Science.gov

Sample records for model-based event detection

  1. Identification of new events in Apollo 16 lunar seismic data by Hidden Markov Model-based event detection and classification

    NASA Astrophysics Data System (ADS)

    Knapmeyer-Endrun, Brigitte; Hammer, Conny

    2015-10-01

    Detection and identification of interesting events in single-station seismic data with little prior knowledge and under tight time constraints is a typical scenario in planetary seismology. The Apollo lunar seismic data, with the only confirmed events recorded on any extraterrestrial body yet, provide a valuable test case. Here we present the application of a stochastic event detector and classifier to the data of station Apollo 16. Based on a single-waveform example for each event class and some hours of background noise, the system is trained to recognize deep moonquakes, impacts, and shallow moonquakes and performs reliably over 3 years of data. The algorithm's demonstrated ability to detect rare events and flag previously undefined signal classes as new event types is of particular interest in the analysis of the first seismic recordings from a completely new environment. We are able to classify more than 50% of previously unclassified lunar events, and additionally find over 200 new events not listed in the current lunar event catalog. These events include deep moonquakes as well as impacts and could be used to update studies on temporal variations in event rate or deep moonquakes stacks used in phase picking for localization. No unambiguous new shallow moonquake was detected, but application to data of the other Apollo stations has the potential for additional new discoveries 40 years after the data were recorded. Besides, the classification system could be useful for future seismometer missions to other planets, e.g., the InSight mission to Mars.

  2. a Topic Modeling Based Representation to Detect Tweet Locations. Example of the Event "je Suis Charlie"

    NASA Astrophysics Data System (ADS)

    Morchid, M.; Josselin, D.; Portilla, Y.; Dufour, R.; Altman, E.; Linarès, G.

    2015-09-01

    Social Networks became a major actor in information propagation. Using the Twitter popular platform, mobile users post or relay messages from different locations. The tweet content, meaning and location, show how an event-such as the bursty one "JeSuisCharlie", happened in France in January 2015, is comprehended in different countries. This research aims at clustering the tweets according to the co-occurrence of their terms, including the country, and forecasting the probable country of a non-located tweet, knowing its content. First, we present the process of collecting a large quantity of data from the Twitter website. We finally have a set of 2,189 located tweets about "Charlie", from the 7th to the 14th of January. We describe an original method adapted from the Author-Topic (AT) model based on the Latent Dirichlet Allocation (LDA) method. We define an homogeneous space containing both lexical content (words) and spatial information (country). During a training process on a part of the sample, we provide a set of clusters (topics) based on statistical relations between lexical and spatial terms. During a clustering task, we evaluate the method effectiveness on the rest of the sample that reaches up to 95% of good assignment. It shows that our model is pertinent to foresee tweet location after a learning process.

  3. Applying a Hidden Markov Model-Based Event Detection and Classification Algorithm to Apollo Lunar Seismic Data

    NASA Astrophysics Data System (ADS)

    Knapmeyer-Endrun, B.; Hammer, C.

    2014-12-01

    The seismometers that the Apollo astronauts deployed on the Moon provide the only recordings of seismic events from any extra-terrestrial body so far. These lunar events are significantly different from ones recorded on Earth, in terms of both signal shape and source processes. Thus they are a valuable test case for any experiment in planetary seismology. In this study, we analyze Apollo 16 data with a single-station event detection and classification algorithm in view of NASA's upcoming InSight mission to Mars. InSight, scheduled for launch in early 2016, has the goal to investigate Mars' internal structure by deploying a seismometer on its surface. As the mission does not feature any orbiter, continuous data will be relayed to Earth at a reduced rate. Full range data will only be available by requesting specific time-windows within a few days after the receipt of the original transmission. We apply a recently introduced algorithm based on hidden Markov models that requires only a single example waveform of each event class for training appropriate models. After constructing the prototypes we detect and classify impacts and deep and shallow moonquakes. Initial results for 1972 (year of station installation with 8 months of data) indicate a high detection rate of over 95% for impacts, of which more than 80% are classified correctly. Deep moonquakes, which occur in large amounts, but often show only very weak signals, are detected with less certainty (~70%). As there is only one weak shallow moonquake covered, results for this event class are not statistically significant. Daily adjustments of the background noise model help to reduce false alarms, which are mainly erroneous deep moonquake detections, by about 25%. The algorithm enables us to classify events that were previously listed in the catalog without classification, and, through the combined use of long period and short period data, identify some unlisted local impacts as well as at least two yet unreported

  4. A Cyber-Attack Detection Model Based on Multivariate Analyses

    NASA Astrophysics Data System (ADS)

    Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

    In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.

  5. Detection of anomalous events

    DOEpatents

    Ferragut, Erik M.; Laska, Jason A.; Bridges, Robert A.

    2016-06-07

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

  6. Detection of solar events

    DOEpatents

    Fischbach, Ephraim; Jenkins, Jere

    2013-08-27

    A flux detection apparatus can include a radioactive sample having a decay rate capable of changing in response to interaction with a first particle or a field, and a detector associated with the radioactive sample. The detector is responsive to a second particle or radiation formed by decay of the radioactive sample. The rate of decay of the radioactive sample can be correlated to flux of the first particle or the field. Detection of the first particle or the field can provide an early warning for an impending solar event.

  7. Scintillation event energy measurement via a pulse model based iterative deconvolution method

    NASA Astrophysics Data System (ADS)

    Deng, Zhenzhou; Xie, Qingguo; Duan, Zhiwen; Xiao, Peng

    2013-11-01

    This work focuses on event energy measurement, a crucial task of scintillation detection systems. We modeled the scintillation detector as a linear system and treated the energy measurement as a deconvolution problem. We proposed a pulse model based iterative deconvolution (PMID) method, which can process pileup events without detection and is adaptive for different signal pulse shapes. The proposed method was compared with digital gated integrator (DGI) and digital delay-line clipping (DDLC) using real world experimental data. For singles data, the energy resolution (ER) produced by PMID matched that of DGI. For pileups, the PMID method outperformed both DGI and DDLC in ER and counts recovery. The encouraging results suggest that the PMID method has great potentials in applications like photon-counting systems and pulse height spectrometers, in which multiple-event pileups are common.

  8. Scintillation event energy measurement via a pulse model based iterative deconvolution method.

    PubMed

    Deng, Zhenzhou; Xie, Qingguo; Duan, Zhiwen; Xiao, Peng

    2013-11-01

    This work focuses on event energy measurement, a crucial task of scintillation detection systems. We modeled the scintillation detector as a linear system and treated the energy measurement as a deconvolution problem. We proposed a pulse model based iterative deconvolution (PMID) method, which can process pileup events without detection and is adaptive for different signal pulse shapes. The proposed method was compared with digital gated integrator (DGI) and digital delay-line clipping (DDLC) using real world experimental data. For singles data, the energy resolution (ER) produced by PMID matched that of DGI. For pileups, the PMID method outperformed both DGI and DDLC in ER and counts recovery. The encouraging results suggest that the PMID method has great potentials in applications like photon-counting systems and pulse height spectrometers, in which multiple-event pileups are common. PMID:24145134

  9. Sequential Bayesian Detection: A Model-Based Approach

    SciTech Connect

    Sullivan, E J; Candy, J V

    2007-08-13

    Sequential detection theory has been known for a long time evolving in the late 1940's by Wald and followed by Middleton's classic exposition in the 1960's coupled with the concurrent enabling technology of digital computer systems and the development of sequential processors. Its development, when coupled to modern sequential model-based processors, offers a reasonable way to attack physics-based problems. In this chapter, the fundamentals of the sequential detection are reviewed from the Neyman-Pearson theoretical perspective and formulated for both linear and nonlinear (approximate) Gauss-Markov, state-space representations. We review the development of modern sequential detectors and incorporate the sequential model-based processors as an integral part of their solution. Motivated by a wealth of physics-based detection problems, we show how both linear and nonlinear processors can seamlessly be embedded into the sequential detection framework to provide a powerful approach to solving non-stationary detection problems.

  10. Sequential Bayesian Detection: A Model-Based Approach

    SciTech Connect

    Candy, J V

    2008-12-08

    Sequential detection theory has been known for a long time evolving in the late 1940's by Wald and followed by Middleton's classic exposition in the 1960's coupled with the concurrent enabling technology of digital computer systems and the development of sequential processors. Its development, when coupled to modern sequential model-based processors, offers a reasonable way to attack physics-based problems. In this chapter, the fundamentals of the sequential detection are reviewed from the Neyman-Pearson theoretical perspective and formulated for both linear and nonlinear (approximate) Gauss-Markov, state-space representations. We review the development of modern sequential detectors and incorporate the sequential model-based processors as an integral part of their solution. Motivated by a wealth of physics-based detection problems, we show how both linear and nonlinear processors can seamlessly be embedded into the sequential detection framework to provide a powerful approach to solving non-stationary detection problems.

  11. Naive Probability: Model-Based Estimates of Unique Events.

    PubMed

    Khemlani, Sangeet S; Lotstein, Max; Johnson-Laird, Philip N

    2015-08-01

    We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory's predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions: P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A). They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning.

  12. Naive Probability: Model-Based Estimates of Unique Events.

    PubMed

    Khemlani, Sangeet S; Lotstein, Max; Johnson-Laird, Philip N

    2015-08-01

    We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory's predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions: P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A). They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning. PMID:25363706

  13. Fuzzy model-based observers for fault detection in CSTR.

    PubMed

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions.

  14. Fuzzy model-based observers for fault detection in CSTR.

    PubMed

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. PMID:26521723

  15. Method for Real-Time Model Based Structural Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Smith, Timothy A. (Inventor); Urnes, James M., Sr. (Inventor); Reichenbach, Eric Y. (Inventor)

    2015-01-01

    A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.

  16. a model based on crowsourcing for detecting natural hazards

    NASA Astrophysics Data System (ADS)

    Duan, J.; Ma, C.; Zhang, J.; Liu, S.; Liu, J.

    2015-12-01

    Remote Sensing Technology provides a new method for the detecting,early warning,mitigation and relief of natural hazards. Given the suddenness and the unpredictability of the location of natural hazards as well as the actual demands for hazards work, this article proposes an evaluation model for remote sensing detecting of natural hazards based on crowdsourcing. Firstly, using crowdsourcing model and with the help of the Internet and the power of hundreds of millions of Internet users, this evaluation model provides visual interpretation of high-resolution remote sensing images of hazards area and collects massive valuable disaster data; secondly, this evaluation model adopts the strategy of dynamic voting consistency to evaluate the disaster data provided by the crowdsourcing workers; thirdly, this evaluation model pre-estimates the disaster severity with the disaster pre-evaluation model based on regional buffers; lastly, the evaluation model actuates the corresponding expert system work according to the forecast results. The idea of this model breaks the boundaries between geographic information professionals and the public, makes the public participation and the citizen science eventually be realized, and improves the accuracy and timeliness of hazards assessment results.

  17. Lightning Detection Efficiency Analysis Process: Modeling Based on Empirical Data

    NASA Technical Reports Server (NTRS)

    Rompala, John T.

    2005-01-01

    A ground based lightning detection system employs a grid of sensors, which record and evaluate the electromagnetic signal produced by a lightning strike. Several detectors gather information on that signal s strength, time of arrival, and behavior over time. By coordinating the information from several detectors, an event solution can be generated. That solution includes the signal s point of origin, strength and polarity. Determination of the location of the lightning strike uses algorithms based on long used techniques of triangulation. Determination of the event s original signal strength relies on the behavior of the generated magnetic field over distance and time. In general the signal from the event undergoes geometric dispersion and environmental attenuation as it progresses. Our knowledge of that radial behavior together with the strength of the signal received by detecting sites permits an extrapolation and evaluation of the original strength of the lightning strike. It also limits the detection efficiency (DE) of the network. For expansive grids and with a sparse density of detectors, the DE varies widely over the area served. This limits the utility of the network in gathering information on regional lightning strike density and applying it to meteorological studies. A network of this type is a grid of four detectors in the Rondonian region of Brazil. The service area extends over a million square kilometers. Much of that area is covered by rain forests. Thus knowledge of lightning strike characteristics over the expanse is of particular value. I have been developing a process that determines the DE over the region [3]. In turn, this provides a way to produce lightning strike density maps, corrected for DE, over the entire region of interest. This report offers a survey of that development to date and a record of present activity.

  18. GPU Accelerated Event Detection Algorithm

    2011-05-25

    Smart grid external require new algorithmic approaches as well as parallel formulations. One of the critical components is the prediction of changes and detection of anomalies within the power grid. The state-of-the-art algorithms are not suited to handle the demands of streaming data analysis. (i) need for events detection algorithms that can scale with the size of data, (ii) need for algorithms that can not only handle multi dimensional nature of the data, but alsomore » model both spatial and temporal dependencies in the data, which, for the most part, are highly nonlinear, (iii) need for algorithms that can operate in an online fashion with streaming data. The GAEDA code is a new online anomaly detection techniques that take into account spatial, temporal, multi-dimensional aspects of the data set. The basic idea behind the proposed approach is to (a) to convert a multi-dimensional sequence into a univariate time series that captures the changes between successive windows extracted from the original sequence using singular value decomposition (SVD), and then (b) to apply known anomaly detection techniques for univariate time series. A key challenge for the proposed approach is to make the algorithm scalable to huge datasets by adopting techniques from perturbation theory, incremental SVD analysis. We used recent advances in tensor decomposition techniques which reduce computational complexity to monitor the change between successive windows and detect anomalies in the same manner as described above. Therefore we propose to develop the parallel solutions on many core systems such as GPUs, because these algorithms involve lot of numerical operations and are highly data-parallelizable.« less

  19. GPU Accelerated Event Detection Algorithm

    SciTech Connect

    2011-05-25

    Smart grid external require new algorithmic approaches as well as parallel formulations. One of the critical components is the prediction of changes and detection of anomalies within the power grid. The state-of-the-art algorithms are not suited to handle the demands of streaming data analysis. (i) need for events detection algorithms that can scale with the size of data, (ii) need for algorithms that can not only handle multi dimensional nature of the data, but also model both spatial and temporal dependencies in the data, which, for the most part, are highly nonlinear, (iii) need for algorithms that can operate in an online fashion with streaming data. The GAEDA code is a new online anomaly detection techniques that take into account spatial, temporal, multi-dimensional aspects of the data set. The basic idea behind the proposed approach is to (a) to convert a multi-dimensional sequence into a univariate time series that captures the changes between successive windows extracted from the original sequence using singular value decomposition (SVD), and then (b) to apply known anomaly detection techniques for univariate time series. A key challenge for the proposed approach is to make the algorithm scalable to huge datasets by adopting techniques from perturbation theory, incremental SVD analysis. We used recent advances in tensor decomposition techniques which reduce computational complexity to monitor the change between successive windows and detect anomalies in the same manner as described above. Therefore we propose to develop the parallel solutions on many core systems such as GPUs, because these algorithms involve lot of numerical operations and are highly data-parallelizable.

  20. Probabilistic model-based approach for heart beat detection.

    PubMed

    Chen, Hugh; Erol, Yusuf; Shen, Eric; Russell, Stuart

    2016-09-01

    Nowadays, hospitals are ubiquitous and integral to modern society. Patients flow in and out of a veritable whirlwind of paperwork, consultations, and potential inpatient admissions, through an abstracted system that is not without flaws. One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system. One longitudinal study reported an 88.8% rate of false alarms, with other studies reporting numbers of similar magnitudes. These false alarm rates lead to deleterious effects that manifest in a lower standard of care across clinics. This paper discusses a model-based probabilistic inference approach to estimate physiological variables at a detection level. We design a generative model that complies with a layman's understanding of human physiology and perform approximate Bayesian inference. One primary goal of this paper is to justify a Bayesian modeling approach to increasing robustness in a physiological domain. In order to evaluate our algorithm we look at the application of heart beat detection using four datasets provided by PhysioNet, a research resource for complex physiological signals, in the form of the PhysioNet 2014 Challenge set-p1 and set-p2, the MIT-BIH Polysomnographic Database, and the MGH/MF Waveform Database. On these data sets our algorithm performs on par with the other top six submissions to the PhysioNet 2014 challenge. The overall evaluation scores in terms of sensitivity and positive predictivity values obtained were as follows: set-p1 (99.72%), set-p2 (93.51%), MIT-BIH (99.66%), and MGH/MF (95.53%). These scores are based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity.

  1. Event oriented dictionary learning for complex event detection.

    PubMed

    Yan, Yan; Yang, Yi; Meng, Deyu; Liu, Gaowen; Tong, Wei; Hauptmann, Alexander G; Sebe, Nicu

    2015-06-01

    Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask lp -norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method. PMID:25794390

  2. Model-Based Detection in a Shallow Water Ocean Environment

    SciTech Connect

    Candy, J V

    2001-07-30

    A model-based detector is developed to process shallow water ocean acoustic data. The function of the detector is to adaptively monitor the environment and decide whether or not a change from normal has occurred. Here we develop a processor incorporating both a normal-mode ocean acoustic model and a vertical hydrophone array. The detector is applied to data acquired from the Hudson Canyon experiments at various ranges and its performance is evaluated.

  3. Sequential Model-Based Detection in a Shallow Ocean Acoustic Environment

    SciTech Connect

    Candy, J V

    2002-03-26

    A model-based detection scheme is developed to passively monitor an ocean acoustic environment along with its associated variations. The technique employs an embedded model-based processor and a reference model in a sequential likelihood detection scheme. The monitor is therefore called a sequential reference detector. The underlying theory for the design is developed and discussed in detail.

  4. How to accurately detect autobiographical events.

    PubMed

    Sartori, Giuseppe; Agosta, Sara; Zogmaister, Cristina; Ferrara, Santo Davide; Castiello, Umberto

    2008-08-01

    We describe a new method, based on indirect measures of implicit autobiographical memory, that allows evaluation of which of two contrasting autobiographical events (e.g., crimes) is true for a given individual. Participants were requested to classify sentences describing possible autobiographical events by pressing one of two response keys. Responses were faster when sentences related to truly autobiographical events shared the same response key with other sentences reporting true events and slower when sentences related to truly autobiographical events shared the same response key with sentences reporting false events. This method has possible application in forensic settings and as a lie-detection technique.

  5. Model-Based Design of Tree WSNs for Decentralized Detection.

    PubMed

    Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

    2015-01-01

    The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches. PMID:26307989

  6. A Biological Hierarchical Model Based Underwater Moving Object Detection

    PubMed Central

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  7. A biological hierarchical model based underwater moving object detection.

    PubMed

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

  8. A novel interacting multiple model based network intrusion detection scheme

    NASA Astrophysics Data System (ADS)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  9. A biological hierarchical model based underwater moving object detection.

    PubMed

    Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

    2014-01-01

    Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

  10. Event synchronous sinusoidal model based on frequency-to-instantaneous frequency mapping

    NASA Astrophysics Data System (ADS)

    Zolfaghari, Parham; Banno, Hideki; Itakura, Fumitada; Kawahara, Hideki

    2002-05-01

    We describe a glottal event synchronous sinusoidal model for speech analysis and synthesis. The sinusoidal components are event synchronously estimated using a mapping from linearly spaced filter center frequencies to the instantaneous frequencies of the filter outputs. Frequency domain fixed points of this mapping correspond to the constituent sinusoidal components of the input signal. A robust technique based on a wavelet representation of this fixed points model is used for fundamental frequency extraction as used in STRAIGHT [Kawahara et al., IEICE (1999)]. The method for event detection and characterization is based on group delay and similar fixed point analysis. This method enables the detection of precise timing and spread of speech events such as vocal fold closure. A trajectory continuation scheme is also applied to the extracted sinusoidal components. The proposed model is capable of high-quality speech synthesis using the overlap-add synthesis method and is also applicable to other sound sources. System evaluation results using spectral distortion measures and mean opinion scores will be reported. A comparison with the fixed frame-rate sinusoidal models will be given.

  11. A study on intrusion detection model based on hybrid classifier

    NASA Astrophysics Data System (ADS)

    Liu, Kewen; Yang, Qingbo

    2013-03-01

    In order to improve the accuracy of classification problem in intrusion detection, a hybrid classifier which was composed by KPCA, BPNN and QGA, has been proposed in this paper. In the hybrid classifier, KPCA was used to reduce dimensions, and then QGA was used to search the best parameters for BPNN. BPNN which has been got the best weights matrix and thresholds by QGA, was used to train classification model. The main core factors of original dataset can be preserved by KPCA, and greatly reduced the computations. The weakness of BPNN, which was usually easy to get stuck in local minimum, can be solved by QGA. Finally, the effectiveness of hybrid classifier was proved by experiments. Compared with traditional methods, the hybrid classifier has better performance in reducing the classify errors.

  12. Detecting Extreme Events in Gridded Climate Data

    SciTech Connect

    Ramachandra, Bharathkumar; Gadiraju, Krishna; Vatsavai, Raju; Kaiser, Dale Patrick; Karnowski, Thomas Paul

    2016-01-01

    Detecting and tracking extreme events in gridded climatological data is a challenging problem on several fronts: algorithms, scalability, and I/O. Successful detection of these events will give climate scientists an alternate view of the behavior of different climatological variables, leading to enhanced scientific understanding of the impacts of events such as heat and cold waves, and on a larger scale, the El Nin o Southern Oscillation. Recent advances in computing power and research in data sciences enabled us to look at this problem with a different perspective from what was previously possible. In this paper we present our computationally efficient algorithms for anomalous cluster detection on climate change big data. We provide results on detection and tracking of surface temperature and geopotential height anomalies, a trend analysis, and a study of relationships between the variables. We also identify the limitations of our approaches, future directions for research and alternate approaches.

  13. Certification Aspects in Critical Embedded Software Development with Model Based Techniques: Detection of Unintended Functions

    NASA Astrophysics Data System (ADS)

    Atencia Yepez, A.; Autrán Cerqueira, J.; Urueña, S.; Jurado, R.

    2012-01-01

    This paper, developed under contract with European Aviation Safety Agency (EASA), analyses in detail which may be the certification implications in the aeronautic industry associated to the application of model-level verification and validation techniques. Particularly, this paper focuses on the problematic of detecting unintended functions by applying Model Coverage Criteria at model level. This point is significantly important for the future extensive use of Model Based approaches in safety critical software, since the uncertainty in the system performance introduced by the unintended functions, which may also lead to unacceptable hazardous or catastrophic events, prevents the system to be compliance with certification requirements. The paper provides a definition and a categorization of unintended functions and gives some relevant examples to assess the efficiency of model- coverage techniques in the detection of UF. The paper explains how this analysis is supported by a methodology based on the study of sources for introducing unintended functions. Finally it is analysed the feasibility of using Model-level verification techniques to support the software certification process.

  14. Semantic Context Detection Using Audio Event Fusion

    NASA Astrophysics Data System (ADS)

    Chu, Wei-Ta; Cheng, Wen-Huang; Wu, Ja-Ling

    2006-12-01

    Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine (SVM)) approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

  15. Monitoring the Ocean Acoustic Environment: A Model-Based Detection Approach

    SciTech Connect

    Candy, J.V.; Sullivan, E.J.

    2000-03-13

    A model-based approach is applied in the development of a processor designed to passively monitor an ocean acoustic environment along with its associated variations. The technique employs an adaptive, model-based processor embedded in a sequential likelihood detection scheme. The trade-off between state-based and innovations-based monitor designs is discussed, conceptually. The underlying theory for the innovations-based design is briefly developed and applied to a simulated data set.

  16. Automated Detection of Events of Scientific Interest

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

    A report presents a slightly different perspective of the subject matter of Fusing Symbolic and Numerical Diagnostic Computations (NPO-42512), which appears elsewhere in this issue of NASA Tech Briefs. Briefly, the subject matter is the X-2000 Anomaly Detection Language, which is a developmental computing language for fusing two diagnostic computer programs one implementing a numerical analysis method, the other implementing a symbolic analysis method into a unified event-based decision analysis software system for real-time detection of events. In the case of the cited companion NASA Tech Briefs article, the contemplated events that one seeks to detect would be primarily failures or other changes that could adversely affect the safety or success of a spacecraft mission. In the case of the instant report, the events to be detected could also include natural phenomena that could be of scientific interest. Hence, the use of X- 2000 Anomaly Detection Language could contribute to a capability for automated, coordinated use of multiple sensors and sensor-output-data-processing hardware and software to effect opportunistic collection and analysis of scientific data.

  17. A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks

    PubMed Central

    Chen, Jiehui; Salim, Mariam B.; Matsumoto, Mitsuji

    2010-01-01

    This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node’s spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains. PMID:22163619

  18. Subnoise detection of a fast random event.

    PubMed

    Ataie, V; Esman, D; Kuo, B P-P; Alic, N; Radic, S

    2015-12-11

    Observation of random, nonrepetitive phenomena is of critical importance in astronomy, spectroscopy, biology, and remote sensing. Heralded by weak signals, hidden in noise, they pose basic detection challenges. In contrast to repetitive waveforms, a single-instance signal cannot be separated from noise through averaging. Here, we show that a fast, randomly occurring event can be detected and extracted from a noisy background without conventional averaging. An isolated 80-picosecond pulse was received with confidence level exceeding 99%, even when accompanied by noise. Our detector relies on instantaneous spectral cloning and a single-step, coherent field processor. The ability to extract fast, subnoise events is expected to increase detection sensitivity in multiple disciplines. Additionally, the new spectral-cloning receiver can potentially intercept communication signals that are presently considered secure. PMID:26659052

  19. Phenological Event Detection from Multitemporal Image Data

    SciTech Connect

    Vatsavai, Raju

    2009-01-01

    Monitoring biomass over large geographic regions for seasonal changes in vegetation and crop phenology is important for many applications. In this paper we a present a novel clustering based change detection method using MODIS NDVI time series data. We used well known EM technique to find GMM parameters and Bayesian Information Criteria (BIC) for determining the number of clusters. KL Divergence measure is then used to establish the cluster correspondence across two years (2001 and 2006) to determine changes between these two years. The changes identied were further analyzed for understanding phenological events. This preliminary study shows interesting relationships between key phenological events such as onset, length, end of growing seasons.

  20. Multimedia event detection using visual concept signatures

    NASA Astrophysics Data System (ADS)

    Younessian, Ehsan; Quinn, Michael; Mitamura, Teruko; Hauptmann, Alex

    2013-03-01

    Multimedia Event Detection (MED) is a multimedia retrieval task with the goal of finding videos of a particular event in a large-scale Internet video archive, given example videos and text descriptions. In this paper, we mainly focus on an 'ad-hoc' scenario in MED where we do not use any example video. We aim to retrieve test videos based on their visual semantics using a Visual Concept Signature (VCS) generated for each event only derived from the event description provided as the query. Visual semantics are described using the Semantic INdexing (SIN) feature which represents the likelihood of predefined visual concepts in a video. To generate a VCS for an event, we project the given event description to a visual concept list using the proposed textual semantic similarity. Exploring SIN feature properties, we harmonize the generated visual concept signature and the SIN feature to improve retrieval performance. We conduct different experiments to assess the quality of generated visual concept signatures with respect to human expectation, and in the context of the MED task to retrieve the SIN feature of videos in the test dataset when we have no or only very few training videos.

  1. Detection and recognition of indoor smoking events

    NASA Astrophysics Data System (ADS)

    Bien, Tse-Lun; Lin, Chang Hong

    2013-03-01

    Smoking in public indoor spaces has become prohibited in many countries since it not only affects the health of the people around you, but also increases the risk of fire outbreaks. This paper proposes a novel scheme to automatically detect and recognize smoking events by using exsiting surveillance cameras. The main idea of our proposed method is to detect human smoking events by recognizing their actions. In this scheme, the human pose estimation is introduced to analyze human actions from their poses. The human pose estimation method segments head and both hands from human body parts by using a skin color detection method. However, the skin color methods may fail in insufficient light conditions. Therefore, the lighting compensation is applied to help the skin color detection method become more accurate. Due to the human body parts may be covered by shadows, which may cause the human pose estimation to fail, the Kalman filter is applied to track the missed body parts. After that, we evaluate the probability features of hands approaching the head. The support vector machine (SVM) is applied to learn and recognize the smoking events by the probability features. To analysis the performance of proposed method, the datasets established in the survillance camera view under indoor enviroment are tested. The experimental results show the effectiveness of our proposed method with accuracy rate of 83.33%.

  2. Model Based Analysis of Clonal Developments Allows for Early Detection of Monoclonal Conversion and Leukemia

    PubMed Central

    Thielecke, Lars; Glauche, Ingmar

    2016-01-01

    The availability of several methods to unambiguously mark individual cells has strongly fostered the understanding of clonal developments in hematopoiesis and other stem cell driven regenerative tissues. While cellular barcoding is the method of choice for experimental studies, patients that underwent gene therapy carry a unique insertional mark within the transplanted cells originating from the integration of the retroviral vector. Close monitoring of such patients allows accessing their clonal dynamics, however, the early detection of events that predict monoclonal conversion and potentially the onset of leukemia are beneficial for treatment. We developed a simple mathematical model of a self-stabilizing hematopoietic stem cell population to generate a wide range of possible clonal developments, reproducing typical, experimentally and clinically observed scenarios. We use the resulting model scenarios to suggest and test a set of statistical measures that should allow for an interpretation and classification of relevant clonal dynamics. Apart from the assessment of several established diversity indices we suggest a measure that quantifies the extension to which the increase in the size of one clone is attributed to the total loss in the size of all other clones. By evaluating the change in relative clone sizes between consecutive measurements, the suggested measure, referred to as maximum relative clonal expansion (mRCE), proves to be highly sensitive in the detection of rapidly expanding cell clones prior to their dominant manifestation. This predictive potential places the mRCE as a suitable means for the early recognition of leukemogenesis especially in gene therapy patients that are closely monitored. Our model based approach illustrates how simulation studies can actively support the design and evaluation of preclinical strategies for the analysis and risk evaluation of clonal developments. PMID:27764218

  3. Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller

    NASA Astrophysics Data System (ADS)

    Kopka, Ryszard

    2014-12-01

    This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.

  4. Phase-Space Detection of Cyber Events

    SciTech Connect

    Hernandez Jimenez, Jarilyn M; Ferber, Aaron E; Prowell, Stacy J; Hively, Lee M

    2015-01-01

    Energy Delivery Systems (EDS) are a network of processes that produce, transfer and distribute energy. EDS are increasingly dependent on networked computing assets, as are many Industrial Control Systems. Consequently, cyber-attacks pose a real and pertinent threat, as evidenced by Stuxnet, Shamoon and Dragonfly. Hence, there is a critical need for novel methods to detect, prevent, and mitigate effects of such attacks. To detect cyber-attacks in EDS, we developed a framework for gathering and analyzing timing data that involves establishing a baseline execution profile and then capturing the effect of perturbations in the state from injecting various malware. The data analysis was based on nonlinear dynamics and graph theory to improve detection of anomalous events in cyber applications. The goal was the extraction of changing dynamics or anomalous activity in the underlying computer system. Takens' theorem in nonlinear dynamics allows reconstruction of topologically invariant, time-delay-embedding states from the computer data in a sufficiently high-dimensional space. The resultant dynamical states were nodes, and the state-to-state transitions were links in a mathematical graph. Alternatively, sequential tabulation of executing instructions provides the nodes with corresponding instruction-to-instruction links. Graph theorems guarantee graph-invariant measures to quantify the dynamical changes in the running applications. Results showed a successful detection of cyber events.

  5. LAN attack detection using Discrete Event Systems.

    PubMed

    Hubballi, Neminath; Biswas, Santosh; Roopa, S; Ratti, Ritesh; Nandi, Sukumar

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed. PMID:20804980

  6. LAN attack detection using Discrete Event Systems.

    PubMed

    Hubballi, Neminath; Biswas, Santosh; Roopa, S; Ratti, Ritesh; Nandi, Sukumar

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed.

  7. Radioactive Threat Detection with Scattering Physics: A Model-Based Application

    SciTech Connect

    Candy, J V; Chambers, D H; Breitfeller, E F; Guidry, B L; Verbeke, J M; Axelrod, M A; Sale, K E; Meyer, A M

    2010-01-21

    The detection of radioactive contraband is a critical problem in maintaining national security for any country. Emissions from threat materials challenge both detection and measurement technologies especially when concealed by various types of shielding complicating the transport physics significantly. The development of a model-based sequential Bayesian processor that captures both the underlying transport physics including scattering offers a physics-based approach to attack this challenging problem. It is shown that this processor can be used to develop an effective detection technique.

  8. Model-Based Detection of Radioactive Contraband for Harbor Defense Incorporating Compton Scattering Physics

    SciTech Connect

    Candy, J V; Chambers, D H; Breitfeller, E F; Guidry, B L; Verbeke, J M; Axelrod, M A; Sale, K E; Meyer, A M

    2010-03-02

    The detection of radioactive contraband is a critical problem is maintaining national security for any country. Photon emissions from threat materials challenge both detection and measurement technologies especially when concealed by various types of shielding complicating the transport physics significantly. This problem becomes especially important when ships are intercepted by U.S. Coast Guard harbor patrols searching for contraband. The development of a sequential model-based processor that captures both the underlying transport physics of gamma-ray emissions including Compton scattering and the measurement of photon energies offers a physics-based approach to attack this challenging problem. The inclusion of a basic radionuclide representation of absorbed/scattered photons at a given energy along with interarrival times is used to extract the physics information available from the noisy measurements portable radiation detection systems used to interdict contraband. It is shown that this physics representation can incorporated scattering physics leading to an 'extended' model-based structure that can be used to develop an effective sequential detection technique. The resulting model-based processor is shown to perform quite well based on data obtained from a controlled experiment.

  9. Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

    DOE PAGES

    Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; Slinkard, Megan Elizabeth

    2016-01-01

    The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less

  10. Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

    SciTech Connect

    Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; Slinkard, Megan Elizabeth

    2016-01-01

    The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events that generated the phase picks.

  11. Prediction models for early risk detection of cardiovascular event.

    PubMed

    Purwanto; Eswaran, Chikkannan; Logeswaran, Rajasvaran; Abdul Rahman, Abdul Rashid

    2012-04-01

    Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron,Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models.The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.

  12. Rare Event Detection Algorithm Of Water Quality

    NASA Astrophysics Data System (ADS)

    Ungs, M. J.

    2011-12-01

    A novel method is presented describing the development and implementation of an on-line water quality event detection algorithm. An algorithm was developed to distinguish between normal variation in water quality parameters and changes in these parameters triggered by the presence of contaminant spikes. Emphasis is placed on simultaneously limiting the number of false alarms (which are called false positives) that occur and the number of misses (called false negatives). The problem of excessive false alarms is common to existing change detection algorithms. EPA's standard measure of evaluation for event detection algorithms is to have a false alarm rate of less than 0.5 percent and a false positive rate less than 2 percent (EPA 817-R-07-002). A detailed description of the algorithm's development is presented. The algorithm is tested using historical water quality data collected by a public water supply agency at multiple locations and using spiking contaminants developed by the USEPA, Water Security Division. The water quality parameters of specific conductivity, chlorine residual, total organic carbon, pH, and oxidation reduction potential are considered. Abnormal data sets are generated by superimposing water quality changes on the historical or baseline data. Eddies-ET has defined reaction expressions which specify how the peak or spike concentration of a particular contaminant affects each water quality parameter. Nine default contaminants (Eddies-ET) were previously derived from pipe-loop tests performed at EPA's National Homeland Security Research Center (NHSRC) Test and Evaluation (T&E) Facility. A contaminant strength value of approximately 1.5 is considered to be a significant threat. The proposed algorithm has been able to achieve a combined false alarm rate of less than 0.03 percent for both false positives and for false negatives using contaminant spikes of strength 2 or more.

  13. Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems

    NASA Technical Reports Server (NTRS)

    Walker, M.; Figueroa, F.

    2015-01-01

    The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.

  14. Model-based fault detection of blade pitch system in floating wind turbines

    NASA Astrophysics Data System (ADS)

    Cho, S.; Gao, Z.; Moan, T.

    2016-09-01

    This paper presents a model-based scheme for fault detection of a blade pitch system in floating wind turbines. A blade pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be detected at the early stage to prevent failures. To detect faults of blade pitch actuators and sensors, an appropriate observer should be designed to estimate the states of the system. Residuals are generated by a Kalman filter and a threshold based on H optimization, and linear matrix inequality (LMI) is used for residual evaluation. The proposed method is demonstrated in a case study that bias and fixed output in pitch sensors and stuck in pitch actuators. The simulation results show that the proposed method detects different realistic fault scenarios of wind turbines under the stochastic external winds.

  15. A model-based approach for detection of objects in low resolution passive millimeter wave images

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Tang, Yuan-Liang; Devadiga, Sadashiva

    1993-01-01

    A model-based vision system to assist the pilots in landing maneuvers under restricted visibility conditions is described. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere, but their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. These data are exploited to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. Also analytical expressions were derived from the accuracy of the camera position estimate obtained by detecting the position of known objects in the image.

  16. Model-based estimation of measures of association for time-to-event outcomes

    PubMed Central

    2014-01-01

    Background Hazard ratios are ubiquitously used in time to event applications to quantify adjusted covariate effects. Although hazard ratios are invaluable for hypothesis testing, other adjusted measures of association, both relative and absolute, should be provided to fully appreciate studies results. The corrected group prognosis method is generally used to estimate the absolute risk reduction and the number needed to be treated for categorical covariates. Methods The goal of this paper is to present transformation models for time-to-event outcomes to obtain, directly from estimated coefficients, the measures of association widely used in biostatistics together with their confidence interval. Pseudo-values are used for a practical estimation of transformation models. Results Using the regression model estimated through pseudo-values with suitable link functions, relative risks, risk differences and the number needed to treat, are obtained together with their confidence intervals. One example based on literature data and one original application to the study of prognostic factors in primary retroperitoneal soft tissue sarcomas are presented. A simulation study is used to show some properties of the different estimation methods. Conclusions Clinically useful measures of treatment or exposure effect are widely available in epidemiology. When time to event outcomes are present, the analysis is performed generally resorting to predicted values from Cox regression model. It is now possible to resort to more general regression models, adopting suitable link functions and pseudo values for estimation, to obtain alternative measures of effect directly from regression coefficients together with their confidence interval. This may be especially useful when, in presence of time dependent covariate effects, it is not straightforward to specify the correct, if any, time dependent functional form. The method can easily be implemented with standard software. PMID:25106903

  17. A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Rinehart, Aidan Walker

    2015-01-01

    This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

  18. A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Rinehart, Aidan W.

    2014-01-01

    This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

  19. A model-based framework for the detection of spiculated masses on mammography

    SciTech Connect

    Sampat, Mehul P.; Bovik, Alan C.; Whitman, Gary J.; Markey, Mia K.

    2008-05-15

    The detection of lesions on mammography is a repetitive and fatiguing task. Thus, computer-aided detection systems have been developed to aid radiologists. The detection accuracy of current systems is much higher for clusters of microcalcifications than for spiculated masses. In this article, the authors present a new model-based framework for the detection of spiculated masses. The authors have invented a new class of linear filters, spiculated lesion filters, for the detection of converging lines or spiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures of spiculated masses. As a part of this algorithm, the authors have also invented a novel technique to enhance spicules on mammograms. This entails filtering in the radon domain. They have also developed models to reduce the false positives due to normal linear structures. A key contribution of this work is that the parameters of the detection algorithm are based on measurements of physical properties of spiculated masses. The results of the detection algorithm are presented in the form of free-response receiver operating characteristic curves on images from the Mammographic Image Analysis Society and Digital Database for Screening Mammography databases.

  20. Model-based fault detection and identification with online aerodynamic model structure selection

    NASA Astrophysics Data System (ADS)

    Lombaerts, T.

    2013-12-01

    This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modification of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identification (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on a simulation data set.

  1. Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

    NASA Astrophysics Data System (ADS)

    Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

    2015-08-01

    The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

  2. Video event detection: from subvolume localization to spatiotemporal path search.

    PubMed

    Tran, Du; Yuan, Junsong; Forsyth, David

    2014-02-01

    Although sliding window-based approaches have been quite successful in detecting objects in images, it is not a trivial problem to extend them to detecting events in videos. We propose to search for spatiotemporal paths for video event detection. This new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intraclass variations of the event. Compared to event detection using spatiotemporal sliding windows, the spatiotemporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video data sets with different event detection tasks, such as anomaly event detection, walking person detection, and running detection. Our proposed method is compatible with different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-the-art methods. PMID:24356358

  3. Detecting seismic events using Benford's Law

    NASA Astrophysics Data System (ADS)

    Diaz, Jordi; Gallart, Josep; Ruiz, Mario

    2015-04-01

    The Benford's Law (BL) states that the distribution of first significant digits is not uniform but follows a logarithmic frequency distribution. Even if a remarkable wide range of natural and socioeconomical data sets, from stock market values to quantum phase transitions, fit this peculiar law, the conformity to it has deserved few scientific applications, being used mainly as a test to pinpoint anomalous or fraudulent data. We developed a procedure to detect the arrival of seismic waves based on the degree of conformity of the amplitude values in the raw seismic trace to the BL. The signal is divided in time windows of appropriate length and the fitting of the first digits distribution to BL is checked in each time window using a conformity estimator. We document that both teleseismic and local earthquakes can be clearly identified in this procedure and we compare its performance with respect to the classical STA/LTA approach. Moreover, we show that the conformity of the seismic record to the BL does not depend on the amplitude of the incoming series, as the occurrence of events with very different amplitudes result in quite similar degree of BL fitting. On the other hand, we show that natural or man-made quasi-monochromatic seismic signals, surface wave trains or engine-generated vibrations can be identified through their very low BL estimator values, when appropriate interval lengths are used. Therefore, we conclude that the degree of conformity of a seismic signal with the BL is primarily dependent on the frequency content of that signal.

  4. AESOP: Adaptive Event detection SOftware using Programming by example

    NASA Astrophysics Data System (ADS)

    Thangali, Ashwin; Prasad, Harsha; Kethamakka, Sai; Demirdjian, David; Checka, Neal

    2015-05-01

    This paper presents AESOP, a software tool for automatic event detection in video. AESOP employs a super- vised learning approach for constructing event models, given training examples from different event classes. A trajectory-based formulation is used for modeling events with an aim towards incorporating invariance to changes in the camera location and orientation parameters. The proposed formulation is designed to accommodate events that involve interactions between two or more entities over an extended period of time. AESOP's event models are formulated as HMMs to improve the event detection algorithm's robustness to noise in input data and to achieve computationally efficient algorithms for event model training and event detection. AESOP's performance is demonstrated on a wide range of different scenarios, including stationary camera surveillance and aerial video footage captured in land and maritime environments.

  5. Observability analysis for model-based fault detection and sensor selection in induction motors

    NASA Astrophysics Data System (ADS)

    Nakhaeinejad, Mohsen; Bryant, Michael D.

    2011-07-01

    Sensors in different types and configurations provide information on the dynamics of a system. For a specific task, the question is whether measurements have enough information or whether the sensor configuration can be changed to improve the performance or to reduce costs. Observability analysis may answer the questions. This paper presents a general algorithm of nonlinear observability analysis with application to model-based diagnostics and sensor selection in three-phase induction motors. A bond graph model of the motor is developed and verified with experiments. A nonlinear observability matrix based on Lie derivatives is obtained from state equations. An observability index based on the singular value decomposition of the observability matrix is obtained. Singular values and singular vectors are used to identify the most and least observable configurations of sensors and parameters. A complex step derivative technique is used in the calculation of Jacobians to improve the computational performance of the observability analysis. The proposed algorithm of observability analysis can be applied to any nonlinear system to select the best configuration of sensors for applications of model-based diagnostics, observer-based controller, or to determine the level of sensor redundancy. Observability analysis on induction motors provides various sensor configurations with corresponding observability indices. Results show the redundancy levels for different sensors, and provide a sensor selection guideline for model-based diagnostics, and for observer-based controllers. The results can also be used for sensor fault detection and to improve the reliability of the system by increasing the redundancy level in measurements.

  6. 3D model-based detection and tracking for space autonomous and uncooperative rendezvous

    NASA Astrophysics Data System (ADS)

    Shang, Yang; Zhang, Yueqiang; Liu, Haibo

    2015-10-01

    In order to fully navigate using a vision sensor, a 3D edge model based detection and tracking technique was developed. Firstly, we proposed a target detection strategy over a sequence of several images from the 3D model to initialize the tracking. The overall purpose of such approach is to robustly match each image with the model views of the target. Thus we designed a line segment detection and matching method based on the multi-scale space technology. Experiments on real images showed that our method is highly robust under various image changes. Secondly, we proposed a method based on 3D particle filter (PF) coupled with M-estimation to track and estimate the pose of the target efficiently. In the proposed approach, a similarity observation model was designed according to a new distance function of line segments. Then, based on the tracking results of PF, the pose was optimized using M-estimation. Experiments indicated that the proposed method can effectively track and accurately estimate the pose of freely moving target in unconstrained environment.

  7. Articulating uncertainty as part of scientific argumentation during model-based exoplanet detection tasks

    NASA Astrophysics Data System (ADS)

    Lee, Hee-Sun; Pallant, Amy; Pryputniewicz, Sarah

    2015-08-01

    Teaching scientific argumentation has emerged as an important goal for K-12 science education. In scientific argumentation, students are actively involved in coordinating evidence with theory based on their understanding of the scientific content and thinking critically about the strengths and weaknesses of the cited evidence in the context of the investigation. We developed a one-week-long online curriculum module called "Is there life in space?" where students conduct a series of four model-based tasks to learn how scientists detect extrasolar planets through the “wobble” and transit methods. The simulation model allows students to manipulate various parameters of an imaginary star and planet system such as planet size, orbit size, planet-orbiting-plane angle, and sensitivity of telescope equipment, and to adjust the display settings for graphs illustrating the relative velocity and light intensity of the star. Students can use model-based evidence to formulate an argument on whether particular signals in the graphs guarantee the presence of a planet. Students' argumentation is facilitated by the four-part prompts consisting of multiple-choice claim, open-ended explanation, Likert-scale uncertainty rating, and open-ended uncertainty rationale. We analyzed 1,013 scientific arguments formulated by 302 high school student groups taught by 7 teachers. We coded these arguments in terms of the accuracy of their claim, the sophistication of explanation connecting evidence to the established knowledge base, the uncertainty rating, and the scientific validity of uncertainty. We found that (1) only 18% of the students' uncertainty rationale involved critical reflection on limitations inherent in data and concepts, (2) 35% of students' uncertainty rationale reflected their assessment of personal ability and knowledge, rather than scientific sources of uncertainty related to the evidence, and (3) the nature of task such as the use of noisy data or the framing of

  8. Model-based approach to the detection and classification of mines in sidescan sonar.

    PubMed

    Reed, Scott; Petillot, Yvan; Bell, Judith

    2004-01-10

    This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis.

  9. Model-based approach to the detection and classification of mines in sidescan sonar.

    PubMed

    Reed, Scott; Petillot, Yvan; Bell, Judith

    2004-01-10

    This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis. PMID:14735943

  10. System for detection of hazardous events

    DOEpatents

    Kulesz, James J.; Worley, Brian A.

    2006-05-23

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  11. System For Detection Of Hazardous Events

    DOEpatents

    Kulesz, James J [Oak Ridge, TN; Worley, Brian A [Knoxville, TN

    2005-08-16

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  12. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  13. Subsurface event detection and classification using Wireless Signal Networks.

    PubMed

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  14. Global Seismic Event Detection Using Surface Waves: 15 Possible Antarctic Glacial Sliding Events

    NASA Astrophysics Data System (ADS)

    Chen, X.; Shearer, P. M.; Walker, K. T.; Fricker, H. A.

    2008-12-01

    To identify overlooked or anomalous seismic events not listed in standard catalogs, we have developed an algorithm to detect and locate global seismic events using intermediate-period (35-70s) surface waves. We apply our method to continuous vertical-component seismograms from the global seismic networks as archived in the IRIS UV FARM database from 1997 to 2007. We first bandpass filter the seismograms, apply automatic gain control, and compute envelope functions. We then examine 1654 target event locations defined at 5 degree intervals and stack the seismogram envelopes along the predicted Rayleigh-wave travel times. The resulting function has spatial and temporal peaks that indicate possible seismic events. We visually check these peaks using a graphical user interface to eliminate artifacts and assign an overall reliability grade (A, B or C) to the new events. We detect 78% of events in the Global Centroid Moment Tensor (CMT) catalog. However, we also find 840 new events not listed in the PDE, ISC and REB catalogs. Many of these new events were previously identified by Ekstrom (2006) using a different Rayleigh-wave detection scheme. Most of these new events are located along oceanic ridges and transform faults. Some new events can be associated with volcanic eruptions such as the 2000 Miyakejima sequence near Japan and others with apparent glacial sliding events in Greenland (Ekstrom et al., 2003). We focus our attention on 15 events detected from near the Antarctic coastline and relocate them using a cross-correlation approach. The events occur in 3 groups which are well-separated from areas of cataloged earthquake activity. We speculate that these are iceberg calving and/or glacial sliding events, and hope to test this by inverting for their source mechanisms and examining remote sensing data from their source regions.

  15. Event Detection using Twitter: A Spatio-Temporal Approach

    PubMed Central

    Cheng, Tao; Wicks, Thomas

    2014-01-01

    Background Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. Methods This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. Results and Conclusion A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection. PMID:24893168

  16. Efficient method for events detection in phonocardiographic signals

    NASA Astrophysics Data System (ADS)

    Martinez-Alajarin, Juan; Ruiz-Merino, Ramon

    2005-06-01

    The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.

  17. Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

    NASA Technical Reports Server (NTRS)

    Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon

    2012-01-01

    A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The

  18. Video Event Detection Framework on Large-Scale Video Data

    ERIC Educational Resources Information Center

    Park, Dong-Jun

    2011-01-01

    Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. Video data present a unique challenge for the information retrieval community because properly representing video events is challenging. We propose a novel approach to analyze temporal aspects of video data. We consider video data…

  19. Automatic detection of iceberg calving events using seismic observations

    NASA Astrophysics Data System (ADS)

    Andersen, M. L.; Larsen, T.; Hamilton, G. S.; Nettles, M.

    2014-12-01

    Iceberg calving at large, marine-terminating glaciers has been shown to be seismogenic. Seismic energy from these events is released slowly, resulting in characteristic low-frequency signals. The events therefore typically escape detection by traditional systematic methods. Here we show the results of a detection algorithm applied to data observed at two stations, both ~100 km from Helheim Glacier, South East Greenland, in 2007 and 2008 for the purpose of detecting calving-related seismic signals. The detector entails sliding a 150 s wide window over the observed vertical displacement seismograms at steps of one second. Relative power in the 1.1-3.3 s band is monitored, and the detector is activated when a pre-defined threshold is exceeded. We determine the threshold by calibrating the detector with a record of known events observed by time lapse cameras at Helheim Glacier and automatic detections of glacial earthquakes from the GSN (Global Seismic Network) stations. The resulting list of detections is then filtered for events overlapping with tectonic events, both local and global. We observe a clear periodicity in the detections, with most events occurring during the late summer and early fall, roughly coinciding with the end of the melt season. This apparent offset from peak melt intensity leads us to speculate that the pattern in calving is the result of a combination of the seasonal development of multiple physical properties of the glacier, i.e., surface crevassing, subglacial melt and crevassing, and the subglacial drainage system.

  20. Detection of flood events in hydrological discharge time series

    NASA Astrophysics Data System (ADS)

    Seibert, S. P.; Ehret, U.

    2012-04-01

    The shortcomings of mean-squared-error (MSE) based distance metrics are well known (Beran 1999, Schaeffli & Gupta 2007) and the development of novel distance metrics (Pappenberger & Beven 2004, Ehret & Zehe 2011) and multi-criteria-approaches enjoy increasing popularity (Reusser 2009, Gupta et al. 2009). Nevertheless, the hydrological community still lacks metrics which identify and thus, allow signature based evaluations of hydrological discharge time series. Signature based information/evaluations are required wherever specific time series features, such as flood events, are of special concern. Calculation of event based runoff coefficients or precise knowledge on flood event characteristics (like onset or duration of rising limp or the volume of falling limp, etc.) are possible applications. The same applies for flood forecasting/simulation models. Directly comparing simulated and observed flood event features may reveal thorough insights into model dynamics. Compared to continuous space-and-time-aggregated distance metrics, event based evaluations may provide answers like the distributions of event characteristics or the percentage of the events which were actually reproduced by a hydrological model. It also may help to provide information on the simulation accuracy of small, medium and/or large events in terms of timing and magnitude. However, the number of approaches which expose time series features is small and their usage is limited to very specific questions (Merz & Blöschl 2009, Norbiato et al. 2009). We believe this is due to the following reasons: i) a generally accepted definition of the signature of interest is missing or difficult to obtain (in our case: what makes a flood event a flood event?) and/or ii) it is difficult to translate such a definition into a equation or (graphical) procedure which exposes the feature of interest in the discharge time series. We reviewed approaches which detect event starts and/or ends in hydrological discharge time

  1. Structuring an event ontology for disease outbreak detection

    PubMed Central

    Kawazoe, Ai; Chanlekha, Hutchatai; Shigematsu, Mika; Collier, Nigel

    2008-01-01

    Background This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available. Construction and content This event ontology integrates a model of experts' knowledge for disease surveillance, and at the same time sets of linguistic expressions which denote disease-related events, and formal definitions of events. In this ontology, rather general event classes, which are suitable for application to language-oriented tasks such as recognition of event expressions, are placed on the upper-level, and more specific events of the experts' interest are in the lower level. Each class is related to other classes which represent participants of events, and linked with multi-lingual synonym sets and axioms. Conclusions We consider that the design of the event ontology and the methodology introduced in this paper are applicable to other domains which require integration of natural language information and machine support for experts to assess them. The first version of the ontology, with about 40 concepts, will be available in March 2008. PMID:18426553

  2. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

    PubMed Central

    Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

    2015-01-01

    With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

  3. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks.

    PubMed

    Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

    2015-01-01

    With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

  4. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks.

    PubMed

    Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

    2015-12-15

    With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

  5. Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

    PubMed Central

    Liu, Changyu; Li, Huiling

    2014-01-01

    We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches. PMID:25147840

  6. Secure access control and large scale robust representation for online multimedia event detection.

    PubMed

    Liu, Changyu; Lu, Bin; Li, Huiling

    2014-01-01

    We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

  7. Bayesian-network-based soccer video event detection and retrieval

    NASA Astrophysics Data System (ADS)

    Sun, Xinghua; Jin, Guoying; Huang, Mei; Xu, Guangyou

    2003-09-01

    This paper presents an event based soccer video retrieval method, where the scoring even is detected based on Bayesian network from six kinds of cue information including gate, face, audio, texture, caption and text. The topology within the Bayesian network is predefined by hand according to the domain knowledge and the probability distributions are learned in the case of the known structure and full observability. The resulting event probability from the Bayesian network is used as the feature vector to perform the video retrieval. Experiments show that the true and false detection rations for the scoring event are about 90% and 16.67% respectively, and that the video retrieval result based on event is superior to that based on low-level features in the human visual perception.

  8. Detection of Energetic Particle Events with SOHO Space Observatory

    NASA Astrophysics Data System (ADS)

    Rodríguez Frías, D.; Gómez Herrero, R.; Gutiérrez, J.; Del Peral, L.

    2004-09-01

    An analysis of eighteen solar energetic particle (SEP) events measured with the EPHIN instrument on board the SOHO spacecraft has been performed. Via parametrization of temporal profiles, differences among events have been shown by the temporal profile parameters obtained. The detected differences are found to depend on the particle acceleration, the magnetic connection with the acceleration zone and the interplanetary physical characteristics transport to the observing point.

  9. Adaptive noise estimation and suppression for improving microseismic event detection

    NASA Astrophysics Data System (ADS)

    Mousavi, S. Mostafa; Langston, Charles A.

    2016-09-01

    Microseismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method might not be compared with more sophisticated and computationally expensive denoising algorithm in terms of preserving detailed features of seismic signal. However, it is fast and data-driven and can be applied in real-time processing of continuous data for event detection purposes. Results from application of this algorithm to synthetic and real seismic data show that it holds a great promise for improving microseismic event detection.

  10. Detection of a second ντ event

    NASA Astrophysics Data System (ADS)

    Ishiguro, Katsumi

    2014-08-01

    OPERA is a neutrino oscillation experiment aiming to prove the tau neutrino appearance for the first time. A tau decay is identified by the detection of its characteristic decay topologies in nuclear emulsion films. OPERA reported the detection of a first tau neutrino event in 2010. We recently detected a second tau neutrino event in the 2011 run sample. The primary vertex has two charged particles of which one exhibits a three-prong vertex in the plastic base of a film after a flight length of 1.54 mm. It satisfies all the selection criteria to be a tau neutrino interaction. Especially the azimuthal angle in the transverse plane between the tau candidate track and the primary track is almost 180 degrees. The number of tau to 3π signal events expected in the analyzed sample is 0.18, the number of background events is 0.05. Considering all modes, the signal expectation is 1.91 events and the background 0.18 events.

  11. Method for early detection of cooling-loss events

    DOEpatents

    Bermudez, Sergio A.; Hamann, Hendrik F.; Marianno, Fernando J.

    2015-12-22

    A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

  12. Method for early detection of cooling-loss events

    DOEpatents

    Bermudez, Sergio A.; Hamann, Hendrik; Marianno, Fernando J.

    2015-06-30

    A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

  13. Detection of atypical seismic events on a regional scale

    NASA Astrophysics Data System (ADS)

    Solano-Hernandez, E. A.; Hjorleifsdottir, V.; Perez-Campos, X.; Iglesias, A.

    2013-12-01

    We propose an event-detection algorithm to locate seismic events on a regional scale. Our goal is to identify non-impulsive or 'atypical' events which are not detected by regional or global networks, due to their low P-wave amplitude. Ekstrom (2006) has developed and implemented a method to detect and locate sources of long-period seismic surface waves on a global scale. Atypical events are generated by, for example, rapid glacial movements (Ekstrom, et al., 2003; Ekstrom, et al., 2006), volcanic events (Schuler and Ekstrom, 2009) and landslides (Ekstrom and Stark, 2013). Furthermore, non-impulsive earthquakes have been located on oceanic transform faults (Abercrombie and Ekstrom, 2001). The current method (Ekstrom, 2006), that is applied on the scale of the globe, routinely detects events with magnitudes around Mw 5 and larger. In this work we wish to lower the detection threshold by using shorter period records registered by regional networks. The difficulty lies in that the shorter period records are strongly influenced by the heterogeneous crust and upper mantle, which need to be accounted for in the modeling process. Our proposed method involves first computing full waveforms, Green's functions or moment tensor responses, between a grid of test locations and existing seismic stations in a 3D medium. We then effectively back propagate observed data through cross correlation with the responses, obtaining a function that localizes in time and space at the source. Our method is a variant of the timereversal method presented by, for example, McMechan (1982), Tromp et al. (2005), Larmat et al. (2006), Gajewski and Tessmer (2005) and Kim et al. (2010). To calibrate the various parameters used by the detection method, we use the aftershocks sequence of the March 20, 2012 Ometepec, Guerrero, Mexico earthquake, recorded by the SSN (Mexican National Network). The lively aftershock sequence provided us with many events of different magnitudes, all occurring approximately

  14. Human Rights Event Detection from Heterogeneous Social Media Graphs.

    PubMed

    Chen, Feng; Neill, Daniel B

    2015-03-01

    Human rights organizations are increasingly monitoring social media for identification, verification, and documentation of human rights violations. Since manual extraction of events from the massive amount of online social network data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights-related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anomalousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clusters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and provides documentation such as links to videos and news reports. Building on our previous work that demonstrates the utility of NPHGS for civil unrest prediction and rare disease outbreak detection, we present an analysis of human rights events detected by NPHGS using two years of Twitter data from Mexico. NPHGS was able to accurately detect relevant clusters of human rights-related tweets prior to international news sources, and in some cases, prior to local news reports. Analysis of social media using NPHGS could enhance the information-gathering missions of human rights organizations by pinpointing specific abuses, revealing events and details that may be blocked from traditional media sources, and providing evidence of emerging patterns of human rights violations. This could lead to more timely, targeted, and effective advocacy, as well as other potential interventions. PMID:27442843

  15. Human Rights Event Detection from Heterogeneous Social Media Graphs.

    PubMed

    Chen, Feng; Neill, Daniel B

    2015-03-01

    Human rights organizations are increasingly monitoring social media for identification, verification, and documentation of human rights violations. Since manual extraction of events from the massive amount of online social network data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights-related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anomalousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clusters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and provides documentation such as links to videos and news reports. Building on our previous work that demonstrates the utility of NPHGS for civil unrest prediction and rare disease outbreak detection, we present an analysis of human rights events detected by NPHGS using two years of Twitter data from Mexico. NPHGS was able to accurately detect relevant clusters of human rights-related tweets prior to international news sources, and in some cases, prior to local news reports. Analysis of social media using NPHGS could enhance the information-gathering missions of human rights organizations by pinpointing specific abuses, revealing events and details that may be blocked from traditional media sources, and providing evidence of emerging patterns of human rights violations. This could lead to more timely, targeted, and effective advocacy, as well as other potential interventions.

  16. Detection of abnormal events via optical flow feature analysis.

    PubMed

    Wang, Tian; Snoussi, Hichem

    2015-03-24

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm.

  17. Detection of Abnormal Events via Optical Flow Feature Analysis

    PubMed Central

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  18. Context-aware event detection smartphone application for first responders

    NASA Astrophysics Data System (ADS)

    Boddhu, Sanjay K.; Dave, Rakesh P.; McCartney, Matt; West, James A.; Williams, Robert L.

    2013-05-01

    The rise of social networking platforms like Twitter, Facebook, etc…, have provided seamless sharing of information (as chat, video and other media) among its user community on a global scale. Further, the proliferation of the smartphones and their connectivity networks has powered the ordinary individuals to share and acquire information regarding the events happening in his/her immediate vicinity in a real-time fashion. This human-centric sensed data being generated in "human-as-sensor" approach is tremendously valuable as it delivered mostly with apt annotations and ground truth that would be missing in traditional machine-centric sensors, besides high redundancy factor (same data thru multiple users). Further, when appropriately employed this real-time data can support in detecting localized events like fire, accidents, shooting, etc…, as they unfold and pin-point individuals being affected by those events. This spatiotemporal information, when made available for first responders in the event vicinity (or approaching it) can greatly assist them to make effective decisions to protect property and life in a timely fashion. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications, that can provide a augmented reality view of the appropriate detected events in a given geographical location (localized) and also provide an event search capability over a large geographic extent. In its current state, the application thru its backend connectivity utilizes a data (Text & Image) processing framework, which deals with data challenges like; identifying and aggregating important events, analyzing and correlating the events temporally and spatially and building a search enabled event database. Further, the smartphone application with its backend data processing workflow has been successfully field tested with live user generated feeds.

  19. On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

    NASA Technical Reports Server (NTRS)

    Joshi, Suresh M.

    2012-01-01

    This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

  20. Human visual system-based smoking event detection

    NASA Astrophysics Data System (ADS)

    Odetallah, Amjad D.; Agaian, Sos S.

    2012-06-01

    Human action (e.g. smoking, eating, and phoning) analysis is an important task in various application domains like video surveillance, video retrieval, human-computer interaction systems, and so on. Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas, public parks, airplanes, hospitals, schools and others. The detection task is challenging since there is no prior knowledge about the object's shape, texture and color. In addition, its visual features will change under different lighting and weather conditions. This paper presents a new scheme of a system for detecting human smoking events, or small smoke, in a sequence of images. In developed system, motion detection and background subtraction are combined with motion-region-saving, skin-based image segmentation, and smoke-based image segmentation to capture potential smoke regions which are further analyzed to decide on the occurrence of smoking events. Experimental results show the effectiveness of the proposed approach. As well, the developed method is capable of detecting the small smoking events of uncertain actions with various cigarette sizes, colors, and shapes.

  1. A model-based approach for detection of objects in low resolution passive-millimeter wave images

    NASA Technical Reports Server (NTRS)

    Tang, Yuan-Liang; Devadiga, Sadashiva; Kasturi, Rangachar; Harris, Randall L., Sr.

    1993-01-01

    We describe a model-based vision system to assist pilots in landing maneuvers under restricted visibility conditions. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere; but, their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. We exploit these data to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. We also derive analytical expressions for the accuracy of the camera position estimate obtained by detecting the position of known objects in the image.

  2. Automatic event detection based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  3. Detecting rare gene transfer events in bacterial populations

    PubMed Central

    Nielsen, Kaare M.; Bøhn, Thomas; Townsend, Jeffrey P.

    2014-01-01

    Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research. PMID:24432015

  4. Detecting rare gene transfer events in bacterial populations.

    PubMed

    Nielsen, Kaare M; Bøhn, Thomas; Townsend, Jeffrey P

    2014-01-01

    Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research.

  5. Comparison of Event Detection Methods for Centralized Sensor Networks

    NASA Technical Reports Server (NTRS)

    Sauvageon, Julien; Agogiono, Alice M.; Farhang, Ali; Tumer, Irem Y.

    2006-01-01

    The development of an Integrated Vehicle Health Management (IVHM) for space vehicles has become a great concern. Smart Sensor Networks is one of the promising technologies that are catching a lot of attention. In this paper, we propose to a qualitative comparison of several local event (hot spot) detection algorithms in centralized redundant sensor networks. The algorithms are compared regarding their ability to locate and evaluate the event under noise and sensor failures. The purpose of this study is to check if the ratio performance/computational power of the Mote Fuzzy Validation and Fusion algorithm is relevant compare to simpler methods.

  6. Detection and interpretation of seismoacoustic events at German infrasound stations

    NASA Astrophysics Data System (ADS)

    Pilger, Christoph; Koch, Karl; Ceranna, Lars

    2016-04-01

    Three infrasound arrays with collocated or nearby installed seismometers are operated by the Federal Institute for Geosciences and Natural Resources (BGR) as the German National Data Center (NDC) for the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Infrasound generated by seismoacoustic events is routinely detected at these infrasound arrays, but air-to-ground coupled acoustic waves occasionally show up in seismometer recordings as well. Different natural and artificial sources like meteoroids as well as industrial and mining activity generate infrasonic signatures that are simultaneously detected at microbarometers and seismometers. Furthermore, many near-surface sources like earthquakes and explosions generate both seismic and infrasonic waves that can be detected successively with both technologies. The combined interpretation of seismic and acoustic signatures provides additional information about the origin time and location of remote infrasound events or about the characterization of seismic events distinguishing man-made and natural origins. Furthermore, seismoacoustic studies help to improve the modelling of infrasound propagation and ducting in the atmosphere and allow quantifying the portion of energy coupled into ground and into air by seismoacoustic sources. An overview of different seismoacoustic sources and their detection by German infrasound stations as well as some conclusions on the benefit of a combined seismoacoustic analysis are presented within this study.

  7. Adaptive Model-Based Mine Detection/Localization using Noisy Laser Doppler Vibration Measurements

    SciTech Connect

    Sullivan, E J; Xiang, N; Candy, J V

    2009-04-06

    The acoustic detection of buried mines is hampered by the fact that at the frequencies required for obtaining useful penetration, the energy is quickly absorbed by the ground. A recent approach which avoids this problem, is to excite the ground with a high-level low frequency sound, which excites low frequency resonances in the mine. These resonances cause a low-level vibration on the surface which can be detected by a Laser Doppler Vibrometer. This paper presents a method of quickly and efficiently detecting these vibrations by sensing a change in the statistics of the signal when the mine is present. Results based on real data are shown.

  8. Model-based assessment of the role of human-induced climate change in the 2005 Caribbean coral bleaching event.

    PubMed

    Donner, Simon D; Knutson, Thomas R; Oppenheimer, Michael

    2007-03-27

    Episodes of mass coral bleaching around the world in recent decades have been attributed to periods of anomalously warm ocean temperatures. In 2005, the sea surface temperature (SST) anomaly in the tropical North Atlantic that may have contributed to the strong hurricane season caused widespread coral bleaching in the Eastern Caribbean. Here, we use two global climate models to evaluate the contribution of natural climate variability and anthropogenic forcing to the thermal stress that caused the 2005 coral bleaching event. Historical temperature data and simulations for the 1870-2000 period show that the observed warming in the region is unlikely to be due to unforced climate variability alone. Simulation of background climate variability suggests that anthropogenic warming may have increased the probability of occurrence of significant thermal stress events for corals in this region by an order of magnitude. Under scenarios of future greenhouse gas emissions, mass coral bleaching in the Eastern Caribbean may become a biannual event in 20-30 years. However, if corals and their symbionts can adapt by 1-1.5 degrees C, such mass bleaching events may not begin to recur at potentially harmful intervals until the latter half of the century. The delay could enable more time to alter the path of greenhouse gas emissions, although long-term "committed warming" even after stabilization of atmospheric CO(2) levels may still represent an additional long-term threat to corals.

  9. Model-based assessment of the role of human-induced climate change in the 2005 Caribbean coral bleaching event

    SciTech Connect

    Donner, S.D.; Knutson, T.R.; Oppenheimer, M.

    2007-03-27

    Episodes of mass coral bleaching around the world in recent decades have been attributed to periods of anomalously warm ocean temperatures. In 2005, the sea surface temperature (SST) anomaly in the tropical North Atlantic that may have contributed to the strong hurricane season caused widespread coral bleaching in the Eastern Caribbean. Here, the authors use two global climate models to evaluate the contribution of natural climate variability and anthropogenic forcing to the thermal stress that caused the 2005 coral bleaching event. Historical temperature data and simulations for the 1870-2000 period show that the observed warming in the region is unlikely to be due to unforced climate variability alone. Simulation of background climate variability suggests that anthropogenic warming may have increased the probability of occurrence of significant thermal stress events for corals in this region by an order of magnitude. Under scenarios of future greenhouse gas emissions, mass coral bleaching in the Eastern Caribbean may become a biannual event in 20-30 years. However, if corals and their symbionts can adapt by 1-1.5{sup o}C, such mass bleaching events may not begin to recur at potentially harmful intervals until the latter half of the century. The delay could enable more time to alter the path of greenhouse gas emissions, although long-term 'committed warming' even after stabilization of atmospheric CO{sub 2} levels may still represent an additional long-term threat to corals.

  10. Multiple Regression Model Based Sequential Probability Ratio Test for Structural Change Detection of Time Series

    NASA Astrophysics Data System (ADS)

    Takeda, Katsunori; Hattori, Tetsuo; Kawano, Hiromichi

    In real time analysis and forecasting of time series data, it is important to detect the structural change as immediately, correctly, and simply as possible. And it is necessary for rebuilding the next prediction model after the change point as soon as possible. For this kind of time series data analysis, in general, multiple linear regression models are used. In this paper, we present two methods, i.e., Sequential Probability Ratio Test (SPRT) and Chow Test that is well-known in economics, and describe those experimental evaluations of the effectiveness in the change detection using the multiple regression models. Moreover, we extend the definition of the detected change point in the SPRT method, and show the improvement of the change detection accuracy.

  11. PMU Data Event Detection: A User Guide for Power Engineers

    SciTech Connect

    Allen, A.; Singh, M.; Muljadi, E.; Santoso, S.

    2014-10-01

    This user guide is intended to accompany a software package containing a Matrix Laboratory (MATLAB) script and related functions for processing phasor measurement unit (PMU) data. This package and guide have been developed by the National Renewable Energy Laboratory and the University of Texas at Austin. The objective of this data processing exercise is to discover events in the vast quantities of data collected by PMUs. This document attempts to cover some of the theory behind processing the data to isolate events as well as the functioning of the MATLAB scripts. The report describes (1) the algorithms and mathematical background that the accompanying MATLAB codes use to detect events in PMU data and (2) the inputs required from the user and the outputs generated by the scripts.

  12. Weakly supervised learning of a classifier for unusual event detection.

    PubMed

    Jäger, Mark; Knoll, Christian; Hamprecht, Fred A

    2008-09-01

    In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

  13. Gait event detection during stair walking using a rate gyroscope.

    PubMed

    Formento, Paola Catalfamo; Acevedo, Ruben; Ghoussayni, Salim; Ewins, David

    2014-03-19

    Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs.

  14. Gait Event Detection during Stair Walking Using a Rate Gyroscope

    PubMed Central

    Formento, Paola Catalfamo; Acevedo, Ruben; Ghoussayni, Salim; Ewins, David

    2014-01-01

    Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs. PMID:24651724

  15. Integrated Seismic Event Detection and Location by Advanced Array Processing

    SciTech Connect

    Kvaerna, T; Gibbons, S J; Ringdal, F; Harris, D B

    2007-02-09

    The principal objective of this two-year study is to develop and test a new advanced, automatic approach to seismic detection/location using array processing. We address a strategy to obtain significantly improved precision in the location of low-magnitude events compared with current fully-automatic approaches, combined with a low false alarm rate. We have developed and evaluated a prototype automatic system which uses as a basis regional array processing with fixed, carefully calibrated, site-specific parameters in conjuction with improved automatic phase onset time estimation. We have in parallel developed tools for Matched Field Processing for optimized detection and source-region identification of seismic signals. This narrow-band procedure aims to mitigate some of the causes of difficulty encountered using the standard array processing system, specifically complicated source-time histories of seismic events and shortcomings in the plane-wave approximation for seismic phase arrivals at regional arrays.

  16. Polygraph lie detection on real events in a laboratory setting.

    PubMed

    Bradley, M T; Cullen, M C

    1993-06-01

    This laboratory study dealt with real-life intense emotional events. Subjects generated embarrassing stories from their experience, then submitted to polygraph testing and, by lying, denied their stories and, by telling the truth, denied a randomly assigned story. Money was given as an incentive to be judged innocent on each story. An interrogator, blind to the stories, used Control Question Tests and found subjects more deceptive when lying than when truthful. Stories interacted with order such that lying on the second story was more easily detected than lying on the first. Embarrassing stories provide an alternative to the use of mock crimes to study lie detection in the laboratory.

  17. ARX model-based gearbox fault detection and localization under varying load conditions

    NASA Astrophysics Data System (ADS)

    Yang, Ming; Makis, Viliam

    2010-11-01

    The development of the fault detection schemes for gearbox systems has received considerable attention in recent years. Both time series modeling and feature extraction based on wavelet methods have been considered, mostly under constant load. Constant load assumption implies that changes in vibration data are caused only by deterioration of the gearbox. However, most real gearbox systems operate under varying load and speed which affect the vibration signature of the system and in general make it difficult to recognize the occurrence of an impending fault. This paper presents a novel approach to detect and localize the gear failure occurrence for a gearbox operating under varying load conditions. First, residual signal is calculated using an autoregressive model with exogenous variables (ARX) fitted to the time-synchronously averaged (TSA) vibration data and filtered TSA envelopes when the gearbox operated under various load conditions in the healthy state. The gear of interest is divided into several sections so that each section includes the same number of adjacent teeth. Then, the fault detection and localization indicator is calculated by applying F-test to the residual signal of the ARX model. The proposed fault detection scheme indicates not only when the gear fault occurs, but also in which section of the gear. Finally, the performance of the fault detection scheme is checked using full lifetime vibration data obtained from the gearbox operating from a new condition to a breakdown under varying load.

  18. Statistical language analysis for automatic exfiltration event detection.

    SciTech Connect

    Robinson, David Gerald

    2010-04-01

    This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

  19. FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

    PubMed Central

    Seeja, K. R.; Zareapoor, Masoumeh

    2014-01-01

    This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers. PMID:25302317

  20. FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

    PubMed

    Seeja, K R; Zareapoor, Masoumeh

    2014-01-01

    This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers. PMID:25302317

  1. FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

    PubMed

    Seeja, K R; Zareapoor, Masoumeh

    2014-01-01

    This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

  2. Hidden Markov Models for Detecting Aseismic Events in Southern California

    NASA Astrophysics Data System (ADS)

    Granat, R.

    2004-12-01

    We employ a hidden Markov model (HMM) to segment surface displacement time series collection by the Southern California Integrated Geodetic Network (SCIGN). These segmented time series are then used to detect regional events by observing the number of simultaneous mode changes across the network; if a large number of stations change at the same time, that indicates an event. The hidden Markov model (HMM) approach assumes that the observed data has been generated by an unobservable dynamical statistical process. The process is of a particular form such that each observation is coincident with the system being in a particular discrete state, which is interpreted as a behavioral mode. The dynamics are the model are constructed so that the next state is directly dependent only on the current state -- it is a first order Markov process. The model is completely described by a set of parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probability distribution of observable outputs associated with each state. The result of this approach is that our segmentation decisions are based entirely on statistical changes in the behavior of the observed daily displacements. In general, finding the optimal model parameters to fit the data is a difficult problem. We present an innovative model fitting method that is unsupervised (i.e., it requires no labeled training data) and uses a regularized version of the expectation-maximization (EM) algorithm to ensure that model solutions are both robust with respect to initial conditions and of high quality. We demonstrate the reliability of the method as compared to standard model fitting methods and show that it results in lower noise in the mode change correlation signal used to detect regional events. We compare candidate events detected by this method to the seismic record and observe that most are not correlated with a significant seismic event. Our analysis

  3. Model-based decision making in early clinical development: minimizing the impact of a blood pressure adverse event.

    PubMed

    Stroh, Mark; Addy, Carol; Wu, Yunhui; Stoch, S Aubrey; Pourkavoos, Nazaneen; Groff, Michelle; Xu, Yang; Wagner, John; Gottesdiener, Keith; Shadle, Craig; Wang, Hong; Manser, Kimberly; Winchell, Gregory A; Stone, Julie A

    2009-03-01

    We describe how modeling and simulation guided program decisions following a randomized placebo-controlled single-rising oral dose first-in-man trial of compound A where an undesired transient blood pressure (BP) elevation occurred in fasted healthy young adult males. We proposed a lumped-parameter pharmacokinetic-pharmacodynamic (PK/PD) model that captured important aspects of the BP homeostasis mechanism. Four conceptual units characterized the feedback PD model: a sinusoidal BP set point, an effect compartment, a linear effect model, and a system response. To explore approaches for minimizing the BP increase, we coupled the PD model to a modified PK model to guide oral controlled-release (CR) development. The proposed PK/PD model captured the central tendency of the observed data. The simulated BP response obtained with theoretical release rate profiles suggested some amelioration of the peak BP response with CR. This triggered subsequent CR formulation development; we used actual dissolution data from these candidate CR formulations in the PK/PD model to confirm a potential benefit in the peak BP response. Though this paradigm has yet to be tested in the clinic, our model-based approach provided a common rational framework to more fully utilize the limited available information for advancing the program.

  4. Automatic Detection of Student Mental Models Based on Natural Language Student Input during Metacognitive Skill Training

    ERIC Educational Resources Information Center

    Lintean, Mihai; Rus, Vasile; Azevedo, Roger

    2012-01-01

    This article describes the problem of detecting the student mental models, i.e. students' knowledge states, during the self-regulatory activity of prior knowledge activation in MetaTutor, an intelligent tutoring system that teaches students self-regulation skills while learning complex science topics. The article presents several approaches to…

  5. Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images

    NASA Astrophysics Data System (ADS)

    Acito, N.; Diani, M.; Corsini, G.

    2005-10-01

    Anomaly detectors reveal the presence of objects/materials in a multi/hyperspectral image simply searching for those pixels whose spectrum differs from the background one (anomalies). This procedure can be applied directly to the radiance at the sensor level and has the great advantage of avoiding the difficult step of atmospheric correction. The most popular anomaly detector is the RX algorithm derived by Yu and Reed. It is based on the assumption that the pixels, in a region around the one under test, follow a single multivariate Gaussian distribution. Unfortunately, such a hypothesis is generally not met in actual scenarios and a large number of false alarms is usually experienced when the RX algorithm is applied in practice. In this paper, a more general approach to anomaly detection is considered based on the assumption that the background contains different terrain types (clusters) each of them Gaussian distributed. In this approach the parameters of each cluster are estimated and used in the detection process. Two detectors are considered: the SEM-RX and the K-means RX. Both the algorithms follow two steps: first, 1) the parameters of the background clusters are estimated, then, 2) a detection rule based on the RX test is applied. The SEM-RX stems from the GMM and employs the SEM algorithm to estimate the clusters' parameters; instead, the K-means RX resorts to the well known K-means algorithm to obtain the background clusters. An automatic procedure is defined, for both the detectors, to select the number of clusters and a novel criterion is proposed to set the test threshold. The performances of the two detectors are also evaluated on an experimental data set and compared to the ones of the RX algorithm. The comparative analysis is carried out in terms of experimental Receiver Operating Characteristics.

  6. Hybrid light transport model based bioluminescence tomography reconstruction for early gastric cancer detection

    NASA Astrophysics Data System (ADS)

    Chen, Xueli; Liang, Jimin; Hu, Hao; Qu, Xiaochao; Yang, Defu; Chen, Duofang; Zhu, Shouping; Tian, Jie

    2012-03-01

    Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues. Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early gastric cancer detection.

  7. Towards perception awareness: Perceptual event detection for Brain computer interfaces.

    PubMed

    Nejati, Hossein; Tsourides, Kleovoulos; Pomponiu, Victor; Ehrenberg, Evan C; Ngai-Man Cheung; Sinha, Pawan

    2015-08-01

    Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting perceptual events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given perceptual event. As this "signature" is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.

  8. Endmember detection in marine environment with oil spill event

    NASA Astrophysics Data System (ADS)

    Andreou, Charoula; Karathanassi, Vassilia

    2011-11-01

    Oil spill events are a crucial environmental issue. Detection of oil spills is important for both oil exploration and environmental protection. In this paper, investigation of hyperspectral remote sensing is performed for the detection of oil spills and the discrimination of different oil types. Spectral signatures of different oil types are very useful, since they may serve as endmembers in unmixing and classification models. Towards this direction, an oil spectral library, resulting from spectral measurements of artificial oil spills as well as of look-alikes in marine environment was compiled. Samples of four different oil types were used; two crude oils, one marine residual fuel oil, and one light petroleum product. Lookalikes comprise sea water, river discharges, shallow water and water with algae. Spectral measurements were acquired with spectro-radiometer GER1500. Moreover, oil and look-alikes spectral signatures have been examined whether they can be served as endmembers. This was accomplished by testifying their linear independence. After that, synthetic hyperspectral images based on the relevant oil spectral library were created. Several simplex-based endmember algorithms such as sequential maximum angle convex cone (SMACC), vertex component analysis (VCA), n-finder algorithm (N-FINDR), and automatic target generation process (ATGP) were applied on the synthetic images in order to evaluate their effectiveness for detecting oil spill events occurred from different oil types. Results showed that different types of oil spills with various thicknesses can be extracted as endmembers.

  9. A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR

    NASA Technical Reports Server (NTRS)

    Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

    2010-01-01

    Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

  10. Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.

    PubMed

    Zheng, Ying; Fang, Huajing; Wang, Hua O

    2006-08-01

    A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches.

  11. An Automated Visual Event Detection System for Cabled Observatory Video

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Cline, D. E.; Mariette, J.

    2007-12-01

    The permanent presence of underwater cameras on oceanic cabled observatories, such as the Victoria Experimental Network Under the Sea (VENUS) and Eye-In-The-Sea (EITS) on Monterey Accelerated Research System (MARS), will generate valuable data that can move forward the boundaries of understanding the underwater world. However, sightings of underwater animal activities are rare, resulting in the recording of many hours of video with relatively few events of interest. The burden of video management and analysis often requires reducing the amount of video recorded and later analyzed. Sometimes enough human resources do not exist to analyze the video; the strains on human attention needed to analyze video demand an automated way to assist in video analysis. Towards this end, an Automated Visual Event Detection System (AVED) is in development at the Monterey Bay Aquarium Research Institute (MBARI) to address the problem of analyzing cabled observatory video. Here we describe the overall design of the system to process video data and enable science users to analyze the results. We present our results analyzing video from the VENUS observatory and test data from EITS deployments. This automated system for detecting visual events includes a collection of custom and open source software that can be run three ways: through a Web Service, through a Condor managed pool of AVED enabled compute servers, or locally on a single computer. The collection of software also includes a graphical user interface to preview or edit detected results and to setup processing options. To optimize the compute-intensive AVED algorithms, a parallel program has been implemented for high-data rate applications like the EITS instrument on MARS.

  12. The waveform correlation event detection system global prototype software design

    SciTech Connect

    Beiriger, J.I.; Moore, S.G.; Trujillo, J.R.; Young, C.J.

    1997-12-01

    The WCEDS prototype software system was developed to investigate the usefulness of waveform correlation methods for CTBT monitoring. The WCEDS prototype performs global seismic event detection and has been used in numerous experiments. This report documents the software system design, presenting an overview of the system operation, describing the system functions, tracing the information flow through the system, discussing the software structures, and describing the subsystem services and interactions. The effectiveness of the software design in meeting project objectives is considered, as well as opportunities for code refuse and lessons learned from the development process. The report concludes with recommendations for modifications and additions envisioned for regional waveform-correlation-based detector.

  13. A Pulse-type Hardware Level Difference Detection Model Based on Sound Source Localization Mechanism in Barn Owl

    NASA Astrophysics Data System (ADS)

    Sakurai, Tsubasa; Sekine, Yoshifumi

    Auditory information processing is very important in the darkness where vision information is extremely limited. Barn owls have excellent hearing information processing function. Barn owls can detect a sound source in the high accuracy of less than two degrees in both of the vertical and horizontal directions. When they perform the sound source localization, the barn owls use the interaural time difference for localization in the horizontal plane, and the interaural level difference for localization in the vertical plane. We are constructing the two-dimensional sound source localization model using pulse-type hardware neuron models based on sound source localization mechanism of barn owl for the purpose of the engineering application. In this paper, we propose a pulse-type hardware model for level difference detection based on sound source localization mechanism of barn owl. Firstly, we discuss the response characteristics of the mathematical model for level difference detection. Next we discuss the response characteristics of the hardware mode. As a result, we show clearly that this proposal model can be used as a sound source localization model of vertical direction.

  14. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

    PubMed Central

    Lu, Huiling; Zhang, Junjie; Shi, Hongbin

    2016-01-01

    In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees. PMID:27722173

  15. Detecting Rare Events in the Time-Domain

    SciTech Connect

    Rest, A; Garg, A

    2008-10-31

    One of the biggest challenges in current and future time-domain surveys is to extract the objects of interest from the immense data stream. There are two aspects to achieving this goal: detecting variable sources and classifying them. Difference imaging provides an elegant technique for identifying new transients or changes in source brightness. Much progress has been made in recent years toward refining the process. We discuss a selection of pitfalls that can afflict an automated difference imagine pipeline and describe some solutions. After identifying true astrophysical variables, we are faced with the challenge of classifying them. For rare events, such as supernovae and microlensing, this challenge is magnified because we must balance having selection criteria that select for the largest number of objects of interest against a high contamination rate. We discuss considerations and techniques for developing classification schemes.

  16. Efficient hemodynamic event detection utilizing relational databases and wavelet analysis

    NASA Technical Reports Server (NTRS)

    Saeed, M.; Mark, R. G.

    2001-01-01

    Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

  17. Barometric pressure and triaxial accelerometry-based falls event detection.

    PubMed

    Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Lovell, Nigel H

    2010-12-01

    Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.

  18. Event Detection and Spatial Analysis for Characterizing Extreme Precipitation

    NASA Astrophysics Data System (ADS)

    Jeon, S.; Prabhat, M.; Byna, S.; Collins, W.; Wehner, M. F.

    2013-12-01

    Atmospheric Rivers (ARs) are large spatially coherent weather systems with high concentrations of elevated water vapor that often cause severe downpours and flooding over western coastal United States. With the availability of more atmospheric moisture in the future under global warming, we expect ARs to play an important role as a potential cause of extreme precipitation. We have recently developed TECA software for automatically identifying and tracking features in climate datasets. In particular, we are able to identify ARs that make landfall on the western coast of North America. This detection tool examines integrated water vapor field above a certain threshold and performs geometric analysis. Based on the detection procedure, we investigate impacts of ARs by exploring spatial extent of AR precipitation for CMIP5 simulations, and characterize spatial pattern of dependence for future projections under climate change within the framework of extreme value theory. The results show that AR events in RCP8.5 scenario (2076-2100) tend to produce heavier rainfall with higher frequency and longer duration than the events from historical run (1981-2005). Range of spatial dependence between extreme precipitations is concentrated on smaller localized area in California under the highest emission scenario than present day. Preliminary results are illustrated in Figure 1 and 2. Fig 1: Boxplot of annual max precipitation (left two) and max AR precipitation (right two) from GFDL-ESM2M during 25-year time period by station in California, US. Fig 2: Spatial dependence of max AR precipitation calculated from Station 4 (triangle) for historical run (left) and for future projections of RCP8.5 (right) from GFDL-ESM2M. Green and orange colors represent complete dependence and independence between two stations respectively.

  19. Barometric pressure and triaxial accelerometry-based falls event detection.

    PubMed

    Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Lovell, Nigel H

    2010-12-01

    Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population. PMID:20805056

  20. Detecting Tidal Disruption Events (TDEs) with the Einstein Probe

    NASA Astrophysics Data System (ADS)

    Yuan, W.; Komossa, S.; Zhang, C.; Feng, H.; Zhang, S.; Osborne, J.; O'Brien, P.; Watson, M.; Fraser, G.

    2014-07-01

    Stars are tidally disrupted and accreted when they approach supermassive black holes (SMBHs) closely, producing a flare of electromagnetic radiation. The majority of the (approximately two dozen) tidal disruption events (TDEs) identified so far have been discovered by their luminous, transient X-ray emission. Once TDEs are detected in much larger numbers, in future dedicated transient surveys, a wealth of new applications will become possible. Including (1) TDE rate measurements in dependence of host galaxy types, (2) an assessment of the population of IMBHs, and (3) new probes of general relativity and accretion processes. Here, we present the proposed X-ray mission Einstein Probe}, which aims at detecting TDEs in large numbers. The mission consists of a wide-field micro-pore Lobster-eye imager (60deg x 60deg, or ˜1 ster), and is designed to carry out an all-sky transient survey at energies of 0.5-4 keV. It will also carry an X-ray telescope of the same micro-pore optics for follow-ups, with a smaller field-of-view. It will be capable of issuing public transient alerts rapidly.

  1. Communication of ALS Patients by Detecting Event-Related Potential

    NASA Astrophysics Data System (ADS)

    Kanou, Naoyuki; Sakuma, Kenji; Nakashima, Kenji

    Amyotrophic Lateral Sclerosis(ALS) patients are unable to successfully communicate their desires, although their mental capacity is the same as non-affected persons. Therefore, the authors put emphasis on Event-Related Potential(ERP) which elicits the highest outcome for the target visual and hearing stimuli. P300 is one component of ERP. It is positive potential that is elicited when the subject focuses attention on stimuli that appears infrequently. In this paper, the authors focused on P200 and N200 components, in addition to P300, for their great improvement in the rate of correct judgment in the target word-specific experiment. Hence the authors propose the algorithm that specifies target words by detecting these three components. Ten healthy subjects and ALS patient underwent the experiment in which a target word out of five words, was specified by this algorithm. The rates of correct judgment in nine of ten healthy subjects were more than 90.0%. The highest rate was 99.7%. The highest rate of ALS patient was 100.0%. Through these results, the authors found the possibility that ALS patients could communicate with surrounding persons by detecting ERP(P200, N200 and P300) as their desire.

  2. Apparatus and method for detecting full-capture radiation events

    DOEpatents

    Odell, Daniel M. C.

    1994-01-01

    An apparatus and method for sampling the output signal of a radiation detector and distinguishing full-capture radiation events from Compton scattering events. The output signal of a radiation detector is continuously sampled. The samples are converted to digital values and input to a discriminator where samples that are representative of events are identified. The discriminator transfers only event samples, that is, samples representing full-capture events and Compton events, to a signal processor where the samples are saved in a three-dimensional count matrix with time (from the time of onset of the pulse) on the first axis, sample pulse current amplitude on the second axis, and number of samples on the third axis. The stored data are analyzed to separate the Compton events from full-capture events, and the energy of the full-capture events is determined without having determined the energies of any of the individual radiation detector events.

  3. Apparatus and method for detecting full-capture radiation events

    DOEpatents

    Odell, D.M.C.

    1994-10-11

    An apparatus and method are disclosed for sampling the output signal of a radiation detector and distinguishing full-capture radiation events from Compton scattering events. The output signal of a radiation detector is continuously sampled. The samples are converted to digital values and input to a discriminator where samples that are representative of events are identified. The discriminator transfers only event samples, that is, samples representing full-capture events and Compton events, to a signal processor where the samples are saved in a three-dimensional count matrix with time (from the time of onset of the pulse) on the first axis, sample pulse current amplitude on the second axis, and number of samples on the third axis. The stored data are analyzed to separate the Compton events from full-capture events, and the energy of the full-capture events is determined without having determined the energies of any of the individual radiation detector events. 4 figs.

  4. Balloon-Borne Infrasound Detection of Energetic Bolide Events

    NASA Astrophysics Data System (ADS)

    Young, Eliot F.; Ballard, Courtney; Klein, Viliam; Bowman, Daniel; Boslough, Mark

    2016-10-01

    Infrasound is usually defined as sound waves below 20 Hz, the nominal limit of human hearing. Infrasound waves propagate over vast distances through the Earth's atmosphere: the CTBTO (Comprehensive Nuclear-Test-Ban Treaty Organization) has 48 installed infrasound-sensing stations around the world to detect nuclear detonations and other disturbances. In February 2013, several CTBTO infrasound stations detected infrasound signals from a large bolide that exploded over Chelyabinsk, Russia. Some stations recorded signals that had circumnavigated the Earth, over a day after the original event. The goal of this project is to improve upon the sensitivity of the CTBTO network by putting microphones on small, long-duration super-pressure balloons, with the overarching goal of studying the small end of the NEO population by using the Earth's atmosphere as a witness plate.A balloon-borne infrasound sensor is expected to have two advantages over ground-based stations: a lack of wind noise and a concentration of infrasound energy in the "stratospheric duct" between roughly 5 - 50 km altitude. To test these advantages, we have built a small balloon payload with five calibrated microphones. We plan to fly this payload on a NASA high-altitude balloon from Ft Sumner, NM in August 2016. We have arranged for three large explosions to take place in Socorro, NM while the balloon is aloft to assess the sensitivity of balloon-borne vs. ground-based infrasound sensors. We will report on the results from this test flight and the prospects for detecting/characterizing small bolides in the stratosphere.

  5. A system for the model based emergency detection and communication for the telerehabilitation training of cardiopulmonary patients.

    PubMed

    Helmer, Axel; Kretschmer, Friedrich; Deparade, Riana; Song, Bianying; Meis, Markus; Hein, Andreas; Marschollek, Michael; Tegtbur, Uwe

    2012-01-01

    Cardiopulmonary diseases affect millions of people and cause high costs in health care systems worldwide. Patients should perform regular endurance exercises to stabilize their health state and prevent further impairment. However, patients are often uncertain about the level of intensity they should exercise in their current condition. The cost of continuous monitoring for these training sessions in clinics is high and additionally requires the patient to travel to a clinic for each single session. Performing the rehabilitation training at home can raise compliance and reduce costs. To ensure safe telerehabilitation training and to enable patients to control their performance and health state, detection of abnormal events during training is a critical prerequisite. Therefore, we created a model that predicts the heart rate of cardiopulmonary patients and that can be used to detect and avoid abnormal health states. To enable external feedback and an immediate reaction in case of a critical situation, the patient should have the possibility to configure the system to communicate warnings and emergency events to clinical and non-clinical actors. To fulfill this task, we coupled a personal health record (PHR) with a new component that extends the classic home emergency systems. The PHR is also used for a training schedule definition that makes use of the predictive HR model. We used statistical methods to evaluate the prediction model and found that our prediction error of 3.2 heart beats per minute is precise enough to enable a detection of critical states. The concept for the communication of alerts was evaluated through focus group interviews with domain experts who judged that it fulfills the needs of potential users.

  6. Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

    NASA Astrophysics Data System (ADS)

    Bhatti, Sohail Masood; Khan, Muhammad Salman; Wuth, Jorge; Huenupan, Fernando; Curilem, Millaray; Franco, Luis; Yoma, Nestor Becerra

    2016-09-01

    In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.

  7. Detection of Local/Regional Events in Kuwait Using Next-Generation Detection Algorithms

    SciTech Connect

    Gok, M. Rengin; Al-Jerri, Farra; Dodge, Douglas; Al-Enezi, Abdullah; Hauk, Terri; Mellors, R.

    2014-12-10

    Seismic networks around the world use conventional triggering algorithms to detect seismic signals in order to locate local/regional seismic events. Kuwait National Seismological Network (KNSN) of Kuwait Institute of Scientific Research (KISR) is operating seven broad-band and short-period three-component stations in Kuwait. The network is equipped with Nanometrics digitizers and uses Antelope and Guralp acquisition software for processing and archiving the data. In this study, we selected 10 days of archived hourly-segmented continuous data of five stations (Figure 1) and 250 days of continuous recording at MIB. For the temporary deployment our selection criteria was based on KNSN catalog intensity for the period of time we test the method. An autonomous event detection and clustering framework is employed to test a more complete catalog of this short period of time. The goal is to illustrate the effectiveness of the technique and pursue the framework for longer period of time.

  8. Using REDItools to Detect RNA Editing Events in NGS Datasets.

    PubMed

    Picardi, Ernesto; D'Erchia, Anna Maria; Montalvo, Antonio; Pesole, Graziano

    2015-03-09

    RNA editing is a post-transcriptional/co-transcriptional molecular phenomenon whereby a genetic message is modified from the corresponding DNA template by means of substitutions, insertions, and/or deletions. It occurs in a variety of organisms and different cellular locations through evolutionally and biochemically unrelated proteins. RNA editing has a plethora of biological effects including the modulation of alternative splicing and fine-tuning of gene expression. RNA editing events by base substitutions can be detected on a genomic scale by NGS technologies through the REDItools package, an ad hoc suite of Python scripts to study RNA editing using RNA-Seq and DNA-Seq data or RNA-Seq data alone. REDItools implement effective filters to minimize biases due to sequencing errors, mapping errors, and SNPs. The package is freely available at Google Code repository (http://code.google.com/p/reditools/) and released under the MIT license. In the present unit we show three basic protocols corresponding to three main REDItools scripts.

  9. Video Event Detection: From Subvolume Localization To Spatio-Temporal Path Search.

    PubMed

    Tran, Du; Yuan, Junsong; Forsyth, David

    2013-07-23

    Although sliding window-based approaches have been quite successful in detecting objects in images, it is not a trivial problem to extend them to detecting events in videos. We propose to search for spatio-temporal paths for video event detection. This new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intra-class variations of the event. Compared to event detection using spatio-temporal sliding windows, the spatio-temporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video datasets with different event detection tasks, such as anomaly event detection, walking person detection, and running detection. Our proposed method is compatible to different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-the-art methods. PMID:23898011

  10. Large Time Projection Chambers for Rare Event Detection

    SciTech Connect

    Heffner, M

    2009-11-03

    The Time Projection Chamber (TPC) concept [add ref to TPC section] has been applied to many projects outside of particle physics and the accelerator based experiments where it was initially developed. TPCs in non-accelerator particle physics experiments are principally focused on rare event detection (e.g. neutrino and darkmater experiments) and the physics of these experiments can place dramatically different constraints on the TPC design (only extensions to the traditional TPCs are discussed here). The drift gas, or liquid, is usually the target or matter under observation and due to very low signal rates a TPC with the largest active mass is desired. The large mass complicates particle tracking of short and sometimes very low energy particles. Other special design issues include, efficient light collection, background rejection, internal triggering and optimal energy resolution. Backgrounds from gamma-rays and neutrons are significant design issues in the construction of these TPCs. They are generally placed deep underground to shield from cosmogenic particles and surrounded with shielding to reduce radiation from the local surroundings. The construction materials have to be carefully screened for radiopurity as they are in close contact with the active mass and can be a signification source of background events. The TPC excels in reducing this internal background because the mass inside the fieldcage forms one monolithic volume from which fiducial cuts can be made ex post facto to isolate quiet drift mass, and can be circulated and purified to a very high level. Self shielding in these large mass systems can be significant and the effect improves with density. The liquid phase TPC can obtain a high density at low pressure which results in very good self-shielding and compact installation with a lightweight containment. The down sides are the need for cryogenics, slower charge drift, tracks shorter than the typical electron diffusion, lower energy resolution (e

  11. A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation.

    PubMed

    Senanayake, Chathuri; Senanayake, S M N Arosha

    2011-10-01

    In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.

  12. A model of human event detection in multiple process monitoring situations

    NASA Technical Reports Server (NTRS)

    Greenstein, J. S.; Rouse, W. B.

    1978-01-01

    It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects.

  13. Method and apparatus for detecting and determining event characteristics with reduced data collection

    NASA Technical Reports Server (NTRS)

    Totman, Peter D. (Inventor); Everton, Randy L. (Inventor); Egget, Mark R. (Inventor); Macon, David J. (Inventor)

    2007-01-01

    A method and apparatus for detecting and determining event characteristics such as, for example, the material failure of a component, in a manner which significantly reduces the amount of data collected. A sensor array, including a plurality of individual sensor elements, is coupled to a programmable logic device (PLD) configured to operate in a passive state and an active state. A triggering event is established such that the PLD records information only upon detection of the occurrence of the triggering event which causes a change in state within one or more of the plurality of sensor elements. Upon the occurrence of the triggering event, the change in state of the one or more sensor elements causes the PLD to record in memory which sensor element detected the event and at what time the event was detected. The PLD may be coupled with a computer for subsequent downloading and analysis of the acquired data.

  14. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    PubMed Central

    Mur, Angel; Dormido, Raquel; Vega, Jesús; Duro, Natividad; Dormido-Canto, Sebastian

    2016-01-01

    In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time. PMID:27120605

  15. Motor task event detection using Subthalamic Nucleus Local Field Potentials.

    PubMed

    Niketeghad, Soroush; Hebb, Adam O; Nedrud, Joshua; Hanrahan, Sara J; Mahoor, Mohammad H

    2015-08-01

    Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC). PMID:26737550

  16. Contamination event detection using multiple types of conventional water quality sensors in source water.

    PubMed

    Liu, Shuming; Che, Han; Smith, Kate; Chen, Lei

    2014-08-01

    Early warning systems are often used to detect deliberate and accidental contamination events in a water system. Conventional methods normally detect a contamination event by comparing the predicted and observed water quality values from one sensor. This paper proposes a new method for event detection by exploring the correlative relationships between multiple types of conventional water quality sensors. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory. Results from these experiments demonstrated the correlative responses of multiple types of sensors. It was observed that the proposed method could detect a contamination event 9 minutes after the introduction of lead nitrate solution with a concentration of 0.01 mg L(-1). The proposed method employs three parameters. Their impact on the detection performance was also analyzed. The initial analysis showed that the correlative response is contaminant-specific, which implies that it can be utilized not only for contamination detection, but also for contaminant identification.

  17. Improved detection of event-related functional MRI signals using probability functions.

    PubMed

    Hagberg, G E; Zito, G; Patria, F; Sanes, J N

    2001-11-01

    Selecting an optimal event distribution for experimental use in event-related fMRI studies can require the generation of large numbers of event sequences with characteristics hard to control. The use of known probability distributions offers the possibility to control event timing and constrain the search space for finding optimal event sequences. We investigated different probability distributions in terms of response estimation (estimation efficiency), detectability (detection power, parameter estimation efficiency, sensitivity to true positives), and false-positive activation. Numerous simulated event sequences were generated selecting interevent intervals (IEI) from the uniform, uniform permuted, Latin square, exponential, binomial, Poisson, chi(2), geometric, and bimodal probability distributions and fixed IEI. Event sequences from the bimodal distribution, like block designs, had the best performance for detection and the poorest for estimation, while high estimation and detectability occurred for the long-decay exponential distribution. The uniform distribution also yielded high estimation efficiency, but probability functions with a long tail toward higher IEI, such as the geometric and the chi(2) distributions, had superior detectability. The distributions with the best detection performance also had a relatively high incidence of false positives, in contrast to the ordered distributions (Latin square and uniform permuted). The predictions of improved sensitivities for distributions with long tails were confirmed with empirical data. Moreover, the Latin square design yielded detection of activated voxels similar to the chi(2) distribution. These results indicate that high detection and suitable behavioral designs have compatibility for application of functional MRI methods to experiments requiring complex designs.

  18. Nonthreshold-based event detection for 3d environment monitoring in sensor networks

    SciTech Connect

    Li, M.; Liu, Y.H.; Chen, L.

    2008-12-15

    Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values and, thus, are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a nonthreshold-based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatiotemporal data patterns. Finally, we conduct trace-driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.

  19. Power System Extreme Event Detection: The VulnerabilityFrontier

    SciTech Connect

    Lesieutre, Bernard C.; Pinar, Ali; Roy, Sandip

    2007-10-17

    In this work we apply graph theoretic tools to provide aclose bound on a frontier relating the number of line outages in a gridto the power disrupted by the outages. This frontier describes theboundary of a space relating the possible severity of a disturbance interms of power disruption, from zero to some maximum on the boundary, tothe number line outages involved in the event. We present the usefulnessof this analysis with a complete analysis of a 30 bus system, and presentresults for larger systems.

  20. Falls event detection using triaxial accelerometry and barometric pressure measurement.

    PubMed

    Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

    2009-01-01

    A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

  1. Neuro-evolutionary event detection technique for downhole microseismic surveys

    NASA Astrophysics Data System (ADS)

    Maity, Debotyam; Salehi, Iraj

    2016-01-01

    Recent years have seen a significant increase in borehole microseismic data acquisition programs associated with unconventional reservoir developments such as hydraulic fracturing programs for shale oil and gas. The data so acquired is used for hydraulic fracture monitoring and diagnostics and therefore, the quality of the data in terms of resolution and accuracy has a significant impact on its value to the industry. Borehole microseismic data acquired in such environments typically suffer from propagation effects due to the presence of thin interbedded shale layers as well as noise and interference effects. Moreover, acquisition geometry has significant impact on detectability across portions of the sensor array. Our work focuses on developing robust first arrival detection and pick selection workflow for both P and S waves specifically designed for such environments. We introduce a novel workflow for refinement of picks with immunity towards significant noise artifacts and applicability over data with very low signal-to-noise ratio provided some accurate picks have already been made. This workflow utilizes multi-step hybrid detection and classification routine which makes use of a neural network based autopicker for initial picking and an evolutionary algorithm for pick refinement. We highlight the results from an actual field case study including multiple examples demonstrating immunity towards noise and compare the effectiveness of the workflow with two contemporary autopicking routines without the application of the shared detection/refinement procedure. Finally, we use a windowed waveform cross-correlation based uncertainty estimation method for potential quality control purposes. While the workflow was developed to work with the neural network based autopicker, it can be used with any other traditional autopicker and provides significant improvements in pick detection across seismic gathers.

  2. Spatial-temporal event detection in climate parameter imagery.

    SciTech Connect

    McKenna, Sean Andrew; Gutierrez, Karen A.

    2011-10-01

    Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.

  3. Detection of intermittent events in atmospheric time series

    NASA Astrophysics Data System (ADS)

    Paradisi, P.; Cesari, R.; Palatella, L.; Contini, D.; Donateo, A.

    2009-04-01

    The modeling approach in atmospheric sciences is based on the assumption that local fluxes of mass, momentum, heat, etc... can be described as linear functions of the local gradient of some intensive property (concentration, flow strain, temperature,...). This is essentially associated with Gaussian statistics and short range (exponential) correlations. However, the atmosphere is a complex dynamical system displaying a wide range of spatial and temporal scales. A global description of the atmospheric dynamics should include a great number of degrees of freedom, strongly interacting on several temporal and spatial scales, thus generating long range (power-law) correlations and non-Gaussian distribution of fluctuations (Lévy flights, Lévy walks, Continuous Time Random Walks) [1]. This is typically associated with anomalous diffusion and scaling, non-trivial memory features and correlation decays and, especially, with the emergence of flux-gradient relationships that are non-linear and/or non-local in time and/or space. Actually, the local flux-gradient relationship is greatly preferred due to a more clear physical meaning, allowing to perform direct comparisons with experimental data, and, especially, to smaller computational costs in numerical models. In particular, the linearity of this relationship allows to define a transport coefficient (e.g., turbulent diffusivity) and the modeling effort is usually focused on this coefficient. However, the validity of the local (and linear) flux-gradient model is strongly dependent on the range of spatial and temporal scales represented by the model and, consequently, by the sub-grid processes included in the flux-gradient relationship. In this work, in order to check the validity of local and linear flux-gradient relationships, an approach based on the concept of renewal critical events [2] is introduced. In fact, in renewal theory [2], the dynamical origin of anomalous behaviour and non-local flux-gradient relation is

  4. A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

    PubMed Central

    Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

    2014-01-01

    The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

  5. Minimal elastographic modeling of breast cancer for model based tumor detection in a digital image elasto tomography (DIET) system

    NASA Astrophysics Data System (ADS)

    Lotz, Thomas F.; Muller, Natalie; Hann, Christopher E.; Chase, J. Geoffrey

    2011-03-01

    Digital Image Elasto Tomography (DIET) is a non-invasive breast cancer screening technology that images the surface motion of a breast under harmonic mechanical actuation. A new approach capturing the dynamics and characteristics of tumor behavior is presented. A simple mechanical model of the breast is used to identify a transfer function relating the input harmonic actuation to the output surface displacements using imaging data of a silicone phantom. Areas of higher stiffness cause significant changes of damping and resonant frequencies as seen in the resulting Bode plots. A case study on a healthy and tumor silicone breast phantom shows the potential for this model-based method to clearly distinguish cancerous and healthy tissue as well as correctly predicting the tumor position.

  6. Object-Oriented Query Language For Events Detection From Images Sequences

    NASA Astrophysics Data System (ADS)

    Ganea, Ion Eugen

    2015-09-01

    In this paper is presented a method to represent the events extracted from images sequences and the query language used for events detection. Using an object oriented model the spatial and temporal relationships between salient objects and also between events are stored and queried. This works aims to unify the storing and querying phases for video events processing. The object oriented language syntax used for events processing allow the instantiation of the indexes classes in order to improve the accuracy of the query results. The experiments were performed on images sequences provided from sport domain and it shows the reliability and the robustness of the proposed language. To extend the language will be added a specific syntax for constructing the templates for abnormal events and for detection of the incidents as the final goal of the research.

  7. Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

    PubMed Central

    Zhao, Liang; Chen, Feng; Dai, Jing; Hua, Ting; Lu, Chang-Tien; Ramakrishnan, Naren

    2014-01-01

    Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. PMID:25350136

  8. Seismic network detection probability assessment using waveforms and accounting to event association logic

    NASA Astrophysics Data System (ADS)

    Pinsky, Vladimir; Shapira, Avi

    2016-05-01

    The geographical area where a seismic event of magnitude M ≥ M t is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.

  9. Find Your Manners: How Do Infants Detect the Invariant Manner of Motion in Dynamic Events?

    ERIC Educational Resources Information Center

    Pruden, Shannon M.; Goksun, Tilbe; Roseberry, Sarah; Hirsh-Pasek, Kathy; Golinkoff, Roberta M.

    2012-01-01

    To learn motion verbs, infants must be sensitive to the specific event features lexicalized in their language. One event feature important for the acquisition of English motion verbs is the manner of motion. This article examines when and how infants detect manners of motion across variations in the figure's path. Experiment 1 shows that 13- to…

  10. An integrated logit model for contamination event detection in water distribution systems.

    PubMed

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

    The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies.

  11. Comparison of the STA/LTA and power spectral density methods for microseismic event detection

    NASA Astrophysics Data System (ADS)

    Vaezi, Yoones; Van der Baan, Mirko

    2015-12-01

    Robust event detection and picking is a prerequisite for reliable (micro-) seismic interpretations. Detection of weak events is a common challenge among various available event detection algorithms. In this paper we compare the performance of two event detection methods, the short-term average/long-term average (STA/LTA) method, which is the most commonly used technique in industry, and a newly introduced method that is based on the power spectral density (PSD) measurements. We have applied both techniques to a 1-hr long segment of the vertical component of some raw continuous data recorded at a borehole geophone in a hydraulic fracturing experiment. The PSD technique outperforms the STA/LTA technique by detecting a higher number of weak events while keeping the number of false alarms at a reasonable level. The time-frequency representations obtained through the PSD method can also help define a more suitable bandpass filter which is usually required for the STA/LTA method. The method offers thus much promise for automated event detection in industrial, local, regional and global seismological data sets.

  12. The waveform correlation event detection system project, Phase I: Issues in prototype development and testing

    SciTech Connect

    Young, C.; Harris, M.; Beiriger, J.; Moore, S.; Trujillo, J.; Withers, M.; Aster, R.

    1996-08-01

    A study using long-period seismic data showed that seismic events can be detected and located based on correlations of processed waveform profiles with the profile expected for an event. In this technique both time and space are discretized and events are found by forming profiles and calculating correlations for all time-distance points. events are declared at points with large correlations. In the first phase of the Waveform Correlation Event Detection System (WCEDS) Project at Sandia Labs we have developed a prototype automatic event detection system based on Shearer`s work which shows promise for treaty monitoring applications. Many modifications have been made to meet the requirements of the monitoring environment. A new full matrix multiplication has been developed which can reduce the number of computations needed for the data correlation by as much as two orders of magnitude for large grids. New methodology has also been developed to deal with the problems caused by false correlations (sidelobes) generated during the correlation process. When an event has been detected, masking matrices are set up which will mask all correlation sidelobes due to the event, allowing other events with intermingled phases to be found. This process is repeated until a detection threshold is reached. The system was tested on one hour of Incorporated Research Institutions for Seismology (IRIS) broadband data and built all 4 of the events listed in the National Earthquake Information Center (NEIC) Preliminary Determination of Epicenters (PDE) which were observable by the IRIS network. A continuous execution scheme has been developed for the system but has not yet been implemented. Improvements to the efficiency of the code are in various stages of development. Many refinements would have to be made to the system before it could be used as part of an actual monitoring system, but at this stage we know of no clear barriers which would prevent an eventual implementation of the system.

  13. Supervisory control design based on hybrid systems and fuzzy events detection. Application to an oxichlorination reactor.

    PubMed

    Altamiranda, Edmary; Torres, Horacio; Colina, Eliezer; Chacón, Edgar

    2002-10-01

    This paper presents a supervisory control scheme based on hybrid systems theory and fuzzy events detection. The fuzzy event detector is a linguistic model, which synthesizes complex relations between process variables and process events incorporating experts' knowledge about the process operation. This kind of detection allows the anticipation of appropriate control actions, which depend upon the selected membership functions used to characterize the process under scrutiny. The proposed supervisory control scheme was successfully implemented for an oxichlorination reactor in a vinyl monomer plant. This implementation has allowed improvement of reactor stability and reduction of raw material consumption. PMID:12398279

  14. Qualitative and event-specific real-time PCR detection methods for Bt brinjal event EE-1.

    PubMed

    Randhawa, Gurinder Jit; Sharma, Ruchi; Singh, Monika

    2012-01-01

    Bt brinjal event EE-1 with cry1Ac gene, expressing insecticidal protein against fruit and shoot borer, is the first genetically modified food crop in the pipeline for commercialization in India. Qualitative polymerase chain reaction (PCR) along with event-specific conventional as well as real-time PCR methods to characterize the event EE-1 is reported. A multiplex (pentaplex) PCR system simultaneously amplifying cry1Ac transgene, Cauliflower Mosaic Virus (CaMV) 35S promoter, nopaline synthase (nos) terminator, aminoglycoside adenyltransferase (aadA) marker gene, and a taxon-specific beta-fructosidase gene in event EE-1 has been developed. Furthermore, construct-specific PCR, targeting the approximate 1.8 kb region of inserted gene construct comprising the region of CaMV 35S promoter and cry1Ac gene has also been developed. The LOD of developed EE-1 specific conventional PCR assay is 0.01%. The method performance of the reported real-time PCR assay was consistent with the acceptance criteria of Codex Alimentarius Commission ALINORM 10/33/23, with the LOD and LOQ values of 0.05%. The developed detection methods would not only facilitate effective regulatory compliance for identification of genetic traits, risk assessment, management, and postrelease monitoring, but also address consumer concerns and resolution of legal disputes. PMID:23451391

  15. Qualitative and event-specific real-time PCR detection methods for Bt brinjal event EE-1.

    PubMed

    Randhawa, Gurinder Jit; Sharma, Ruchi; Singh, Monika

    2012-01-01

    Bt brinjal event EE-1 with cry1Ac gene, expressing insecticidal protein against fruit and shoot borer, is the first genetically modified food crop in the pipeline for commercialization in India. Qualitative polymerase chain reaction (PCR) along with event-specific conventional as well as real-time PCR methods to characterize the event EE-1 is reported. A multiplex (pentaplex) PCR system simultaneously amplifying cry1Ac transgene, Cauliflower Mosaic Virus (CaMV) 35S promoter, nopaline synthase (nos) terminator, aminoglycoside adenyltransferase (aadA) marker gene, and a taxon-specific beta-fructosidase gene in event EE-1 has been developed. Furthermore, construct-specific PCR, targeting the approximate 1.8 kb region of inserted gene construct comprising the region of CaMV 35S promoter and cry1Ac gene has also been developed. The LOD of developed EE-1 specific conventional PCR assay is 0.01%. The method performance of the reported real-time PCR assay was consistent with the acceptance criteria of Codex Alimentarius Commission ALINORM 10/33/23, with the LOD and LOQ values of 0.05%. The developed detection methods would not only facilitate effective regulatory compliance for identification of genetic traits, risk assessment, management, and postrelease monitoring, but also address consumer concerns and resolution of legal disputes.

  16. Real-time detection and classification of anomalous events in streaming data

    DOEpatents

    Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

    2016-04-19

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

  17. A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection

    PubMed Central

    2014-01-01

    Background In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known. Results To deal with small samples for particular subfamilies of HIV-1, we introduce a novel model-based information sharing protocol. It estimates the emission probabilities of the pHMM modeling a particular subfamily not only based on the nucleotide frequencies of the respective subfamily but also incorporating the nucleotide frequencies of all available subfamilies. To this end, the underlying probabilistic model mimics the pattern of commonality and variation between the subtypes with regards to the biological characteristics of HI viruses. In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine. Conclusions We apply the modified algorithm to classify HIV-1 sequence data in the form of partial HIV-1 sequences and semi-artificial recombinants. Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique. Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning. PMID:24946781

  18. Event Detection and Visualization of Ocean Eddies based on SSH and Velocity Field

    NASA Astrophysics Data System (ADS)

    Matsuoka, Daisuke; Araki, Fumiaki; Inoue, Yumi; Sasaki, Hideharu

    2016-04-01

    Numerical studies of ocean eddies have been progressed using high-resolution ocean general circulation models. In order to understand ocean eddies from simulation results with large amount of information volume, it is necessary to visualize not only distribution of eddies of each time step, but also events or phenomena of eddies. However, previous methods cannot precisely detect eddies, especially, during the events such as eddies' amalgamation, bifurcation. In the present study, we propose a new approach of eddy's detection, tracking and event visualization based on sea surface height (SSH) and velocity field. The proposed method detects eddies region as well as streams and currents region, and classifies detected eddies into several types. By tracking the time-varying change of classified eddies, it is possible to detect not only eddies event such as amalgamation and bifurcation but also the interaction between eddy and ocean current. As a result of visualizing detected eddies and events, we succeeded in creating the movie which enables us to intuitively understand the region of interest.

  19. The Cognitive Processes Underlying Event-Based Prospective Memory In School Age Children and Young Adults: A Formal Model-Based Study

    PubMed Central

    Smith, Rebekah E.; Bayen, Ute Johanna; Martin, Claudia

    2010-01-01

    Fifty 7-year-olds (29 female), 53 10-year-olds (29 female), and 36 young adults (19 female), performed a computerized event-based prospective memory task. All three groups differed significantly in prospective memory performance with adults showing the best performance and 7-year-olds the poorest performance. We used a formal multinomial process tree model of event-based prospective memory to decompose age differences in cognitive processes that jointly contribute to prospective memory performance. The formal modeling results demonstrated that adults differed significantly from the 7-year-olds and 10-year-olds on both the prospective component and the retrospective component of the task. The 7-year-olds and 10-year-olds differed only in the ability to recognize prospective memory target events. The prospective memory task imposed a cost to ongoing activities in all three age groups. PMID:20053020

  20. Event Detection in Aerospace Systems using Centralized Sensor Networks: A Comparative Study of Several Methodologies

    NASA Technical Reports Server (NTRS)

    Mehr, Ali Farhang; Sauvageon, Julien; Agogino, Alice M.; Tumer, Irem Y.

    2006-01-01

    Recent advances in micro electromechanical systems technology, digital electronics, and wireless communications have enabled development of low-cost, low-power, multifunctional miniature smart sensors. These sensors can be deployed throughout a region in an aerospace vehicle to build a network for measurement, detection and surveillance applications. Event detection using such centralized sensor networks is often regarded as one of the most promising health management technologies in aerospace applications where timely detection of local anomalies has a great impact on the safety of the mission. In this paper, we propose to conduct a qualitative comparison of several local event detection algorithms for centralized redundant sensor networks. The algorithms are compared with respect to their ability to locate and evaluate an event in the presence of noise and sensor failures for various node geometries and densities.

  1. Cooperative object tracking and composite event detection with wireless embedded smart cameras.

    PubMed

    Wang, Youlu; Velipasalar, Senem; Casares, Mauricio

    2010-10-01

    Embedded smart cameras have limited processing power, memory, energy, and bandwidth. Thus, many system- and algorithm-wise challenges remain to be addressed to have operational, battery-powered wireless smart-camera networks. We present a wireless embedded smart-camera system for cooperative object tracking and detection of composite events spanning multiple camera views. Each camera is a CITRIC mote consisting of a camera board and wireless mote. Lightweight and robust foreground detection and tracking algorithms are implemented on the camera boards. Cameras exchange small-sized data wirelessly in a peer-to-peer manner. Instead of transferring or saving every frame or trajectory, events of interest are detected. Simpler events are combined in a time sequence to define semantically higher-level events. Event complexity can be increased by increasing the number of primitives and/or number of camera views they span. Examples of consistently tracking objects across different cameras, updating location of occluded/lost objects from other cameras, and detecting composite events spanning two or three camera views, are presented. All the processing is performed on camera boards. Operating current plots of smart cameras, obtained when performing different tasks, are also presented. Power consumption is analyzed based upon these measurements. PMID:20551001

  2. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    SciTech Connect

    Grimm, Lars J. Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-03-15

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

  3. Assessing the Probability of Detection of Horizontal Gene Transfer Events in Bacterial Populations

    PubMed Central

    Townsend, Jeffrey P.; Bøhn, Thomas; Nielsen, Kaare Magne

    2012-01-01

    Experimental approaches to identify horizontal gene transfer (HGT) events of non-mobile DNA in bacteria have typically relied on detection of the initial transformants or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to be detected in a short time frame. Population genetic modeling of the growth dynamics of bacterial genotypes is therefore necessary to account for natural selection and genetic drift during the time lag and to predict realistic time frames for detection with a given sampling design. Here we draw on statistical approaches to population genetic theory to construct a cohesive probabilistic framework for investigation of HGT of exogenous DNA into bacteria. In particular, the stochastic timing of rare HGT events is accounted for. Integrating over all possible event timings, we provide an equation for the probability of detection, given that HGT actually occurred. Furthermore, we identify the key variables determining the probability of detecting HGT events in four different case scenarios that are representative of bacterial populations in various environments. Our theoretical analysis provides insight into the temporal aspects of dissemination of genetic material, such as antibiotic resistance genes or transgenes present in genetically modified organisms. Due to the long time scales involved and the exponential growth of bacteria with differing fitness, quantitative analyses incorporating bacterial generation time, and levels of selection, such as the one presented here, will be a necessary component of any future experimental design and analysis of HGT as it occurs in natural settings. PMID:22363321

  4. Assessing the probability of detection of horizontal gene transfer events in bacterial populations.

    PubMed

    Townsend, Jeffrey P; Bøhn, Thomas; Nielsen, Kaare Magne

    2012-01-01

    Experimental approaches to identify horizontal gene transfer (HGT) events of non-mobile DNA in bacteria have typically relied on detection of the initial transformants or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to be detected in a short time frame. Population genetic modeling of the growth dynamics of bacterial genotypes is therefore necessary to account for natural selection and genetic drift during the time lag and to predict realistic time frames for detection with a given sampling design. Here we draw on statistical approaches to population genetic theory to construct a cohesive probabilistic framework for investigation of HGT of exogenous DNA into bacteria. In particular, the stochastic timing of rare HGT events is accounted for. Integrating over all possible event timings, we provide an equation for the probability of detection, given that HGT actually occurred. Furthermore, we identify the key variables determining the probability of detecting HGT events in four different case scenarios that are representative of bacterial populations in various environments. Our theoretical analysis provides insight into the temporal aspects of dissemination of genetic material, such as antibiotic resistance genes or transgenes present in genetically modified organisms. Due to the long time scales involved and the exponential growth of bacteria with differing fitness, quantitative analyses incorporating bacterial generation time, and levels of selection, such as the one presented here, will be a necessary component of any future experimental design and analysis of HGT as it occurs in natural settings.

  5. Model-based analysis supports interglacial refugia over long-dispersal events in the diversification of two South American cactus species.

    PubMed

    Perez, M F; Bonatelli, I A S; Moraes, E M; Carstens, B C

    2016-06-01

    Pilosocereus machrisii and P. aurisetus are cactus species within the P. aurisetus complex, a group of eight cacti that are restricted to rocky habitats within the Neotropical savannas of eastern South America. Previous studies have suggested that diversification within this complex was driven by distributional fragmentation, isolation leading to allopatric differentiation, and secondary contact among divergent lineages. These events have been associated with Quaternary climatic cycles, leading to the hypothesis that the xerophytic vegetation patches which presently harbor these populations operate as refugia during the current interglacial. However, owing to limitations of the standard phylogeographic approaches used in these studies, this hypothesis was not explicitly tested. Here we use Approximate Bayesian Computation to refine the previous inferences and test the role of different events in the diversification of two species within P. aurisetus group. We used molecular data from chloroplast DNA and simple sequence repeats loci of P. machrisii and P. aurisetus, the two species with broadest distribution in the complex, in order to test if the diversification in each species was driven mostly by vicariance or by long-dispersal events. We found that both species were affected primarily by vicariance, with a refuge model as the most likely scenario for P. aurisetus and a soft vicariance scenario most probable for P. machrisii. These results emphasize the importance of distributional fragmentation in these species, and add support to the hypothesis of long-term isolation in interglacial refugia previously proposed for the P. aurisetus species complex diversification.

  6. Model-based analysis supports interglacial refugia over long-dispersal events in the diversification of two South American cactus species.

    PubMed

    Perez, M F; Bonatelli, I A S; Moraes, E M; Carstens, B C

    2016-06-01

    Pilosocereus machrisii and P. aurisetus are cactus species within the P. aurisetus complex, a group of eight cacti that are restricted to rocky habitats within the Neotropical savannas of eastern South America. Previous studies have suggested that diversification within this complex was driven by distributional fragmentation, isolation leading to allopatric differentiation, and secondary contact among divergent lineages. These events have been associated with Quaternary climatic cycles, leading to the hypothesis that the xerophytic vegetation patches which presently harbor these populations operate as refugia during the current interglacial. However, owing to limitations of the standard phylogeographic approaches used in these studies, this hypothesis was not explicitly tested. Here we use Approximate Bayesian Computation to refine the previous inferences and test the role of different events in the diversification of two species within P. aurisetus group. We used molecular data from chloroplast DNA and simple sequence repeats loci of P. machrisii and P. aurisetus, the two species with broadest distribution in the complex, in order to test if the diversification in each species was driven mostly by vicariance or by long-dispersal events. We found that both species were affected primarily by vicariance, with a refuge model as the most likely scenario for P. aurisetus and a soft vicariance scenario most probable for P. machrisii. These results emphasize the importance of distributional fragmentation in these species, and add support to the hypothesis of long-term isolation in interglacial refugia previously proposed for the P. aurisetus species complex diversification. PMID:27071846

  7. 3He-Rich SEP Events Detected by EPHIN 1996-2000

    NASA Astrophysics Data System (ADS)

    Gómez-Herrero, R.; Rodríguez-Frías, D.; Del Peral, L.; Sequeiros, J.; Gutiérrez, J.; Müller-Mellin, R.; Kunow, H.

    2003-07-01

    Thirteen 3 He-rich impulsive events have been identified in EPHIN data between 1996 and 2000. Energy spectra, abundance ratios, and association with solar activity have been evaluated. association with radio bursts I I I-type has been observed for all of them, but solar flare association has been found only for 8 of them. No acceleration above 30 MeV/nucleon has been appreciated. No deuterium has been detected for any event under study.

  8. On-line Machine Learning and Event Detection in Petascale Data Streams

    NASA Astrophysics Data System (ADS)

    Thompson, David R.; Wagstaff, K. L.

    2012-01-01

    Traditional statistical data mining involves off-line analysis in which all data are available and equally accessible. However, petascale datasets have challenged this premise since it is often impossible to store, let alone analyze, the relevant observations. This has led the machine learning community to investigate adaptive processing chains where data mining is a continuous process. Here pattern recognition permits triage and followup decisions at multiple stages of a processing pipeline. Such techniques can also benefit new astronomical instruments such as the Large Synoptic Survey Telescope (LSST) and Square Kilometre Array (SKA) that will generate petascale data volumes. We summarize some machine learning perspectives on real time data mining, with representative cases of astronomical applications and event detection in high volume datastreams. The first is a "supervised classification" approach currently used for transient event detection at the Very Long Baseline Array (VLBA). It injects known signals of interest - faint single-pulse anomalies - and tunes system parameters to recover these events. This permits meaningful event detection for diverse instrument configurations and observing conditions whose noise cannot be well-characterized in advance. Second, "semi-supervised novelty detection" finds novel events based on statistical deviations from previous patterns. It detects outlier signals of interest while considering known examples of false alarm interference. Applied to data from the Parkes pulsar survey, the approach identifies anomalous "peryton" phenomena that do not match previous event models. Finally, we consider online light curve classification that can trigger adaptive followup measurements of candidate events. Classifier performance analyses suggest optimal survey strategies, and permit principled followup decisions from incomplete data. These examples trace a broad range of algorithm possibilities available for online astronomical data

  9. A canonical correlation analysis based method for contamination event detection in water sources.

    PubMed

    Li, Ruonan; Liu, Shuming; Smith, Kate; Che, Han

    2016-06-15

    In this study, a general framework integrating a data-driven estimation model is employed for contamination event detection in water sources. Sequential canonical correlation coefficients are updated in the model using multivariate water quality time series. The proposed method utilizes canonical correlation analysis for studying the interplay between two sets of water quality parameters. The model is assessed by precision, recall and F-measure. The proposed method is tested using data from a laboratory contaminant injection experiment. The proposed method could detect a contamination event 1 minute after the introduction of 1.600 mg l(-1) acrylamide solution. With optimized parameter values, the proposed method can correctly detect 97.50% of all contamination events with no false alarms. The robustness of the proposed method can be explained using the Bauer-Fike theorem. PMID:27264637

  10. Detecting deception in children: an experimental study of the effect of event familiarity on CBCA ratings.

    PubMed

    Blandon-Gitlin, Iris; Pezdek, Kathy; Rogers, Martha; Brodie, Laura

    2005-04-01

    The CBCA is the most commonly used deception detection. technique worldwide. Pezdek et al. (2004) used a quasi-experimental design to assess children's accounts of a traumatic medical procedure; CBCA ratings were higher for descriptions of familiar than unfamiliar events. This study tested this effect using an experimental design and assessed the joint effect of familiarity and veracity on CBCA ratings. Children described a true or a fabricated event. Half described a familiar event; half described an unfamiliar event. Two CBCA-trained judges rated transcripts of the descriptions. CBCA scores were more strongly influenced by the familiarity than the actual veracity of the event, and CBCA scores were significantly correlated with age. CBCA results were compared with results from other measures. Together with the results of K. Pezdek et al. (2004) these findings suggest that in its current form, CBCA is of limited utility as a credibility assessment tool. PMID:15912723

  11. Anomalous optical events detected by rocket-borne sensor in the WIPP campaign

    NASA Technical Reports Server (NTRS)

    Li, Ya QI; Holzworth, Robert H.; Hu, Hua; Mccarthy, Michael; Massey, R. Dayle

    1991-01-01

    This paper describes the instruments used in the Wave Induced Particle Precipitation campaign in 1987, in which one rocket and four balloons were launched near thunderstorms from the NASA Wallops Flight Facility. Examples of both the lightning events and the anomalous optical events (AOEs) detected on July 31, 1987 are presented. It is shown that the signatures of these AOEs differ from those of well-known optical sources in the atmosphere. It was found that AOEs were sometimes associated with subionospheric VLF signal perturbations (Trimpi events), suggesting a possibility that AOEs might be linked to a disturbance of the electron concentration in the high-altitude atmosphere or lower ionosphere.

  12. Automated detection of apnea/hypopnea events in healthy children polysomnograms: preliminary results.

    PubMed

    Held, Claudio M; Causa, Leonardo; Jaillet, Fabrice; Chamorro, Rodrigo; Garrido, Marcelo; Algarin, Cecilia; Peirano, Patricio

    2013-01-01

    A methodology to detect sleep apnea/hypopnea events in the respiratory signals of polysomnographic recordings is presented. It applies empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), fuzzy logic and signal preprocessing techniques for feature extraction, expert criteria and context analysis. EMD, HHT and fuzzy logic are used for artifact detection and preliminary detection of respiration signal zones with significant variations in the amplitude of the signal; feature extraction, expert criteria and context analysis are used to characterize and validate the respiratory events. An annotated database of 30 all-night polysomnographic recordings, acquired from 30 healthy ten-year-old children, was divided in a training set of 15 recordings (485 sleep apnea/hypopnea events), a validation set of five recordings (109 sleep apnea/hypopnea events), and a testing set of ten recordings (281 sleep apnea/hypopnea events). The overall detection performance on the testing data set was 89.7% sensitivity and 16.3% false-positive rate. The next step is to include discrimination among apneas, hypopneas and respiratory pauses.

  13. Impulsive 3He-rich Solar Energetic Particle Events detected with EPHIN

    NASA Astrophysics Data System (ADS)

    Rodriguez-Frias, M. D.; Gomez-Herrero, R.; del Peral, L.; Sequeiros, J.; Kunow, H.; Mueller-Mellin, R.

    2001-08-01

    We report observation of 3 He-rich solar energetic particles (SEP) events detected by Electron Proton and Helium Instrument (EPHIN) aboard the Solar and Heliospheric Observatory (SOHO) spacecraft. EPHIN has been detecting Helium isotopes in the energy range 4-53 MeV/n since December 1995 using a ˜E-E sensor system with solid-state detectors. In this paper we concentrate on observations of SEP with excess in the 3 He abundance. The abundances 3 He/4 He and 4 He/1 H have been obtained and compared among different events. Energy spectra of protons, 3 He, 4 He have been studied.

  14. Method for detecting binding events using micro-X-ray fluorescence spectrometry

    DOEpatents

    Warner, Benjamin P.; Havrilla, George J.; Mann, Grace

    2010-12-28

    Method for detecting binding events using micro-X-ray fluorescence spectrometry. Receptors are exposed to at least one potential binder and arrayed on a substrate support. Each member of the array is exposed to X-ray radiation. The magnitude of a detectable X-ray fluorescence signal for at least one element can be used to determine whether a binding event between a binder and a receptor has occurred, and can provide information related to the extent of binding between the binder and receptor.

  15. Detection of invisible and crucial events: from seismic fluctuations to the war against terrorism

    NASA Astrophysics Data System (ADS)

    Allegrini, Paolo; Fronzoni, Leone; Grigolini, Paolo; Latora, Vito; Mega, Mirko S.; Palatella, Luigi; Rapisarda, Andrea; Vinciguerra, Sergio

    2004-04-01

    We prove the efficiency of a new method for the detection of crucial events that might have useful applications to the war against terrorism. This has to do with the search for rare but significant events, a theme of research that has been made of extreme importance by the tragedy of September 11. This method is applied here to defining the statistics of seismic main-shocks, as done in cond-mat/0212529. The emphasis here is on the conceptual issues behind the results obtained in cond-mat/0212529 than on geophysics. This discussion suggests that the method has a wider range of validity. We support this general discussion with a dynamic model originally proposed in cond-mat/0107597 for purposes different from geophysical applications. However, it is a case where the crucial events to detect are under our control, thereby making it possible for us to check the accuracy of the method of detection of invisible and crucial events that we propose here for a general purpose, including the war against terrorism. For this model an analytical treatment has been recently found [cond-mat/0209038], supporting the claims that we make in this paper for the accuracy of the method of detection. For the reader's convenience, the results on the seismic fluctuations are suitably reviewed, and discussed in the light of the more general perspective of this paper. We also review the model for seismic fluctuations, proposed in the earlier work of cond-mat/0212529. This model shares with the model of cond-mat/0107597 the property that the crucial events are imbedded in a sea of secondary events, but it allows us to reveal with accuracy the statistics of the crucial events for different mathematical reasons.

  16. Why conventional detection methods fail in identifying the existence of contamination events.

    PubMed

    Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading.

  17. Why conventional detection methods fail in identifying the existence of contamination events.

    PubMed

    Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. PMID:26905801

  18. Autonomous Detection of Eruptions, Plumes, and Other Transient Events in the Outer Solar System

    NASA Astrophysics Data System (ADS)

    Bunte, M. K.; Lin, Y.; Saripalli, S.; Bell, J. F.

    2012-12-01

    The outer solar system abounds with visually stunning examples of dynamic processes such as eruptive events that jettison materials from satellites and small bodies into space. The most notable examples of such events are the prominent volcanic plumes of Io, the wispy water jets of Enceladus, and the outgassing of comet nuclei. We are investigating techniques that will allow a spacecraft to autonomously detect those events in visible images. This technique will allow future outer planet missions to conduct sustained event monitoring and automate prioritization of data for downlink. Our technique detects plumes by searching for concentrations of large local gradients in images. Applying a Scale Invariant Feature Transform (SIFT) to either raw or calibrated images identifies interest points for further investigation based on the magnitude and orientation of local gradients in pixel values. The interest points are classified as possible transient geophysical events when they share characteristics with similar features in user-classified images. A nearest neighbor classification scheme assesses the similarity of all interest points within a threshold Euclidean distance and classifies each according to the majority classification of other interest points. Thus, features marked by multiple interest points are more likely to be classified positively as events; isolated large plumes or multiple small jets are easily distinguished from a textured background surface due to the higher magnitude gradient of the plume or jet when compared with the small, randomly oriented gradients of the textured surface. We have applied this method to images of Io, Enceladus, and comet Hartley 2 from the Voyager, Galileo, New Horizons, Cassini, and Deep Impact EPOXI missions, where appropriate, and have successfully detected up to 95% of manually identifiable events that our method was able to distinguish from the background surface and surface features of a body. Dozens of distinct features

  19. Wenchuan Event Detection And Localization Using Waveform Correlation Coupled With Double Difference

    NASA Astrophysics Data System (ADS)

    Slinkard, M.; Heck, S.; Schaff, D. P.; Young, C. J.; Richards, P. G.

    2014-12-01

    The well-studied Wenchuan aftershock sequence triggered by the May 12, 2008, Ms 8.0, mainshock offers an ideal test case for evaluating the effectiveness of using waveform correlation coupled with double difference relocation to detect and locate events in a large aftershock sequence. We use Sandia's SeisCorr detector to process 3 months of data recorded by permanent IRIS and temporary ASCENT stations using templates from events listed in a global catalog to find similar events in the raw data stream. Then we take the detections and relocate them using the double difference method. We explore both the performance that can be expected with using just a small number of stations, and, the benefits of reprocessing a well-studied sequence such as this one using waveform correlation to find even more events. We benchmark our results against previously published results describing relocations of regional catalog data. Before starting this project, we had examples where with just a few stations at far-regional distances, waveform correlation combined with double difference did and impressive job of detection and location events with precision at the few hundred and even tens of meters level.

  20. Model-Based Fault Tolerant Control

    NASA Technical Reports Server (NTRS)

    Kumar, Aditya; Viassolo, Daniel

    2008-01-01

    The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

  1. Testing the ability of different seismic detections approaches to monitor aftershocks following a moderate magnitude event.

    NASA Astrophysics Data System (ADS)

    Romero, Paula; Díaz, Jordi; Ruiz, Mario; Cantavella, Juan Vicente; Gomez-García, Clara

    2016-04-01

    The detection and picking of seismic events is a permanent concern for seismic surveying, in particular when dealing with aftershocks of moderate magnitude events. Many efforts have been done to find the balance between computer efficiency and the robustness of the detection methods. In this work, data recorded by a high density seismic network deployed following a 5.2 magnitude event located close to Albacete, SE Spain, is used to test the ability of classical and recently proposed detection methodologies. Two days after the main shock, occurred the 23th February, a network formed by 11 stations from ICTJA-CSIC and 2 stations from IGN were deployed over the region, with inter-station distances ranging between 5 and 10 km. The network remained in operation until April 6th, 2015 and allowed to manually identify up to 552 events with magnitudes from 0.2 to 3.5 located in an area of just 25 km2 inside the network limits. The detection methods here studied applied are the classical STA/LTA, a power spectral method, a detector based in the Benford's law and a waveform similarity method. The STA/LTA method, based in the comparison of background noise and seismic signal amplitudes, is taken as a reference to evaluate the results arising from the other approaches. The power spectral density method is based in the inspection of the characteristic frequency pattern associated to seismic events. The Benford's Law detector analyses the distribution of the first-digit of displacement count in the histogram of a seismic waveform, considering that only the windows containing seismic wave arrivals will match the logarithmic law. Finally, the waveform similarity method is based in the analysis of the normalized waveform amplitude, detecting those events with waveform similar to a previously defined master event. The aim of this contribution is to inspect the ability of the different approaches to accurately detect the aftershocks events for this kind of seismic crisis and to

  2. Detection and identification of multiple genetically modified events using DNA insert fingerprinting.

    PubMed

    Raymond, Philippe; Gendron, Louis; Khalf, Moustafa; Paul, Sylvianne; Dibley, Kim L; Bhat, Somanath; Xie, Vicki R D; Partis, Lina; Moreau, Marie-Eve; Dollard, Cheryl; Coté, Marie-José; Laberge, Serge; Emslie, Kerry R

    2010-03-01

    Current screening and event-specific polymerase chain reaction (PCR) assays for the detection and identification of genetically modified organisms (GMOs) in samples of unknown composition or for the detection of non-regulated GMOs have limitations, and alternative approaches are required. A transgenic DNA fingerprinting methodology using restriction enzyme digestion, adaptor ligation, and nested PCR was developed where individual GMOs are distinguished by the characteristic fingerprint pattern of the fragments generated. The inter-laboratory reproducibility of the amplified fragment sizes using different capillary electrophoresis platforms was compared, and reproducible patterns were obtained with an average difference in fragment size of 2.4 bp. DNA insert fingerprints for 12 different maize events, including two maize hybrids and one soy event, were generated that reflected the composition of the transgenic DNA constructs. Once produced, the fingerprint profiles were added to a database which can be readily exchanged and shared between laboratories. This approach should facilitate the process of GMO identification and characterization.

  3. Detecting Continuity Violations in Infancy: A New Account and New Evidence from Covering and Tube Events

    ERIC Educational Resources Information Center

    Wang, S.h.; Baillargeon, R.; Paterson, S.

    2005-01-01

    Recent research on infants' responses to occlusion and containment events indicates that, although some violations of the continuity principle are detected at an early age e.g. Aguiar, A., & Baillargeon, R. (1999). 2.5-month-old infants' reasoning about when objects should and should not be occluded. Cognitive Psychology 39, 116-157; Hespos, S.…

  4. A novel seizure detection algorithm informed by hidden Markov model event states

    NASA Astrophysics Data System (ADS)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h‑1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  5. A novel seizure detection algorithm informed by hidden Markov model event states

    NASA Astrophysics Data System (ADS)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  6. Low time resolution analysis of polar ice cores cannot detect impulsive nitrate events

    NASA Astrophysics Data System (ADS)

    Smart, D. F.; Shea, M. A.; Melott, A. L.; Laird, C. M.

    2014-12-01

    Ice cores are archives of climate change and possibly large solar proton events (SPEs). Wolff et al. (2012) used a single event, a nitrate peak in the GISP2-H core, which McCracken et al. (2001a) time associated with the poorly quantified 1859 Carrington event, to discredit SPE-produced, impulsive nitrate deposition in polar ice. This is not the ideal test case. We critique the Wolff et al. analysis and demonstrate that the data they used cannot detect impulsive nitrate events because of resolution limitations. We suggest reexamination of the top of the Greenland ice sheet at key intervals over the last two millennia with attention to fine resolution and replicate sampling of multiple species. This will allow further insight into polar depositional processes on a subseasonal scale, including atmospheric sources, transport mechanisms to the ice sheet, postdepositional interactions, and a potential SPE association.

  7. Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft

    NASA Technical Reports Server (NTRS)

    Wilson, Edward (Inventor)

    2008-01-01

    The present invention is a method for detecting and isolating fault modes in a system having a model describing its behavior and regularly sampled measurements. The models are used to calculate past and present deviations from measurements that would result with no faults present, as well as with one or more potential fault modes present. Algorithms that calculate and store these deviations, along with memory of when said faults, if present, would have an effect on the said actual measurements, are used to detect when a fault is present. Related algorithms are used to exonerate false fault modes and finally to isolate the true fault mode. This invention is presented with application to detection and isolation of thruster faults for a thruster-controlled spacecraft. As a supporting aspect of the invention, a novel, effective, and efficient filtering method for estimating the derivative of a noisy signal is presented.

  8. Pre-trained D-CNN models for detecting complex events in unconstrained videos

    NASA Astrophysics Data System (ADS)

    Robinson, Joseph P.; Fu, Yun

    2016-05-01

    Rapid event detection faces an emergent need to process large videos collections; whether surveillance videos or unconstrained web videos, the ability to automatically recognize high-level, complex events is a challenging task. Motivated by pre-existing methods being complex, computationally demanding, and often non-replicable, we designed a simple system that is quick, effective and carries minimal overhead in terms of memory and storage. Our system is clearly described, modular in nature, replicable on any Desktop, and demonstrated with extensive experiments, backed by insightful analysis on different Convolutional Neural Networks (CNNs), as stand-alone and fused with others. With a large corpus of unconstrained, real-world video data, we examine the usefulness of different CNN models as features extractors for modeling high-level events, i.e., pre-trained CNNs that differ in architectures, training data, and number of outputs. For each CNN, we use 1-fps from all training exemplar to train one-vs-rest SVMs for each event. To represent videos, frame-level features were fused using a variety of techniques. The best being to max-pool between predetermined shot boundaries, then average-pool to form the final video-level descriptor. Through extensive analysis, several insights were found on using pre-trained CNNs as off-the-shelf feature extractors for the task of event detection. Fusing SVMs of different CNNs revealed some interesting facts, finding some combinations to be complimentary. It was concluded that no single CNN works best for all events, as some events are more object-driven while others are more scene-based. Our top performance resulted from learning event-dependent weights for different CNNs.

  9. Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)

    NASA Astrophysics Data System (ADS)

    Caffagni, Enrico; Eaton, David W.; Jones, Joshua P.; van der Baan, Mirko

    2016-07-01

    A new Matched Filtering Algorithm (MFA) is proposed for detecting and analysing microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template (`parent') events, which are obtained using conventional microseismic processing and selected on the basis of high signal-to-noise (S/N) ratio and representative spatial distribution of the recorded microseismicity. Detection and extraction of `child' events are based on stacked, multichannel cross-correlation of the continuous waveform data, using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multicomponent waveforms into the ray-centred co-ordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent's hypocentre. After correction for geometrical spreading and attenuation, the relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. Since only a small number of parent events require interactive analysis such as picking P- and S-wave arrivals, the MFA approach offers the potential for significant reduction in effort for downhole microseismic processing. Our algorithm also facilitates the analysis of single-phase child events, that is, microseismic events for which only one of the S- or P-wave arrivals is evident due to unfavourable S/N conditions. A real-data example using microseismic monitoring data from four stages of an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents (in this case, 4.6 per cent of the originally located events) yields a significant (more than fourfold increase) in the number of located events compared with the original catalogue. Moreover, analysis of the new MFA catalogue suggests that this approach leads to more robust interpretation

  10. Remote monitoring of cardiovascular implantable devices in the pediatric population improves detection of adverse events.

    PubMed

    Malloy, Lindsey E; Gingerich, Jean; Olson, Mark D; Atkins, Dianne L

    2014-02-01

    With the exponential growth of cardiovascular implantable electronic devices (CIEDs) in pediatric patients, a new method of long-term surveillance, remote monitoring (RM), has become the standard of care. The purpose of this study was to determine the usefulness of RM as a monitoring tool in the pediatric population. A retrospective review was performed of 198 patients at the University of Iowa Children's Hospital who had CIEDs. Data transmitted by RM were analyzed. The following data were examined: patient demographics; median interval between transmissions; detection of adverse events requiring corrective measures, including detection of lead failure; detection of arrhythmias and device malfunctions independent of symptoms; time gained in the detection of events using RM versus standard practice; the validity of RM; and the impact of RM on data management. Of 198 patients, 162 submitted 615 RM transmissions. The median time between remote transmissions was 91 days. Of 615 total transmissions, 16 % had true adverse events with 11 % prompting clinical intervention. Of those events requiring clinical response, 61 % of patients reported symptoms. The median interval between last follow-up and occurrence of events detected by RM was 46 days, representing a gain of 134 days for patients followed-up at 6-month intervals and 44 days for patients followed-up at 3 month-intervals. The sensitivity and specificity of RM were found to be 99 and 72 %, respectively. The positive and negative predictive values were found to be 41 and 99 %, respectively. RM allows for early identification of arrhythmias and device malfunctions, thus prompting earlier corrective measures and improving care and safety in pediatric patients.

  11. Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection

    PubMed Central

    Olson, Sarah H.; Benedum, Corey M.; Mekaru, Sumiko R.; Preston, Nicholas D.; Mazet, Jonna A.K.; Joly, Damien O.

    2015-01-01

    The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data. PMID:26196106

  12. Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

    PubMed Central

    Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

    2014-01-01

    National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

  13. Effects of performing two visual tasks on single-trial detection of event-related potentials.

    PubMed

    Cecotti, Hubert; Eckstein, Miguel P; Giesbrecht, Barry

    2012-01-01

    The detection of event-related potentials (ERPs) in brain-computer interface (BCI) depends on the ability of the subject to pay attention to specific stimuli presented during the BCI task. For healthy users, a BCI shall be used as a complement to other existing devices, which involve the response to other tasks. Those tasks may impair selective attention, particularly if the stimuli have the same modality e.g. visual. It is therefore critical to analyze how single-trial detection of brain evoked response is impaired by the addition of tasks concerning the same modality. We tested 10 healthy participants using an application that has two visual target detection tasks. The first one corresponds to a rapid serial visual presentation paradigm where target detection is achieved by brain-evoked single-trial detection in the recorded electroencephalogram (EEG) signal. The second task is the detection of a visual event on a tactical map by a behavioral response. These tasks were tested individually (single task) and in parallel (dual-task). Whereas the performance of single-trial detection was not impaired between single and dual-task conditions, the behavioral performance decreased during the dual-task condition. These results quantify the performance drop that can occur in a dual-task system using both brain-evoked responses and behavioral responses. PMID:23366242

  14. Event Detection and Location of Earthquakes Using the Cascadia Initiative Dataset

    NASA Astrophysics Data System (ADS)

    Morton, E.; Bilek, S. L.; Rowe, C. A.

    2015-12-01

    The Cascadia subduction zone (CSZ) produces a range of slip behavior along the plate boundary megathrust, from great earthquakes to episodic slow slip and tremor (ETS). Unlike other subduction zones that produce great earthquakes and ETS, the CSZ is notable for the lack of small and moderate magnitude earthquakes recorded. The seismogenic zone extent is currently estimated to be primarily offshore, thus the lack of observed small, interplate earthquakes may be partially due to the use of only land seismometers. The Cascadia Initiative (CI) community seismic experiment seeks to address this issue by including ocean bottom seismometers (OBS) deployed directly over the locked seismogenic zone, in addition to land seismometers. We use these seismic data to explore whether small magnitude earthquakes are occurring on the plate interface, but have gone undetected by the land-based seismic networks. We select a subset of small magnitude (M0.1-3.7) earthquakes from existing earthquake catalogs, based on land seismic data, whose preliminary hypocentral locations suggest they may have occurred on the plate interface. We window the waveforms on CI OBS and land seismometers around the phase arrival times for these earthquakes to generate templates for subspace detection, which allows for additional flexibility over traditional matched filter detection methods. Here we present event detections from the first year of CI deployment and preliminary locations for the detected events. Initial results of scanning the first year of the CI deployment using one cluster of template events, located near a previously identified subducted seamount, include 473 detections on OBS station M08A (~61.6 km offshore) and 710 detections on OBS station J25A (~44.8 km northeast of M08A). Ongoing efforts include detection using additional OBS stations along the margin, as well as determining locations of clusters detected in the first year of deployment.

  15. Event-specific quantitative detection of nine genetically modified maizes using one novel standard reference molecule.

    PubMed

    Yang, Litao; Guo, Jinchao; Pan, Aihu; Zhang, Haibo; Zhang, Kewei; Wang, Zhengming; Zhang, Dabing

    2007-01-10

    With the development of genetically modified organism (GMO) detection techniques, the Polymerase Chain Reaction (PCR) technique has been the mainstay for GMO detection, and real-time PCR is the most effective and important method for GMO quantification. An event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity. This study establishes the event-specific detection methods for TC1507 and CBH351 maizes. In addition, the event-specific TaqMan real-time PCR detection methods for another seven GM maize events (Bt11, Bt176, GA21, MON810, MON863, NK603, and T25) were systematically optimized and developed. In these PCR assays, the fluorescent quencher, TAMRA, was dyed on the T-base of the probe at the internal position to improve the intensity of the fluorescent signal. To overcome the difficulties in obtaining the certified reference materials of these GM maizes, one novel standard reference molecule containing all nine specific integration junction sequences of these GM maizes and the maize endogenous reference gene, zSSIIb, was constructed and used for quantitative analysis. The limits of detection of these methods were 20 copies for these different GM maizes, the limits of quantitation were about 20 copies, and the dynamic ranges for quantification were from 0.05 to 100% in 100 ng of DNA template. Furthermore, nine groups of the mixed maize samples of these nine GM maize events were quantitatively analyzed to evaluate the accuracy and precision. The accuracy expressed as bias varied from 0.67 to 28.00% for the nine tested groups of GM maize samples, and the precision expressed as relative standard deviations was from 0.83 to 26.20%. All of these indicated that the established event-specific real-time PCR detection systems and the reference molecule in this study are suitable for the identification and quantification of these GM

  16. Event-specific quantitative detection of nine genetically modified maizes using one novel standard reference molecule.

    PubMed

    Yang, Litao; Guo, Jinchao; Pan, Aihu; Zhang, Haibo; Zhang, Kewei; Wang, Zhengming; Zhang, Dabing

    2007-01-10

    With the development of genetically modified organism (GMO) detection techniques, the Polymerase Chain Reaction (PCR) technique has been the mainstay for GMO detection, and real-time PCR is the most effective and important method for GMO quantification. An event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity. This study establishes the event-specific detection methods for TC1507 and CBH351 maizes. In addition, the event-specific TaqMan real-time PCR detection methods for another seven GM maize events (Bt11, Bt176, GA21, MON810, MON863, NK603, and T25) were systematically optimized and developed. In these PCR assays, the fluorescent quencher, TAMRA, was dyed on the T-base of the probe at the internal position to improve the intensity of the fluorescent signal. To overcome the difficulties in obtaining the certified reference materials of these GM maizes, one novel standard reference molecule containing all nine specific integration junction sequences of these GM maizes and the maize endogenous reference gene, zSSIIb, was constructed and used for quantitative analysis. The limits of detection of these methods were 20 copies for these different GM maizes, the limits of quantitation were about 20 copies, and the dynamic ranges for quantification were from 0.05 to 100% in 100 ng of DNA template. Furthermore, nine groups of the mixed maize samples of these nine GM maize events were quantitatively analyzed to evaluate the accuracy and precision. The accuracy expressed as bias varied from 0.67 to 28.00% for the nine tested groups of GM maize samples, and the precision expressed as relative standard deviations was from 0.83 to 26.20%. All of these indicated that the established event-specific real-time PCR detection systems and the reference molecule in this study are suitable for the identification and quantification of these GM

  17. Long-Duration Neutron Production in Solar Eruptive Events Detected with the MESSENGER Neutron Spectrometer

    NASA Astrophysics Data System (ADS)

    Feldman, W. C.; Lawrence, D. J.; Vestrand, W. T.; Peplowski, P. N.

    2014-12-01

    Nine long-duration neutron solar eruptive events (SEEs) between 31 December 2007 and 16 March 2013 appear to be excellent candidates for detection of fast neutrons from the Sun by the MESSENGER Neutron Spectrometer (NS). One event (on 4 June 2011) is the cleanest example, because it was not accompanied by energetic ions at MESSENGER having energies greater than 50±10 MeV/nuc. The purpose of this study is to assemble a set of conditions common to all events that can help identify the physical conditions at their origin. We classified the nine events into three categories: (1) those having tight magnetic connection to the Sun as well as to spacecraft at 1 AU that can separately measure the energetic proton, alpha particle, and electron flux spectra, (2) those with sufficiently close connection that the energetic flux spectra can be compared, (3) those that have only marginal connections, and (4) those that are also seen at Earth. Four events fall into category (1), three into category (2), two into category (3), and parts of four events overlapped neutron events also seen by the scintillation FIBer solar neutron telescope (FIB) detector placed on the International Space Station in 2009. Seven of the nine events that have either tight or marginal magnetic connection have alpha particle abundances less than 2%. For each event, we modeled expected fast neutron count rates from the 1 AU ion spectrum, a process that accounts for the transport of the neutrons through the spacecraft to the NS. The ratios of measured to predicted fast-neutron counts range between 2.0 and 12.1.

  18. Ontology-based knowledge management for personalized adverse drug events detection.

    PubMed

    Cao, Feng; Sun, Xingzhi; Wang, Xiaoyuan; Li, Bo; Li, Jing; Pan, Yue

    2011-01-01

    Since Adverse Drug Event (ADE) has become a leading cause of death around the world, there arises high demand for helping clinicians or patients to identify possible hazards from drug effects. Motivated by this, we present a personalized ADE detection system, with the focus on applying ontology-based knowledge management techniques to enhance ADE detection services. The development of electronic health records makes it possible to automate the personalized ADE detection, i.e., to take patient clinical conditions into account during ADE detection. Specifically, we define the ADE ontology to uniformly manage the ADE knowledge from multiple sources. We take advantage of the rich semantics from the terminology SNOMED-CT and apply it to ADE detection via the semantic query and reasoning. PMID:21893837

  19. Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database

    PubMed Central

    2016-01-01

    We conducted pharmacovigilance data mining for a β-lactam antibiotics, amoxicillin, and compare the adverse events (AEs) with the drug labels of 9 countries including Korea, USA, UK, Japan, Germany, Swiss, Italy, France, and Laos. We used the Korea Adverse Event Reporting System (KAERS) database, a nationwide database of AE reports, between December 1988 and June 2014. Frequentist and Bayesian methods were used to calculate disproportionality distribution of drug-AE pairs. The AE which was detected by all the three indices of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) was defined as a signal. The KAERS database contained a total of 807,582 AE reports, among which 1,722 reports were attributed to amoxicillin. Among the 192,510 antibiotics-AE pairs, the number of amoxicillin-AE pairs was 2,913. Among 241 AEs, 52 adverse events were detected as amoxicillin signals. Comparing the drug labels of 9 countries, 12 adverse events including ineffective medicine, bronchitis, rhinitis, sinusitis, dry mouth, gastroesophageal reflux, hypercholesterolemia, gastric carcinoma, abnormal crying, induration, pulmonary carcinoma, and influenza-like symptoms were not listed on any of the labels of nine countries. In conclusion, we detected 12 new signals of amoxicillin which were not listed on the labels of 9 countries. Therefore, it should be followed by signal evaluation including causal association, clinical significance, and preventability. PMID:27510377

  20. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

    PubMed

    Aung, Min S H; Thies, Sibylle B; Kenney, Laurence P J; Howard, David; Selles, Ruud W; Findlow, Andrew H; Goulermas, John Y

    2013-11-01

    Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains. PMID:23322764

  1. A Heuristic Indication and Warning Staging Model for Detection and Assessment of Biological Events

    PubMed Central

    Wilson, James M.; Polyak, Marat G.; Blake, Jane W.; Collmann, Jeff

    2008-01-01

    Objective This paper presents a model designed to enable rapid detection and assessment of biological threats that may require swift intervention by the international public health community. Design We utilized Strauss’ grounded theory to develop an expanded model of social disruption due to biological events based on retrospective and prospective case studies. We then applied this model to the temporal domain and propose a heuristic staging model, the Wilson–Collmann Scale for assessing biological event evolution. Measurements We retrospectively and manually examined hard copy archival local media reports in the native vernacular for three biological events associated with substantial social disruption. The model was then tested prospectively through media harvesting based on keywords corresponding to the model parameters. Results Our heuristic staging model provides valuable information about the features of a biological event that can be used to determine the level of concern warranted, such as whether the pathogen in question is responding to established public health disease control measures, including the use of antimicrobials or vaccines; whether the public health and medical infrastructure of the country involved is adequate to mount the necessary response; whether the country’s officials are providing an appropriate level of information to international public health authorities; and whether the event poses a international threat. The approach is applicable for monitoring open-source (public-domain) media for indications and warnings of such events, and specifically for markers of the social disruption that commonly occur as these events unfold. These indications and warnings can then be used as the basis for staging the biological threat in the same manner that the United States National Weather Service currently uses storm warning models (such as the Saffir-Simpson Hurricane Scale) to detect and assess threatening weather conditions. Conclusion

  2. A method for detecting and locating geophysical events using groups of arrays

    NASA Astrophysics Data System (ADS)

    de Groot-Hedlin, Catherine D.; Hedlin, Michael A. H.

    2015-11-01

    We have developed a novel method to detect and locate geophysical events that makes use of any sufficiently dense sensor network. This method is demonstrated using acoustic sensor data collected in 2013 at the USArray Transportable Array (TA). The algorithm applies Delaunay triangulation to divide the sensor network into a mesh of three-element arrays, called triads. Because infrasound waveforms are incoherent between the sensors within each triad, the data are transformed into envelopes, which are cross-correlated to find signals that satisfy a consistency criterion. The propagation azimuth, phase velocity and signal arrival time are computed for each signal. Triads with signals that are consistent with a single source are bundled as an event group. The ensemble of arrival times and azimuths of detected signals within each group are used to locate a common source in space and time. A total of 513 infrasonic stations that were active for part or all of 2013 were divided into over 2000 triads. Low (0.5-2 Hz) and high (2-8 Hz) catalogues of infrasonic events were created for the eastern USA. The low-frequency catalogue includes over 900 events and reveals several highly active source areas on land that correspond with coal mining regions. The high-frequency catalogue includes over 2000 events, with most occurring offshore. Although their cause is not certain, most events are clearly anthropogenic as almost all occur during regular working hours each week. The regions to which the TA is most sensitive vary seasonally, with the direction of reception dependent on the direction of zonal winds. The catalogue has also revealed large acoustic events that may provide useful insight into the nature of long-range infrasound propagation in the atmosphere.

  3. Automatic Acoustic Events Detection, Classification, and Semantic Annotation for Persistent Surveillance Applications

    NASA Astrophysics Data System (ADS)

    Alkilani, Amjad H. I.

    Acoustic surveillance and human behavior analysis represent some of the ongoing research topics in signal processing. Acoustic sensors offer a promising sensing modality, primarily because they can capture a huge amount of information from the environment. Moreover, they can be rapidly deployed and are low-cost. In the past, significant efforts have been devoted to detecting sounds of individual objects or events. However, the issue of understanding human activities based on sporadic acoustic sound events has received unequal attention in the literature and hence is not well understood. This dissertation presents an extensive literature survey on this topic and discusses existing advanced techniques for acoustic signal processing and pattern recognition. A novel theoretic framework (Acoustic Events Detection, Classification, and Annotation (AEDCA)) is proposed which accommodates sound events ontology for improved human activities recognition. Based on a generalized taxonomy, three sound categories signifying interaction of human with each other, with vehicles, and with other objects are introduced. In order to understand different type of human interactions salient sound events are preliminarily identified and classified based on trained set of data. To interlink salient events representing an ontology-based hypothesis, a Hidden Markov Model-Acoustic Activity Recognizer (HMM-AAR) is modeled to recognize spatiotemporally correlated events. Once such a connection is established, an annotation of perceived sound activity is generated. The performance of the AEDCA system was tested and measured experimentally in both indoor and outdoor environments. Appropriate confusion matrices are developed for the assessment of performance reliability, and computational efficiency of the AEDCA system. The obtained results are very promising and strongly demonstrate the AEDCA is both reliable and effective, and can be extended to future surveillance applications.

  4. Exploring the limits of waveform correlation event detection as applied to three earthquake aftershock sequences.

    SciTech Connect

    Resor, Megan E.; Carr, Dorthe Bame; Young, Christopher John

    2010-05-01

    Swarms of earthquakes and/or aftershock sequences can dramatically increase the level of seismicity in a region for a period of time lasting from days to months, depending on the swarm or sequence. Such occurrences can provide a large amount of useful information to seismologists. For those who monitor seismic events for possible nuclear explosions, however, these swarms/sequences are a nuisance. In an explosion monitoring system, each event must be treated as a possible nuclear test until it can be proven, to a high degree of confidence, not to be. Seismic events recorded by the same station with highly correlated waveforms almost certainly have a similar location and source type, so clusters of events within a swarm can quickly be identified as earthquakes. We have developed a number of tools that can be used to exploit the high degree of waveform similarity expected to be associated with swarms/sequences. Dendro Tool measures correlations between known events. The Waveform Correlation Detector is intended to act as a detector, finding events in raw data which correlate with known events. The Self Scanner is used to find all correlated segments within a raw data steam and does not require an event library. All three techniques together provide an opportunity to study the similarities of events in an aftershock sequence in different ways. To comprehensively characterize the benefits and limits of waveform correlation techniques, we studied 3 aftershock sequences, using our 3 tools, at multiple stations. We explored the effects of station distance and event magnitudes on correlation results. Lastly, we show the reduction in detection threshold and analyst workload offered by waveform correlation techniques compared to STA/LTA based detection. We analyzed 4 days of data from each aftershock sequence using all three methods. Most known events clustered in a similar manner across the toolsets. Up to 25% of catalogued events were found to be a member of a cluster. In

  5. Characterization and Analysis of Networked Array of Sensors for Event Detection (CANARY-EDS)

    2011-05-27

    CANARY -EDS provides probabilistic event detection based on analysis of time-series data from water quality or other sensors. CANARY also can compare patterns against a library of previously seen data to indicate that a certain pattern has reoccurred, suppressing what would otherwise be considered an event. CANARY can be configured to analyze previously recorded data from files or databases, or it can be configured to run in real-time mode directory from a database, or throughmore » the US EPA EDDIES software.« less

  6. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

    PubMed

    Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

    2016-07-13

    The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

  7. Detection of seismic events triggered by P-waves from the 2011 Tohoku-Oki earthquake

    NASA Astrophysics Data System (ADS)

    Miyazawa, Masatoshi

    2012-12-01

    Large-amplitude surface waves from the 2011 Tohoku-Oki earthquake triggered many seismic events across Japan, while the smaller amplitude P-wave triggering remains unclear. A spectral method was used to detect seismic events triggered by the first arriving P-waves over Japan. This method uses a reference event to correct for source and propagation effects, so that the local response near the station can be examined in detail. P-wave triggering was found in the regions where triggered non-volcanic tremor (NVT) has been observed, and some seismic and volcanic regions. The triggering strain due to P-waves is of the order of 10-8 to 10-7, which is 1 to 2 orders of magnitude smaller than the triggering strain necessary for the surface wave triggering. In the regions of NVT, the triggered event was not identified with slow events, but with other seismic events such as tectonic earthquakes. The sequence of triggering in the regions started with P-wave arrivals. The subsequent surface waves contributed to triggering of NVT, possibly together with slow slip, which resulted in the large amplitude of the NVT.

  8. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

    PubMed

    Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

    2016-01-01

    The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

  9. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)

    PubMed Central

    Yan, Lixin; Zhang, Yishi; He, Yi; Gao, Song; Zhu, Dunyao; Ran, Bin; Wu, Qing

    2016-01-01

    The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles. PMID:27420073

  10. CTBT infrasound network performance to detect the 2013 Russian fireball event

    NASA Astrophysics Data System (ADS)

    Pilger, Christoph; Ceranna, Lars; Ross, J. Ole; Le Pichon, Alexis; Mialle, Pierrick; Garcés, Milton A.

    2015-04-01

    The explosive fragmentation of the 2013 Chelyabinsk meteorite generated a large airburst with an equivalent yield of 500 kT TNT. It is the most energetic event recorded by the infrasound component of the Comprehensive Nuclear-Test-Ban Treaty-International Monitoring System (CTBT-IMS), globally detected by 20 out of 42 operational stations. This study performs a station-by-station estimation of the IMS detection capability to explain infrasound detections and nondetections from short to long distances, using the Chelyabinsk meteorite as global reference event. Investigated parameters influencing the detection capability are the directivity of the line source signal, the ducting of acoustic energy, and the individual noise conditions at each station. Findings include a clear detection preference for stations perpendicular to the meteorite trajectory, even over large distances. Only a weak influence of stratospheric ducting is observed for this low-frequency case. Furthermore, a strong dependence on the diurnal variability of background noise levels at each station is observed, favoring nocturnal detections.

  11. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

    PubMed Central

    Vilar, Santiago; Harpaz, Rave; Chase, Herbert S; Costanzi, Stefano; Rabadan, Raul

    2011-01-01

    Background Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. Objective To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. Results The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. Conclusion The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection. PMID:21946238

  12. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    NASA Astrophysics Data System (ADS)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  13. Women's perceptions of events impeding or facilitating the detection, investigation and treatment of breast cancer.

    PubMed

    Bairati, I; Fillion, L; Meyer, F A; Héry, C; Larochelle, M

    2006-05-01

    An integrated network is currently being implemented in the province of Quebec in order to improve the cancer care continuum. In this context, formal trajectories for cancer patients through healthcare services are being established. The investigation of patients' perceptions of the healthcare continuum is essential as it allows us to identify the issue of continuity/discontinuity of health services. In addition, patients' perceptions of continuity of cancer care should be documented since they could influence the implementation of optimal trajectories through the healthcare services. An exploratory qualitative study was conducted in order to identify events, based on the perceptions of women with breast cancer, that made the patient progress more rapidly, facilitating events, or more slowly, impeding events, within the cancer care continuum. Two consecutive series of women receiving adjuvant radiation therapy in 2002 and 2003 at the University Hospital of Quebec City were recruited, for a total of 120 participants. A semi-structured interview was administered in order to identify women's perceptions regarding impeding and facilitating events during the detection, investigation and treatment periods of cancer, as well as the actors and reasons involved. Overall, 64% of women reported having at least one impeding event, while 68% reported at least one facilitating event. The periods most frequently affected by impeding or facilitating events were the investigation period, followed by the treatment period. The main stages affected by impeding or facilitating events were the scheduling of an appointment, during the investigation period, and the onset of treatment. Impeding events particularly affected the scheduling of mammography, the initial exam of the investigation for breast cancer, as well as the onset of radiation treatment. On the other hand, facilitating events mainly occurred at the time of the scheduling of medical consultations with specialists, during the

  14. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

    SciTech Connect

    Perez, Rafael B; Protopopescu, Vladimir A; Worley, Brian Addison; Perez, Cristina

    2006-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

  15. Solar Origin of Solar Particle Events Detected by the Standard Radiation Environment Monitor of ESA

    NASA Astrophysics Data System (ADS)

    Tziotziou, K.; Sandberg, I.; Anastasiadis, A.; Daglis, I. A.; Panagopoulos, I.; Mavromichalaki, H.; Papaioannou, A.; Gerontidou, M.; Nieminen, P.; Glover, A.

    2010-07-01

    Solar Particle Events (SPEs) of the 23rd Solar Cycle detected by the ESA Standard Radiation Environment Monitor (SREM) onboard the INTEGRAL satellite have been studied in order to find their connection to solar sources. X-ray, optical and radio data of solar flares that were observed by several space-based instruments during the aforementioned solar cycle have been selected. The data were reduced and thoroughly analyzed in order to establish the corresponding solar origin of the selected SPEs. The extensive scientific analysis has produced clear correlations with X class solar flares for the events of the October-November 2003, January 2005 and December 2006 periods while for the events that occurred during September 2005, correlations with X class flares are possible but not straightforward due to the complexity of the registered solar particle fluxes.

  16. Event Detection: A Clinical Notification Service on a Health Information Exchange Platform

    PubMed Central

    Moore, Thomas; Shapiro, Jason S.; Doles, Luke; Calman, Neil; Camhi, Eli; Check, Thomas; Onyile, Arit; Kuperman, Gilad

    2012-01-01

    Notifying ambulatory providers when their patients visit the hospital is a simple concept but potentially a powerful tool for improving care coordination. A health information exchange (HIE) can provide automatic notifications to its members by building services on top of their existing infrastructure. NYCLIX, Inc., a functioning HIE in New York City, has developed a system that detects hospital admissions, discharges and emergency department visits and notifies their providers. The system has been in use since November 2010. Out of 63,305 patients enrolled 6,913 (11%) had one or more events in the study period and on average there were 238 events per day. While event notifications have a clinical value, their use also involves non-clinical care coordination; new workflows should be designed to incorporate a broader care team in their use. This paper describes the user requirements for the notification system, system design, current status, lessons learned and future directions. PMID:23304336

  17. Detection of Severe Rain on Snow events using passive microwave remote sensing

    NASA Astrophysics Data System (ADS)

    Grenfell, T. C.; Putkonen, J.

    2007-12-01

    Severe wintertime rain-on-snow (ROS) events create a strong ice layer or layers in the snow on arctic tundra that act as a barrier to ungulate grazing. These events are linked with large-scale ungulate herd declines via starvation and reduced calf production rate when the animals are unable to penetrate through the resulting ice layer. ROS events also produce considerable perturbation in the mean wintertime soil temperature beneath the snow pack. ROS is a sporadic but well-known and significant phenomenon that is currently very poorly documented. Characterization of the distribution and occurrence of severe rain-on-snow events is based only on anecdotal evidence, indirect observations of carcasses found adjacent to iced snow packs, and irregular detection by a sparse observational weather network. We have analyzed in detail a particular well-identified ROS event that took place on Banks Island in early October 2003 that resulted in the death of 20,000 musk oxen. We make use of multifrequency passive microwave imagery from the special sensing microwave imager satellite sensor suite (SSM/I) in conjunction with a strong-fluctuation-theory (SFT) emissivity model. We show that a combination of time series analysis and cluster analysis based on microwave spectral gradients and polarization ratios provides a means to detect the stages of the ROS event resulting from the modification of the vertical structure of the snow pack, specifically wetting the snow, the accumulation of liquid water at the base of the snow during the rain event, and the subsequent modification of the snowpack after refreezing. SFT model analysis provides quantitative confirmation of our interpretation of the evolution of the microwave properties of the snowpack as a result of the ROS event. In particular, in addition to the grain coarsening due to destructive metamorphism, we detect the presence of the internal water and ice layers, directly identifying the physical properties producing the

  18. Assessment and validation of a simple automated method for the detection of gait events and intervals.

    PubMed

    Ghoussayni, Salim; Stevens, Christopher; Durham, Sally; Ewins, David

    2004-12-01

    A simple and rapid automatic method for detection of gait events at the foot could speed up and possibly increase the repeatability of gait analysis and evaluations of treatments for pathological gaits. The aim of this study was to compare and validate a kinematic-based algorithm used in the detection of four gait events, heel contact, heel rise, toe contact and toe off. Force platform data is often used to obtain start and end of contact phases, but not usually heel rise and toe contact events. For this purpose synchronised kinematic, kinetic and video data were captured from 12 healthy adult subjects walking both barefoot and shod at slow and normal self-selected speeds. The data were used to determine the gait events using three methods: force, visual inspection and algorithm methods. Ninety percent of all timings given by the algorithm were within one frame (16.7 ms) when compared to visual inspection. There were no statistically significant differences between the visual and algorithm timings. For both heel and toe contact the differences between the three methods were within 1.5 frames, whereas for heel rise and toe off the differences between the force on one side and the visual and algorithm on the other were higher and more varied (up to 175 ms). In addition, the algorithm method provided the duration of three intervals, heel contact to toe contact, toe contact to heel rise and heel rise to toe off, which are not readily available from force platform data. The ability to automatically and reliably detect the timings of these four gait events and three intervals using kinematic data alone is an asset to clinical gait analysis.

  19. Flow detection via sparse frame analysis for suspicious event recognition in infrared imagery

    NASA Astrophysics Data System (ADS)

    Fernandes, Henrique C.; Batista, Marcos A.; Barcelos, Celia A. Z.; Maldague, Xavier P. V.

    2013-05-01

    It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

  20. Detection and analysis of high-temperature events in the BIRD mission

    NASA Astrophysics Data System (ADS)

    Zhukov, Boris; Briess, Klaus; Lorenz, Eckehard; Oertel, Dieter; Skrbek, Wolfgang

    2005-01-01

    The primary mission objective of a new small Bi-spectral InfraRed Detection (BIRD) satellite is detection and quantitative analysis of high-temperature events like fires and volcanoes. An absence of saturation in the BIRD infrared channels makes it possible to improve false alarm rejection as well as to retrieve quantitative characteristics of hot targets, including their effective fire temperature, area and the radiative energy release. Examples are given of detection and analysis of wild and coal seam fires, of volcanic activity as well as of oil fires in Iraq. The smallest fires detected by BIRD, which were verified on ground, had an area of 12m2 at daytime and 4m2 at night.

  1. Automatic Event Detection in Noisy Environment for Material Process Monitoring by Laser AE Method

    NASA Astrophysics Data System (ADS)

    Ito, K.; Kuriki, H.; Araki, H.; Kuroda, S.; Enoki, M.

    2014-06-01

    Laser acoustic emission (AE) method is a unique in-situ and non-contact nondestructive evaluation (NDE) method. It has a capability to detect signals generated from crack generation and propagation, friction and other physical phenomena in materials even in high temperature environment. However, laser AE system has lower signal-to-noise ratio compared to the conventional AE system using PZT sensors, so it is difficult to apply this method in noisy environment. A novel AE measurement system to detect events in such difficult environments was developed. This system could continuously record all AE waveforms and enable unrestricted post-analyses. Noise reduction filters in frequency domain coupling with a new AE event extraction using multiple threshold values showed a good potential for AE signal processing. This system was successfully applied for crack monitoring of plasma spray deposition process of ceramic coating.

  2. How unusual are the "unusual events" detected by control chart techniques in healthcare settings?

    PubMed

    Borckardt, Jeffrey J; Nash, Michael R; Hardesty, Susan; Herbert, Joan; Cooney, Harriet; Pelic, Christopher

    2006-01-01

    Statistical process control (SPC) charts have become widely implemented tools for quality monitoring and assurance in healthcare settings across the United States. SPC methods have been successfully used in industrial settings to track the quality of products manufactured by machines and to detect deviations from acceptable Levels of product quality. However, problems may arise when SPC methods are used to evaluate human behavior. Specifically, when human behavior is tracked over time, the data stream generated usually exhibits periodicity and gradualism with respect to behavioral changes over time. These tendencies can be quantified and are recognized in the statistical field as autocorrelation. When autocorrelation is present, conventional SPC methods too often identify events as "unusuaL" when they really should be understood as products of random fluctuation. This article discusses the concept of autocorrelation and demonstrates the negative impact of autocorrelation on traditional SPC methods, with a specific focus on the use of SPC charts to detect unusual events. PMID:16944647

  3. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use

    PubMed Central

    Moghaddam, Athena K.; Yuen, Hiu Kim; Archambault, Philippe S.; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user’s driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user’s PW driving behavior. PMID:27170879

  4. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

    PubMed

    Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior. PMID:27170879

  5. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

    PubMed

    Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior.

  6. Nanoporous niobium oxide for label-free detection of DNA hybridization events.

    PubMed

    Choi, Jinsub; Lim, Jae Hoon; Rho, Sangchul; Jahng, Deokjin; Lee, Jaeyoung; Kim, Kyung Ja

    2008-01-15

    We found that DNA probes can be immobilized on anodically prepared porous niobium oxide without a chemical modification of both the DNA probes and the substrate. By using the porous niobium oxide with a positive surface charge, DNA hybridization events are detected on the basis of the blue-shift of a maximum absorption peak in UV-vis-NIR spectroscopy. The blue-shift is ascribed to the change of surface charge upon single- or double-stranded DNA. The method does not require a label and shows high sensitivity with the detection limit of the concentration of 1nM.

  7. A multivariate based event detection method and performance comparison with two baseline methods.

    PubMed

    Liu, Shuming; Smith, Kate; Che, Han

    2015-09-01

    Early warning systems have been widely deployed to protect water systems from accidental and intentional contamination events. Conventional detection algorithms are often criticized for having high false positive rates and low true positive rates. This mainly stems from the inability of these methods to determine whether variation in sensor measurements is caused by equipment noise or the presence of contamination. This paper presents a new detection method that identifies the existence of contamination by comparing Euclidean distances of correlation indicators, which are derived from the correlation coefficients of multiple water quality sensors. The performance of the proposed method was evaluated using data from a contaminant injection experiment and compared with two baseline detection methods. The results show that the proposed method can differentiate between fluctuations caused by equipment noise and those due to the presence of contamination. It yielded higher possibility of detection and a lower false alarm rate than the two baseline methods. With optimized parameter values, the proposed method can correctly detect 95% of all contamination events with a 2% false alarm rate.

  8. Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence

    PubMed Central

    Ohyama, Junji; Watanabe, Katsumi

    2016-01-01

    We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images. PMID:26869966

  9. Event Detection for Hydrothermal Plumes: A case study at Grotto Vent

    NASA Astrophysics Data System (ADS)

    Bemis, K. G.; Ozer, S.; Xu, G.; Rona, P. A.; Silver, D.

    2012-12-01

    Evidence is mounting that geologic events such as volcanic eruptions (and intrusions) and earthquakes (near and far) influence the flow rates and temperatures of hydrothermal systems. Connecting such suppositions to observations of hydrothermal output is challenging, but new ongoing time series have the potential to capture such events. This study explores using activity detection, a technique modified from computer vision, to identify pre-defined events within an extended time series recorded by COVIS (Cabled Observatory Vent Imaging Sonar) and applies it to a time series, with gaps, from Sept 2010 to the present; available measurements include plume orientation, plume rise rate, and diffuse flow area at the NEPTUNE Canada Observatory at Grotto Vent, Main Endeavour Field, Juan de Fuca Ridge. Activity detection is the process of finding a pattern (activity) in a data set containing many different types of patterns. Among many approaches proposed to model and detect activities, we have chosen a graph-based technique, Petri Nets, as they do not require training data to model the activity. They use the domain expert's knowledge to build the activity as a combination of feature states and their transitions (actions). Starting from a conceptual model of how hydrothermal plumes respond to daily tides, we have developed a Petri Net based detection algorithm that identifies deviations from the specified response. Initially we assumed that the orientation of the plume would change smoothly and symmetrically in a consistent daily pattern. However, results indicate that the rate of directional changes varies. The present Petri Net detects unusually large and rapid changes in direction or amount of bending; however inspection of Figure 1 suggests that many of the events detected may be artifacts resulting from gaps in the data or from the large temporal spacing. Still, considerable complexity overlies the "normal" tidal response pattern (the data has a dominant frequency of

  10. Adverse event detection (AED) system for continuously monitoring and evaluating structural health status

    NASA Astrophysics Data System (ADS)

    Yun, Jinsik; Ha, Dong Sam; Inman, Daniel J.; Owen, Robert B.

    2011-03-01

    Structural damage for spacecraft is mainly due to impacts such as collision of meteorites or space debris. We present a structural health monitoring (SHM) system for space applications, named Adverse Event Detection (AED), which integrates an acoustic sensor, an impedance-based SHM system, and a Lamb wave SHM system. With these three health-monitoring methods in place, we can determine the presence, location, and severity of damage. An acoustic sensor continuously monitors acoustic events, while the impedance-based and Lamb wave SHM systems are in sleep mode. If an acoustic sensor detects an impact, it activates the impedance-based SHM. The impedance-based system determines if the impact incurred damage. When damage is detected, it activates the Lamb wave SHM system to determine the severity and location of the damage. Further, since an acoustic sensor dissipates much less power than the two SHM systems and the two systems are activated only when there is an acoustic event, our system reduces overall power dissipation significantly. Our prototype system demonstrates the feasibility of the proposed concept.

  11. Real-time gait event detection for transfemoral amputees during ramp ascending and descending.

    PubMed

    Maqbool, H F; Husman, M A B; Awad, M I; Abouhossein, A; Dehghani-Sanij, A A

    2015-01-01

    Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and ampute-eprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results.

  12. Real-time gait event detection for transfemoral amputees during ramp ascending and descending.

    PubMed

    Maqbool, H F; Husman, M A B; Awad, M I; Abouhossein, A; Dehghani-Sanij, A A

    2015-01-01

    Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and ampute-eprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results. PMID:26737364

  13. Detection of DNA recognition events using multi-well field effect devices.

    PubMed

    Sakata, Toshiya; Miyahara, Yuji

    2005-11-15

    We proposed the multi-well field effect device for detection of charged biomolecules and demonstrated the detection principle for DNA recognition events using quasi-static capacitance-voltage (QSCV) measurement. The multi-well field effect device is based on the electrostatic interaction between molecular charges induced by DNA recognition and surface electrons in silicon through the Si(3)N(4)/SiO(2) thin double-layer. Since DNA molecules and DNA binders such as Hoechst 33258 have intrinsic charges in aqueous solutions, respectively, the charge density changes due to DNA recognition events at the Si(3)N(4) surface were directly translated into electrical signal such as a flat band voltage change in the QSCV measurement. The average flat band shifts were 20.7 mV for hybridization and -13.5 mV for binding of Hoechst 33258. From the results of flat band voltage shifts due to hybridization and binding of Hoechst 33258, the immobilization density of oligonucleotide probes at the Si(3)N(4) surface was estimated to be 10(8) cm(-2). The platform based on the multi-well field effect device is suitable for a simple and arrayed detection system for DNA recognition events.

  14. Data Flow Design for Event Detection and Qualification in TES X-Ray Detectors

    NASA Astrophysics Data System (ADS)

    Ceballos, M. T.; Cobo, B.; Fraga-Encinas, R.; van der Kuur, J.; Schuurmans, J.; Gottardi, L.

    2013-10-01

    The current and forthcoming research lines in X-ray astronomy will require unprecedented spectral resolution with imaging capabilities. The most promising detectors able to provide these capabilities are the calorimeters based on Transition Edge Sensor (TES) technologies, like the one that has been under development for the proposed ATHENA x-ray space mission. We present here the Data Flow designed for one of such instruments covering the detection algorithms to extract the x-ray events (photons) from the noisy signal (as well as to cope with a possible pile-up), the event qualification (event grade) according to the event arrival time and proximity to other events, and finally the filtering process applied to these pulses to get their energy content, and thus the astronomical source spectrum. This development is currently part of a collaboration between IFCA (Spain) and SRON (NL) institutes, as part of a larger project initiated in 2005 and named EURECA (de Korte et al. 2009) involving many other institutes in Europe and the USA. This project was created to design the TES prototype proposed for the XEUS/IXO/ATHENA ESA missions.

  15. Method for the depth corrected detection of ionizing events from a co-planar grids sensor

    DOEpatents

    De Geronimo, Gianluigi; Bolotnikov, Aleksey E.; Carini, Gabriella

    2009-05-12

    A method for the detection of ionizing events utilizing a co-planar grids sensor comprising a semiconductor substrate, cathode electrode, collecting grid and non-collecting grid. The semiconductor substrate is sensitive to ionizing radiation. A voltage less than 0 Volts is applied to the cathode electrode. A voltage greater than the voltage applied to the cathode is applied to the non-collecting grid. A voltage greater than the voltage applied to the non-collecting grid is applied to the collecting grid. The collecting grid and the non-collecting grid are summed and subtracted creating a sum and difference respectively. The difference and sum are divided creating a ratio. A gain coefficient factor for each depth (distance between the ionizing event and the collecting grid) is determined, whereby the difference between the collecting electrode and the non-collecting electrode multiplied by the corresponding gain coefficient is the depth corrected energy of an ionizing event. Therefore, the energy of each ionizing event is the difference between the collecting grid and the non-collecting grid multiplied by the corresponding gain coefficient. The depth of the ionizing event can also be determined from the ratio.

  16. Advanced Geospatial Hydrodynamic Signals Analysis for Tsunami Event Detection and Warning

    NASA Astrophysics Data System (ADS)

    Arbab-Zavar, Banafshe; Sabeur, Zoheir

    2013-04-01

    Current early tsunami warning can be issued upon the detection of a seismic event which may occur at a given location offshore. This also provides an opportunity to predict the tsunami wave propagation and run-ups at potentially affected coastal zones by selecting the best matching seismic event from a database of pre-computed tsunami scenarios. Nevertheless, it remains difficult and challenging to obtain the rupture parameters of the tsunamigenic earthquakes in real time and simulate the tsunami propagation with high accuracy. In this study, we propose a supporting approach, in which the hydrodynamic signal is systematically analysed for traces of a tsunamigenic signal. The combination of relatively low amplitudes of a tsunami signal at deep waters and the frequent occurrence of background signals and noise contributes to a generally low signal to noise ratio for the tsunami signal; which in turn makes the detection of this signal difficult. In order to improve the accuracy and confidence of detection, a re-identification framework in which a tsunamigenic signal is detected via the scan of a network of hydrodynamic stations with water level sensing is performed. The aim is to attempt the re-identification of the same signatures as the tsunami wave spatially propagates through the hydrodynamic stations sensing network. The re-identification of the tsunamigenic signal is technically possible since the tsunami signal at the open ocean itself conserves its birthmarks relating it to the source event. As well as supporting the initial detection and improving the confidence of detection, a re-identified signal is indicative of the spatial range of the signal, and thereby it can be used to facilitate the identification of certain background signals such as wind waves which do not have as large a spatial reach as tsunamis. In this paper, the proposed methodology for the automatic detection of tsunamigenic signals has been achieved using open data from NOAA with a recorded

  17. Group localisation and unsupervised detection and classification of basic crowd behaviour events for surveillance applications

    NASA Astrophysics Data System (ADS)

    Roubtsova, Nadejda S.; de With, Peter H. N.

    2013-02-01

    Technology for monitoring crowd behaviour is in demand for surveillance and security applications. The trend in research is to tackle detection of complex crowd behaviour events (panic, ght, evacuation etc.) directly using machine learning techniques. In this paper, we present a contrary, bottom-up approach seeking basic group information: (1) instantaneous location and (2) the merge, split and lateral slide-by events - the three basic motion patterns comprising any crowd behaviour. The focus on such generic group information makes our algorithm suitable as a building block in a variety of surveillance systems, possibly integrated with static content analysis solutions. Our feature extraction framework has optical ow in its core. The framework is universal being motion-based, rather than object-detection-based and generates a large variety of motion-blob- characterising features useful for an array of classi cation problems. Motion-based characterisation is performed on a group as an atomic whole and not by means of superposition of individual human motions. Within that feature space, our classi cation system makes decisions based on heuristic rules and thresholds, without machine learning. Our system performs well on group localisation, consistently generating contours around both moving and halted groups. The visual output of our periodical group localisation is equivalent to tracking and the group contour accuracy ranges from adequate to exceptionally good. The system successfully detects and classi es within our merge/split/slide-by event space in surveillance-type video sequences, di ering in resolution, scale, quality and motion content. Quantitatively, its performance is characterised by a good recall: 83% on detection and 71% on combined detection and classi cation.

  18. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    SciTech Connect

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-30

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  19. A Patch-Based Method for Repetitive and Transient Event Detection in Fluorescence Imaging

    PubMed Central

    Boulanger, Jérôme; Gidon, Alexandre; Kervran, Charles; Salamero, Jean

    2010-01-01

    Automatic detection and characterization of molecular behavior in large data sets obtained by fast imaging in advanced light microscopy become key issues to decipher the dynamic architectures and their coordination in the living cell. Automatic quantification of the number of sudden and transient events observed in fluorescence microscopy is discussed in this paper. We propose a calibrated method based on the comparison of image patches expected to distinguish sudden appearing/vanishing fluorescent spots from other motion behaviors such as lateral movements. We analyze the performances of two statistical control procedures and compare the proposed approach to a frame difference approach using the same controls on a benchmark of synthetic image sequences. We have then selected a molecular model related to membrane trafficking and considered real image sequences obtained in cells stably expressing an endocytic-recycling trans-membrane protein, the Langerin-YFP, for validation. With this model, we targeted the efficient detection of fast and transient local fluorescence concentration arising in image sequences from a data base provided by two different microscopy modalities, wide field (WF) video microscopy using maximum intensity projection along the axial direction and total internal reflection fluorescence microscopy. Finally, the proposed detection method is briefly used to statistically explore the effect of several perturbations on the rate of transient events detected on the pilot biological model. PMID:20976222

  20. Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by Dmsp/ols Night Light Image

    NASA Astrophysics Data System (ADS)

    Yuki, Akiyama; Satoshi, Ueyama; Ryosuke, Shibasaki; Adachi, Ryuichiro

    2016-06-01

    In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

  1. Fault detection and isolation in manufacturing systems with an identified discrete event model

    NASA Astrophysics Data System (ADS)

    Roth, Matthias; Schneider, Stefan; Lesage, Jean-Jacques; Litz, Lothar

    2012-10-01

    In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.

  2. Exupery volcano fast response system - The event detection and waveform classification system

    NASA Astrophysics Data System (ADS)

    Hammer, Conny; Ohrnberger, Matthias

    2010-05-01

    Volcanic eruptions are often preceded by seismic activity which can be used to quantify the volcanic activity since the number and the size of certain types of seismic events usually increase before periods of volcanic crisis. The implementation of an automatic detection and classification system for seismic signals of volcanic origin allows not only for the processing of large amounts of data in short time, but also provides consistent and time-invariant results. Here, we have developed a system based upon a combination of different methods. To enable a first robust event detection in the continuous data stream different modules are implemented in the real time system Earthworm which is widely distributed in active volcano monitoring observatories worldwide. Among those software modules are classical trigger algorithm like STA/LTA and cross-correlation master event matching which is also used to detect different classes of signals. Furthermore an additional module is implemented in the real time system to compute continuous activity parameters which are also used to quantify the volcanic activity. Most automatic classification systems need a sufficiently large pre-classified data set for training the system. However in case of a volcanic crisis we are often confronted with a lack of training data due to insufficient prior observations because prior data acquisition might be carried out with different equipment at a low number of sites and due to the imminent crisis there might be no time for the time-consuming and tedious process of preparing a training data set. For this reason we have developed a novel seismic event spotting technique in order to be less dependent on the existence of previously acquired data bases of event classes. One main goal is therefore to provide observatory staff with a robust event classification based on a minimum number of reference waveforms. By using a "learning-while-recording" approach we are allowing for the fast build-up of a

  3. Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations.

    PubMed

    Rueterbories, Jan; Spaich, Erika G; Andersen, Ole K

    2014-04-01

    Gait rehabilitation by Functional Electrical Stimulations (FESs) requires a reliable trigger signal to start the stimulations. This could be obtained by a simple switch under the heel or by means of an inertial sensor system. This study provides an algorithm to detect gait events in differential acceleration signals of the foot. The key feature of differential measurements is that they compensate the impact of gravity. The real time detection capability of a rule based algorithm in healthy and hemiparetic individuals was investigated. Detection accuracy and precision compared to signals from foot switches were evaluated. The algorithm detected curve features of the vectorial sum of radial and tangential accelerations and mapped those to discrete gait states. The results showed detection rates for healthy and hemiparetic gait ranging form 84.2% to 108.5%. The sensitivity was between 0.81 and 1, and the specificity between 0.85 and 1, depending on gait phase and group of subjects. The algorithm detected gait phase changes earlier than the reference. Differential acceleration signals combined with the proposed algorithm have the potential to be implemented in a future FES system.

  4. Detection and identification of multiple genetically modified events using DNA insert fingerprinting.

    PubMed

    Raymond, Philippe; Gendron, Louis; Khalf, Moustafa; Paul, Sylvianne; Dibley, Kim L; Bhat, Somanath; Xie, Vicki R D; Partis, Lina; Moreau, Marie-Eve; Dollard, Cheryl; Coté, Marie-José; Laberge, Serge; Emslie, Kerry R

    2010-03-01

    Current screening and event-specific polymerase chain reaction (PCR) assays for the detection and identification of genetically modified organisms (GMOs) in samples of unknown composition or for the detection of non-regulated GMOs have limitations, and alternative approaches are required. A transgenic DNA fingerprinting methodology using restriction enzyme digestion, adaptor ligation, and nested PCR was developed where individual GMOs are distinguished by the characteristic fingerprint pattern of the fragments generated. The inter-laboratory reproducibility of the amplified fragment sizes using different capillary electrophoresis platforms was compared, and reproducible patterns were obtained with an average difference in fragment size of 2.4 bp. DNA insert fingerprints for 12 different maize events, including two maize hybrids and one soy event, were generated that reflected the composition of the transgenic DNA constructs. Once produced, the fingerprint profiles were added to a database which can be readily exchanged and shared between laboratories. This approach should facilitate the process of GMO identification and characterization. PMID:19943159

  5. Clinical outcome of subchromosomal events detected by whole‐genome noninvasive prenatal testing

    PubMed Central

    Helgeson, J.; Wardrop, J.; Boomer, T.; Almasri, E.; Paxton, W. B.; Saldivar, J. S.; Dharajiya, N.; Monroe, T. J.; Farkas, D. H.; Grosu, D. S.

    2015-01-01

    Abstract Objective A novel algorithm to identify fetal microdeletion events in maternal plasma has been developed and used in clinical laboratory‐based noninvasive prenatal testing. We used this approach to identify the subchromosomal events 5pdel, 22q11del, 15qdel, 1p36del, 4pdel, 11qdel, and 8qdel in routine testing. We describe the clinical outcomes of those samples identified with these subchromosomal events. Methods Blood samples from high‐risk pregnant women submitted for noninvasive prenatal testing were analyzed using low coverage whole genome massively parallel sequencing. Sequencing data were analyzed using a novel algorithm to detect trisomies and microdeletions. Results In testing 175 393 samples, 55 subchromosomal deletions were reported. The overall positive predictive value for each subchromosomal aberration ranged from 60% to 100% for cases with diagnostic and clinical follow‐up information. The total false positive rate was 0.0017% for confirmed false positives results; false negative rate and sensitivity were not conclusively determined. Conclusion Noninvasive testing can be expanded into the detection of subchromosomal copy number variations, while maintaining overall high test specificity. In the current setting, our results demonstrate high positive predictive values for testing of rare subchromosomal deletions. © 2015 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd. PMID:26088833

  6. Piezoelectric energy-harvesting power source and event detection sensors for gun-fired munitions

    NASA Astrophysics Data System (ADS)

    Rastegar, Jahangir; Feng, Dake; Pereira, Carlos M.

    2015-05-01

    This paper presents a review of piezoelectric based energy harvesting devices and their charge collection electronics for use in very harsh environment of gun-fired munitions. A number of novel classes of such energy harvesting power sources have been developed for gun-fired munitions and similar applications, including those with integrated safety and firing setback event detection electronics and logic circuitry. The power sources are designed to harvest energy from firing acceleration and vibratory motions during the flight. As an example, the application of the developed piezoelectric based energy harvesting devices with event detection circuitry for the development of self-powered initiators with full no-fire safety circuitry for protection against accidental drops, transportation vibration, and other similar low amplitude accelerations and/or high amplitude but short duration acceleration events is presented. The design allows the use of a very small piezoelectric element, thereby allowing such devices to be highly miniaturized. These devices can be readily hardened to withstand very high G firing setback accelerations in excess of 100,000 G and the harsh firing environment. The design of prototypes and testing under realistic conditions are presented.

  7. Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification

    PubMed Central

    Sohn, Sunghwan; Wagholikar, Kavishwar B; Li, Dingcheng; Jonnalagadda, Siddhartha R; Tao, Cui; Komandur Elayavilli, Ravikumar; Liu, Hongfang

    2013-01-01

    Background Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge. Objective To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text. Materials and methods The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework. Results Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537. Conclusions Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance. PMID:23558168

  8. Detecting regular sound changes in linguistics as events of concerted evolution

    SciTech Connect

    Hruschka, Daniel  J.; Branford, Simon; Smith, Eric  D.; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2014-12-18

    Background: Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Results: Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. Conclusions: We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group.

  9. Detection of Visual Events in Underwater Video Using a Neuromorphic Saliency-based Attention System

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Walther, D.; Cline, D. E.; Sherlock, R.; Salamy, K. A.; Wilson, A.; Koch, C.

    2003-12-01

    The Monterey Bay Aquarium Research Institute (MBARI) uses high-resolution video equipment on remotely operated vehicles (ROV) to obtain quantitative data on the distribution and abundance of oceanic animals. High-quality video data supplants the traditional approach of assessing the kinds and numbers of animals in the oceanic water column through towing collection nets behind ships. Tow nets are limited in spatial resolution, and often destroy abundant gelatinous animals resulting in species undersampling. Video camera-based quantitative video transects (QVT) are taken through the ocean midwater, from 50m to 4000m, and provide high-resolution data at the scale of the individual animals and their natural aggregation patterns. However, the current manual method of analyzing QVT video by trained scientists is labor intensive and poses a serious limitation to the amount of information that can be analyzed from ROV dives. Presented here is an automated system for detecting marine animals (events) visible in the videos. Automated detection is difficult due to the low contrast of many translucent animals and due to debris ("marine snow") cluttering the scene. Video frames are processed with an artificial intelligence attention selection algorithm that has proven a robust means of target detection in a variety of natural terrestrial scenes. The candidate locations identified by the attention selection module are tracked across video frames using linear Kalman filters. Typically, the occurrence of visible animals in the video footage is sparse in space and time. A notion of "boring" video frames is developed by detecting whether or not there is an interesting candidate object for an animal present in a particular sequence of underwater video -- video frames that do not contain any "interesting" events. If objects can be tracked successfully over several frames, they are stored as potentially "interesting" events. Based on low-level properties, interesting events are

  10. Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray.

    PubMed

    Xu, Xiaodan; Li, Yingcong; Zhao, Heng; Wen, Si-yuan; Wang, Sheng-qi; Huang, Jian; Huang, Kun-lun; Luo, Yun-bo

    2005-05-18

    To devise a rapid and reliable method for the detection and identification of genetically modified (GM) events, we developed a multiplex polymerase chain reaction (PCR) coupled with a DNA microarray system simultaneously aiming at many targets in a single reaction. The system included probes for screening gene, species reference gene, specific gene, construct-specific gene, event-specific gene, and internal and negative control genes. 18S rRNA was combined with species reference genes as internal controls to assess the efficiency of all reactions and to eliminate false negatives. Two sets of the multiplex PCR system were used to amplify four and five targets, respectively. Eight different structure genes could be detected and identified simultaneously for Roundup Ready soybean in a single microarray. The microarray specificity was validated by its ability to discriminate two GM maizes Bt176 and Bt11. The advantages of this method are its high specificity and greatly reduced false-positives and -negatives. The multiplex PCR coupled with microarray technology presented here is a rapid and reliable tool for the simultaneous detection of GM organism ingredients.

  11. Robust cardiac event change detection method for long-term healthcare monitoring applications.

    PubMed

    Satija, Udit; Ramkumar, Barathram; Manikandan, M Sabarimalai

    2016-06-01

    A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals.

  12. Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

    NASA Technical Reports Server (NTRS)

    Albayrak, Arif; Teng, William

    2015-01-01

    As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

  13. Detecting tidal disruption events of massive black holes in normal galaxies with the Einstein Probe

    NASA Astrophysics Data System (ADS)

    Yuan, W.; Komossa, S.; Zhang, C.; Feng, H.; Ling, Z.-X.; Zhao, D. H.; Zhang, S.-N.; Osborne, J. P.; O'Brien, P.; Willingale, R.; Lapington, J.; Lapington

    2016-02-01

    Stars are tidally disrupted and accreted when they approach massive black holes (MBHs) closely, producing a flare of electromagnetic radiation. The majority of the (approximately two dozen) tidal disruption events (TDEs) identified so far have been discovered by their luminous, transient X-ray emission. Once TDEs are detected in much larger numbers, in future dedicated transient surveys, a wealth of new applications will become possible. Here, we present the proposed Einstein Probe mission, which is a dedicated time-domain soft X-ray all-sky monitor aiming at detecting X-ray transients including TDEs in large numbers. The mission consists of a wide-field micro-pore Lobster-eye imager (60° × 60°), and is designed to carry out an all-sky transient survey at energies of 0.5-4 keV. It will also carry a more sensitive telescope for X-ray follow-ups, and will be capable of issuing public transient alerts rapidly. Einstein Probe is expected to revolutionise the field of TDE research by detecting several tens to hundreds of events per year from the early phase of flares, many with long-term, well sampled lightcurves.

  14. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

    PubMed

    Cron, Andrew; Gouttefangeas, Cécile; Frelinger, Jacob; Lin, Lin; Singh, Satwinder K; Britten, Cedrik M; Welters, Marij J P; van der Burg, Sjoerd H; West, Mike; Chan, Cliburn

    2013-01-01

    Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling

  15. BIRD detection and analysis of high-temperature events: first results

    NASA Astrophysics Data System (ADS)

    Zhukov, Boris; Briess, Klaus; Lorenz, Eckehard; Oertel, Dieter; Skrbek, Wolfgang

    2003-03-01

    The primary mission objective of a new small Bi-spectral InfraRed Detection (BIRD) satellite, which was put in a 570 km circular sun-synchronous orbit on 22 October 2001, is detection and quantitative analysis of high-temperature events (HTE) like fires and volcanoes. A unique feature of the BIRD mid- and thermal infrared channels is a real-time adjustment of their integration time that allows a HTE observation without sensor saturation, preserving a good radiometric resolution of 0.1-0.2 K for pixels at normal temperatures. This makes it possible: (a) to improve false alarm rejection capability and (b) to estimate HTE temperature, area and radiative energy release. Due to a higher spatial resolution, BIRD can detect an order of magnitude smaller HTE than AVHRR and MODIS. The smallest verified fire that was detected in the BIRD data had an area of ~12 m2. The first BIRD HTE detection and analysis results are presented including bush fires in Australia, forest fires in Russia, coal seam fires in China, and a time-varying thermal activity at Etna.

  16. Gait event detection on level ground and incline walking using a rate gyroscope.

    PubMed

    Catalfamo, Paola; Ghoussayni, Salim; Ewins, David

    2010-01-01

    Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. Accurate determination of the Initial Contact of the foot with the floor (IC) and the final contact or Foot Off (FO) on different terrains is important. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects walking outdoors on level ground, and up and down an incline. Performance was compared with a reference pressure measurement system. The mean difference between the gyroscope and the reference was less than -25 ms for IC and less than 75 ms for FO for all terrains. Detection success was over 98%. These results provide preliminary evidence supporting the use of the gyroscope for gait event detection on inclines as well as level walking.

  17. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event.

    PubMed

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-01-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM(-1) s(-1)) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM(-1) s(-1)) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules. PMID:27435527

  18. Detecting event-related recurrences by symbolic analysis: applications to human language processing

    PubMed Central

    beim Graben, Peter; Hutt, Axel

    2015-01-01

    Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states. PMID:25548270

  19. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event

    NASA Astrophysics Data System (ADS)

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-07-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM‑1 s‑1) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM‑1 s‑1) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules.

  20. Label-Free Detection of Single Living Bacteria via Electrochemical Collision Event

    PubMed Central

    Lee, Ji Young; Kim, Byung-Kwon; Kang, Mijeong; Park, Jun Hui

    2016-01-01

    We detected single living bacterial cells on ultramicroelectrode (UME) using a single-particle collision method and optical microscopic methods. The number of collision events involving the bacterial cells indicated in current-time (i-t) curves corresponds to the number of bacterial cells (i.e., Escherichia coli) on the UME surface, as observed visually. Simulations were performed to determine the theoretical current response (75 pA) and frequency (0.47 pM−1 s−1) of single Escherichia coli collisions. The experimental current response (83 pA) and frequency (0.26 pM−1 s−1) were on the same order of magnitude as the theoretical values. This single-particle collision approach facilitates detecting living bacteria and determining their concentration in solution and could be widely applied to studying other bacteria and biomolecules. PMID:27435527

  1. Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-06

    Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

  2. Gait Event Detection on Level Ground and Incline Walking Using a Rate Gyroscope

    PubMed Central

    Catalfamo, Paola; Ghoussayni, Salim; Ewins, David

    2010-01-01

    Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. Accurate determination of the Initial Contact of the foot with the floor (IC) and the final contact or Foot Off (FO) on different terrains is important. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects walking outdoors on level ground, and up and down an incline. Performance was compared with a reference pressure measurement system. The mean difference between the gyroscope and the reference was less than −25 ms for IC and less than 75 ms for FO for all terrains. Detection success was over 98%. These results provide preliminary evidence supporting the use of the gyroscope for gait event detection on inclines as well as level walking. PMID:22219682

  3. Detection of planets in extremely weak central perturbation microlensing events via next-generation ground-based surveys

    SciTech Connect

    Chung, Sun-Ju; Lee, Chung-Uk; Koo, Jae-Rim E-mail: leecu@kasi.re.kr

    2014-04-20

    Even though the recently discovered high-magnification event MOA-2010-BLG-311 had complete coverage over its peak, confident planet detection did not happen due to extremely weak central perturbations (EWCPs, fractional deviations of ≲ 2%). For confident detection of planets in EWCP events, it is necessary to have both high cadence monitoring and high photometric accuracy better than those of current follow-up observation systems. The next-generation ground-based observation project, Korea Microlensing Telescope Network (KMTNet), satisfies these conditions. We estimate the probability of occurrence of EWCP events with fractional deviations of ≤2% in high-magnification events and the efficiency of detecting planets in the EWCP events using the KMTNet. From this study, we find that the EWCP events occur with a frequency of >50% in the case of ≲ 100 M {sub E} planets with separations of 0.2 AU ≲ d ≲ 20 AU. We find that for main-sequence and sub-giant source stars, ≳ 1 M {sub E} planets in EWCP events with deviations ≤2% can be detected with frequency >50% in a certain range that changes with the planet mass. However, it is difficult to detect planets in EWCP events of bright stars like giant stars because it is easy for KMTNet to be saturated around the peak of the events because of its constant exposure time. EWCP events are caused by close, intermediate, and wide planetary systems with low-mass planets and close and wide planetary systems with massive planets. Therefore, we expect that a much greater variety of planetary systems than those already detected, which are mostly intermediate planetary systems, regardless of the planet mass, will be significantly detected in the near future.

  4. Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

    PubMed Central

    Dafna, Eliran; Tarasiuk, Ariel; Zigel, Yaniv

    2013-01-01

    Objective Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. Design Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. Patients Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. Measurements and Results To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise). Conclusions Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients. PMID:24391903

  5. Large-Scale Disturbance Events in Terrestrial Ecosystems Detected using Global Satellite Data Sets

    NASA Astrophysics Data System (ADS)

    Potter, C.; Tan, P.; Kumar, V.; Klooster, S.

    2004-12-01

    Studies are being conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the Advanced Very High Resolution Radiometer (AVHRR), as a means to characterize large-scale ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8-km globally. Potential disturbance events were identified in the FPAR time series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We can find verifiable evidence of numerous disturbance types across North America, including major regional patterns of cold and heat waves, forest fires, tropical storms, and large-scale forest logging. Based on this analysis, an historical picture is emerging of periodic droughts and heat waves, possibly coupled with herbivorous insect outbreaks, as among the most important causes of ecosystem disturbance in North America. In South America, large areas of northeastern Brazil appear to have been impacted in the early 1990s by severe drought. Amazon tropical forest disturbance can be detected at large scales particularly in the mid 1990s. In Asia, large-scale disturbance events appear in the mid 1980s and the late 1990s across boreal and temperate forest zones, as well as in cropland areas of western India. In northern Europe and central Africa, large-scale forest disturbance appears in the mid 1990s.

  6. Endpoint Visual Detection of Three Genetically Modified Rice Events by Loop-Mediated Isothermal Amplification

    PubMed Central

    Chen, Xiaoyun; Wang, Xiaofu; Jin, Nuo; Zhou, Yu; Huang, Sainan; Miao, Qingmei; Zhu, Qing; Xu, Junfeng

    2012-01-01

    Genetically modified (GM) rice KMD1, TT51-1, and KF6 are three of the most well known transgenic Bt rice lines in China. A rapid and sensitive molecular assay for risk assessment of GM rice is needed. Polymerase chain reaction (PCR), currently the most common method for detecting genetically modified organisms, requires temperature cycling and relatively complex procedures. Here we developed a visual and rapid loop-mediated isothermal amplification (LAMP) method to amplify three GM rice event-specific junction sequences. Target DNA was amplified and visualized by two indicators (SYBR green or hydroxy naphthol blue [HNB]) within 60 min at an isothermal temperature of 63 °C. Different kinds of plants were selected to ensure the specificity of detection and the results of the non-target samples were negative, indicating that the primer sets for the three GM rice varieties had good levels of specificity. The sensitivity of LAMP, with detection limits at low concentration levels (0.01%–0.005% GM), was 10- to 100-fold greater than that of conventional PCR. Additionally, the LAMP assay coupled with an indicator (SYBR green or HNB) facilitated analysis. These findings revealed that the rapid detection method was suitable as a simple field-based test to determine the status of GM crops. PMID:23203072

  7. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    PubMed Central

    Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

    2013-01-01

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. PMID:24351629

  8. Online least squares one-class support vector machines-based abnormal visual event detection.

    PubMed

    Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

    2013-12-12

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

  9. Online least squares one-class support vector machines-based abnormal visual event detection.

    PubMed

    Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

    2013-01-01

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. PMID:24351629

  10. The Waveform Correlation Event Detection System project, Phase II: Testing with the IDC primary network

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Moore, S.G.

    1998-04-01

    Further improvements to the Waveform Correlation Event Detection System (WCEDS) developed by Sandia Laboratory have made it possible to test the system on the accepted Comprehensive Test Ban Treaty (CTBT) seismic monitoring network. For our test interval we selected a 24-hour period from December 1996, and chose to use the Reviewed Event Bulletin (REB) produced by the Prototype International Data Center (PIDC) as ground truth for evaluating the results. The network is heterogeneous, consisting of array and three-component sites, and as a result requires more flexible waveform processing algorithms than were available in the first version of the system. For simplicity and superior performance, we opted to use the spatial coherency algorithm of Wagner and Owens (1996) for both types of sites. Preliminary tests indicated that the existing version of WCEDS, which ignored directional information, could not achieve satisfactory detection or location performance for many of the smaller events in the REB, particularly those in the south Pacific where the network coverage is unusually sparse. To achieve an acceptable level of performance, we made modifications to include directional consistency checks for the correlations, making the regions of high correlation much less ambiguous. These checks require the production of continuous azimuth and slowness streams for each station, which is accomplished by means of FK processing for the arrays and power polarization processing for the three-component sites. In addition, we added the capability to use multiple frequency-banded data streams for each site to increase sensitivity to phases whose frequency content changes as a function of distance.

  11. Automatic Event Detection and Characterization of solar events with IRIS, SDO/AIA and Hi-C

    NASA Astrophysics Data System (ADS)

    Alexander, Caroline; Fayock, Brian; Winebarger, Amy

    2016-05-01

    Dynamic, low-lying loops with peak temperatures <1 MK are observed throughout the solar transition region. These loops can be observed in SDO/AIA data due to some lower temperature spectral lines in the passbands, but have not been studied in great detail. We have developed a technique to automatically identify events (i.e., brightenings) on a pixel-by-pixel basis applying a set of selection criteria. The pixels are then grouped according to their proximity in space and relative progression of the event. This method allows us to characterize their overall lifetime and the rate at which these events occur. Our current progress includes identification of these groups of events in IRIS data, determination of their existence in AIA data, and characterization based on a comparison between the two. This technique has also been used on Hi-C data in preparation for the rocket re-flight in July 2016. Results on the success of this technique at identifying real structures and sources of heating will be shown.

  12. Detecting regular sound changes in linguistics as events of concerted evolution

    DOE PAGES

    Hruschka, Daniel  J.; Branford, Simon; Smith, Eric  D.; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2014-12-18

    Background: Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Results: Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular soundmore » change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. Conclusions: We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group.« less

  13. Comparison and applicability of landslide susceptibility models based on landslide ratio-based logistic regression, frequency ratio, weight of evidence, and instability index methods in an extreme rainfall event

    NASA Astrophysics Data System (ADS)

    Wu, Chunhung

    2016-04-01

    Few researches have discussed about the applicability of applying the statistical landslide susceptibility (LS) model for extreme rainfall-induced landslide events. The researches focuses on the comparison and applicability of LS models based on four methods, including landslide ratio-based logistic regression (LRBLR), frequency ratio (FR), weight of evidence (WOE), and instability index (II) methods, in an extreme rainfall-induced landslide cases. The landslide inventory in the Chishan river watershed, Southwestern Taiwan, after 2009 Typhoon Morakot is the main materials in this research. The Chishan river watershed is a tributary watershed of Kaoping river watershed, which is a landslide- and erosion-prone watershed with the annual average suspended load of 3.6×107 MT/yr (ranks 11th in the world). Typhoon Morakot struck Southern Taiwan from Aug. 6-10 in 2009 and dumped nearly 2,000 mm of rainfall in the Chishan river watershed. The 24-hour, 48-hour, and 72-hours accumulated rainfall in the Chishan river watershed exceeded the 200-year return period accumulated rainfall. 2,389 landslide polygons in the Chishan river watershed were extracted from SPOT 5 images after 2009 Typhoon Morakot. The total landslide area is around 33.5 km2, equals to the landslide ratio of 4.1%. The main landslide types based on Varnes' (1978) classification are rotational and translational slides. The two characteristics of extreme rainfall-induced landslide event are dense landslide distribution and large occupation of downslope landslide areas owing to headward erosion and bank erosion in the flooding processes. The area of downslope landslide in the Chishan river watershed after 2009 Typhoon Morakot is 3.2 times higher than that of upslope landslide areas. The prediction accuracy of LS models based on LRBLR, FR, WOE, and II methods have been proven over 70%. The model performance and applicability of four models in a landslide-prone watershed with dense distribution of rainfall

  14. Unreported seismic events found far off-shore Mexico using full-waveform, cross-correlation detection method.

    NASA Astrophysics Data System (ADS)

    Solano, ErickaAlinne; Hjorleifsdottir, Vala; Perez-Campos, Xyoli

    2015-04-01

    A large subset of seismic events do not have impulsive arrivals, such as low frequency events in volcanoes, earthquakes in the shallow part of the subduction interface and further down dip from the traditional seismogenic part, glacial events, volcanic and non-volcanic tremors and landslides. A suite of methods can be used to detect these non-impulsive events. One of this methods is the full-waveform detection based on time reversal methods (Solano, et al , submitted to GJI). The method uses continuous observed seismograms, together with Greens functions and moment tensor responses calculated for an arbitrary 3D structure. This method was applied to the 2012 Ometepec-Pinotepa Nacional earthquake sequence in Guerrero, Mexico. During the span time of the study, we encountered three previously unknown events. One of this events was an impulsive earthquake in the Ometepec area, that only has clear arrivals on three stations and was therefore not located and reported by the SSN. The other two events are previously undetected events, very depleted in high frequencies, that occurred far outside the search area. A very rough estimate gives the location of this two events in the portion of the East Pacific Rise around 9 N. These two events are detected despite their distance from the search area, due to favorable move-out on the array of the Mexican National Seismological Service network (SSN). We are expanding the study area to the EPR and to a larger period of time, with the objective of finding more events in that region. We will present an analysis of the newly detected events, as well as any further findings at the meeting.

  15. Validity assessment of the detection method of maize event Bt10 through investigation of its molecular structure.

    PubMed

    Milcamps, Anne; Rabe, Scott; Cade, Rebecca; De Framond, Anic J; Henriksson, Peter; Kramer, Vance; Lisboa, Duarte; Pastor-Benito, Susana; Willits, Michael G; Lawrence, David; Van den Eede, Guy

    2009-04-22

    In March 2005, U.S. authorities informed the European Commission of the inadvertent release of unauthorized maize GM event Bt10 in their market and subsequently the grain channel. In the United States measures were taken to eliminate Bt10 from seed and grain supplies; in the European Union an embargo for maize gluten and brewer's grain import was implemented unless certified of Bt10 absence with a Bt10-specific PCR detection method. With the aim of assessing the validity of the Bt10 detection method, an in-depth analysis of the molecular organization of the genetic modification of this event was carried out by both the company Syngenta, who produced the event, and the European Commission Joint Research Centre, who validated the detection method. Using a variety of molecular analytical tools, both organizations found the genetic modification of event Bt10 to be very complex in structure, with rearrangements, inversions, and multiple copies of the structural elements (cry1Ab, pat, and the amp gene), interspersed with small genomic maize fragments. Southern blot analyses demonstrated that all Bt10 elements were found tightly linked on one large fragment, including the region that would generate the event-specific PCR amplicon of the Bt10 detection method. This study proposes a hypothetical map of the insert of event Bt10 and concludes that the validated detection method for event Bt10 is fit for its purpose.

  16. Validity assessment of the detection method of maize event Bt10 through investigation of its molecular structure.

    PubMed

    Milcamps, Anne; Rabe, Scott; Cade, Rebecca; De Framond, Anic J; Henriksson, Peter; Kramer, Vance; Lisboa, Duarte; Pastor-Benito, Susana; Willits, Michael G; Lawrence, David; Van den Eede, Guy

    2009-04-22

    In March 2005, U.S. authorities informed the European Commission of the inadvertent release of unauthorized maize GM event Bt10 in their market and subsequently the grain channel. In the United States measures were taken to eliminate Bt10 from seed and grain supplies; in the European Union an embargo for maize gluten and brewer's grain import was implemented unless certified of Bt10 absence with a Bt10-specific PCR detection method. With the aim of assessing the validity of the Bt10 detection method, an in-depth analysis of the molecular organization of the genetic modification of this event was carried out by both the company Syngenta, who produced the event, and the European Commission Joint Research Centre, who validated the detection method. Using a variety of molecular analytical tools, both organizations found the genetic modification of event Bt10 to be very complex in structure, with rearrangements, inversions, and multiple copies of the structural elements (cry1Ab, pat, and the amp gene), interspersed with small genomic maize fragments. Southern blot analyses demonstrated that all Bt10 elements were found tightly linked on one large fragment, including the region that would generate the event-specific PCR amplicon of the Bt10 detection method. This study proposes a hypothetical map of the insert of event Bt10 and concludes that the validated detection method for event Bt10 is fit for its purpose. PMID:19368351

  17. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    PubMed Central

    Silva, Rodrigo Tavares; Martinelli Filho, Martino; Peixoto, Giselle de Lima; de Lima, José Jayme Galvão; de Siqueira, Sérgio Freitas; Costa, Roberto; Gowdak, Luís Henrique Wolff; de Paula, Flávio Jota; Kalil Filho, Roberto; Ramires, José Antônio Franchini

    2015-01-01

    Background The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. Objective We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). Methods A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. Results During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). Conclusions In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT. PMID:26351983

  18. Rare-event detection and process control for a biomedical application

    NASA Astrophysics Data System (ADS)

    Kegelmeyer, Laura N.

    1990-05-01

    Medical researchers are seeking a method for detecting chromosomal abnormalities in unborn children without requiring invasive procedures such as anmiocentesis. Software has been developed to utilize a light microscope to detect fetal cells that occur with very low frequency in a sample of maternal blood. This rare event detection involves dividing a microscope slide containing a maternal blood sample into as many as 40,000 fields, automatically focusing on each field-of-view, and searching for fetal cells. Size and shape information is obtained by calculating a figure of merit through various binary operations and is used to discriminate fetal cells from noise and artifacts. Once the rare fetal cells are located, the slide is automatically rescanned to count the total number of cells on the slide. Binary operations and image processing hardware are used as much as possible to reduce the total amount of time to analyze one slide. Current runtime for scoring one full slide is about four hours, with motorized stage movement and focusing being the speed-limiting factors. Fetal cells occurring with a frequency of less than 1 in 200,000 maternal cells have been consistently found with this system.

  19. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm.

    PubMed

    Fan, Kai; Sun, Xingzhi; Tao, Ying; Xu, Linhao; Wang, Chen; Mao, Xianling; Peng, Bo; Pan, Yue

    2010-01-01

    Post-marketing pharmacovigilance is important for public health, as many Adverse Drug Events (ADEs) are unknown when those drugs were approved for marketing. However, due to the large number of reported drugs and drug combinations, detecting ADE signals by mining these reports is becoming a challenging task in terms of computational complexity. Recently, a parallel programming model, MapReduce has been introduced by Google to support large-scale data intensive applications. In this study, we proposed a MapReduce-based algorithm, for common ADE detection approach, Proportional Reporting Ratio (PRR), and tested it in mining spontaneous ADE reports from FDA. The purpose is to investigate the possibility of using MapReduce principle to speed up biomedical data mining tasks using this pharmacovigilance case as one specific example. The results demonstrated that MapReduce programming model could improve the performance of common signal detection algorithm for pharmacovigilance in a distributed computation environment at approximately liner speedup rates. PMID:21347109

  20. The WISE Detection of an Infrared Echo in Tidal Disruption Event ASASSN-14li

    NASA Astrophysics Data System (ADS)

    Jiang, Ning; Dou, Liming; Wang, Tinggui; Yang, Chenwei; Lyu, Jianwei; Zhou, Hongyan

    2016-09-01

    We report the detection of a significant infrared variability of the nearest tidal disruption event (TDE) ASASSN-14li using Wide-field Infrared Survey Explorer and newly released Near-Earth Object WISE Reactivation data. In comparison with the quiescent state, the infrared flux is brightened by 0.12 and 0.16 mag in the W1 (3.4 μm) and W2 (4.6 μm) bands at 36 days after the optical discovery (or ∼110 days after the peak disruption date). The flux excess is still detectable ∼170 days later. Assuming that the flare-like infrared emission is from the dust around the black hole, its blackbody temperature is estimated to be ∼2.1 × 103 K, slightly higher than the dust sublimation temperature, indicating that the dust is likely located close to the dust sublimation radius. The equilibrium between the heating and radiation of the dust claims a bolometric luminosity of ∼1043–1045 erg s‑1, comparable with the observed peak luminosity. This result has for the first time confirmed the detection of infrared emission from the dust echoes of TDEs.

  1. Automatic Detection of Swallowing Events by Acoustical Means for Applications of Monitoring of Ingestive Behavior

    PubMed Central

    Sazonov, Edward S.; Makeyev, Oleksandr; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Edward L.; Neuman, Michael R.

    2010-01-01

    Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for Monitoring of Ingestive Behavior (MIB) in the free living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 hours with a total of 9,966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch recognition accuracy for intra-visit individual models was 96.8% which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals. PMID:19789095

  2. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-05

    Solar power ramp events (SPREs) significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to enhance the state of the art in SPRE detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

  3. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm: Preprint

    SciTech Connect

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-07

    Solar power ramp events (SPREs) are those that significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

  4. The WISE Detection of an Infrared Echo in Tidal Disruption Event ASASSN-14li

    NASA Astrophysics Data System (ADS)

    Jiang, Ning; Dou, Liming; Wang, Tinggui; Yang, Chenwei; Lyu, Jianwei; Zhou, Hongyan

    2016-09-01

    We report the detection of a significant infrared variability of the nearest tidal disruption event (TDE) ASASSN-14li using Wide-field Infrared Survey Explorer and newly released Near-Earth Object WISE Reactivation data. In comparison with the quiescent state, the infrared flux is brightened by 0.12 and 0.16 mag in the W1 (3.4 μm) and W2 (4.6 μm) bands at 36 days after the optical discovery (or ˜110 days after the peak disruption date). The flux excess is still detectable ˜170 days later. Assuming that the flare-like infrared emission is from the dust around the black hole, its blackbody temperature is estimated to be ˜2.1 × 103 K, slightly higher than the dust sublimation temperature, indicating that the dust is likely located close to the dust sublimation radius. The equilibrium between the heating and radiation of the dust claims a bolometric luminosity of ˜1043-1045 erg s-1, comparable with the observed peak luminosity. This result has for the first time confirmed the detection of infrared emission from the dust echoes of TDEs.

  5. DADA: Data Assimilation for the Detection and Attribution of Weather and Climate-related Events

    NASA Astrophysics Data System (ADS)

    Hannart, Alexis; Bocquet, Marc; Carrassi, Alberto; Ghil, Michael; Naveau, Philippe; Pulido, Manuel; Ruiz, Juan; Tandeo, Pierre

    2015-04-01

    We describe a new approach allowing for near real time, systematic causal attribution of weather and climate-related events. The method is purposely designed to allow its operability at meteorological centers by synergizing causal attribution with data treatments that are routinely performed when numerically forecasting the weather, thereby taking advantage of their powerful computational and observational capacity. Namely, we show that causal attribution can be obtained as a by-product of the so-called data assimilation procedures that are run on a daily basis to update the meteorological model with new atmospheric observations. We explain the theoretical rationale of this approach and sketch the most prominent features of a "data assimilation-based detection and attribution" (DADA) procedure. The proposal is illustrated in the context of the 3-variables Lorenz model. Several practical and theoretical research questions that need to be addressed to make the proposal readily operational within weather forecasting centers are finally laid out.

  6. Event detection and localization for small mobile robots using reservoir computing.

    PubMed

    Antonelo, E A; Schrauwen, B; Stroobandt, D

    2008-08-01

    Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

  7. The Event Detection and the Apparent Velocity Estimation Based on Computer Vision

    NASA Astrophysics Data System (ADS)

    Shimojo, M.

    2012-08-01

    The high spatial and time resolution data obtained by the telescopes aboard Hinode revealed the new interesting dynamics in solar atmosphere. In order to detect such events and estimate the velocity of dynamics automatically, we examined the estimation methods of the optical flow based on the OpenCV that is the computer vision library. We applied the methods to the prominence eruption observed by NoRH, and the polar X-ray jet observed by XRT. As a result, it is clear that the methods work well for solar images if the images are optimized for the methods. It indicates that the optical flow estimation methods in the OpenCV library are very useful to analyze the solar phenomena.

  8. Detection and Attribution of Simulated Climatic Extreme Events and Impacts: High Sensitivity to Bias Correction

    NASA Astrophysics Data System (ADS)

    Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

    2015-12-01

    Understanding, quantifying and attributing the impacts of climatic extreme events and variability is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit pronounced biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. We assess how biases and their correction affect the quantification and attribution of simulated extremes and variability in i) climatological variables and ii) impacts on ecosystem functioning as simulated by a terrestrial biosphere model. Our study demonstrates that assessments of simulated climatic extreme events and impacts in the terrestrial biosphere are highly sensitive to bias correction schemes with major implications for the detection and attribution of these events. We introduce a novel ensemble-based resampling scheme based on a large regional climate model ensemble generated by the distributed weather@home setup[1], which fully preserves the physical consistency and multivariate correlation structure of the model output. We use extreme value statistics to show that this procedure considerably improves the representation of climatic extremes and variability. Subsequently, biosphere-atmosphere carbon fluxes are simulated using a terrestrial ecosystem model (LPJ-GSI) to further demonstrate the sensitivity of ecosystem impacts to the methodology of bias correcting climate model output. We find that uncertainties arising from bias correction schemes are comparable in magnitude to model structural and parameter uncertainties. The present study consists of a first attempt to alleviate climate model biases in a physically consistent way and demonstrates that this yields improved simulations of

  9. Fusion of waveform events and radionuclide detections with the help of atmospheric transport modelling

    NASA Astrophysics Data System (ADS)

    Krysta, Monika; Kushida, Noriyuki; Kotselko, Yuriy; Carter, Jerry

    2016-04-01

    Possibilities of associating information from four pillars constituting CTBT monitoring and verification regime, namely seismic, infrasound, hydracoustic and radionuclide networks, have been explored by the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) for a long time. Based on a concept of overlying waveform events with the geographical regions constituting possible sources of the detected radionuclides, interactive and non-interactive tools were built in the past. Based on the same concept, a design of a prototype of a Fused Event Bulletin was proposed recently. One of the key design elements of the proposed approach is the ability to access fusion results from either the radionuclide or from the waveform technologies products, which are available on different time scales and through various different automatic and interactive products. To accommodate various time scales a dynamic product evolving while the results of the different technologies are being processed and compiled is envisioned. The product would be available through the Secure Web Portal (SWP). In this presentation we describe implementation of the data fusion functionality in the test framework of the SWP. In addition, we address possible refinements to the already implemented concepts.

  10. Applying a New Event Detection Algorithm to an Ocean Bottom Seismometer Dataset Recorded Offshore Southern California

    NASA Astrophysics Data System (ADS)

    Bishop, J.; Kohler, M. D.; Bunn, J.; Chandy, K. M.

    2015-12-01

    A number of active southern California offshore faults are capable of M>6 earthquakes, and the only permanent Southern California Seismic Network stations that can contribute to ongoing, small-magnitude earthquake detection and location are those located on the coastline and islands. To obtain a more detailed picture of the seismicity of the region, an array of 34 ocean bottom seismometers (OBSs) was deployed to record continuous waveform data off the coast of Southern California for 12 months (2010-2011) as part of the ALBACORE (Asthenospheric and Lithospheric Broadband Architecture from the California Offshore Region Experiment) project. To obtain a local event catalog based on OBS data, we make use of a newly developed data processing platform based on Python. The data processing procedure comprises a multi-step analysis that starts with the identification of significant signals above the time-adjusted noise floor for each sensor. This is followed by a time-dependent statistical estimate of the likelihood of an earthquake based on the aggregated signals in the array. For periods with elevated event likelihood, an adaptive grid-fitting procedure is used that yields candidate earthquake hypocenters with confidence estimates that best match the observed sensor signals. The results are validated with synthetic travel times and manual picks. Using results from ALBACORE, we have created a more complete view of active faulting in the California Borderland.

  11. Using AHRQ patient safety indicators to detect postdischarge adverse events in the Veterans Health Administration.

    PubMed

    Mull, Hillary J; Borzecki, Ann M; Chen, Qi; Shin, Marlena H; Rosen, Amy K

    2014-01-01

    Patient safety indicators (PSIs) use inpatient administrative data to flag cases with potentially preventable adverse events (AEs) attributable to hospital care. This study explored how many AEs the PSIs identified in the 30 days post discharge. PSI software was run on Veterans Health Administration 2003-2007 administrative data for 10 recently validated PSIs. Among PSI-eligible index hospitalizations not flagged with an AE, this study evaluated how many AEs occurred within 1 to 14 and 15 to 30 days post discharge using inpatient and outpatient administrative data. Considering all PSI-eligible index hospitalizations, 11 141 postdischarge AEs were identified, compared with 40 578 inpatient-flagged AEs. More than 60% of postdischarge AEs were detected within 14 days of discharge. The majority of postdischarge AEs were decubitus ulcers and postoperative pulmonary embolisms or deep vein thromboses. Extending PSI algorithms to the postdischarge period may provide a more complete picture of hospital quality. Future work should use chart review to validate postdischarge PSI events. PMID:23939485

  12. Application of remote sensing in coastal change detection after the tsunami event in Indonesia

    NASA Astrophysics Data System (ADS)

    Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Saleh, N. Mohd.; Surbakti, M. S.

    2008-10-01

    Shoreline mapping and shoreline change detection are critical in many coastal zone applications. This study focuses on applying remote sensing technology to identify and assess coastal changes in the Banda Aceh, Indonesia. Major changes to land cover were found along the coastal line. Using remote sensing data to detect coastal line change requires high spatial resolution data. In this study, two high spatial data with 30 meter resolution of Landsat TM images captured before and after the Tsunami event were acquired for this purpose. The two satellite images was overlain and compared with pre-Tsunami imagery and with after Tsunami. The two Landsat TM images also were used to generate land cover classification maps for the 24 December 2004 and 27 March 2005, before and after the Tsunami event respectively. The standard supervised classifier was performed to the satellite images such as the Maximum Likelihood, Minimum Distance-to-mean and Parallelepiped. High overall accuracy (>80%) and Kappa coefficient (>0.80) was achieved by the Maximum Likelihood classifier in this study. Estimation of the damage areas between the two dated was estimated from the different between the two classified land cover maps. Visible damage could be seen in either before and after image pair. The visible damage land areas were determined and draw out using the polygon tool included in the PCI Geomatica image processing software. The final set of polygons containing the major changes in the coastal line. An overview of the coastal line changes using Landsat TM images is also presented in this study. This study provided useful information that helps local decision makers make better plan and land management choices.

  13. Automated Visual Event Detection, Tracking, and Data Management System for Cabled- Observatory Video

    NASA Astrophysics Data System (ADS)

    Edgington, D. R.; Cline, D. E.; Schlining, B.; Raymond, E.

    2008-12-01

    Ocean observatories and underwater video surveys have the potential to unlock important discoveries with new and existing camera systems. Yet the burden of video management and analysis often requires reducing the amount of video recorded through time-lapse video or similar methods. It's unknown how many digitized video data sets exist in the oceanographic community, but we suspect that many remain under analyzed due to lack of good tools or human resources to analyze the video. To help address this problem, the Automated Visual Event Detection (AVED) software and The Video Annotation and Reference System (VARS) have been under development at MBARI. For detecting interesting events in the video, the AVED software has been developed over the last 5 years. AVED is based on a neuromorphic-selective attention algorithm, modeled on the human vision system. Frames are decomposed into specific feature maps that are combined into a unique saliency map. This saliency map is then scanned to determine the most salient locations. The candidate salient locations are then segmented from the scene using algorithms suitable for the low, non-uniform light and marine snow typical of deep underwater video. For managing the AVED descriptions of the video, the VARS system provides an interface and database for describing, viewing, and cataloging the video. VARS was developed by the MBARI for annotating deep-sea video data and is currently being used to describe over 3000 dives by our remotely operated vehicles (ROV), making it well suited to this deepwater observatory application with only a few modifications. To meet the compute and data intensive job of video processing, a distributed heterogeneous network of computers is managed using the Condor workload management system. This system manages data storage, video transcoding, and AVED processing. Looking to the future, we see high-speed networks and Grid technology as an important element in addressing the problem of processing and

  14. Beat-by-Beat Quantification of Cardiac Cycle Events Detected From Three-Dimensional Precordial Acceleration Signals.

    PubMed

    Paukkunen, Mikko; Parkkila, Petteri; Hurnanen, Tero; Pänkäälä, Mikko; Koivisto, Tero; Nieminen, Tuomo; Kettunen, Raimo; Sepponen, Raimo

    2016-03-01

    The vibrations produced by the cardiovascular system that are coupled to the precordium can be noninvasively detected using accelerometers. This technique is called seismocardiography. Although clinical applications have been proposed for seismocardiography, the physiology underlying the signal is still not clear. The relationship of seismocardiograms of on the back-to-front axis and cardiac events is fairly well known. However, the 3-D seismocardiograms detectable with modern accelerometers have not been quantified in terms of cardiac cycle events. A major reason for this might be the degree of intersubject variability observed in 3-D seismocardiograms. We present a method to quantify 3-D seismocardiography in terms of cardiac cycle events. First, cardiac cycle events are identified from the seismocardiograms, and then, assigned a number based on the location in which the corresponding event was found. 396 cardiac cycle events from 9 healthy subjects and 120 cardiac cycle events from patients suffering from atrial flutter were analyzed. Despite the weak intersubject correlation of the waveforms (0.05, 0.27, and 0.15 for the x-, y-, and z-axes, respectively), the present method managed to find latent similarities in the seismocardiograms of healthy subjects. We observed that in healthy subjects the distribution of cardiac cycle event coordinates was centered on specific locations. These locations were different in patients with atrial flutter. The results suggest that spatial distribution of seismocardiographic cardiac cycle events might be used to discriminate healthy individuals and those with a failing heart.

  15. Using GPS to Rapidly Detect and Model Earthquakes and Transient Deformation Events

    NASA Astrophysics Data System (ADS)

    Crowell, Brendan W.

    The rapid modeling and detection of earthquakes and transient deformation is a problem of extreme societal importance for earthquake early warning and rapid hazard response. To date, GPS data is not used in earthquake early warning or rapid source modeling even in Japan or California where the most extensive geophysical networks exist. This dissertation focuses on creating algorithms for automated modeling of earthquakes and transient slip events using GPS data in the western United States and Japan. First, I focus on the creation and use of high-rate GPS and combined seismogeodetic data for applications in earthquake early warning and rapid slip inversions. Leveraging data from earthquakes in Japan and southern California, I demonstrate that an accurate magnitude estimate can be made within seconds using P wave displacement scaling, and that a heterogeneous static slip model can be generated within 2-3 minutes. The preliminary source characterization is sufficiently robust to independently confirm the extent of fault slip used for rapid assessment of strong ground motions and improved tsunami warning in subduction zone environments. Secondly, I investigate the automated detection of transient slow slip events in Cascadia using daily positional estimates from GPS. Proper geodetic characterization of transient deformation is necessary for studies of regional interseismic, coseismic and postseismic tectonics, and miscalculations can affect our understanding of the regional stress field. I utilize the relative strength index (RSI) from financial forecasting to create a complete record of slow slip from continuous GPS stations in the Cascadia subduction zone between 1996 and 2012. I create a complete history of slow slip across the Cascadia subduction zone, fully characterizing the timing, progression, and magnitude of events. Finally, using a combination of continuous and campaign GPS measurements, I characterize the amount of extension, shear and subsidence in the

  16. PREFACE: Fourth Symposium on Large TPCs for Low Energy Rare Event Detection

    NASA Astrophysics Data System (ADS)

    Irastorza, Igor G.; Colas, Paul; Giomataris, Ioannis

    2009-07-01

    The Fourth International Symposium on large TPCs for low-energy rare-event detection was held at the Hermite auditorium of the Insitute Henri Poincaréte, 11 rue Pierre et Marie Curie in Paris on 18-19 December 2008. As in previous instances of the meeting, held always in Paris in 2006, 2004 and 2002, it gathered a significant community of physicists involved in rare event searches and/or development of time projection chambers (TPCs). The purpose of the meeting was to present and discuss the status of current experiments or projects involving the use of large TPCs for the search of rare events, like low-energy neutrinos, double beta decay, dark matter or axion experiments, as well as to discuss new results and ideas in the framework of the last developments of Micro Pattern Gaseous Detectors (MPGD), and how these are being - or could be - applied to the mentioned searches. The rapid evolvement of these devices and the relevance of their latest results need to be efficiently transferred to the rare event community. The creation of this series of meetings followed the motivation of bringing together both know-hows and it is proving to be a fruitful area of collaboration. Once more, the format of the meeting proved to be a success. A short (2 days) and relatively informal program with some recent highlighted results, rather than exhaustive reviews, attracted the interest of the audience. The symposium, fourth of the series, is becoming consolidated as a regular meeting place for the synergic interplay between the fields of rare events and TPC development. Apart from the usual topics central to the conference subject, like the status of some low-energy neutrino physics and double beta decay experiments, dark matter experiments (and in general physics in underground laboratories), axion searches, or development results, every year the conference programme is enriched with original slightly off-topic contributions that trigger the curiosity and stimulate further thought

  17. Fully Autonomous Multiplet Event Detection: Application to Local-Distance Monitoring of Blood Falls Seismicity

    SciTech Connect

    Carmichael, Joshua Daniel; Carr, Christina; Pettit, Erin C.

    2015-06-18

    We apply a fully autonomous icequake detection methodology to a single day of high-sample rate (200 Hz) seismic network data recorded from the terminus of Taylor Glacier, ANT that temporally coincided with a brine release episode near Blood Falls (May 13, 2014). We demonstrate a statistically validated procedure to assemble waveforms triggered by icequakes into populations of clusters linked by intra-event waveform similarity. Our processing methodology implements a noise-adaptive power detector coupled with a complete-linkage clustering algorithm and noise-adaptive correlation detector. This detector-chain reveals a population of 20 multiplet sequences that includes ~150 icequakes and produces zero false alarms on the concurrent, diurnally variable noise. Our results are very promising for identifying changes in background seismicity associated with the presence or absence of brine release episodes. We thereby suggest that our methodology could be applied to longer time periods to establish a brine-release monitoring program for Blood Falls that is based on icequake detections.

  18. Detecting concealed information using feedback related event-related brain potentials.

    PubMed

    Sai, Liyang; Lin, Xiaohong; Hu, Xiaoqing; Fu, Genyue

    2014-10-01

    Employing an event-related potential (ERP)-based concealed information test (CIT), the present study investigated (1) the neurocognitive processes when people received feedbacks regarding their deceptive/truthful responses and (2) whether such feedback-related ERP activities can be used to detect concealed information above and beyond the recognition-related P300. During the CIT, participants were presented with rare, meaningful probes (their own names) embedded within a series of frequent yet meaningless irrelevants (others' names). Participants were instructed to deny their recognition of the probes. Critically, following participants' responses, they were provided with feedbacks regarding whether they succeeded or failed in the CIT. Replicating previous ERP-based CITs, we found a larger P300 elicited by probe compared to irrelevant. Regarding feedback-related ERPs, a temporospatial Principle Component Analyses found two ERP components that were not only sensitive to feedback manipulations but also can discriminate probe from irrelevant: an earlier, central-distributed positivity that was elicited by "success" feedbacks peaked around 219ms; and a later, right central-distributed positivity that was also elicited by "success" feedbacks, peaked around 400ms. Importantly, the feedback ERPs were not correlated with P300 that was elicited by probe/irrelevant, suggesting that these two ERPs reflect independent processes underlying memory concealment. These findings illustrate the feasibility and promise of using feedback-related ERPs to detect concealed memory and thus deception. PMID:25058495

  19. Improving single-trial detection of event-related potentials through artificial deformed signals.

    PubMed

    Cecotti, H; Rivet, B

    2014-01-01

    To propose a reliable and robust Brain-Computer Interface (BCI), efficient machine learning and signal processing methods have to be used. However, it is often necessary to have a sufficient number of labeled brain responses to create a model. A large database that would represent all of the possible variabilities of the signal is not always possible to obtain, because calibration sessions have to be short. In the case of BCIs based on the detection of event-related potentials (ERPs), we propose to tackle this problem by including additional deformed patterns in the training database to increase the number of labeled brain responses. The creation of the additional deformed patterns is based on two approaches: (i) smooth deformation fields, and (ii) right and left shifted signals. The evaluation is performed with data from 10 healthy subjects participating in a P300 speller experiment. The results show that small shifts of the signal allow a better estimation of both spatial filters, and a linear classifier. The best performance, AUC=0.828 ± 0.061, is obtained by combining the smooth deformation fields and the shifts, after spatial filtering, compared to AUC=0.543 ± 0.025, without additional deformed patterns. The results support the conclusion that adding signals with small deformations can significantly improve the performance of single-trial detection when the amount of training data is limited.

  20. Localized Detection of Frozen Precipitation Events and the Rain/Snow Transition

    NASA Astrophysics Data System (ADS)

    Strachan, S.

    2014-12-01

    Frozen precipitation in the mid-latitudes and semi-arid environments frequently serves a crucial role in the annual water budget. Often occurring along elevational gradients, the rain/snow transition (or, "snow line") in mountain systems determines the amount of water which enters the system slowly during melt phases as opposed to rain which immediately infiltrates or runs off to lower elevations. This in turn influences the location and composition of ecological communities such as conifer forests, as well as timing and nature of the entire mountain block annual hydrologic cycle. Characterization of the rain/snow transition is becoming a priority in mountainous semi-arid regions, as increasing human populations and repeated drought episodes combine to create water shortages. Atmospheric conditions (temperature and relative humidity) which signal the rain/snow transition have been described, but variability within the conditions window can create error in estimating true areal cover of frozen versus liquid precipitation. In populated, flood-prone regions, radar installations specifically tuned to the detection of the "bright band" transition elevation can be deployed; however these cannot be permanently installed at remote, solar-power-dependent climate stations or with fine geographical scale. Characterization of current trends in rain/snow transition can be made using automated weather stations placed along the elevational gradient fielding sensors for high-frequency (e.g. 1-10 minute) measurement of air temperature, relative humidity, liquid precipitation, and precipitation mass. Visual validation of precipitation modes detected through automated means is performed using time-series records from digital cameras placed at each station. Refinements of geographically-explicit relationships of atmospheric conditions to precipitation mode can be made over time, as well as detection of seasonally-anomalous but eco-hydrologically-significant frozen precipitation events

  1. Monkeying around with the gorillas in our midst: familiarity with an inattentional-blindness task does not improve the detection of unexpected events.

    PubMed

    Simons, Daniel J

    2010-01-01

    When people know to look for an unexpected event (eg, a gorilla in a basketball game), they tend to notice that event. But does knowledge that an unexpected event might occur improve the detection of other unexpected events in a similar scene? Subjects watched a new video in which, in addition to the gorilla, two other unexpected events occurred: a curtain changed color, and one player left the scene. Subjects who knew about videos like this one consistently spotted the gorilla in the new video, but they were slightly less likely to notice the other events. Foreknowledge that unexpected events might occur does not enhance the ability to detect other such events.

  2. FOREWORD: 3rd Symposium on Large TPCs for Low Energy Event Detection

    NASA Astrophysics Data System (ADS)

    Irastorza, Igor G.; Colas, Paul; Gorodetzky, Phillippe

    2007-05-01

    The Third International Symposium on large TPCs for low-energy rare-event detection was held at Carré des sciences, Poincaré auditorium, 25 rue de la Montagne Ste Geneviève in Paris on 11 12 December 2006. This prestigious location belonging to the Ministry of Research is hosted in the former Ecole Polytechnique. The meeting, held in Paris every two years, gathers a significant community of physicists involved in rare event detection. Its purpose is an extensive discussion of present and future projects using large TPCs for low energy, low background detection of rare events (low-energy neutrinos, dark matter, solar axions). The use of a new generation of Micro-Pattern Gaseous Detectors (MPGD) appears to be a promising way to reach this goal. The program this year was enriched by a new session devoted to the detection challenge of polarized gamma rays, relevant novel experimental techniques and the impact on particle physics, astrophysics and astronomy. A very particular feature of this conference is the large variety of talks ranging from purely theoretical to purely experimental subjects including novel technological aspects. This allows discussion and exchange of useful information and new ideas that are emerging to address particle physics experimental challenges. The scientific highlights at the Symposium came on many fronts: Status of low-energy neutrino physics and double-beta decay New ideas on double-beta decay experiments Gamma ray polarization measurement combining high-precision TPCs with MPGD read-out Dark Matter challenges in both axion and WIMP search with new emerging ideas for detection improvements Progress in gaseous and liquid TPCs for rare event detection Georges Charpak opened the meeting with a talk on gaseous detectors for applications in the bio-medical field. He also underlined the importance of new MPGD detectors for both physics and applications. There were about 100 registered participants at the symposium. The successful

  3. Detection of Rapid Events at Mantle Depth by Future Gravity Missions

    NASA Astrophysics Data System (ADS)

    Ivins, Erik; Watkins, Michael

    2015-04-01

    The robust detection of gravity changes associated with relatively shallow subduction zone earthquakes (0-50 km depth co-seismic rupture) has been one of the success stories of the GRACE (e.g., Han et al. 2013, JGR-B, doi:10.1002/jgrb.50116) and GOCE (e.g., Fuchs et al., 2013, JGR-B, doi: 10.1002/jgrb.50381) missions. This surprise is a testament to the sensitivity of the measurement system, for the satellites must map the gravity potential field changes while flying at orbital altitudes exceeding 400 km (in the case of GRACE). It is clear that these observations contribute to advancing our understanding of large subduction zone earthquakes, if for no other reason than they allow comprehensive observation over the ocean covered solid Earth. The observations aid studies of both the mass transport associated with coseismic and post-seismic. Measurement capability for missions proposed to be flown after GRACE-2 are anticipated to be an order of magnitude, or greater, in accuracy and resolution (e.g., Wiese et al., 2012, J. Geodesy, doi: 10.1007/s00190-011-0493-8; Elsaka et al. 2014, J. Geodesy, doi: 10.1007/s00190-013-0665-9). Deep subduction zone earthquakes have not been detected, nor have any other non-seismic solid Earth deformations - with the exception of the glacial isostatic adjustment vertical response to the last glacial age. We examine the possibility that earthquakes occurring at, or near, the major transition zone in the mantle should be detected in the region where mantle phases become unstable and undergoes transition to a stable perovskite phase below 660 km depth. The Mw 8.2 1994 Bolivian Earthquake and the May 24, 2013 Mw 8.3 earthquake beneath the Sea of Okhotsk, Russia, are prototypes of events that can be studied with future gravity missions. Observation of gravity changes associated with deep subduction zone earthquakes could provide new clues on the enigmatic questions currently in debate over faulting mechanism (e.g., Zhan et al., 2014, Science

  4. Detection of air pollution events over Évora-Portugal during 2009

    NASA Astrophysics Data System (ADS)

    Filipa Domingues, Ana; Bortoli, Daniele; Silva, Ana Maria; Kulkarni, Pavan; Antón, Manuel

    2010-05-01

    All over the world pollutant industries, traffic and other natural and anthropogenic sources are responsible for air pollution affecting health and also the climate. At the moment the monitoring of air quality in urban and country regions become an urgent concern in the atmospheric studies due to the impact of global air pollution on climate and on the environment. One of the evidences of the global character of air pollution is that it not only affects industrialized countries but also reaches less developed countries with pollution gases and particles generated for elsewhere. The development and the employment of instruments and techniques for measure the variation of atmospheric trace gases and perform their monitoring are crucial for the improvement of the air quality and the control of pollutants emissions. One of the instruments able to perform the air quality monitoring is the Spectrometer for Atmospheric TRacers Measurements (SPATRAM) and it is installed at the CGÉs Observatory in Évora (38.5° N, 7.9° W, 300 m asl). This UV-VIS Spectrometer is used to carry out measurements of the zenith scattered radiation (290- 900 nm) to retrieve the vertical content of some atmospheric trace gases such as O3 and NO2 in stratosphere, using Differential Optical Absorption Spectroscopy (DOAS) methodology. Although SPATRAM, in its actual geometric and operational configuration - zenith sky looking and passive mode measurements, is not able to detect small variations of tracers in the troposphere it is possible to identify enhancements in the pollution loads due to air masses movements from polluted sites. In spite of the fact that Evora is a quite unpolluted city the deep analysis of the DOAS output, namely the quantity of gas (in this case NO2) present along the optical path of measurements (SCD - Slant Column Density) allows for the detection of unpredicted variations in the diurnal NO2 cycle. The SPATRAḾs data allows the identification of polluting events which

  5. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    PubMed

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    Mobility characteristics associated with activity of daily living such as sitting down, lying down, rising up, and walking are considered to be important in maintaining functional independence and healthy life style especially for the growing elderly population. Characteristics of postural transitions such as sit-to-stand are widely used by clinicians as a physical indicator of health, and walking is used as an important mobility assessment tool. Many tools have been developed to assist in the assessment of functional levels and to detect a person’s activities during daily life. These include questionnaires, observation, diaries, kinetic and kinematic systems, and validated functional tests. These measures are costly and time consuming, rely on subjective patient recall and may not accurately reflect functional ability in the patient’s home. In order to provide a low-cost, objective assessment of functional ability, inertial measurement unit (IMU) using MEMS technology has been employed to ascertain ADLs. These measures facilitate long-term monitoring of activity of daily living using wearable sensors. IMU system are desirable in monitoring human postures since they respond to both frequency and the intensity of movements and measure both dc (gravitational acceleration vector) and ac (acceleration due to body movement) components at a low cost. This has enabled the development of a small, lightweight, portable system that can be worn by a free-living subject without motion impediment – TEMPO (Technology Enabled Medical Precision Observation). Using this IMU system, we acquired indirect measures of biomechanical variables that can be used as an assessment of individual mobility characteristics with accuracy and recognition rates that are comparable to the modern motion capture systems. In this study, five subjects performed various ADLs and mobility measures such as posture transitions and gait characteristics were obtained. We developed postural event detection

  6. An automated cross-correlation based event detection technique and its application to surface passive data set

    USGS Publications Warehouse

    Forghani-Arani, Farnoush; Behura, Jyoti; Haines, Seth S.; Batzle, Mike

    2013-01-01

    In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

  7. Exploiting semantics for scheduling real-time data collection from sensors to maximize event detection

    NASA Astrophysics Data System (ADS)

    Vaisenberg, Ronen; Mehrotra, Sharad; Ramanan, Deva

    2009-01-01

    A distributed camera network allows for many compelling applications such as large-scale tracking or event detection. In most practical systems, resources are constrained. Although one would like to probe every camera at every time instant and store every frame, this is simply not feasible. Constraints arise from network bandwidth restrictions, I/O and disk usage from writing images, and CPU usage needed to extract features from the images. Assume that, due to resource constraints, only a subset of sensors can be probed at any given time unit. This paper examines the problem of selecting the "best" subset of sensors to probe under some user-specified objective - e.g., detecting as much motion as possible. With this objective, we would like to probe a camera when we expect motion, but would not like to waste resources on a non-active camera. The main idea behind our approach is the use of sensor semantics to guide the scheduling of resources. We learn a dynamic probabilistic model of motion correlations between cameras, and use the model to guide resource allocation for our sensor network. Although previous work has leveraged probabilistic models for sensor-scheduling, our work is distinct in its focus on real-time building-monitoring using a camera network. We validate our approach on a sensor network of a dozen cameras spread throughout a university building, recording measurements of unscripted human activity over a two week period. We automatically learnt a semantic model of typical behaviors, and show that one can significantly improve effciency of resource allocation by exploiting this model.

  8. Elastomeric optical fiber sensors and method for detecting and measuring events occurring in elastic materials

    DOEpatents

    Muhs, Jeffrey D.; Capps, Gary J.; Smith, David B.; White, Clifford P.

    1994-01-01

    Fiber optic sensing means for the detection and measurement of events such as dynamic loadings imposed upon elastic materials including cementitious materials, elastomers, and animal body components and/or the attrition of such elastic materials are provided. One or more optical fibers each having a deformable core and cladding formed of an elastomeric material such as silicone rubber are embedded in the elastic material. Changes in light transmission through any of the optical fibers due the deformation of the optical fiber by the application of dynamic loads such as compression, tension, or bending loadings imposed on the elastic material or by the attrition of the elastic material such as by cracking, deterioration, aggregate break-up, and muscle, tendon, or organ atrophy provide a measurement of the dynamic loadings and attrition. The fiber optic sensors can be embedded in elastomers subject to dynamic loadings and attrition such as commonly used automobiles and in shoes for determining the amount and frequency of the dynamic loadings and the extent of attrition. The fiber optic sensors are also useable in cementitious material for determining the maturation thereof.

  9. First Satellite-detected Perturbations of Outgoing Longwave Radiation Associated with Blowing Snow Events over Antarctica

    NASA Technical Reports Server (NTRS)

    Yang, Yuekui; Palm, Stephen P.; Marshak, Alexander; Wu, Dong L.; Yu, Hongbin; Fu, Qiang

    2014-01-01

    We present the first satellite-detected perturbations of the outgoing longwave radiation (OLR) associated with blowing snow events over the Antarctic ice sheet using data from Cloud-Aerosol Lidar with Orthogonal Polarization and Clouds and the Earth's Radiant Energy System. Significant cloud-free OLR differences are observed between the clear and blowing snow sky, with the sign andmagnitude depending on season and time of the day. During nighttime, OLRs are usually larger when blowing snow is present; the average difference in OLRs between without and with blowing snow over the East Antarctic Ice Sheet is about 5.2 W/m2 for the winter months of 2009. During daytime, in contrast, the OLR perturbation is usually smaller or even has the opposite sign. The observed seasonal variations and day-night differences in the OLR perturbation are consistent with theoretical calculations of the influence of blowing snow on OLR. Detailed atmospheric profiles are needed to quantify the radiative effect of blowing snow from the satellite observations.

  10. PREFACE: 7th International Symposium on Large TPCs for Low-Energy Rare Event Detection

    NASA Astrophysics Data System (ADS)

    Colas, P.; Giomataris, I.; Irastorza, I.; Patzak, Th

    2015-11-01

    The seventh "International Symposium on Large TPCs for Low-Energy Rare Event Detection", took place in Paris between the 15th and 17th of December 2014 at the Institute of Astroparticle Physics (APC) campus - Paris Diderot University. As usual the conference was organized during the week before Christmas, which seems to be convenient for most of the people and occurs every two years with almost 120 participants attending. Many people contributed to the success of the conference, but the organizers would particularly like to thank the management of APC for providing the nice Buffon auditorium and infrastructure. We also acknowledge the valuable support of DSM-Irfu and the University of Zaragoza. The scientific program consisted of plenary sessions including the following topics with theoretical and experimental lectures: • Low energy neutrino physics • Neutrinoless double beta decay process • Dark matter searches • Axion and especially solar axion searches • Space experiments and gamma-ray polarimetry • New detector R&D and future experiments

  11. Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France.

    PubMed

    Abat, Cédric; Chaudet, Hervé; Colson, Philippe; Rolain, Jean-Marc; Raoult, Didier

    2015-08-01

    Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved.

  12. Solar Energetic Particle Events detected by the Standard Radiation Environment Monitor (SREM) onboard INTEGRAL

    NASA Astrophysics Data System (ADS)

    Georgoulis, M.; Daglis, I. A.; Anastasiadis, A.; Sandberg, I.; Balasis, G.; Nieminen, P.

    2012-01-01

    The SREM is a cost-effective instrument mounted onboard multiple ESA missions. The SREM objective is the in-situ measurement of high-energy solar particles at the spacecraft location. Within the previous solar cycle 23, SREM units onboard ESA's INTEGRAL and Rosetta missions detected several tens of SEPEs and accurately pinpointed their onset, rise, and decay times. We have undertaken a detailed study to determine the solar sources and subsequent interplanetary coronal mass ejections (ICMEs) that gave rise to these events, as well as the timing of SEPEs with the onset of possible geomagnetic activity triggered by these ICMEs. We find that virtually all SREM SEPEs may be associated with CME-driven shocks. For a number of well-studied INTEGRAL/SREM SEPEs, moreover, we see an association between the SEPE peak and the shock passage at L1. Shortly (typically within a few hours) after the SEPE peak, the ICME-driven modulation of the magnetosphere kicks in, with either an increase or a dip of the Dst index, indicating stormy conditions in geospace. We conclude that, pending additional investigation, SREM units may prove useful for a short-term prediction of inclement space-weather conditions in Geospace, especially if mounted onboard dayside missions ahead of the magnetospheric bow shock.

  13. Model-based tomographic reconstruction

    DOEpatents

    Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.

    2012-06-26

    A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

  14. Event-driven simulation in SELMON: An overview of EDSE

    NASA Technical Reports Server (NTRS)

    Rouquette, Nicolas F.; Chien, Steve A.; Charest, Leonard, Jr.

    1992-01-01

    EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.

  15. Effects of rainfall events on the occurrence and detection efficiency of viruses in river water impacted by combined sewer overflows.

    PubMed

    Hata, Akihiko; Katayama, Hiroyuki; Kojima, Keisuke; Sano, Shoichi; Kasuga, Ikuro; Kitajima, Masaaki; Furumai, Hiroaki

    2014-01-15

    Rainfall events can introduce large amount of microbial contaminants including human enteric viruses into surface water by intermittent discharges from combined sewer overflows (CSOs). The present study aimed to investigate the effect of rainfall events on viral loads in surface waters impacted by CSO and the reliability of molecular methods for detection of enteric viruses. The reliability of virus detection in the samples was assessed by using process controls for virus concentration, nucleic acid extraction and reverse transcription (RT)-quantitative PCR (qPCR) steps, which allowed accurate estimation of virus detection efficiencies. Recovery efficiencies of poliovirus in river water samples collected during rainfall events (<10%) were lower than those during dry weather conditions (>10%). The log10-transformed virus concentration efficiency was negatively correlated with suspended solid concentration (r(2)=0.86) that increased significantly during rainfall events. Efficiencies of DNA extraction and qPCR steps determined with adenovirus type 5 and a primer sharing control, respectively, were lower in dry weather. However, no clear relationship was observed between organic water quality parameters and efficiencies of these two steps. Observed concentrations of indigenous enteric adenoviruses, GII-noroviruses, enteroviruses, and Aichi viruses increased during rainfall events even though the virus concentration efficiency was presumed to be lower than in dry weather. The present study highlights the importance of using appropriate process controls to evaluate accurately the concentration of water borne enteric viruses in natural waters impacted by wastewater discharge, stormwater, and CSOs.

  16. Unsupervised spatio-temporal detection of brain functional activation based on hidden Markov multiple event sequence models

    NASA Astrophysics Data System (ADS)

    Faisan, Sylvain; Thoraval, Laurent; Armspach, Jean-Paul; Heitz, Fabrice; Foucher, Jack

    2005-04-01

    This paper presents a novel, completely unsupervised fMRI brain mapping approach that addresses the three problems of hemodynamic response function (HRF) shape variability, neural event timing, and fMRI response linearity. To make it robust, the method takes into account spatial and temporal information directly into the core of the activation detection process. In practice, activation detection is formulated in terms of temporal alignment between the sequence of hemodynamic response onsets (HROs) detected in the fMRI signal at υ and in the spatial neighbourhood of υ, and the sequence of "off-on" transitions observed in the input blocked stimulation paradigm (when considering epoch-related fMRI data), or the sequence of stimuli of the event-based paradigm (when considering event-related fMRI data). This multiple event sequence alignment problem, which comes under multisensor data fusion, is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a special class of hidden Markov models. Results obtained on real and synthetic data compete with those obtained with the popular statistical parametric mapping (SPM) approach, but without necessitating any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

  17. Effects of rainfall events on the occurrence and detection efficiency of viruses in river water impacted by combined sewer overflows.

    PubMed

    Hata, Akihiko; Katayama, Hiroyuki; Kojima, Keisuke; Sano, Shoichi; Kasuga, Ikuro; Kitajima, Masaaki; Furumai, Hiroaki

    2014-01-15

    Rainfall events can introduce large amount of microbial contaminants including human enteric viruses into surface water by intermittent discharges from combined sewer overflows (CSOs). The present study aimed to investigate the effect of rainfall events on viral loads in surface waters impacted by CSO and the reliability of molecular methods for detection of enteric viruses. The reliability of virus detection in the samples was assessed by using process controls for virus concentration, nucleic acid extraction and reverse transcription (RT)-quantitative PCR (qPCR) steps, which allowed accurate estimation of virus detection efficiencies. Recovery efficiencies of poliovirus in river water samples collected during rainfall events (<10%) were lower than those during dry weather conditions (>10%). The log10-transformed virus concentration efficiency was negatively correlated with suspended solid concentration (r(2)=0.86) that increased significantly during rainfall events. Efficiencies of DNA extraction and qPCR steps determined with adenovirus type 5 and a primer sharing control, respectively, were lower in dry weather. However, no clear relationship was observed between organic water quality parameters and efficiencies of these two steps. Observed concentrations of indigenous enteric adenoviruses, GII-noroviruses, enteroviruses, and Aichi viruses increased during rainfall events even though the virus concentration efficiency was presumed to be lower than in dry weather. The present study highlights the importance of using appropriate process controls to evaluate accurately the concentration of water borne enteric viruses in natural waters impacted by wastewater discharge, stormwater, and CSOs. PMID:24064345

  18. Development of a multiplex polymerase chain reaction method for simultaneous detection of eight events of genetically modified maize.

    PubMed

    Onishi, Mari; Matsuoka, Takeshi; Kodama, Takashi; Kashiwaba, Koichi; Futo, Satoshi; Akiyama, Hiroshi; Maitani, Tamio; Furui, Satoshi; Oguchi, Taichi; Hino, Akihiro

    2005-12-14

    In this study, we developed a novel multiplex polymerase chain reaction (PCR) method for simultaneous detection of up to eight events of genetically modified (GM) maize within a single reaction. The eight detection primer pairs designed to be construct specific for eight respective GM events (i.e., Bt11, Event176, GA21, MON810, MON863, NK603, T25, and TC1507) and a primer pair for an endogenous reference gene, ssIIb, were included in the nonaplex(9plex) PCR system, and its amplified products could be distinguished by agarose gel and capillary electrophoreses based on their different lengths. The optimal condition enabled us to reliably amplify two fragments corresponding to a construct specific sequence and a taxon specific ssIIb in each of the eight events of GM maize and all of nine fragments in a simulated GM mixture containing as little as 0.25% (w/w) each of eight events of GM maize. These results indicate that this multiplex PCR method could be an effective qualitative detection method for screening GM maize. PMID:16332120

  19. Model based manipulator control

    NASA Technical Reports Server (NTRS)

    Petrosky, Lyman J.; Oppenheim, Irving J.

    1989-01-01

    The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.

  20. A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits.

    PubMed

    Gouwanda, Darwin; Gopalai, Alpha Agape

    2015-02-01

    Gait events detection allows clinicians and biomechanics researchers to determine timing of gait events, to estimate duration of stance phase and swing phase and to segment gait data. It also aids biomedical engineers to improve the design of orthoses and FES (functional electrical stimulation) systems. In recent years, researchers have resorted to using gyroscopes to determine heel-strike (HS) and toe-off (TO) events in gait cycles. However, these methods are subjected to significant delays when implemented in real-time gait monitoring devices, orthoses, and FES systems. Therefore, the work presented in this paper proposes a method that addresses these delays, to ensure real-time gait event detection. The proposed algorithm combines the use of heuristics and zero-crossing method to identify HS and TO. Experiments involving: (1) normal walking; (2) walking with knee brace; and (3) walking with ankle brace for overground walking and treadmill walking were designed to verify and validate the identified HS and TO. The performance of the proposed method was compared against the established gait detection algorithms. It was observed that the proposed method produced detection rate that was comparable to earlier reported methods and recorded reduced time delays, at an average of 100 ms.

  1. A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits.

    PubMed

    Gouwanda, Darwin; Gopalai, Alpha Agape

    2015-02-01

    Gait events detection allows clinicians and biomechanics researchers to determine timing of gait events, to estimate duration of stance phase and swing phase and to segment gait data. It also aids biomedical engineers to improve the design of orthoses and FES (functional electrical stimulation) systems. In recent years, researchers have resorted to using gyroscopes to determine heel-strike (HS) and toe-off (TO) events in gait cycles. However, these methods are subjected to significant delays when implemented in real-time gait monitoring devices, orthoses, and FES systems. Therefore, the work presented in this paper proposes a method that addresses these delays, to ensure real-time gait event detection. The proposed algorithm combines the use of heuristics and zero-crossing method to identify HS and TO. Experiments involving: (1) normal walking; (2) walking with knee brace; and (3) walking with ankle brace for overground walking and treadmill walking were designed to verify and validate the identified HS and TO. The performance of the proposed method was compared against the established gait detection algorithms. It was observed that the proposed method produced detection rate that was comparable to earlier reported methods and recorded reduced time delays, at an average of 100 ms. PMID:25619613

  2. Comparison among solar energetic particle events detected by COSTEP/SOHO experiment

    NASA Astrophysics Data System (ADS)

    Gomez-Herrero, Raul

    A comparative study of three impulsive solar events produced in July 1996 and two gradual-CME associated events produced in November 1997 has been performed using data from EPHIN instrument aboard SOHO spacecraft. Temporal evolution of the differential energy spectra of electrons in the energy range 0.25-10.4 MeV and H & He nuclei in the energy range 4.3-53.0 MeV/n has been determined for these events. Isotopic abundances of H and He have been obtained separately for each event.

  3. A new method to detect anisotropic electron events with SOHO/EPHIN

    NASA Astrophysics Data System (ADS)

    Banjac, Saša; Kühl, Patrick; Heber, Bernd

    2016-07-01

    The EPHIN instrument (Electron Proton Helium INstrument) forms a part of the COSTEP experiment (COmprehensive SupraThermal and Energetic Particle Analyzer) within the CEPAC collaboration on board of the SOHO spacecraft (SOlar and Heliospheric Observatory). The EPHIN sensor is a stack of six solid-state detectors surrounded by an anti-coincidence. It measures energy spectra of electrons in the range 250 keV to >8.7 MeV, and hydrogen and helium isotopes in the range 4~MeV/n to >53~MeV/n. In order to improve the isotopic resolution, the first two detectors have been segmented: 5 segments form a ring enclosing a central segment. This does not only allow to correct the energy-losses in the detectors for the different path-length in the detectors but allows also an estimation of the arrival direction of the particles with respect to the sensor axis. Utilizing an extensive GEANT 4 Monte-Carlo simulation of the sensor head we computed the scattering-induced modifications to the input angular distribution and developed an inversion method that takes into account the poor counting statistics by optimizing the corresponding algorithm. This improvement makes it possible for the first time to detect long lasting anisotropies in the 1~MeV-3~MeV electron flux with a single telescope on a three-axis stabilized spacecraft. We present the method and its application to several events with strong anisotropies. For validation, we compare our data with the WIND-3DP results.

  4. Automated Detection and Predictive Modeling of Flux Transfer Events using CLUSTER Data

    NASA Astrophysics Data System (ADS)

    Sipes, T. B.; Karimabadi, H.; Driscoll, J.; Wang, Y.; Lavraud, B.; Slavin, J. A.

    2006-12-01

    Almost all statistical studies of flux ropes (FTEs) and traveling compression regions (TCRs) have been based on (i) visual inspection of data to compile a list of events and (ii) use of histograms and simple linear correlation analysis to study their properties and potential causes and dependencies. This approach has several major drawbacks including being highly subjective and inefficient. The traditional use of histograms and simple linear correlation analysis is also only useful for analysis of systems that show dominant dependencies on one or two variables at the most. However, if the system has complex dependencies, more sophisticated statistical techniques are required. For example, Wang et al. [2006] showed evidence that FTE occurrence rate are affected by IMF Bygsm, Bzgsm, and magnitude, and the IMF clock, tilt, spiral, and cone angles. If the initial findings were correct that FTEs occur only during periods of southward IMF, one could use the direction of IMF as a predictor of occurrence of FTEs. But in light of Wang et al. result, one cannot draw quantitative conclusions about conditions under which FTEs occur. It may be that a certain combination of these parameters is the true controlling parameter. To uncover this, one needs to deploy more sophisticated techniques. We have developed a new, sophisticated data mining tool called MineTool. MineTool is highly accurate, flexible and capable of handling difficult and even noisy datasets extremely well. It has the ability to outperform standard data mining tools such as artificial neural networks, decision/regression trees and support vector machines. Here we present preliminary results of the application of this tool to the CLUSTER data to perform two tasks: (i) automated detection of FTEs, and (ii) predictive modeling of occurrences of FTEs based on IMF and magnetospheric conditions.

  5. A secure distributed logistic regression protocol for the detection of rare adverse drug events

    PubMed Central

    El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

    2013-01-01

    Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for

  6. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier

    PubMed Central

    Kambhampati, Satya Samyukta; Singh, Vishal; Ramkumar, Barathram

    2015-01-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%. PMID:26609414

  7. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

    SciTech Connect

    Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

    2015-10-01

    Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phases are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.

  8. A new method for producing automated seismic bulletins: Probabilistic event detection, association, and location

    DOE PAGES

    Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

    2015-10-01

    Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phasesmore » are considered. In addition, once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified.« less

  9. Discriminating Famous from Fictional Names Based on Lifetime Experience: Evidence in Support of a Signal-Detection Model Based on Finite Mixture Distributions

    ERIC Educational Resources Information Center

    Bowles, Ben; Harlow, Iain M.; Meeking, Melissa M.; Kohler, Stefan

    2012-01-01

    It is widely accepted that signal-detection mechanisms contribute to item-recognition memory decisions that involve discriminations between targets and lures based on a controlled laboratory study episode. Here, the authors employed mathematical modeling of receiver operating characteristics (ROC) to determine whether and how a signal-detection…

  10. IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data

    PubMed Central

    2015-01-01

    Background The emergence of next-generation RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate detection of intron retention (IR) events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies. Results We propose two new methods: IRcall and IRclassifier to detect IR events from RNA-Seq data. Our methods combine together gene expression information, read coverage within an intron, and read counts (within introns, within flanking exons, supporting splice junctions, and overlapping with 5' splice site/ 3' splice site), employing ranking strategy and classifiers to detect IR events. We applied our approaches to one published RNA-Seq data on contrasting skip mutant and wild-type in Arabidopsis thaliana. Compared with three state-of-the-art methods, IRcall and IRclassifier could effectively filter out false positives, and predict more accurate IR events. Availability The data and codes of IRcall and IRclassifier are available at http://mlg.hit.edu.cn/ybai/IR/IRcallAndIRclass.html PMID:25707295

  11. Accuracy and Precision of Equine Gait Event Detection during Walking with Limb and Trunk Mounted Inertial Sensors

    PubMed Central

    Olsen, Emil; Andersen, Pia Haubro; Pfau, Thilo

    2012-01-01

    The increased variations of temporal gait events when pathology is present are good candidate features for objective diagnostic tests. We hypothesised that the gait events hoof-on/off and stance can be detected accurately and precisely using features from trunk and distal limb-mounted Inertial Measurement Units (IMUs). Four IMUs were mounted on the distal limb and five IMUs were attached to the skin over the dorsal spinous processes at the withers, fourth lumbar vertebrae and sacrum as well as left and right tuber coxae. IMU data were synchronised to a force plate array and a motion capture system. Accuracy (bias) and precision (SD of bias) was calculated to compare force plate and IMU timings for gait events. Data were collected from seven horses. One hundred and twenty three (123) front limb steps were analysed; hoof-on was detected with a bias (SD) of −7 (23) ms, hoof-off with 0.7 (37) ms and front limb stance with −0.02 (37) ms. A total of 119 hind limb steps were analysed; hoof-on was found with a bias (SD) of −4 (25) ms, hoof-off with 6 (21) ms and hind limb stance with 0.2 (28) ms. IMUs mounted on the distal limbs and sacrum can detect gait events accurately and precisely. PMID:22969392

  12. An innovative methodological approach in the frame of Marine Strategy Framework Directive: a statistical model based on ship detection SAR data for monitoring programmes.

    PubMed

    Pieralice, Francesca; Proietti, Raffaele; La Valle, Paola; Giorgi, Giordano; Mazzolena, Marco; Taramelli, Andrea; Nicoletti, Luisa

    2014-12-01

    The Marine Strategy Framework Directive (MSFD, 2008/56/EC) is focused on protection, preservation and restoration of the marine environment by achieving and maintaining Good Environmental Status (GES) by 2020. Within this context, this paper presents a methodological approach for a fast and repeatable monitoring that allows quantitative assessment of seabed abrasion pressure due to recreational boat anchoring. The methodology consists of two steps: a semi-automatic procedure based on an algorithm for the ship detection in SAR imagery and a statistical model to obtain maps of spatial and temporal distribution density of anchored boats. Ship detection processing has been performed on 36 ASAR VV-pol images of Liguria test site, for the three years 2008, 2009 and 2010. Starting from the pointwise distribution layer produced by ship detection in imagery, boats points have been subdivided into 4 areas where a constant distribution density has been assumed for the entire period 2008-2010. In the future, this methodology will be applied also to higher resolution data of Sentinel-1 mission, specifically designed for the operational needs of the European Programme Copernicus. PMID:25096752

  13. An innovative methodological approach in the frame of Marine Strategy Framework Directive: a statistical model based on ship detection SAR data for monitoring programmes.

    PubMed

    Pieralice, Francesca; Proietti, Raffaele; La Valle, Paola; Giorgi, Giordano; Mazzolena, Marco; Taramelli, Andrea; Nicoletti, Luisa

    2014-12-01

    The Marine Strategy Framework Directive (MSFD, 2008/56/EC) is focused on protection, preservation and restoration of the marine environment by achieving and maintaining Good Environmental Status (GES) by 2020. Within this context, this paper presents a methodological approach for a fast and repeatable monitoring that allows quantitative assessment of seabed abrasion pressure due to recreational boat anchoring. The methodology consists of two steps: a semi-automatic procedure based on an algorithm for the ship detection in SAR imagery and a statistical model to obtain maps of spatial and temporal distribution density of anchored boats. Ship detection processing has been performed on 36 ASAR VV-pol images of Liguria test site, for the three years 2008, 2009 and 2010. Starting from the pointwise distribution layer produced by ship detection in imagery, boats points have been subdivided into 4 areas where a constant distribution density has been assumed for the entire period 2008-2010. In the future, this methodology will be applied also to higher resolution data of Sentinel-1 mission, specifically designed for the operational needs of the European Programme Copernicus.

  14. Acoustic events semantic detection, classification, and annotation for persistent surveillance applications

    NASA Astrophysics Data System (ADS)

    Alkilani, Amjad; Shirkhodaie, Amir

    2014-06-01

    Understanding of group activity based on analysis of spatiotemporally correlated acoustic sound events has received a minimum attention in the literature and hence is not well understood. Identification of group sub-activities such as: Human-Vehicle Interactions (HVI), Human-Object Interactions (HOI), and Human-Human Interactions (HHI) can significantly improve Situational Awareness (SA) in Persistent Surveillance Systems (PSS). In this paper, salient sound events associated with group activities are preliminary identified and applied for training a Gaussian Mixture Model (GMM) whose features are employed as feature vectors for training of algorithms for acoustic sound recognition. In this paper, discrimination of salient sounds associated with the HVI, HHI, and HOI events is achieved via a Correlation Based Template Matching (CMTM) classifier. To interlinked salient events representing an ontology-based hypothesis, a Hidden Markov Model (HMM) is employed to recognize spatiotemporally correlated events. Once such a connection is established, then, the system generates an annotation of each perceived sound event. This paper discusses the technical aspects of this approach and presents the experimental results for several outdoor group activities monitored by an array of acoustic sensors.

  15. Utility of Clinical Breast Exams in Detecting Local-Regional Breast Events after Breast-Conservation in Women with a Personal History of High-risk Breast Cancer

    PubMed Central

    Neuman, Heather B.; Schumacher, Jessica R.; Francescatti, Amanda B.; Adesoye, Taiwo; SB, Edge; ES, Burnside; DJ, Vanness; M, Yu; Y, Si; D, McKellar; DP, Winchester; Greenberg, Caprice C.

    2016-01-01

    Introduction Although breast cancer follow-up guidelines emphasize the importance of clinical exams, prior studies suggest a small fraction of local-regional events occurring after breast conservation are detected by exam alone. Our objective was to examine how local-regional events are detected in a contemporary, national cohort of high-risk breast cancer survivors. Methods A stage-stratified sample of stage II/III breast cancer patients diagnosed in 2006-2007 (n=11,099) were identified from 1,217 facilities within the National Cancer Data Base. Additional data on local-regional and distant breast events, method of event detection, imaging received, and mortality was collected. We further limited the cohort to patients with breast conservation (n=4,854). Summary statistics describe local-regional event rates and detection method. Results Local-regional events were detected in 5.5% (n=265). 83% were ipsilateral or contralateral in-breast events, and 17% within ipsilateral lymph nodes. 48% of local-regional events were detected on asymptomatic breast imaging, 29% by patients, and 10% on clinical exam. Overall, 0.5% of the 4,854 patients had a local-regional event detected on exam. Exams detected a higher proportion of lymph node (8/45) compared to in-breast events (18/220). No factors were associated with method of event detection. Discussion Clinical exams, as an adjunct to screening mammography, have a modest effect on local-regional event detection. This contradicts current belief that exams are a critical adjunct to mammographic screening. These findings can help to streamline follow-up care, potentially improving follow-up efficiency and quality. PMID:27491784

  16. Detecting Recent Atmospheric River Induced Flood Events over the Russian River Basin

    NASA Astrophysics Data System (ADS)

    Mehran, A.; Lettenmaier, D. P.; Ralph, F. M.; Lavers, D. A.

    2015-12-01

    Almost all major flood events in the coastal Western U.S. occur as a result of multi-day extreme precipitation during the winter and late fall, and most such events are now known to be Atmospheric Rivers (ARs). AR events are defined as having integrated water vapor (IWV) exceeding 2 cm in an area at least 2000 km long and no more than 1000 km wide. The dominant moisture source in many AR events, including those associated with most floods in the Russian River basin in Northern California, is the tropics. We report on a hydrological analysis of selected floods in the Russian River basin using the Distributed Hydrology Soil Vegetation Model (DHSVM), forced alternately by gridded station data, NWS WSR-88D radar data, and output from a regional atmospheric model. We also report results of river state forecasts using a river hydrodynamics model to reconstruct flood inundation from selected AR events. We diagnose errors in both the hydrological and river stage predictions, and discuss alternatives for future error reduction.

  17. A Systematic Analysis of the Sensitivity of Plasma Pharmacokinetics to Detect Differences in the Pulmonary Performance of Inhaled Fluticasone Propionate Products Using a Model-Based Simulation Approach.

    PubMed

    Weber, Benjamin; Hochhaus, Guenther

    2015-07-01

    The role of plasma pharmacokinetics (PK) for assessing bioequivalence at the target site, the lung, for orally inhaled drugs remains unclear. A validated semi-mechanistic model, considering the presence of mucociliary clearance in central lung regions, was expanded for quantifying the sensitivity of PK studies in detecting differences in the pulmonary performance (total lung deposition, central-to-peripheral lung deposition ratio, and pulmonary dissolution characteristics) between test (T) and reference (R) inhaled fluticasone propionate (FP) products. PK bioequivalence trials for inhaled FP were simulated based on this PK model for a varying number of subjects and T products. The statistical power to conclude bioequivalence when T and R products are identical was demonstrated to be 90% for approximately 50 subjects. Furthermore, the simulations demonstrated that PK metrics (area under the concentration time curve (AUC) and C max) are capable of detecting differences between T and R formulations of inhaled FP products when the products differ by more than 20%, 30%, and 25% for total lung deposition, central-to-peripheral lung deposition ratio, and pulmonary dissolution characteristics, respectively. These results were derived using a rather conservative risk assessment approach with an error rate of <10%. The simulations thus indicated that PK studies might be a viable alternative to clinical studies comparing pulmonary efficacy biomarkers for slowly dissolving inhaled drugs. PK trials for pulmonary efficacy equivalence testing should be complemented by in vitro studies to avoid false positive bioequivalence assessments that are theoretically possible for some specific scenarios. Moreover, a user-friendly web application for simulating such PK equivalence trials with inhaled FP is provided.

  18. Externally Sensitized Deprotection of PPG-Masked Carbonyls as a Spatial Proximity Probe in Photoamplified Detection of Binding Events

    PubMed Central

    Gustafson, Tiffany P.; Metzel, Greg A.

    2013-01-01

    Externally-sensitized electron-transfer fragmentation in dithiane PPG-protected carbonyls is adopted for detection and amplification of molecular recognition events. The new methodology allows for detection of as low as 50 attomoles of avidin utilizing an imager based on a low sensitivity mass-produced consumer CCD camera. Numeric modelling is carried out to demonstrate the intrinsic limitations of 2D amplification on surfaces and the advantages of unconstrained amplification in a compartmentalized volume of spatially addressable 3D solutions. PMID:22252455

  19. Event-specific detection of seven genetically modified soybean and maizes using multiplex-PCR coupled with oligonucleotide microarray.

    PubMed

    Xu, Jia; Zhu, Shuifang; Miao, Haizhen; Huang, Wensheng; Qiu, Minyan; Huang, Yan; Fu, Xuping; Li, Yao

    2007-07-11

    With the increasing development of genetically modified organism (GMO) detection techniques, the polymerase chain reaction (PCR) technique has been the mainstay for GMO detection. An oligonucleotide microarray is a glass chip to the surface of which an array of oligonucleotides was fixed as spots, each containing numerous copies of a sequence-specific probe that is complementary to a gene of interest. So it is used to detect ten or more targets synchronously. In this research, an event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity using multiplex-PCR together with oligonucleotide microarray. A commercial GM soybean (GTS 40-3-2) and six GM maize events (MON810, MON863, Bt176, Bt11, GA21, and T25) were detected by this method. The results indicate that it is a suitable method for the identification of these GM soybean and maizes. PMID:17559227

  20. Event-specific detection of seven genetically modified soybean and maizes using multiplex-PCR coupled with oligonucleotide microarray.

    PubMed

    Xu, Jia; Zhu, Shuifang; Miao, Haizhen; Huang, Wensheng; Qiu, Minyan; Huang, Yan; Fu, Xuping; Li, Yao

    2007-07-11

    With the increasing development of genetically modified organism (GMO) detection techniques, the polymerase chain reaction (PCR) technique has been the mainstay for GMO detection. An oligonucleotide microarray is a glass chip to the surface of which an array of oligonucleotides was fixed as spots, each containing numerous copies of a sequence-specific probe that is complementary to a gene of interest. So it is used to detect ten or more targets synchronously. In this research, an event-specific detection strategy based on the unique and specific integration junction sequences between the host plant genome DNA and the integrated gene is being developed for its high specificity using multiplex-PCR together with oligonucleotide microarray. A commercial GM soybean (GTS 40-3-2) and six GM maize events (MON810, MON863, Bt176, Bt11, GA21, and T25) were detected by this method. The results indicate that it is a suitable method for the identification of these GM soybean and maizes.

  1. Detecting deception in children: event familiarity affects criterion-based content analysis ratings.

    PubMed

    Pezdek, Kathy; Morrow, Anne; Blandon-Gitlin, Iris; Goodman, Gail S; Quas, Jodi A; Saywitz, Karen J; Bidrose, Sue; Pipe, Margaret-Ellen; Rogers, Martha; Brodie, Laura

    2004-02-01

    Statement Validity Assessment (SVA) is a comprehensive credibility assessment system, with the Criterion-Based Content Analysis (CBCA) as a core component. Worldwide, the CBCA is reported to be the most widely used veracity assessment instrument. We tested and confirmed the hypothesis that CBCA scores are affected by event familiarity; descriptions of familiar events are more likely to be judged true than are descriptions of unfamiliar events. CBCA scores were applied to transcripts of 114 children who recalled a routine medical procedure (control) or a traumatic medical procedure that they had experienced one time (relatively unfamiliar) or multiple times (relatively familiar). CBCA scores were higher for children in the relatively familiar than the relatively unfamiliar condition, and CBCA scores were significantly correlated with age. Results raise serious questions regarding the forensic suitability of the CBCA for assessing the veracity of children's accounts.

  2. PortVis: A Tool for Port-Based Detection of Security Events

    SciTech Connect

    McPherson, J; Ma, K; Krystosk, P; Bartoletti, T; Christensen, M

    2004-06-29

    Most visualizations of security-related network data require large amounts of finely detailed, high-dimensional data. However, in some cases, the data available can only be coarsely detailed because of security concerns or other limitations. How can interesting security events still be discovered in data that lacks important details, such as IP addresses, network security alarms, and labels? In this paper, we discuss a system we have designed that takes very coarsely detailed data-basic, summarized information of the activity on each TCP port during each given hour-and uses visualization to help uncover interesting security events.

  3. Wideband acoustic activation and detection of droplet vaporization events using a capacitive micromachined ultrasonic transducer.

    PubMed

    Novell, Anthony; Arena, Christopher B; Oralkan, Omer; Dayton, Paul A

    2016-06-01

    An ongoing challenge exists in understanding and optimizing the acoustic droplet vaporization (ADV) process to enhance contrast agent effectiveness for biomedical applications. Acoustic signatures from vaporization events can be identified and differentiated from microbubble or tissue signals based on their frequency content. The present study exploited the wide bandwidth of a 128-element capacitive micromachined ultrasonic transducer (CMUT) array for activation (8 MHz) and real-time imaging (1 MHz) of ADV events from droplets circulating in a tube. Compared to a commercial piezoelectric probe, the CMUT array provides a substantial increase of the contrast-to-noise ratio. PMID:27369143

  4. Event-specific qualitative and quantitative PCR methods for the detection of genetically modified rapeseed Oxy-235.

    PubMed

    Wu, Gang; Wu, Yuhua; Xiao, Ling; Lu, Changming

    2008-10-01

    Oxy-235 is an oxynil-tolerant genetically modified rapeseed approved for commercialized planting in Canada. The aim of this study was to establish event-specific qualitative and quantitative detection methods for Oxy-235. Both the 5'- and 3'-junction sequences spanning the plant DNA and the integrated gene construct of the Oxy-235 event were isolated, sequenced and analyzed. A 1298-bp deletion of the rapeseed genomic DNA that showed a high similarity to the mRNA sequence of Arabidopsis thaliana was found in the integration site of the insert DNA. Event-specific qualitative PCR methods were established, with one method producing a 105-bp product specific for the 5'-integration junction and the other method producing a 124-bp product specific for the 3'-junction. The absolute detection limits for the qualitative PCR were determined to be 100 initial template copies for the 5'-junction and ten for the 3'-junction. Quantitative methods were also developed that targeted both of the junction fragments. The limit of detection of the quantitative PCR analysis was ten initial template copies for either the 5'- or 3'-junction, while the limit of quantification was determined to be approximately 50 initial template copies. The real-time PCR systems so established were examined with two mixed rapeseed samples with known Oxy-235 contents and found to obtain the expected results.

  5. Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

    SciTech Connect

    Teuton, Jeremy R.; Griswold, Richard L.; Mehdi, Beata L.; Browning, Nigel D.

    2015-09-23

    Precise analysis of both (S)TEM images and video are time and labor intensive processes. As an example, determining when crystal growth and shrinkage occurs during the dynamic process of Li dendrite deposition and stripping involves manually scanning through each frame in the video to extract a specific set of frames/images. For large numbers of images, this process can be very time consuming, so a fast and accurate automated method is desirable. Given this need, we developed software that uses analysis of video compression statistics for detecting and characterizing events in large data sets. This software works by converting the data into a series of images which it compresses into an MPEG-2 video using the open source “avconv” utility [1]. The software does not use the video itself, but rather analyzes the video statistics from the first pass of the video encoding that avconv records in the log file. This file contains statistics for each frame of the video including the frame quality, intra-texture and predicted texture bits, forward and backward motion vector resolution, among others. In all, avconv records 15 statistics for each frame. By combining different statistics, we have been able to detect events in various types of data. We have developed an interactive tool for exploring the data and the statistics that aids the analyst in selecting useful statistics for each analysis. Going forward, an algorithm for detecting and possibly describing events automatically can be written based on statistic(s) for each data type.

  6. Systematic analysis of tropospheric NO2 long-range transport events detected in GOME-2 satellite data

    NASA Astrophysics Data System (ADS)

    Zien, A. W.; Richter, A.; Hilboll, A.; Blechschmidt, A.-M.; Burrows, J. P.

    2014-07-01

    Intercontinental long-range transport (LRT) events of NO2 relocate the effects of air pollution from emission regions to remote, pristine regions. We detect transported plumes in tropospheric NO2 columns measured by the GOME-2/MetOp-A instrument with a specialized algorithm and trace the plumes to their sources using the HYSPLIT Lagrangian transport model. With this algorithm we find 3808 LRT events over the ocean for the period 2007 to 2011. LRT events occur frequently in the mid-latitudes, emerging usually from coastal high-emission regions. In the free troposphere, plumes of NO2 can travel for several days to the polar oceanic atmosphere or to other continents. They travel along characteristic routes and originate from both continuous anthropogenic emission and emission events such as bush fires. Most NO2 LRT events occur during autumn and winter months, when meteorological conditions and emissions are most favorable. The evaluation of meteorological data shows that the observed NO2 LRT is often linked to cyclones passing over an emission region.

  7. Systematic analysis of tropospheric NO2 long-range transport events detected in GOME-2 satellite data

    NASA Astrophysics Data System (ADS)

    Zien, A. W.; Richter, A.; Hilboll, A.; Blechschmidt, A.-M.; Burrows, J. P.

    2013-11-01

    Intercontinental long-range transport (LRT) events of NO2 relocate the effects of air pollution from emission regions to remote, pristine regions. We detect transported plumes in tropospheric NO2 columns measured by the GOME-2/MetOp-A instrument with a specialized algorithm and trace the plumes to their sources using the HYSPLIT lagrangian transport model. With this algorithm we find 3808 LRT events over the ocean for the period 2007 to 2011. LRT events occur frequently in the mid-latitudes, emerging usually from coastal high-emission regions. In the free troposphere, plumes of NO2 can travel for several days to the polar oceanic atmosphere or to other continents. They travel along characteristic routes and originate from both continuous anthropogenic emission and emission events such as bush fires. Most NO2 LRT events occur during autumn and winter months, when meteorological conditions and emissions are most favorable. The evaluation of meteorological data shows that the observed NO2 LRT is often linked to cyclones passing over an emission region.

  8. A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Devadiga, Sadashiva; Tang, Yuan-Liang

    1994-01-01

    This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included.

  9. Dune Detective, Using Ecological Studies to Reconstruct Events Which Shaped a Barrier Island.

    ERIC Educational Resources Information Center

    Godfrey, Paul J.; Hon, Will

    This publication is designed for use as part of a curriculum series developed by the Regional Marine Science Project. Students in grades 11 and 12 are exposed to research methods through a series of field exercises guiding investigators in reconstructing the events which have shaped the natural communities of a barrier beach. Background…

  10. Model-Based Systems

    NASA Technical Reports Server (NTRS)

    Frisch, Harold P.

    2007-01-01

    Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.

  11. A new PCR-CGE (size and color) method for simultaneous detection of genetically modified maize events.

    PubMed

    Nadal, Anna; Coll, Anna; La Paz, Jose-Luis; Esteve, Teresa; Pla, Maria

    2006-10-01

    We present a novel multiplex PCR assay for simultaneous detection of multiple transgenic events in maize. Initially, five PCR primers pairs specific to events Bt11, GA21, MON810, and NK603, and Zea mays L. (alcohol dehydrogenase) were included. The event specificity was based on amplification of transgene/plant genome flanking regions, i.e., the same targets as for validated real-time PCR assays. These short and similarly sized amplicons were selected to achieve high and similar amplification efficiency for all targets; however, its unambiguous identification was a technical challenge. We achieved a clear distinction by a novel CGE approach that combined the identification by size and color (CGE-SC). In one single step, all five targets were amplified and specifically labeled with three different fluorescent dyes. The assay was specific and displayed an LOD of 0.1% of each genetically modified organism (GMO). Therefore, it was adequate to fulfill legal thresholds established, e.g., in the European Union. Our CGE-SC based strategy in combination with an adequate labeling design has the potential to simultaneously detect higher numbers of targets. As an example, we present the detection of up to eight targets in a single run. Multiplex PCR-CGE-SC only requires a conventional sequencer device and enables automation and high throughput. In addition, it proved to be transferable to a different laboratory. The number of authorized GMO events is rapidly growing; and the acreage of genetically modified (GM) varieties cultivated and commercialized worldwide is rapidly increasing. In this context, our multiplex PCR-CGE-SC can be suitable for screening GM contents in food.

  12. A new PCR-CGE (size and color) method for simultaneous detection of genetically modified maize events.

    PubMed

    Nadal, Anna; Coll, Anna; La Paz, Jose-Luis; Esteve, Teresa; Pla, Maria

    2006-10-01

    We present a novel multiplex PCR assay for simultaneous detection of multiple transgenic events in maize. Initially, five PCR primers pairs specific to events Bt11, GA21, MON810, and NK603, and Zea mays L. (alcohol dehydrogenase) were included. The event specificity was based on amplification of transgene/plant genome flanking regions, i.e., the same targets as for validated real-time PCR assays. These short and similarly sized amplicons were selected to achieve high and similar amplification efficiency for all targets; however, its unambiguous identification was a technical challenge. We achieved a clear distinction by a novel CGE approach that combined the identification by size and color (CGE-SC). In one single step, all five targets were amplified and specifically labeled with three different fluorescent dyes. The assay was specific and displayed an LOD of 0.1% of each genetically modified organism (GMO). Therefore, it was adequate to fulfill legal thresholds established, e.g., in the European Union. Our CGE-SC based strategy in combination with an adequate labeling design has the potential to simultaneously detect higher numbers of targets. As an example, we present the detection of up to eight targets in a single run. Multiplex PCR-CGE-SC only requires a conventional sequencer device and enables automation and high throughput. In addition, it proved to be transferable to a different laboratory. The number of authorized GMO events is rapidly growing; and the acreage of genetically modified (GM) varieties cultivated and commercialized worldwide is rapidly increasing. In this context, our multiplex PCR-CGE-SC can be suitable for screening GM contents in food. PMID:16972302

  13. Data-Driven Multimodal Sleep Apnea Events Detection : Synchrosquezing Transform Processing and Riemannian Geometry Classification Approaches.

    PubMed

    Rutkowski, Tomasz M

    2016-07-01

    A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification. PMID:27194241

  14. Detection and location of multiple events by MARS. Final report. [Multiple Arrival Recognition System

    SciTech Connect

    Wang, J.; Masso, J.F.; Archambeau, C.B.; Savino, J.M.

    1980-09-01

    Seismic data from two explosions was processed using the Systems Science and Software MARS (Multiple Arrival Recognition System) seismic event detector in an effort to determine their relative spatial and temporal separation on the basis of seismic data alone. The explosions were less than 1.0 kilometer apart and were separated by less than 0.5 sec in origin times. The seismic data consisted of nine local accelerograms (r < 1.0 km) and four regional (240 through 400 km) seismograms. The MARS processing clearly indicates the presence of multiple explosions, but the restricted frequency range of the data inhibits accurate time picks and hence limits the precision of the event location.

  15. Event detection and control co-design of sampled-data systems

    NASA Astrophysics Data System (ADS)

    Meng, Xiangyu; Chen, Tongwen

    2014-04-01

    This paper proposes event detector and controller co-design criteria for sampled-data systems in which static or dynamic output feedback controllers are used. The resulting criteria provide sufficient conditions to ensure the asymptotic stability of the closed-loop system with reduced communication rates among sensors, controllers, and actuators. The transmissions are mediated by event detectors at both sensor and controller nodes. For static output feedback control, the sufficient condition is given in terms of the feasibility of bilinear matrix inequalities (BMIs). The BMI feasibility problem is converted into a nonlinear optimisation problem involving linear matrix inequalities (LMIs), which is solved via the complementarity linearisation algorithm. For dynamic output feedback control, the sufficient condition is formulated into an LMI feasibility problem, which is readily solved with existing tools. Numerical examples are included to show the effectiveness of the proposed methods.

  16. Signature Based Detection of User Events for Post-mortem Forensic Analysis

    NASA Astrophysics Data System (ADS)

    James, Joshua Isaac; Gladyshev, Pavel; Zhu, Yuandong

    This paper introduces a novel approach to user event reconstruction by showing the practicality of generating and implementing signature-based analysis methods to reconstruct high-level user actions from a collection of low-level traces found during a post-mortem forensic analysis of a system. Traditional forensic analysis and the inferences an investigator normally makes when given digital evidence, are examined. It is then demonstrated that this natural process of inferring high-level events from low-level traces may be encoded using signature-matching techniques. Simple signatures using the defined method are created and applied for three popular Windows-based programs as a proof of concept.

  17. Detecting flood event trends assigned to changes in urbanisation levels using a bivariate copula model

    NASA Astrophysics Data System (ADS)

    Requena, Ana; Prosdocimi, Ilaria; Kjeldsen, Thomas R.; Mediero, Luis

    2014-05-01

    Flood frequency analyses based on stationary assumptions are usually employed for estimating design floods. However, more complex non-stationarity approaches are trying to be incorporated with the aim of improving such estimates. In this study, the effect of changing urbanisation on maximum flood peak (Q) and volume (V) series is analysed. The potential changes in an urbanised catchment and in a nearby hydrologically similar rural catchment in northwest England are investigated. The urbanised catchment is characterised by a noticeable increase of the urbanisation level in time, while the rural catchment has not been altered by anthropogenic actions. Winter, summer and annual maximum flood events are studied. With the aim of analysing changes in time, two non-superimposed time-windows are defined covering the periods 1976-1992 and 1993-2008, respectively. A preliminary analysis of temporal trends in Q, V and Kendall's tau is visually done, being formal tested by a resampling procedure. Differences were found among winter, summer and annual maximum flood events. As annual maximum flood events are commonly used for designing purposes, the corresponding bivariate distribution (margins and copula) was obtained for the different time-windows. Trends regarding both time-windows were analysed by comparing bivariate return period curves in the Q-V space. Different behaviours were found depending on the catchment. As a result, the application of the proposed methodology provides useful information in describing changes in flood events, regarding different flood variables and their relationship. In addition, the methodology can inform practitioners on the potential changes connected with urbanisation for appropriate design flood estimation.

  18. Vy-PER: eliminating false positive detection of virus integration events in next generation sequencing data.

    PubMed

    Forster, Michael; Szymczak, Silke; Ellinghaus, David; Hemmrich, Georg; Rühlemann, Malte; Kraemer, Lars; Mucha, Sören; Wienbrandt, Lars; Stanulla, Martin; Franke, Andre

    2015-01-01

    Several pathogenic viruses such as hepatitis B and human immunodeficiency viruses may integrate into the host genome. These virus/host integrations are detectable using paired-end next generation sequencing. However, the low number of expected true virus integrations may be difficult to distinguish from the noise of many false positive candidates. Here, we propose a novel filtering approach that increases specificity without compromising sensitivity for virus/host chimera detection. Our detection pipeline termed Vy-PER (Virus integration detection bY Paired End Reads) outperforms existing similar tools in speed and accuracy. We analysed whole genome data from childhood acute lymphoblastic leukemia (ALL), which is characterised by genomic rearrangements and usually associated with radiation exposure. This analysis was motivated by the recently reported virus integrations at genomic rearrangement sites and association with chromosomal instability in liver cancer. However, as expected, our analysis of 20 tumour and matched germline genomes from ALL patients finds no significant evidence for integrations by known viruses. Nevertheless, our method eliminates 12,800 false positives per genome (80× coverage) and only our method detects singleton human-phiX174-chimeras caused by optical errors of the Illumina HiSeq platform. This high accuracy is useful for detecting low virus integration levels as well as non-integrated viruses. PMID:26166306

  19. Vy-PER: eliminating false positive detection of virus integration events in next generation sequencing data.

    PubMed

    Forster, Michael; Szymczak, Silke; Ellinghaus, David; Hemmrich, Georg; Rühlemann, Malte; Kraemer, Lars; Mucha, Sören; Wienbrandt, Lars; Stanulla, Martin; Franke, Andre

    2015-07-13

    Several pathogenic viruses such as hepatitis B and human immunodeficiency viruses may integrate into the host genome. These virus/host integrations are detectable using paired-end next generation sequencing. However, the low number of expected true virus integrations may be difficult to distinguish from the noise of many false positive candidates. Here, we propose a novel filtering approach that increases specificity without compromising sensitivity for virus/host chimera detection. Our detection pipeline termed Vy-PER (Virus integration detection bY Paired End Reads) outperforms existing similar tools in speed and accuracy. We analysed whole genome data from childhood acute lymphoblastic leukemia (ALL), which is characterised by genomic rearrangements and usually associated with radiation exposure. This analysis was motivated by the recently reported virus integrations at genomic rearrangement sites and association with chromosomal instability in liver cancer. However, as expected, our analysis of 20 tumour and matched germline genomes from ALL patients finds no significant evidence for integrations by known viruses. Nevertheless, our method eliminates 12,800 false positives per genome (80× coverage) and only our method detects singleton human-phiX174-chimeras caused by optical errors of the Illumina HiSeq platform. This high accuracy is useful for detecting low virus integration levels as well as non-integrated viruses.

  20. Vy-PER: eliminating false positive detection of virus integration events in next generation sequencing data

    PubMed Central

    Forster, Michael; Szymczak, Silke; Ellinghaus, David; Hemmrich, Georg; Rühlemann, Malte; Kraemer, Lars; Mucha, Sören; Wienbrandt, Lars; Stanulla, Martin; Franke, Andre

    2015-01-01

    Several pathogenic viruses such as hepatitis B and human immunodeficiency viruses may integrate into the host genome. These virus/host integrations are detectable using paired-end next generation sequencing. However, the low number of expected true virus integrations may be difficult to distinguish from the noise of many false positive candidates. Here, we propose a novel filtering approach that increases specificity without compromising sensitivity for virus/host chimera detection. Our detection pipeline termed Vy-PER (Virus integration detection bY Paired End Reads) outperforms existing similar tools in speed and accuracy. We analysed whole genome data from childhood acute lymphoblastic leukemia (ALL), which is characterised by genomic rearrangements and usually associated with radiation exposure. This analysis was motivated by the recently reported virus integrations at genomic rearrangement sites and association with chromosomal instability in liver cancer. However, as expected, our analysis of 20 tumour and matched germline genomes from ALL patients finds no significant evidence for integrations by known viruses. Nevertheless, our method eliminates 12,800 false positives per genome (80× coverage) and only our method detects singleton human-phiX174-chimeras caused by optical errors of the Illumina HiSeq platform. This high accuracy is useful for detecting low virus integration levels as well as non-integrated viruses. PMID:26166306

  1. Enriched encoding: reward motivation organizes cortical networks for hippocampal detection of unexpected events.

    PubMed

    Murty, Vishnu P; Adcock, R Alison

    2014-08-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expectancy violations. During functional magnetic resonance imaging, participants performed a reaction-time task in which goal-irrelevant expectancy violations were encountered during states of high- or low-reward motivation. Motivation amplified hippocampal activation to and declarative memory for expectancy violations. Connectivity of the ventral tegmental area (VTA) with medial prefrontal, ventrolateral prefrontal, and visual cortices preceded and predicted this increase in hippocampal sensitivity. These findings elucidate a novel mechanism whereby reward motivation can enhance hippocampus-dependent memory: anticipatory VTA-cortical-hippocampal interactions. Further, the findings integrate literatures on dopaminergic neuromodulation of prefrontal function and hippocampus-dependent memory. We conclude that during reward motivation, VTA modulation induces distributed neural changes that amplify hippocampal signals and records of expectancy violations to improve predictions-a potentially unique contribution of the hippocampus to reward learning.

  2. Invariance of exocytotic events detected by amperometry as a function of the carbon fiber microelectrode diameter.

    PubMed

    Amatore, Christian; Arbault, Stéphane; Bouret, Yann; Guille, Manon; Lemaître, Frédéric; Verchier, Yann

    2009-04-15

    Etched carbon fiber microelectrodes of different radii have been used for amperometric measurements of single exocytotic events occurring at adrenal chromaffin cells. Frequency, kinetic, and quantitative information on exocytosis provided by amperometric spikes were analyzed as a function of the surface area of the microelectrodes. Interestingly, the percentage of spikes with foot (as well as their own characteristics), a category revealing the existence of sufficient long-lasting fusion pores, was found to be constant whatever the microelectrode diameter was, whereas the probability of overlapping spikes decreased with the electrode size. This confirmed that the prespike foot could not feature accidental superimposition of separated events occurring at different places. Moreover, the features of amperometric spikes investigated here (charge, intensity and kinetics) were found constant for all microelectrode diameters. This demonstrated that the electrochemical measurement does not introduce significant bias onto the kinetics and thermodynamics of release during individual exocytotic events. All in all, this work evidences that information on exocytosis amperometrically recorded with the usual 7 microm diameter carbon fiber electrodes is biologically relevant, although the frequent overlap between spikes requires a censorship of the data during the analytical treatment. PMID:19290664

  3. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection.

    PubMed

    Ahn, Junho; Han, Richard

    2016-01-01

    We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users' daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period. PMID:27223292

  4. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection.

    PubMed

    Ahn, Junho; Han, Richard

    2016-05-23

    We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users' daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period.

  5. Network Event Recording Device: An automated system for Network anomaly detection, and notification. Draft

    SciTech Connect

    Simmons, D.G.; Wilkins, R.

    1994-09-01

    The goal of the Network Event Recording Device (NERD) is to provide a flexible autonomous system for network logging and notification when significant network anomalies occur. The NERD is also charged with increasing the efficiency and effectiveness of currently implemented network security procedures. While it has always been possible for network and security managers to review log files for evidence of network irregularities, the NERD provides real-time display of network activity, as well as constant monitoring and notification services for managers. Similarly, real-time display and notification of possible security breaches will provide improved effectiveness in combating resource infiltration from both inside and outside the immediate network environment.

  6. Detection of centimeter-sized meteoroid impact events in Saturn's F ring.

    PubMed

    Showalter, M R

    1998-11-01

    Voyager images reveal that three prominent clumps in Saturn's F ring were short-lived, appearing rapidly and then spreading and decaying in brightness over periods of approximately 2 weeks. These features arise from hypervelocity impacts by approximately 10-centimeter meteoroids into F ring bodies. Future ring observations of these impact events could constrain the centimeter-sized component of the meteoroid population, which is otherwise unmeasurable but plays an important role in the evolution of rings and surfaces in the outer solar system. The F ring's numerous other clumps are much longer lived and appear to be unrelated to impacts.

  7. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection

    PubMed Central

    Ahn, Junho; Han, Richard

    2016-01-01

    We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users’ daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period. PMID:27223292

  8. Systems and methods for detecting a failure event in a field programmable gate array

    NASA Technical Reports Server (NTRS)

    Ng, Tak-Kwong (Inventor); Herath, Jeffrey A. (Inventor)

    2009-01-01

    An embodiment generally relates to a method of self-detecting an error in a field programmable gate array (FPGA). The method includes writing a signature value into a signature memory in the FPGA and determining a conclusion of a configuration refresh operation in the FPGA. The method also includes reading an outcome value from the signature memory.

  9. Maximizing the probability of detecting an electromagnetic counterpart of gravitational-wave events

    NASA Astrophysics Data System (ADS)

    Coughlin, Michael; Stubbs, Christopher

    2016-07-01

    Compact binary coalescences are a promising source of gravitational waves for second-generation interferometric gravitational-wave detectors such as advanced LIGO and advanced Virgo. These are among the most promising sources for joint detection of electromagnetic (EM) and gravitational-wave (GW) emission. To maximize the science performed with these objects, it is essential to undertake a followup observing strategy that maximizes the likelihood of detecting the EM counterpart. We present a follow-up strategy that maximizes the counterpart detection probability, given a fixed investment of telescope time. We show how the prior assumption on the luminosity function of the electro-magnetic counterpart impacts the optimized followup strategy. Our results suggest that if the goal is to detect an EM counterpart from among a succession of GW triggers, the optimal strategy is to perform long integrations in the highest likelihood regions. For certain assumptions about source luminosity and mass distributions, we find that an optimal time investment that is proportional to the 2/3 power of the surface density of the GW location probability on the sky. In the future, this analysis framework will benefit significantly from the 3-dimensional localization probability.

  10. Electrical detection of specific versus non-specific binding events in breast cancer cells

    NASA Astrophysics Data System (ADS)

    King, Benjamin C.; Clark, Michael; Burkhead, Thomas; Sethu, Palaniappan; Rai, Shesh; Kloecker, Goetz; Panchapakesan, Balaji

    2012-10-01

    Detection of circulating tumor cells (CTCs) from patient blood samples offers a desirable alternative to invasive tissue biopsies for screening of malignant carcinomas. A rigorous CTC detection method must identify CTCs from millions of other formed elements in blood and distinguish them from healthy tissue cells also present in the blood. CTCs are known to overexpress surface receptors, many of which aid them in invading other tissue, and these provide an avenue for their detection. We have developed carbon nanotube (CNT) thin film devices to specifically detect these receptors in intact cells. The CNT sidewalls are functionalized with antibodies specific to Epithelial Cell Adhesion Molecule (EpCAM), a marker overexpressed by breast and other carcinomas. Specific binding of EpCAM to anti-EpCAM antibodies causes a change in the local charge environment of the CNT surface which produces a characteristic electrical signal. Two cell lines were tested in the device: MCF7, a mammary adenocarcinoma line which overexpresses EpCAM, and MCF10A, a non-tumorigenic mammary epithelial line which does not. Introduction of MCF7s caused significant changes in the electrical conductance of the devices due to specific binding and associated charge environment change near the CNT sidewalls. Introduction of MCF10A displays a different profile due to purely nonspecific interactions. The profile of specific vs. nonspecific interaction signatures using carbon based devices will guide development of this diagnostic tool towards clinical sample volumes with wide variety of markers.

  11. DETECTION OF SUPERSONIC DOWNFLOWS AND ASSOCIATED HEATING EVENTS IN THE TRANSITION REGION ABOVE SUNSPOTS

    SciTech Connect

    Kleint, L.; Martínez-Sykora, J.; Antolin, P.; Tian, H.; Testa, P.; Reeves, K. K.; McKillop, S.; Saar, S.; Golub, L.; Judge, P.; Carlsson, M.; Hansteen, V.; Jaeggli, S.; and others

    2014-07-10

    Interface Region Imaging Spectrograph data allow us to study the solar transition region (TR) with an unprecedented spatial resolution of 0.''33. On 2013 August 30, we observed bursts of high Doppler shifts suggesting strong supersonic downflows of up to 200 km s{sup –1} and weaker, slightly slower upflows in the spectral lines Mg II h and k, C II 1336, Si IV 1394 Å, and 1403 Å, that are correlated with brightenings in the slitjaw images (SJIs). The bursty behavior lasts throughout the 2 hr observation, with average burst durations of about 20 s. The locations of these short-lived events appear to be the umbral and penumbral footpoints of EUV loops. Fast apparent downflows are observed along these loops in the SJIs and in the Atmospheric Imaging Assembly, suggesting that the loops are thermally unstable. We interpret the observations as cool material falling from coronal heights, and especially coronal rain produced along the thermally unstable loops, which leads to an increase of intensity at the loop footpoints, probably indicating an increase of density and temperature in the TR. The rain speeds are on the higher end of previously reported speeds for this phenomenon, and possibly higher than the free-fall velocity along the loops. On other observing days, similar bright dots are sometimes aligned into ribbons, resembling small flare ribbons. These observations provide a first insight into small-scale heating events in sunspots in the TR.

  12. Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

    NASA Technical Reports Server (NTRS)

    Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

    2006-01-01

    Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

  13. Probabilistic Swinging Door Algorithm as Applied to Photovoltaic Power Ramping Event Detection

    SciTech Connect

    Florita, Anthony; Zhang, Jie; Brancucci Martinez-Anido, Carlo; Hodge, Bri-Mathias; Cui, Mingjian

    2015-10-02

    Photovoltaic (PV) power generation experiences power ramping events due to cloud interference. Depending on the extent of PV aggregation and local grid features, such power variability can be constructive or destructive to measures of uncertainty regarding renewable power generation; however, it directly influences contingency planning, production costs, and the overall reliable operation of power systems. For enhanced power system flexibility, and to help mitigate the negative impacts of power ramping, it is desirable to analyze events in a probabilistic fashion so degrees of beliefs concerning system states and forecastability are better captured and uncertainty is explicitly quantified. A probabilistic swinging door algorithm is developed and presented in this paper. It is then applied to a solar data set of PV power generation. The probabilistic swinging door algorithm builds on results from the original swinging door algorithm, first used for data compression in trend logging, and it is described by two uncertain parameters: (i) e, the threshold sensitivity to a given ramp, and (ii) s, the residual of the piecewise linear ramps. These two parameters determine the distribution of ramps and capture the uncertainty in PV power generation.

  14. Development and application of absolute quantitative detection by duplex chamber-based digital PCR of genetically modified maize events without pretreatment steps.

    PubMed

    Zhu, Pengyu; Fu, Wei; Wang, Chenguang; Du, Zhixin; Huang, Kunlun; Zhu, Shuifang; Xu, Wentao

    2016-04-15

    The possibility of the absolute quantitation of GMO events by digital PCR was recently reported. However, most absolute quantitation methods based on the digital PCR required pretreatment steps. Meanwhile, singleplex detection could not meet the demand of the absolute quantitation of GMO events that is based on the ratio of foreign fragments and reference genes. Thus, to promote the absolute quantitative detection of different GMO events by digital PCR, we developed a quantitative detection method based on duplex digital PCR without pretreatment. Moreover, we tested 7 GMO events in our study to evaluate the fitness of our method. The optimized combination of foreign and reference primers, limit of quantitation (LOQ), limit of detection (LOD) and specificity were validated. The results showed that the LOQ of our method for different GMO events was 0.5%, while the LOD is 0.1%. Additionally, we found that duplex digital PCR could achieve the detection results with lower RSD compared with singleplex digital PCR. In summary, the duplex digital PCR detection system is a simple and stable way to achieve the absolute quantitation of different GMO events. Moreover, the LOQ and LOD indicated that this method is suitable for the daily detection and quantitation of GMO events. PMID:27016439

  15. Development and application of absolute quantitative detection by duplex chamber-based digital PCR of genetically modified maize events without pretreatment steps.

    PubMed

    Zhu, Pengyu; Fu, Wei; Wang, Chenguang; Du, Zhixin; Huang, Kunlun; Zhu, Shuifang; Xu, Wentao

    2016-04-15

    The possibility of the absolute quantitation of GMO events by digital PCR was recently reported. However, most absolute quantitation methods based on the digital PCR required pretreatment steps. Meanwhile, singleplex detection could not meet the demand of the absolute quantitation of GMO events that is based on the ratio of foreign fragments and reference genes. Thus, to promote the absolute quantitative detection of different GMO events by digital PCR, we developed a quantitative detection method based on duplex digital PCR without pretreatment. Moreover, we tested 7 GMO events in our study to evaluate the fitness of our method. The optimized combination of foreign and reference primers, limit of quantitation (LOQ), limit of detection (LOD) and specificity were validated. The results showed that the LOQ of our method for different GMO events was 0.5%, while the LOD is 0.1%. Additionally, we found that duplex digital PCR could achieve the detection results with lower RSD compared with singleplex digital PCR. In summary, the duplex digital PCR detection system is a simple and stable way to achieve the absolute quantitation of different GMO events. Moreover, the LOQ and LOD indicated that this method is suitable for the daily detection and quantitation of GMO events.

  16. Detecting alpha spindle events in EEG time series using adaptive autoregressive models

    PubMed Central

    2013-01-01

    Background Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection. Methods In this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling. The DAR model uses a discounting rate to weigh points measured further in the past less heavily than points more recently observed. This model is used together with predictive loss scoring to identify periods of EEG data that are statistically significant. Results Our algorithm accurately captures changes in the statistical properties of the alpha frequency band. These statistical changes are highly correlated with alpha spindle occurrences and form a reliable measure for detecting alpha spindles in EEG. We achieve approximately 95% accuracy in detecting alpha spindles, with timing precision to within approximately 150 ms, for two datasets from an experiment of prolonged simulated driving, as well as in simulated EEG. Sensitivity and specificity values are above 0.9, and in many cases are above

  17. Event-related brain potentials reveal the time-course of language change detection in early bilinguals.

    PubMed

    Kuipers, Jan-Rouke; Thierry, Guillaume

    2010-05-01

    Using event-related brain potentials, we investigated the temporal course of language change detection in proficient bilinguals as compared to matched controls. Welsh-English bilingual participants and English controls were presented with a variant of the oddball paradigm involving picture-word pairs. The language of the spoken word was manipulated such that English was the frequent stimulus (75%) and Welsh the infrequent stimulus (25%). We also manipulated semantic relatedness between pictures and words, such that only half of the pictures were followed by a word that corresponded with the identity of the picture. The P2 wave was significantly modulated by language in the bilingual group only, suggesting that this group detected a language change as early as 200 ms after word onset. Monolinguals also reliably detected the language change, but at a later stage of semantic integration (N400 range), since Welsh words were perceived as meaningless. The early detection of a language change in bilinguals triggered stimulus re-evaluation mechanisms reflected by a significant P600 modulation by Welsh words. Furthermore, compared to English unrelated words, English words matching the picture identity elicited significantly greater P2 amplitudes in the bilingual group only, suggesting that proficient bilinguals validate an incoming word against their expectation based on the context. Overall, highly proficient bilinguals appear to detect language changes very early on during speech perception and to consciously monitor language changes when they occur.

  18. Model Based Definition

    NASA Technical Reports Server (NTRS)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  19. Detection and Analysis of High Ice Concentration Events and Supercooled Drizzle from IAGOS Commercial Aircraft

    NASA Astrophysics Data System (ADS)

    Gallagher, Martin; Baumgardner, Darrel; Lloyd, Gary; Beswick, Karl; Freer, Matt; Durant, Adam

    2016-04-01

    Hazardous encounters with high ice concentrations that lead to temperature and airspeed sensor measurement errors, as well as engine rollback and flameout, continue to pose serious problems for flight operations of commercial air carriers. Supercooled liquid droplets (SLD) are an additional hazard, especially for smaller commuter aircraft that do not have sufficient power to fly out of heavy icing conditions or heat to remove the ice. New regulations issued by the United States and European regulatory agencies are being implemented that will require aircraft below a certain weight class to carry sensors that will detect and warn of these types of icing conditions. Commercial aircraft do not currently carry standard sensors to detect the presence of ice crystals in high concentrations because they are typical found in sizes that are below the detection range of aircraft weather radar. Likewise, the sensors that are currently used to detect supercooled water do not respond well to drizzle-sized drops. Hence, there is a need for a sensor that can fill this measurement void. In addition, the forecast models that are used to predict regions of icing rely on pilot observations as the only means to validate the model products and currently there are no forecasts for the prevalence of high altitude ice crystals. Backscatter Cloud Probes (BCP) have been flying since 2011 under the IAGOS project on six Airbus commercial airliners operated by Lufthansa, Air France, China Air, Iberia and Cathay Pacific, and measure cloud droplets, ice crystals and aerosol particles larger than 5 μm. The BCP can detect these particles and measures an optical equivalent diameter (OED) but is not able to distinguish the type of particle, i.e. whether they are droplets, ice crystals, dust or ash. However, some qualification can be done based on measured temperature to discriminate between liquid water and ice. The next generation BCP (BCPD, Backscatter Cloud Probe with polarization detection) is

  20. Embedding surveillance into clinical care to detect serious adverse events in pregnancy

    PubMed Central

    Seale, Anna C; Barsosio, Helen C; Koech, Angela; Berkley, James A

    2016-01-01

    Severe maternal complications in pregnancy in sub-Saharan Africa contribute to high maternal mortality and morbidity. Incidence data on severe maternal complications, life-threatening conditions, maternal deaths and birth outcomes are essential for clinical audit and to inform trial design of the types and frequency of expected severe adverse events (SAEs). However, such data are very limited, especially in sub-Saharan Africa. We set up standardized, systematic clinical surveillance embedded into routine clinical care in a rural county hospital in Kenya. Pregnant women and newborns are systematically assessed and investigated. Data are reported using a standardized Maternal Admission Record that forms both the hospital’s clinical record and the data collection tool. Integrating clinical surveillance with routine clinical care is feasible and should be expanded in sub-Saharan Africa, both for improving clinical practice and as a basis for intervention studies to reduce maternal and newborn mortality and morbidity where rates are highest. PMID:26254977

  1. Real-time detection of an extreme scattering event: Constraints on Galactic plasma lenses

    NASA Astrophysics Data System (ADS)

    Bannister, Keith W.; Stevens, Jamie; Tuntsov, Artem V.; Walker, Mark A.; Johnston, Simon; Reynolds, Cormac; Bignall, Hayley

    2016-01-01

    Extreme scattering events (ESEs) are distinctive fluctuations in the brightness of astronomical radio sources caused by occulting plasma lenses in the interstellar medium. The inferred plasma pressures of the lenses are ~103 times the ambient pressure, challenging our understanding of gas conditions in the Milky Way. Using a new survey technique, we discovered an ESE while it was in progress. Here we report radio and optical follow-up observations. Modeling of the radio data demonstrates that the lensing structure is a density enhancement and the lens is diverging, ruling out one of two competing physical models. Our technique will uncover many more ESEs, addressing a long-standing mystery of the small-scale gas structure of our Galaxy.

  2. Detection of overflow events in the shag rocks passage, scotia ridge.

    PubMed

    Zenk, W

    1981-09-01

    During an almost yearlong period of observations made with a current meter in the fracture zone between the Falkland Islands (Islas Malvinas) and South Georgia, several overflow events were recorded at a depth of 3000 meters carrying cold bottom water from the Scotia Sea into the Argentine Basin. The outflow bursts of Scotia Sea bottom water, a mixing product of Weddell Sea and eastern Pacific bottom water, were associated with typical speeds of more than 28 centimeters per second toward the northwest and characteristic temperatures below 0.6 degrees C. The maximum 24-hour average speed of 65 centimeters per second, together with a temperature of 0.29 degrees C, was encountered on 14 November 1980 at a water depth of 2973 meters, 35 meters above the sea floor. PMID:17741101

  3. Real-time detection of an extreme scattering event: Constraints on Galactic plasma lenses.

    PubMed

    Bannister, Keith W; Stevens, Jamie; Tuntsov, Artem V; Walker, Mark A; Johnston, Simon; Reynolds, Cormac; Bignall, Hayley

    2016-01-22

    Extreme scattering events (ESEs) are distinctive fluctuations in the brightness of astronomical radio sources caused by occulting plasma lenses in the interstellar medium. The inferred plasma pressures of the lenses are ~10(3) times the ambient pressure, challenging our understanding of gas conditions in the Milky Way. Using a new survey technique, we discovered an ESE while it was in progress. Here we report radio and optical follow-up observations. Modeling of the radio data demonstrates that the lensing structure is a density enhancement and the lens is diverging, ruling out one of two competing physical models. Our technique will uncover many more ESEs, addressing a long-standing mystery of the small-scale gas structure of our Galaxy.

  4. Detection of overflow events in the shag rocks passage, scotia ridge.

    PubMed

    Zenk, W

    1981-09-01

    During an almost yearlong period of observations made with a current meter in the fracture zone between the Falkland Islands (Islas Malvinas) and South Georgia, several overflow events were recorded at a depth of 3000 meters carrying cold bottom water from the Scotia Sea into the Argentine Basin. The outflow bursts of Scotia Sea bottom water, a mixing product of Weddell Sea and eastern Pacific bottom water, were associated with typical speeds of more than 28 centimeters per second toward the northwest and characteristic temperatures below 0.6 degrees C. The maximum 24-hour average speed of 65 centimeters per second, together with a temperature of 0.29 degrees C, was encountered on 14 November 1980 at a water depth of 2973 meters, 35 meters above the sea floor.

  5. Real-time detection of an extreme scattering event: Constraints on Galactic plasma lenses.

    PubMed

    Bannister, Keith W; Stevens, Jamie; Tuntsov, Artem V; Walker, Mark A; Johnston, Simon; Reynolds, Cormac; Bignall, Hayley

    2016-01-22

    Extreme scattering events (ESEs) are distinctive fluctuations in the brightness of astronomical radio sources caused by occulting plasma lenses in the interstellar medium. The inferred plasma pressures of the lenses are ~10(3) times the ambient pressure, challenging our understanding of gas conditions in the Milky Way. Using a new survey technique, we discovered an ESE while it was in progress. Here we report radio and optical follow-up observations. Modeling of the radio data demonstrates that the lensing structure is a density enhancement and the lens is diverging, ruling out one of two competing physical models. Our technique will uncover many more ESEs, addressing a long-standing mystery of the small-scale gas structure of our Galaxy. PMID:26798008

  6. An electronic circuit that detects left ventricular ejection events by processing the arterial pressure waveform

    NASA Technical Reports Server (NTRS)

    Gebben, V. D.; Webb, J. A., Jr.

    1972-01-01

    An electronic circuit for processing arterial blood pressure waveform signals is described. The circuit detects blood pressure as the heart pumps blood through the aortic valve and the pressure distribution caused by aortic valve closure. From these measurements, timing signals for use in measuring the left ventricular ejection time is determined, and signals are provided for computer monitoring of the cardiovascular system. Illustrations are given of the circuit and pressure waveforms.

  7. 3D-nanostructured Au electrodes for the event-specific detection of MON810 transgenic maize.

    PubMed

    Barroso, M Fátima; Freitas, Maria; Oliveira, M Beatriz P P; de-los-Santos-Álvarez, Noemí; Lobo-Castañón, María Jesús; Delerue-Matos, Cristina

    2015-03-01

    In the present work, the development of a genosensor for the event-specific detection of MON810 transgenic maize is proposed. Taking advantage of nanostructuration, a cost-effective three dimensional electrode was fabricated and a ternary monolayer containing a dithiol, a monothiol and the thiolated capture probe was optimized to minimize the unspecific signals. A sandwich format assay was selected as a way of precluding inefficient hybridization associated with stable secondary target structures. A comparison between the analytical performance of the Au nanostructured electrodes and commercially available screen-printed electrodes highlighted the superior performance of the nanostructured ones. Finally, the genosensor was effectively applied to detect the transgenic sequence in real samples, showing its potential for future quantitative analysis. PMID:25618653

  8. 3D-nanostructured Au electrodes for the event-specific detection of MON810 transgenic maize.

    PubMed

    Barroso, M Fátima; Freitas, Maria; Oliveira, M Beatriz P P; de-los-Santos-Álvarez, Noemí; Lobo-Castañón, María Jesús; Delerue-Matos, Cristina

    2015-03-01

    In the present work, the development of a genosensor for the event-specific detection of MON810 transgenic maize is proposed. Taking advantage of nanostructuration, a cost-effective three dimensional electrode was fabricated and a ternary monolayer containing a dithiol, a monothiol and the thiolated capture probe was optimized to minimize the unspecific signals. A sandwich format assay was selected as a way of precluding inefficient hybridization associated with stable secondary target structures. A comparison between the analytical performance of the Au nanostructured electrodes and commercially available screen-printed electrodes highlighted the superior performance of the nanostructured ones. Finally, the genosensor was effectively applied to detect the transgenic sequence in real samples, showing its potential for future quantitative analysis.

  9. I. The detectability of occultation events for outer solar system objects. II. Measuring temporal symmetry in quasar variation

    NASA Astrophysics Data System (ADS)

    Nihei, Taryn Candace

    This is a two-part dissertation in which I discuss two topics. Each utilizes time-series analysis to quantitatively parameterize observed and simulated astronomical light curves. In Chapter 2 I present a study of the indirect detection of objects in the outer solar system, from the Kuiper Belt to the Oort Cloud. By parameterization of the width and depth of simulated occultation light curves which account for diffraction effects, background source size, stellar spectra, finite passbands, and finite sampling, I quantify the detectability of occultation events. I then consider the specifications of three telescope systems to determine the sensitivity of these systems to outer solar system occultation events. From this study, I conclude that a modest ground survey sampling at a rate of 5 Hz with signal-to-noise ratio of ~ 8 is sufficient for the detection of Kuiper Belt objects with radii r [Special characters omitted.] 1.5 km. In addition, such a survey would be able to detect occultation events by objects in the Oort Cloud. Detection of occultation events by inner Oort Cloud objects with radii r [Special characters omitted.] 10 km are possible. Larger objects with r [Special characters omitted.] 100 km will be detectable in the outer Oort Cloud regions. In addition, I show that surveys with higher signal-to-noise ratios and higher sampling rates will be able to probe the lower range of the Kuiper Belt size distribution down to objects with radii r ~ 0.3 km. Chapter 3 is an inquiry into the nature of quasar optical variations that explores the feasibility of a microlensing source for quasar variation. The microlensing hypothesis posits that microlensing by compact objects is a significant contributor to quasar optical variation. Microlensing does not exhibit any arrow of time--there is no consistent temporal asymmetry across microlensing events which exhibit either a rapid-rise and slow-decay or a slow- rise and rapid-decay. Therefore, a predicted consequence of a

  10. Architecture design of the multi-functional wavelet-based ECG microprocessor for realtime detection of abnormal cardiac events.

    PubMed

    Cheng, Li-Fang; Chen, Tung-Chien; Chen, Liang-Gee

    2012-01-01

    Most of the abnormal cardiac events such as myocardial ischemia, acute myocardial infarction (AMI) and fatal arrhythmia can be diagnosed through continuous electrocardiogram (ECG) analysis. According to recent clinical research, early detection and alarming of such cardiac events can reduce the time delay to the hospital, and the clinical outcomes of these individuals can be greatly improved. Therefore, it would be helpful if there is a long-term ECG monitoring system with the ability to identify abnormal cardiac events and provide realtime warning for the users. The combination of the wireless body area sensor network (BASN) and the on-sensor ECG processor is a possible solution for this application. In this paper, we aim to design and implement a digital signal processor that is suitable for continuous ECG monitoring and alarming based on the continuous wavelet transform (CWT) through the proposed architectures--using both programmable RISC processor and application specific integrated circuits (ASIC) for performance optimization. According to the implementation results, the power consumption of the proposed processor integrated with an ASIC for CWT computation is only 79.4 mW. Compared with the single-RISC processor, about 91.6% of the power reduction is achieved.

  11. Characterization and event specific-detection by quantitative real-time PCR of T25 maize insert.

    PubMed

    Collonnier, Cécile; Schattner, Alexandra; Berthier, Georges; Boyer, Francine; Coué-Philippe, Géraldine; Diolez, Annick; Duplan, Marie-Noëlle; Fernandez, Sophie; Kebdani, Naïma; Kobilinsky, André; Romaniuk, Marcel; de Beuckeleer, Marc; de Loose, Marc; Windels, Pieter; Bertheau, Yves

    2005-01-01

    T25 is one of the 4 maize transformation events from which commercial lines have so far been authorized in Europe. It was created by polyethylene glycol-mediated transformation using a construct bearing one copy of the synthetic pat gene associated with both promoter and terminator of the 35S ribosomal gene from cauliflower mosaic virus. In this article, we report the sequencing of the whole T25 insert and the characterization of its integration site by using a genome walking strategy. Our results confirmed that one intact copy of the initial construct had been integrated in the plant genome. They also revealed, at the 5' junction of the insert, the presence of a second truncated 35S promoter, probably resulting from rearrangements which may have occurred before or during integration of the plasmid DNA. The analysis of the junction fragments showed that the integration site of the insert presented high homologies with the Huck retrotransposon family. By using one primer annealing in the maize genome and the other in the 5' end of the integrated DNA, we developed a reliable event-specific detection system for T25 maize. To provide means to comply with the European regulation, a real-time PCR test was designed for specific quantitation of T25 event by using Taqman chemistry.

  12. Characterization and event specific-detection by quantitative real-time PCR of T25 maize insert.

    PubMed

    Collonnier, Cécile; Schattner, Alexandra; Berthier, Georges; Boyer, Francine; Coué-Philippe, Géraldine; Diolez, Annick; Duplan, Marie-Noëlle; Fernandez, Sophie; Kebdani, Naïma; Kobilinsky, André; Romaniuk, Marcel; de Beuckeleer, Marc; de Loose, Marc; Windels, Pieter; Bertheau, Yves

    2005-01-01

    T25 is one of the 4 maize transformation events from which commercial lines have so far been authorized in Europe. It was created by polyethylene glycol-mediated transformation using a construct bearing one copy of the synthetic pat gene associated with both promoter and terminator of the 35S ribosomal gene from cauliflower mosaic virus. In this article, we report the sequencing of the whole T25 insert and the characterization of its integration site by using a genome walking strategy. Our results confirmed that one intact copy of the initial construct had been integrated in the plant genome. They also revealed, at the 5' junction of the insert, the presence of a second truncated 35S promoter, probably resulting from rearrangements which may have occurred before or during integration of the plasmid DNA. The analysis of the junction fragments showed that the integration site of the insert presented high homologies with the Huck retrotransposon family. By using one primer annealing in the maize genome and the other in the 5' end of the integrated DNA, we developed a reliable event-specific detection system for T25 maize. To provide means to comply with the European regulation, a real-time PCR test was designed for specific quantitation of T25 event by using Taqman chemistry. PMID:15859082

  13. Fast joint detection-estimation of evoked brain activity in event-related FMRI using a variational approach

    PubMed Central

    Chaari, Lotfi; Vincent, Thomas; Forbes, Florence; Dojat, Michel; Ciuciu, Philippe

    2013-01-01

    In standard within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery. PMID:23096056

  14. Comparison of real-time PCR detection chemistries and cycling modes using Mon810 event-specific assays as model.

    PubMed

    La Paz, José Luis; Esteve, Teresa; Pla, Maria

    2007-05-30

    The most widely accepted methods for accurate quantitative detection of genetically modified organisms rely on real-time PCR. Various detection chemistries are available for real-time PCR. They include sequence-unspecific DNA labeling dyes such SYBR-Green I and the use of both universal (e.g., AmpliFluor) and sequence-specific double-labeled probes, the latter comprising hybridization (e.g., Molecular Beacon) and hydrolysis (e.g., TaqMan or MGB) probes. Also, new real-time PCR devices and reagents allowing fast cycling reactions exist. Five Mon810 real-time PCR assays were developed in which the event specificity was based on the detection of transgene and plant rearranged sequences found to 3' flank the insertion site. Every assay was specifically designed for one particular detection chemistry, that is, AmpliFluor, Molecular Beacon, MGB, TaqMan, and SYBR-Green I. When possible, the assays were adapted to fast cycling mode. All assays displayed satisfactory performance parameters, although Molecular Beacon, MGB, and TaqMan chemistries were the most suitable for quantification purposes in both conventional and fast cycling modes.

  15. The direct detection of boosted dark matter at high energies and PeV events at IceCube

    SciTech Connect

    Bhattacharya, A.; Gandhi, R.; Gupta, A.

    2015-03-13

    We study the possibility of detecting dark matter directly via a small but energetic component that is allowed within present-day constraints. Drawing closely upon the fact that neutral current neutrino nucleon interactions are indistinguishable from DM-nucleon interactions at low energies, we extend this feature to high energies for a small, non-thermal but highly energetic population of DM particle χ, created via the decay of a significantly more massive and long-lived non-thermal relic Φ, which forms the bulk of DM. If χ interacts with nucleons, its cross-section, like the neutrino-nucleus coherent cross-section, can rise sharply with energy leading to deep inelastic scattering, similar to neutral current neutrino-nucleon interactions at high energies. Thus, its direct detection may be possible via cascades in very large neutrino detectors. As a specific example, we apply this notion to the recently reported three ultra-high energy PeV cascade events clustered around 1 – 2 PeV at IceCube (IC). We discuss the features which may help discriminate this scenario from one in which only astrophysical neutrinos constitute the event sample in detectors like IC.

  16. The direct detection of boosted dark matter at high energies and PeV events at IceCube

    SciTech Connect

    Bhattacharya, A.; Gandhi, R.; Gupta, A.

    2015-03-13

    We study the possibility of detecting dark matter directly via a small but energetic component that is allowed within present-day constraints. Drawing closely upon the fact that neutral current neutrino nucleon interactions are indistinguishable from DM-nucleon interactions at low energies, we extend this feature to high energies for a small, non-thermal but highly energetic population of DM particle χ, created via the decay of a significantly more massive and long-lived non-thermal relic ϕ, which forms the bulk of DM. If χ interacts with nucleons, its cross-section, like the neutrino-nucleus coherent cross-section, can rise sharply with energy leading to deep inelastic scattering, similar to neutral current neutrino-nucleon interactions at high energies. Thus, its direct detection may be possible via cascades in very large neutrino detectors. As a specific example, we apply this notion to the recently reported three ultra-high energy PeV cascade events clustered around 1−2 PeV at IceCube (IC). We discuss the features which may help discriminate this scenario from one in which only astrophysical neutrinos constitute the event sample in detectors like IC.

  17. The direct detection of boosted dark matter at high energies and PeV events at IceCube

    DOE PAGES

    Bhattacharya, A.; Gandhi, R.; Gupta, A.

    2015-03-13

    We study the possibility of detecting dark matter directly via a small but energetic component that is allowed within present-day constraints. Drawing closely upon the fact that neutral current neutrino nucleon interactions are indistinguishable from DM-nucleon interactions at low energies, we extend this feature to high energies for a small, non-thermal but highly energetic population of DM particle χ, created via the decay of a significantly more massive and long-lived non-thermal relic Φ, which forms the bulk of DM. If χ interacts with nucleons, its cross-section, like the neutrino-nucleus coherent cross-section, can rise sharply with energy leading to deep inelasticmore » scattering, similar to neutral current neutrino-nucleon interactions at high energies. Thus, its direct detection may be possible via cascades in very large neutrino detectors. As a specific example, we apply this notion to the recently reported three ultra-high energy PeV cascade events clustered around 1 – 2 PeV at IceCube (IC). We discuss the features which may help discriminate this scenario from one in which only astrophysical neutrinos constitute the event sample in detectors like IC.« less

  18. Qualitative and quantitative event-specific PCR detection methods for oxy-235 canola based on the 3' integration flanking sequence.

    PubMed

    Yang, Litao; Guo, Jinchao; Zhang, Haibo; Liu, Jia; Zhang, Dabing

    2008-03-26

    As more genetically modified plant events are approved for commercialization worldwide, the event-specific PCR method has become the key method for genetically modified organism (GMO) identification and quantification. This study reveals the 3' flanking sequence of the exogenous integration of Oxy-235 canola employing thermal asymmetric interlaced PCR (TAIL-PCR). On the basis of the revealed 3' flanking sequence, PCR primers and TaqMan probe were designed and qualitative and quantitative PCR assays were established for Oxy-235 canola. The specificity and limits of detection (LOD) and quantification (LOQ) of these two PCR assays were validated to as low as 0.1% for the relative LOD of qualitative PCR assay; the absolute LOD and LOQ were low to 10 and 20 copies of canola genomic DNA in quantitative PCR assay, respectively. Furthermore, ideal quantified results were obtained in the practical canola sample detection. All of the results indicate that the developed qualitative and quantitative PCR methods based on the revealed 3' integration flanking sequence are suitable for GM canola Oxy-235 identification and quantification.

  19. APASVO: A free software tool for automatic P-phase picking and event detection in seismic traces

    NASA Astrophysics Data System (ADS)

    Romero, José Emilio; Titos, Manuel; Bueno, Ángel; Álvarez, Isaac; García, Luz; Torre, Ángel de la; Benítez, M.a. Carmen

    2016-05-01

    The accurate estimation of the arrival time of seismic waves or picking is a problem of major interest in seismic research given its relevance in many seismological applications, such as earthquake source location and active seismic tomography. In the last decades, several automatic picking methods have been proposed with the ultimate goal of implementing picking algorithms whose results are comparable to those obtained by manual picking. In order to facilitate the use of these automated methods in the analysis of seismic traces, this paper presents a new free, open source, software graphical tool, named APASVO, which allows picking tasks in an easy and user-friendly way. The tool also provides event detection functionality, where a relatively imprecise estimation of the onset time is sufficient. The application implements the STA-LTA detection algorithm and the AMPA picking algorithm. An autoregressive AIC-based picking method can also be applied. Besides, this graphical tool is complemented with two additional command line tools, an event picking tool and a synthetic earthquake generator. APASVO is a multiplatform tool that works on Windows, Linux and OS X. The application can process data in a large variety of file formats. It is implemented in Python and relies on well-known scientific computing packages such as ObsPy, NumPy, SciPy and Matplotlib.

  20. Analyzing Protease Specificity and Detecting in Vivo Proteolytic Events Using Tandem Mass Spectrometry

    SciTech Connect

    Gupta, Nitin; Hixson, Kim K.; Culley, David E.; Smith, Richard D.; Pevzner, Pavel A.

    2010-07-01

    While trypsin remains the most commonly used protease in mass spectrometry, other proteases may be employed for increasing peptide-coverage or generating overlapping peptides. Knowledge of the accurate specifcity rules of these proteases is helpful for database search tools to detect peptides, and becomes crucial when mass spectrometry is used to discover in vivo proteolytic cleavages. In this study, we use tandem mass spectrometry to analyze the specifcity rules of selected proteases and describe MS- Proteolysis, a software tool for identifying putative sites of in vivo proteolytic cleavage. Our analysis suggests that the specifcity rules for some commonly used proteases can be improved, e.g., we find that V8 protease cuts not only after Asp and Glu, as currently expected, but also shows a smaller propensity to cleave after Gly for the conditions tested in this study. Finally, we show that comparative analysis of multiple proteases can be used to detect putative in vivo proteolytic sites on a proteome-wide scale.

  1. Event-related potential correlates of language change detection in bilingual toddlers.

    PubMed

    Kuipers, Jan Rouke; Thierry, Guillaume

    2012-01-01

    Children raised in a bilingual environment are faced with the daunting task of learning to extract meaning from language input that can differ between caregivers but, depending on the social context, also within caregivers. Here, we investigated monolingual and bilingual toddlers' brain responses to an unexpected language change. We presented 2-3 year old children with picture-word pairs and occasionally changed the language of the spoken word while recording event-related potentials (ERPs). In line with previous results obtained in adults, bilingual children differentiated between the languages of input faster than their monolingual peers, i.e., within 200 ms of spoken word onset, a time range previously associated with lexical access. However, while adult bilinguals displayed a late stimulus re-evaluation ERP response to a language change, no such modulation was found in bilingual toddlers. These results suggest that although bilingual individuals are sensitive to phonemic language cues already from an early age, language awareness and language monitoring mechanisms probably develop later in life.

  2. Clinical Experiments of Communication by ALS Patient Utilizing Detecting Event-Related Potential

    NASA Astrophysics Data System (ADS)

    Kanou, Naoyuki; Sakuma, Kenji; Nakashima, Kenji

    Amyotrophic Lateral Sclerosis(ALS) patients are unable to successfully communicate their desires, although their mentality is normal, and so, the necessity of Communication Aids(CA) for ALS patients is realized. Therefore, the authors are focused on Event-Related Potential(ERP) which is elicited primarily for the target by visual and auditory stimuli. P200, N200 and P300 are components of ERP. These are potentials that are elicited when the subject focuses attention on stimuli that appears infrequently. ALS patient participated in two experiments. In the first experiment, a target word out of five words on a computer display was specified. The five words were linked to an each electric appliance, allowing the ALS patient to switch on a target appliance by ERP. In the second experiment, a target word in a 5×5 matrix was specified by measure of ERP. The rows and columns of the matrix were reversed randomly. The word on a crossing point of rows and columns including the target word, was specified as the target word. The rate of correct judgment in the first and second experiments were 100% in N200 and 96% in P200. For practical use of this system, it is very important to determine suitable communication algorithms for each patient by performing these experiments evaluating the results.

  3. Microlensing events by Proxima Centauri in 2014 and 2016: Opportunities for mass determination and possible planet detection

    SciTech Connect

    Sahu, Kailash C.; Bond, Howard E.; Anderson, Jay; Dominik, Martin E-mail: jayander@stsci.edu E-mail: md35@st-andrews.ac.uk

    2014-02-20

    We have found that Proxima Centauri, the star closest to our Sun, will pass close to a pair of faint background stars in the next few years. Using Hubble Space Telescope (HST) images obtained in 2012 October, we determine that the passage close to a mag 20 star will occur in 2014 October (impact parameter 1.''6), and to a mag 19.5 star in 2016 February (impact parameter 0.''5). As Proxima passes in front of these stars, the relativistic deflection of light will cause shifts in the positions of the background stars of ∼0.5 and 1.5 mas, respectively, readily detectable by HST imaging, and possibly by Gaia and ground-based facilities such as the Very Large Telescope. Measurement of these astrometric shifts offers a unique and direct method to measure the mass of Proxima. Moreover, if Proxima has a planetary system, the planets may be detectable through their additional microlensing signals, although the probability of such detections is small. With astrometric accuracies of 0.03 mas (achievable with HST spatial scanning), centroid shifts caused by Jovian planets are detectable at separations of up to 2.''0 (corresponding to 2.6 AU at the distance of Proxima), and centroid shifts by Earth-mass planets are detectable within a small band of 8 mas (corresponding to 0.01 AU) around the source trajectories. Jovian planets within a band of about 28 mas (corresponding to 0.036 AU) around the source trajectories would produce a brightening of the source by >0.01 mag and could hence be detectable. Estimated timescales of the astrometric and photometric microlensing events due to a planet range from a few hours to a few days, and both methods would provide direct measurements of the planetary mass.

  4. Reconstruction of the Magnetkoepfl rockfall event - Detecting rock fall release zones using terrestrial laser scanning, Hohe Tauern, Austria

    NASA Astrophysics Data System (ADS)

    Hartmeyer, I.; Keuschnig, M.; Delleske, R.; Schrott, L.

    2012-04-01

    Instability of rock faces in high mountain areas is an important risk factor for man and infrastructure, particularly within the context of climate change. Numerous rock fall events in the European Alps suggest an increasing occurrence of mass movements due to rising temperatures in recent years. Within the MOREXPERT project ('Monitoring Expert System for Hazardous Rock Walls') a new long-term monitoring site for mass movement and permafrost interaction has been initiated in the Austrian Alps. The study area is located at the Kitzsteinhorn (Hohe Tauern), a particularly interesting site for the investigation of glacier retreat and potential permafrost degradation and their respective consequences for the stability of alpine rock faces. To detect and quantify changes occurring at the terrain surface an extensive terrestrial laser scanning (TLS) monitoring campaign was started in 2011. TLS creates three-dimensional high-resolution images of the scanned area allowing precise quantification of changes in geometry and volume in steep rock faces over distances of up to several hundreds of meters. Within the TLS monitoring campaign at the Kitzsteinhorn a large number of differently dimensioned rock faces is examined (varying size, slope inclination etc.). Scanned areas include the Kitzsteinhorn northwest and south face, the Magnetkoepfl east face as well as a couple of small rock faces in the vicinity of the summit station. During the night from August 27th to August 28th 2011 a rock fall event was documented by employees of the cable car company. The release zone could not immediately be detected. The east face of the Magnetkoepfl covers approximately 70 meters in height and about 200 meters in width. It is made up of calcareous mica-schist and displays an abundance of well-developed joint sets with large joint apertures. Meteorological data from a weather station located at the same altitude (2.950m) and just 500m away from the release zone show that the rock fall event

  5. ON THE DETECTABILITY OF A PREDICTED MESOLENSING EVENT ASSOCIATED WITH THE HIGH PROPER MOTION STAR VB 10

    SciTech Connect

    Lepine, Sebastien; DiStefano, Rosanne E-mail: rd@cfa.harvard.edu

    2012-04-10

    Extrapolation of the astrometric motion of the nearby low-mass star VB 10 indicates that sometime in late 2011 December or during the first 2-3 months of 2012, the star will make a close approach to a background point source. Based on astrometric uncertainties, we estimate a 1 in 2 chance that the distance of closest approach {rho}{sub min} will be less than 100 mas, a 1 in 5 chance that {rho}{sub min} < 50 mas, and a 1 in 10 chance that {rho}{sub min} < 20 mas. The last would result in a microlensing event with a 6% magnification in the light from the background source and an astrometric shift of 3.3 mas. The lensing signal will however be significantly diluted by the light from VB 10, which is 1.5 mag brighter than the background source in B band, 5 mag brighter in I band, and 10 mag brighter in K band, making the event undetectable in all but the bluer optical bands. However, we show that if VB 10 happens to harbor a {approx}1 M{sub J} planet on a moderately wide ( Almost-Equal-To 0.18 AU-0.84 AU) orbit, there is a chance (1% to more than 10%, depending on the distance of closest approach and orbital period and inclination) that a passage of the planet closer to the background source will result in a secondary event of higher magnification. The detection of secondary events could be made possible with a several-times-per-night multi-site monitoring campaign.

  6. Detection mechanisms employing single event upsets in dynamic random access memories used as radiation sensors

    NASA Astrophysics Data System (ADS)

    Darambara, D. G.; Spyrou, N. M.

    1994-12-01

    A hardware system is being designed and constructed for the detection of neutrons, with a view to using it in neutron imaging and elemental analysis. A feasibility study was initially carried out to demonstrate that dynamic Random Access Memories (dRAMs) can be used as heavy charged particle detectors and furthermore be made sensitive to neutrons. We are interested, however, in constructing a detector that will be position sensitive, and hence carried out experiments to investigate the relative sensitivity of specific elements within the dRAM chips. The findings from these initial system tests highlight the usefulness of such a device as a position sensitive radiation detector. This paper aims to explain and give a review of most aspects concerning the soft error (SE) performance using dRAM as a radiation sensor.

  7. Application of stochastic discrete event system framework for detection of induced low rate TCP attack.

    PubMed

    Barbhuiya, F A; Agarwal, Mayank; Purwar, Sanketh; Biswas, Santosh; Nandi, Sukumar

    2015-09-01

    TCP is the most widely accepted transport layer protocol. The major emphasis during the development of TCP was its functionality and efficiency. However, not much consideration was given on studying the possibility of attackers exploiting the protocol, which has lead to several attacks on TCP. This paper deals with the induced low rate TCP attack. Since the attack is relatively new, only a few schemes have been proposed to mitigate it. However, the main issues with these schemes are scalability, change in TCP header, lack of formal frameworks, etc. In this paper, we have adapted the stochastic DES framework for detecting the attack, which addresses most of these issues. We have successfully deployed and tested the proposed DES based IDS on a test bed.

  8. Satellite remote sensing for detection and inventory of mass wasting events in British Columbia

    NASA Astrophysics Data System (ADS)

    Martin, Y.; Franklin, S.; Barlow, J.

    2003-04-01

    Landsliding is a major mechanism of mass wasting in humid, alpine terrain. The collection of large, landsliding inventories is critical from both an applied and theoretical perspective. These inventories can be used to improve our understanding of the contribution of landsliding to sediment transfers in steep terrain and also form a key component of many hazard assessment studies. The collection of large inventories has traditionally been based on interpretation of aerial photographs, a methodology which is labour-intensive and not cost-effective. Automated approaches based on satellite image digital data sources have much potential for improving the efficiency of data collection and may help remove interpretation bias. As the quality and quantity of such data increases, methods of analyzing the imagery together with digital elevation models (DEMs) must be developed and tested in a variety of environmental conditions. We examined the potential of Landsat satellite data in conjunction with DEM data to detect translational landslides in the Cascade Mountains of British Columbia. Image segmentation, followed by object-based classification using spectral and geomorphometric data derived from the DEM, resulted in an overall accuracy of 75% in the detection of landslides that were over 1 ha in area. Use of a geomorphometric software package enabled the calculation of slope shape parameters and the path of steepest slope, two key discriminators of landslide morphology. We tested fusion of the Landsat optical/infrared spectral channels with the 15 m spatial resolution panchromatic data to obtain additional increase in accuracy. Texture analysis was used to discriminate between shallow and bedrock landslides. This research suggests there is a strong potential to develop accurate large-area landslide inventories from satellite imagery in British Columbia.

  9. High-throughput, low-cost, and event-specific polymerase chain reaction detection of herbicide tolerance in genetically modified soybean A2704-12.

    PubMed

    Ma, H; Li, H; Li, J; Wang, X F; Wei, P C; Li, L; Yang, J B

    2014-01-01

    The aim of this study was to develop an event-specific qualitative and real-time quantitative polymerase chain reaction (PCR) method for detection of herbicide-tolerance genetically modified (GM) soybean A2704-12. The event-specific PCR primers were designed, based on the 5'-flanking integration sequence in the soybean genome, to amplify the 239-bp target fragment. Employing the same event-specific primers, qualitative PCR and real-time quantitative PCR detection methods were successfully developed. The results showed that the A2704-12 event could be specifically distinguished from other GM soybean events. In the qualitative PCR assay, the limit of detection was 0.05%, and in the real-time quantitative PCR assay, the limit of detection was less than 0.01%. Moreover, our genomic DNA (gDNA) extraction protocol is high-throughput, safe, and low-cost. The event-specific PCR assay system is cost-efficient by using SYBR Green I in real-time PCR, and by using the same primers in both the qualitative and quantitative PCR assays. We therefore developed a high-throughput, low-cost, and event-specific qualitative and quantitative PCR detection method for GM soybean A2704-12. The method would be useful for market supervision and management of GM soybean A2704-12 due to its high specificity and sensitivity. PMID:24615034

  10. Principles of models based engineering

    SciTech Connect

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  11. An automatic rules extraction approach to support OSA events detection in an mHealth system.

    PubMed

    Sannino, Giovanna; De Falco, Ivanoe; De Pietro, Giuseppe

    2014-09-01

    Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out. PMID:25192565

  12. Age Dating Merger Events in Early Type Galaxies via the Detection of AGB Light

    NASA Technical Reports Server (NTRS)

    Bothun, G.

    2005-01-01

    A thorough statistical analysis of the J-H vs. H-K color plane of all detected early type galaxies in the 2MASS catalog with velocities less than 5000 km/s has been performed. This all sky survey is not sensitive to one particular galactic environment and therefore a representative range of early type galaxy environments have been sampled. Virtually all N-body simulation so major mergers produces a central starburst due to rapid collection of gas. This central starburst is of sufficient amplitude to change the stellar population in the central regions of the galaxy. Intermediate age populations are given away by the presence of AGB stars which will drive the central colors redder in H-K relative to the J- H baseline. This color anomaly has a lifetime of 2-5 billion years depending on the amplitude of the initial starburst Employing this technique on the entire 2MASS sample (several hundred galaxies) reveals that the AGB signature occurs less than 1% of the time. This is a straightforward indication that virtually all nearby early type galaxies have not had a major merger occur within the last few billion years.

  13. An automatic rules extraction approach to support OSA events detection in an mHealth system.

    PubMed

    Sannino, Giovanna; De Falco, Ivanoe; De Pietro, Giuseppe

    2014-09-01

    Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.

  14. SPR and SPR Imaging: Recent Trends in Developing Nanodevices for Detection and Real-Time Monitoring of Biomolecular Events

    PubMed Central

    Puiu, Mihaela; Bala, Camelia

    2016-01-01

    In this paper we review the underlying principles of the surface plasmon resonance (SPR) technique, particularly emphasizing its advantages along with its limitations regarding the ability to discriminate between the specific binding response and the interfering effects from biological samples. While SPR sensors were developed almost three decades, SPR detection is not yet able to reduce the time-consuming steps of the analysis, and is hardly amenable for miniaturized, portable platforms required in point-of-care (POC) testing. Recent advances in near-field optics have emerged, resulting in the development of SPR imaging (SPRi) as a powerful optical, label-free monitoring tool for multiplexed detection and monitoring of biomolecular events. The microarrays design of the SPRi chips incorporating various metallic nanostructures make these optofluidic devices more suitable for diagnosis and near-patient testing than the traditional SPR sensors. The latest developments indicate SPRi detection as being the most promising surface plasmon-based technique fulfilling the demands for implementation in lab-on-a-chip (LOC) technologies. PMID:27314345

  15. Detection of explosive events by monitoring acoustically-induced geomagnetic perturbations

    SciTech Connect

    Lewis, J P; Rock, D R; Shaeffer, D L; Warshaw, S I

    1999-10-07

    The Black Thunder Coal Mine (BTCM) near Gillette, Wyoming was used as a test bed to determine the feasibility of detecting explosion-induced geomagnetic disturbances with ground-based induction magnetometers. Two magnetic observatories were fielded at distances of 50 km and 64 km geomagnetically north from the northernmost edge of BTCM. Each observatory consisted of three separate but mutually orthogonal magnetometers, Global Positioning System (GPS) timing, battery and solar power, a data acquisition and storage system, and a three-axis seismometer. Explosions with yields of 1 to 3 kT of TNT equivalent occur approximately every three weeks at BTCM. We hypothesize that explosion-induced acoustic waves propagate upward and interact collisionally with the ionosphere to produce ionospheric electron density (and concomitant current density) perturbations which act as sources for geomagnetic disturbances. These disturbances propagate through an ionospheric Alfven waveguide that we postulate to be leaky (due to the imperfectly conducting lower ionospheric boundary). Consequently, wave energy may be observed on the ground. We observed transient pulses, known as Q-bursts, with pulse widths about 0.5 s and with spectral energy dominated by the Schumann resonances. These resonances appear to be excited in the earth-ionosphere cavity by Alfven solitons that may have been generated by the explosion-induced acoustic waves reaching the ionospheric E and F regions and that subsequently propagate down through the ionosphere to the atmosphere. In addition, we observe late time (> 800 s) ultra low frequency (ULF) geomagnetic perturbations that appear to originate in the upper F region ({approximately}300 km) and appear to be caused by the explosion-induced acoustic wave interacting with that part of the ionosphere. We suggest that explosion-induced Q-bursts may be discriminated from naturally occurring Q-bursts by association of the former with the late time explosion-induced ULF

  16. Event-specific qualitative and quantitative polymerase chain reaction methods for detection of genetically modified rapeseed Ms8xRf3 based on the right border junctions.

    PubMed

    Wu, Gang; Wu, Yuhua; Xiao, Ling; Lu, Changming

    2008-01-01

    Ms8xRf3 is a genetically modified rapeseed hybrid which is widely cultivated in Canada and exported to some other countries for production of foodstuffs or fodder. In this study, the genomic sequences flanking the right borders of the integrated transgenic sequences in the Ms8xRf3 genome were characterized and showed high similarities with the bacterial artificial chromosome clone of Chinese cabbage. Event-specific qualitative polymerase chain reaction (PCR) methods were established with the primers and probes targeting the junction regions to produce a 123 base pair (bp) product for the Ms8 event and 92 bp for the Rf3 event. The absolute detection limit of qualitative PCR was 2.5 initial template copies for the Ms8 event and 50 copies for the Rf3 event. Quantitiative detection methods were established, with the absolute quantification limit being approximately 25 initial template copies.

  17. Stable operation of a 300-m laser interferometer with sufficient sensitivity to detect gravitational-wave events within our galaxy.

    PubMed

    Ando, M; Arai, K; Takahashi, R; Heinzel, G; Kawamura, S; Tatsumi, D; Kanda, N; Tagoshi, H; Araya, A; Asada, H; Aso, Y; Barton, M A; Fujimoto, M K; Fukushima, M; Futamase, T; Hayama, K; Horikoshi, G; Ishizuka, H; Kamikubota, N; Kawabe, K; Kawashima, N; Kobayashi, Y; Kojima, Y; Kondo, K; Kozai, Y; Kuroda, K; Matsuda, N; Mio, N; Miura, K; Miyakawa, O; Miyama, S M; Miyoki, S; Moriwaki, S; Musha, M; Nagano, S; Nakagawa, K; Nakamura, T; Nakao, K; Numata, K; Ogawa, Y; Ohashi, M; Ohishi, N; Okutomi, S; Oohara, K; Otsuka, S; Saito, Y; Sasaki, M; Sato, S; Sekiya, A; Shibata, M; Somiya, K; Suzuki, T; Takamori, A; Tanaka, T; Taniguchi, S; Telada, S; Tochikubo, K; Tomaru, T; Tsubono, K; Tsuda, N; Uchiyama, T; Ueda, A; Ueda, K; Waseda, K; Watanabe, Y; Yakura, H; Yamamoto, K; Yamazaki, T

    2001-04-30

    TAMA300, an interferometric gravitational-wave detector with 300-m baseline length, has been developed and operated with sufficient sensitivity to detect gravitational-wave events within our galaxy and sufficient stability for observations; the interferometer was operated for over 10 hours stably and continuously. With a strain-equivalent noise level of h approximately 5x10(-21)/sqrt[Hz], a signal-to-noise ratio of 30 is expected for gravitational waves generated by a coalescence of 1.4M-1.4M binary neutron stars at 10 kpc distance. We evaluated the stability of the detector sensitivity with a 2-week data-taking run, collecting 160 hours of data to be analyzed in the search for gravitational waves.

  18. Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Abreu, Carlos; Miranda, Francisco; Mendes, Paulo M.

    2016-06-01

    The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular, they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless, due to the critical nature of the data conveyed by such patient monitoring applications, they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context, vis-à-vis the quality of service being provided by the wireless sensor network, this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric's value.

  19. Collaborative trial for the validation of event-specific PCR detection methods of genetically modified papaya Huanong No.1.

    PubMed

    Wei, Jiaojun; Le, Huangying; Pan, Aihu; Xu, Junfeng; Li, Feiwu; Li, Xiang; Quan, Sheng; Guo, Jinchao; Yang, Litao

    2016-03-01

    For transferring the event-specific PCR methods of genetically modified papaya Huanong No.1 to other laboratories, we validated the previous developed PCR assays of Huanong No.1 according to the international standard organization (ISO) guidelines. A total of 11 laboratories participated and returned their test results in this trial. In qualitative PCR assay, the high specificity and limit of detection as low as 0.1% was confirmed. For the quantitative PCR assay, the limit of quantification was as low as 25 copies. The quantitative biases among ten blind samples were within the range between 0.21% and 10.04%. Furthermore, the measurement uncertainty of the quantitative PCR results was calculated within the range between 0.28% and 2.92% for these ten samples. All results demonstrated that the Huanong No.1 qualitative and quantitative PCR assays were creditable and applicable for identification and quantification of GM papaya Huanong No.1 in further routine lab analysis.

  20. Automatic Event Detection in Search for Inter-Moss Loops in IRIS Si IV Slit-Jaw Images

    NASA Technical Reports Server (NTRS)

    Fayock, Brian; Winebarger, Amy R.; De Pontieu, Bart

    2015-01-01

    The high-resolution capabilities of the Interface Region Imaging Spectrometer (IRIS) mission have allowed the exploration of the finer details of the solar magnetic structure from the chromosphere to the lower corona that have previously been unresolved. Of particular interest to us are the relatively short-lived, low-lying magnetic loops that have foot points in neighboring moss regions. These inter-moss loops have also appeared in several AIA pass bands, which are generally associated with temperatures that are at least an order of magnitude higher than that of the Si IV emission seen in the 1400 angstrom pass band of IRIS. While the emission lines seen in these pass bands can be associated with a range of temperatures, the simultaneous appearance of these loops in IRIS 1400 and AIA 171, 193, and 211 suggest that they are not in ionization equilibrium. To study these structures in detail, we have developed a series of algorithms to automatically detect signal brightening or events on a pixel-by-pixel basis and group them together as structures for each of the above data sets. These algorithms have successfully picked out all activity fitting certain adjustable criteria. The resulting groups of events are then statistically analyzed to determine which characteristics can be used to distinguish the inter-moss loops from all other structures. While a few characteristic histograms reveal that manually selected inter-moss loops lie outside the norm, a combination of several characteristics will need to be used to determine the statistical likelihood that a group of events be categorized automatically as a loop of interest. The goal of this project is to be able to automatically pick out inter-moss loops from an entire data set and calculate the characteristics that have previously been determined manually, such as length, intensity, and lifetime. We will discuss the algorithms, preliminary results, and current progress of automatic characterization.

  1. Generation of a Solar Cycle of Sunspot Metadata Using the AIA Event Detection Framework - A Test of the System

    NASA Astrophysics Data System (ADS)

    Slater, G. L.; Zharkov, S.

    2008-12-01

    The soon-to-be-launched Solar Dynamics Observatory (SDO) will generate roughly 2 TB of image data per day, far more than previous solar missions. Because of the difficulty of widely distributing this enormous volume of data and in order to maximize discovery and scientific return, a sophisticated automated metadata extraction system is being developed at Stanford University and Lockheed Martin Solar and Astrophysics Laboratory in Palo Alto, CA. A key component in this system is the Event Detection System, which will supervise the execution of a set of feature and event extraction algorithms running in parallel, in real time, on all images recorded by the four telescopes of the key imaging instrument, the Atmospheric Imaging Assembly (AIA). The system will run on a beowulf cluster of 160 processors. As a test of the new system, we will run feature extraction software developed under the European Grid of Solar Observatories (EGSO) program to extract sunspot metadata from the 12 year SOHO MDI mission archive of full disk continuum and magnetogram images and also from the TRACE high resolution image archive. Although the main goal will be to test the performance of the production line framework, the resulting database will have applications for both research and space weather prediction. We examine some of these applications and compare the databases generated with others currently available.

  2. Measurement of patient safety: a systematic review of the reliability and validity of adverse event detection with record review

    PubMed Central

    Hanskamp-Sebregts, Mirelle; Zegers, Marieke; Vincent, Charles; van Gurp, Petra J; de Vet, Henrica C W; Wollersheim, Hub

    2016-01-01

    Objectives Record review is the most used method to quantify patient safety. We systematically reviewed the reliability and validity of adverse event detection with record review. Design A systematic review of the literature. Methods We searched PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Library and from their inception through February 2015. We included all studies that aimed to describe the reliability and/or validity of record review. Two reviewers conducted data extraction. We pooled κ values (κ) and analysed the differences in subgroups according to number of reviewers, reviewer experience and training level, adjusted for the prevalence of adverse events. Results In 25 studies, the psychometric data of the Global Trigger Tool (GTT) and the Harvard Medical Practice Study (HMPS) were reported and 24 studies were included for statistical pooling. The inter-rater reliability of the GTT and HMPS showed a pooled κ of 0.65 and 0.55, respectively. The inter-rater agreement was statistically significantly higher when the group of reviewers within a study consisted of a maximum five reviewers. We found no studies reporting on the validity of the GTT and HMPS. Conclusions The reliability of record review is moderate to substantial and improved when a small group of reviewers carried out record review. The validity of the record review method has never been evaluated, while clinical data registries, autopsy or direct observations of patient care are potential reference methods that can be used to test concurrent validity. PMID:27550650

  3. Model Based Testing for Agent Systems

    NASA Astrophysics Data System (ADS)

    Zhang, Zhiyong; Thangarajah, John; Padgham, Lin

    Although agent technology is gaining world wide popularity, a hindrance to its uptake is the lack of proper testing mechanisms for agent based systems. While many traditional software testing methods can be generalized to agent systems, there are many aspects that are different and which require an understanding of the underlying agent paradigm. In this paper we present certain aspects of a testing framework that we have developed for agent based systems. The testing framework is a model based approach using the design models of the Prometheus agent development methodology. In this paper we focus on model based unit testing and identify the appropriate units, present mechanisms for generating suitable test cases and for determining the order in which the units are to be tested, present a brief overview of the unit testing process and an example. Although we use the design artefacts from Prometheus the approach is suitable for any plan and event based agent system.

  4. Method and device for detecting impact events on a security barrier which includes a hollow rebar allowing insertion and removal of an optical fiber

    DOEpatents

    Pies, Ross E.

    2016-03-29

    A method and device for the detection of impact events on a security barrier. A hollow rebar is farmed within a security barrier, whereby the hollow rebar is completely surrounded by the security barrier. An optical fiber passes through the interior of the hollow rebar. An optical transmitter and an optical receiver are both optically connected to the optical fiber and connected to optical electronics. The optical electronics are configured to provide notification upon the detection of an impact event at the security barrier based on the detection of disturbances within the optical fiber.

  5. Microlensing Events by Proxima Centauri in 2014 and 2016: Opportunities for Mass Determination and Possible Planet Detection

    NASA Astrophysics Data System (ADS)

    Sahu, Kailash C.; Bond, Howard E.; Anderson, Jay; Dominik, Martin

    2014-02-01

    We have found that Proxima Centauri, the star closest to our Sun, will pass close to a pair of faint background stars in the next few years. Using Hubble Space Telescope (HST) images obtained in 2012 October, we determine that the passage close to a mag 20 star will occur in 2014 October (impact parameter 1.''6), and to a mag 19.5 star in 2016 February (impact parameter 0.''5). As Proxima passes in front of these stars, the relativistic deflection of light will cause shifts in the positions of the background stars of ~0.5 and 1.5 mas, respectively, readily detectable by HST imaging, and possibly by Gaia and ground-based facilities such as the Very Large Telescope. Measurement of these astrometric shifts offers a unique and direct method to measure the mass of Proxima. Moreover, if Proxima has a planetary system, the planets may be detectable through their additional microlensing signals, although the probability of such detections is small. With astrometric accuracies of 0.03 mas (achievable with HST spatial scanning), centroid shifts caused by Jovian planets are detectable at separations of up to 2.''0 (corresponding to 2.6 AU at the distance of Proxima), and centroid shifts by Earth-mass planets are detectable within a small band of 8 mas (corresponding to 0.01 AU) around the source trajectories. Jovian planets within a band of about 28 mas (corresponding to 0.036 AU) around the source trajectories would produce a brightening of the source by >0.01 mag and could hence be detectable. Estimated timescales of the astrometric and photometric microlensing events due to a planet range from a few hours to a few days, and both methods would provide direct measurements of the planetary mass. Based in part on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

  6. Accelerometry-based gait analysis and its application to Parkinson's disease assessment--part 1: detection of stride event.

    PubMed

    Yoneyama, Mitsuru; Kurihara, Yosuke; Watanabe, Kajiro; Mitoma, Hiroshi

    2014-05-01

    Gait analysis is widely recognized as a promising tool for obtaining objective information on the walking behavior of Parkinson's disease (PD) patients. It is especially useful in clinical practices if gait properties can be captured with minimal instrumentation that does not interfere with the subject's usual behavioral pattern under ambulatory conditions. In this study, we propose a new gait analysis system based on a trunk-mounted acceleration sensor and automatic gait detection algorithm. The algorithm identifies the acceleration signal with high intensity, periodicity, and biphasicity as a possible gait sequence, from which gait peaks due to stride events are extracted by utilizing the cross-correlation and anisotropy properties of the signal. A total of 11 healthy subjects and 12 PD patients were tested to evaluate the performance of the algorithm. The result indicates that gait peaks can be detected with an accuracy of more than 94%. The proposed method may serve as a practical component in the accelerometry-based assessment of daily gait characteristics.

  7. The Detection of a Type IIn Supernova in Optical Follow-up Observations of IceCube Neutrino Events

    NASA Astrophysics Data System (ADS)

    Aartsen, M. G.; Abraham, K.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Altmann, D.; Anderson, T.; Archinger, M.; Arguelles, C.; Arlen, T. C.; Auffenberg, J.; Bai, X.; Barwick, S. W.; Baum, V.; Bay, R.; Beatty, J. J.; Becker Tjus, J.; Becker, K.-H.; Beiser, E.; BenZvi, S.; Berghaus, P.; Berley, D.; Bernardini, E.; Bernhard, A.; Besson, D. Z.; Binder, G.; Bindig, D.; Bissok, M.; Blaufuss, E.; Blumenthal, J.; Boersma, D. J.; Bohm, C.; Börner, M.; Bos, F.; Bose, D.; Böser, S.; Botner, O.; Braun, J.; Brayeur, L.; Bretz, H.-P.; Brown, A. M.; Buzinsky, N.; Casey, J.; Casier, M.; Cheung, E.; Chirkin, D.; Christov, A.; Christy, B.; Clark, K.; Classen, L.; Coenders, S.; Cowen, D. F.; Cruz Silva, A. H.; Daughhetee, J.; Davis, J. C.; Day, M.; de André, J. P. A. M.; De Clercq, C.; Dembinski, H.; De Ridder, S.; Desiati, P.; de Vries, K. D.; de Wasseige, G.; de With, M.; DeYoung, T.; Díaz-Vélez, J. C.; Dumm, J. P.; Dunkman, M.; Eagan, R.; Eberhardt, B.; Ehrhardt, T.; Eichmann, B.; Euler, S.; Evenson, P. A.; Fadiran, O.; Fahey, S.; Fazely, A. R.; Fedynitch, A.; Feintzeig, J.; Felde, J.; Filimonov, K.; Finley, C.; Fischer-Wasels, T.; Flis, S.; Fuchs, T.; Glagla, M.; Gaisser, T. K.; Gaior, R.; Gallagher, J.; Gerhardt, L.; Ghorbani, K.; Gier, D.; Gladstone, L.; Glüsenkamp, T.; Goldschmidt, A.; Golup, G.; Gonzalez, J. G.; Góra, D.; Grant, D.; Gretskov, P.; Groh, J. C.; Gross, A.; Ha, C.; Haack, C.; Haj Ismail, A.; Hallgren, A.; Halzen, F.; Hansmann, B.; Hanson, K.; Hebecker, D.; Heereman, D.; Helbing, K.; Hellauer, R.; Hellwig, D.; Hickford, S.; Hignight, J.; Hill, G. C.; Hoffman, K. D.; Hoffmann, R.; Holzapfe, K.; Homeier, A.; Hoshina, K.; Huang, F.; Huber, M.; Huelsnitz, W.; Hulth, P. O.; Hultqvist, K.; In, S.; Ishihara, A.; Jacobi, E.; Japaridze, G. S.; Jero, K.; Jurkovic, M.; Kaminsky, B.; Kappes, A.; Karg, T.; Karle, A.; Kauer, M.; Keivani, A.; Kelley, J. L.; Kemp, J.; Kheirandish, A.; Kiryluk, J.; Kläs, J.; Klein, S. R.; Kohnen, G.; Koirala, R.; Kolanoski, H.; Konietz, R.; Koob, A.; Köpke, L.; Kopper, C.; Kopper, S.; Koskinen, D. J.; Kowalski, M.; Krings, K.; Kroll, G.; Kroll, M.; Kunnen, J.; Kurahashi, N.; Kuwabara, T.; Labare, M.; Lanfranchi, J. L.; Larson, M. J.; Lesiak-Bzdak, M.; Leuermann, M.; Leuner, J.; Lünemann, J.; Madsen, J.; Maggi, G.; Mahn, K. B. M.; Maruyama, R.; Mase, K.; Matis, H. S.; Maunu, R.; McNally, F.; Meagher, K.; Medici, M.; Meli, A.; Menne, T.; Merino, G.; Meures, T.; Miarecki, S.; Middell, E.; Middlemas, E.; Miller, J.; Mohrmann, L.; Montaruli, T.; Morse, R.; Nahnhauer, R.; Naumann, U.; Niederhausen, H.; Nowicki, S. C.; Nygren, D. R.; Obertacke, A.; Olivas, A.; Omairat, A.; O'Murchadha, A.; Palczewski, T.; Pandya, H.; Paul, L.; Pepper, J. A.; Pérez de los Heros, C.; Pfendner, C.; Pieloth, D.; Pinat, E.; Posselt, J.; Price, P. B.; Przybylski, G. T.; Pütz, J.; Quinnan, M.; Rädel, L.; Rameez, M.; Rawlins, K.; Redl, P.; Reimann, R.; Relich, M.; Resconi, E.; Rhode, W.; Richman, M.; Richter, S.; Riedel, B.; Robertson, S.; Rongen, M.; Rott, C.; Ruhe, T.; Ryckbosch, D.; Saba, S. M.; Sabbatini, L.; Sander, H.-G.; Sandrock, A.; Sandroos, J.; Sarkar, S.; Schatto, K.; Scheriau, F.; Schimp, M.; Schmidt, T.; Schmitz, M.; Schoenen, S.; Schöneberg, S.; Schönwald, A.; Schukraft, A.; Schulte, L.; Seckel, D.; Seunarine, S.; Shanidze, R.; Smith, M. W. E.; Soldin, D.; Spiczak, G. M.; Spiering, C.; Stahlberg, M.; Stamatikos, M.; Stanev, T.; Stanisha, N. A.; Stasik, A.; Stezelberger, T.; Stokstad, R. G.; Stössl, A.; Strahler, E. A.; Ström, R.; Strotjohann, N. L.; Sullivan, G. W.; Sutherland, M.; Taavola, H.; Taboada, I.; Ter-Antonyan, S.; Terliuk, A.; Tešić, G.; Tilav, S.; Toale, P. A.; Tobin, M. N.; Tosi, D.; Tselengidou, M.; Turcati, A.; Unger, E.; Usner, M.; Vallecorsa, S.; van Eijndhoven, N.; Vandenbroucke, J.; van Santen, J.; Vanheule, S.; Veenkamp, J.; Vehring, M.; Voge, M.; Vraeghe, M.; Walck, C.; Wallraff, M.; Wandkowsky, N.; Weaver, Ch.; Wendt, C.; Westerhoff, S.; Whelan, B. J.; Whitehorn, N.; Wichary, C.; Wiebe, K.; Wiebusch, C. H.; Wille, L.; Williams, D. R.; Wissing, H.; Wolf, M.; Wood, T. R.; Woschnagg, K.; Xu, D. L.; Xu, X. W.; Xu, Y.; Yanez, J. P.; Yodh, G.; Yoshida, S.; Zarzhitsky, P.; Zoll, M.; IceCube Collaboration; Ofek, Eran O.; Kasliwal, Mansi M.; Nugent, Peter E.; Arcavi, Iair; Bloom, Joshua S.; Kulkarni, Shrinivas R.; Perley, Daniel A.; Barlow, Tom; Horesh, Assaf; Gal-Yam, Avishay; Howell, D. A.; Dilday, Ben; PTF Collaboration; Evans, Phil A.; Kennea, Jamie A.; Swift Collaboration; Burgett, W. S.; Chambers, K. C.; Kaiser, N.; Waters, C.; Flewelling, H.; Tonry, J. L.; Rest, A.; Smartt, S. J.; Pan-STARRS1 Science Consortium

    2015-09-01

    The IceCube neutrino observatory pursues a follow-up program selecting interesting neutrino events in real-time and issuing alerts for electromagnetic follow-up observations. In 2012 March, the most significant neutrino alert during the first three years of operation was issued by IceCube. In the follow-up observations performed by the Palomar Transient Factory (PTF), a Type IIn supernova (SN IIn) PTF12csy was found 0.°2 away from the neutrino alert direction, with an error radius of 0.°54. It has a redshift of z = 0.0684, corresponding to a luminosity distance of about 300 Mpc and the Pan-STARRS1 survey shows that its explosion time was at least 158 days (in host galaxy rest frame) before the neutrino alert, so that a causal connection is unlikely. The a posteriori significance of the chance detection of both the neutrinos and the SN at any epoch is 2.2σ within IceCube's 2011/12 data acquisition season. Also, a complementary neutrino analysis reveals no long-term signal over the course of one year. Therefore, we consider the SN detection coincidental and the neutrinos uncorrelated to the SN. However, the SN is unusual and interesting by itself: it is luminous and energetic, bearing strong resemblance to the SN IIn 2010jl, and shows signs of interaction of the SN ejecta with a dense circumstellar medium. High-energy neutrino emission is expected in models of diffusive shock acceleration, but at a low, non-detectable level for this specific SN. In this paper, we describe the SN PTF12csy and present both the neutrino and electromagnetic data, as well as their analysis.

  8. Characterisations of adverse events detected in a university hospital: a 4-year study using the Global Trigger Tool method

    PubMed Central

    Rutberg, Hans; Borgstedt Risberg, Madeleine; Sjödahl, Rune; Nordqvist, Pernilla; Valter, Lars; Nilsson, Lena

    2014-01-01

    Objectives To describe the level, preventability and categories of adverse events (AEs) identified by medical record review using the Global Trigger Tool (GTT). To estimate when the AE occurred in the course of the hospital stay and to compare voluntary AE reporting with medical record reviewing. Design Two-stage retrospective record review. Setting 650-bed university hospital. Participants 20 randomly selected medical records were reviewed every month from 2009 to 2012. Primary and secondary outcome measures AE/1000 patient-days. Proportion of AEs found by GTT found also in the voluntary reporting system. AE categorisation. Description of when during hospital stay AEs occur. Results A total of 271 AEs were detected in the 960 medical records reviewed, corresponding to 33.2 AEs/1000 patient-days or 20.5% of the patients. Of the AEs, 6.3% were reported in the voluntary AE reporting system. Hospital-acquired infections were the most common AE category. The AEs occurred and were detected during the hospital stay in 65.5% of cases; the rest occurred or were detected within 30 days before or after the hospital stay. The AE usually occurred early during the hospital stay, and the hospital stay was 5 days longer on average for patients with an AE. Conclusions Record reviewing identified AEs to a much larger extent than voluntary AE reporting. Healthcare organisations should consider using a portfolio of tools to gain a comprehensive picture of AEs. Substantial costs could be saved if AEs were prevented. PMID:24871538

  9. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks.

    PubMed

    Acir, Nurettin; Oztura, Ibrahim; Kuntalp, Mehmet; Baklan, Bariş; Güzeliş, Cüneyt

    2005-01-01

    This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.

  10. Detection of Rain-on-Snow (ROS) Events Using the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Weather Station Observations

    NASA Astrophysics Data System (ADS)

    Ryan, E. M.; Brucker, L.; Forman, B. A.

    2015-12-01

    During the winter months, the occurrence of rain-on-snow (ROS) events can impact snow stratigraphy via generation of large scale ice crusts, e.g., on or within the snowpack. The formation of such layers significantly alters the electromagnetic response of the snowpack, which can be witnessed using space-based microwave radiometers. In addition, ROS layers can hinder the ability of wildlife to burrow in the snow for vegetation, which limits their foraging capability. A prime example occurred on 23 October 2003 in Banks Island, Canada, where an ROS event is believed to have caused the deaths of over 20,000 musk oxen. Through the use of passive microwave remote sensing, ROS events can be detected by utilizing observed brightness temperatures (Tb) from AMSR-E. Tb observed at different microwave frequencies and polarizations depends on snow properties. A wet snowpack formed from an ROS event yields a larger Tb than a typical dry snowpack would. This phenomenon makes observed Tb useful when detecting ROS events. With the use of data retrieved from AMSR-E, in conjunction with observations from ground-based weather station networks, a database of estimated ROS events over the past twelve years was generated. Using this database, changes in measured Tb following the ROS events was also observed. This study adds to the growing knowledge of ROS events and has the potential to help inform passive microwave snow water equivalent (SWE) retrievals or snow cover properties in polar regions.

  11. Efficient In Planta Detection and Dissection of De Novo Mutation Events in the Arabidopsis thaliana Disease Resistance Gene UNI.

    PubMed

    Ogawa, Tomohiko; Mori, Akiko; Igari, Kadunari; Morita, Miyo Terao; Tasaka, Masao; Uchida, Naoyuki

    2016-06-01

    Plants possess disease resistance (R) proteins encoded by R genes, and each R protein recognizes a specific pathogen factor(s) for immunity. Interestingly, a remarkably high degree of polymorphisms in R genes, which are traces of past mutation events during evolution, suggest the rapid diversification of R genes. However, little is known about molecular aspects that facilitate the rapid change of R genes because of the lack of tools that enable us to monitor de novo R gene mutations efficiently in an experimentally feasible time scale, especially in living plants. Here we introduce a model assay system that enables efficient in planta detection of de novo mutation events in the Arabidopsis thaliana R gene UNI in one generation. The uni-1D mutant harbors a gain-of-function allele of the UNI gene. uni-1D heterozygous individuals originally exhibit dwarfism with abnormally short stems. However, interestingly, morphologically normal stems sometimes emerge spontaneously from the uni-1D plants, and the morphologically reverted tissues carry additional de novo mutations in the UNI gene. Strikingly, under an extreme condition, almost half of the examined population shows the reversion phenomenon. By taking advantage of this phenomenon, we demonstrate that the reversion frequency is remarkably sensitive to a variety of fluctuations in DNA stability, underlying a mutable tendency of the UNI gene. We also reveal that activities of the salicylic acid pathway and DNA damage sensor pathway are involved in the reversion phenomenon. Thus, we provide an experimentally feasible model tool to explore factors and conditions that significantly affect the R gene mutation phenomenon. PMID:27016096

  12. Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance application

    PubMed Central

    2011-01-01

    Background The establishment of robust biosurveillance capabilities is an important component of the U.S. strategy for identifying disease outbreaks, environmental exposures and bioterrorism events. Currently, U.S. Departments of Defense (DoD) and Veterans Affairs (VA) perform biosurveillance independently. This article describes a joint VA/DoD biosurveillance project at North Chicago-VA Medical Center (NC-VAMC). The Naval Health Clinics-Great Lakes facility physically merged with NC-VAMC beginning in 2006 with the full merger completed in October 2010 at which time all DoD care and medical personnel had relocated to the expanded and remodeled NC-VAMC campus and the combined facility was renamed the Lovell Federal Health Care Center (FHCC). The goal of this study was to evaluate disease surveillance using a biosurveillance application which combined data from both populations. Methods A retrospective analysis of NC-VAMC/Lovell FHCC and other Chicago-area VAMC data was performed using the ESSENCE biosurveillance system, including one infectious disease outbreak (Salmonella/Taste of Chicago-July 2007) and one weather event (Heat Wave-July 2006). Influenza-like-illness (ILI) data from these same facilities was compared with CDC/Illinois Sentinel Provider and Cook County ESSENCE data for 2007-2008. Results Following consolidation of VA and DoD facilities in North Chicago, median number of visits more than doubled, median patient age dropped and proportion of females rose significantly in comparison with the pre-merger NC-VAMC facility. A high-level gastrointestinal alert was detected in July 2007, but only low-level alerts at other Chicago-area VAMCs. Heat-injury alerts were triggered for the merged facility in June 2006, but not at the other facilities. There was also limited evidence in these events that surveillance of the combined population provided utility above and beyond the VA-only and DoD-only components. Recorded ILI activity for NC-VAMC/Lovell FHCC was more

  13. Gaseous time projection chambers for rare event detection: results from the T-REX project. I. Double beta decay

    SciTech Connect

    Irastorza, I.G.; Aznar, F.; Castel, J. E-mail: faznar@unizar.es [Grupo de Física Nuclear y Astropartículas, Departamento de Física Teórica, Universidad de Zaragoza, C and others

    2016-01-01

    As part of the T-REX project, a number of R and D and prototyping activities have been carried out during the last years to explore the applicability of gaseous Time Projection Chambers (TPCs) with Micromesh Gas Structures (Micromegas) in rare event searches like double beta decay, axion research and low-mass WIMP searches. In both this and its companion paper, we compile the main results of the project and give an outlook of application prospects for this detection technique. While in the companion paper we focus on axions and WIMPs, in this paper we focus on the results regarding the measurement of the double beta decay (DBD) of {sup 136}Xe in a high pressure Xe (HPXe) TPC. Micromegas of the microbulk type have been extensively studied in high pressure Xe and Xe mixtures. Particularly relevant are the results obtained in Xe + trimethylamine (TMA) mixtures, showing very promising results in terms of gain, stability of operation, and energy resolution at high pressures up to 10 bar. The addition of TMA at levels of ∼ 1% reduces electron diffusion by up to a factor of 10 with respect to pure Xe, improving the quality of the topological pattern, with a positive impact on the discrimination capability. Operation with a medium size prototype of 30 cm diameter and 38 cm of drift (holding about 1 kg of Xe at 10 bar in the fiducial volume, enough to contain high energy electron tracks in the detector volume) has allowed to test the detection concept in realistic experimental conditions. Microbulk Micromegas are able to image the DBD ionization signature with high quality while, at the same time, measuring its energy deposition with a resolution of at least a ∼ 3% FWHM @ Q{sub ββ}. This value was experimentally demonstrated for high-energy extended tracks at 10 bar, and is probably improvable down to the ∼ 1% FWHM levels as extrapolated from low energy events. In addition, first results on the topological signature information (one straggling track ending in two

  14. Analysis of Inter-Moss Loops in the Solar Region with IRIS and SDO AIA: Automatic Event Detection and Characterization

    NASA Technical Reports Server (NTRS)

    Fayock, Brian; Winebarger, Amy; De Pontieu, Bart

    2014-01-01

    The Interface Region Imaging Spectrograph (IRIS), launched in the summer of 2013, is designed specifically to observe and investigate the transition region and adjacent layers of the solar atmosphere, obtaining images in high spatial, temporal, and spectral resolution. Our particular work is focused on the evolution of inter-moss loops, which have been detected in the lower corona by the Atmospheric Imaging Assembly (AIA) and the High-Resolution Coronal Imager (Hi- C), but are known to have foot points below the transition region. With the high-resolution capabilities of IRIS and its Si IV pass band, which measures activity in the upper chromosphere, we can study these magnetic loops in detail and compare their characteristic length and time scales to those obtained from several AIA image sets, particularly the 171, 193, and 211 pass bands. By comparing the results between these four data sets, one can potentially establish a measure of the ionization equilibrium for the location in question. To explore this idea, we found a large, sit-and-stare observation within the IRIS database that fit our specifications. This data set contained a number of well-defined inter-moss loops (by visual inspection) with a cadence less than or equal to that of AIA (approximately 12 seconds). This particular data set was recorded on October 23, 2013 at 07:09:30, lasting for 3219 seconds with a field of view of 120.6 by 128.1 arcseconds, centered on -53.9 by 59.1 arcseconds from disk center. For ease of comparison, the AIA data has been interpolated to match the IRIS cadence and resolution. In the main portion of the poster, we demonstrate the detection of events, the information collected, and the immediate results to the right, showing the progress of an event with green as the start, blue as the peak, and red as the end. Below here, we demonstrate how pixels are combined to form groups. The 3D results are shown to the right.

  15. Analysis of Inter-Moss Loops in the Solar Region with IRIS and SDO AIA: Automatic Event Detection and Characterization

    NASA Technical Reports Server (NTRS)

    Fayock, Brian; Winebarger, Amy; De Pontieu, Bart; Alexander, Caroline

    2016-01-01

    The Interface Region Imaging Spectrograph (IRIS), launched in the summer of 2013, is designed specifically to observe and investigate the transition region and adjacent layers of the solar atmosphere, obtaining images in high spatial, temporal, and spectral resolution. Our particular work is focused on the evolution of inter-moss loops, which have been detected in the lower corona by the Atmospheric Imaging Assembly (AIA) and the High-Resolution Coronal Imager (Hi- C), but are known to have foot points below the transition region. With the high-resolution capabilities of IRIS and its Si IV pass band, which measures activity in the upper chromosphere, we can study these magnetic loops in detail and compare their characteristic length and time scales to those obtained from several AIA image sets, particularly the 171, 193, and 211 pass bands. By comparing the results between these four data sets, one can potentially establish a measure of the ionization equilibrium for the location in question. To explore this idea, we found a large, sit-and-stare observation within the IRIS database that fit our specifications. This data set contained a number of well-defined inter-moss loops (by visual inspection) with a cadence less than or equal to that of AIA (approximately 12 seconds). This particular data set was recorded on October 23, 2013 at 07:09:30, lasting for 3219 seconds with a field of view of 120.6 by 128.1 arcseconds, centered on -53.9 by 59.1 arcseconds from disk center. For ease of comparison, the AIA data has been interpolated to match the IRIS cadence and resolution. In the main portion of the poster, we demonstrate the detection of events, the information collected, and the immediate results to the right, showing the progress of an event with green as the start, blue as the peak, and red as the end. Below here, we demonstrate how pixels are combined to form groups. The 3D results are shown to the right

  16. A multi-station matched filter and coherent network processing approach to the automatic detection and relative location of seismic events

    NASA Astrophysics Data System (ADS)

    Gibbons, Steven J.; Näsholm, Sven Peter; Kværna, Tormod

    2014-05-01

    Correlation detectors facilitate seismic monitoring in the near vicinity of previously observed events at far lower detection thresholds than are possible using the methods applied in most existing processing pipelines. The use of seismic arrays has been demonstrated to be highly beneficial in pressing down the detection threshold, due to superior noise suppression, and also in eliminating vast numbers of false alarms by performing array processing on the multi-channel output of the correlation detectors. This last property means that it is highly desirable to run continuous detectors for sites of repeating seismic events on a single-array basis for many arrays across a global network. Spurious detections for a given signal template on a single array can however still occur when an unrelated wavefront crosses the array from a very similar direction to that of the master event wavefront. We present an algorithm which scans automatically the output from multiple stations - both array and 3-component - for coherence between the individual station correlator outputs that is consistent with a disturbance in the vicinity of the master event. The procedure results in a categorical rejection of an event hypothesis in the absence of support from stations other than the one generating the trigger and provides a fully automatic relative event location estimate when patterns in the correlation detector outputs are found to be consistent with a common event. This coherence-based approach removes the need to make explicit measurements of the time-differences for single stations and this eliminates a potential source of error. The method is demonstrated for the North Korea nuclear test site and the relative event location estimates obtained for the 2006, 2009, and 2013 events are compared with previous estimates from different station configurations.

  17. Detecting pop-out targets in contexts of varying homogeneity: investigating homogeneity coding with event-related brain potentials (ERPs).

    PubMed

    Schubö, Anna; Wykowska, Agnieszka; Müller, Hermann J

    2007-03-23

    Searching for a target among many distracting context elements might be an easy or a demanding task. Duncan and Humphreys (Duncan, J., Humphreys, G.W., 1989. Visual search and stimulus similarity. Psychol. Rev. 96, 433-458) showed that not only the target itself plays a role in the difficulty of target detection. Similarity among context elements and dissimilarity of target and context are two main factors also affecting search efficiency. Moreover, many studies have shown that search becomes particularly efficient with large set sizes and perfectly homogeneous context elements, presumably due to grouping processes involved in target-context segmentation. Especially N2p amplitude has been found to be modulated by the number of context elements and their homogeneity. The aim of the present study was to investigate the influence of context elements of different heterogeneities on search performance using event-related brain potentials (ERPs). Results showed that contexts with perfectly homogeneous elements were indeed special: they were most efficient in visual search and elicited a large N2p differential amplitude effect. Increasing context heterogeneity led to a decrease in search performance and a reduction in N2p differential amplitude. Reducing the number of context elements led to a marked performance decrease for random heterogeneous contexts but not for grouped heterogeneous contexts. Behavioral and N2p results delivered evidence (a) in favor of specific processing modes operating on different spatial scales (b) for the existence of homogeneity coding postulated by Duncan and Humphreys.

  18. Collaborative trial for the validation of event-specific PCR detection methods of genetically modified papaya Huanong No.1.

    PubMed

    Wei, Jiaojun; Le, Huangying; Pan, Aihu; Xu, Junfeng; Li, Feiwu; Li, Xiang; Quan, Sheng; Guo, Jinchao; Yang, Litao

    2016-03-01

    For transferring the event-specific PCR methods of genetically modified papaya Huanong No.1 to other laboratories, we validated the previous developed PCR assays of Huanong No.1 according to the international standard organization (ISO) guidelines. A total of 11 laboratories participated and returned their test results in this trial. In qualitative PCR assay, the high specificity and limit of detection as low as 0.1% was confirmed. For the quantitative PCR assay, the limit of quantification was as low as 25 copies. The quantitative biases among ten blind samples were within the range between 0.21% and 10.04%. Furthermore, the measurement uncertainty of the quantitative PCR results was calculated within the range between 0.28% and 2.92% for these ten samples. All results demonstrated that the Huanong No.1 qualitative and quantitative PCR assays were creditable and applicable for identification and quantification of GM papaya Huanong No.1 in further routine lab analysis. PMID:26471522

  19. A measurement of the muon number in showers using inclined events detected at the Pierre Auger Observatory

    NASA Astrophysics Data System (ADS)

    Rodriguez, G.

    2013-06-01

    The average muon content of measured showers with zenith angles between 62∘ and 80∘ detected at the Pierre Auger Observatory is obtained as a function of shower energy using a reconstruction method specifically designed for inclined showers and the hybrid character of the detector. The reconstruction of inclined showers relies on a comparison between the measured signals at ground and reference patterns at ground level from which an overall normalization factor is obtained. Since inclined showers are dominated by muons this factor gives the relative muon size. It can be calibrated using a subsample of showers simultaneously recorded with the fluorescence detector (FD) and the surface detector (SD) which provides an independent calorimetric measurement of the energy. The muon size obtained for each shower becomes a measurement of the relative number of muons with respect to the reference distributions. The precision of the measurement is assessed using simulated events which are reconstructed using exactly the same procedure. We compare the relative number of muons versus energy as obtained to simulations. Proton simulations with QGSJETII show a factor of 2.13 ± 0.04(stat) ± 0.11(sys) at 1019eV without significant variations in the energy range explored between 4 × 1018eV to 7 × 1019eV. We find that none of the current shower models, neither for proton nor for iron primaries, are able to predict as many muons as are observed.

  20. 1.3 mm WAVELENGTH VLBI OF SAGITTARIUS A*: DETECTION OF TIME-VARIABLE EMISSION ON EVENT HORIZON SCALES

    SciTech Connect

    Fish, Vincent L.; Doeleman, Sheperd S.; Beaudoin, Christopher; Bolin, David E.; Rogers, Alan E. E.; Blundell, Ray; Gurwell, Mark A.; Moran, James M.; Primiani, Rurik; Bower, Geoffrey C.; Plambeck, Richard; Chamberlin, Richard; Freund, Robert; Friberg, Per; Honma, Mareki; Oyama, Tomoaki; Inoue, Makoto; Krichbaum, Thomas P.; Lamb, James; Marrone, Daniel P.

    2011-02-01

    Sagittarius A*, the {approx}4 x 10{sup 6} M{sub sun} black hole candidate at the Galactic center, can be studied on Schwarzschild radius scales with (sub)millimeter wavelength very long baseline interferometry (VLBI). We report on 1.3 mm wavelength observations of Sgr A* using a VLBI array consisting of the JCMT on Mauna Kea, the Arizona Radio Observatory's Submillimeter Telescope on Mt. Graham in Arizona, and two telescopes of the CARMA array at Cedar Flat in California. Both Sgr A* and the quasar calibrator 1924-292 were observed over three consecutive nights, and both sources were clearly detected on all baselines. For the first time, we are able to extract 1.3 mm VLBI interferometer phase information on Sgr A* through measurement of closure phase on the triangle of baselines. On the third night of observing, the correlated flux density of Sgr A* on all VLBI baselines increased relative to the first two nights, providing strong evidence for time-variable change on scales of a few Schwarzschild radii. These results suggest that future VLBI observations with greater sensitivity and additional baselines will play a valuable role in determining the structure of emission near the event horizon of Sgr A*.

  1. Detection of the brain response during a cognitive task using perfusion-based event-related functional MRI.

    PubMed

    Yee, S H; Liu, H L; Hou, J; Pu, Y; Fox, P T; Gao, J H

    2000-08-01

    Event-related (ER) fMRI has evoked great interest due to the ability to depict the dynamic features of human brain function during various cognitive tasks. Thus far, all cognitive ER-fMRI studies have been based on blood oxygenation level-dependent (BOLD) contrast techniques. Compared with BOLD-based fMRI techniques, perfusion-based fMRI is able to localize the region of neuronal activity more accurately. This report demonstrates, for the first time, the detection of the brain response to a cognitive task using high temporal resolution perfusion-based ER-fMRI. An English verb generation task was used in this study. Results show that perfusion-based ER-fMRI accurately depicts the activation in Broca's area. Average changes in regional relative cerebral blood flow reached a maximum value of 30.7% at approximately 6.5 s after the start of stimulation and returned to 10% of the maximum value at approximately 12.8 s. Our results show that perfusion-based ER-fMRI is a useful tool for cognitive neuroscience studies, providing comparable temporal resolution and better localization of brain function than BOLD ER-fMRI. PMID:10943717

  2. PREFACE: 5th Symposium on Large TPCs for Low Energy Rare Event Detection and Workshop on Neutrinos from Supernovae

    NASA Astrophysics Data System (ADS)

    Irastorza, Igor G.; Scholberg, Kate; Colas, Paul; Giomataris, Ioannis

    2011-08-01

    The Fifth International Symposium on large TPCs for low-energy rare-event detection was held at the auditorium of the Astroparticle and Cosmology (APC) Laboratory in Paris, on 14-17 December 2010. As for all previous meetings, always held in Paris in 2008, 2006, 2004 and 2002, it brought together a significant community of physicists involved in rare event searches and/or development of time projection chambers (TPCs). As a novelty this year, the meeting was extended with two half-day sessions on Supernova physics. These proceedings also include the contributions corresponding to the supernova sessions. The purpose of the meeting was to present and discuss the status of current experiments or projects involving the use of TPCs to search for rare events, like low-energy neutrinos, double beta decay, dark matter or axion experiments, as well as to discuss new results and ideas in the framework of the last developments of Micro Pattern Gaseous Detectors (MPGD), and how these are being - or could be - applied to these searches. As in previous meetings in this series, the format included an informal program with some recent highlighted results, rather than exhaustive reviews, with time for discussion and interaction. The symposium, the fifth of the series, is becoming consolidated as a regular meeting place for the synergic interplay between the fields of rare events and TPC development. The meeting started with a moving tribute by Ioannis Giomataris to the memory of George Charpak, who recently passed away. We then moved on to the usual topics like the status of some low-energy neutrino physics and double beta decay experiments, dark matter experiments with directional detectors, axion searches, or development results. A relevant subject this time was the electroluminescence in Xe TPCs, covered by several speakers. Every time the conference program is enriched with original slightly off-topic contributions that trigger the curiosity and stimulate further thought. As

  3. Multiplex polymerase chain reaction-capillary gel electrophoresis: a promising tool for GMO screening--assay for simultaneous detection of five genetically modified cotton events and species.

    PubMed

    Nadal, Anna; Esteve, Teresa; Pla, Maria

    2009-01-01

    A multiplex polymerase chain reaction assay coupled to capillary gel electrophoresis for amplicon identification by size and color (multiplex PCR-CGE-SC) was developed for simultaneous detection of cotton species and 5 events of genetically modified (GM) cotton. Validated real-time-PCR reactions targeting Bollgard, Bollgard II, Roundup Ready, 3006-210-23, and 281-24-236 junction sequences, and the cotton reference gene acp1 were adapted to detect more than half of the European Union-approved individual or stacked GM cotton events in one reaction. The assay was fully specific (<1.7% of false classification rate), with limit of detection values of 0.1% for each event, which were also achieved with simulated mixtures at different relative percentages of targets. The assay was further combined with a second multiplex PCR-CGE-SC assay to allow simultaneous detection of 6 cotton and 5 maize targets (two endogenous genes and 9 GM events) in two multiplex PCRs and a single CGE, making the approach more economic. Besides allowing simultaneous detection of many targets with adequate specificity and sensitivity, the multiplex PCR-CGE-SC approach has high throughput and automation capabilities, while keeping a very simple protocol, e.g., amplification and labeling in one step. Thus, it is an easy and inexpensive tool for initial screening, to be complemented with quantitative assays if necessary.

  4. Multiplex polymerase chain reaction-capillary gel electrophoresis: a promising tool for GMO screening--assay for simultaneous detection of five genetically modified cotton events and species.

    PubMed

    Nadal, Anna; Esteve, Teresa; Pla, Maria

    2009-01-01

    A multiplex polymerase chain reaction assay coupled to capillary gel electrophoresis for amplicon identification by size and color (multiplex PCR-CGE-SC) was developed for simultaneous detection of cotton species and 5 events of genetically modified (GM) cotton. Validated real-time-PCR reactions targeting Bollgard, Bollgard II, Roundup Ready, 3006-210-23, and 281-24-236 junction sequences, and the cotton reference gene acp1 were adapted to detect more than half of the European Union-approved individual or stacked GM cotton events in one reaction. The assay was fully specific (<1.7% of false classification rate), with limit of detection values of 0.1% for each event, which were also achieved with simulated mixtures at different relative percentages of targets. The assay was further combined with a second multiplex PCR-CGE-SC assay to allow simultaneous detection of 6 cotton and 5 maize targets (two endogenous genes and 9 GM events) in two multiplex PCRs and a single CGE, making the approach more economic. Besides allowing simultaneous detection of many targets with adequate specificity and sensitivity, the multiplex PCR-CGE-SC approach has high throughput and automation capabilities, while keeping a very simple protocol, e.g., amplification and labeling in one step. Thus, it is an easy and inexpensive tool for initial screening, to be complemented with quantitative assays if necessary. PMID:19610365

  5. Comparison of drought events detected by SPI calculated from different historical precipitation data sets - case study from Southern Alps

    NASA Astrophysics Data System (ADS)

    Brencic, M.; Hictaler, J.

    2012-04-01

    During recent years substantial efforts were directed toward the reconstruction of past meteorological data sets of precipitation, air temperature, air pressure and sunshine. In Alpine space of Europe long tradition of meteorological data monitoring exist starting with the first modern measurements in late 18th century. However, older data were obtained under very different conditions, standards and quality. Consequently direct comparison between data sets of different observation points is not possible. Several methods defined as data homogenisation procedures were developed intended to enable comparison of data from different observation points and sources. In spite of the fact that homogenisation procedures are scientifically agreed final result represented as homogenised data series depends on the ability and approach of the interpreters. Well know data set from the Greater Alpine region based on the common homogenisation procedure is HISTALP data series. However, HISTALP data set is not the only available homogenised data set in the region. Local agencies responsible for meteorological observations (e.g. in Slovenia Environmental Agency of Slovenia - ARSO) perform their own homogenisation procedures. Because more detailed information about measuring procedures and locations for the particular stations is available for them one can expect differences between homogenised data sets. Longer meteorological data sets can be used to detect past drought events of various magnitudes. They can help to discern past droughts and their characteristics. A very frequently used meteorological drought index is standardized precipitation index - SPI. The nature of SPI is designed to detect events of low frequency. With the help of this index periods of extremely low precipitation can be defined. It is usually based on monthly amount of precipitation where cumulative precipitation amount for the particular time period is calculated. During the calculation of SPI with a time

  6. Final report for LDRD project 11-0029 : high-interest event detection in large-scale multi-modal data sets : proof of concept.

    SciTech Connect

    Rohrer, Brandon Robinson

    2011-09-01

    Events of interest to data analysts are sometimes difficult to characterize in detail. Rather, they consist of anomalies, events that are unpredicted, unusual, or otherwise incongruent. The purpose of this LDRD was to test the hypothesis that a biologically-inspired anomaly detection algorithm could be used to detect contextual, multi-modal anomalies. There currently is no other solution to this problem, but the existence of a solution would have a great national security impact. The technical focus of this research was the application of a brain-emulating cognition and control architecture (BECCA) to the problem of anomaly detection. One aspect of BECCA in particular was discovered to be critical to improved anomaly detection capabilities: it's feature creator. During the course of this project the feature creator was developed and tested against multiple data types. Development direction was drawn from psychological and neurophysiological measurements. Major technical achievements include the creation of hierarchical feature sets created from both audio and imagery data.

  7. Detection of atrial high-rate events by continuous Home Monitoring: clinical significance in the heart failure–cardiac resynchronization therapy population

    PubMed Central

    Shanmugam, Nesan; Boerdlein, Annegret; Proff, Jochen; Ong, Peter; Valencia, Oswaldo; Maier, Sebastian K.G.; Bauer, Wolfgang R.; Paul, Vince; Sack, Stefan

    2012-01-01

    Aims Uncertainty exists over the importance of device-detected short-duration atrial arrhythmias. Continuous atrial diagnostics, through home monitoring (HM) technology (BIOTRONIK, Berlin, Germany), provides a unique opportunity to assess frequency and quantity of atrial fibrillation (AF) episodes defined as atrial high-rate events (AHRE). Methods and results Prospective data from 560 heart failure (HF) patients (age 67 ± 10 years, median ejection fraction 27%) patients with a cardiac resynchronization therapy (CRT) device capable of HM from two multi-centre studies were analysed. Atrial high-rate events burden was defined as the duration of mode switch in a 24-h period with atrial rates of >180 beats for at least 1% or total of 14 min per day. The primary endpoint was incidence of a thromboembolic (TE) event. Secondary endpoints were cardiovascular death, hospitalization because of AF, or worsening HF. Over a median 370-day follow-up AHRE occurred in 40% of patients with 11 (2%) patients developing TE complications and mortality rate of 4.3% (24 deaths, 16 with cardiovascular aetiology). Compared with patients without detected AHRE, patients with detected AHRE>3.8 h over a day were nine times more likely to develop TE complications (P= 0.006). The majority of patients (73%) did not show a temporal association with the detected atrial episode and their adverse event, with a mean interval of 46.7 ± 71.9 days (range 0–194) before the TE complication. Conclusion In a high-risk cohort of HF patients, device-detected atrial arrhythmias are associated with an increased incidence of TE events. A cut-off point of 3.8 h over 24 h was associated with significant increase in the event rate. Routine assessment of AHRE should be considered with other data when assessing stroke risk and considering anti-coagulation initiation and should also prompt the optimization of cardioprotective HF therapy in CRT patients. PMID:21933802

  8. A novel “correlated ion and neutral time of flight” method: Event-by-event detection of neutral and charged fragments in collision induced dissociation of mass selected ions

    SciTech Connect

    Teyssier, C.; Fillol, R.; Abdoul-Carime, H.; Farizon, B.; Farizon, M.

    2014-01-15

    A new tandem mass spectrometry (MS/MS) method based on time of flight measurements performed on an event-by-event detection technique is presented. This “correlated ion and neutral time of flight” method allows to explore Collision Induced Dissociation (CID) fragmentation processes by directly identifying not only all ions and neutral fragments produced but also their arrival time correlations within each single fragmentation event from a dissociating molecular ion. This constitutes a new step in the characterization of molecular ions. The method will be illustrated here for a prototypical case involving CID of protonated water clusters H{sup +}(H{sub 2}O){sub n=1–5} upon collisions with argon atoms.

  9. Detection of Healthcare-Related Extended-Spectrum Beta-Lactamase-Producing Escherichia coli Transmission Events Using Combined Genetic and Phenotypic Epidemiology

    PubMed Central

    Boers, Stefan A.; Jansen, Ruud; Hays, John P.; Goessens, Wil H. F.; Vos, Margreet C.

    2016-01-01

    Background Since the year 2000 there has been a sharp increase in the prevalence of healthcare-related infections caused by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli. However, the high community prevalence of ESBL-producing E. coli isolates means that many E. coli typing techniques may not be suitable for detecting E. coli transmission events. Therefore, we investigated if High-throughput MultiLocus Sequence Typing (HiMLST) and/or Raman spectroscopy were suitable techniques for detecting recent E. coli transmission events. Methods This study was conducted from January until December 2010 at Erasmus University Medical Center, Rotterdam, the Netherlands. Isolates were typed using HiMLST and Raman spectroscopy. A genetic cluster was defined as two or more patients carrying identical isolates. We used predefined definitions for epidemiological relatedness to assess healthcare-related transmission. Results We included 194 patients; strains of 112 patients were typed using HiMLST and strains of 194 patients were typed using Raman spectroscopy. Raman spectroscopy identified 16 clusters while HiMLST identified 10 clusters. However, no healthcare-related transmission events were detected. When combining data from both typing techniques, we identified eight clusters (n = 34 patients), as well as 78 patients with a non-cluster isolate. However, we could not detect any healthcare-related transmission in these 8 clusters. Conclusions Although clusters were genetically detected using HiMLST and Raman spectroscopy, no definite epidemiological relationships could be demonstrated which makes the possibility of healthcare-related transmission events highly unlikely. Our results suggest that typing of ESBL-producing E. coli using HiMLST and/or Raman spectroscopy is not helpful in detecting E. coli healthcare-related transmission events. PMID:27463231

  10. Development of multiplex PCR method for simultaneous detection of four events of genetically modified maize: DAS-59122-7, MIR604, MON863 and MON88017.

    PubMed

    Oguchi, Taichi; Onishi, Mari; Mano, Junichi; Akiyama, Hiroshi; Teshima, Reiko; Futo, Satoshi; Furui, Satoshi; Kitta, Kazumi

    2010-01-01

    A novel multiplex PCR method was developed for simultaneous event-specific detection of four events of GM maize, i.e., DAS-59122-7, MIR604, MON88017, and MON863. The single laboratory examination of analytical performance using simulated DNA mixtures containing GM DNA at various concentrations in non-GM DNA suggested that the limits of detection (LOD) of the multiplex PCR method were 0.16% for MON863, MIR604, and MON88017, and 0.078% for DAS-59122-7. We previously developed a nonaplex (9plex) PCR method for eight events of GM maize, i.e., Bt11, Bt176, GA21, MON810, MON863, NK603, T25, and TC1507. Together with the nonaplex PCR method, the newly developed method enabled the detection and identification of eleven GM maize events that are frequently included in commercial GM seed used in Japan. In addition, this combinational analysis may be useful for the identification of combined event products of GM maize. PMID:20595789

  11. Development of multiplex PCR method for simultaneous detection of four events of genetically modified maize: DAS-59122-7, MIR604, MON863 and MON88017.

    PubMed

    Oguchi, Taichi; Onishi, Mari; Mano, Junichi; Akiyama, Hiroshi; Teshima, Reiko; Futo, Satoshi; Furui, Satoshi; Kitta, Kazumi

    2010-01-01

    A novel multiplex PCR method was developed for simultaneous event-specific detection of four events of GM maize, i.e., DAS-59122-7, MIR604, MON88017, and MON863. The single laboratory examination of analytical performance using simulated DNA mixtures containing GM DNA at various concentrations in non-GM DNA suggested that the limits of detection (LOD) of the multiplex PCR method were 0.16% for MON863, MIR604, and MON88017, and 0.078% for DAS-59122-7. We previously developed a nonaplex (9plex) PCR method for eight events of GM maize, i.e., Bt11, Bt176, GA21, MON810, MON863, NK603, T25, and TC1507. Together with the nonaplex PCR method, the newly developed method enabled the detection and identification of eleven GM maize events that are frequently included in commercial GM seed used in Japan. In addition, this combinational analysis may be useful for the identification of combined event products of GM maize.

  12. Concordance of nuclear and mitochondrial DNA markers in detecting a founder event in Lake Clark sockeye salmon

    USGS Publications Warehouse

    Ramstad, Kristina M.; Woody, Carol Ann; Habicht, Chris; Sage, G. Kevin; Seeb, James E.; Allendorf, Fred W.

    2007-01-01

    Genetic bottleneck effects can reduce genetic variation, persistence probability, and evolutionary potential of populations. Previous microsatellite analysis suggested a bottleneck associated with a common founding of sock-eye salmon Oncorhynchus nerka populations of Lake Clark, Alaska, about 100 to 400 generations ago. The common foundingevent occurred after the last glacial recession and resulted in reduced allelic diversity and strong divergence of Lake Clarksockeye salmon relative to neighboring Six Mile Lake and LakeIliamna populations. Here we used two additional genetic marker types (allozymes and mtDNA) to examine these patterns further. Allozyme and mtDNA results were congruent with the microsatellite data in suggesting a common founder event in LakeClark sockeye salmon and confirmed the divergence of Lake Clarkpopulations from neighboring Six Mile Lake and Lake Iliamna populations. The use of multiple marker types provided better understanding of the bottleneck in Lake Clark. For example, the Sucker Bay Lake population had an exceptionally severe reduction in allelic diversity at microsatellite loci, but not at mtDNA. This suggests that the reduced microsatellite variation in Sucker Bay Lake fish is due to consistently smaller effective population size than other Lake Clark populations, rather than a more acute or additional bottleneck since founding. Caution is urged in using reduced heterozygosity as a measure of genetic bottleneck effects because stochastic variance among loci resulted in an overall increase in allozyme heterozygosity within bottlenecked Lake Clark populations. However, heterozygosity excess, which assesses heterozygosity relative to allelic variation, detected genetic bottleneck effects in both allozyme and microsatellite loci. 

  13. Line Identifications of Type I Supernovae: On the Detection of Si II for These Hydrogen-poor Events

    NASA Astrophysics Data System (ADS)

    Parrent, J. T.; Milisavljevic, D.; Soderberg, A. M.; Parthasarathy, M.

    2016-03-01

    Here we revisit line identifications of type I supernovae (SNe I) and highlight trace amounts of unburned hydrogen as an important free parameter for the composition of the progenitor. Most one-dimensional stripped-envelope models of supernovae indicate that observed features near 6000-6400 Å in type I spectra are due to more than Si ii λ6355. However, while an interpretation of conspicuous Si ii λ6355 can approximate 6150 Å absorption features for all SNe Ia during the first month of free expansion, similar identifications applied to 6250 Å features of SNe Ib and Ic have not been as successful. When the corresponding synthetic spectra are compared with high-quality timeseries observations, the computed spectra are frequently too blue in wavelength. Some improvement can be achieved with Fe ii lines that contribute redward of 6150 Å however, the computed spectra either remain too blue or the spectrum only reaches a fair agreement when the rise-time to peak brightness of the model conflicts with observations by a factor of two. This degree of disagreement brings into question the proposed explosion scenario. Similarly, a detection of strong Si ii λ6355 in the spectra of broadlined Ic and super-luminous events of type I/R is less convincing despite numerous model spectra used to show otherwise. Alternatively, we suggest 6000-6400 Å features are possibly influenced by either trace amounts of hydrogen or blueshifted absorption and emission in Hα, the latter being an effect which is frequently observed in the spectra of hydrogen-rich, SNe II.

  14. Quantification and identification of genetically modified maize events in non-identity preserved maize samples in 2009 using an individual kernel detection system.

    PubMed

    Akiyama, Hiroshi; Minegishi, Yasutaka; Makiyama, Daiki; Mano, Junichi; Sakata, Kozue; Nakamura, Kosuke; Noguchi, Akio; Takabatake, Reona; Futo, Satoshi; Kondo, Kazunari; Kitta, Kazumi; Kato, Yasuo; Teshima, Reiko

    2012-01-01

    We investigated the GM maize grain content of non-identity preserved (non-IP) maize samples produced in 2009 in the USA using our individual kernel detection system, involving two multiplex qualitative PCR methods coupled to microchip electrophoresis and partially real-time PCR array analysis, to clarify how many GM event maize grains were present in the samples and which GM events frequently appeared in 2009. The average percentage and standard deviation of GM maize grains on a kernel basis in five non-IP sample lots were 81.9%±2.8%, the average percentage of single GM event grains was 46.9%, and the average percentage of stacked GM event grains was 35.0%. MON88017 grains and NK603 grains were the most frequently observed as single GM event grains. The most frequent stacked GM event grains were MON88017×MON810 grains. This study shows that our method can provide information about GM maize events present in imported maize samples on a kernel basis. PMID:23132354

  15. A novel quadruplex real-time PCR method for simultaneous detection of Cry2Ae and two genetically modified cotton events (GHB119 and T304-40)

    PubMed Central

    2014-01-01

    Background To date, over 150 genetically modified (GM) crops are widely cultivated. To comply with regulations developed for genetically modified organisms (GMOs), including labeling policies, many detection methods for GMO identification and quantification have been developed. Results To detect the entrance and exit of unauthorized GM crop events in China, we developed a novel quadruplex real-time PCR method for simultaneous detection and quantification of GM cotton events GHB119 and T304-40 in cotton-derived products (based on the 5′-flanking sequence) and the insect-resistance gene Cry2Ae. The limit of detection was 10 copies for GHB119 and Cry2Ae and 25 copies for T304-40. The limit of quantification was 25 copies for GHB119 and Cry2Ae and 50 copies for T304-40. Moreover, low bias and acceptable standard deviation and relative standard deviation values were obtained in quantification analysis of six blind samples containing different GHB119 and T304-40 ingredients. Conclusions The developed quadruplex quantitative method could be used for quantitative detection of two GM cotton events (GHB119 and T304-40) and Cry2Ae gene ingredient in cotton derived products. PMID:24884946

  16. The chemically homogeneous evolutionary channel for binary black hole mergers: rates and properties of gravitational-wave events detectable by advanced LIGO

    NASA Astrophysics Data System (ADS)

    de Mink, S. E.; Mandel, I.

    2016-08-01

    We explore the predictions for detectable gravitational-wave signals from merging binary black holes formed through chemically homogeneous evolution in massive short-period stellar binaries. We find that ˜500 events per year could be detected with advanced ground-based detectors operating at full sensitivity. We analyse the distribution of detectable events, and conclude that there is a very strong preference for detecting events with nearly equal components (mass ratio >0.66 at 90 per cent confidence in our default model) and high masses (total source-frame mass between 57 and 103 M⊙ at 90 per cent confidence). We consider multiple alternative variations to analyse the sensitivity to uncertainties in the evolutionary physics and cosmological parameters, and conclude that while the rates are sensitive to assumed variations, the mass distributions are robust predictions. Finally, we consider the recently reported results of the analysis of the first 16 double-coincident days of the O1 LIGO (Laser Interferometer Gravitational-wave Observatory) observing run, and find that this formation channel is fully consistent with the inferred parameters of the GW150914 binary black hole detection and the inferred merger rate.

  17. Event specific qualitative and quantitative polymerase chain reaction detection of genetically modified MON863 maize based on the 5'-transgene integration sequence.

    PubMed

    Yang, Litao; Xu, Songci; Pan, Aihu; Yin, Changsong; Zhang, Kewei; Wang, Zhenying; Zhou, Zhigang; Zhang, Dabing

    2005-11-30

    Because of the genetically modified organisms (GMOs) labeling policies issued in many countries and areas, polymerase chain reaction (PCR) methods were developed for the execution of GMO labeling policies, such as screening, gene specific, construct specific, and event specific PCR detection methods, which have become a mainstay of GMOs detection. The event specific PCR detection method is the primary trend in GMOs detection because of its high specificity based on the flanking sequence of the exogenous integrant. This genetically modified maize, MON863, contains a Cry3Bb1 coding sequence that produces a protein with enhanced insecticidal activity against the coleopteran pest, corn rootworm. In this study, the 5'-integration junction sequence between the host plant DNA and the integrated gene construct of the genetically modified maize MON863 was revealed by means of thermal asymmetric interlaced-PCR, and the specific PCR primers and TaqMan probe were designed based upon the revealed 5'-integration junction sequence; the conventional qualitative PCR and quantitative TaqMan real-time PCR detection methods employing these primers and probes were successfully developed. In conventional qualitative PCR assay, the limit of detection (LOD) was 0.1% for MON863 in 100 ng of maize genomic DNA for one reaction. In the quantitative TaqMan real-time PCR assay, the LOD and the limit of quantification were eight and 80 haploid genome copies, respectively. In addition, three mixed maize samples with known MON863 contents were detected using the established real-time PCR systems, and the ideal results indicated that the established event specific real-time PCR detection systems were reliable, sensitive, and accurate.

  18. Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children.

    PubMed

    Garde, Ainara; Dehkordi, Parastoo; Wensley, David; Ansermino, J Mark; Dumont, Guy A

    2015-08-01

    Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home. PMID:26738074

  19. Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children.

    PubMed

    Garde, Ainara; Dehkordi, Parastoo; Wensley, David; Ansermino, J Mark; Dumont, Guy A

    2015-01-01

    Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home.

  20. On the feasibility of detecting the ionospheric effects of solar energetic particle events at Mars using spacecraft-spacecraft radio links

    NASA Astrophysics Data System (ADS)

    Withers, Paul

    2016-04-01

    Indirect evidence and theoretical modeling suggests that the effects of solar energetic particle (SEP) events on the ionosphere of Mars are substantial, but observations have not yet provided quantitative information on the magnitude or vertical distribution of the plasma produced below 100 km by SEP events. Strong radio wave absorption is anticipated during a SEP event due to the production of plasma at low altitudes where the neutral atmosphere is relatively dense. Here we test the feasibility of measuring the ionospheric effects of SEP events using power losses in spacecraft-spacecraft UHF radio links. Both lander-orbiter and orbiter-orbiter cases are considered for the UHF radio frequency of 400 MHz. A large SEP event should cause an ionospheric power loss at 400 MHz of 1.5 dB in lander-orbiter communications and, due to the longer path length, a larger power loss of 35 dB in orbiter-orbiter communications. Multiple SEP events occur each year that can cause a lander-orbiter power loss of 0.1 dB, which is shown to be theoretically detectable by current instrumentation, and an orbiter-orbiter power loss of 2 dB. The vertical profile of electron density at low altitudes can be determined from orbiter-orbiter power losses.

  1. Model-Based Safety Analysis

    NASA Technical Reports Server (NTRS)

    Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.

    2006-01-01

    System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

  2. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  3. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine