISS. The potential for free text information accompanying AIS coding to improve the quality of AIS mapping was explored. Results A total of 128280 AIS98-coded injuries were evaluated in 32134 patients, 15471 patients of whom were classified as major trauma. Although only 4.5% of dictionary codes decreased in severity from AIS98 to AIS08, this represented almost 13% of injuries in the registry. In 4.9% of patients, no injuries could be mapped. ISS was potentially unreliable in one-third of patients, as they had at least one AIS98 code which could not be mapped. Using AIS08, the number of patients classified as major trauma decreased by between 17.3% and 30.3%. Evaluation of free text descriptions for some injuries demonstrated the potential to improve mapping between AIS versions. Conclusions Converting AIS98-coded data to AIS08 results in a significant decrease in the number of patients classified as major trauma. Many AIS98 codes are missing from the

  2. Improving Decision Making in Ocean Race Sailing using Sensor Data

    NARCIS (Netherlands)

    van Hillegersberg, Jos; Vroling, Mark; Smit, Floris

    While in some sports, experiences have been gained using traditional information and decision support systems, using large sensor datasets for sports analytics is a recent phenomenon. Using sensor data to arrive at effective decision support for sports encompasses various challenges: (1) Sensor data

  3. Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

    Directory of Open Access Journals (Sweden)

    Miao Jin

    2017-06-01

    Full Text Available This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of the measurement error at one sensor node is limited within a small area of the network around the node. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing the superior performance of the proposed algorithm.

  4. Localization Algorithm Based on a Spring Model (LASM for Large Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shuai Li

    2008-03-01

    Full Text Available A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are greatly increasedwith the increase of the network scales. A localization algorithm based on a spring model(LASM method is proposed to reduce the computational complexity, while maintainingthe localization accuracy in large scale sensor networks. The algorithm simulates thedynamics of physical spring system to estimate the positions of nodes. The sensor nodesare set as particles with masses and connected with neighbor nodes by virtual springs. Thevirtual springs will force the particles move to the original positions, the node positionscorrespondingly, from the randomly set positions. Therefore, a blind node position can bedetermined from the LASM algorithm by calculating the related forces with the neighbornodes. The computational and communication complexity are O(1 for each node, since thenumber of the neighbor nodes does not increase proportionally with the network scale size.Three patches are proposed to avoid local optimization, kick out bad nodes and deal withnode variation. Simulation results show that the computational and communicationcomplexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps arealmost unrelated with the network scale size.

  5. Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines

    Directory of Open Access Journals (Sweden)

    Alexandre Presas

    2018-03-01

    Full Text Available Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined

  6. Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines.

    Science.gov (United States)

    Presas, Alexandre; Valentin, David; Egusquiza, Mònica; Valero, Carme; Egusquiza, Eduard

    2018-03-30

    Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW) have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined based on the

  7. Insights into social disparities in smoking prevalence using Mosaic, a novel measure of socioeconomic status: an analysis using a large primary care dataset

    Directory of Open Access Journals (Sweden)

    Szatkowski Lisa

    2010-12-01

    Full Text Available Abstract Background There are well-established socio-economic differences in the prevalence of smoking in the UK, but conventional socio-economic measures may not capture the range and degree of these associations. We have used a commercial geodemographic profiling system, Mosaic, to explore associations with smoking prevalence in a large primary care dataset and to establish whether this tool provides new insights into socio-economic determinants of smoking. Methods We analysed anonymised data on over 2 million patients from The Health Improvement Network (THIN database, linked via patients' postcodes to Mosaic classifications (11 groups and 61 types and quintiles of Townsend Index of Multiple Deprivation. Patients' current smoking status was identified using Read Codes, and logistic regression was used to explore the associations between the available measures of socioeconomic status and smoking prevalence. Results As anticipated, smoking prevalence increased with increasing deprivation according to the Townsend Index (age and sex adjusted OR for highest vs lowest quintile 2.96, 95% CI 2.92-2.99. There were more marked differences in prevalence across Mosaic groups (OR for group G vs group A 4.41, 95% CI 4.33-4.49. Across the 61 Mosaic types, smoking prevalence varied from 8.6% to 42.7%. Mosaic types with high smoking prevalence were characterised by relative deprivation, but also more specifically by single-parent households living in public rented accommodation in areas with little community support, having no access to a car, few qualifications and high TV viewing behaviour. Conclusion Conventional socio-economic measures may underplay social disparities in smoking prevalence. Newer classification systems, such as Mosaic, encompass a wider range of demographic, lifestyle and behaviour data, and are valuable in identifying characteristics of groups of heavy smokers which might be used to tailor cessation interventions.

  8. Design, Fabrication, and Characteristics Experiment of a Large LVDT Sensor for of Bottom Mounted CRDM

    Energy Technology Data Exchange (ETDEWEB)

    Huh, Hyung; Lee, Jin-Haeng; Cho, Yeong-Garp; Yoo, Yeon-Sik; Ryu, Jeong-Soo [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    The stepping motor directly drives the ball screw, and the nut of the ball screw makes the electromagnet move up and down along the guide tube. At this time the higher force of an electromagnet will greatly result in less position fluctuation of the armature for a given variation of loadings. The magnetic rigidity represents one of the most important characteristics of the electromagnet. For this reason, it is necessary to measure control rod position including sagging rate due to loadings exactly. Therefore, KAERI has developed electromagnet rigidity measuring sensor using LVDT. This paper presents the case numerical and experimental research of prototyping a large LVDT sensor for BMCRDM. The FEM and experimental results for optimized large LVDT shows good linearity agreement of displacement vs. induced currents between 0[mm] and ±22[mm] intervals. The experimental result has shorter linearity interval than that of FEM result due to 100[mm] core length using experimental test. The developed FE model and analysis procedure could be useful tools for predicting the linearity of displacement of a large LVDT.

  9. Performance Analysis of the Ironless Inductive Position Sensor in the Large Hadron Collider Collimators Environment

    CERN Document Server

    Danisi, Alessandro; Losito, Roberto

    2015-01-01

    The Ironless Inductive Position Sensor (I2PS) has been introduced as a valid alternative to Linear Variable Differential Transformers (LVDTs) when external magnetic fields are present. Potential applications of this linear position sensor can be found in critical systems such as nuclear plants, tokamaks, satellites and particle accelerators. This paper analyzes the performance of the I2PS in the harsh environment of the collimators of the Large Hadron Collider (LHC), where position uncertainties of less than 20 μm are demanded in the presence of nuclear radiation and external magnetic fields. The I2PS has been targeted for installation for LHC Run 2, in order to solve the magnetic interference problem which standard LVDTs are experiencing. The paper describes in detail the chain of systems which belong to the new I2PS measurement task, their impact on the sensor performance and their possible further optimization. The I2PS performance is analyzed evaluating the position uncertainty (on 30 s), the magnetic im...

  10. Large Size High Performance Transparent Amorphous Silicon Sensors for Laser Beam Position Detection and Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Calderon, A.; Martinez Rivero, C.; Matorras, F.; Rodrigo, T.; Sobron, M.; Vila, I.; Virto; Alberdi, J.; Arce, P.; Barcala, J. M.; Calvo, E.; Ferrando, A.; Josa, M. I.; Luque, J. M.; Molinero, A.; Navarrete, J.; Oller, J. C.; Kohler, C.; Lutz, B.; Schubert, M. B.

    2006-09-04

    We present the measured performance of a new generation of semitransparente amorphous silicon position detectors. They have a large sensitive area (30 x 30 mm2) and show good properties such as a high response (about 20 mA/W), an intinsic position resolution better than 3 m, a spatial point reconstruction precision better than 10 m, deflection angles smaller than 10 rad and a transmission power in the visible and NIR higher than 70%. In addition, multipoint alignment monitoring, using up to five sensors lined along a light path of about 5 meters, can be achieved with a resolution better than 20m. (Author)

  11. Large Size High Performance Transparent Amorphous Silicon Sensors for Laser Beam Position Detection and Monitoring

    International Nuclear Information System (INIS)

    Calderon, A.; Martinez Rivero, C.; Matorras, F.; Rodrigo, T.; Sobron, M.; Vila, I.; Virto; Alberdi, J.; Arce, P.; Barcala, J. M.; Calvo, E.; Ferrando, A.; Josa, M. I.; Luque, J. M.; Molinero, A.; Navarrete, J.; Oller, J. C.; Kohler, C.; Lutz, B.; Schubert, M. B.

    2006-01-01

    We present the measured performance of a new generation of semitransparente amorphous silicon position detectors. They have a large sensitive area (30 x 30 mm2) and show good properties such as a high response (about 20 mA/W), an intinsic position resolution better than 3 m, a spatial point reconstruction precision better than 10 m, deflection angles smaller than 10 rad and a transmission power in the visible and NIR higher than 70%. In addition, multipoint alignment monitoring, using up to five sensors lined along a light path of about 5 meters, can be achieved with a resolution better than 20m. (Author)

  12. Sensors

    CERN Document Server

    Pigorsch, Enrico

    1997-01-01

    This is the 5th edition of the Metra Martech Directory "EUROPEAN CENTRES OF EXPERTISE - SENSORS." The entries represent a survey of European sensors development. The new edition contains 425 detailed profiles of companies and research institutions in 22 countries. This is reflected in the diversity of sensors development programmes described, from sensors for physical parameters to biosensors and intelligent sensor systems. We do not claim that all European organisations developing sensors are included, but this is a good cross section from an invited list of participants. If you see gaps or omissions, or would like your organisation to be included, please send details. The data base invites the formation of effective joint ventures by identifying and providing access to specific areas in which organisations offer collaboration. This issue is recognised to be of great importance and most entrants include details of collaboration offered and sought. We hope the directory on Sensors will help you to find the ri...

  13. Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Jensen, H. [PBI-Dansensor A/S (Denmark); Toft Soerensen, O. [Risoe National Lab., Materials Research Dept. (Denmark)

    1999-10-01

    A new type of ceramic oxygen sensors based on semiconducting oxides was developed in this project. The advantage of these sensors compared to standard ZrO{sub 2} sensors is that they do not require a reference gas and that they can be produced in small sizes. The sensor design and the techniques developed for production of these sensors are judged suitable by the participating industry for a niche production of a new generation of oxygen sensors. Materials research on new oxygen ion conducting conductors both for applications in oxygen sensors and in fuel was also performed in this project and finally a new process was developed for fabrication of ceramic tubes by dip-coating. (EHS)

  14. Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

    Science.gov (United States)

    Fries, K. J.; Kerkez, B.

    2017-12-01

    We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.

  15. Geostatistical and multivariate modelling for large scale quantitative mapping of seafloor sediments using sparse datasets, a case study from the Cleaverbank area (the Netherlands)

    NARCIS (Netherlands)

    Alevizos, Evangelos; Siemes, K.; Janmaat, J.; Snellen, M.; Simons, D.G.; Greinert, J

    2016-01-01

    Quantitative mapping of seafloor sediment properties (eg. grain size) requires the input of comprehensive Multi-Beam Echo Sounder (MBES) datasets along with adequate ground truth for establishing a functional relation between them. MBES surveys in extensive shallow shelf areas can be a rather

  16. Large-area compatible fabrication and encapsulation of inkjet-printed humidity sensors on flexible foils with integrated thermal compensation

    International Nuclear Information System (INIS)

    Molina-Lopez, F; Quintero, A Vásquez; Mattana, G; Briand, D; De Rooij, N F

    2013-01-01

    This work presents the simultaneous fabrication of ambient relative humidity (RH) and temperature sensors arrays, inkjet-printed on flexible substrates and subsequently encapsulated at foil level. These sensors are based on planar interdigitated capacitors with an inkjet-printed sensing layer and meander-shaped resistors. Their combination allows the compensation of the RH signals variations at different temperatures. The whole fabrication of the system is carried out at foil level and involves the utilization of additive methods such as inkjet-printing and electrodeposition. Electrodeposition of the printed lines resulted in an improvement of the thermoresistors. The sensors have been characterized and their performances analyzed. The encapsulation layer does not modify the performances of the sensors in terms of sensitivity or response time. This work demonstrates the potential of inkjet-printing in the large-area fabrication of light-weight and cost-efficient gas sensors on flexible substrates. (paper)

  17. Collecting big datasets of human activity one checkin at a time

    OpenAIRE

    Hossmann, Theus; Efstratiou, Christos; Mascolo, Cecilia

    2012-01-01

    A variety of cutting edge applications for mobile phones exploit the availability of phone sensors to accurately infer the user activity and location to offer more effective services. To validate and evaluate these new applications, appropriate and extensive datasets are needed: in particular, large sets of traces of sensor data (accelerometer, GPS, micro- phone, etc.), labelled with corresponding user activities. So far, such traces have only been collected in short-lived, small-scale setups...

  18. Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

    KAUST Repository

    Douik, Ahmed

    2017-08-30

    Consider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.

  19. Mathematical Model and Calibration Experiment of a Large Measurement Range Flexible Joints 6-UPUR Six-Axis Force Sensor

    Directory of Open Access Journals (Sweden)

    Yanzhi Zhao

    2016-08-01

    Full Text Available Nowadays improving the accuracy and enlarging the measuring range of six-axis force sensors for wider applications in aircraft landing, rocket thrust, and spacecraft docking testing experiments has become an urgent objective. However, it is still difficult to achieve high accuracy and large measuring range with traditional parallel six-axis force sensors due to the influence of the gap and friction of the joints. Therefore, to overcome the mentioned limitations, this paper proposed a 6-Universal-Prismatic-Universal-Revolute (UPUR joints parallel mechanism with flexible joints to develop a large measurement range six-axis force sensor. The structural characteristics of the sensor are analyzed in comparison with traditional parallel sensor based on the Stewart platform. The force transfer relation of the sensor is deduced, and the force Jacobian matrix is obtained using screw theory in two cases of the ideal state and the state of flexibility of each flexible joint is considered. The prototype and loading calibration system are designed and developed. The K value method and least squares method are used to process experimental data, and in errors of kind Ι and kind II linearity are obtained. The experimental results show that the calibration error of the K value method is more than 13.4%, and the calibration error of the least squares method is 2.67%. The experimental results prove the feasibility of the sensor and the correctness of the theoretical analysis which are expected to be adopted in practical applications.

  20. Mathematical Model and Calibration Experiment of a Large Measurement Range Flexible Joints 6-UPUR Six-Axis Force Sensor.

    Science.gov (United States)

    Zhao, Yanzhi; Zhang, Caifeng; Zhang, Dan; Shi, Zhongpan; Zhao, Tieshi

    2016-08-11

    Nowadays improving the accuracy and enlarging the measuring range of six-axis force sensors for wider applications in aircraft landing, rocket thrust, and spacecraft docking testing experiments has become an urgent objective. However, it is still difficult to achieve high accuracy and large measuring range with traditional parallel six-axis force sensors due to the influence of the gap and friction of the joints. Therefore, to overcome the mentioned limitations, this paper proposed a 6-Universal-Prismatic-Universal-Revolute (UPUR) joints parallel mechanism with flexible joints to develop a large measurement range six-axis force sensor. The structural characteristics of the sensor are analyzed in comparison with traditional parallel sensor based on the Stewart platform. The force transfer relation of the sensor is deduced, and the force Jacobian matrix is obtained using screw theory in two cases of the ideal state and the state of flexibility of each flexible joint is considered. The prototype and loading calibration system are designed and developed. The K value method and least squares method are used to process experimental data, and in errors of kind Ι and kind II linearity are obtained. The experimental results show that the calibration error of the K value method is more than 13.4%, and the calibration error of the least squares method is 2.67%. The experimental results prove the feasibility of the sensor and the correctness of the theoretical analysis which are expected to be adopted in practical applications.

  1. Single Photon Counting Large Format Imaging Sensors with High Spatial and Temporal Resolution

    Science.gov (United States)

    Siegmund, O. H. W.; Ertley, C.; Vallerga, J. V.; Cremer, T.; Craven, C. A.; Lyashenko, A.; Minot, M. J.

    High time resolution astronomical and remote sensing applications have been addressed with microchannel plate based imaging, photon time tagging detector sealed tube schemes. These are being realized with the advent of cross strip readout techniques with high performance encoding electronics and atomic layer deposited (ALD) microchannel plate technologies. Sealed tube devices up to 20 cm square have now been successfully implemented with sub nanosecond timing and imaging. The objective is to provide sensors with large areas (25 cm2 to 400 cm2) with spatial resolutions of 5 MHz and event timing accuracy of 100 ps. High-performance ASIC versions of these electronics are in development with better event rate, power and mass suitable for spaceflight instruments.

  2. Measuring Accurate Body Parameters of Dressed Humans with Large-Scale Motion Using a Kinect Sensor

    Directory of Open Access Journals (Sweden)

    Sidan Du

    2013-08-01

    Full Text Available Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods.

  3. Equation Chapter 1 Section 1Cross Layer Design for Localization in Large-Scale Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yuanfeng ZHANG

    2014-02-01

    Full Text Available There are many technical challenges for designing large-scale underwater sensor networks, especially the sensor node localization. Although many papers studied for large-scale sensor node localization, previous studies mainly study the location algorithm without the cross layer design for localization. In this paper, by utilizing the network hierarchical structure of underwater sensor networks, we propose a new large-scale underwater acoustic localization scheme based on cross layer design. In this scheme, localization is performed in a hierarchical way, and the whole localization process focused on the physical layer, data link layer and application layer. We increase the pipeline parameters which matched the acoustic channel, added in MAC protocol to increase the authenticity of the large-scale underwater sensor networks, and made analysis of different location algorithm. We conduct extensive simulations, and our results show that MAC layer protocol and the localization algorithm all would affect the result of localization which can balance the trade-off between localization accuracy, localization coverage, and communication cost.

  4. Prototype of a laser guide star wavefront sensor for the Extremely Large Telescope

    Science.gov (United States)

    Patti, M.; Lombini, M.; Schreiber, L.; Bregoli, G.; Arcidiacono, C.; Cosentino, G.; Diolaiti, E.; Foppiani, I.

    2018-06-01

    The new class of large telescopes, like the future Extremely Large Telescope (ELT), are designed to work with a laser guide star (LGS) tuned to a resonance of atmospheric sodium atoms. This wavefront sensing technique presents complex issues when applied to big telescopes for many reasons, mainly linked to the finite distance of the LGS, the launching angle, tip-tilt indetermination and focus anisoplanatism. The implementation of a laboratory prototype for the LGS wavefront sensor (WFS) at the beginning of the phase study of MAORY (Multi-conjugate Adaptive Optics Relay) for ELT first light has been indispensable in investigating specific mitigation strategies for the LGS WFS issues. This paper presents the test results of the LGS WFS prototype under different working conditions. The accuracy within which the LGS images are generated on the Shack-Hartmann WFS has been cross-checked with the MAORY simulation code. The experiments show the effect of noise on centroiding precision, the impact of LGS image truncation on wavefront sensing accuracy as well as the temporal evolution of the sodium density profile and LGS image under-sampling.

  5. High Resolution and Large Dynamic Range Resonant Pressure Sensor Based on Q-Factor Measurement

    Science.gov (United States)

    Gutierrez, Roman C. (Inventor); Stell, Christopher B. (Inventor); Tang, Tony K. (Inventor); Vorperian, Vatche (Inventor); Wilcox, Jaroslava (Inventor); Shcheglov, Kirill (Inventor); Kaiser, William J. (Inventor)

    2000-01-01

    A pressure sensor has a high degree of accuracy over a wide range of pressures. Using a pressure sensor relying upon resonant oscillations to determine pressure, a driving circuit drives such a pressure sensor at resonance and tracks resonant frequency and amplitude shifts with changes in pressure. Pressure changes affect the Q-factor of the resonating portion of the pressure sensor. Such Q-factor changes are detected by the driving/sensing circuit which in turn tracks the changes in resonant frequency to maintain the pressure sensor at resonance. Changes in the Q-factor are reflected in changes of amplitude of the resonating pressure sensor. In response, upon sensing the changes in the amplitude, the driving circuit changes the force or strength of the electrostatic driving signal to maintain the resonator at constant amplitude. The amplitude of the driving signals become a direct measure of the changes in pressure as the operating characteristics of the resonator give rise to a linear response curve for the amplitude of the driving signal. Pressure change resolution is on the order of 10(exp -6) torr over a range spanning from 7,600 torr to 10(exp -6) torr. No temperature compensation for the pressure sensor of the present invention is foreseen. Power requirements for the pressure sensor are generally minimal due to the low-loss mechanical design of the resonating pressure sensor and the simple control electronics.

  6. Use of a Recursive-Rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset

    Directory of Open Access Journals (Sweden)

    Yoichi Hayashi

    Full Text Available To assist physicians in the diagnosis of breast cancer and thereby improve survival, a highly accurate computer-aided diagnostic system is necessary. Although various machine learning and data mining approaches have been devised to increase diagnostic accuracy, most current methods are inadequate. The recently developed Recursive-Rule eXtraction (Re-RX algorithm provides a hierarchical, recursive consideration of discrete variables prior to analysis of continuous data, and can generate classification rules that have been trained on the basis of both discrete and continuous attributes. The objective of this study was to extract highly accurate, concise, and interpretable classification rules for diagnosis using the Re-RX algorithm with J48graft, a class for generating a grafted C4.5 decision tree. We used the Wisconsin Breast Cancer Dataset (WBCD. Nine research groups provided 10 kinds of highly accurate concrete classification rules for the WBCD. We compared the accuracy and characteristics of the rule set for the WBCD generated using the Re-RX algorithm with J48graft with five rule sets obtained using 10-fold cross validation (CV. We trained the WBCD using the Re-RX algorithm with J48graft and the average classification accuracies of 10 runs of 10-fold CV for the training and test datasets, the number of extracted rules, and the average number of antecedents for the WBCD. Compared with other rule extraction algorithms, the Re-RX algorithm with J48graft resulted in a lower average number of rules for diagnosing breast cancer, which is a substantial advantage. It also provided the lowest average number of antecedents per rule. These features are expected to greatly aid physicians in making accurate and concise diagnoses for patients with breast cancer. Keywords: Breast cancer diagnosis, Rule extraction, Re-RX algorithm, J48graft, C4.5

  7. A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ahmadreza Vajdi

    2018-05-01

    Full Text Available We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP. Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

  8. A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

    Science.gov (United States)

    Vajdi, Ahmadreza; Zhang, Gongxuan; Zhou, Junlong; Wei, Tongquan; Wang, Yongli; Wang, Tianshu

    2018-05-04

    We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

  9. A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

    Science.gov (United States)

    Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

    2018-01-01

    We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach. PMID:29734718

  10. Multi-level infrastructure of interconnected testbeds of large-scale wireless sensor networks (MI2T-WSN)

    CSIR Research Space (South Africa)

    Abu-Mahfouz, Adnan M

    2012-06-01

    Full Text Available are still required for further testing before the real implementation. In this paper we propose a multi-level infrastructure of interconnected testbeds of large- scale WSNs. This testbed consists of 1000 sensor motes that will be distributed into four...

  11. EPA Nanorelease Dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — EPA Nanorelease Dataset. This dataset is associated with the following publication: Wohlleben, W., C. Kingston, J. Carter, E. Sahle-Demessie, S. Vazquez-Campos, B....

  12. The New world of ';Big Data' Analytics and High Performance Data: A Paradigm shift in the way we interact with very large Earth Observation datasets (Invited)

    Science.gov (United States)

    Purss, M. B.; Lewis, A.; Ip, A.; Evans, B.

    2013-12-01

    The next decade promises an exponential increase in volumes of open data from Earth observing satellites. The ESA Sentinels, the Japan Meteorological Agency's Himawari 8/9 geostationary satellites, various NASA missions, and of course the many EO satellites planned from China, will produce petabyte scale datasets of national and global significance. It is vital that we develop new ways of managing, accessing and using this ';big-data' from satellites, to produce value added information within realistic timeframes. A paradigm shift is required away from traditional ';scene based' (and labour intensive) approaches with data storage and delivery for processing at local sites, to emerging High Performance Data (HPD) models where the data are organised and co-located with High Performance Computational (HPC) infrastructures in a way that enables users to bring themselves, their algorithms and the HPC processing power to the data. Automated workflows, that allow the entire archive of data to be rapidly reprocessed from raw data to fully calibrated products, are a crucial requirement for the effective stewardship of these datasets. New concepts such as arranging and viewing data as ';data objects' which underpin the delivery of ';information as a service' are also integral to realising the transition into HPD analytics. As Australia's national remote sensing and geoscience agency, Geoscience Australia faces a pressing need to solve the problems of ';big-data', in particular around the 25-year archive of calibrated Landsat data. The challenge is to ensure standardised information can be extracted from the entire archive and applied to nationally significant problems in hazards, water management, land management, resource development and the environment. Ultimately, these uses justify government investment in these unique systems. A key challenge was how best to organise the archive of calibrated Landsat data (estimated to grow to almost 1 PB by the end of 2014) in a way

  13. Autonomous construction agents: An investigative framework for large sensor network self-management

    Energy Technology Data Exchange (ETDEWEB)

    Koch, Joshua Bruce [Iowa State Univ., Ames, IA (United States)

    2008-01-01

    Recent technological advances have made it cost effective to utilize massive, heterogeneous sensor networks. To gain appreciable value from these informational systems, there must be a control scheme that coordinates information flow to produce meaningful results. This paper will focus on tools developed to manage the coordination of autonomous construction agents using stigmergy, in which a set of basic low-level rules are implemented through various environmental cues. Using VE-Suite, an open-source virtual engineering software package, an interactive environment is created to explore various informational configurations for the construction problem. A simple test case is developed within the framework, and construction times are analyzed for possible functional relationships pertaining to performance of a particular set of parameters and a given control process. Initial experiments for the test case show sensor saturation occurs relatively quickly with 5-7 sensors, and construction time is generally independent of sensor range except for small numbers of sensors. Further experiments using this framework are needed to define other aspects of sensor performance. These trends can then be used to help decide what kinds of sensing capabilities are required to simultaneously achieve the most cost-effective solution and provide the required value of information when applied to the development of real world sensor applications.

  14. Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm

    International Nuclear Information System (INIS)

    Lian, Jijian; He, Longjun; Ma, Bin; Peng, Wenxiang; Li, Huokun

    2013-01-01

    Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures. (paper)

  15. Sensor

    OpenAIRE

    Gleeson, Helen; Dierking, Ingo; Grieve, Bruce; Woodyatt, Christopher; Brimicombe, Paul

    2015-01-01

    An electrical temperature sensor (10) comprises a liquid crystalline material (12). First and second electrically conductive contacts (14), (16), having a spaced relationship there between, contact the liquid crystalline material (12). An electric property measuring device is electrically connected to the first and second contacts (14), (16) and is arranged to measure an electric property of the liquid crystalline material (12). The liquid crystalline material (12) has a transition temperatur...

  16. Six-axis force–torque sensor with a large range for biomechanical applications

    International Nuclear Information System (INIS)

    + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Brookhuis, R A; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Droogendijk, H; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >De Boer, M J; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Sanders, R G P; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Lammerink, T S J; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Wiegerink, R J; + Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" data-affiliation=" (MESA+ Institute for Nanotechnology, University of Twente, Enschede (Netherlands))" >Krijnen, G J M

    2014-01-01

    A silicon six-axis force–torque sensor is designed and realized to be used for measurement of the power transfer between the human body and the environment. Capacitive read-out is used to detect all axial force components and all torque components simultaneously. Small electrode gaps in combination with mechanical amplification by the sensor structure result in a high sensitivity. The miniature sensor has a wide force range of up to 50 N in normal direction, 10 N in shear direction and 25 N mm of maximum torque around each axis. (paper)

  17. In vitro and in vivo evaluation of a new large animal spirometry device using mainstream CO2 flow sensors.

    Science.gov (United States)

    Ambrisko, T D; Lammer, V; Schramel, J P; Moens, Y P S

    2014-07-01

    A spirometry device equipped with mainstream CO2 flow sensor is not available for large animal anaesthesia. To measure the resistance of a new large animal spirometry device and assess its agreement with reference methods for volume measurements. In vitro experiment and crossover study using anaesthetised horses. A flow partitioning device (FPD) equipped with 4 human CO2 flow sensors was tested. Pressure differences were measured across the whole FPD and across each sensor separately using air flows (range: 90-720 l/min). One sensor was connected to a spirometry monitor for in vitro volume (3, 5 and 7 l) measurements. These measurements were compared with a reference method. Five anaesthetised horses were used for tidal volume (VT) measurements using the FPD and a horse-lite sensor (reference method). Bland-Altman analysis, ANOVA and linear regression analysis were used for data analysis. Pressure differences across each sensor were similar suggesting equal flow partitioning. The resistance of the device increased with flow (range: 0.3-1.5 cmH2 O s/l) and was higher than that of the horse-lite. The limits of agreement for volume measurements were within -1 and 2% in vitro and -12 and 0% in vivo. Nine of 147 VT measurements in horses were outside of the ± 10% limits of acceptance but most of these erroneous measurements occurred with VTs lower than 4 l. The determined correction factor for volume measurements was 3.97 ± 0.03. The limits of agreement for volume measurements by the new device were within ± 10% using clinically relevant range of volumes. The new spirometry device can be recommended for measurement of VT in adult Warmblood horses. © 2013 EVJ Ltd.

  18. Large Scale Applications Using FBG Sensors: Determination of In-Flight Loads and Shape of a Composite Aircraft Wing

    Directory of Open Access Journals (Sweden)

    Matthew J. Nicolas

    2016-06-01

    Full Text Available Technological advances have enabled the development of a number of optical fiber sensing methods over the last few years. The most prevalent optical technique involves the use of fiber Bragg grating (FBG sensors. These small, lightweight sensors have many attributes that enable their use for a number of measurement applications. Although much literature is available regarding the use of FBGs for laboratory level testing, few publications in the public domain exist of their use at the operational level. Therefore, this paper gives an overview of the implementation of FBG sensors for large scale structures and applications. For demonstration, a case study is presented in which FBGs were used to determine the deflected wing shape and the out-of-plane loads of a 5.5-m carbon-composite wing of an ultralight aerial vehicle. The in-plane strains from the 780 FBG sensors were used to obtain the out-of-plane loads as well as the wing shape at various load levels. The calculated out-of-plane displacements and loads were within 4.2% of the measured data. This study demonstrates a practical method in which direct measurements are used to obtain critical parameters from the high distribution of FBG sensors. This procedure can be used to obtain information for structural health monitoring applications to quantify healthy vs. unhealthy structures.

  19. Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks

    Directory of Open Access Journals (Sweden)

    Raja Jurdak

    2008-11-01

    Full Text Available Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

  20. Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks.

    Science.gov (United States)

    Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

    2008-11-24

    Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

  1. Retina-like sensor based on a lens array with a large field of view.

    Science.gov (United States)

    Fan, Fan; Hao, Qun; Cheng, Xuemin

    2015-12-20

    This paper puts forward a retina-like sensor based on a lens array, which can be used in conventional optical systems. This sensor achieves log-polar mapping by dividing the imaging optical system's image plane using a lens array. In this paper the mathematical model has been set up with the relative structural parameters. Also, the simulation experiments and parameter analysis have been discussed to verify the reliability of this system. From the experiment results, it can be seen that this sensor realized the log-polar mapping with the transformed image output. Each lens corresponded to a circular region in the image plane with no crossover between different fields of view of adjacent lenses. When the number of rings changed, the relative error did not significantly change, and this error could be reduced to 1% when the number of lenses in each ring was increased. The work widely enlarged the application of this kind of sensor, which will lay a theoretical foundation for retina-like sensors.

  2. iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

    OpenAIRE

    Wu, Xudong; Shen, Ruofei; Fu, Luoyi; Tian, Xiaohua; Liu, Peng; Wang, Xinbing

    2017-01-01

    As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper pre...

  3. Design and implementation of PAVEMON: A GIS web-based pavement monitoring system based on large amounts of heterogeneous sensors data

    Science.gov (United States)

    Shahini Shamsabadi, Salar

    A web-based PAVEment MONitoring system, PAVEMON, is a GIS oriented platform for accommodating, representing, and leveraging data from a multi-modal mobile sensor system. Stated sensor system consists of acoustic, optical, electromagnetic, and GPS sensors and is capable of producing as much as 1 Terabyte of data per day. Multi-channel raw sensor data (microphone, accelerometer, tire pressure sensor, video) and processed results (road profile, crack density, international roughness index, micro texture depth, etc.) are outputs of this sensor system. By correlating the sensor measurements and positioning data collected in tight time synchronization, PAVEMON attaches a spatial component to all the datasets. These spatially indexed outputs are placed into an Oracle database which integrates seamlessly with PAVEMON's web-based system. The web-based system of PAVEMON consists of two major modules: 1) a GIS module for visualizing and spatial analysis of pavement condition information layers, and 2) a decision-support module for managing maintenance and repair (Mℝ) activities and predicting future budget needs. PAVEMON weaves together sensor data with third-party climate and traffic information from the National Oceanic and Atmospheric Administration (NOAA) and Long Term Pavement Performance (LTPP) databases for an organized data driven approach to conduct pavement management activities. PAVEMON deals with heterogeneous and redundant observations by fusing them for jointly-derived higher-confidence results. A prominent example of the fusion algorithms developed within PAVEMON is a data fusion algorithm used for estimating the overall pavement conditions in terms of ASTM's Pavement Condition Index (PCI). PAVEMON predicts PCI by undertaking a statistical fusion approach and selecting a subset of all the sensor measurements. Other fusion algorithms include noise-removal algorithms to remove false negatives in the sensor data in addition to fusion algorithms developed for

  4. Circular High-Q Resonating Isotropic Strain Sensors with Large Shift of Resonance Frequency under Stress

    Directory of Open Access Journals (Sweden)

    Hilmi Volkan Demir

    2009-11-01

    Full Text Available We present circular architecture bioimplant strain sensors that facilitate a strong resonance frequency shift with mechanical deformation. The clinical application area of these sensors is for in vivo assessment of bone fractures. Using a rectangular geometry, we obtain a resonance shift of 330 MHz for a single device and 170 MHz for its triplet configuration (with three side-by-side resonators on chip under an applied load of 3,920 N. Using the same device parameters with a circular isotropic architecture, we achieve a resonance frequency shift of 500 MHz for the single device and 260 MHz for its triplet configuration, demonstrating substantially increased sensitivity.

  5. A coastal seawater temperature dataset for biogeographical studies: large biases between in situ and remotely-sensed data sets around the Coast of South Africa.

    Directory of Open Access Journals (Sweden)

    Albertus J Smit

    Full Text Available Gridded SST products developed particularly for offshore regions are increasingly being applied close to the coast for biogeographical applications. The purpose of this paper is to demonstrate the dangers of doing so through a comparison of reprocessed MODIS Terra and Pathfinder v5.2 SSTs, both at 4 km resolution, with instrumental in situ temperatures taken within 400 m from the coast. We report large biases of up to +6°C in places between satellite-derived and in situ climatological temperatures for 87 sites spanning the entire ca. 2 700 km of the South African coastline. Although biases are predominantly warm (i.e. the satellite SSTs being higher, smaller or even cold biases also appear in places, especially along the southern and western coasts of the country. We also demonstrate the presence of gradients in temperature biases along shore-normal transects - generally SSTs extracted close to the shore demonstrate a smaller bias with respect to the in situ temperatures. Contributing towards the magnitude of the biases are factors such as SST data source, proximity to the shore, the presence/absence of upwelling cells or coastal embayments. Despite the generally large biases, from a biogeographical perspective, species distribution retains a correlative relationship with underlying spatial patterns in SST, but in order to arrive at a causal understanding of the determinants of biogeographical patterns we suggest that in shallow, inshore marine habitats, temperature is best measured directly.

  6. A Coastal Seawater Temperature Dataset for Biogeographical Studies: Large Biases between In Situ and Remotely-Sensed Data Sets around the Coast of South Africa

    Science.gov (United States)

    Smit, Albertus J.; Roberts, Michael; Anderson, Robert J.; Dufois, Francois; Dudley, Sheldon F. J.; Bornman, Thomas G.; Olbers, Jennifer; Bolton, John J.

    2013-01-01

    Gridded SST products developed particularly for offshore regions are increasingly being applied close to the coast for biogeographical applications. The purpose of this paper is to demonstrate the dangers of doing so through a comparison of reprocessed MODIS Terra and Pathfinder v5.2 SSTs, both at 4 km resolution, with instrumental in situ temperatures taken within 400 m from the coast. We report large biases of up to +6°C in places between satellite-derived and in situ climatological temperatures for 87 sites spanning the entire ca. 2 700 km of the South African coastline. Although biases are predominantly warm (i.e. the satellite SSTs being higher), smaller or even cold biases also appear in places, especially along the southern and western coasts of the country. We also demonstrate the presence of gradients in temperature biases along shore-normal transects — generally SSTs extracted close to the shore demonstrate a smaller bias with respect to the in situ temperatures. Contributing towards the magnitude of the biases are factors such as SST data source, proximity to the shore, the presence/absence of upwelling cells or coastal embayments. Despite the generally large biases, from a biogeographical perspective, species distribution retains a correlative relationship with underlying spatial patterns in SST, but in order to arrive at a causal understanding of the determinants of biogeographical patterns we suggest that in shallow, inshore marine habitats, temperature is best measured directly. PMID:24312609

  7. Comparing vector-based and Bayesian memory models using large-scale datasets: User-generated hashtag and tag prediction on Twitter and Stack Overflow.

    Science.gov (United States)

    Stanley, Clayton; Byrne, Michael D

    2016-12-01

    The growth of social media and user-created content on online sites provides unique opportunities to study models of human declarative memory. By framing the task of choosing a hashtag for a tweet and tagging a post on Stack Overflow as a declarative memory retrieval problem, 2 cognitively plausible declarative memory models were applied to millions of posts and tweets and evaluated on how accurately they predict a user's chosen tags. An ACT-R based Bayesian model and a random permutation vector-based model were tested on the large data sets. The results show that past user behavior of tag use is a strong predictor of future behavior. Furthermore, past behavior was successfully incorporated into the random permutation model that previously used only context. Also, ACT-R's attentional weight term was linked to an entropy-weighting natural language processing method used to attenuate high-frequency words (e.g., articles and prepositions). Word order was not found to be a strong predictor of tag use, and the random permutation model performed comparably to the Bayesian model without including word order. This shows that the strength of the random permutation model is not in the ability to represent word order, but rather in the way in which context information is successfully compressed. The results of the large-scale exploration show how the architecture of the 2 memory models can be modified to significantly improve accuracy, and may suggest task-independent general modifications that can help improve model fit to human data in a much wider range of domains. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  8. Edgeless silicon sensors for Medipix-based large-area X-ray imaging detectors

    International Nuclear Information System (INIS)

    Bosma, M J; Visser, J; Koffeman, E N; Evrard, O; De Moor, P; De Munck, K; Tezcan, D Sabuncuoglu

    2011-01-01

    Some X-ray imaging applications demand sensitive areas exceeding the active area of a single sensor. This requires a seamless tessellation of multiple detector modules with edgeless sensors. Our research is aimed at minimising the insensitive periphery that isolates the active area from the edge. Reduction of the edge-defect induced charge injection, caused by the deleterious effects of dicing, is an important step. We report on the electrical characterisation of 300 μm thick edgeless silicon p + -ν-n + diodes, diced using deep reactive ion etching. Sensors with both n-type and p-type stop rings were fabricated in various edge topologies. Leakage currents in the active area are compared with those of sensors with a conventional design. As expected, we observe an inverse correlation between leakage-current density and both the edge distance and stop-ring width. From this correlation we determine a minimum acceptable edge distance of 50 μm. We also conclude that structures with a p-type stop ring show lower leakage currents and higher breakdown voltages than the ones with an n-type stop ring.

  9. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease

    NARCIS (Netherlands)

    Silva de Lima, A.L.; Hahn, T.; Evers, L.J.W.; Vries, N.M. de; Cohen, E.; Afek, M.; Bataille, L.; Daeschler, M.; Claes, K.; Boroojerdi, B.; Terricabras, D.; Little, M.A.; Baldus, H.; Bloem, B.R.; Faber, M.J.

    2017-01-01

    Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort

  10. Near real-time large scale (sensor) data provisioning for PLF

    NARCIS (Netherlands)

    Vonder, M.R.; Waaij, B.D. van der; Harmsma, E.J.; Donker, G.

    2015-01-01

    Think big, start small. With that thought in mind, Smart Dairy Farming (SDF) developed a platform to make real-time sensor data from different farms available, for model developers to support dairy farmers in Precision Livestock Farming. The data has been made available via a standard interface on

  11. A Novel Method for Proximity Detection of Moving Targets Using a Large-Scale Planar Capacitive Sensor System

    Directory of Open Access Journals (Sweden)

    Yong Ye

    2016-05-01

    Full Text Available A novel method for proximity detection of moving targets (with high dielectric constants using a large-scale (the size of each sensor is 31 cm × 19 cm planar capacitive sensor system (PCSS is proposed. The capacitive variation with distance is derived, and a pair of electrodes in a planar capacitive sensor unit (PCSU with a spiral shape is found to have better performance on sensitivity distribution homogeneity and dynamic range than three other shapes (comb shape, rectangular shape, and circular shape. A driving excitation circuit with a Clapp oscillator is proposed, and a capacitance measuring circuit with sensitivity of 0.21 V p − p / pF is designed. The results of static experiments and dynamic experiments demonstrate that the voltage curves of static experiments are similar to those of dynamic experiments; therefore, the static data can be used to simulate the dynamic curves. The dynamic range of proximity detection for three projectiles is up to 60 cm, and the results of the following static experiments show that the PCSU with four neighboring units has the highest sensitivity (the sensitivities of other units are at least 4% lower; when the attack angle decreases, the intensity of sensor signal increases. This proposed method leads to the design of a feasible moving target detector with simple structure and low cost, which can be applied in the interception system.

  12. SIMADL: Simulated Activities of Daily Living Dataset

    Directory of Open Access Journals (Sweden)

    Talal Alshammari

    2018-04-01

    Full Text Available With the realisation of the Internet of Things (IoT paradigm, the analysis of the Activities of Daily Living (ADLs, in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator, which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

  13. Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets

    Science.gov (United States)

    Shrimankar, D. D.; Sathe, S. R.

    2016-01-01

    Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today’s supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures. PMID:27932868

  14. Sirenomelia: an epidemiologic study in a large dataset from the International Clearinghouse of Birth Defects Surveillance and Research, and literature review.

    Science.gov (United States)

    Orioli, Iêda M; Amar, Emmanuelle; Arteaga-Vazquez, Jazmin; Bakker, Marian K; Bianca, Sebastiano; Botto, Lorenzo D; Clementi, Maurizio; Correa, Adolfo; Csaky-Szunyogh, Melinda; Leoncini, Emanuele; Li, Zhu; López-Camelo, Jorge S; Lowry, R Brian; Marengo, Lisa; Martínez-Frías, María-Luisa; Mastroiacovo, Pierpaolo; Morgan, Margery; Pierini, Anna; Ritvanen, Annukka; Scarano, Gioacchino; Szabova, Elena; Castilla, Eduardo E

    2011-11-15

    Sirenomelia is a very rare limb anomaly in which the normally paired lower limbs are replaced by a single midline limb. This study describes the prevalence, associated malformations, and maternal characteristics among cases with sirenomelia. Data originated from 19 birth defect surveillance system members of the International Clearinghouse for Birth Defects Surveillance and Research, and were reported according to a single pre-established protocol. Cases were clinically evaluated locally and reviewed centrally. A total of 249 cases with sirenomelia were identified among 25,290,172 births, for a prevalence of 0.98 per 100,000, with higher prevalence in the Mexican registry. An increase of sirenomelia prevalence with maternal age less than 20 years was statistically significant. The proportion of twinning was 9%, higher than the 1% expected. Sex was ambiguous in 47% of cases, and no different from expectation in the rest. The proportion of cases born alive, premature, and weighting less than 2,500 g were 47%, 71.2%, and 88.2%, respectively. Half of the cases with sirenomelia also presented with genital, large bowel, and urinary defects. About 10-15% of the cases had lower spinal column defects, single or anomalous umbilical artery, upper limb, cardiac, and central nervous system defects. There was a greater than expected association of sirenomelia with other very rare defects such as bladder exstrophy, cyclopia/holoprosencephaly, and acardia-acephalus. The application of the new biological network analysis approach, including molecular results, to these associated very rare diseases is suggested for future studies. Copyright © 2011 Wiley Periodicals, Inc.

  15. Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets.

    Science.gov (United States)

    Shrimankar, D D; Sathe, S R

    2016-01-01

    Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today's supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures.

  16. Estruturação topológica de grandes bases de dados de bacias hidrográficas Topologically-structureo large datasets for watersheds

    Directory of Open Access Journals (Sweden)

    Carlos Antonio Alvares Soares Ribeiro

    2008-08-01

    Full Text Available O presente trabalho teve como objetivo avaliar o método para delimitação da bacia de contribuição à montante de um ponto selecionado sobre o hidrografia e a obtenção das respectivas características morfométricas, a partir de bases de dados estruturadas topologicamente. Para tanto, utilizou-se o aplicativo Hidrodata 2.0, desenvolvido para o ArcINFO workstation, comparando os seus resultados com os do processo convencional. Os resultados comprovaram que o tempo de processamento demandado para delimitação de bacias e extração de suas características morfométricas a partir de uma base de dados estruturada topologicamente se manteve baixo e constante. Concluiu-se que o método apresentado poderá ser aplicado em qualquer bacia hidrográfica, independentemente do seu tamanho, mesmo com o uso de computadores de configuração mais modesta.The present work aims to present and to evaluate a method for topologically structuring large databases, implemented as a set of AML routines for ArcINFO workstation named Hidrodata. The results proved that the processing time for delineating drainage areas and extracting their morphometric characteristics was kept low and constant. The use of a topologically structured database resulted in a lower demand of processing capacity. It was concluded that the presented approach can be applied for any watershed, independently of its size, even with the use of less-sophisticated computers.

  17. Delay-Tolerant, Low-Power Protocols for Large Security-Critical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Claudio S. Malavenda

    2012-01-01

    Full Text Available This paper reports the analysis, implementation, and experimental testing of a delay-tolerant and energy-aware protocol for a wireless sensor node, oriented to security applications. The solution proposed takes advantages from different domains considering as a guideline the low power consumption and facing the problems of seamless and lossy connectivity offered by the wireless medium along with very limited resources offered by a wireless network node. The paper is organized as follows: first we give an overview on delay-tolerant wireless sensor networking (DTN; then we perform a simulation-based comparative analysis of state-of-the-art DTN approaches and illustrate the improvement offered by the proposed protocol; finally we present experimental data gathered from the implementation of the proposed protocol on a proprietary hardware node.

  18. A Large-Scale Multibody Manipulator Soft Sensor Model and Experiment Validation

    Directory of Open Access Journals (Sweden)

    Wu Ren

    2014-01-01

    Full Text Available Stress signal is difficult to obtain in the health monitoring of multibody manipulator. In order to solve this problem, a soft sensor method is presented. In the method, stress signal is considered as dominant variable and angle signal is regarded as auxiliary variable. By establishing the mathematical relationship between them, a soft sensor model is proposed. In the model, the stress information can be deduced by angle information which can be easily measured for such structures by experiments. Finally, test of ground and wall working conditions is done on a multibody manipulator test rig. The results show that the stress calculated by the proposed method is closed to the test one. Thus, the stress signal is easier to get than the traditional method. All of these prove that the model is correct and the method is feasible.

  19. Development of n+-in-p large-area silicon microstrip sensors for very high radiation environments – ATLAS12 design and initial results

    International Nuclear Information System (INIS)

    Unno, Y.; Edwards, S.O.; Pyatt, S.; Thomas, J.P.; Wilson, J.A.; Kierstead, J.; Lynn, D.; Carter, J.R.; Hommels, L.B.A.; Robinson, D.; Bloch, I.; Gregor, I.M.; Tackmann, K.; Betancourt, C.; Jakobs, K.; Kuehn, S.; Mori, R.; Parzefall, U.; Wiik-Fucks, L.; Clark, A.

    2014-01-01

    We have been developing a novel radiation-tolerant n + -in-p silicon microstrip sensor for very high radiation environments, aiming for application in the high luminosity large hadron collider. The sensors are fabricated in 6 in., p-type, float-zone wafers, where large-area strip sensor designs are laid out together with a number of miniature sensors. Radiation tolerance has been studied with ATLAS07 sensors and with independent structures. The ATLAS07 design was developed into new ATLAS12 designs. The ATLAS12A large-area sensor is made towards an axial strip sensor and the ATLAS12M towards a stereo strip sensor. New features to the ATLAS12 sensors are two dicing lines: standard edge space of 910 μm and slim edge space of 450 μm, a gated punch-through protection structure, and connection of orphan strips in a triangular corner of stereo strips. We report the design of the ATLAS12 layouts and initial measurements of the leakage current after dicing and the resistivity of the wafers

  20. Silver nanowire/polymer composite soft conductive film fabricated by large-area compatible coating for flexible pressure sensor array

    Science.gov (United States)

    Chen, Sujie; Li, Siying; Peng, Sai; Huang, Yukun; Zhao, Jiaqing; Tang, Wei; Guo, Xiaojun

    2018-01-01

    Soft conductive films composed of a silver nanowire (AgNW) network, a neutral-pH PEDOT:PSS over-coating layer and a polydimethylsiloxane (PDMS) elastomer substrate are fabricated by large area compatible coating processes. The neutral-pH PEDOT:PSS layer is shown to be able to significantly improve the conductivity, stretchability and air stability of the conductive films. The soft conductive films are patterned using a simple maskless patterning approach to fabricate an 8 × 8 flexible pressure sensor array. It is shown that such soft conductive films can help to improve the sensitivity and reduce the signal crosstalk over the pressure sensor array. Project supported by the Science and Technology Commission of Shanghai Municipality (No. 16JC1400603).

  1. Large-scale syntheses of uniform ZnO nanorods and ethanol gas sensors application

    International Nuclear Information System (INIS)

    Chen Jin; Li Jin; Li Jiahui; Xiao Guoqing; Yang Xiaofeng

    2011-01-01

    Research highlights: → The uniform ZnO nanorods could be synthesized by a low temperature, solution-based method. → The results showed that the sample had uniform rod-like morphology with a narrow size distribution and highly crystallinity. → Room-temperature photoluminescence spectra of these nanorods show an exciton emission around 382 nm and a weak deep level emission, indicating the nanorods have high quality. → The sensor exhibited high sensitivity and fast response to ethanol gas at a work temperature of 400 deg. C. - Abstract: Uniform ZnO nanorods with a gram scale were prepared by a low temperature and solution-based method. The samples are characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and photoluminescence (PL). The results showed that the sample had uniform rod-like morphology with a narrow size distribution and highly crystallinity. Room-temperature PL spectra of these nanorods show an exciton emission around 382 nm and a negligible deep level emission, indicating the nanorods have high quality. The gas-sensing properties of the materials have been investigated. The results indicate that the as-prepared nanorods show much better sensitivity and stability. The n-type semiconductor gas sensor exhibited high sensitivity and fast response to ethanol gas at a work temperature of 400 deg. C. ZnO nanorods are excellent potential candidates for highly sensitive gas sensors and ultraviolet laser.

  2. Aaron Journal article datasets

    Data.gov (United States)

    U.S. Environmental Protection Agency — All figures used in the journal article are in netCDF format. This dataset is associated with the following publication: Sims, A., K. Alapaty , and S. Raman....

  3. Integrated Surface Dataset (Global)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Integrated Surface (ISD) Dataset (ISD) is composed of worldwide surface weather observations from over 35,000 stations, though the best spatial coverage is...

  4. Control Measure Dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — The EPA Control Measure Dataset is a collection of documents describing air pollution control available to regulated facilities for the control and abatement of air...

  5. National Hydrography Dataset (NHD)

    Data.gov (United States)

    Kansas Data Access and Support Center — The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that comprise the...

  6. Market Squid Ecology Dataset

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains ecological information collected on the major adult spawning and juvenile habitats of market squid off California and the US Pacific Northwest....

  7. Tables and figure datasets

    Data.gov (United States)

    U.S. Environmental Protection Agency — Soil and air concentrations of asbestos in Sumas study. This dataset is associated with the following publication: Wroble, J., T. Frederick, A. Frame, and D....

  8. Optimal placement of excitations and sensors for verification of large dynamical systems

    Science.gov (United States)

    Salama, M.; Rose, T.; Garba, J.

    1987-01-01

    The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.

  9. Development of a Large-Format Science-Grade CMOS Active Pixel Sensor, for Extreme Ultra Violet Spectroscopy and Imaging in Space Science

    National Research Council Canada - National Science Library

    Waltham, N. R; Prydderch, M; Mapson-Menard, H; Morrissey, Q; Turchetta, R; Pool, P; Harris, A

    2005-01-01

    We describe our programme to develop a large-format science-grade CMOS active pixel sensor for future space science missions, and in particular an extreme ultra-violet spectrograph for solar physics...

  10. Embedded Electro-Optic Sensor Network for the On-Site Calibration and Real-Time Performance Monitoring of Large-Scale Phased Arrays

    National Research Council Canada - National Science Library

    Yang, Kyoung

    2005-01-01

    This final report summarizes the progress during the Phase I SBIR project entitled "Embedded Electro-Optic Sensor Network for the On-Site Calibration and Real-Time Performance Monitoring of Large-Scale Phased Arrays...

  11. Vapor transport deposition of large-area polycrystalline CdTe for radiation image sensor application

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Keedong; Cha, Bokyung; Heo, Duchang; Jeon, Sungchae [Korea Electrotechnology Research Institute, 111 Hanggaul-ro, Ansan-si, Gyeonggi-do 426-170 (Korea, Republic of)

    2014-07-15

    Vapor transport deposition (VTD) process delivers saturated vapor to substrate, resulting in high-throughput and scalable process. In addition, VTD can maintain lower substrate temperature than close-spaced sublimation (CSS). The motivation of this work is to adopt several advantages of VTD for radiation image sensor application. Polycrystalline CdTe films were obtained on 300 mm x 300 mm indium tin oxide (ITO) coated glass. The polycrystalline CdTe film has columnar structure with average grain size of 3 μm ∝ 9 μm, which can be controlled by changing the substrate temperature. In order to analyze electrical and X-ray characteristics, ITO-CdTe-Al sandwich structured device was fabricated. Effective resistivity of the polycrystalline CdTe film was ∝1.4 x 10{sup 9}Ωcm. The device was operated under hole-collection mode. The responsivity and the μτ product estimated to be 6.8 μC/cm{sup 2}R and 5.5 x 10{sup -7} cm{sup 2}/V. The VTD can be a process of choice for monolithic integration of CdTe thick film for radiation image sensor and CMOS/TFT circuitry. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  12. Fundamentals for remote structural health monitoring of wind turbine blades - a preproject. Annex A. Cost-benefit for embedded sensors in large wind turbine blades

    OpenAIRE

    Hansen, L.G.; Lading, Lars

    2002-01-01

    This report contains the results of a cost-benefit analysis for the use of embed-ded sensors for damage detection in large wind turbine blades - structural health monitoring - (in connection with remote surveillance) of large wind turbine placedoff-shore. The total operating costs of a three-bladed 2MW turbine placed offshore either without sensors or with sensors are compared. The price of a structural health monitoring system of a price of 100 000 DKK (per tur-bine) results in a break-event...

  13. Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

    Science.gov (United States)

    Kubota, Ken J; Chen, Jason A; Little, Max A

    2016-09-01

    For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, "wearable," sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that "learn" from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

  14. Large-Area High-Performance Flexible Pressure Sensor with Carbon Nanotube Active Matrix for Electronic Skin.

    Science.gov (United States)

    Nela, Luca; Tang, Jianshi; Cao, Qing; Tulevski, George; Han, Shu-Jen

    2018-03-14

    Artificial "electronic skin" is of great interest for mimicking the functionality of human skin, such as tactile pressure sensing. Several important performance metrics include mechanical flexibility, operation voltage, sensitivity, and accuracy, as well as response speed. In this Letter, we demonstrate a large-area high-performance flexible pressure sensor built on an active matrix of 16 × 16 carbon nanotube thin-film transistors (CNT TFTs). Made from highly purified solution tubes, the active matrix exhibits superior flexible TFT performance with high mobility and large current density, along with a high device yield of nearly 99% over 4 inch sample area. The fully integrated flexible pressure sensor operates within a small voltage range of 3 V and shows superb performance featuring high spatial resolution of 4 mm, faster response than human skin (<30 ms), and excellent accuracy in sensing complex objects on both flat and curved surfaces. This work may pave the road for future integration of high-performance electronic skin in smart robotics and prosthetic solutions.

  15. Qualification of Sub-Atmospheric Pressure Sensors for the Cryomagnet Bayonet Heat Exchangers of the Large Hadron Collider

    Science.gov (United States)

    Bager, T.; Casas-Cubillos, J.; Jeanmonod, N.

    2006-04-01

    The superconducting magnets of the Large Hadron Collider (LHC) will be cooled at 1.9 K by distributed cooling loops working with saturated two-phase superfluid helium flowing in 107 m long bayonet heat exchangers located in each magnet cold-mass cell. The temperature of the magnets could be difficult to control because of the large dynamic heat load variations. Therefore, it is foreseen to measure the heat exchangers pressure to feed the regulation loops with the corresponding saturation temperature. The required uncertainty of the sub-atmospheric saturation pressure measurement shall be of the same order of the one associated to the magnet thermometers, in pressure it translates as ±5 Pa at 1.6 kPa. The transducers shall be radiation hard as they will endure, in the worst case, doses up to 10 kGy and 1015 neutronsṡcm-2 over 10 years. The sensors under evaluation were installed underground in the dump section of the SPS accelerator with a radiation environment close to the one expected for the LHC. The monitoring equipment was installed in a remote radiation protected area. This paper presents the results of the radiation qualification campaign with emphasis on the reliability and accuracy of the pressure sensors under the test conditions.

  16. A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chunyang Lei

    2016-07-01

    Full Text Available Super dense wireless sensor networks (WSNs have become popular with the development of Internet of Things (IoT, Machine-to-Machine (M2M communications and Vehicular-to-Vehicular (V2V networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

  17. A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks.

    Science.gov (United States)

    Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Gaura, Elena; Brusey, James; Zhang, Xuekun; Dutkiewicz, Eryk

    2016-07-18

    Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

  18. Isfahan MISP Dataset.

    Science.gov (United States)

    Kashefpur, Masoud; Kafieh, Rahele; Jorjandi, Sahar; Golmohammadi, Hadis; Khodabande, Zahra; Abbasi, Mohammadreza; Teifuri, Nilufar; Fakharzadeh, Ali Akbar; Kashefpoor, Maryam; Rabbani, Hossein

    2017-01-01

    An online depository was introduced to share clinical ground truth with the public and provide open access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming and MySQL for database managing. The website was entitled "biosigdata.com." It was a fast, secure, and easy-to-use online database for medical signals and images. Freely registered users could download the datasets and could also share their own supplementary materials while maintaining their privacies (citation and fee). Commenting was also available for all datasets, and automatic sitemap and semi-automatic SEO indexing have been set for the site. A comprehensive list of available websites for medical datasets is also presented as a Supplementary (http://journalonweb.com/tempaccess/4800.584.JMSS_55_16I3253.pdf).

  19. Mridangam stroke dataset

    OpenAIRE

    CompMusic

    2014-01-01

    The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studio conditions by Akshay Anantapadmanabhan using SM-58 microphones and an H4n ZOOM recorder. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The dataset can be used for training models for each Mridangam stroke. /n/nA detailed description of the Mridangam and its strokes can be found in the paper below. A part of the dataset was used in the following paper. /nAkshay Anantapadman...

  20. The GTZAN dataset

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2013-01-01

    The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge...... of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN...

  1. A Multi-Sensor Approach to Documenting a Large Collapse Sinkhole in West-Central Florida

    Science.gov (United States)

    Collins, L. D.; Kiflu, H. G.; Robinson, T.; Doering, T.; Eilers, D.; Rodgers, M.; Kruse, S.; Landry, S.; Braunmiller, J.; Speed, G.; Gonzalez, J.; McKenzie, R.

    2017-12-01

    The Saxon Lake sinkhole collapse of July 14, 2017 in Land O Lakes, Florida, caused the destruction of two homes and the evacuation of nine additional residences. The sinkhole is slightly oval with dimensions of approximately 51 meters east-west and 42 meters north-south, and it is reportedly 15 meters deep. This is presumably the largest sinkhole to form in Pasco County during the last 30 years. The surface collapse happened rapidly and continued over three days, with slumping and erosion increasing the size. The site is located near two natural lakes in a housing development from the late 1960s. This occurrence is within an area of well-developed karst, with a number of natural lakes. We present preliminary analysis of the sequence of deformation, sinkhole geometry, surrounding subsurface structures, and seismic activity. Data are assembled from terrestrial and aerial LiDAR, UAS survey and PhoDAR modeling, aerial imagery, ground penetrating radar, lake-bottom profiling, and seismic monitoring. Additionally, multi-sensor data were brought together in a Geographic Information Systems (GIS) and included an analysis of georeferenced historic imagery and maps. These spatial data indicate historic land use change and development alterations that included lake shore reconfiguration, canal construction, and connection of lake water systems in the area of impact. Three subsidence reports from the 1980s are also recorded within 500 meters of the collapse.

  2. Dataset - Adviesregel PPL 2010

    NARCIS (Netherlands)

    Evert, van F.K.; Schans, van der D.A.; Geel, van W.C.A.; Slabbekoorn, J.J.; Booij, R.; Jukema, J.N.; Meurs, E.J.J.; Uenk, D.

    2011-01-01

    This dataset contains experimental data from a number of field experiments with potato in The Netherlands (Van Evert et al., 2011). The data are presented as an SQL dump of a PostgreSQL database (version 8.4.4). An outline of the entity-relationship diagram of the database is given in an

  3. Generation of large scale urban environments to support advanced sensor and seeker simulation

    Science.gov (United States)

    Giuliani, Joseph; Hershey, Daniel; McKeown, David, Jr.; Willis, Carla; Van, Tan

    2009-05-01

    One of the key aspects for the design of a next generation weapon system is the need to operate in cluttered and complex urban environments. Simulation systems rely on accurate representation of these environments and require automated software tools to construct the underlying 3D geometry and associated spectral and material properties that are then formatted for various objective seeker simulation systems. Under an Air Force Small Business Innovative Research (SBIR) contract, we have developed an automated process to generate 3D urban environments with user defined properties. These environments can be composed from a wide variety of source materials, including vector source data, pre-existing 3D models, and digital elevation models, and rapidly organized into a geo-specific visual simulation database. This intermediate representation can be easily inspected in the visible spectrum for content and organization and interactively queried for accuracy. Once the database contains the required contents, it can then be exported into specific synthetic scene generation runtime formats, preserving the relationship between geometry and material properties. To date an exporter for the Irma simulation system developed and maintained by AFRL/Eglin has been created and a second exporter to Real Time Composite Hardbody and Missile Plume (CHAMP) simulation system for real-time use is currently being developed. This process supports significantly more complex target environments than previous approaches to database generation. In this paper we describe the capabilities for content creation for advanced seeker processing algorithms simulation and sensor stimulation, including the overall database compilation process and sample databases produced and exported for the Irma runtime system. We also discuss the addition of object dynamics and viewer dynamics within the visual simulation into the Irma runtime environment.

  4. The use of large area silicon sensors for thermal neutron detection

    International Nuclear Information System (INIS)

    Schulte, R.L.; Swanson, F.; Kesselman, M.

    1994-01-01

    The use of large area planar silicon detectors coupled with gadolinium foils has been investigated to develop a thermal neutron detector having a large area-efficiency (Aε) product. Noise levels due to high detector capacitance limit the size of silicon detectors that can be utilized. Calculations using the Monte Carlo code, MCNP, have been made to determine the variation of intrinsic detection efficiency as a function of the discriminator threshold level required to eliminate the detector noise. Measurements of the noise levels for planar silicon detectors of various resistivities (400, 3000 and 5000 Ω cm) have been made and the optimal detector area-efficiency products have been determined. The response of a Si-Gd-Si sandwich detector with areas between 1 cm 2 and 10.5 cm 2 is presented and the effects of the detector capacitance and reverse current are discussed. ((orig.))

  5. The use of large area silicon sensors for thermal neutron detection

    Energy Technology Data Exchange (ETDEWEB)

    Schulte, R.L. (Research and Development Center, Mail Stop: A01-26, Grumman Aerospace Corporation, Bethpage, NY 11714 (United States)); Swanson, F. (Research and Development Center, Mail Stop: A01-26, Grumman Aerospace Corporation, Bethpage, NY 11714 (United States)); Kesselman, M. (Research and Development Center, Mail Stop: A01-26, Grumman Aerospace Corporation, Bethpage, NY 11714 (United States))

    1994-12-30

    The use of large area planar silicon detectors coupled with gadolinium foils has been investigated to develop a thermal neutron detector having a large area-efficiency (A[epsilon]) product. Noise levels due to high detector capacitance limit the size of silicon detectors that can be utilized. Calculations using the Monte Carlo code, MCNP, have been made to determine the variation of intrinsic detection efficiency as a function of the discriminator threshold level required to eliminate the detector noise. Measurements of the noise levels for planar silicon detectors of various resistivities (400, 3000 and 5000 [Omega] cm) have been made and the optimal detector area-efficiency products have been determined. The response of a Si-Gd-Si sandwich detector with areas between 1 cm[sup 2] and 10.5 cm[sup 2] is presented and the effects of the detector capacitance and reverse current are discussed. ((orig.))

  6. Large-size high-performance transparent amorphous silicon sensors for laser beam position detection

    International Nuclear Information System (INIS)

    Calderon, A.; Martinez-Rivero, C.; Matorras, F.; Rodrigo, T.; Sobron, M.; Vila, I.; Virto, A.L.; Alberdi, J.; Arce, P.; Barcala, J.M.; Calvo, E.; Ferrando, A.; Josa, M.I.; Luque, J.M.; Molinero, A.; Navarrete, J.; Oller, J.C.; Yuste, C.; Koehler, C.; Lutz, B.; Schubert, M.B.; Werner, J.H.

    2006-01-01

    We present the measured performance of a new generation of semitransparent amorphous silicon position detectors. They have a large sensitive area (30x30mm 2 ) and show good properties such as a high response (about 20mA/W), an intrinsic position resolution better than 3μm, a spatial-point reconstruction precision better than 10μm, deflection angles smaller than 10μrad and a transmission power in the visible and NIR higher than 70%

  7. The Potential Applications of Real-Time Monitoring of Water Quality in a Large Shallow Lake (Lake Taihu, China) Using a Chromophoric Dissolved Organic Matter Fluorescence Sensor

    OpenAIRE

    Niu, Cheng; Zhang, Yunlin; Zhou, Yongqiang; Shi, Kun; Liu, Xiaohan; Qin, Boqiang

    2014-01-01

    This study presents results from field surveys performed over various seasons in a large, eutrophic, shallow lake (Lake Taihu, China) using an in situ chromophoric dissolved organic matter (CDOM) fluorescence sensor as a surrogate for other water quality parameters. These measurements identified highly significant empirical relationships between CDOM concentration measured using the in situ fluorescence sensor and CDOM absorption, fluorescence, dissolved organic carbon (DOC), chemical oxygen ...

  8. Process Simulation and Characterization of Substrate Engineered Silicon Thin Film Transistor for Display Sensors and Large Area Electronics

    International Nuclear Information System (INIS)

    Hashmi, S M; Ahmed, S

    2013-01-01

    Design, simulation, fabrication and post-process qualification of substrate-engineered Thin Film Transistors (TFTs) are carried out to suggest an alternate manufacturing process step focused on display sensors and large area electronics applications. Damage created by ion implantation of Helium and Silicon ions into single-crystalline n-type silicon substrate provides an alternate route to create an amorphized region responsible for the fabrication of TFT structures with controllable and application-specific output parameters. The post-process qualification of starting material and full-cycle devices using Rutherford Backscattering Spectrometry (RBS) and Proton or Particle induced X-ray Emission (PIXE) techniques also provide an insight to optimize the process protocols as well as their applicability in the manufacturing cycle

  9. Large-size high-performance transparent amorphous silicon sensors for laser beam position detection

    Energy Technology Data Exchange (ETDEWEB)

    Calderon, A. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Martinez-Rivero, C. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Matorras, F. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Rodrigo, T. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Sobron, M. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Vila, I. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Virto, A.L. [Instituto de Fisica de Cantabria. CSIC-University of Cantabria, Santander (Spain); Alberdi, J. [CIEMAT, Madrid (Spain); Arce, P. [CIEMAT, Madrid (Spain); Barcala, J.M. [CIEMAT, Madrid (Spain); Calvo, E. [CIEMAT, Madrid (Spain); Ferrando, A. [CIEMAT, Madrid (Spain)]. E-mail: antonio.ferrando@ciemat.es; Josa, M.I. [CIEMAT, Madrid (Spain); Luque, J.M. [CIEMAT, Madrid (Spain); Molinero, A. [CIEMAT, Madrid (Spain); Navarrete, J. [CIEMAT, Madrid (Spain); Oller, J.C. [CIEMAT, Madrid (Spain); Yuste, C. [CIEMAT, Madrid (Spain); Koehler, C. [Steinbeis-Transferzentrum fuer Angewandte Photovoltaik und Duennschichttechnik, Stuttgart (Germany); Lutz, B. [Steinbeis-Transferzentrum fuer Angewandte Photovoltaik und Duennschichttechnik, Stuttgart (Germany); Schubert, M.B. [Steinbeis-Transferzentrum fuer Angewandte Photovoltaik und Duennschichttechnik, Stuttgart (Germany); Werner, J.H. [Steinbeis-Transferzentrum fuer Angewandte Photovoltaik und Duennschichttechnik, Stuttgart (Germany)

    2006-09-15

    We present the measured performance of a new generation of semitransparent amorphous silicon position detectors. They have a large sensitive area (30x30mm{sup 2}) and show good properties such as a high response (about 20mA/W), an intrinsic position resolution better than 3{mu}m, a spatial-point reconstruction precision better than 10{mu}m, deflection angles smaller than 10{mu}rad and a transmission power in the visible and NIR higher than 70%.

  10. Multi-dimensional two-phase flow measurements in a large-diameter pipe using wire-mesh sensor

    International Nuclear Information System (INIS)

    Kanai, Taizo; Furuya, Masahiro; Arai, Takahiro; Shirakawa, Kenetsu; Nishi, Yoshihisa; Ueda, Nobuyuki

    2011-01-01

    The authors developed a method of measurement to determine the multi-dimensionality of two phase flow. A wire-mesh sensor (WMS) can acquire a void fraction distribution at a high temporal and spatial resolution and also estimate the velocity of a vertical rising flow by investigating the signal time-delay of the upstream WMS relative to downstream. Previously, one-dimensional velocity was estimated by using the same point of each WMS at a temporal resolution of 1.0 - 5.0 s. The authors propose to extend this time series analysis to estimate the multi-dimensional velocity profile via cross-correlation analysis between a point of upstream WMS and multiple points downstream. Bubbles behave in various ways according to size, which is used to classify them into certain groups via wavelet analysis before cross-correlation analysis. This method was verified by air-water straight and swirl flows within a large-diameter vertical pipe. A high-speed camera is used to set the parameter of cross-correlation analysis. The results revealed that for the rising straight and swirl flows, large scale bubbles tend to move to the center, while the small bubble is pushed to the outside or sucked into the space where the large bubbles existed. Moreover, it is found that this method can estimate the rotational component of velocity of the swirl flow as well as measuring the multi-dimensional velocity vector at high temporal resolutions of 0.2 s. (author)

  11. Analysis of Public Datasets for Wearable Fall Detection Systems.

    Science.gov (United States)

    Casilari, Eduardo; Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

    2017-06-27

    Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

  12. Analysis of Public Datasets for Wearable Fall Detection Systems

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    2017-06-01

    Full Text Available Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs. In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.. Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

  13. A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data.

    Science.gov (United States)

    Dell'Acqua, Fabio; Iannelli, Gianni Cristian; Torres, Marco A; Martina, Mario L V

    2018-02-14

    Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data-such as municipality-level records of crop seeding-for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using "good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem

  14. A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data

    Directory of Open Access Journals (Sweden)

    Fabio Dell’Acqua

    2018-02-01

    Full Text Available Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The

  15. Satellite-Based Precipitation Datasets

    Science.gov (United States)

    Munchak, S. J.; Huffman, G. J.

    2017-12-01

    Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

  16. Fundamentals for remote structural health monitoring of wind turbine blades - a preproject. Annex A. Cost-benefit for embedded sensors in large wind turbine blades

    DEFF Research Database (Denmark)

    Hansen, L.G.; Lading, Lars

    2002-01-01

    -bladed 2MW turbine placed offshore either without sensors or with sensors are compared. The price of a structural health monitoring system of a price of 100 000 DKK (per tur-bine) results in a break-eventime of about 3 years. For a price of 300 000 DKK the break-even time is about 8 years. However......This report contains the results of a cost-benefit analysis for the use of embed-ded sensors for damage detection in large wind turbine blades - structural health monitoring - (in connection with remote surveillance) of large wind turbine placedoff-shore. The total operating costs of a three......, the cost/benefit analysis has large uncertainties....

  17. NAVIGATION IN LARGE-FORMAT BUILDINGS BASED ON RFID SENSORS AND QR AND AR MARKERS

    Directory of Open Access Journals (Sweden)

    Tomasz Szymczyk

    2016-09-01

    Full Text Available The authors address the problem of passive navigation in large buildings. Based on the example of several interconnected buildings housing departments of the Lublin University of Technology, as well as the conceptual navigation system, the paper presents one of the possible ways of leading the user from the entrance of the building to a particular room. An analysis of different types of users is made and different (best for them ways of navigating the intricate corridors are proposed. Three ways of user localisation are suggested: RFID, AR and QR markers. A graph of connections between specific rooms was made and weights proposed, representing “the difficulty of covering a given distance”. In the process of navigation Dijkstra’s algorithm was used. The road is indicated as multimedia information: a voice-over or animated arrow showing the direction displayed on the smart phone screen with proprietary software installed. It is also possible to inform the user of the position of the location in which he currently is, based on the static information stored in the QR code.

  18. Development of microstructured large area magnetic calorimeters with Au:Er- and Ag:Er-sensors for the detection of x-ray quanta and high energetic particles

    International Nuclear Information System (INIS)

    Burck, Andreas

    2008-01-01

    This thesis describes the development of large-area magnetic calorimeters which could for example be used for the investigation of the dissociative recombination or the measurement of the Lamb-shift for hydrogenlike heavy ions. The detectors consist of two meandershaped niobium thin film pickup coils and a paramagnetic sensor. The deposition of energy in the sensor results in a temperature change and therefore in a change of magnetisation of the sensor, which can be measured by a SQUID-magnetometer with high precision. As sensormaterials a dilute alloy of gold-erbium (Au:Er) as well as silver-erbium (Ag:Er) were used. Whereas the Ag:Er-sensor was glued on the pickup coil the Au:Er-sensor was for the first time microstructured by a novel microstructuring process established in this thesis. For the characterisation of the detectors and the sensormaterials a fluorescence source and a 55 Fe source were used. The thermodynamic properties of the Au:Er-sensors thereby show promising results, as the magnetisation shows bulk properties down to 20 mK. The measurements of the signalize and the magnetisation with the detector which was equipped with a Ag:Er-sensor showed that the thermodynamic properties of the Ag:Eralloy could be fully described. Furthermore the shape of the pulses, the noise and the energy resolution of both detectors will be discussed. (orig.)

  19. Qualification of Sub-atmospheric Pressure Sensors for the Cryomagnet Bayonet Heat Exchangers of the Large Hadron Collider

    CERN Document Server

    Jeanmonod, N; Casas-Cubillos, J

    2006-01-01

    The superconducting magnets of the Large Hadron Collider (LHC) will be cooled at 1.9 K by distributed cooling loops working with saturated two-phase superfluid helium flowing in 107 m long bayonet heat exchangers [1] located in each magnet cold-mass cell. The temperature of the magnets could be difficult to control because of the large dynamic heat load variations. Therefore, it is foreseen to measure the heat exchangers pressure to feed the regulation loops with the corresponding saturation temperature. The required uncertainty of the sub-atmospheric saturation pressure measurement shall be of the same order of the one associated to the magnet thermometers, in pressure it translates as ±5 Pa at 1.6 kPa. The transducers shall be radiation hard as they will endure, in the worst case, doses up to 10 kGy and 10**15 neutrons·cm**-2 over 10 years. The sensors under evaluation were installed underground in the dump section of the SPS accelerator with a radiation environment close to the one expected for the L...

  20. Magnetic field sensor based on the magnetic-fluid-clad combined with singlemode-multimode-singlemode fiber and large core-offset splicing structure

    Science.gov (United States)

    Lv, Ri-qing; Qian, Jun-kai; Zhao, Yong

    2018-03-01

    A simple, compact optical fiber magnetic field sensor is proposed and experimentally demonstrated in this paper. It is based on the magnetic-fluid-clad combined with singlemode-multimode-singlemode fiber structure and large core-offset splicing structure. It was protected by a section of capillary tube and was sealed by UV glue. A sensing property study of the combined optical fiber structure and the proposed sensor were carried out. The experimental results show that the sensitivity of the refractive index of the optical fiber sensing structure is up to 156.63 nm/RIU and the magnetic field sensitivity of the proposed sensor is up to -97.24 pm/Oe in the range from 72.4 Oe to 297.8 Oe. The proposed sensor has several other advantages, such as simple structure, small size, easy fabrication and low cost.

  1. A Dual-Mode Large-Arrayed CMOS ISFET Sensor for Accurate and High-Throughput pH Sensing in Biomedical Diagnosis.

    Science.gov (United States)

    Huang, Xiwei; Yu, Hao; Liu, Xu; Jiang, Yu; Yan, Mei; Wu, Dongping

    2015-09-01

    The existing ISFET-based DNA sequencing detects hydrogen ions released during the polymerization of DNA strands on microbeads, which are scattered into microwell array above the ISFET sensor with unknown distribution. However, false pH detection happens at empty microwells due to crosstalk from neighboring microbeads. In this paper, a dual-mode CMOS ISFET sensor is proposed to have accurate pH detection toward DNA sequencing. Dual-mode sensing, optical and chemical modes, is realized by integrating a CMOS image sensor (CIS) with ISFET pH sensor, and is fabricated in a standard 0.18-μm CIS process. With accurate determination of microbead physical locations with CIS pixel by contact imaging, the dual-mode sensor can correlate local pH for one DNA slice at one location-determined microbead, which can result in improved pH detection accuracy. Moreover, toward a high-throughput DNA sequencing, a correlated-double-sampling readout that supports large array for both modes is deployed to reduce pixel-to-pixel nonuniformity such as threshold voltage mismatch. The proposed CMOS dual-mode sensor is experimentally examined to show a well correlated pH map and optical image for microbeads with a pH sensitivity of 26.2 mV/pH, a fixed pattern noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/s. A dual-mode CMOS ISFET sensor with suppressed FPN for accurate large-arrayed pH sensing is proposed and demonstrated with state-of-the-art measured results toward accurate and high-throughput DNA sequencing. The developed dual-mode CMOS ISFET sensor has great potential for future personal genome diagnostics with high accuracy and low cost.

  2. Eraser-based eco-friendly fabrication of a skin-like large-area matrix of flexible carbon nanotube strain and pressure sensors.

    Science.gov (United States)

    Sahatiya, Parikshit; Badhulika, Sushmee

    2017-03-03

    This paper reports a new type of electronic, recoverable skin-like pressure and strain sensor, produced on a flexible, biodegradable pencil-eraser substrate and fabricated using a solvent-free, low-cost and energy efficient process. Multi-walled carbon nanotube (MWCNT) film, the strain sensing element, was patterned on pencil eraser with a rolling pin and a pre-compaction mechanical press. This induces high interfacial bonding between the MWCNTs and the eraser substrate, which enables the sensor to achieve recoverability under ambient conditions. The eraser serves as a substrate for strain sensing, as well as acting as a dielectric for capacitive pressure sensing, thereby eliminating the dielectric deposition step, which is crucial in capacitive-based pressure sensors. The strain sensing transduction mechanism is attributed to the tunneling effect, caused by the elastic behavior of the MWCNTs and the strong mechanical interlock between MWCNTs and the eraser substrate, which restricts slippage of MWCNTs on the eraser thereby minimizing hysteresis. The gauge factor of the strain sensor was calculated to be 2.4, which is comparable to and even better than most of the strain and pressure sensors fabricated with more complex designs and architectures. The sensitivity of the capacitive pressure sensor was found to be 0.135 MPa -1 .To demonstrate the applicability of the sensor as artificial electronic skin, the sensor was assembled on various parts of the human body and corresponding movements and touch sensation were monitored. The entire fabrication process is scalable and can be integrated into large areas to map spatial pressure distributions. This low-cost, easily scalable MWCNT pin-rolled eraser-based pressure and strain sensor has huge potential in applications such as artificial e-skin in flexible electronics and medical diagnostics, in particular in surgery as it provides high spatial resolution without a complex nanostructure architecture.

  3. Kinota: An Open-Source NoSQL implementation of OGC SensorThings for large-scale high-resolution real-time environmental monitoring

    Science.gov (United States)

    Miles, B.; Chepudira, K.; LaBar, W.

    2017-12-01

    The Open Geospatial Consortium (OGC) SensorThings API (STA) specification, ratified in 2016, is a next-generation open standard for enabling real-time communication of sensor data. Building on over a decade of OGC Sensor Web Enablement (SWE) Standards, STA offers a rich data model that can represent a range of sensor and phenomena types (e.g. fixed sensors sensing fixed phenomena, fixed sensors sensing moving phenomena, mobile sensors sensing fixed phenomena, and mobile sensors sensing moving phenomena) and is data agnostic. Additionally, and in contrast to previous SWE standards, STA is developer-friendly, as is evident from its convenient JSON serialization, and expressive OData-based query language (with support for geospatial queries); with its Message Queue Telemetry Transport (MQTT), STA is also well-suited to efficient real-time data publishing and discovery. All these attributes make STA potentially useful for use in environmental monitoring sensor networks. Here we present Kinota(TM), an Open-Source NoSQL implementation of OGC SensorThings for large-scale high-resolution real-time environmental monitoring. Kinota, which roughly stands for Knowledge from Internet of Things Analyses, relies on Cassandra its underlying data store, which is a horizontally scalable, fault-tolerant open-source database that is often used to store time-series data for Big Data applications (though integration with other NoSQL or rational databases is possible). With this foundation, Kinota can scale to store data from an arbitrary number of sensors collecting data every 500 milliseconds. Additionally, Kinota architecture is very modular allowing for customization by adopters who can choose to replace parts of the existing implementation when desirable. The architecture is also highly portable providing the flexibility to choose between cloud providers like azure, amazon, google etc. The scalable, flexible and cloud friendly architecture of Kinota makes it ideal for use in next

  4. The CMS dataset bookkeeping service

    Science.gov (United States)

    Afaq, A.; Dolgert, A.; Guo, Y.; Jones, C.; Kosyakov, S.; Kuznetsov, V.; Lueking, L.; Riley, D.; Sekhri, V.

    2008-07-01

    The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems.

  5. The CMS dataset bookkeeping service

    Energy Technology Data Exchange (ETDEWEB)

    Afaq, A; Guo, Y; Kosyakov, S; Lueking, L; Sekhri, V [Fermilab, Batavia, Illinois 60510 (United States); Dolgert, A; Jones, C; Kuznetsov, V; Riley, D [Cornell University, Ithaca, New York 14850 (United States)

    2008-07-15

    The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems.

  6. The CMS dataset bookkeeping service

    International Nuclear Information System (INIS)

    Afaq, A; Guo, Y; Kosyakov, S; Lueking, L; Sekhri, V; Dolgert, A; Jones, C; Kuznetsov, V; Riley, D

    2008-01-01

    The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems

  7. The CMS dataset bookkeeping service

    International Nuclear Information System (INIS)

    Afaq, Anzar; Dolgert, Andrew; Guo, Yuyi; Jones, Chris; Kosyakov, Sergey; Kuznetsov, Valentin; Lueking, Lee; Riley, Dan; Sekhri, Vijay

    2007-01-01

    The CMS Dataset Bookkeeping Service (DBS) has been developed to catalog all CMS event data from Monte Carlo and Detector sources. It provides the ability to identify MC or trigger source, track data provenance, construct datasets for analysis, and discover interesting data. CMS requires processing and analysis activities at various service levels and the DBS system provides support for localized processing or private analysis, as well as global access for CMS users at large. Catalog entries can be moved among the various service levels with a simple set of migration tools, thus forming a loose federation of databases. DBS is available to CMS users via a Python API, Command Line, and a Discovery web page interfaces. The system is built as a multi-tier web application with Java servlets running under Tomcat, with connections via JDBC to Oracle or MySQL database backends. Clients connect to the service through HTTP or HTTPS with authentication provided by GRID certificates and authorization through VOMS. DBS is an integral part of the overall CMS Data Management and Workflow Management systems

  8. A Cross-Layered Communication Protocol for Load Balancing in Large Scale Multi-sink Wireless Sensor Networks

    NARCIS (Netherlands)

    Erman-Tüysüz, A.; Mutter, T.; van Hoesel, L.F.W.; Havinga, Paul J.M.

    2008-01-01

    One of the fundamental operations in sensor networks is convergecast which refers to the communication pattern in which data is collected from a set of sensor nodes and forwarded to a common end-point gateway, namely sink node, in the network. In case of multiple sinks within the network, the total

  9. A Cross-Layered Communication Protocol for Load Balancing in Large Scale Multi-sink Wireless Sensor Networks

    NARCIS (Netherlands)

    Erman-Tüysüz, A.; Mutter, T.; van Hoesel, L.F.W.; Havinga, Paul J.M.

    One of the fundamental operations in sensor networks is convergecast which refers to the communication pattern in which data is collected from a set of sensor nodes and forwarded to a common end-point gateway, namely sink node, in the network. In case of multiple sinks within the network, the total

  10. High performance architecture design for large scale fibre-optic sensor arrays using distributed EDFAs and hybrid TDM/DWDM

    Science.gov (United States)

    Liao, Yi; Austin, Ed; Nash, Philip J.; Kingsley, Stuart A.; Richardson, David J.

    2013-09-01

    A distributed amplified dense wavelength division multiplexing (DWDM) array architecture is presented for interferometric fibre-optic sensor array systems. This architecture employs a distributed erbium-doped fibre amplifier (EDFA) scheme to decrease the array insertion loss, and employs time division multiplexing (TDM) at each wavelength to increase the number of sensors that can be supported. The first experimental demonstration of this system is reported including results which show the potential for multiplexing and interrogating up to 4096 sensors using a single telemetry fibre pair with good system performance. The number can be increased to 8192 by using dual pump sources.

  11. The Design and Implementation of Smart Monitoring System for Large-Scale Railway Maintenance Equipment Cab Based on ZigBee Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hairui Wang

    2014-06-01

    Full Text Available In recent years, organizations use IEEE 802.15.4 and ZigBee technology to deliver solution in variety areas including home environment monitoring. ZigBee technology has advantages on low-cost, low power consumption and self-forming. With the rapid expansion of the Internet, there is the requirement for remote monitoring large-scale railway maintenance equipment cab. This paper discusses the disadvantages of the existing smart monitoring system, and proposes a solution. A ZigBee wireless sensor network smart monitoring system and Wi-Fi network is integrated through a home gateway to increase the system flexibility. At the same time the home gateway cooperated with a pre- processing system provide a flexible user interface, and the security and safety of the smart monitoring system. To testify the efficiency of the proposed system, the temperature and humidity sensors and light sensors have developed and evaluated in the smart monitoring system.

  12. National Elevation Dataset

    Science.gov (United States)

    ,

    2002-01-01

    The National Elevation Dataset (NED) is a new raster product assembled by the U.S. Geological Survey. NED is designed to provide National elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections were made in the NED assembly process to minimize artifacts, perform edge matching, and fill sliver areas of missing data. NED has a resolution of one arc-second (approximately 30 meters) for the conterminous United States, Hawaii, Puerto Rico and the island territories and a resolution of two arc-seconds for Alaska. NED data sources have a variety of elevation units, horizontal datums, and map projections. In the NED assembly process the elevation values are converted to decimal meters as a consistent unit of measure, NAD83 is consistently used as horizontal datum, and all the data are recast in a geographic projection. Older DEM's produced by methods that are now obsolete have been filtered during the NED assembly process to minimize artifacts that are commonly found in data produced by these methods. Artifact removal greatly improves the quality of the slope, shaded-relief, and synthetic drainage information that can be derived from the elevation data. Figure 2 illustrates the results of this artifact removal filtering. NED processing also includes steps to adjust values where adjacent DEM's do not match well, and to fill sliver areas of missing data between DEM's. These processing steps ensure that NED has no void areas and artificial discontinuities have been minimized. The artifact removal filtering process does not eliminate all of the artifacts. In areas where the only available DEM is produced by older methods, then "striping" may still occur.

  13. The Potential Applications of Real-Time Monitoring of Water Quality in a Large Shallow Lake (Lake Taihu, China) Using a Chromophoric Dissolved Organic Matter Fluorescence Sensor

    Science.gov (United States)

    Niu, Cheng; Zhang, Yunlin; Zhou, Yongqiang; Shi, Kun; Liu, Xiaohan; Qin, Boqiang

    2014-01-01

    This study presents results from field surveys performed over various seasons in a large, eutrophic, shallow lake (Lake Taihu, China) using an in situ chromophoric dissolved organic matter (CDOM) fluorescence sensor as a surrogate for other water quality parameters. These measurements identified highly significant empirical relationships between CDOM concentration measured using the in situ fluorescence sensor and CDOM absorption, fluorescence, dissolved organic carbon (DOC), chemical oxygen demand (COD) and total phosphorus (TP) concentrations. CDOM concentration expressed in quinine sulfate equivalent units, was highly correlated with the CDOM absorption coefficient (r2 = 0.80, p CDOM concentration measured using the in situ fluorescence sensor could act as a substitute for the CDOM absorption coefficient and fluorescence measured in the laboratory. Similarly, CDOM concentration was highly correlated with DOC concentration (r2 = 0.68, p CDOM fluorescence sensor measurements could be a proxy for DOC concentration. In addition, significant positive correlations were found between laboratory CDOM absorption coefficients and COD (r2 = 0.83, p CDOM fluorescence sensor. PMID:24984060

  14. The potential applications of real-time monitoring of water quality in a large shallow lake (Lake Taihu, China) using a chromophoric dissolved organic matter fluorescence sensor.

    Science.gov (United States)

    Niu, Cheng; Zhang, Yunlin; Zhou, Yongqiang; Shi, Kun; Liu, Xiaohan; Qin, Boqiang

    2014-06-30

    This study presents results from field surveys performed over various seasons in a large, eutrophic, shallow lake (Lake Taihu, China) using an in situ chromophoric dissolved organic matter (CDOM) fluorescence sensor as a surrogate for other water quality parameters. These measurements identified highly significant empirical relationships between CDOM concentration measured using the in situ fluorescence sensor and CDOM absorption, fluorescence, dissolved organic carbon (DOC), chemical oxygen demand (COD) and total phosphorus (TP) concentrations. CDOM concentration expressed in quinine sulfate equivalent units, was highly correlated with the CDOM absorption coefficient (r(2) = 0.80, p CDOM concentration measured using the in situ fluorescence sensor could act as a substitute for the CDOM absorption coefficient and fluorescence measured in the laboratory. Similarly, CDOM concentration was highly correlated with DOC concentration (r(2) = 0.68, p CDOM fluorescence sensor measurements could be a proxy for DOC concentration. In addition, significant positive correlations were found between laboratory CDOM absorption coefficients and COD (r(2) = 0.83, p CDOM fluorescence sensor.

  15. NP-PAH Interaction Dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — Dataset presents concentrations of organic pollutants, such as polyaromatic hydrocarbon compounds, in water samples. Water samples of known volume and concentration...

  16. 3D silicon sensors: Design, large area production and quality assurance for the ATLAS IBL pixel detector upgrade

    Science.gov (United States)

    Da Via, Cinzia; Boscardin, Maurizio; Dalla Betta, Gian-Franco; Darbo, Giovanni; Fleta, Celeste; Gemme, Claudia; Grenier, Philippe; Grinstein, Sebastian; Hansen, Thor-Erik; Hasi, Jasmine; Kenney, Chris; Kok, Angela; Parker, Sherwood; Pellegrini, Giulio; Vianello, Elisa; Zorzi, Nicola

    2012-12-01

    3D silicon sensors, where electrodes penetrate the silicon substrate fully or partially, have successfully been fabricated in different processing facilities in Europe and USA. The key to 3D fabrication is the use of plasma micro-machining to etch narrow deep vertical openings allowing dopants to be diffused in and form electrodes of pin junctions. Similar openings can be used at the sensor's edge to reduce the perimeter's dead volume to as low as ˜4 μm. Since 2009 four industrial partners of the 3D ATLAS R&D Collaboration started a joint effort aimed at one common design and compatible processing strategy for the production of 3D sensors for the LHC Upgrade and in particular for the ATLAS pixel Insertable B-Layer (IBL). In this project, aimed for installation in 2013, a new layer will be inserted as close as 3.4 cm from the proton beams inside the existing pixel layers of the ATLAS experiment. The detector proximity to the interaction point will therefore require new radiation hard technologies for both sensors and front end electronics. The latter, called FE-I4, is processed at IBM and is the biggest front end of this kind ever designed with a surface of ˜4 cm2. The performance of 3D devices from several wafers was evaluated before and after bump-bonding. Key design aspects, device fabrication plans and quality assurance tests during the 3D sensors prototyping phase are discussed in this paper.

  17. Invitation to a forum: architecting operational `next generation' earth monitoring satellites based on best modeling, existing sensor capabilities, with constellation efficiencies to secure trusted datasets for the next 20 years

    Science.gov (United States)

    Helmuth, Douglas B.; Bell, Raymond M.; Grant, David A.; Lentz, Christopher A.

    2012-09-01

    Architecting the operational Next Generation of earth monitoring satellites based on matured climate modeling, reuse of existing sensor & satellite capabilities, attention to affordability and evolutionary improvements integrated with constellation efficiencies - becomes our collective goal for an open architectural design forum. Understanding the earth's climate and collecting requisite signatures over the next 30 years is a shared mandate by many of the world's governments. But there remains a daunting challenge to bridge scientific missions to 'operational' systems that truly support the demands of decision makers, scientific investigators and global users' requirements for trusted data. In this paper we will suggest an architectural structure that takes advantage of current earth modeling examples including cross-model verification and a first order set of critical climate parameters and metrics; that in turn, are matched up with existing space borne collection capabilities and sensors. The tools used and the frameworks offered are designed to allow collaborative overlays by other stakeholders nominating different critical parameters and their own treaded connections to existing international collection experience. These aggregate design suggestions will be held up to group review and prioritized as potential constellation solutions including incremental and spiral developments - including cost benefits and organizational opportunities. This Part IV effort is focused on being an inclusive 'Next Gen Constellation' design discussion and is the natural extension to earlier papers.

  18. Editorial: Datasets for Learning Analytics

    NARCIS (Netherlands)

    Dietze, Stefan; George, Siemens; Davide, Taibi; Drachsler, Hendrik

    2018-01-01

    The European LinkedUp and LACE (Learning Analytics Community Exchange) project have been responsible for setting up a series of data challenges at the LAK conferences 2013 and 2014 around the LAK dataset. The LAK datasets consists of a rich collection of full text publications in the domain of

  19. Comparison of Shallow Survey 2012 Multibeam Datasets

    Science.gov (United States)

    Ramirez, T. M.

    2012-12-01

    The purpose of the Shallow Survey common dataset is a comparison of the different technologies utilized for data acquisition in the shallow survey marine environment. The common dataset consists of a series of surveys conducted over a common area of seabed using a variety of systems. It provides equipment manufacturers the opportunity to showcase their latest systems while giving hydrographic researchers and scientists a chance to test their latest algorithms on the dataset so that rigorous comparisons can be made. Five companies collected data for the Common Dataset in the Wellington Harbor area in New Zealand between May 2010 and May 2011; including Kongsberg, Reson, R2Sonic, GeoAcoustics, and Applied Acoustics. The Wellington harbor and surrounding coastal area was selected since it has a number of well-defined features, including the HMNZS South Seas and HMNZS Wellington wrecks, an armored seawall constructed of Tetrapods and Akmons, aquifers, wharves and marinas. The seabed inside the harbor basin is largely fine-grained sediment, with gravel and reefs around the coast. The area outside the harbor on the southern coast is an active environment, with moving sand and exposed reefs. A marine reserve is also in this area. For consistency between datasets, the coastal research vessel R/V Ikatere and crew were used for all surveys conducted for the common dataset. Using Triton's Perspective processing software multibeam datasets collected for the Shallow Survey were processed for detail analysis. Datasets from each sonar manufacturer were processed using the CUBE algorithm developed by the Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC). Each dataset was gridded at 0.5 and 1.0 meter resolutions for cross comparison and compliance with International Hydrographic Organization (IHO) requirements. Detailed comparisons were made of equipment specifications (transmit frequency, number of beams, beam width), data density, total uncertainty, and

  20. Open University Learning Analytics dataset.

    Science.gov (United States)

    Kuzilek, Jakub; Hlosta, Martin; Zdrahal, Zdenek

    2017-11-28

    Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

  1. Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization.

    Science.gov (United States)

    Zhao, Chumin; Kanicki, Jerzy; Konstantinidis, Anastasios C; Patel, Tushita

    2015-11-01

    Large area x-ray imagers based on complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology have been proposed for various medical imaging applications including digital breast tomosynthesis (DBT). The low electronic noise (50-300 e-) of CMOS APS x-ray imagers provides a possible route to shrink the pixel pitch to smaller than 75 μm for microcalcification detection and possible reduction of the DBT mean glandular dose (MGD). In this study, imaging performance of a large area (29×23 cm2) CMOS APS x-ray imager [Dexela 2923 MAM (PerkinElmer, London)] with a pixel pitch of 75 μm was characterized and modeled. The authors developed a cascaded system model for CMOS APS x-ray imagers using both a broadband x-ray radiation and monochromatic synchrotron radiation. The experimental data including modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were theoretically described using the proposed cascaded system model with satisfactory consistency to experimental results. Both high full well and low full well (LFW) modes of the Dexela 2923 MAM CMOS APS x-ray imager were characterized and modeled. The cascaded system analysis results were further used to extract the contrast-to-noise ratio (CNR) for microcalcifications with sizes of 165-400 μm at various MGDs. The impact of electronic noise on CNR was also evaluated. The LFW mode shows better DQE at low air kerma (Ka<10 μGy) and should be used for DBT. At current DBT applications, air kerma (Ka∼10 μGy, broadband radiation of 28 kVp), DQE of more than 0.7 and ∼0.3 was achieved using the LFW mode at spatial frequency of 0.5 line pairs per millimeter (lp/mm) and Nyquist frequency ∼6.7 lp/mm, respectively. It is shown that microcalcifications of 165-400 μm in size can be resolved using a MGD range of 0.3-1 mGy, respectively. In comparison to a General Electric GEN2 prototype DBT system (at MGD of 2.5 mGy), an increased CNR (by ∼10) for

  2. Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization

    International Nuclear Information System (INIS)

    Zhao, Chumin; Kanicki, Jerzy; Konstantinidis, Anastasios C.; Patel, Tushita

    2015-01-01

    Purpose: Large area x-ray imagers based on complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology have been proposed for various medical imaging applications including digital breast tomosynthesis (DBT). The low electronic noise (50–300 e − ) of CMOS APS x-ray imagers provides a possible route to shrink the pixel pitch to smaller than 75 μm for microcalcification detection and possible reduction of the DBT mean glandular dose (MGD). Methods: In this study, imaging performance of a large area (29 × 23 cm 2 ) CMOS APS x-ray imager [Dexela 2923 MAM (PerkinElmer, London)] with a pixel pitch of 75 μm was characterized and modeled. The authors developed a cascaded system model for CMOS APS x-ray imagers using both a broadband x-ray radiation and monochromatic synchrotron radiation. The experimental data including modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were theoretically described using the proposed cascaded system model with satisfactory consistency to experimental results. Both high full well and low full well (LFW) modes of the Dexela 2923 MAM CMOS APS x-ray imager were characterized and modeled. The cascaded system analysis results were further used to extract the contrast-to-noise ratio (CNR) for microcalcifications with sizes of 165–400 μm at various MGDs. The impact of electronic noise on CNR was also evaluated. Results: The LFW mode shows better DQE at low air kerma (K a < 10 μGy) and should be used for DBT. At current DBT applications, air kerma (K a ∼ 10 μGy, broadband radiation of 28 kVp), DQE of more than 0.7 and ∼0.3 was achieved using the LFW mode at spatial frequency of 0.5 line pairs per millimeter (lp/mm) and Nyquist frequency ∼6.7 lp/mm, respectively. It is shown that microcalcifications of 165–400 μm in size can be resolved using a MGD range of 0.3–1 mGy, respectively. In comparison to a General Electric GEN2 prototype DBT system (at MGD of 2.5 m

  3. Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Chumin; Kanicki, Jerzy, E-mail: kanicki@eecs.umich.edu [Solid-State Electronics Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109 (United States); Konstantinidis, Anastasios C. [Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom and Diagnostic Radiology and Radiation Protection, Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX (United Kingdom); Patel, Tushita [Department of Physics, University of Virginia, Charlottesville, Virginia 22908 (United States)

    2015-11-15

    Purpose: Large area x-ray imagers based on complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology have been proposed for various medical imaging applications including digital breast tomosynthesis (DBT). The low electronic noise (50–300 e{sup −}) of CMOS APS x-ray imagers provides a possible route to shrink the pixel pitch to smaller than 75 μm for microcalcification detection and possible reduction of the DBT mean glandular dose (MGD). Methods: In this study, imaging performance of a large area (29 × 23 cm{sup 2}) CMOS APS x-ray imager [Dexela 2923 MAM (PerkinElmer, London)] with a pixel pitch of 75 μm was characterized and modeled. The authors developed a cascaded system model for CMOS APS x-ray imagers using both a broadband x-ray radiation and monochromatic synchrotron radiation. The experimental data including modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were theoretically described using the proposed cascaded system model with satisfactory consistency to experimental results. Both high full well and low full well (LFW) modes of the Dexela 2923 MAM CMOS APS x-ray imager were characterized and modeled. The cascaded system analysis results were further used to extract the contrast-to-noise ratio (CNR) for microcalcifications with sizes of 165–400 μm at various MGDs. The impact of electronic noise on CNR was also evaluated. Results: The LFW mode shows better DQE at low air kerma (K{sub a} < 10 μGy) and should be used for DBT. At current DBT applications, air kerma (K{sub a} ∼ 10 μGy, broadband radiation of 28 kVp), DQE of more than 0.7 and ∼0.3 was achieved using the LFW mode at spatial frequency of 0.5 line pairs per millimeter (lp/mm) and Nyquist frequency ∼6.7 lp/mm, respectively. It is shown that microcalcifications of 165–400 μm in size can be resolved using a MGD range of 0.3–1 mGy, respectively. In comparison to a General Electric GEN2 prototype DBT system (at

  4. 3D silicon sensors: Design, large area production and quality assurance for the ATLAS IBL pixel detector upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Da Via, Cinzia [School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Boscardin, Maurizio [Fondazione Bruno Kessler, FBK-CMM, Via Sommarive 18, I-38123 Trento (Italy); Dalla Betta, Gian-Franco, E-mail: dallabe@disi.unitn.it [DISI, Universita degli Studi di Trento and INFN, Via Sommarive 14, I-38123 Trento (Italy); Darbo, Giovanni [INFN Sezione di Genova, Via Dodecaneso 33, I-14146 Genova (Italy); Fleta, Celeste [Centro Nacional de Microelectronica, CNM-IMB (CSIC), Barcelona E-08193 (Spain); Gemme, Claudia [INFN Sezione di Genova, Via Dodecaneso 33, I-14146 Genova (Italy); Grenier, Philippe [SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Grinstein, Sebastian [Institut de Fisica d' Altes Energies (IFAE) and ICREA, Universitat Autonoma de Barcelona (UAB), E-08193 Bellaterra, Barcelona (Spain); Hansen, Thor-Erik [SINTEF MiNaLab, Blindern, N-0314 Oslo (Norway); Hasi, Jasmine; Kenney, Chris [SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Kok, Angela [SINTEF MiNaLab, Blindern, N-0314 Oslo (Norway); Parker, Sherwood [University of Hawaii, c/o Lawrence Berkeley Laboratory, Berkeley, CA 94720 (United States); Pellegrini, Giulio [Centro Nacional de Microelectronica, CNM-IMB (CSIC), Barcelona E-08193 (Spain); Vianello, Elisa; Zorzi, Nicola [Fondazione Bruno Kessler, FBK-CMM, Via Sommarive 18, I-38123 Trento (Italy)

    2012-12-01

    3D silicon sensors, where electrodes penetrate the silicon substrate fully or partially, have successfully been fabricated in different processing facilities in Europe and USA. The key to 3D fabrication is the use of plasma micro-machining to etch narrow deep vertical openings allowing dopants to be diffused in and form electrodes of pin junctions. Similar openings can be used at the sensor's edge to reduce the perimeter's dead volume to as low as {approx}4 {mu}m. Since 2009 four industrial partners of the 3D ATLAS R and D Collaboration started a joint effort aimed at one common design and compatible processing strategy for the production of 3D sensors for the LHC Upgrade and in particular for the ATLAS pixel Insertable B-Layer (IBL). In this project, aimed for installation in 2013, a new layer will be inserted as close as 3.4 cm from the proton beams inside the existing pixel layers of the ATLAS experiment. The detector proximity to the interaction point will therefore require new radiation hard technologies for both sensors and front end electronics. The latter, called FE-I4, is processed at IBM and is the biggest front end of this kind ever designed with a surface of {approx}4 cm{sup 2}. The performance of 3D devices from several wafers was evaluated before and after bump-bonding. Key design aspects, device fabrication plans and quality assurance tests during the 3D sensors prototyping phase are discussed in this paper.

  5. Error characterisation of global active and passive microwave soil moisture datasets

    Directory of Open Access Journals (Sweden)

    W. A. Dorigo

    2010-12-01

    Full Text Available Understanding the error structures of remotely sensed soil moisture observations is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available datasets is often hampered by the limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT and passive (AMSR-E and SSM/I microwave sensors. The triple collocation is a powerful statistical tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three linearly related data sources with independent error structures. Prerequisite for this technique is the availability of a sufficiently large number of timely corresponding observations. In addition to the active and passive satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture datasets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active, the use of different frequencies (C-, X-, and Ku-band for passive microwave observations, and the choice of the independent reference dataset (ERA-Interim versus GLDAS-NOAH. The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different

  6. Framework for Interactive Parallel Dataset Analysis on the Grid

    Energy Technology Data Exchange (ETDEWEB)

    Alexander, David A.; Ananthan, Balamurali; /Tech-X Corp.; Johnson, Tony; Serbo, Victor; /SLAC

    2007-01-10

    We present a framework for use at a typical Grid site to facilitate custom interactive parallel dataset analysis targeting terabyte-scale datasets of the type typically produced by large multi-institutional science experiments. We summarize the needs for interactive analysis and show a prototype solution that satisfies those needs. The solution consists of desktop client tool and a set of Web Services that allow scientists to sign onto a Grid site, compose analysis script code to carry out physics analysis on datasets, distribute the code and datasets to worker nodes, collect the results back to the client, and to construct professional-quality visualizations of the results.

  7. Automatic processing of multimodal tomography datasets.

    Science.gov (United States)

    Parsons, Aaron D; Price, Stephen W T; Wadeson, Nicola; Basham, Mark; Beale, Andrew M; Ashton, Alun W; Mosselmans, J Frederick W; Quinn, Paul D

    2017-01-01

    With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.

  8. The Potential Applications of Real-Time Monitoring of Water Quality in a Large Shallow Lake (Lake Taihu, China Using a Chromophoric Dissolved Organic Matter Fluorescence Sensor

    Directory of Open Access Journals (Sweden)

    Cheng Niu

    2014-06-01

    Full Text Available This study presents results from field surveys performed over various seasons in a large, eutrophic, shallow lake (Lake Taihu, China using an in situ chromophoric dissolved organic matter (CDOM fluorescence sensor as a surrogate for other water quality parameters. These measurements identified highly significant empirical relationships between CDOM concentration measured using the in situ fluorescence sensor and CDOM absorption, fluorescence, dissolved organic carbon (DOC, chemical oxygen demand (COD and total phosphorus (TP concentrations. CDOM concentration expressed in quinine sulfate equivalent units, was highly correlated with the CDOM absorption coefficient (r2 = 0.80, p < 0.001, fluorescence intensities (Ex./Em. 370/460 nm (r2 = 0.91, p < 0.001, the fluorescence index (r2 = 0.88, p < 0.001 and the humification index (r2 = 0.78, p < 0.001, suggesting that CDOM concentration measured using the in situ fluorescence sensor could act as a substitute for the CDOM absorption coefficient and fluorescence measured in the laboratory. Similarly, CDOM concentration was highly correlated with DOC concentration (r2 = 0.68, p < 0.001, indicating that in situ CDOM fluorescence sensor measurements could be a proxy for DOC concentration. In addition, significant positive correlations were found between laboratory CDOM absorption coefficients and COD (r2 = 0.83, p < 0.001, TP (r2 = 0.82, p < 0.001 concentrations, suggesting a potential further application for the real-time monitoring of water quality using an in situ CDOM fluorescence sensor.

  9. Large Scale Automatic Analysis and Classification of Roof Surfaces for the Installation of Solar Panels Using a Multi-Sensor Aerial Platform

    Directory of Open Access Journals (Sweden)

    Luis López-Fernández

    2015-09-01

    Full Text Available A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbor solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the areas, tilts, orientations and the existence of obstacles to locate the optimal zones inside each roof surface for the installation of solar panels. This information is complemented with the estimation of the solar irradiation received by each surface. This way, large areas may be efficiently analyzed obtaining as final result the optimal locations for the placement of solar panels as well as the information necessary (location, orientation, tilt, area and solar irradiation to estimate the productivity of a solar panel from its technical characteristics.

  10. Turkey Run Landfill Emissions Dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — landfill emissions measurements for the Turkey run landfill in Georgia. This dataset is associated with the following publication: De la Cruz, F., R. Green, G....

  11. Dataset of NRDA emission data

    Data.gov (United States)

    U.S. Environmental Protection Agency — Emissions data from open air oil burns. This dataset is associated with the following publication: Gullett, B., J. Aurell, A. Holder, B. Mitchell, D. Greenwell, M....

  12. Chemical product and function dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — Merged product weight fraction and chemical function data. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K....

  13. An Efficient Audio Coding Scheme for Quantitative and Qualitative Large Scale Acoustic Monitoring Using the Sensor Grid Approach

    Directory of Open Access Journals (Sweden)

    Félix Gontier

    2017-11-01

    Full Text Available The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on the recent development of low cost hardware acoustic sensors, we propose in this paper to consider a sensor grid approach to tackle this issue. In this kind of approach, the crucial question is the nature of the data that are transmitted from the sensors to the processing and archival servers. To this end, we propose an efficient audio coding scheme based on third octave band spectral representation that allows: (1 the estimation of standard acoustic indicators; and (2 the recognition of acoustic events at state-of-the-art performance rate. The former is useful to provide quantitative information about the acoustic environment, while the latter is useful to gather qualitative information and build perceptually motivated indicators using for example the emergence of a given sound source. The coding scheme is also demonstrated to transmit spectrally encoded data that, reverted to the time domain using state-of-the-art techniques, are not intelligible, thus protecting the privacy of citizens.

  14. A Novel Mechanism for Chemical Sensing Based on Solvent-Fluorophore-Substrate Interaction: Highly Selective Alcohol and Water Sensor with Large Fluorescence Signal Contrast.

    Science.gov (United States)

    Chung, Kyeongwoon; Yang, Da Seul; Jung, Jaehun; Seo, Deokwon; Kwon, Min Sang; Kim, Jinsang

    2016-10-06

    Differentiation of solvents having similar physicochemical properties, such as ethanol and methanol, is an important issue of interest. However, without performing chemical analyses, discrimination between methanol and ethanol is highly challenging due to their similarity in chemical structure as well as properties. Here, we present a novel type of alcohol and water sensor based on the subtle differences in interaction among solvent analytes, fluorescent organic molecules, and a mesoporous silica gel substrate. A gradual change in the chemical structure of the fluorescent diketopyrrolopyrrole (DPP) derivatives alters their interaction with the substrate and solvent analyte, which creates a distinct intermolecular aggregation of the DPP derivatives on the silica gel substrate depending on the solvent environment and produces a change in the fluorescence color and intensity as a sensory signal. The devised sensor device, which is fabricated with simple drop-casting of the DPP derivative solutions onto a silica gel substrate, exhibited a completely reversible fluorescence signal change with large fluorescence signal contrast, which allows selective solvent detection by simple optical observation with the naked eye under UV light. Superior selectivity of the alcohol and water sensor system, which can clearly distinguish among ethanol, methanol, ethylene glycol, and water, is demonstrated.

  15. Experimental investigations on bubble turbulent diffusion in a vertical large diameter pipe by means of wire-mesh sensors and correlation techniques

    International Nuclear Information System (INIS)

    Annalisa Manera; Horst-Michael Prasser; Dirk Lucas

    2005-01-01

    Full text of publication follows: A large number of experiments for water-air vertical flows in a large-diameter pipe has been carried out at the TOPFLOW facility (Forschunszentrum Rossendorf). The experiments cover a wide range of liquid and superficial gas velocity. The test section consists of a vertical pipe of ∼194 mm and 8.5 m long. At a distance of 7.6 m from the air injection, two wire-mesh sensors are installed. The two sensors are mounted at a distance of 63.3 mm from each other. The wire-mesh sensors measure sequences of instantaneous two-dimensional gas-fraction distributions in the cross-section in which they are mounted with a spatial resolution of 3 mm and a frequency of 2500 Hz. The total dimension of the matrix of measuring points for each mesh sensor is 64 x 64. In a central region of the measuring plane, where the void-fraction gradients are small, points of the first wire-mesh sensor are individually cross-correlated in time domain with measuring points belonging to the second wire-mesh sensor. The cross-correlation functions were calculated for pairs of points that are located accurately above each other as well as for points with a lateral distance. The lateral distance was varied from 0 to 48 mm (16 points), which is still within 50% of the pipe radius, i.e. in the region of small void-fraction gradients. The maximum of each of the 17 correlations is selected in order to derive a spatial correlation in the radial direction. The obtained spatial cross-correlations shows a maximum at zero lateral distance and decrease with growing lateral shift. In a region without gradients, the lateral displacement of bubbles is dominated by turbulent diffusion. This gives the opportunity to derive bubble turbulent diffusion coefficients from the spreading of the spatial correlations. At this aim, the spatial correlations have been first corrected to take into account the finite spatial resolution of the sensor and the finite dimension of the bubbles. The

  16. The NOAA Dataset Identifier Project

    Science.gov (United States)

    de la Beaujardiere, J.; Mccullough, H.; Casey, K. S.

    2013-12-01

    The US National Oceanic and Atmospheric Administration (NOAA) initiated a project in 2013 to assign persistent identifiers to datasets archived at NOAA and to create informational landing pages about those datasets. The goals of this project are to enable the citation of datasets used in products and results in order to help provide credit to data producers, to support traceability and reproducibility, and to enable tracking of data usage and impact. A secondary goal is to encourage the submission of datasets for long-term preservation, because only archived datasets will be eligible for a NOAA-issued identifier. A team was formed with representatives from the National Geophysical, Oceanographic, and Climatic Data Centers (NGDC, NODC, NCDC) to resolve questions including which identifier scheme to use (answer: Digital Object Identifier - DOI), whether or not to embed semantics in identifiers (no), the level of granularity at which to assign identifiers (as coarsely as reasonable), how to handle ongoing time-series data (do not break into chunks), creation mechanism for the landing page (stylesheet from formal metadata record preferred), and others. Decisions made and implementation experience gained will inform the writing of a Data Citation Procedural Directive to be issued by the Environmental Data Management Committee in 2014. Several identifiers have been issued as of July 2013, with more on the way. NOAA is now reporting the number as a metric to federal Open Government initiatives. This paper will provide further details and status of the project.

  17. Highly Sensitive Electromechanical Piezoresistive Pressure Sensors Based on Large-Area Layered PtSe2 Films.

    Science.gov (United States)

    Wagner, Stefan; Yim, Chanyoung; McEvoy, Niall; Kataria, Satender; Yokaribas, Volkan; Kuc, Agnieszka; Pindl, Stephan; Fritzen, Claus-Peter; Heine, Thomas; Duesberg, Georg S; Lemme, Max C

    2018-05-23

    Two-dimensional (2D) layered materials are ideal for micro- and nanoelectromechanical systems (MEMS/NEMS) due to their ultimate thinness. Platinum diselenide (PtSe 2 ), an exciting and unexplored 2D transition metal dichalcogenide material, is particularly interesting because its low temperature growth process is scalable and compatible with silicon technology. Here, we report the potential of thin PtSe 2 films as electromechanical piezoresistive sensors. All experiments have been conducted with semimetallic PtSe 2 films grown by thermally assisted conversion of platinum at a complementary metal-oxide-semiconductor (CMOS)-compatible temperature of 400 °C. We report high negative gauge factors of up to -85 obtained experimentally from PtSe 2 strain gauges in a bending cantilever beam setup. Integrated NEMS piezoresistive pressure sensors with freestanding PMMA/PtSe 2 membranes confirm the negative gauge factor and exhibit very high sensitivity, outperforming previously reported values by orders of magnitude. We employ density functional theory calculations to understand the origin of the measured negative gauge factor. Our results suggest PtSe 2 as a very promising candidate for future NEMS applications, including integration into CMOS production lines.

  18. Sharing Video Datasets in Design Research

    DEFF Research Database (Denmark)

    Christensen, Bo; Abildgaard, Sille Julie Jøhnk

    2017-01-01

    This paper examines how design researchers, design practitioners and design education can benefit from sharing a dataset. We present the Design Thinking Research Symposium 11 (DTRS11) as an exemplary project that implied sharing video data of design processes and design activity in natural settings...... with a large group of fellow academics from the international community of Design Thinking Research, for the purpose of facilitating research collaboration and communication within the field of Design and Design Thinking. This approach emphasizes the social and collaborative aspects of design research, where...... a multitude of appropriate perspectives and methods may be utilized in analyzing and discussing the singular dataset. The shared data is, from this perspective, understood as a design object in itself, which facilitates new ways of working, collaborating, studying, learning and educating within the expanding...

  19. The Harvard organic photovoltaic dataset.

    Science.gov (United States)

    Lopez, Steven A; Pyzer-Knapp, Edward O; Simm, Gregor N; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R; Hachmann, Johannes; Aspuru-Guzik, Alán

    2016-09-27

    The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

  20. The Harvard organic photovoltaic dataset

    Science.gov (United States)

    Lopez, Steven A.; Pyzer-Knapp, Edward O.; Simm, Gregor N.; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R.; Hachmann, Johannes; Aspuru-Guzik, Alán

    2016-01-01

    The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications. PMID:27676312

  1. A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks.

    Science.gov (United States)

    Cheikhrouhou, Omar; M Bhatti, Ghulam; Alroobaea, Roobaea

    2018-05-08

    With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only those wireless nodes that are within one-hop distance from the anchor nodes. Multi-hop localization algorithms, on the other hand, aim at localizing the wireless nodes that can physically be residing at multiple hops away from anchor nodes. These latter algorithms have attracted a growing interest from research community due to the smaller number of required anchor nodes. One such algorithm, known as DV-Hop (Distance Vector Hop), has gained popularity due to its simplicity and lower cost. However, DV-Hop suffers from reduced accuracy due to the fact that it exploits only the network topology (i.e., number of hops to anchors) rather than the distances between pairs of nodes. In this paper, we propose an enhanced DV-Hop localization algorithm that also uses the RSSI values associated with links between one-hop neighbors. Moreover, we exploit already localized nodes by promoting them to become additional anchor nodes. Our simulations have shown that the proposed algorithm significantly outperforms the original DV-Hop localization algorithm and two of its recently published variants, namely RSSI Auxiliary Ranging and the Selective 3-Anchor DV-hop algorithm. More precisely, in some scenarios, the proposed algorithm improves the localization accuracy by almost 95%, 90% and 70% as compared to the basic DV-Hop, Selective 3-Anchor, and RSSI DV-Hop algorithms, respectively.

  2. Fluxnet Synthesis Dataset Collaboration Infrastructure

    Energy Technology Data Exchange (ETDEWEB)

    Agarwal, Deborah A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Humphrey, Marty [Univ. of Virginia, Charlottesville, VA (United States); van Ingen, Catharine [Microsoft. San Francisco, CA (United States); Beekwilder, Norm [Univ. of Virginia, Charlottesville, VA (United States); Goode, Monte [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Jackson, Keith [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rodriguez, Matt [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Weber, Robin [Univ. of California, Berkeley, CA (United States)

    2008-02-06

    The Fluxnet synthesis dataset originally compiled for the La Thuile workshop contained approximately 600 site years. Since the workshop, several additional site years have been added and the dataset now contains over 920 site years from over 240 sites. A data refresh update is expected to increase those numbers in the next few months. The ancillary data describing the sites continues to evolve as well. There are on the order of 120 site contacts and 60proposals have been approved to use thedata. These proposals involve around 120 researchers. The size and complexity of the dataset and collaboration has led to a new approach to providing access to the data and collaboration support and the support team attended the workshop and worked closely with the attendees and the Fluxnet project office to define the requirements for the support infrastructure. As a result of this effort, a new website (http://www.fluxdata.org) has been created to provide access to the Fluxnet synthesis dataset. This new web site is based on a scientific data server which enables browsing of the data on-line, data download, and version tracking. We leverage database and data analysis tools such as OLAP data cubes and web reports to enable browser and Excel pivot table access to the data.

  3. Application of Density Estimation Methods to Datasets from a Glider

    Science.gov (United States)

    2014-09-30

    humpback and sperm whales as well as different dolphin species. OBJECTIVES The objective of this research is to extend existing methods for cetacean...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...estimation from single sensor datasets. Required steps for a cue counting approach, where a cue has been defined as a clicking event (Küsel et al., 2011), to

  4. A dynamic response model for pressure sensors in continuum and high Knudsen number flows with large temperature gradients

    Science.gov (United States)

    Whitmore, Stephen A.; Petersen, Brian J.; Scott, David D.

    1996-01-01

    This paper develops a dynamic model for pressure sensors in continuum and rarefied flows with longitudinal temperature gradients. The model was developed from the unsteady Navier-Stokes momentum, energy, and continuity equations and was linearized using small perturbations. The energy equation was decoupled from momentum and continuity assuming a polytropic flow process. Rarefied flow conditions were accounted for using a slip flow boundary condition at the tubing wall. The equations were radially averaged and solved assuming gas properties remain constant along a small tubing element. This fundamental solution was used as a building block for arbitrary geometries where fluid properties may also vary longitudinally in the tube. The problem was solved recursively starting at the transducer and working upstream in the tube. Dynamic frequency response tests were performed for continuum flow conditions in the presence of temperature gradients. These tests validated the recursive formulation of the model. Model steady-state behavior was analyzed using the final value theorem. Tests were performed for rarefied flow conditions and compared to the model steady-state response to evaluate the regime of applicability. Model comparisons were excellent for Knudsen numbers up to 0.6. Beyond this point, molecular affects caused model analyses to become inaccurate.

  5. A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhilu Wu

    2015-01-01

    Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

  6. A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink.

    Science.gov (United States)

    Zhu, Chuan; Zhang, Sai; Han, Guangjie; Jiang, Jinfang; Rodrigues, Joel J P C

    2016-09-06

    Mobile sink is widely used for data collection in wireless sensor networks. It can avoid 'hot spot' problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.

  7. Towards Sensor Database Systems

    DEFF Research Database (Denmark)

    Bonnet, Philippe; Gehrke, Johannes; Seshadri, Praveen

    2001-01-01

    . These systems lack flexibility because data is extracted in a predefined way; also, they do not scale to a large number of devices because large volumes of raw data are transferred regardless of the queries that are submitted. In our new concept of sensor database system, queries dictate which data is extracted...... from the sensors. In this paper, we define the concept of sensor databases mixing stored data represented as relations and sensor data represented as time series. Each long-running query formulated over a sensor database defines a persistent view, which is maintained during a given time interval. We...... also describe the design and implementation of the COUGAR sensor database system....

  8. A Dataset for Visual Navigation with Neuromorphic Methods

    Directory of Open Access Journals (Sweden)

    Francisco eBarranco

    2016-02-01

    Full Text Available Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.

  9. Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

    DEFF Research Database (Denmark)

    Cao, Bin; Zhao, Jianwei; Yang, Po

    2018-01-01

    -objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively.......Using immune algorithms is generally a time-intensive process especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface...... (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large...

  10. Detecting Significant Stress Drop Variations in Large Micro-Earthquake Datasets: A Comparison Between a Convergent Step-Over in the San Andreas Fault and the Ventura Thrust Fault System, Southern California

    Science.gov (United States)

    Goebel, T. H. W.; Hauksson, E.; Plesch, A.; Shaw, J. H.

    2017-06-01

    A key parameter in engineering seismology and earthquake physics is seismic stress drop, which describes the relative amount of high-frequency energy radiation at the source. To identify regions with potentially significant stress drop variations, we perform a comparative analysis of source parameters in the greater San Gorgonio Pass (SGP) and Ventura basin (VB) in southern California. The identification of physical stress drop variations is complicated by large data scatter as a result of attenuation, limited recording bandwidth and imprecise modeling assumptions. In light of the inherently high uncertainties in single stress drop measurements, we follow the strategy of stacking large numbers of source spectra thereby enhancing the resolution of our method. We analyze more than 6000 high-quality waveforms between 2000 and 2014, and compute seismic moments, corner frequencies and stress drops. Significant variations in stress drop estimates exist within the SGP area. Moreover, the SGP also exhibits systematically higher stress drops than VB and shows more scatter. We demonstrate that the higher scatter in SGP is not a generic artifact of our method but an expression of differences in underlying source processes. Our results suggest that higher differential stresses, which can be deduced from larger focal depth and more thrust faulting, may only be of secondary importance for stress drop variations. Instead, the general degree of stress field heterogeneity and strain localization may influence stress drops more strongly, so that more localized faulting and homogeneous stress fields favor lower stress drops. In addition, higher loading rates, for example, across the VB potentially result in stress drop reduction whereas slow loading rates on local fault segments within the SGP region result in anomalously high stress drop estimates. Our results show that crustal and fault properties systematically influence earthquake stress drops of small and large events and should

  11. Method of making large area conformable shape structures for detector/sensor applications using glass drawing technique and postprocessing

    Science.gov (United States)

    Ivanov, Ilia N [Knoxville, TN; Simpson, John T [Clinton, IN

    2012-01-24

    A method of making a large area conformable shape structure comprises drawing a plurality of tubes to form a plurality of drawn tubes, and cutting the plurality of drawn tubes into cut drawn tubes of a predetermined shape. The cut drawn tubes have a first end and a second end along the longitudinal direction of the cut drawn tubes. The method further comprises conforming the first end of the cut drawn tubes into a predetermined curve to form the large area conformable shape structure, wherein the cut drawn tubes contain a material.

  12. CERC Dataset (Full Hadza Data)

    DEFF Research Database (Denmark)

    2016-01-01

    The dataset includes demographic, behavioral, and religiosity data from eight different populations from around the world. The samples were drawn from: (1) Coastal and (2) Inland Tanna, Vanuatu; (3) Hadzaland, Tanzania; (4) Lovu, Fiji; (5) Pointe aux Piment, Mauritius; (6) Pesqueiro, Brazil; (7......) Kyzyl, Tyva Republic; and (8) Yasawa, Fiji. Related publication: Purzycki, et al. (2016). Moralistic Gods, Supernatural Punishment and the Expansion of Human Sociality. Nature, 530(7590): 327-330....

  13. Viking Seismometer PDS Archive Dataset

    Science.gov (United States)

    Lorenz, R. D.

    2016-12-01

    The Viking Lander 2 seismometer operated successfully for over 500 Sols on the Martian surface, recording at least one likely candidate Marsquake. The Viking mission, in an era when data handling hardware (both on board and on the ground) was limited in capability, predated modern planetary data archiving, and ad-hoc repositories of the data, and the very low-level record at NSSDC, were neither convenient to process nor well-known. In an effort supported by the NASA Mars Data Analysis Program, we have converted the bulk of the Viking dataset (namely the 49,000 and 270,000 records made in High- and Event- modes at 20 and 1 Hz respectively) into a simple ASCII table format. Additionally, since wind-generated lander motion is a major component of the signal, contemporaneous meteorological data are included in summary records to facilitate correlation. These datasets are being archived at the PDS Geosciences Node. In addition to brief instrument and dataset descriptions, the archive includes code snippets in the freely-available language 'R' to demonstrate plotting and analysis. Further, we present examples of lander-generated noise, associated with the sampler arm, instrument dumps and other mechanical operations.

  14. PHYSICS PERFORMANCE AND DATASET (PPD)

    CERN Multimedia

    L. Silvestris

    2013-01-01

    The first part of the Long Shutdown period has been dedicated to the preparation of the samples for the analysis targeting the summer conferences. In particular, the 8 TeV data acquired in 2012, including most of the “parked datasets”, have been reconstructed profiting from improved alignment and calibration conditions for all the sub-detectors. A careful planning of the resources was essential in order to deliver the datasets well in time to the analysts, and to schedule the update of all the conditions and calibrations needed at the analysis level. The newly reprocessed data have undergone detailed scrutiny by the Dataset Certification team allowing to recover some of the data for analysis usage and further improving the certification efficiency, which is now at 91% of the recorded luminosity. With the aim of delivering a consistent dataset for 2011 and 2012, both in terms of conditions and release (53X), the PPD team is now working to set up a data re-reconstruction and a new MC pro...

  15. Integration of Point Clouds Dataset from Different Sensors

    Science.gov (United States)

    Abdullah, C. K. A. F. Che Ku; Baharuddin, N. Z. S.; Ariff, M. F. M.; Majid, Z.; Lau, C. L.; Yusoff, A. R.; Idris, K. M.; Aspuri, A.

    2017-02-01

    Laser Scanner technology become an option in the process of collecting data nowadays. It is composed of Airborne Laser Scanner (ALS) and Terrestrial Laser Scanner (TLS). ALS like Phoenix AL3-32 can provide accurate information from the viewpoint of rooftop while TLS as Leica C10 can provide complete data for building facade. However if both are integrated, it is able to produce more accurate data. The focus of this study is to integrate both types of data acquisition of ALS and TLS and determine the accuracy of the data obtained. The final results acquired will be used to generate models of three-dimensional (3D) buildings. The scope of this study is focusing on data acquisition of UTM Eco-home through laser scanning methods such as ALS which scanning on the roof and the TLS which scanning on building façade. Both device is used to ensure that no part of the building that are not scanned. In data integration process, both are registered by the selected points among the manmade features which are clearly visible in Cyclone 7.3 software. The accuracy of integrated data is determined based on the accuracy assessment which is carried out using man-made registration methods. The result of integration process can achieve below 0.04m. This integrated data then are used to generate a 3D model of UTM Eco-home building using SketchUp software. In conclusion, the combination of the data acquisition integration between ALS and TLS would produce the accurate integrated data and able to use for generate a 3D model of UTM eco-home. For visualization purposes, the 3D building model which generated is prepared in Level of Detail 3 (LOD3) which recommended by City Geographic Mark-Up Language (CityGML).

  16. Odometry and Low-Cost Sensor Fusion in Tmm Dataset

    Science.gov (United States)

    Manzino, A. M.; Taglioretti, C.

    2016-03-01

    The aim of this study is to identify the most powerful motion model and filtering technique to represent an urban terrestrial mobile mapping (TMM) survey and ultimately to obtain the best representation of the car trajectory. The authors want to test how far a motion model and a more or less refined filtering technique could bring benefits in the determination of the car trajectory. To achieve the necessary data for the application of the motion models and the filtering techniques described in the article, the authors realized a TMM survey in the urban centre of Turin by equipping a vehicle with various instruments: a low-cost action-cam also able to record the GPS trace of the vehicle even in the presence of obstructions, an inertial measurement system and an odometer. The results of analysis show in the article indicate that the Unscented Kalman Filter (UKF) technique provides good results in the determination of the vehicle trajectory, especially if the motion model considers more states (such as the positions, the tangential velocity, the angular velocity, the heading, the acceleration). The authors also compared the results obtained with a motion model characterized by four, five and six states. A natural corollary to this work would be the introduction to the UKF of the photogrammetric information obtained by the same camera placed on board the vehicle. These data would permit to establish how photogrammetric measurements can improve the quality of TMM solutions, especially in the absence of GPS signals (like urban canyons).

  17. RARD: The Related-Article Recommendation Dataset

    OpenAIRE

    Beel, Joeran; Carevic, Zeljko; Schaible, Johann; Neusch, Gabor

    2017-01-01

    Recommender-system datasets are used for recommender-system evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music, there are rather few datasets from research-paper recommender systems. In this paper, we introduce RARD, the Related-Article Recommendation Dataset, from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains ...

  18. Something From Nothing (There): Collecting Global IPv6 Datasets from DNS

    NARCIS (Netherlands)

    Fiebig, T.; Borgolte, Kevin; Hao, Shuang; Kruegel, Christopher; Vigna, Giovanny; Spring, Neil; Riley, George F.

    2017-01-01

    Current large-scale IPv6 studies mostly rely on non-public datasets, asmost public datasets are domain specific. For instance, traceroute-based datasetsare biased toward network equipment. In this paper, we present a new methodologyto collect IPv6 address datasets that does not require access to

  19. Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

    Science.gov (United States)

    Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

    2014-01-01

    SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

  20. Thermal and hydrodynamic studies for micro-channel cooling for large area silicon sensors in high energy physics experiments

    Energy Technology Data Exchange (ETDEWEB)

    Flaschel, Nils; Ariza, Dario; Diez, Sergio; Gregor, Ingrid-Maria; Tackmann, Kerstin [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Gerboles, Marta; Jorda, Xavier; Mas, Roser; Quirion, David; Ullan, Miguel [Centro Nacional de Microelectronica, Barcelona (Spain)

    2017-01-15

    Micro-channel cooling initially aiming at small-sized high-power integrated circuits is being transferred to the field of high energy physics. Today's prospects of micro-fabricating silicon opens a door to a more direct cooling of detector modules. The challenge in high energy physics is to save material in the detector construction and to cool large areas. In this paper, we are investigating micro-channel cooling as a candidate for a future cooling system for silicon detectors in a generic research and development approach. The work presented in this paper includes the production and the hydrodynamic and thermal testing of a micro-channel equipped prototype optimized to achieve a homogeneous flow distribution. Furthermore, the device was simulated using finite element methods.

  1. Large, Linear, and Tunable Positive Magnetoresistance of Mechanically Stable Graphene Foam-Toward High-Performance Magnetic Field Sensors.

    Science.gov (United States)

    Sagar, Rizwan Ur Rehman; Galluzzi, Massimiliano; Wan, Caihua; Shehzad, Khurram; Navale, Sachin T; Anwar, Tauseef; Mane, Rajaram S; Piao, Hong-Guang; Ali, Abid; Stadler, Florian J

    2017-01-18

    Here, we present the first observation of magneto-transport properties of graphene foam (GF) composed of a few layers in a wide temperature range of 2-300 K. Large room-temperature linear positive magnetoresistance (PMR ≈ 171% at B ≈ 9 T) has been detected. The largest PMR (∼213%) has been achieved at 2 K under a magnetic field of 9 T, which can be tuned by the addition of poly(methyl methacrylate) to the porous structure of the foam. This remarkable magnetoresistance may be the result of quadratic magnetoresistance. The excellent magneto-transport properties of GF open a way toward three-dimensional graphene-based magnetoelectronic devices.

  2. Thermal and hydrodynamic studies for micro-channel cooling for large area silicon sensors in high energy physics experiments

    International Nuclear Information System (INIS)

    Flaschel, Nils; Ariza, Dario; Diez, Sergio; Gregor, Ingrid-Maria; Tackmann, Kerstin; Gerboles, Marta; Jorda, Xavier; Mas, Roser; Quirion, David; Ullan, Miguel

    2017-01-01

    Micro-channel cooling initially aiming at small-sized high-power integrated circuits is being transferred to the field of high energy physics. Today's prospects of micro-fabricating silicon opens a door to a more direct cooling of detector modules. The challenge in high energy physics is to save material in the detector construction and to cool large areas. In this paper, we are investigating micro-channel cooling as a candidate for a future cooling system for silicon detectors in a generic research and development approach. The work presented in this paper includes the production and the hydrodynamic and thermal testing of a micro-channel equipped prototype optimized to achieve a homogeneous flow distribution. Furthermore, the device was simulated using finite element methods.

  3. Passive Containment DataSet

    Science.gov (United States)

    This data is for Figures 6 and 7 in the journal article. The data also includes the two EPANET input files used for the analysis described in the paper, one for the looped system and one for the block system.This dataset is associated with the following publication:Grayman, W., R. Murray , and D. Savic. Redesign of Water Distribution Systems for Passive Containment of Contamination. JOURNAL OF THE AMERICAN WATER WORKS ASSOCIATION. American Water Works Association, Denver, CO, USA, 108(7): 381-391, (2016).

  4. Highly sensitive digital optical sensor with large measurement range based on the dual-microring resonator with waveguide-coupled feedback

    International Nuclear Information System (INIS)

    Xiang Xing-Ye; Wang Kui-Ru; Yuan Jin-Hui; Jin Bo-Yuan; Sang Xin-Zhu; Yu Chong-Xiu

    2014-01-01

    We propose a novel high-performance digital optical sensor based on the Mach—Zehnder interferential effect and the dual-microring resonators with the waveguide-coupled feedback. The simulation results show that the sensitivity of the sensor can be orders of magnitude higher than that of a conventional sensor, and high quality factor is not critical in it. Moreover, by optimizing the length of the feedback waveguide to be equal to the perimeter of the ring, the measurement range of the proposed sensor is twice as much as that of the conventional sensor in the weak coupling case

  5. A robust dataset-agnostic heart disease classifier from Phonocardiogram.

    Science.gov (United States)

    Banerjee, Rohan; Dutta Choudhury, Anirban; Deshpande, Parijat; Bhattacharya, Sakyajit; Pal, Arpan; Mandana, K M

    2017-07-01

    Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.

  6. A Comparative Analysis of Classification Algorithms on Diverse Datasets

    Directory of Open Access Journals (Sweden)

    M. Alghobiri

    2018-04-01

    Full Text Available Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

  7. Homogenised Australian climate datasets used for climate change monitoring

    International Nuclear Information System (INIS)

    Trewin, Blair; Jones, David; Collins; Dean; Jovanovic, Branislava; Braganza, Karl

    2007-01-01

    Full text: The Australian Bureau of Meteorology has developed a number of datasets for use in climate change monitoring. These datasets typically cover 50-200 stations distributed as evenly as possible over the Australian continent, and have been subject to detailed quality control and homogenisation.The time period over which data are available for each element is largely determined by the availability of data in digital form. Whilst nearly all Australian monthly and daily precipitation data have been digitised, a significant quantity of pre-1957 data (for temperature and evaporation) or pre-1987 data (for some other elements) remains to be digitised, and is not currently available for use in the climate change monitoring datasets. In the case of temperature and evaporation, the start date of the datasets is also determined by major changes in instruments or observing practices for which no adjustment is feasible at the present time. The datasets currently available cover: Monthly and daily precipitation (most stations commence 1915 or earlier, with many extending back to the late 19th century, and a few to the mid-19th century); Annual temperature (commences 1910); Daily temperature (commences 1910, with limited station coverage pre-1957); Twice-daily dewpoint/relative humidity (commences 1957); Monthly pan evaporation (commences 1970); Cloud amount (commences 1957) (Jovanovic etal. 2007). As well as the station-based datasets listed above, an additional dataset being developed for use in climate change monitoring (and other applications) covers tropical cyclones in the Australian region. This is described in more detail in Trewin (2007). The datasets already developed are used in analyses of observed climate change, which are available through the Australian Bureau of Meteorology website (http://www.bom.gov.au/silo/products/cli_chg/). They are also used as a basis for routine climate monitoring, and in the datasets used for the development of seasonal

  8. Toward computational cumulative biology by combining models of biological datasets.

    Science.gov (United States)

    Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

    2014-01-01

    A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

  9. Hydrostatic force sensor

    International Nuclear Information System (INIS)

    Evans, M.S.; Stoughton, R.S.; Kazerooni, H.

    1994-08-01

    This paper presents a theoretical and experimental investigation of a new kind of force sensor which detects forces by measuring an induced pressure change in a material of large Poisson's ratio. In this investigation we develop mathematical expressions for the sensor's sensitivity and bandwidth, and show that its sensitivity can be much larger and its bandwidth is usually smaller than those of existing strain-gage-type sensors. This force sensor is well-suited for measuring large but slowly varying forces. It can be installed in a space smaller than that required by existing sensors

  10. 2008 TIGER/Line Nationwide Dataset

    Data.gov (United States)

    California Natural Resource Agency — This dataset contains a nationwide build of the 2008 TIGER/Line datasets from the US Census Bureau downloaded in April 2009. The TIGER/Line Shapefiles are an extract...

  11. Large-scale Machine Learning in High-dimensional Datasets

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen

    Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed...... are better at modeling local heterogeneities. In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of f...

  12. NCBI Mass Sequence Downloader–Large dataset downloading made easy

    Directory of Open Access Journals (Sweden)

    F. Pina-Martins

    2016-01-01

    Source code is licensed under the GPLv3, and is supported on Linux, Windows and Mac OSX. Available at https://github.com/ElsevierSoftwareX/SOFTX-D-15-00072.git, https://github.com/StuntsPT/NCBI_Mass_Downloader

  13. Interactive Visualization of Large High-Dimensional Datasets

    Science.gov (United States)

    Ding, Wei; Chen, Ping

    Nowadays many companies and public organizations use powerful database systems for collecting and managing information. Huge amount of data records are often accumulated within a short period of time. Valuable information is embedded in these data, which could help discover interesting knowledge and significantly assist in decision-making process. However, human beings are not capable of understanding so many data records which often have lots of attributes. The need for automated knowledge extraction is widely recognized, and leads to a rapidly developing market of data analysis and knowledge discovery tools.

  14. Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

    Litvinenko, Alexander

    2017-09-03

    We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H-matrix technique allows us to work with general covariance matrices in an efficient way, since H-matrices can approximate inhomogeneous covariance functions, with a fairly general mesh that is not necessarily axes-parallel, and neither the covariance matrix itself nor its inverse have to be sparse. We demonstrate our method with Monte Carlo simulations and an application to soil moisture data. The C, C++ codes and data are freely available.

  15. Distributed Large Dataset Deployment with Improved Load Balancing and Performance

    OpenAIRE

    Siddharth Bhandari

    2016-01-01

    Cloud computing is a prototype for permitting universal, appropriate, on-demand network access. Cloud is a method of computing where enormously scalable IT-enabled proficiencies are delivered „as a service‟ using Internet tools to multiple outdoor clients. Virtualization is the establishment of a virtual form of something such as computing device or server, an operating system, or network devices and storage device. The different names for cloud data management are DaaS Data as a ...

  16. Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

    Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.

    2017-01-01

    algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H

  17. The Importance of Normalization on Large and Heterogeneous Microarray Datasets

    Science.gov (United States)

    DNA microarray technology is a powerful functional genomics tool increasingly used for investigating global gene expression in environmental studies. Microarrays can also be used in identifying biological networks, as they give insight on the complex gene-to-gene interactions, ne...

  18. Oil palm mapping for Malaysia using PALSAR-2 dataset

    Science.gov (United States)

    Gong, P.; Qi, C. Y.; Yu, L.; Cracknell, A.

    2016-12-01

    Oil palm is one of the most productive vegetable oil crops in the world. The main oil palm producing areas are distributed in humid tropical areas such as Malaysia, Indonesia, Thailand, western and central Africa, northern South America, and central America. Increasing market demands, high yields and low production costs of palm oil are the primary factors driving large-scale commercial cultivation of oil palm, especially in Malaysia and Indonesia. Global demand for palm oil has grown exponentially during the last 50 years, and the expansion of oil palm plantations is linked directly to the deforestation of natural forests. Satellite remote sensing plays an important role in monitoring expansion of oil palm. However, optical remote sensing images are difficult to acquire in the Tropics because of the frequent occurrence of thick cloud cover. This problem has led to the use of data obtained by synthetic aperture radar (SAR), which is a sensor capable of all-day/all-weather observation for studies in the Tropics. In this study, the ALOS-2 (Advanced Land Observing Satellite) PALSAR-2 (Phased Array type L-band SAR) datasets for year 2015 were used as an input to a support vector machine (SVM) based machine learning algorithm. Oil palm/non-oil palm samples were collected using a hexagonal equal-area sampling design. High-resolution images in Google Earth and PALSAR-2 imagery were used in human photo-interpretation to separate oil palm from others (i.e. cropland, forest, grassland, shrubland, water, hard surface and bareland). The characteristics of oil palms from various aspects, including PALSAR-2 backscattering coefficients (HH, HV), terrain and climate by using this sample set were further explored to post-process the SVM output. The average accuracy of oil palm type is better than 80% in the final oil palm map for Malaysia.

  19. Development of a SPARK Training Dataset

    Energy Technology Data Exchange (ETDEWEB)

    Sayre, Amanda M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Olson, Jarrod R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2015-03-01

    In its first five years, the National Nuclear Security Administration’s (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and across locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK’s intended analysis capability. The analysis demonstration sought to answer the

  20. Development of a SPARK Training Dataset

    International Nuclear Information System (INIS)

    Sayre, Amanda M.; Olson, Jarrod R.

    2015-01-01

    In its first five years, the National Nuclear Security Administration's (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and across locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK's intended analysis capability. The analysis demonstration sought to answer

  1. Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology.

    Science.gov (United States)

    Kalid, Naser; Zaidan, A A; Zaidan, B B; Salman, Omar H; Hashim, M; Albahri, O S; Albahri, A S

    2018-03-02

    This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and

  2. Heuristics for Relevancy Ranking of Earth Dataset Search Results

    Science.gov (United States)

    Lynnes, Christopher; Quinn, Patrick; Norton, James

    2016-01-01

    As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.

  3. Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

    OpenAIRE

    Limin Liao; Jinling Song; Jindi Wang; Zhiqiang Xiao; Jian Wang

    2016-01-01

    Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resol...

  4. Climatic Analysis of Oceanic Water Vapor Transports Based on Satellite E-P Datasets

    Science.gov (United States)

    Smith, Eric A.; Sohn, Byung-Ju; Mehta, Vikram

    2004-01-01

    Understanding the climatically varying properties of water vapor transports from a robust observational perspective is an essential step in calibrating climate models. This is tantamount to measuring year-to-year changes of monthly- or seasonally-averaged, divergent water vapor transport distributions. This cannot be done effectively with conventional radiosonde data over ocean regions where sounding data are generally sparse. This talk describes how a methodology designed to derive atmospheric water vapor transports over the world oceans from satellite-retrieved precipitation (P) and evaporation (E) datasets circumvents the problem of inadequate sampling. Ultimately, the method is intended to take advantage of the relatively complete and consistent coverage, as well as continuity in sampling, associated with E and P datasets obtained from satellite measurements. Independent P and E retrievals from Special Sensor Microwave Imager (SSM/I) measurements, along with P retrievals from Tropical Rainfall Measuring Mission (TRMM) measurements, are used to obtain transports by solving a potential function for the divergence of water vapor transport as balanced by large scale E - P conditions.

  5. A synthetic dataset for evaluating soft and hard fusion algorithms

    Science.gov (United States)

    Graham, Jacob L.; Hall, David L.; Rimland, Jeffrey

    2011-06-01

    There is an emerging demand for the development of data fusion techniques and algorithms that are capable of combining conventional "hard" sensor inputs such as video, radar, and multispectral sensor data with "soft" data including textual situation reports, open-source web information, and "hard/soft" data such as image or video data that includes human-generated annotations. New techniques that assist in sense-making over a wide range of vastly heterogeneous sources are critical to improving tactical situational awareness in counterinsurgency (COIN) and other asymmetric warfare situations. A major challenge in this area is the lack of realistic datasets available for test and evaluation of such algorithms. While "soft" message sets exist, they tend to be of limited use for data fusion applications due to the lack of critical message pedigree and other metadata. They also lack corresponding hard sensor data that presents reasonable "fusion opportunities" to evaluate the ability to make connections and inferences that span the soft and hard data sets. This paper outlines the design methodologies, content, and some potential use cases of a COIN-based synthetic soft and hard dataset created under a United States Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office (ARO). The dataset includes realistic synthetic reports from a variety of sources, corresponding synthetic hard data, and an extensive supporting database that maintains "ground truth" through logical grouping of related data into "vignettes." The supporting database also maintains the pedigree of messages and other critical metadata.

  6. Estimating parameters for probabilistic linkage of privacy-preserved datasets.

    Science.gov (United States)

    Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H

    2017-07-10

    Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher

  7. Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

    Science.gov (United States)

    Lary, D. J.

    2013-12-01

    A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

  8. PHYSICS PERFORMANCE AND DATASET (PPD)

    CERN Multimedia

    L. Silvestris

    2012-01-01

      Introduction The first part of the year presented an important test for the new Physics Performance and Dataset (PPD) group (cf. its mandate: http://cern.ch/go/8f77). The activity was focused on the validation of the new releases meant for the Monte Carlo (MC) production and the data-processing in 2012 (CMSSW 50X and 52X), and on the preparation of the 2012 operations. In view of the Chamonix meeting, the PPD and physics groups worked to understand the impact of the higher pile-up scenario on some of the flagship Higgs analyses to better quantify the impact of the high luminosity on the CMS physics potential. A task force is working on the optimisation of the reconstruction algorithms and on the code to cope with the performance requirements imposed by the higher event occupancy as foreseen for 2012. Concerning the preparation for the analysis of the new data, a new MC production has been prepared. The new samples, simulated at 8 TeV, are already being produced and the digitisation and recons...

  9. Pattern Analysis On Banking Dataset

    Directory of Open Access Journals (Sweden)

    Amritpal Singh

    2015-06-01

    Full Text Available Abstract Everyday refinement and development of technology has led to an increase in the competition between the Tech companies and their going out of way to crack the system andbreak down. Thus providing Data mining a strategically and security-wise important area for many business organizations including banking sector. It allows the analyzes of important information in the data warehouse and assists the banks to look for obscure patterns in a group and discover unknown relationship in the data.Banking systems needs to process ample amount of data on daily basis related to customer information their credit card details limit and collateral details transaction details risk profiles Anti Money Laundering related information trade finance data. Thousands of decisionsbased on the related data are taken in a bank daily. This paper analyzes the banking dataset in the weka environment for the detection of interesting patterns based on its applications ofcustomer acquisition customer retention management and marketing and management of risk fraudulence detections.

  10. PHYSICS PERFORMANCE AND DATASET (PPD)

    CERN Multimedia

    L. Silvestris

    2013-01-01

    The PPD activities, in the first part of 2013, have been focused mostly on the final physics validation and preparation for the data reprocessing of the full 8 TeV datasets with the latest calibrations. These samples will be the basis for the preliminary results for summer 2013 but most importantly for the final publications on the 8 TeV Run 1 data. The reprocessing involves also the reconstruction of a significant fraction of “parked data” that will allow CMS to perform a whole new set of precision analyses and searches. In this way the CMSSW release 53X is becoming the legacy release for the 8 TeV Run 1 data. The regular operation activities have included taking care of the prolonged proton-proton data taking and the run with proton-lead collisions that ended in February. The DQM and Data Certification team has deployed a continuous effort to promptly certify the quality of the data. The luminosity-weighted certification efficiency (requiring all sub-detectors to be certified as usab...

  11. Systematic evaluation of a secondary method for measuring diagnostic-level medical ultrasound transducer output power based on a large-area pyroelectric sensor

    Science.gov (United States)

    Zeqiri, B.; Žauhar, G.; Rajagopal, S.; Pounder, A.

    2012-06-01

    A systematic study of the application of a novel pyroelectric technique to the measurement of diagnostic-level medical ultrasound output power is described. The method exploits the pyroelectric properties of a 0.028 mm thick membrane of polyvinylidene fluoride (PVDF), backed by an acoustic absorber whose ultrasonic absorption coefficient approaches 1000 dB cm-1 at 3 MHz. When exposed to an ultrasonic field, absorption of ultrasound adjacent to the PVDF-absorber interface results in heating and the generation of a pyroelectric output voltage across gold electrodes deposited on the membrane. For a sensor large enough to intercept the whole of the acoustic beam, the output voltage can be calibrated for the measurement of acoustic output power. A number of key performance properties of the method have been investigated. The technique is very sensitive, with a power to voltage conversion factor of typically 0.23 V W-1. The frequency response of a particular embodiment of the sensor in which acoustic power reflected at the absorber-PVDF interface is subsequently returned to the pyroelectric membrane to be absorbed, has been evaluated over the frequency range 1.5 MHz to 10 MHz. This has shown the frequency response to be flat to within ±4%, above 2.5 MHz. Below this frequency, the sensitivity falls by 20% at 1.5 MHz. Linearity of the technique has been demonstrated to within ±1.6% for applied acoustic power levels from 1 mW up to 120 mW. A number of other studies targeted at assessing the achievable measurement uncertainties are presented. These involve: the effects of soaking, the influence of the angle of incidence of the acoustic beam, measurement repeatability and sensitivity to transducer positioning. Additionally, over the range 20 °C to 30 °C, the rate of change in sensitivity with ambient temperature has been shown to be +0.5% °C-1. Implications of the work for the development of a sensitive, traceable, portable, secondary method of ultrasound output power

  12. A high-resolution European dataset for hydrologic modeling

    Science.gov (United States)

    Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

    2013-04-01

    There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as

  13. GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

    Science.gov (United States)

    Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

    2015-07-02

    A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

  14. An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures.

    Science.gov (United States)

    Meoni, Andrea; D'Alessandro, Antonella; Downey, Austin; García-Macías, Enrique; Rallini, Marco; Materazzi, A Luigi; Torre, Luigi; Laflamme, Simon; Castro-Triguero, Rafael; Ubertini, Filippo

    2018-03-09

    The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

  15. An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

    Directory of Open Access Journals (Sweden)

    Andrea Meoni

    2018-03-01

    Full Text Available The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs, and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

  16. Dataset of herbarium specimens of threatened vascular plants in Catalonia.

    Science.gov (United States)

    Nualart, Neus; Ibáñez, Neus; Luque, Pere; Pedrol, Joan; Vilar, Lluís; Guàrdia, Roser

    2017-01-01

    This data paper describes a specimens' dataset of the Catalonian threatened vascular plants conserved in five public Catalonian herbaria (BC, BCN, HGI, HBIL and MTTE). Catalonia is an administrative region of Spain that includes large autochthon plants diversity and 199 taxa with IUCN threatened categories (EX, EW, RE, CR, EN and VU). This dataset includes 1,618 records collected from 17 th century to nowadays. For each specimen, the species name, locality indication, collection date, collector, ecology and revision label are recorded. More than 94% of the taxa are represented in the herbaria, which evidence the paper of the botanical collections as an essential source of occurrence data.

  17. A review of continent scale hydrological datasets available for Africa

    OpenAIRE

    Bonsor, H.C.

    2010-01-01

    As rainfall becomes less reliable with predicted climate change the ability to assess the spatial and seasonal variations in groundwater availability on a large-scale (catchment and continent) is becoming increasingly important (Bates, et al. 2007; MacDonald et al. 2009). The scarcity of observed hydrological data, or difficulty in obtaining such data, within Africa means remotely sensed (RS) datasets must often be used to drive large-scale hydrological models. The different ap...

  18. The Geometry of Finite Equilibrium Datasets

    DEFF Research Database (Denmark)

    Balasko, Yves; Tvede, Mich

    We investigate the geometry of finite datasets defined by equilibrium prices, income distributions, and total resources. We show that the equilibrium condition imposes no restrictions if total resources are collinear, a property that is robust to small perturbations. We also show that the set...... of equilibrium datasets is pathconnected when the equilibrium condition does impose restrictions on datasets, as for example when total resources are widely non collinear....

  19. IPCC Socio-Economic Baseline Dataset

    Data.gov (United States)

    National Aeronautics and Space Administration — The Intergovernmental Panel on Climate Change (IPCC) Socio-Economic Baseline Dataset consists of population, human development, economic, water resources, land...

  20. Veterans Affairs Suicide Prevention Synthetic Dataset

    Data.gov (United States)

    Department of Veterans Affairs — The VA's Veteran Health Administration, in support of the Open Data Initiative, is providing the Veterans Affairs Suicide Prevention Synthetic Dataset (VASPSD). The...

  1. Nanoparticle-organic pollutant interaction dataset

    Data.gov (United States)

    U.S. Environmental Protection Agency — Dataset presents concentrations of organic pollutants, such as polyaromatic hydrocarbon compounds, in water samples. Water samples of known volume and concentration...

  2. An Annotated Dataset of 14 Meat Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille

    2002-01-01

    This note describes a dataset consisting of 14 annotated images of meat. Points of correspondence are placed on each image. As such, the dataset can be readily used for building statistical models of shape. Further, format specifications and terms of use are given.......This note describes a dataset consisting of 14 annotated images of meat. Points of correspondence are placed on each image. As such, the dataset can be readily used for building statistical models of shape. Further, format specifications and terms of use are given....

  3. The OXL format for the exchange of integrated datasets

    Directory of Open Access Journals (Sweden)

    Taubert Jan

    2007-12-01

    Full Text Available A prerequisite for systems biology is the integration and analysis of heterogeneous experimental data stored in hundreds of life-science databases and millions of scientific publications. Several standardised formats for the exchange of specific kinds of biological information exist. Such exchange languages facilitate the integration process; however they are not designed to transport integrated datasets. A format for exchanging integrated datasets needs to i cover data from a broad range of application domains, ii be flexible and extensible to combine many different complex data structures, iii include metadata and semantic definitions, iv include inferred information, v identify the original data source for integrated entities and vi transport large integrated datasets. Unfortunately, none of the exchange formats from the biological domain (e.g. BioPAX, MAGE-ML, PSI-MI, SBML or the generic approaches (RDF, OWL fulfil these requirements in a systematic way.

  4. Cross-Dataset Analysis and Visualization Driven by Expressive Web Services

    Science.gov (United States)

    Alexandru Dumitru, Mircea; Catalin Merticariu, Vlad

    2015-04-01

    The deluge of data that is hitting us every day from satellite and airborne sensors is changing the workflow of environmental data analysts and modelers. Web geo-services play now a fundamental role, and are no longer needed to preliminary download and store the data, but rather they interact in real-time with GIS applications. Due to the very large amount of data that is curated and made available by web services, it is crucial to deploy smart solutions for optimizing network bandwidth, reducing duplication of data and moving the processing closer to the data. In this context we have created a visualization application for analysis and cross-comparison of aerosol optical thickness datasets. The application aims to help researchers identify and visualize discrepancies between datasets coming from various sources, having different spatial and time resolutions. It also acts as a proof of concept for integration of OGC Web Services under a user-friendly interface that provides beautiful visualizations of the explored data. The tool was built on top of the World Wind engine, a Java based virtual globe built by NASA and the open source community. For data retrieval and processing we exploited the OGC Web Coverage Service potential: the most exciting aspect being its processing extension, a.k.a. the OGC Web Coverage Processing Service (WCPS) standard. A WCPS-compliant service allows a client to execute a processing query on any coverage offered by the server. By exploiting a full grammar, several different kinds of information can be retrieved from one or more datasets together: scalar condensers, cross-sectional profiles, comparison maps and plots, etc. This combination of technology made the application versatile and portable. As the processing is done on the server-side, we ensured that the minimal amount of data is transferred and that the processing is done on a fully-capable server, leaving the client hardware resources to be used for rendering the visualization

  5. The LANDFIRE Refresh strategy: updating the national dataset

    Science.gov (United States)

    Nelson, Kurtis J.; Connot, Joel A.; Peterson, Birgit E.; Martin, Charley

    2013-01-01

    The LANDFIRE Program provides comprehensive vegetation and fuel datasets for the entire United States. As with many large-scale ecological datasets, vegetation and landscape conditions must be updated periodically to account for disturbances, growth, and natural succession. The LANDFIRE Refresh effort was the first attempt to consistently update these products nationwide. It incorporated a combination of specific systematic improvements to the original LANDFIRE National data, remote sensing based disturbance detection methods, field collected disturbance information, vegetation growth and succession modeling, and vegetation transition processes. This resulted in the creation of two complete datasets for all 50 states: LANDFIRE Refresh 2001, which includes the systematic improvements, and LANDFIRE Refresh 2008, which includes the disturbance and succession updates to the vegetation and fuel data. The new datasets are comparable for studying landscape changes in vegetation type and structure over a decadal period, and provide the most recent characterization of fuel conditions across the country. The applicability of the new layers is discussed and the effects of using the new fuel datasets are demonstrated through a fire behavior modeling exercise using the 2011 Wallow Fire in eastern Arizona as an example.

  6. Process mining in oncology using the MIMIC-III dataset

    Science.gov (United States)

    Prima Kurniati, Angelina; Hall, Geoff; Hogg, David; Johnson, Owen

    2018-03-01

    Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research.

  7. Comparison of CORA and EN4 in-situ datasets validation methods, toward a better quality merged dataset.

    Science.gov (United States)

    Szekely, Tanguy; Killick, Rachel; Gourrion, Jerome; Reverdin, Gilles

    2017-04-01

    CORA and EN4 are both global delayed time mode validated in-situ ocean temperature and salinity datasets distributed by the Met Office (http://www.metoffice.gov.uk/) and Copernicus (www.marine.copernicus.eu). A large part of the profiles distributed by CORA and EN4 in recent years are Argo profiles from the ARGO DAC, but profiles are also extracted from the World Ocean Database and TESAC profiles from GTSPP. In the case of CORA, data coming from the EUROGOOS Regional operationnal oserving system( ROOS) operated by European institutes no managed by National Data Centres and other datasets of profiles povided by scientific sources can also be found (Sea mammals profiles from MEOP, XBT datasets from cruises ...). (EN4 also takes data from the ASBO dataset to supplement observations in the Arctic). First advantage of this new merge product is to enhance the space and time coverage at global and european scales for the period covering 1950 till a year before the current year. This product is updated once a year and T&S gridded fields are alos generated for the period 1990-year n-1. The enhancement compared to the revious CORA product will be presented Despite the fact that the profiles distributed by both datasets are mostly the same, the quality control procedures developed by the Met Office and Copernicus teams differ, sometimes leading to different quality control flags for the same profile. Started in 2016 a new study started that aims to compare both validation procedures to move towards a Copernicus Marine Service dataset with the best features of CORA and EN4 validation.A reference data set composed of the full set of in-situ temperature and salinity measurements collected by Coriolis during 2015 is used. These measurements have been made thanks to wide range of instruments (XBTs, CTDs, Argo floats, Instrumented sea mammals,...), covering the global ocean. The reference dataset has been validated simultaneously by both teams.An exhaustive comparison of the

  8. Principal Component Analysis of Process Datasets with Missing Values

    Directory of Open Access Journals (Sweden)

    Kristen A. Severson

    2017-07-01

    Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.

  9. Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

    Science.gov (United States)

    Ali, Abdulbaset; Hu, Bing; Ramahi, Omar

    2015-05-15

    This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

  10. ASSISTments Dataset from Multiple Randomized Controlled Experiments

    Science.gov (United States)

    Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil

    2016-01-01

    In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…

  11. Synthetic and Empirical Capsicum Annuum Image Dataset

    NARCIS (Netherlands)

    Barth, R.

    2016-01-01

    This dataset consists of per-pixel annotated synthetic (10500) and empirical images (50) of Capsicum annuum, also known as sweet or bell pepper, situated in a commercial greenhouse. Furthermore, the source models to generate the synthetic images are included. The aim of the datasets are to

  12. Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

    Science.gov (United States)

    Yurtman, Aras; Barshan, Billur

    2017-08-09

    Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

  13. Novel Battery Management System with Distributed Wireless and Fiber Optic Sensors for Early Detection and Suppression of Thermal Runaway in Large Battery Packs, FY13 Q4 Report, ARPA-E Program: Advanced Management Protection of Energy Storage Devices (AMPE

    Energy Technology Data Exchange (ETDEWEB)

    Farmer, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chang, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zumstein, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kovotsky, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Puglia, F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Dobley, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Moore, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Osswald, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Wolf, K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kaschmitter, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Eaves, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-10-08

    Technology has been developed that enables monitoring of individual cells in highcapacity lithium-ion battery packs, with a distributed array of wireless Bluetooth 4.0 tags and sensors, and without proliferation of extensive wiring harnesses. Given the safety challenges facing lithium-ion batteries in electric vehicle, civilian aviation and defense applications, these wireless sensors may be particularly important to these emerging markets. These wireless sensors will enhance the performance, reliability and safety of such energy storage systems. Specific accomplishments to date include, but are not limited to: (1) the development of wireless tags using Bluetooth 4.0 standard to monitor a large array of sensors in battery pack; (2) sensor suites enabling the simultaneous monitoring of cell voltage, cell current, cell temperature, and package strain, indicative of swelling and increased internal pressure, (3) small receivers compatible with USB ports on portable computers; (4) software drivers and logging software; (5) a 7S2P battery simulator, enabling the safe development of wireless BMS hardware in the laboratory; (6) demonstrated data transmission out of metal enclosures, including battery box, with small variable aperture opening; (7) test data demonstrating the accurate and reliable operation of sensors, with transmission of terminal voltage, cell temperature and package strain at distances up to 110 feet; (8) quantification of the data transmission error as a function of distance, in both indoor and outdoor operation; (9) electromagnetic interference testing during operation with live, high-capacity battery management system at Yardney Technical Products; (10) demonstrated operation with live high-capacity lithium-ion battery pack during charge-discharge cycling; (11) development of special polymer-gel lithium-ion batteries with embedded temperature sensors, capable of measuring the core temperature of individual of the cells during charge-discharge cycling

  14. Design of an audio advertisement dataset

    Science.gov (United States)

    Fu, Yutao; Liu, Jihong; Zhang, Qi; Geng, Yuting

    2015-12-01

    Since more and more advertisements swarm into radios, it is necessary to establish an audio advertising dataset which could be used to analyze and classify the advertisement. A method of how to establish a complete audio advertising dataset is presented in this paper. The dataset is divided into four different kinds of advertisements. Each advertisement's sample is given in *.wav file format, and annotated with a txt file which contains its file name, sampling frequency, channel number, broadcasting time and its class. The classifying rationality of the advertisements in this dataset is proved by clustering the different advertisements based on Principal Component Analysis (PCA). The experimental results show that this audio advertisement dataset offers a reliable set of samples for correlative audio advertisement experimental studies.

  15. A dataset of human decision-making in teamwork management

    Science.gov (United States)

    Yu, Han; Shen, Zhiqi; Miao, Chunyan; Leung, Cyril; Chen, Yiqiang; Fauvel, Simon; Lin, Jun; Cui, Lizhen; Pan, Zhengxiang; Yang, Qiang

    2017-01-01

    Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members' capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.

  16. Taste sensor; Mikaku sensor

    Energy Technology Data Exchange (ETDEWEB)

    Toko, K. [Kyushu University, Fukuoka (Japan)

    1998-03-05

    This paper introduces a taste sensor having a lipid/polymer membrane to work as a receptor of taste substances. The paper describes the following matters: this sensor uses a hollow polyvinyl chloride rod filled with KCl aqueous solution, and placed with silver and silver chloride wires, whose cross section is affixed with a lipid/polymer membrane as a lipid membrane electrode to identify taste from seven or eight kinds of response patterns of electric potential output from the lipid/polymer membrane; measurements of different substances presenting acidic taste, salty taste, bitter taste, sweet taste and flavor by using this sensor identified clearly each taste (similar response is shown to a similar taste even if the substances are different); different responses are indicated on different brands of beers; from the result of measuring a great variety of mineral waters, a possibility was suggested that this taste sensor could be used for water quality monitoring sensors; and application of this taste sensor may be expected as a maturation control sensor for Japanese sake (wine) and miso (bean paste) manufacturing. 2 figs., 1 tab.

  17. Quartz enhanced photoacoustic H{sub 2}S gas sensor based on a fiber-amplifier source and a custom tuning fork with large prong spacing

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Hongpeng; Liu, Xiaoli; Zheng, Huadan; Yin, Xukun; Ma, Weiguang; Zhang, Lei; Yin, Wangbao; Jia, Suotang [State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan 030006 (China); Sampaolo, Angelo [Dipartimento Interateneo di Fisica, Università degli Studi di Bari and Politecnico di Bari, CNR-IFN UOS BARI, Via Amendola 173, Bari 70126 (Italy); Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005 (United States); Dong, Lei, E-mail: donglei@sxu.edu.cn [State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan 030006 (China); Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005 (United States); Patimisco, Pietro; Spagnolo, Vincenzo [Dipartimento Interateneo di Fisica, Università degli Studi di Bari and Politecnico di Bari, CNR-IFN UOS BARI, Via Amendola 173, Bari 70126 (Italy); Tittel, Frank K. [Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005 (United States)

    2015-09-14

    A quartz enhanced photoacoustic spectroscopy (QEPAS) sensor, employing an erbium-doped fiber amplified laser source and a custom quartz tuning fork (QTF) with its two prongs spaced ∼800 μm apart, is reported. The sensor employs an acoustic micro-resonator (AmR) which is assembled in an “on-beam” QEPAS configuration. Both length and vertical position of the AmR are optimized in terms of signal-to-noise ratio, significantly improving the QEPAS detection sensitivity by a factor of ∼40, compared to the case of a sensor using a bare custom QTF. The fiber-amplifier-enhanced QEPAS sensor is applied to H{sub 2}S trace gas detection, reaching a sensitivity of ∼890 ppb at 1 s integration time, similar to those obtained with a power-enhanced QEPAS sensor equipped with a standard QTF, but with the advantages of easy optical alignment, simple installation, and long-term stability.

  18. ASSESSING SMALL SAMPLE WAR-GAMING DATASETS

    Directory of Open Access Journals (Sweden)

    W. J. HURLEY

    2013-10-01

    Full Text Available One of the fundamental problems faced by military planners is the assessment of changes to force structure. An example is whether to replace an existing capability with an enhanced system. This can be done directly with a comparison of measures such as accuracy, lethality, survivability, etc. However this approach does not allow an assessment of the force multiplier effects of the proposed change. To gauge these effects, planners often turn to war-gaming. For many war-gaming experiments, it is expensive, both in terms of time and dollars, to generate a large number of sample observations. This puts a premium on the statistical methodology used to examine these small datasets. In this paper we compare the power of three tests to assess population differences: the Wald-Wolfowitz test, the Mann-Whitney U test, and re-sampling. We employ a series of Monte Carlo simulation experiments. Not unexpectedly, we find that the Mann-Whitney test performs better than the Wald-Wolfowitz test. Resampling is judged to perform slightly better than the Mann-Whitney test.

  19. Virtual Sensor Test Instrumentation

    Science.gov (United States)

    Wang, Roy

    2011-01-01

    functions. The sensor data is processed in a distributed fashion across the network, providing a large pool of resources in real time to meet stringent latency requirements.

  20. Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management

    OpenAIRE

    Morrison, James J.; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L.

    2014-01-01

    Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was conve...

  1. PHYSICS PERFORMANCE AND DATASET (PPD)

    CERN Multimedia

    L. Silvestris

    2012-01-01

      Introduction Since July, the activities have been focused on very diverse subjects: operations activities for the 2012 data-taking, Monte Carlo production and data re-processing plans for 2013 conferences (winter and summer), preparation for the Upgrades TDRs and readiness after LS1. The regular operations activities have included: changing to the 53X release at the Tier-0, regular calibrations updates, and data certification to guarantee certified data for analysis with the shortest delay from data taking. The samples, simulated at 8 TeV, have been re-reconstructed using 53X. A lot of effort has been put in their prioritisation to ensure that the samples needed for HCP and future conferences are produced on time. Given the large amount of data that have been collected in 2012 and the available computing resources, a careful planning is needed. The PPD and Physics groups worked on a master schedule for the Monte Carlo production, new conditions validation and data reprocessing. The ...

  2. The Kinetics Human Action Video Dataset

    OpenAIRE

    Kay, Will; Carreira, Joao; Simonyan, Karen; Zhang, Brian; Hillier, Chloe; Vijayanarasimhan, Sudheendra; Viola, Fabio; Green, Tim; Back, Trevor; Natsev, Paul; Suleyman, Mustafa; Zisserman, Andrew

    2017-01-01

    We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some ...

  3. A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

    KAUST Repository

    Nassar, Joanna M.

    2017-02-07

    We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

  4. A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

    KAUST Repository

    Nassar, Joanna M.; Sevilla, Galo T.; Velling, Seneca J.; Cordero, Marlon D.; Hussain, Muhammad Mustafa

    2017-01-01

    We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

  5. Digital Sensor Technology

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, Ken D. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Quinn, Edward L. [Technology Resources, Dana Point, CA (United States); Mauck, Jerry L. [Technology Resources, Dana Point, CA (United States); Bockhorst, Richard M. [Technology Resources, Dana Point, CA (United States)

    2015-02-01

    The nuclear industry has been slow to incorporate digital sensor technology into nuclear plant designs due to concerns with digital qualification issues. However, the benefits of digital sensor technology for nuclear plant instrumentation are substantial in terms of accuracy and reliability. This paper, which refers to a final report issued in 2013, demonstrates these benefits in direct comparisons of digital and analog sensor applications. Improved accuracy results from the superior operating characteristics of digital sensors. These include improvements in sensor accuracy and drift and other related parameters which reduce total loop uncertainty and thereby increase safety and operating margins. An example instrument loop uncertainty calculation for a pressure sensor application is presented to illustrate these improvements. This is a side-by-side comparison of the instrument loop uncertainty for both an analog and a digital sensor in the same pressure measurement application. Similarly, improved sensor reliability is illustrated with a sample calculation for determining the probability of failure on demand, an industry standard reliability measure. This looks at equivalent analog and digital temperature sensors to draw the comparison. The results confirm substantial reliability improvement with the digital sensor, due in large part to ability to continuously monitor the health of a digital sensor such that problems can be immediately identified and corrected. This greatly reduces the likelihood of a latent failure condition of the sensor at the time of a design basis event. Notwithstanding the benefits of digital sensors, there are certain qualification issues that are inherent with digital technology and these are described in the report. One major qualification impediment for digital sensor implementation is software common cause failure (SCCF).

  6. BASE MAP DATASET, LOS ANGELES COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  7. BASE MAP DATASET, CHEROKEE COUNTY, SOUTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  8. SIAM 2007 Text Mining Competition dataset

    Data.gov (United States)

    National Aeronautics and Space Administration — Subject Area: Text Mining Description: This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining...

  9. Harvard Aging Brain Study : Dataset and accessibility

    NARCIS (Netherlands)

    Dagley, Alexander; LaPoint, Molly; Huijbers, Willem; Hedden, Trey; McLaren, Donald G.; Chatwal, Jasmeer P.; Papp, Kathryn V.; Amariglio, Rebecca E.; Blacker, Deborah; Rentz, Dorene M.; Johnson, Keith A.; Sperling, Reisa A.; Schultz, Aaron P.

    2017-01-01

    The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging.

  10. BASE MAP DATASET, HONOLULU COUNTY, HAWAII, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  11. BASE MAP DATASET, EDGEFIELD COUNTY, SOUTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  12. Environmental Dataset Gateway (EDG) REST Interface

    Data.gov (United States)

    U.S. Environmental Protection Agency — Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other...

  13. BASE MAP DATASET, INYO COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  14. BASE MAP DATASET, JACKSON COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  15. BASE MAP DATASET, SANTA CRIZ COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  16. Climate Prediction Center IR 4km Dataset

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — CPC IR 4km dataset was created from all available individual geostationary satellite data which have been merged to form nearly seamless global (60N-60S) IR...

  17. BASE MAP DATASET, MAYES COUNTY, OKLAHOMA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications: cadastral, geodetic control,...

  18. BASE MAP DATASET, KINGFISHER COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme,...

  19. Comparison of recent SnIa datasets

    International Nuclear Information System (INIS)

    Sanchez, J.C. Bueno; Perivolaropoulos, L.; Nesseris, S.

    2009-01-01

    We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization w(a) = w 0 +w 1 (1−a), according to their Figure of Merit (FoM), their consistency with the cosmological constant (ΛCDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with ΛCDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy (w 0 ,w 1 ) = (−1.4,2) crossing the phantom divide line w = −1 (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample

  20. The Ringcore Fluxgate Sensor

    DEFF Research Database (Denmark)

    Brauer, Peter

    1997-01-01

    A model describing the fundamental working principle of the "ringcore fluxgate sensor" is derived. The model is solely based on geometrical and measurable magnetic properties of the sensor and from this a number of fluxgate phenomenon can be described and estimated. The sensitivity of ringcore...... fluxgate sensors is measured for a large variety of geometries and is for all measurements found to fall between two limits obtained by the fluxgate model. The model is used to explain the zero field odd harmonic output of the fluxgate sensor, called the "feedthrough". By assuming a non ideal sensor...... with spatially distributed magnetization, the model predicts feedthrough signals which exactly reflects the measured signals. The non-linearities in a feedback compensated ringcore fluxgate sensors, called the "transverse field effect", can also be explained by the model. Measurements on stress annealed...

  1. Flexible magnetoimpedance sensor

    KAUST Repository

    Li, Bodong

    2015-03-01

    Flexible magnetoimpedance (MI) sensors fabricated using a NiFe/Cu/NiFe tri-layer on Kapton substrate have been studied. A customized flexible microstrip transmission line was employed to investigate the MI sensors\\'s magnetic field and frequency responses and their dependence on the sensors\\'s deflection. For the first time, the impedance characteristic is obtained through reflection coefficient analysis over a wide range of frequencies from 0.1 MHz to 3 GHz and for deflections ranging from zero curvature to a radius of 7.2 cm. The sensor element maintains a high MI ratio of up to 90% and magnetic sensitivity of up to 9.2%/Oe over different bending curvatures. The relationship between the curvature and material composition is discussed based on the magnetostriction effect and stress simulations. The sensor\\'s large frequency range, simple fabrication process and high sensitivity provide a great potential for flexible electronics and wireless applications.

  2. Resolution testing and limitations of geodetic and tsunami datasets for finite fault inversions along subduction zones

    Science.gov (United States)

    Williamson, A.; Newman, A. V.

    2017-12-01

    Finite fault inversions utilizing multiple datasets have become commonplace for large earthquakes pending data availability. The mixture of geodetic datasets such as Global Navigational Satellite Systems (GNSS) and InSAR, seismic waveforms, and when applicable, tsunami waveforms from Deep-Ocean Assessment and Reporting of Tsunami (DART) gauges, provide slightly different observations that when incorporated together lead to a more robust model of fault slip distribution. The merging of different datasets is of particular importance along subduction zones where direct observations of seafloor deformation over the rupture area are extremely limited. Instead, instrumentation measures related ground motion from tens to hundreds of kilometers away. The distance from the event and dataset type can lead to a variable degree of resolution, affecting the ability to accurately model the spatial distribution of slip. This study analyzes the spatial resolution attained individually from geodetic and tsunami datasets as well as in a combined dataset. We constrain the importance of distance between estimated parameters and observed data and how that varies between land-based and open ocean datasets. Analysis focuses on accurately scaled subduction zone synthetic models as well as analysis of the relationship between slip and data in recent large subduction zone earthquakes. This study shows that seafloor deformation sensitive datasets, like open-ocean tsunami waveforms or seafloor geodetic instrumentation, can provide unique offshore resolution for understanding most large and particularly tsunamigenic megathrust earthquake activity. In most environments, we simply lack the capability to resolve static displacements using land-based geodetic observations.

  3. An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

    Science.gov (United States)

    Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

    2015-10-21

    The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

  4. CAMEX-4 DC-8 NEVZOROV TOTAL CONDENSED WATER CONTENT SENSOR V1

    Data.gov (United States)

    National Aeronautics and Space Administration — The CAMEX-4 DC-8 Nevzorov Total Condensed Water Content Sensor dataset was collected by the Nevzorov total condensed water content sensor which was used to measure...

  5. Compliant Tactile Sensors

    Science.gov (United States)

    Torres-Jara, Eduardo R.

    2011-01-01

    Tactile sensors are currently being designed to sense interactions with human hands or pen-like interfaces. They are generally embedded in screens, keyboards, mousepads, and pushbuttons. However, they are not well fitted to sense interactions with all kinds of objects. A novel sensor was originally designed to investigate robotics manipulation where not only the contact with an object needs to be detected, but also where the object needs to be held and manipulated. This tactile sensor has been designed with features that allow it to sense a large variety of objects in human environments. The sensor is capable of detecting forces coming from any direction. As a result, this sensor delivers a force vector with three components. In contrast to most of the tactile sensors that are flat, this one sticks out from the surface so that it is likely to come in contact with objects. The sensor conforms to the object with which it interacts. This augments the contact's surface, consequently reducing the stress applied to the object. This feature makes the sensor ideal for grabbing objects and other applications that require compliance with objects. The operational range of the sensor allows it to operate well with objects found in peoples' daily life. The fabrication of this sensor is simple and inexpensive because of its compact mechanical configuration and reduced electronics. These features are convenient for mass production of individual sensors as well as dense arrays. The biologically inspired tactile sensor is sensitive to both normal and lateral forces, providing better feedback to the host robot about the object to be grabbed. It has a high sensitivity, enabling its use in manipulation fingers, which typically have low mechanical impedance in order to be very compliant. The construction of the sensor is simple, using inexpensive technologies like silicon rubber molding and standard stock electronics.

  6. Sensor for volatile organic compounds using an interdigitated gold electrode modified with a nanocomposite made from poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) and ultra-large graphene oxide

    International Nuclear Information System (INIS)

    Hasani, Amirhossein; Salehi, Alireza; Dehsari, Hamed Sharifi; Gavgani, Jaber Nasrollah; Shalamzari, Elham Khodabakhshi; Taromi, Farmarz Afshar; Mahyari, Mojtaba

    2015-01-01

    A highly efficient gas sensor is described based on the use of a nanocomposite fabricated from poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) and ultra-large graphene oxide (UL-GO). The nanocomposite was placed by drop casting in high uniformity on interdigitated gold electrodes over a large area of silicon substrate and investigated for its response to volatile organic compounds (VOCs) at room temperature. Monolayers of UL-GOs were synthesized based on a novel solution-phase method involving pre-exfoliation of graphite flakes. The nanocomposite was optimized in terms of composition, and the resulting vapor sensor (containing 0.04 wt% of UL-GO) exhibits strong response to various VOC vapors. The improved gas-sensing performance is attributed to several effects, viz. (a) an enhanced transport of charge carriers, probably a result of the weakening of columbic attraction between PEDOT and PSS by the functional groups on the UL-GO sheets; (b) the increase in the specific surface area on adding UL-GO sheets; and (c) enhanced interactions between the sensing film and VOC molecules via the network of π-electrons. The sensitivity, response and recovery times of the PEDOT-PSS/UL-GO nanocomposite gas sensor with 0.04 wt% of UL-GO are 11.3 %, 3.2 s, and 16 s, respectively. At a methanol vapor concentration as low as 35 ppm, this is an improvement by factors of 110, 10, and 6 respectively, compared to a PEDOT-PSS reference gas sensor without UL-GO. (author)

  7. Evaluation of Application Space Expansion for the Sensor Fish

    Energy Technology Data Exchange (ETDEWEB)

    DeRolph, Christopher R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bevelhimer, Mark S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-02-01

    The Pacific Northwest National Laboratory has developed an instrument known as the sensor fish that can be released into downstream passage routes at hydropower facilities to collect data on the physical conditions that a fish might be exposed to during passage through a turbine. The US Department of Energy Wind and Water Power Program sees value in expanding the sensor fish application space beyond large Kaplan turbines in the northwest United States to evaluate conditions to which a greater variety of fish species are exposed. Development of fish-friendly turbines requires an understanding of both physical passage conditions and biological responses to those conditions. Expanding the use of sensor fish into other application spaces will add to the knowledge base of physical passage conditions and could also enhance the use of sensor fish as a site-specific tool in mitigating potential impacts to fish populations from hydropower. The Oak Ridge National Laboratory (ORNL) National Hydropower Assessment Program (NHAAP) database contains hydropower facility characteristics that, along with national fish distribution data, were used to evaluate potential interactions between fish species and project characteristics related to downstream passage issues. ORNL developed rankings for the turbine types in the NHAAP database in terms of their potential to impact fish through injury or mortality during downstream turbine passage. National-scale fish distributions for 31 key migratory species were spatially intersected with hydropower plant locations to identify facilities where turbines with a high threat to fish injury or mortality overlap with the potential range of a sensitive fish species. A dataset was produced that identifies hydropower facilities where deployment of the sensor fish technology might be beneficial in addressing issues related to downstream fish passage. The dataset can be queried to target specific geographic regions, fish species, license expiration

  8. Ambient Sensors

    NARCIS (Netherlands)

    Börner, Dirk; Specht, Marcus

    2014-01-01

    This software sketches comprise two custom-built ambient sensors, i.e. a noise and a movement sensor. Both sensors measure an ambient value and process the values to a color gradient (green > yellow > red). The sensors were built using the Processing 1.5.1 development environment. Available under

  9. On sample size and different interpretations of snow stability datasets

    Science.gov (United States)

    Schirmer, M.; Mitterer, C.; Schweizer, J.

    2009-04-01

    Interpretations of snow stability variations need an assessment of the stability itself, independent of the scale investigated in the study. Studies on stability variations at a regional scale have often chosen stability tests such as the Rutschblock test or combinations of various tests in order to detect differences in aspect and elevation. The question arose: ‘how capable are such stability interpretations in drawing conclusions'. There are at least three possible errors sources: (i) the variance of the stability test itself; (ii) the stability variance at an underlying slope scale, and (iii) that the stability interpretation might not be directly related to the probability of skier triggering. Various stability interpretations have been proposed in the past that provide partly different results. We compared a subjective one based on expert knowledge with a more objective one based on a measure derived from comparing skier-triggered slopes vs. slopes that have been skied but not triggered. In this study, the uncertainties are discussed and their effects on regional scale stability variations will be quantified in a pragmatic way. An existing dataset with very large sample sizes was revisited. This dataset contained the variance of stability at a regional scale for several situations. The stability in this dataset was determined using the subjective interpretation scheme based on expert knowledge. The question to be answered was how many measurements were needed to obtain similar results (mainly stability differences in aspect or elevation) as with the complete dataset. The optimal sample size was obtained in several ways: (i) assuming a nominal data scale the sample size was determined with a given test, significance level and power, and by calculating the mean and standard deviation of the complete dataset. With this method it can also be determined if the complete dataset consists of an appropriate sample size. (ii) Smaller subsets were created with similar

  10. Semantically-Enabled Sensor Plug & Play for the Sensor Web

    Science.gov (United States)

    Bröring, Arne; Maúe, Patrick; Janowicz, Krzysztof; Nüst, Daniel; Malewski, Christian

    2011-01-01

    Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research. PMID:22164033

  11. New public dataset for spotting patterns in medieval document images

    Science.gov (United States)

    En, Sovann; Nicolas, Stéphane; Petitjean, Caroline; Jurie, Frédéric; Heutte, Laurent

    2017-01-01

    With advances in technology, a large part of our cultural heritage is becoming digitally available. In particular, in the field of historical document image analysis, there is now a growing need for indexing and data mining tools, thus allowing us to spot and retrieve the occurrences of an object of interest, called a pattern, in a large database of document images. Patterns may present some variability in terms of color, shape, or context, making the spotting of patterns a challenging task. Pattern spotting is a relatively new field of research, still hampered by the lack of available annotated resources. We present a new publicly available dataset named DocExplore dedicated to spotting patterns in historical document images. The dataset contains 1500 images and 1464 queries, and allows the evaluation of two tasks: image retrieval and pattern localization. A standardized benchmark protocol along with ad hoc metrics is provided for a fair comparison of the submitted approaches. We also provide some first results obtained with our baseline system on this new dataset, which show that there is room for improvement and that should encourage researchers of the document image analysis community to design new systems and submit improved results.

  12. A New Dataset Size Reduction Approach for PCA-Based Classification in OCR Application

    Directory of Open Access Journals (Sweden)

    Mohammad Amin Shayegan

    2014-01-01

    Full Text Available A major problem of pattern recognition systems is due to the large volume of training datasets including duplicate and similar training samples. In order to overcome this problem, some dataset size reduction and also dimensionality reduction techniques have been introduced. The algorithms presently used for dataset size reduction usually remove samples near to the centers of classes or support vector samples between different classes. However, the samples near to a class center include valuable information about the class characteristics and the support vector is important for evaluating system efficiency. This paper reports on the use of Modified Frequency Diagram technique for dataset size reduction. In this new proposed technique, a training dataset is rearranged and then sieved. The sieved training dataset along with automatic feature extraction/selection operation using Principal Component Analysis is used in an OCR application. The experimental results obtained when using the proposed system on one of the biggest handwritten Farsi/Arabic numeral standard OCR datasets, Hoda, show about 97% accuracy in the recognition rate. The recognition speed increased by 2.28 times, while the accuracy decreased only by 0.7%, when a sieved version of the dataset, which is only as half as the size of the initial training dataset, was used.

  13. Animated analysis of geoscientific datasets: An interactive graphical application

    Science.gov (United States)

    Morse, Peter; Reading, Anya; Lueg, Christopher

    2017-12-01

    Geoscientists are required to analyze and draw conclusions from increasingly large volumes of data. There is a need to recognise and characterise features and changing patterns of Earth observables within such large datasets. It is also necessary to identify significant subsets of the data for more detailed analysis. We present an innovative, interactive software tool and workflow to visualise, characterise, sample and tag large geoscientific datasets from both local and cloud-based repositories. It uses an animated interface and human-computer interaction to utilise the capacity of human expert observers to identify features via enhanced visual analytics. 'Tagger' enables users to analyze datasets that are too large in volume to be drawn legibly on a reasonable number of single static plots. Users interact with the moving graphical display, tagging data ranges of interest for subsequent attention. The tool provides a rapid pre-pass process using fast GPU-based OpenGL graphics and data-handling and is coded in the Quartz Composer visual programing language (VPL) on Mac OSX. It makes use of interoperable data formats, and cloud-based (or local) data storage and compute. In a case study, Tagger was used to characterise a decade (2000-2009) of data recorded by the Cape Sorell Waverider Buoy, located approximately 10 km off the west coast of Tasmania, Australia. These data serve as a proxy for the understanding of Southern Ocean storminess, which has both local and global implications. This example shows use of the tool to identify and characterise 4 different types of storm and non-storm events during this time. Events characterised in this way are compared with conventional analysis, noting advantages and limitations of data analysis using animation and human interaction. Tagger provides a new ability to make use of humans as feature detectors in computer-based analysis of large-volume geosciences and other data.

  14. Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.

    2014-10-01

    We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

  15. A multimodal MRI dataset of professional chess players.

    Science.gov (United States)

    Li, Kaiming; Jiang, Jing; Qiu, Lihua; Yang, Xun; Huang, Xiaoqi; Lui, Su; Gong, Qiyong

    2015-01-01

    Chess is a good model to study high-level human brain functions such as spatial cognition, memory, planning, learning and problem solving. Recent studies have demonstrated that non-invasive MRI techniques are valuable for researchers to investigate the underlying neural mechanism of playing chess. For professional chess players (e.g., chess grand masters and masters or GM/Ms), what are the structural and functional alterations due to long-term professional practice, and how these alterations relate to behavior, are largely veiled. Here, we report a multimodal MRI dataset from 29 professional Chinese chess players (most of whom are GM/Ms), and 29 age matched novices. We hope that this dataset will provide researchers with new materials to further explore high-level human brain functions.

  16. Automatic registration method for multisensor datasets adopted for dimensional measurements on cutting tools

    International Nuclear Information System (INIS)

    Shaw, L; Mehari, F; Weckenmann, A; Ettl, S; Häusler, G

    2013-01-01

    Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece. (paper)

  17. An organic dye with very large Stokes-shift and broad tunability of fluorescence: Potential two-photon probe for bioimaging and ultra-sensitive solid-state gas sensor

    Energy Technology Data Exchange (ETDEWEB)

    He, Tingchao; Tian, Xiaoqing; Lin, Xiaodong, E-mail: linxd@szu.edu.cn, E-mail: hdsun@ntu.edu.sg [College of Physics Science and Technology, Shenzhen University, Shenzhen 518060 (China); Wang, Yue; Zhao, Xin; Sun, Handong, E-mail: linxd@szu.edu.cn, E-mail: hdsun@ntu.edu.sg [Division of Physics and Applied Physics, and Centre for Disruptive Photonic Technologies (CDPT), School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371 (Singapore); Gao, Yang; Grimsdale, Andrew C. [School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798 (Singapore)

    2016-01-04

    Light-emitting nonlinear optical molecules, especially those with large Stokes shifts and broad tunability of their emission wavelength, have attracted considerable attention for various applications including biomedical imaging and fluorescent sensors. However, most fluorescent chromophores have only limited potential for such applications due to small Stokes shifts, narrow tunability of fluorescence emissions, and small optical nonlinearity in highly polar solvents. In this work, we demonstrate that a two-photon absorbing stilbene chromophore exhibits a large two-photon absorption action cross-section (ηδ = 320 GM) in dimethylsulfoxide (DMSO) and shows broad fluorescence tunability (125 nm) by manipulating the polarity of the surrounding medium. Importantly, a very large Stokes shift of up to 227 nm is achieved in DMSO. Thanks to these features, this chromophore can be utilized as a two-photon probe for bioimaging applications and in an ultrasensitive solid-state gas detector.

  18. 3DSEM: A 3D microscopy dataset

    Directory of Open Access Journals (Sweden)

    Ahmad P. Tafti

    2016-03-01

    Full Text Available The Scanning Electron Microscope (SEM as a 2D imaging instrument has been widely used in many scientific disciplines including biological, mechanical, and materials sciences to determine the surface attributes of microscopic objects. However the SEM micrographs still remain 2D images. To effectively measure and visualize the surface properties, we need to truly restore the 3D shape model from 2D SEM images. Having 3D surfaces would provide anatomic shape of micro-samples which allows for quantitative measurements and informative visualization of the specimens being investigated. The 3DSEM is a dataset for 3D microscopy vision which is freely available at [1] for any academic, educational, and research purposes. The dataset includes both 2D images and 3D reconstructed surfaces of several real microscopic samples. Keywords: 3D microscopy dataset, 3D microscopy vision, 3D SEM surface reconstruction, Scanning Electron Microscope (SEM

  19. Data Mining for Imbalanced Datasets: An Overview

    Science.gov (United States)

    Chawla, Nitesh V.

    A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.

  20. Genomics dataset of unidentified disclosed isolates

    Directory of Open Access Journals (Sweden)

    Bhagwan N. Rekadwad

    2016-09-01

    Full Text Available Analysis of DNA sequences is necessary for higher hierarchical classification of the organisms. It gives clues about the characteristics of organisms and their taxonomic position. This dataset is chosen to find complexities in the unidentified DNA in the disclosed patents. A total of 17 unidentified DNA sequences were thoroughly analyzed. The quick response codes were generated. AT/GC content of the DNA sequences analysis was carried out. The QR is helpful for quick identification of isolates. AT/GC content is helpful for studying their stability at different temperatures. Additionally, a dataset on cleavage code and enzyme code studied under the restriction digestion study, which helpful for performing studies using short DNA sequences was reported. The dataset disclosed here is the new revelatory data for exploration of unique DNA sequences for evaluation, identification, comparison and analysis. Keywords: BioLABs, Blunt ends, Genomics, NEB cutter, Restriction digestion, Short DNA sequences, Sticky ends

  1. Soil chemistry in lithologically diverse datasets: the quartz dilution effect

    Science.gov (United States)

    Bern, Carleton R.

    2009-01-01

    National- and continental-scale soil geochemical datasets are likely to move our understanding of broad soil geochemistry patterns forward significantly. Patterns of chemistry and mineralogy delineated from these datasets are strongly influenced by the composition of the soil parent material, which itself is largely a function of lithology and particle size sorting. Such controls present a challenge by obscuring subtler patterns arising from subsequent pedogenic processes. Here the effect of quartz concentration is examined in moist-climate soils from a pilot dataset of the North American Soil Geochemical Landscapes Project. Due to variable and high quartz contents (6.2–81.7 wt.%), and its residual and inert nature in soil, quartz is demonstrated to influence broad patterns in soil chemistry. A dilution effect is observed whereby concentrations of various elements are significantly and strongly negatively correlated with quartz. Quartz content drives artificial positive correlations between concentrations of some elements and obscures negative correlations between others. Unadjusted soil data show the highly mobile base cations Ca, Mg, and Na to be often strongly positively correlated with intermediately mobile Al or Fe, and generally uncorrelated with the relatively immobile high-field-strength elements (HFS) Ti and Nb. Both patterns are contrary to broad expectations for soils being weathered and leached. After transforming bulk soil chemistry to a quartz-free basis, the base cations are generally uncorrelated with Al and Fe, and negative correlations generally emerge with the HFS elements. Quartz-free element data may be a useful tool for elucidating patterns of weathering or parent-material chemistry in large soil datasets.

  2. Harvard Aging Brain Study: Dataset and accessibility.

    Science.gov (United States)

    Dagley, Alexander; LaPoint, Molly; Huijbers, Willem; Hedden, Trey; McLaren, Donald G; Chatwal, Jasmeer P; Papp, Kathryn V; Amariglio, Rebecca E; Blacker, Deborah; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Schultz, Aaron P

    2017-01-01

    The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Dual-Analyte Fluorescent Sensor Based on [5]Helicene Derivative with Super Large Stokes Shift for the Selective Determinations of Cu2+ or Zn2+ in Buffer Solutions and Its Application in a Living Cell.

    Science.gov (United States)

    Sakunkaewkasem, Siwakorn; Petdum, Anuwut; Panchan, Waraporn; Sirirak, Jitnapa; Charoenpanich, Adisri; Sooksimuang, Thanasat; Wanichacheva, Nantanit

    2018-05-10

    A new fluorescent sensor, M201-DPA, based on [5]helicene derivative was utilized as dual-analyte sensor for determination of Cu 2+ or Zn 2+ in different media and different emission wavelengths. The sensor could provide selective and bifunctional determination of Cu 2+ in HEPES buffer containing Triton-X100 and Zn 2+ in Tris buffer/methanol without interference from each other and other ions. In HEPES buffer, M201-DPA demonstrated the selective ON-OFF fluorescence quenching at 524 nm toward Cu 2+ . On the other hand, in Tris buffer/methanol, M201-DPA showed the selective OFF-ON fluorescence enhancement upon the addition of Zn 2+ , which was specified by the hypsochromic shift at 448 nm. Additionally, M201-DPA showed extremely large Stokes shifts up to ∼150 nm. By controlling the concentration of Zn 2+ and Cu 2+ in a living cell, the imaging of a HepG2 cellular system was performed, in which the fluorescence of M201-DPA in the blue channel was decreased upon addition of Cu 2+ and was enhanced in UV channel upon addition of Zn 2+ . The detection limits of M201-DPA for Cu 2+ and Zn 2+ in buffer solutions were 5.6 and 3.8 ppb, respectively. Importantly, the Cu 2+ and Zn 2+ detection limits of the developed sensors were significantly lower than permitted Cu 2+ and Zn 2+ concentrations in drinking water as established by the U.S. EPA and WHO.

  4. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

    Directory of Open Access Journals (Sweden)

    Juan Carlos Davila

    2017-06-01

    Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  5. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

    Science.gov (United States)

    Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

    2017-06-07

    The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  6. CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data

    Directory of Open Access Journals (Sweden)

    K.-G. Karlsson

    2013-05-01

    Full Text Available A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data" – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates and extension of the temporal coverage.

  7. Thesaurus Dataset of Educational Technology in Chinese

    Science.gov (United States)

    Wu, Linjing; Liu, Qingtang; Zhao, Gang; Huang, Huan; Huang, Tao

    2015-01-01

    The thesaurus dataset of educational technology is a knowledge description of educational technology in Chinese. The aims of this thesaurus were to collect the subject terms in the domain of educational technology, facilitate the standardization of terminology and promote the communication between Chinese researchers and scholars from various…

  8. Smart and Intelligent Sensors

    Science.gov (United States)

    Lansaw, John; Schmalzel, John; Figueroa, Jorge

    2009-01-01

    John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make

  9. Kernel-based discriminant feature extraction using a representative dataset

    Science.gov (United States)

    Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

    2002-07-01

    Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

  10. Decoys Selection in Benchmarking Datasets: Overview and Perspectives

    Science.gov (United States)

    Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Lagarde, Nathalie; Montes, Matthieu

    2018-01-01

    Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets. PMID:29416509

  11. Sensor Alerting Capability

    Science.gov (United States)

    Henriksson, Jakob; Bermudez, Luis; Satapathy, Goutam

    2013-04-01

    There is a large amount of sensor data generated today by various sensors, from in-situ buoys to mobile underwater gliders. Providing sensor data to the users through standardized services, language and data model is the promise of OGC's Sensor Web Enablement (SWE) initiative. As the amount of data grows it is becoming difficult for data providers, planners and managers to ensure reliability of data and services and to monitor critical data changes. Intelligent Automation Inc. (IAI) is developing a net-centric alerting capability to address these issues. The capability is built on Sensor Observation Services (SOSs), which is used to collect and monitor sensor data. The alerts can be configured at the service level and at the sensor data level. For example it can alert for irregular data delivery events or a geo-temporal statistic of sensor data crossing a preset threshold. The capability provides multiple delivery mechanisms and protocols, including traditional techniques such as email and RSS. With this capability decision makers can monitor their assets and data streams, correct failures or be alerted about a coming phenomena.

  12. GLEAM version 3: Global Land Evaporation Datasets and Model

    Science.gov (United States)

    Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

    2015-12-01

    Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ

  13. Omicseq: a web-based search engine for exploring omics datasets

    Science.gov (United States)

    Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng

    2017-01-01

    Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462

  14. Omicseq: a web-based search engine for exploring omics datasets.

    Science.gov (United States)

    Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S

    2017-07-03

    The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Attention Sensor

    NARCIS (Netherlands)

    Börner, Dirk; Kalz, Marco; Specht, Marcus

    2014-01-01

    This software sketch was used in the context of an experiment for the PhD project “Ambient Learning Displays”. The sketch comprises a custom-built attention sensor. The sensor measured (during the experiment) whether a participant looked at and thus attended a public display. The sensor was built

  16. Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.

    Science.gov (United States)

    Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh

    2018-03-01

    Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

  17. Interpolation of diffusion weighted imaging datasets

    DEFF Research Database (Denmark)

    Dyrby, Tim B; Lundell, Henrik; Burke, Mark W

    2014-01-01

    anatomical details and signal-to-noise-ratio for reliable fibre reconstruction. We assessed the potential benefits of interpolating DWI datasets to a higher image resolution before fibre reconstruction using a diffusion tensor model. Simulations of straight and curved crossing tracts smaller than or equal......Diffusion weighted imaging (DWI) is used to study white-matter fibre organisation, orientation and structural connectivity by means of fibre reconstruction algorithms and tractography. For clinical settings, limited scan time compromises the possibilities to achieve high image resolution for finer...... interpolation methods fail to disentangle fine anatomical details if PVE is too pronounced in the original data. As for validation we used ex-vivo DWI datasets acquired at various image resolutions as well as Nissl-stained sections. Increasing the image resolution by a factor of eight yielded finer geometrical...

  18. Artificial intelligence based event detection in wireless sensor networks

    NARCIS (Netherlands)

    Bahrepour, M.

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more

  19. Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain)

    OpenAIRE

    Antonio Jesús Pérez-Luque; Cristina Patricia Sánchez-Rojas; Regino Zamora; Ramón Pérez-Pérez; Francisco Javier Bonet

    2015-01-01

    Abstract Sierra Nevada mountain range (southern Spain) hosts a high number of endemic plant species, being one of the most important biodiversity hotspots in the Mediterranean basin. The high-mountain meadow ecosystems (borreguiles) harbour a large number of endemic and threatened plant species. In this data paper, we describe a dataset of the flora inhabiting this threatened ecosystem in this Mediterranean mountain. The dataset includes occurrence data for flora collected in those ecosystems...

  20. Data assimilation and model evaluation experiment datasets

    Science.gov (United States)

    Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

    1994-01-01

    The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.