Science.gov

Sample records for sequential anomaly detection

  1. An anomaly detection and isolation scheme with instance-based learning and sequential analysis

    SciTech Connect

    Yoo, T. S.; Garcia, H. E.

    2006-07-01

    This paper presents an online anomaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance. (authors)

  2. Anomaly Detection in Dynamic Networks

    SciTech Connect

    Turcotte, Melissa

    2014-10-14

    Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

  3. Automated anomaly detection processor

    NASA Astrophysics Data System (ADS)

    Kraiman, James B.; Arouh, Scott L.; Webb, Michael L.

    2002-07-01

    Robust exploitation of tracking and surveillance data will provide an early warning and cueing capability for military and civilian Law Enforcement Agency operations. This will improve dynamic tasking of limited resources and hence operational efficiency. The challenge is to rapidly identify threat activity within a huge background of noncombatant traffic. We discuss development of an Automated Anomaly Detection Processor (AADP) that exploits multi-INT, multi-sensor tracking and surveillance data to rapidly identify and characterize events and/or objects of military interest, without requiring operators to specify threat behaviors or templates. The AADP has successfully detected an anomaly in traffic patterns in Los Angeles, analyzed ship track data collected during a Fleet Battle Experiment to detect simulated mine laying behavior amongst maritime noncombatants, and is currently under development for surface vessel tracking within the Coast Guard's Vessel Traffic Service to support port security, ship inspection, and harbor traffic control missions, and to monitor medical surveillance databases for early alert of a bioterrorist attack. The AADP can also be integrated into combat simulations to enhance model fidelity of multi-sensor fusion effects in military operations.

  4. Seismic data fusion anomaly detection

    NASA Astrophysics Data System (ADS)

    Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

    2014-06-01

    Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

  5. Model selection for anomaly detection

    NASA Astrophysics Data System (ADS)

    Burnaev, E.; Erofeev, P.; Smolyakov, D.

    2015-12-01

    Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.

  6. Sequential detection of web defects

    DOEpatents

    Eichel, Paul H.; Sleefe, Gerard E.; Stalker, K. Terry; Yee, Amy A.

    2001-01-01

    A system for detecting defects on a moving web having a sequential series of identical frames uses an imaging device to form a real-time camera image of a frame and a comparitor to comparing elements of the camera image with corresponding elements of an image of an exemplar frame. The comparitor provides an acceptable indication if the pair of elements are determined to be statistically identical; and a defective indication if the pair of elements are determined to be statistically not identical. If the pair of elements is neither acceptable nor defective, the comparitor recursively compares the element of said exemplar frame with corresponding elements of other frames on said web until one of the acceptable or defective indications occur.

  7. Survey of Anomaly Detection Methods

    SciTech Connect

    Ng, B

    2006-10-12

    This survey defines the problem of anomaly detection and provides an overview of existing methods. The methods are categorized into two general classes: generative and discriminative. A generative approach involves building a model that represents the joint distribution of the input features and the output labels of system behavior (e.g., normal or anomalous) then applies the model to formulate a decision rule for detecting anomalies. On the other hand, a discriminative approach aims directly to find the decision rule, with the smallest error rate, that distinguishes between normal and anomalous behavior. For each approach, we will give an overview of popular techniques and provide references to state-of-the-art applications.

  8. Efficient Computer Network Anomaly Detection by Changepoint Detection Methods

    NASA Astrophysics Data System (ADS)

    Tartakovsky, Alexander G.; Polunchenko, Aleksey S.; Sokolov, Grigory

    2013-02-01

    We consider the problem of efficient on-line anomaly detection in computer network traffic. The problem is approached statistically, as that of sequential (quickest) changepoint detection. A multi-cyclic setting of quickest change detection is a natural fit for this problem. We propose a novel score-based multi-cyclic detection algorithm. The algorithm is based on the so-called Shiryaev-Roberts procedure. This procedure is as easy to employ in practice and as computationally inexpensive as the popular Cumulative Sum chart and the Exponentially Weighted Moving Average scheme. The likelihood ratio based Shiryaev-Roberts procedure has appealing optimality properties, particularly it is exactly optimal in a multi-cyclic setting geared to detect a change occurring at a far time horizon. It is therefore expected that an intrusion detection algorithm based on the Shiryaev-Roberts procedure will perform better than other detection schemes. This is confirmed experimentally for real traces. We also discuss the possibility of complementing our anomaly detection algorithm with a spectral-signature intrusion detection system with false alarm filtering and true attack confirmation capability, so as to obtain a synergistic system.

  9. Conscious and unconscious detection of semantic anomalies.

    PubMed

    Hannon, Brenda

    2015-01-01

    When asked What superhero is associated with bats, Robin, the Penguin, Metropolis, Catwoman, the Riddler, the Joker, and Mr. Freeze? people frequently fail to notice the anomalous word Metropolis. The goals of this study were to determine whether detection of semantic anomalies, like Metropolis, is conscious or unconscious and whether this detection is immediate or delayed. To achieve these goals, participants answered anomalous and nonanomalous questions as their reading times for words were recorded. Comparisons between detected versus undetected anomalies revealed slower reading times for detected anomalies-a finding that suggests that people immediately and consciously detected anomalies. Further, comparisons between first and second words following undetected anomalies versus nonanomalous controls revealed some slower reading times for first and second words-a finding that suggests that people may have unconsciously detected anomalies but this detection was delayed. Taken together, these findings support the idea that when we are immediately aware of a semantic anomaly (i.e., immediate conscious detection) our language processes make immediate adjustments in order to reconcile contradictory information of anomalies with surrounding text; however, even when we are not consciously aware of semantic anomalies, our language processes still make these adjustments, although these adjustments are delayed (i.e., delayed unconscious detection). PMID:25624136

  10. Data Mining for Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

    2013-01-01

    The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

  11. A New, Principled Approach to Anomaly Detection

    SciTech Connect

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

    2012-01-01

    Intrusion detection is often described as having two main approaches: signature-based and anomaly-based. We argue that only unsupervised methods are suitable for detecting anomalies. However, there has been a tendency in the literature to conflate the notion of an anomaly with the notion of a malicious event. As a result, the methods used to discover anomalies have typically been ad hoc, making it nearly impossible to systematically compare between models or regulate the number of alerts. We propose a new, principled approach to anomaly detection that addresses the main shortcomings of ad hoc approaches. We provide both theoretical and cyber-specific examples to demonstrate the benefits of our more principled approach.

  12. Analytic sequential methods for detecting network intrusions

    NASA Astrophysics Data System (ADS)

    Chen, Xinjia; Walker, Ernest

    2014-05-01

    In this paper, we propose an analytic sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. We have developed explicit formulae for quick determination of the parameters of the new detection algorithm.

  13. A sequential framework for image change detection.

    PubMed

    Lingg, Andrew J; Zelnio, Edmund; Garber, Fred; Rigling, Brian D

    2014-05-01

    We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions. PMID:24818249

  14. Anomaly detection for internet surveillance

    NASA Astrophysics Data System (ADS)

    Bouma, Henri; Raaijmakers, Stephan; Halma, Arvid; Wedemeijer, Harry

    2012-06-01

    Many threats in the real world can be related to activity of persons on the internet. Internet surveillance aims to predict and prevent attacks and to assist in finding suspects based on information from the web. However, the amount of data on the internet rapidly increases and it is time consuming to monitor many websites. In this paper, we present a novel method to automatically monitor trends and find anomalies on the internet. The system was tested on Twitter data. The results showed that it can successfully recognize abnormal changes in activity or emotion.

  15. Modeling And Detecting Anomalies In Scada Systems

    NASA Astrophysics Data System (ADS)

    Svendsen, Nils; Wolthusen, Stephen

    The detection of attacks and intrusions based on anomalies is hampered by the limits of specificity underlying the detection techniques. However, in the case of many critical infrastructure systems, domain-specific knowledge and models can impose constraints that potentially reduce error rates. At the same time, attackers can use their knowledge of system behavior to mask their manipulations, causing adverse effects to observed only after a significant period of time. This paper describes elementary statistical techniques that can be applied to detect anomalies in critical infrastructure networks. A SCADA system employed in liquefied natural gas (LNG) production is used as a case study.

  16. Spectral anomaly detection in deep shadows.

    PubMed

    Kanaev, Andrey V; Murray-Krezan, Jeremy

    2010-03-20

    Although several hyperspectral anomaly detection algorithms have proven useful when illumination conditions provide for enough light, many of these same detection algorithms fail to perform well when shadows are also present. To date, no general approach to the problem has been demonstrated. In this paper, a novel hyperspectral anomaly detection algorithm that adapts the dimensionality of the spectral detection subspace to multiple illumination levels is described. The novel detection algorithm is applied to reflectance domain hyperspectral data that represents a variety of illumination conditions: well illuminated and poorly illuminated (i.e., shadowed). Detection results obtained for objects located in deep shadows and light-shadow transition areas suggest superiority of the novel algorithm over standard subspace RX detection. PMID:20300158

  17. Anomaly Detection Using Behavioral Approaches

    NASA Astrophysics Data System (ADS)

    Benferhat, Salem; Tabia, Karim

    Behavioral approaches, which represent normal/abnormal activities, have been widely used during last years in intrusion detection and computer security. Nevertheless, most works showed that they are ineffective for detecting novel attacks involving new behaviors. In this paper, we first study this recurring problem due on one hand to inadequate handling of anomalous and unusual audit events and on other hand to insufficient decision rules which do not meet behavioral approach objectives. We then propose to enhance the standard decision rules in order to fit behavioral approach requirements and better detect novel attacks. Experimental studies carried out on real and simulated http traffic show that these enhanced decision rules improve detecting most novel attacks without triggering higher false alarm rates.

  18. Anomaly Detection for Discrete Sequences: A Survey

    SciTech Connect

    Chandola, Varun; Banerjee, Arindam; Kumar, Vipin

    2012-01-01

    This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose frequency of occurrence is anomalous. We show how each of these problem formulations is characteristically distinct from each other and discuss their relevance in various application domains. We review techniques from many disparate and disconnected application domains that address each of these formulations. Within each problem formulation, we group techniques into categories based on the nature of the underlying algorithm. For each category, we provide a basic anomaly detection technique, and show how the existing techniques are variants of the basic technique. This approach shows how different techniques within a category are related or different from each other. Our categorization reveals new variants and combinations that have not been investigated before for anomaly detection. We also provide a discussion of relative strengths and weaknesses of different techniques. We show how techniques developed for one problem formulation can be adapted to solve a different formulation, thereby providing several novel adaptations to solve the different problem formulations. We also highlight the applicability of the techniques that handle discrete sequences to other related areas such as online anomaly detection and time series anomaly detection.

  19. Hyperspectral Anomaly Detection in Urban Scenarios

    NASA Astrophysics Data System (ADS)

    Rejas Ayuga, J. G.; Martínez Marín, R.; Marchamalo Sacristán, M.; Bonatti, J.; Ojeda, J. C.

    2016-06-01

    We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used. In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

  20. Detecting data anomalies methods in distributed systems

    NASA Astrophysics Data System (ADS)

    Mosiej, Lukasz

    2009-06-01

    Distributed systems became most popular systems in big companies. Nowadays many telecommunications companies want to hold large volumes of data about all customers. Obviously, those data cannot be stored in single database because of many technical difficulties, such as data access efficiency, security reasons, etc. On the other hand there is no need to hold all data in one place, because companies already have dedicated systems to perform specific tasks. In the distributed systems there is a redundancy of data and each system holds only interesting data in appropriate form. Data updated in one system should be also updated in the rest of systems, which hold that data. There are technical problems to update those data in all systems in transactional way. This article is about data anomalies in distributed systems. Avail data anomalies detection methods are shown. Furthermore, a new initial concept of new data anomalies detection methods is described on the last section.

  1. Network Anomaly Detection Based on Wavelet Analysis

    NASA Astrophysics Data System (ADS)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  2. Fusion and normalization to enhance anomaly detection

    NASA Astrophysics Data System (ADS)

    Mayer, R.; Atkinson, G.; Antoniades, J.; Baumback, M.; Chester, D.; Edwards, J.; Goldstein, A.; Haas, D.; Henderson, S.; Liu, L.

    2009-05-01

    This study examines normalizing the imagery and the optimization metrics to enhance anomaly and change detection, respectively. The RX algorithm, the standard anomaly detector for hyperspectral imagery, more successfully extracts bright rather than dark man-made objects when applied to visible hyperspectral imagery. However, normalizing the imagery prior to applying the anomaly detector can help detect some of the problematic dark objects, but can also miss some bright objects. This study jointly fuses images of RX applied to normalized and unnormalized imagery and has a single decision surface. The technique was tested using imagery of commercial vehicles in urban environment gathered by a hyperspectral visible/near IR sensor mounted in an airborne platform. Combining detections first requires converting the detector output to a target probability. The observed anomaly detections were fitted with a linear combination of chi square distributions and these weights were used to help compute the target probability. Receiver Operator Characteristic (ROC) quantitatively assessed the target detection performance. The target detection performance is highly variable depending on the relative number of candidate bright and dark targets and false alarms and controlled in this study by using vegetation and street line masks. The joint Boolean OR and AND operations also generate variable performance depending on the scene. The joint SUM operation provides a reasonable compromise between OR and AND operations and has good target detection performance. In addition, new transforms based on normalizing correlation coefficient and least squares generate new transforms related to canonical correlation analysis (CCA) and a normalized image regression (NIR). Transforms based on CCA and NIR performed better than the standard approaches. Only RX detection of the unnormalized of the difference imagery in change detection provides adequate change detection performance.

  3. Sequential decision rules for failure detection

    NASA Technical Reports Server (NTRS)

    Chow, E. Y.; Willsky, A. S.

    1981-01-01

    The formulation of the decision making of a failure detection process as a Bayes sequential decision problem (BSDP) provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Baysian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is a useful one.

  4. Adaptive sequential methods for detecting network intrusions

    NASA Astrophysics Data System (ADS)

    Chen, Xinjia; Walker, Ernest

    2013-06-01

    In this paper, we propose new sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. Moreover, our method guarantees that the maximum amount of observational time is bounded. In contrast to the previous most effective method, Threshold Random Walk Algorithm, which is explicit and analytical in nature, our proposed algorithm involve parameters to be determined by numerical methods. We have introduced computational techniques such as iterative minimax optimization for quick determination of the parameters of the new detection algorithm. A framework of multi-valued decision for detecting portscanners and DoS attacks is also proposed.

  5. Anomaly Detection Techniques for Ad Hoc Networks

    ERIC Educational Resources Information Center

    Cai, Chaoli

    2009-01-01

    Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

  6. Automatic detection of anomalies in Space Shuttle Main Engine turbopumps

    NASA Astrophysics Data System (ADS)

    Lo, Ching F.; Whitehead, B. A.; Wu, Kewei

    1992-07-01

    A prototype expert system (developed on both PC and Symbolics 3670 lisp machine) for detecting anomalies in turbopump vibration data has been tested with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine (SSME). The expert system has been utilized to analyze vibration data from each of the following SSME components: high-pressure oxidizer turbopump, high-pressure fuel turbopump, low-pressure fuel turbopump, and preburner boost pump. The expert system locates and classifies peaks in the power spectral density of each 0.4-sec window of steady-state data. Peaks representing the fundamental and harmonic frequencies of both shaft rotation and bearing cage rotation are identified by the expert system. Anomalies are then detected on the basis of sequential criteria and two threshold criteria set individually for the amplitude of each of these peaks: a prior threshold used during the first few windows of data in a test, and a posterior threshold used thereafter. In most cases the anomalies detected by the expert system agree with those reported by NASA. The two cases where there is significant disagreement will be further studied and the system design refined accordingly.

  7. Automatic detection of anomalies in Space Shuttle Main Engine turbopumps

    NASA Technical Reports Server (NTRS)

    Lo, Ching F.; Whitehead, B. A.; Wu, Kewei

    1992-01-01

    A prototype expert system (developed on both PC and Symbolics 3670 lisp machine) for detecting anomalies in turbopump vibration data has been tested with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine (SSME). The expert system has been utilized to analyze vibration data from each of the following SSME components: high-pressure oxidizer turbopump, high-pressure fuel turbopump, low-pressure fuel turbopump, and preburner boost pump. The expert system locates and classifies peaks in the power spectral density of each 0.4-sec window of steady-state data. Peaks representing the fundamental and harmonic frequencies of both shaft rotation and bearing cage rotation are identified by the expert system. Anomalies are then detected on the basis of sequential criteria and two threshold criteria set individually for the amplitude of each of these peaks: a prior threshold used during the first few windows of data in a test, and a posterior threshold used thereafter. In most cases the anomalies detected by the expert system agree with those reported by NASA. The two cases where there is significant disagreement will be further studied and the system design refined accordingly.

  8. OPAD data analysis. [Optical Plumes Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Buntine, Wray L.; Kraft, Richard; Whitaker, Kevin; Cooper, Anita E.; Powers, W. T.; Wallace, Tim L.

    1993-01-01

    Data obtained in the framework of an Optical Plume Anomaly Detection (OPAD) program intended to create a rocket engine health monitor based on spectrometric detections of anomalous atomic and molecular species in the exhaust plume are analyzed. The major results include techniques for handling data noise, methods for registration of spectra to wavelength, and a simple automatic process for estimating the metallic component of a spectrum.

  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. Gravity anomaly detection: Apollo/Soyuz

    NASA Technical Reports Server (NTRS)

    Vonbun, F. O.; Kahn, W. D.; Bryan, J. W.; Schmid, P. E.; Wells, W. T.; Conrad, D. T.

    1976-01-01

    The Goddard Apollo-Soyuz Geodynamics Experiment is described. It was performed to demonstrate the feasibility of tracking and recovering high frequency components of the earth's gravity field by utilizing a synchronous orbiting tracking station such as ATS-6. Gravity anomalies of 5 MGLS or larger having wavelengths of 300 to 1000 kilometers on the earth's surface are important for geologic studies of the upper layers of the earth's crust. Short wavelength Earth's gravity anomalies were detected from space. Two prime areas of data collection were selected for the experiment: (1) the center of the African continent and (2) the Indian Ocean Depression centered at 5% north latitude and 75% east longitude. Preliminary results show that the detectability objective of the experiment was met in both areas as well as at several additional anomalous areas around the globe. Gravity anomalies of the Karakoram and Himalayan mountain ranges, ocean trenches, as well as the Diamantina Depth, can be seen. Maps outlining the anomalies discovered are shown.

  12. System and method for anomaly detection

    DOEpatents

    Scherrer, Chad

    2010-06-15

    A system and method for detecting one or more anomalies in a plurality of observations is provided. In one illustrative embodiment, the observations are real-time network observations collected from a stream of network traffic. The method includes performing a discrete decomposition of the observations, and introducing derived variables to increase storage and query efficiencies. A mathematical model, such as a conditional independence model, is then generated from the formatted data. The formatted data is also used to construct frequency tables which maintain an accurate count of specific variable occurrence as indicated by the model generation process. The formatted data is then applied to the mathematical model to generate scored data. The scored data is then analyzed to detect anomalies.

  13. Sequential detection of learning in cognitive diagnosis.

    PubMed

    Ye, Sangbeak; Fellouris, Georgios; Culpepper, Steven; Douglas, Jeff

    2016-05-01

    In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broadly defined skills, when the objective is to hasten learning. If these fine-grained skills can be identified, a sharpened focus on learning and remediation can be achieved. The focus here is on how to detect when learning has taken place for a particular attribute and efficiently guide a student through a sequence of items to ultimately attain mastery of all attributes while administering as few items as possible. This can be seen as a problem in sequential change-point detection for which there is a long history and a well-developed literature. Though some ad hoc rules for determining learning may be used, such as stopping after M consecutive items have been successfully answered, more efficient methods that are optimal under various conditions are available. The CUSUM, Shiryaev-Roberts and Shiryaev procedures can dramatically reduce the time required to detect learning while maintaining rigorous Type I error control, and they are studied in this context through simulation. Future directions for modelling and detection of learning are discussed. PMID:26931602

  14. Sequential Changepoint Approach for Online Community Detection

    NASA Astrophysics Data System (ADS)

    Marangoni-Simonsen, David; Xie, Yao

    2015-08-01

    We present new algorithms for detecting the emergence of a community in large networks from sequential observations. The networks are modeled using Erdos-Renyi random graphs with edges forming between nodes in the community with higher probability. Based on statistical changepoint detection methodology, we develop three algorithms: the Exhaustive Search (ES), the mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these methods is evaluated by the average run length (ARL), which captures the frequency of false alarms, and the detection delay. Numerical comparisons show that the ES method performs the best; however, it is exponentially complex. The mixture method is polynomially complex by exploiting the fact that the size of the community is typically small in a large network. However, it may react to a group of active edges that do not form a community. This issue is resolved by the H-Mix method, which is based on a dendrogram decomposition of the network. We present an asymptotic analytical expression for ARL of the mixture method when the threshold is large. Numerical simulation verifies that our approximation is accurate even in the non-asymptotic regime. Hence, it can be used to determine a desired threshold efficiently. Finally, numerical examples show that the mixture and the H-Mix methods can both detect a community quickly with a lower complexity than the ES method.

  15. Anomaly detection in the maritime domain

    NASA Astrophysics Data System (ADS)

    Roy, Jean

    2008-04-01

    Defence R&D Canada is developing a Collaborative Knowledge Exploitation Framework (CKEF) to support the analysts in efficiently managing and exploiting relevant knowledge assets to achieve maritime domain awareness in joint operations centres of the Canadian Forces. While developing the CKEF, anomaly detection has been clearly recognized as an important aspect requiring R&D. An activity has thus been undertaken to implement, within the CKEF, a proof-of-concept prototype of a rule-based expert system to support the analysts regarding this aspect. This expert system has to perform automated reasoning and output recommendations (or alerts) about maritime anomalies, thereby supporting the identification of vessels of interest and threat analysis. The system must contribute to a lower false alarm rate and a better probability of detection in drawing operator's attention to vessels worthy of their attention. It must provide explanations as to why the vessels may be of interest, with links to resources that help the operators dig deeper. Mechanisms are necessary for the analysts to fine tune the system, and for the knowledge engineer to maintain the knowledge base as the expertise of the operators evolves. This paper portrays the anomaly detection prototype, and describes the knowledge acquisition and elicitation session conducted to capture the know-how of the experts, the formal knowledge representation enablers and the ontology required for aspects of the maritime domain that are relevant to anomaly detection, vessels of interest, and threat analysis, the prototype high-level design and implementation on the service-oriented architecture of the CKEF, and other findings and results of this ongoing activity.

  16. Geomagnetic anomaly detected at hydromagnetic wave frequencies

    NASA Astrophysics Data System (ADS)

    Meloni, A.; Medford, L. V.; Lanzerotti, L. J.

    1985-04-01

    We report the discovery, in northwestern Illinois, of a geomagnetic anomaly, using hydromagnetic wave frequencies as the source spectrum. Three portable magnetometer stations with computer-compatible digital data acquisition systems were operated in a longitude array at Piano and Ashton, Illinois, and Cascade, Iowa (total separation ˜200 km), in 1981-1982. Analysis of the natural geomagnetic field fluctuations in the hydromagnetic wave regime reveals that the vertical components of the detected fluctuations are essentially 180° out of phase between Plano/Ashton and Cascade for variations with periods ˜30-120 s. The observations can be modeled in terms of a shallow (˜10-20 km) north-south oriented geomagnetic anomaly of enhanced conductivity located between Ashton and Cascade, approximately parallel to the Mississippi River valley.

  17. Detecting syntactic and semantic anomalies in schizophrenia.

    PubMed

    Moro, Andrea; Bambini, Valentina; Bosia, Marta; Anselmetti, Simona; Riccaboni, Roberta; Cappa, Stefano F; Smeraldi, Enrico; Cavallaro, Roberto

    2015-12-01

    One of the major challenges in the study of language in schizophrenia is to identify specific levels of the linguistic structure that might be selectively impaired. While historically a main semantic deficit has been widely claimed, results are mixed, with also evidence of syntactic impairment. This might be due to heterogeneity in materials and paradigms across studies, which often do not allow to tap into single linguistic components. Moreover, the interaction between linguistic and neurocognitive deficits is still unclear. In this study, we concentrated on syntactic and semantic knowledge. We employed an anomaly detection task including short and long sentences with either syntactic errors violating the principles of Universal Grammar, or a novel form of semantic errors, resulting from a contradiction in the computation of the whole sentence meaning. Fifty-eight patients with diagnosis of schizophrenia were compared to 30 healthy subjects. Results showed that, in patients, only the ability to identify syntactic anomaly, both in short and long sentences, was impaired. This result cannot be explained by working memory abilities or psychopathological features. These findings suggest the presence of an impairment of syntactic knowledge in schizophrenia, at least partially independent of the cognitive and psychopathological profile. On the contrary, we cannot conclude that there is a semantic impairment, at least in terms of compositional semantics abilities. PMID:26519554

  18. Recursive SAM-based band selection for hyperspectral anomaly detection

    NASA Astrophysics Data System (ADS)

    He, Yuanlei; Liu, Daizhi; Yi, Shihua

    2010-10-01

    Band selection has been widely used in hyperspectral image processing for dimension reduction. In this paper, a recursive SAM-based band selection (RSAM-BBS) method is proposed. Once two initial bands are given, RSAM-BBS is performed in a sequential manner, and at each step the band that can best describe the spectral separation of two hyperspectral signatures is added to the bands already selected until the spectral angle reaches its maximum. In order to demonstrate the utility of the proposed band selection method, an anomaly detection algorithm is developed, which first extracts the anomalous target spectrum from the original image using automatic target detection and classification algorithm (ATDCA), followed by maximum spectral screening (MSS) to estimate the background average spectrum, then implements RSAM-BBS to select bands that participate in the subsequent adaptive cosine estimator (ACE) target detection. As shown in the experimental result on the AVIRIS dataset, less than five bands selected by the RSAM-BBS can achieve comparable detection performance using the full bands.

  19. Clustering and Recurring Anomaly Identification: Recurring Anomaly Detection System (ReADS)

    NASA Technical Reports Server (NTRS)

    McIntosh, Dawn

    2006-01-01

    This viewgraph presentation reviews the Recurring Anomaly Detection System (ReADS). The Recurring Anomaly Detection System is a tool to analyze text reports, such as aviation reports and maintenance records: (1) Text clustering algorithms group large quantities of reports and documents; Reduces human error and fatigue (2) Identifies interconnected reports; Automates the discovery of possible recurring anomalies; (3) Provides a visualization of the clusters and recurring anomalies We have illustrated our techniques on data from Shuttle and ISS discrepancy reports, as well as ASRS data. ReADS has been integrated with a secure online search

  20. A model for anomaly classification in intrusion detection systems

    NASA Astrophysics Data System (ADS)

    Ferreira, V. O.; Galhardi, V. V.; Gonçalves, L. B. L.; Silva, R. C.; Cansian, A. M.

    2015-09-01

    Intrusion Detection Systems (IDS) are traditionally divided into two types according to the detection methods they employ, namely (i) misuse detection and (ii) anomaly detection. Anomaly detection has been widely used and its main advantage is the ability to detect new attacks. However, the analysis of anomalies generated can become expensive, since they often have no clear information about the malicious events they represent. In this context, this paper presents a model for automated classification of alerts generated by an anomaly based IDS. The main goal is either the classification of the detected anomalies in well-defined taxonomies of attacks or to identify whether it is a false positive misclassified by the IDS. Some common attacks to computer networks were considered and we achieved important results that can equip security analysts with best resources for their analyses.

  1. Anomaly detection enhanced classification in computer intrusion detection

    SciTech Connect

    Fugate, M. L.; Gattiker, J. R.

    2002-01-01

    This report describes work with the goal of enhancing capabilities in computer intrusion detection. The work builds upon a study of classification performance, that compared various methods of classifying information derived from computer network packets into attack versus normal categories, based on a labeled training dataset. This previous work validates our classification methods, and clears the ground for studying whether and how anomaly detection can be used to enhance this performance, The DARPA project that initiated the dataset used here concluded that anomaly detection should be examined to boost the performance of machine learning in the computer intrusion detection task. This report investigates the data set for aspects that will be valuable for anomaly detection application, and supports these results with models constructed from the data. In this report, the term anomaly detection means learning a model from unlabeled data, and using this to make some inference about future data. Our data is a feature vector derived from network packets: an 'example' or 'sample'. On the other hand, classification means building a model from labeled data, and using that model to classify unlabeled (future) examples. There is some precedent in the literature for combining these methods. One approach is to stage the two techniques, using anomaly detection to segment data into two sets for classification. An interpretation of this is a method to combat nonstationarity in the data. In our previous work, we demonstrated that the data has substantial temporal nonstationarity. With classification methods that can be thought of as learning a decision surface between two statistical distributions, performance is expected to degrade significantly when classifying examples that are from regions not well represented in the training set. Anomaly detection can be seen as a problem of learning the density (landscape) or the support (boundary) of a statistical distribution so that

  2. Statistical Anomaly Detection for Monitoring of Human Dynamics

    NASA Astrophysics Data System (ADS)

    Kamiya, K.; Fuse, T.

    2015-05-01

    Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to lead to dynamic traffic control in the future. Although this monitoring focuses on detecting anomalous states of human dynamics, anomaly detection methods are developed ad hoc and not fully systematized. This research aims to define an anomaly detection problem of the human dynamics monitoring with gridded population data and develop an anomaly detection method based on the definition. According to the result of a review we have comprehensively conducted, we discussed the characteristics of the anomaly detection of human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky HDP-HMM, which is able to estimate the number of hidden states according to input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiment with real gridded population data, an anomaly was detected when and where an actual social event had occurred.

  3. Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

    PubMed

    Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

    2016-06-01

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets. PMID:26336154

  4. Automated Network Anomaly Detection with Learning, Control and Mitigation

    ERIC Educational Resources Information Center

    Ippoliti, Dennis

    2014-01-01

    Anomaly detection is a challenging problem that has been researched within a variety of application domains. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the specific signatures of every possible attack.…

  5. Fluorescence sensor for sequential detection of zinc and phosphate ions.

    PubMed

    An, Miran; Kim, Bo-Yeon; Seo, Hansol; Helal, Aasif; Kim, Hong-Seok

    2016-12-01

    A new, highly selective turn-on fluorescent chemosensor based on 2-(2'-tosylamidophenyl)thiazole (1) for the detection of zinc and phosphate ions in ethanol was synthesized and characterized. Sensor 1 showed a high selectivity for zinc compared to other cations and sequentially detected hydrogen pyrophosphate and hydrogen phosphate. The fluorescence mechanism can be explained by two different mechanisms: (i) the inhibition of excited-state intramolecular proton transfer (ESIPT) and (ii) chelation-induced enhanced fluorescence by binding with Zn(2+). The sequential detection of phosphate anions was achieved by the quenching and subsequent revival of ESIPT. PMID:27343439

  6. Fluorescence sensor for sequential detection of zinc and phosphate ions

    NASA Astrophysics Data System (ADS)

    An, Miran; Kim, Bo-Yeon; Seo, Hansol; Helal, Aasif; Kim, Hong-Seok

    2016-12-01

    A new, highly selective turn-on fluorescent chemosensor based on 2-(2‧-tosylamidophenyl)thiazole (1) for the detection of zinc and phosphate ions in ethanol was synthesized and characterized. Sensor 1 showed a high selectivity for zinc compared to other cations and sequentially detected hydrogen pyrophosphate and hydrogen phosphate. The fluorescence mechanism can be explained by two different mechanisms: (i) the inhibition of excited-state intramolecular proton transfer (ESIPT) and (ii) chelation-induced enhanced fluorescence by binding with Zn2 +. The sequential detection of phosphate anions was achieved by the quenching and subsequent revival of ESIPT.

  7. Network Anomaly Detection System with Optimized DS Evidence Theory

    PubMed Central

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  8. Network anomaly detection system with optimized DS evidence theory.

    PubMed

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  9. Post-processing for improving hyperspectral anomaly detection accuracy

    NASA Astrophysics Data System (ADS)

    Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

    2015-10-01

    Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

  10. Hyperspectral anomaly detection method based on auto-encoder

    NASA Astrophysics Data System (ADS)

    Bati, Emrecan; ćalışkan, Akın.; Koz, Alper; Alatan, A. A.

    2015-10-01

    A major drawback of most of the existing hyperspectral anomaly detection methods is the lack of an efficient background representation, which can successfully adapt to the varying complexity of hyperspectral images. In this paper, we propose a novel anomaly detection method which represents the hyperspectral scenes of different complexity with the state-of-the-art representation learning method, namely auto-encoder. The proposed method first encodes the spectral image into a sparse code, then decodes the coded image, and finally, assesses the coding error at each pixel as a measure of anomaly. Predictive Sparse Decomposition Auto-encoder is utilized in the proposed anomaly method due to its efficient joint learning for the encoding and decoding functions. The performance of the proposed anomaly detection method is both tested on visible-near infrared (VNIR) and long wave infrared (LWIR) hyperspectral images and compared with the conventional anomaly detection method, namely Reed-Xiaoli (RX) detector.1 The experiments has verified the superiority of the proposed anomaly detection method in terms of receiver operating characteristics (ROC) performance.

  11. Load characterization and anomaly detection for voice over IP traffic.

    PubMed

    Mandjes, Michel; Saniee, Iraj; Stolyar, Alexander L

    2005-09-01

    We consider the problem of traffic anomaly detection in IP networks. Traffic anomalies typically arise when there is focused overload or when a network element fails and it is desired to infer these purely from the measured traffic. We derive new general formulae for the variance of the cumulative traffic over a fixed time interval and show how the derived analytical expression simplifies for the case of voice over IP traffic, the focus of this paper. To detect load anomalies, we show it is sufficient to consider cumulative traffic over relatively long intervals such as 5 min. We also propose simple anomaly detection tests including detection of over/underload. This approach substantially extends the current practice in IP network management where only the first-order statistics and fixed thresholds are used to identify abnormal behavior. We conclude with the application of the scheme to field data from an operational network. PMID:16252813

  12. Lidar detection algorithm for time and range anomalies

    NASA Astrophysics Data System (ADS)

    Ben-David, Avishai; Davidson, Charles E.; Vanderbeek, Richard G.

    2007-10-01

    A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t1 to t2" is addressed, and for range anomaly where the question "is a target present at time t within ranges R1 and R2" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO2 lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.

  13. Lidar detection algorithm for time and range anomalies.

    PubMed

    Ben-David, Avishai; Davidson, Charles E; Vanderbeek, Richard G

    2007-10-10

    A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t(1) to t(2)" is addressed, and for range anomaly where the question "is a target present at time t within ranges R(1) and R(2)" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO(2) lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed. PMID:17932542

  14. Sequential Detection of Fission Processes for Harbor Defense

    SciTech Connect

    Candy, J V; Walston, S E; Chambers, D H

    2015-02-12

    With the large increase in terrorist activities throughout the world, the timely and accurate detection of special nuclear material (SNM) has become an extremely high priority for many countries concerned with national security. The detection of radionuclide contraband based on their γ-ray emissions has been attacked vigorously with some interesting and feasible results; however, the fission process of SNM has not received as much attention due to its inherent complexity and required predictive nature. In this paper, on-line, sequential Bayesian detection and estimation (parameter) techniques to rapidly and reliably detect unknown fissioning sources with high statistical confidence are developed.

  15. A New Methodology for Early Anomaly Detection of BWR Instabilities

    SciTech Connect

    Ivanov, K. N.

    2005-11-27

    The objective of the performed research is to develop an early anomaly detection methodology so as to enhance safety, availability, and operational flexibility of Boiling Water Reactor (BWR) nuclear power plants. The technical approach relies on suppression of potential power oscillations in BWRs by detecting small anomalies at an early stage and taking appropriate prognostic actions based on an anticipated operation schedule. The research utilizes a model of coupled (two-phase) thermal-hydraulic and neutron flux dynamics, which is used as a generator of time series data for anomaly detection at an early stage. The model captures critical nonlinear features of coupled thermal-hydraulic and nuclear reactor dynamics and (slow time-scale) evolution of the anomalies as non-stationary parameters. The time series data derived from this nonlinear non-stationary model serves as the source of information for generating the symbolic dynamics for characterization of model parameter changes that quantitatively represent small anomalies. The major focus of the presented research activity was on developing and qualifying algorithms of pattern recognition for power instability based on anomaly detection from time series data, which later can be used to formulate real-time decision and control algorithms for suppression of power oscillations for a variety of anticipated operating conditions. The research being performed in the framework of this project is essential to make significant improvement in the capability of thermal instability analyses for enhancing safety, availability, and operational flexibility of currently operating and next generation BWRs.

  16. Evaluation schemes for video and image anomaly detection algorithms

    NASA Astrophysics Data System (ADS)

    Parameswaran, Shibin; Harguess, Josh; Barngrover, Christopher; Shafer, Scott; Reese, Michael

    2016-05-01

    Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.

  17. Multiple-Instance Learning for Anomaly Detection in Digital Mammography.

    PubMed

    Quellec, Gwenole; Lamard, Mathieu; Cozic, Michel; Coatrieux, Gouenou; Cazuguel, Guy

    2016-07-01

    This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation. PMID:26829783

  18. Anomaly Detection in Power Quality at Data Centers

    NASA Technical Reports Server (NTRS)

    Grichine, Art; Solano, Wanda M.

    2015-01-01

    The goal during my internship at the National Center for Critical Information Processing and Storage (NCCIPS) is to implement an anomaly detection method through the StruxureWare SCADA Power Monitoring system. The benefit of the anomaly detection mechanism is to provide the capability to detect and anticipate equipment degradation by monitoring power quality prior to equipment failure. First, a study is conducted that examines the existing techniques of power quality management. Based on these findings, and the capabilities of the existing SCADA resources, recommendations are presented for implementing effective anomaly detection. Since voltage, current, and total harmonic distortion demonstrate Gaussian distributions, effective set-points are computed using this model, while maintaining a low false positive count.

  19. Anomaly Detection In Additively Manufactured Parts Using Laser Doppler Vibrometery

    SciTech Connect

    Hernandez, Carlos A.

    2015-09-29

    Additively manufactured parts are susceptible to non-uniform structure caused by the unique manufacturing process. This can lead to structural weakness or catastrophic failure. Using laser Doppler vibrometry and frequency response analysis, non-contact detection of anomalies in additively manufactured parts may be possible. Preliminary tests show promise for small scale detection, but more future work is necessary.

  20. Visual analytics of anomaly detection in large data streams

    NASA Astrophysics Data System (ADS)

    Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.; Sharma, Ratnesh K.; Mehta, Abhay

    2009-01-01

    Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Many analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream. Mining results are quickly presented in graphical representations (i.e., tooltip) for the user to zoom into the problem regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have successfully applied this technique to detect data stream anomalies in large real-world enterprise server performance and data center energy management.

  1. Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, Mehdi

    2015-07-01

    Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

  2. [Anomaly Detection of Multivariate Time Series Based on Riemannian Manifolds].

    PubMed

    Xu, Yonghong; Hou, Xiaoying; Li Shuting; Cui, Jie

    2015-06-01

    Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective. PMID:26485975

  3. A hybrid approach for efficient anomaly detection using metaheuristic methods.

    PubMed

    Ghanem, Tamer F; Elkilani, Wail S; Abdul-Kader, Hatem M

    2015-07-01

    Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms. PMID:26199752

  4. Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

    PubMed Central

    Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra

    2015-01-01

    Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. PMID:25633599

  5. Anomaly detection based on sensor data in petroleum industry applications.

    PubMed

    Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra

    2015-01-01

    Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. PMID:25633599

  6. Anomalies.

    ERIC Educational Resources Information Center

    Online-Offline, 1999

    1999-01-01

    This theme issue on anomalies includes Web sites, CD-ROMs and software, videos, books, and additional resources for elementary and junior high school students. Pertinent activities are suggested, and sidebars discuss UFOs, animal anomalies, and anomalies from nature; and resources covering unexplained phenonmenas like crop circles, Easter Island,…

  7. Anomaly detection using classified eigenblocks in GPR image

    NASA Astrophysics Data System (ADS)

    Kim, Min Ju; Kim, Seong Dae; Lee, Seung-eui

    2016-05-01

    Automatic landmine detection system using ground penetrating radar has been widely researched. For the automatic mine detection system, system speed is an important factor. Many techniques for mine detection have been developed based on statistical background. Among them, a detection technique employing the Principal Component Analysis(PCA) has been used for clutter reduction and anomaly detection. However, the PCA technique can retard the entire process, because of large basis dimension and a numerous number of inner product operations. In order to overcome this problem, we propose a fast anomaly detection system using 2D DCT and PCA. Our experiments use a set of data obtained from a test site where the anti-tank and anti- personnel mines are buried. We evaluate the proposed system in terms of the ROC curve. The result shows that the proposed system performs much better than the conventional PCA systems from the viewpoint of speed and false alarm rate.

  8. Profile-based adaptive anomaly detection for network security.

    SciTech Connect

    Zhang, Pengchu C. (Sandia National Laboratories, Albuquerque, NM); Durgin, Nancy Ann

    2005-11-01

    As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress, rather than after the fact). We also build a prototype anomaly detection tool that demonstrates how the techniques might

  9. Locality-constrained anomaly detection for hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Liu, Jiabin; Li, Wei; Du, Qian; Liu, Kui

    2015-12-01

    Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.

  10. Attention focusing and anomaly detection in systems monitoring

    NASA Technical Reports Server (NTRS)

    Doyle, Richard J.

    1994-01-01

    Any attempt to introduce automation into the monitoring of complex physical systems must start from a robust anomaly detection capability. This task is far from straightforward, for a single definition of what constitutes an anomaly is difficult to come by. In addition, to make the monitoring process efficient, and to avoid the potential for information overload on human operators, attention focusing must also be addressed. When an anomaly occurs, more often than not several sensors are affected, and the partially redundant information they provide can be confusing, particularly in a crisis situation where a response is needed quickly. The focus of this paper is a new technique for attention focusing. The technique involves reasoning about the distance between two frequency distributions, and is used to detect both anomalous system parameters and 'broken' causal dependencies. These two forms of information together isolate the locus of anomalous behavior in the system being monitored.

  11. The use of Compton scattering in detecting anomaly in soil-possible use in pyromaterial detection

    NASA Astrophysics Data System (ADS)

    Abedin, Ahmad Firdaus Zainal; Ibrahim, Noorddin; Zabidi, Noriza Ahmad; Demon, Siti Zulaikha Ngah

    2016-01-01

    The Compton scattering is able to determine the signature of land mine detection based on dependency of density anomaly and energy change of scattered photons. In this study, 4.43 MeV gamma of the Am-Be source was used to perform Compton scattering. Two detectors were placed between source with distance of 8 cm and radius of 1.9 cm. Detectors of thallium-doped sodium iodide NaI(TI) was used for detecting gamma ray. There are 9 anomalies used in this simulation. The physical of anomaly is in cylinder form with radius of 10 cm and 8.9 cm height. The anomaly is buried 5 cm deep in the bed soil measured 80 cm radius and 53.5 cm height. Monte Carlo methods indicated the scattering of photons is directly proportional to density of anomalies. The difference between detector response with anomaly and without anomaly namely contrast ratio values are in a linear relationship with density of anomalies. Anomalies of air, wood and water give positive contrast ratio values whereas explosive, sand, concrete, graphite, limestone and polyethylene give negative contrast ratio values. Overall, the contrast ratio values are greater than 2 % for all anomalies. The strong contrast ratios result a good detection capability and distinction between anomalies.

  12. On-line Flagging of Anomalies and Adaptive Sequential Hypothesis Testing for Fine-feature Characterization of Geosynchronous Satellites

    NASA Astrophysics Data System (ADS)

    Chaudhary, A.; Payne, T.; Kinateder, K.; Dao, P.; Beecher, E.; Boone, D.; Elliott, B.

    The objective of on-line flagging in this paper is to perform interactive assessment of geosynchronous satellites anomalies such as cross-tagging of a satellites in a cluster, solar panel offset change, etc. This assessment will utilize a Bayesian belief propagation procedure and will include automated update of baseline signature data for the satellite, while accounting for the seasonal changes. Its purpose is to enable an ongoing, automated assessment of satellite behavior through its life cycle using the photometry data collected during the synoptic search performed by a ground or space-based sensor as a part of its metrics mission. The change in the satellite features will be reported along with the probabilities of Type I and Type II errors. The objective of adaptive sequential hypothesis testing in this paper is to define future sensor tasking for the purpose of characterization of fine features of the satellite. The tasking will be designed in order to maximize new information with the least number of photometry data points to be collected during the synoptic search by a ground or space-based sensor. Its calculation is based on the utilization of information entropy techniques. The tasking is defined by considering a sequence of hypotheses in regard to the fine features of the satellite. The optimal observation conditions are then ordered in order to maximize new information about a chosen fine feature. The combined objective of on-line flagging and adaptive sequential hypothesis testing is to progressively discover new information about the features of a geosynchronous satellites by leveraging the regular but sparse cadence of data collection during the synoptic search performed by a ground or space-based sensor. Automated Algorithm to Detect Changes in Geostationary Satellite's Configuration and Cross-Tagging Phan Dao, Air Force Research Laboratory/RVB By characterizing geostationary satellites based on photometry and color photometry, analysts can

  13. Robust and efficient anomaly detection using heterogeneous representations

    NASA Astrophysics Data System (ADS)

    Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

    2015-05-01

    Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

  14. Solar cell anomaly detection method and apparatus

    NASA Technical Reports Server (NTRS)

    Miller, Emmett L. (Inventor); Shumka, Alex (Inventor); Gauthier, Michael K. (Inventor)

    1981-01-01

    A method is provided for detecting cracks and other imperfections in a solar cell, which includes scanning a narrow light beam back and forth across the cell in a raster pattern, while monitoring the electrical output of the cell to find locations where the electrical output varies significantly. The electrical output can be monitored on a television type screen containing a raster pattern with each point on the screen corresponding to a point on the solar cell surface, and with the brightness of each point on the screen corresponding to the electrical output from the cell which was produced when the light beam was at the corresponding point on the cell. The technique can be utilized to scan a large array of interconnected solar cells, to determine which ones are defective.

  15. A spring window for geobotanical anomaly detection

    NASA Technical Reports Server (NTRS)

    Bell, R.; Labovitz, M. L.; Masuoka, E. J.

    1985-01-01

    The observation of senescence of deciduous vegetation to detect soil heavy metal mineralization is discussed. A gridded sampling of two sites of Quercus alba L. in south-central Virginia in 1982 is studied. The data reveal that smaller leaf blade lengths are observed in the soil site with copper, lead, and zinc concentrations. A random study in 1983 of red and white Q. rubra L., Q. prinus L., and Acer rubrum L., to confirm previous results is described. The observations of blade length and bud breaks show a 7-10 day lag in growth in the mineral site for the oak trees; however, the maple trees are not influenced by the minerals.

  16. Extending TOPS: Knowledge Management System for Anomaly Detection and Analysis

    NASA Astrophysics Data System (ADS)

    Votava, P.; Nemani, R. R.; Michaelis, A.

    2009-12-01

    Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. While there are large numbers of anomaly detection algorithms for multivariate datasets, we are extending this capability beyond the anomaly detection itself and towards an automated analysis that would discover the possible causes of the anomalies. There are often indirect connections between datasets that manifest themselves during occurrence of external events and rather than searching exhaustively throughout all the datasets, our goal is to capture this knowledge and provide it to the system during automated analysis. This results in more efficient processing. Since we don’t need to process all the datasets using the original anomaly detection algorithms, which is often compute intensive; we achieve data reduction as we don’t need to store all the datasets in order to search for possible connections but we can download selected data on-demand based on our analysis. For example, an anomaly observed in vegetation Net Primary Production (NPP) can relate to an anomaly in vegetation Leaf Area Index (LAI), which is a fairly direct connection, as LAI is one of the inputs for NPP, however the change in LAI could be caused by a fire event, which is not directly connected with NPP. Because we are able to capture this knowledge we can analyze fire datasets and if there is a match with the NPP anomaly, we can infer that a fire is a likely cause. The knowledge is captured using OWL ontology language, where connections are defined in a schema

  17. 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.

  18. Automated anomaly detection for Orbiter High Temperature Reusable Surface Insulation

    NASA Astrophysics Data System (ADS)

    Cooper, Eric G.; Jones, Sharon M.; Goode, Plesent W.; Vazquez, Sixto L.

    1992-11-01

    The description, analysis, and experimental results of a method for identifying possible defects on High Temperature Reusable Surface Insulation (HRSI) of the Orbiter Thermal Protection System (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.

  19. Gaussian Process for Activity Modeling and Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Liao, W.; Rosenhahn, B.; Yang, M. Ying

    2015-08-01

    Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.

  20. Sequential damage detection based on the continuous wavelet transform

    NASA Astrophysics Data System (ADS)

    Liao, Yizheng; Balafas, Konstantinos; Rajagopal, Ram; Kiremidjian, Anne S.

    2015-03-01

    This paper presents a sequential structural damage detection algorithm that is based on a statistical model for the wavelet transform of the structural responses. The detector uses the coefficients of the wavelet model and does not require prior knowledge of the structural properties. Principal Component Analysis is applied to select and extract the most sensitive features from the wavelet coefficients as the damage sensitive features. The damage detection algorithm is validated using the simulation data collected from a four-story steel moment frame. Various features have been explored and the detection algorithm was able to identify damage. Additionally, we show that for a desired probability of false alarm, the proposed detector is asymptotically optimal on the expected delay.

  1. Limitations of Aneuploidy and Anomaly Detection in the Obese Patient.

    PubMed

    Zozzaro-Smith, Paula; Gray, Lisa M; Bacak, Stephen J; Thornburg, Loralei L

    2014-01-01

    Obesity is a worldwide epidemic and can have a profound effect on pregnancy risks. Obese patients tend to be older and are at increased risk for structural fetal anomalies and aneuploidy, making screening options critically important for these women. Failure rates for first-trimester nuchal translucency (NT) screening increase with obesity, while the ability to detect soft-markers declines, limiting ultrasound-based screening options. Obesity also decreases the chances of completing the anatomy survey and increases the residual risk of undetected anomalies. Additionally, non-invasive prenatal testing (NIPT) is less likely to provide an informative result in obese patients. Understanding the limitations and diagnostic accuracy of aneuploidy and anomaly screening in obese patients can help guide clinicians in counseling patients on the screening options. PMID:26237478

  2. Extending TOPS: Ontology-driven Anomaly Detection and Analysis System

    NASA Astrophysics Data System (ADS)

    Votava, P.; Nemani, R. R.; Michaelis, A.

    2010-12-01

    Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include a capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. In order to best capture the knowledge about data hierarchies, Earth science models and implied dependencies between anomalies and occurrences of observable events such as urbanization, deforestation, or fires, we have developed an ontology to serve as a knowledge base. We can query the knowledge base and answer questions about dataset compatibilities, similarities and dependencies so that we can, for example, automatically analyze similar datasets in order to verify a given anomaly occurrence in multiple data sources. We are further extending the system to go beyond anomaly detection towards reasoning about possible causes of anomalies that are also encoded in the knowledge base as either learned or implied knowledge. This enables us to scale up the analysis by eliminating a large number of anomalies early on during the processing by either failure to verify them from other sources, or matching them directly with other observable events without having to perform an extensive and time-consuming exploration and analysis. The knowledge is captured using OWL ontology language, where connections are defined in a schema that is later extended by including specific instances of datasets and models. The information is stored using Sesame server and is accessible through both Java API and web services using SeRQL and SPARQL query languages. Inference is provided using OWLIM component integrated with Sesame.

  3. Anomaly detection for machine learning redshifts applied to SDSS galaxies

    NASA Astrophysics Data System (ADS)

    Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

    2015-10-01

    We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

  4. Sparsity-driven anomaly detection for ship detection and tracking in maritime video

    NASA Astrophysics Data System (ADS)

    Shafer, Scott; Harguess, Josh; Forero, Pedro A.

    2015-05-01

    This work examines joint anomaly detection and dictionary learning approaches for identifying anomalies in persistent surveillance applications that require data compression. We have developed a sparsity-driven anomaly detector that can be used for learning dictionaries to address these challenges. In our approach, each training datum is modeled as a sparse linear combination of dictionary atoms in the presence of noise. The noise term is modeled as additive Gaussian noise and a deterministic term models the anomalies. However, no model for the statistical distribution of the anomalies is made. An estimator is postulated for a dictionary that exploits the fact that since anomalies by definition are rare, only a few anomalies will be present when considering the entire dataset. From this vantage point, we endow the deterministic noise term (anomaly-related) with a group-sparsity property. A robust dictionary learning problem is postulated where a group-lasso penalty is used to encourage most anomaly-related noise components to be zero. The proposed estimator achieves robustness by both identifying the anomalies and removing their effect from the dictionary estimate. Our approach is applied to the problem of ship detection and tracking from full-motion video with promising results.

  5. A multilevel approach to sequential detection of pictorial features

    NASA Technical Reports Server (NTRS)

    Ramapriyan, H. K.

    1976-01-01

    The problem of detecting the local similarity between templates in a given class and a given image using a hierarchically ordered sequential decision rule is examined. It is proposed that the set of templates be partitioned and a 'representative template' be defined for each of the partitions. Several levels of partitioning are defined. Elimination of mismatching locations and termination of computation can take place at each level of detection. Each level of testing is over a more restrictive subset of the template class than the previous level. Criteria are given for selecting representative templates, the ordering of components of a template vector for error evaluation, and the threshold sequences to be used in deciding about a 'match'. Suboptimal solutions are given satisfying these criteria. Examples showing recognition of linear features in test patterns and photographs obtained by aerial and spaceborne sensors are provided.

  6. Spectral anomaly methods for aerial detection using KUT nuisance rejection

    NASA Astrophysics Data System (ADS)

    Detwiler, R. S.; Pfund, D. M.; Myjak, M. J.; Kulisek, J. A.; Seifert, C. E.

    2015-06-01

    This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land-water interfaces.

  7. Anomaly detection in clutter using spectrally enhanced LADAR

    NASA Astrophysics Data System (ADS)

    Chhabra, Puneet S.; Wallace, Andrew M.; Hopgood, James R.

    2015-05-01

    Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camflouaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral signature that does not conform to a prior expectation, represented using a learnt subspace (dictionary) and set of coefficients that capture co-occurring local-patterns using an overlapping temporal window. A modified optimization scheme is proposed for subspace learning based on stochastic approximations. The objective function is augmented with a discriminative term that represents the subspace's separability properties and supports anomaly characterisation. The algorithm detects several man-made objects and anomalous spectra hidden in a dense clutter of vegetation and also allows tree species classification.

  8. Anomaly Detection for Next-Generation Space Launch Ground Operations

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Iverson, David L.; Hall, David R.; Taylor, William M.; Patterson-Hine, Ann; Brown, Barbara; Ferrell, Bob A.; Waterman, Robert D.

    2010-01-01

    NASA is developing new capabilities that will enable future human exploration missions while reducing mission risk and cost. The Fault Detection, Isolation, and Recovery (FDIR) project aims to demonstrate the utility of integrated vehicle health management (IVHM) tools in the domain of ground support equipment (GSE) to be used for the next generation launch vehicles. In addition to demonstrating the utility of IVHM tools for GSE, FDIR aims to mature promising tools for use on future missions and document the level of effort - and hence cost - required to implement an application with each selected tool. One of the FDIR capabilities is anomaly detection, i.e., detecting off-nominal behavior. The tool we selected for this task uses a data-driven approach. Unlike rule-based and model-based systems that require manual extraction of system knowledge, data-driven systems take a radically different approach to reasoning. At the basic level, they start with data that represent nominal functioning of the system and automatically learn expected system behavior. The behavior is encoded in a knowledge base that represents "in-family" system operations. During real-time system monitoring or during post-flight analysis, incoming data is compared to that nominal system operating behavior knowledge base; a distance representing deviation from nominal is computed, providing a measure of how far "out of family" current behavior is. We describe the selected tool for FDIR anomaly detection - Inductive Monitoring System (IMS), how it fits into the FDIR architecture, the operations concept for the GSE anomaly monitoring, and some preliminary results of applying IMS to a Space Shuttle GSE anomaly.

  9. Anomaly-based intrusion detection for SCADA systems

    SciTech Connect

    Yang, D.; Usynin, A.; Hines, J. W.

    2006-07-01

    Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA. These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an auto-associative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks. (authors)

  10. Anomaly detection based on the statistics of hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Catterall, Stephen P.

    2004-10-01

    The purpose of this paper is to introduce a new anomaly detection algorithm for application to hyperspectral imaging (HSI) data. The algorithm uses characterisations of the joint (among wavebands) probability density function (pdf) of HSI data. Traditionally, the pdf has been assumed to be multivariate Gaussian or a mixture of multivariate Gaussians. Other distributions have been considered by previous authors, in particular Elliptically Contoured Distributions (ECDs). In this paper we focus on another distribution, which has only recently been defined and studied. This distribution has a more flexible and extensive set of parameters than the multivariate Gaussian does, yet the pdf takes on a relatively simple mathematical form. The result of all this is a model for the pdf of a hyperspectral image, consisting of a mixture of these distributions. Once a model for the pdf of a hyperspectral image has been obtained, it can be incorporated into an anomaly detector. The new anomaly detector is implemented and applied to some medium wave infra-red (MWIR) hyperspectral imagery. Comparison is made with a well-known anomaly detector, and it will be seen that the results are promising.

  11. GPR anomaly detection with robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Masarik, Matthew P.; Burns, Joseph; Thelen, Brian T.; Kelly, Jack; Havens, Timothy C.

    2015-05-01

    This paper investigates the application of Robust Principal Component Analysis (RPCA) to ground penetrating radar as a means to improve GPR anomaly detection. The method consists of a preprocessing routine to smoothly align the ground and remove the ground response (haircut), followed by mapping to the frequency domain, applying RPCA, and then mapping the sparse component of the RPCA decomposition back to the time domain. A prescreener is then applied to the time-domain sparse component to perform anomaly detection. The emphasis of the RPCA algorithm on sparsity has the effect of significantly increasing the apparent signal-to-clutter ratio (SCR) as compared to the original data, thereby enabling improved anomaly detection. This method is compared to detrending (spatial-mean removal) and classical principal component analysis (PCA), and the RPCA-based processing is seen to provide substantial improvements in the apparent SCR over both of these alternative processing schemes. In particular, the algorithm has been applied to both field collected impulse GPR data and has shown significant improvement in terms of the ROC curve relative to detrending and PCA.

  12. Claycap anomaly detection using hyperspectral remote sensing and lidargrammetric techniques

    NASA Astrophysics Data System (ADS)

    Garcia Quijano, Maria Jose

    Clay capped waste sites are a common method to dispose of the more than 40 million tons of hazardous waste produced in the United States every year (EPA, 2003). Due to the potential threat that hazardous waste poses, it is essential to monitor closely the performance of these facilities. Development of a monitoring system that exploits spectral and topographic changes over hazardous waste sites is presented. Spectral anomaly detection is based upon the observed changes in absolute reflectance and spectral derivatives in centipede grass (Eremochloa ophiuroides) under different irrigation levels. The spectral features that provide the best separability among irrigation levels were identified using Stepwise Discriminant Analyses. The Red Edge Position was selected as a suitable discriminant variable to compare the performance of a global and a local anomaly detection algorithm using a DAIS 3715 hyperspectral image. Topographical anomaly detection is assessed by evaluating the vertical accuracy of two LIDAR datasets acquired from two different altitudes (700 m and 1,200 m AGL) over a clay-capped hazardous site at the Savannah River National Laboratory, SC using the same Optech ALTM 2050 and Cessna 337 platform. Additionally, a quantitative comparison is performed to determine the effect that decreasing platform altitude and increasing posting density have on the vertical accuracy of the LIDAR data collected.

  13. Automatic detection of anomalies in Space Shuttle Main Engine turbopumps

    NASA Technical Reports Server (NTRS)

    Lo, Ching F. (Principal Investigator); Whitehead, Bruce; Wu, Kewei; Rogers, George

    1992-01-01

    A prototype expert system for detecting anomalies in turbopump vibration data has been tested with data from ground tests 902-473, 902-501 902-519, and 904-097 of the Space Shuttle Main Engine!nc (SSME). The expert system has been utilized to analyze vibration ion data from each of the following SSME components: pressure oxidizer turbopump, high-pressure fuel turbo pump, low-pressure fuel turbopump, and preburner boost pump. The expert system locates and classifies peaks in the power spectral density of each 0.4 s window of steady-state data. Peaks representing the fundamental and harmonic frequencies of both shaft rotation and bearing cage rotation are identified by the expert system. Anomalies are then detected on the basis of of two thresholds set individually for the amplitude of each of these peaks: a prior threshold used during the first few windows of data in a test, and a posterior threshold used thereafter. In most cases the anomalies detected by the expert system agree with those reported by NASA. The two cases where there is significant disagreement will be further studied and the system design refined accordingly.

  14. Colorimetric multiplexed immunoassay for sequential detection of tumor markers.

    PubMed

    Wang, Jing; Cao, Ya; Xu, Yuanyuan; Li, Genxi

    2009-10-15

    In this paper, a very simple and easily-operated colorimetric multiplexed immunoassay method for sequential detection of tumor markers has been presented. Magnetic microparticles which are conjugated with biotinylated antibodies are firstly added into the test solution. After fast magnetic collection, these complexes are separated from non-specific proteins. Through different enzymatic reactions of 3,3',5,5'-tetramethylbenzidine (TMB) and o-phenylenediamine (OPD) catalyzed by horseradish peroxidase molecules which are loaded on the surfaces of gold nanoparticles, two antigens carcinoembryonic antigen and alpha-fetoprotein can be detected even with naked eyes. The detection limit obtained from the spectrophotometric measurements is as low as 0.02 ng/mL. This proposed method also has high specificity and reproducibility, as well as excellent efficiency of 94 min for the detection of serum samples. So, this new multiplexed immunoassay method might be a promising approach for the diagnosis of cancer and some other diseases in clinical applications. PMID:19726177

  15. Detection of Low Temperature Volcanogenic Thermal Anomalies with ASTER

    NASA Astrophysics Data System (ADS)

    Pieri, D. C.; Baxter, S.

    2009-12-01

    Predicting volcanic eruptions is a thorny problem, as volcanoes typically exhibit idiosyncratic waxing and/or waning pre-eruption emission, geodetic, and seismic behavior. It is no surprise that increasing our accuracy and precision in eruption prediction depends on assessing the time-progressions of all relevant precursor geophysical, geochemical, and geological phenomena, and on more frequently observing volcanoes when they become restless. The ASTER instrument on the NASA Terra Earth Observing System satellite in low earth orbit provides important capabilities in the area of detection of volcanogenic anomalies such as thermal precursors and increased passive gas emissions. Its unique high spatial resolution multi-spectral thermal IR imaging data (90m/pixel; 5 bands in the 8-12um region), bore-sighted with visible and near-IR imaging data, and combined with off-nadir pointing and stereo-photogrammetric capabilities make ASTER a potentially important volcanic precursor detection tool. We are utilizing the JPL ASTER Volcano Archive (http://ava.jpl.nasa.gov) to systematically examine 80,000+ ASTER volcano images to analyze (a) thermal emission baseline behavior for over 1500 volcanoes worldwide, (b) the form and magnitude of time-dependent thermal emission variability for these volcanoes, and (c) the spatio-temporal limits of detection of pre-eruption temporal changes in thermal emission in the context of eruption precursor behavior. We are creating and analyzing a catalog of the magnitude, frequency, and distribution of volcano thermal signatures worldwide as observed from ASTER since 2000 at 90m/pixel. Of particular interest as eruption precursors are small low contrast thermal anomalies of low apparent absolute temperature (e.g., melt-water lakes, fumaroles, geysers, grossly sub-pixel hotspots), for which the signal-to-noise ratio may be marginal (e.g., scene confusion due to clouds, water and water vapor, fumarolic emissions, variegated ground emissivity, and

  16. Using Physical Models for Anomaly Detection in Control Systems

    NASA Astrophysics Data System (ADS)

    Svendsen, Nils; Wolthusen, Stephen

    Supervisory control and data acquisition (SCADA) systems are increasingly used to operate critical infrastructure assets. However, the inclusion of advanced information technology and communications components and elaborate control strategies in SCADA systems increase the threat surface for external and subversion-type attacks. The problems are exacerbated by site-specific properties of SCADA environments that make subversion detection impractical; and by sensor noise and feedback characteristics that degrade conventional anomaly detection systems. Moreover, potential attack mechanisms are ill-defined and may include both physical and logical aspects.

  17. Inflight and Preflight Detection of Pitot Tube Anomalies

    NASA Technical Reports Server (NTRS)

    Mitchell, Darrell W.

    2014-01-01

    The health and integrity of aircraft sensors play a critical role in aviation safety. Inaccurate or false readings from these sensors can lead to improper decision making, resulting in serious and sometimes fatal consequences. This project demonstrated the feasibility of using advanced data analysis techniques to identify anomalies in Pitot tubes resulting from blockage such as icing, moisture, or foreign objects. The core technology used in this project is referred to as noise analysis because it relates sensors' response time to the dynamic component (noise) found in the signal of these same sensors. This analysis technique has used existing electrical signals of Pitot tube sensors that result from measured processes during inflight conditions and/or induced signals in preflight conditions to detect anomalies in the sensor readings. Analysis and Measurement Services Corporation (AMS Corp.) has routinely used this technology to determine the health of pressure transmitters in nuclear power plants. The application of this technology for the detection of aircraft anomalies is innovative. Instead of determining the health of process monitoring at a steady-state condition, this technology will be used to quickly inform the pilot when an air-speed indication becomes faulty under any flight condition as well as during preflight preparation.

  18. Hierarchical Kohonenen net for anomaly detection in network security.

    PubMed

    Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

    2005-04-01

    A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate. PMID:15828658

  19. Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

    NASA Technical Reports Server (NTRS)

    Viswanathan, Arun

    2012-01-01

    This report describes the results of research into the effects of environment-induced noise on the evaluation process for anomaly detectors in the cyber security domain. This research was conducted during a 10-week summer internship program from the 19th of August, 2012 to the 23rd of August, 2012 at the Jet Propulsion Laboratory in Pasadena, California. The research performed lies within the larger context of the Los Angeles Department of Water and Power (LADWP) Smart Grid cyber security project, a Department of Energy (DoE) funded effort involving the Jet Propulsion Laboratory, California Institute of Technology and the University of Southern California/ Information Sciences Institute. The results of the present effort constitute an important contribution towards building more rigorous evaluation paradigms for anomaly-based intrusion detectors in complex cyber physical systems such as the Smart Grid. Anomaly detection is a key strategy for cyber intrusion detection and operates by identifying deviations from profiles of nominal behavior and are thus conceptually appealing for detecting "novel" attacks. Evaluating the performance of such a detector requires assessing: (a) how well it captures the model of nominal behavior, and (b) how well it detects attacks (deviations from normality). Current evaluation methods produce results that give insufficient insight into the operation of a detector, inevitably resulting in a significantly poor characterization of a detectors performance. In this work, we first describe a preliminary taxonomy of key evaluation constructs that are necessary for establishing rigor in the evaluation regime of an anomaly detector. We then focus on clarifying the impact of the operational environment on the manifestation of attacks in monitored data. We show how dynamic and evolving environments can introduce high variability into the data stream perturbing detector performance. Prior research has focused on understanding the impact of this

  20. Computationally efficient strategies to perform anomaly detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Rossi, Alessandro; Acito, Nicola; Diani, Marco; Corsini, Giovanni

    2012-11-01

    In remote sensing, hyperspectral sensors are effectively used for target detection and recognition because of their high spectral resolution that allows discrimination of different materials in the sensed scene. When a priori information about the spectrum of the targets of interest is not available, target detection turns into anomaly detection (AD), i.e. searching for objects that are anomalous with respect to the scene background. In the field of AD, anomalies can be generally associated to observations that statistically move away from background clutter, being this latter intended as a local neighborhood surrounding the observed pixel or as a large part of the image. In this context, many efforts have been put to reduce the computational load of AD algorithms so as to furnish information for real-time decision making. In this work, a sub-class of AD methods is considered that aim at detecting small rare objects that are anomalous with respect to their local background. Such techniques not only are characterized by mathematical tractability but also allow the design of real-time strategies for AD. Within these methods, one of the most-established anomaly detectors is the RX algorithm which is based on a local Gaussian model for background modeling. In the literature, the RX decision rule has been employed to develop computationally efficient algorithms implemented in real-time systems. In this work, a survey of computationally efficient methods to implement the RX detector is presented where advanced algebraic strategies are exploited to speed up the estimate of the covariance matrix and of its inverse. The comparison of the overall number of operations required by the different implementations of the RX algorithms is given and discussed by varying the RX parameters in order to show the computational improvements achieved with the introduced algebraic strategy.

  1. Visual Mismatch Negativity Reveals Automatic Detection of Sequential Regularity Violation

    PubMed Central

    Stefanics, Gábor; Kimura, Motohiro; Czigler, István

    2011-01-01

    Sequential regularities are abstract rules based on repeating sequences of environmental events, which are useful to make predictions about future events. Here, we tested whether the visual system is capable to detect sequential regularity in unattended stimulus sequences. The visual mismatch negativity (vMMN) component of the event-related potentials is sensitive to the violation of complex regularities (e.g., object-related characteristics, temporal patterns). We used the vMMN component as an index of violation of conditional (if, then) regularities. In the first experiment, to investigate emergence of vMMN and other change-related activity to the violation of conditional rules, red and green disk patterns were delivered in pairs. The majority of pairs comprised of disk patterns with identical colors, whereas in deviant pairs the colors were different. The probabilities of the two colors were equal. The second member of the deviant pairs elicited a vMMN with longer latency and more extended spatial distribution to deviants with lower probability (10 vs. 30%). In the second (control) experiment the emergence of vMMN to violation of a simple, feature-related rule was studied using oddball sequences of stimulus pairs where deviant colors were presented with 20% probabilities. Deviant colored patterns elicited a vMMN, and this component was larger for the second member of the pair, i.e., after a shorter inter-stimulus interval. This result corresponds to the SOA/(v)MMN relationship, expected on the basis of a memory-mismatch process. Our results show that the system underlying vMMN is sensitive to abstract, conditional rules. Representation of such rules implicates expectation of a subsequent event, therefore vMMN can be considered as a correlate of violated predictions about the characteristics of environmental events. PMID:21629766

  2. Rule-based expert system for maritime anomaly detection

    NASA Astrophysics Data System (ADS)

    Roy, Jean

    2010-04-01

    Maritime domain operators/analysts have a mandate to be aware of all that is happening within their areas of responsibility. This mandate derives from the needs to defend sovereignty, protect infrastructures, counter terrorism, detect illegal activities, etc., and it has become more challenging in the past decade, as commercial shipping turned into a potential threat. In particular, a huge portion of the data and information made available to the operators/analysts is mundane, from maritime platforms going about normal, legitimate activities, and it is very challenging for them to detect and identify the non-mundane. To achieve such anomaly detection, they must establish numerous relevant situational facts from a variety of sensor data streams. Unfortunately, many of the facts of interest just cannot be observed; the operators/analysts thus use their knowledge of the maritime domain and their reasoning faculties to infer these facts. As they are often overwhelmed by the large amount of data and information, automated reasoning tools could be used to support them by inferring the necessary facts, ultimately providing indications and warning on a small number of anomalous events worthy of their attention. Along this line of thought, this paper describes a proof-of-concept prototype of a rule-based expert system implementing automated rule-based reasoning in support of maritime anomaly detection.

  3. Detection of Anomalies in Hydrometric Data Using Artificial Intelligence Techniques

    NASA Astrophysics Data System (ADS)

    Lauzon, N.; Lence, B. J.

    2002-12-01

    This work focuses on the detection of anomalies in hydrometric data sequences, such as 1) outliers, which are individual data having statistical properties that differ from those of the overall population; 2) shifts, which are sudden changes over time in the statistical properties of the historical records of data; and 3) trends, which are systematic changes over time in the statistical properties. For the purpose of the design and management of water resources systems, it is important to be aware of these anomalies in hydrometric data, for they can induce a bias in the estimation of water quantity and quality parameters. These anomalies may be viewed as specific patterns affecting the data, and therefore pattern recognition techniques can be used for identifying them. However, the number of possible patterns is very large for each type of anomaly and consequently large computing capacities are required to account for all possibilities using the standard statistical techniques, such as cluster analysis. Artificial intelligence techniques, such as the Kohonen neural network and fuzzy c-means, are clustering techniques commonly used for pattern recognition in several areas of engineering and have recently begun to be used for the analysis of natural systems. They require much less computing capacity than the standard statistical techniques, and therefore are well suited for the identification of outliers, shifts and trends in hydrometric data. This work constitutes a preliminary study, using synthetic data representing hydrometric data that can be found in Canada. The analysis of the results obtained shows that the Kohonen neural network and fuzzy c-means are reasonably successful in identifying anomalies. This work also addresses the problem of uncertainties inherent to the calibration procedures that fit the clusters to the possible patterns for both the Kohonen neural network and fuzzy c-means. Indeed, for the same database, different sets of clusters can be

  4. Segmentation of laser range image for pipe anomaly detection

    NASA Astrophysics Data System (ADS)

    Liu, Zheng; Krys, Dennis

    2010-04-01

    Laser-based scanning can provide a precise surface profile. It has been widely applied to the inspection of pipe inner walls and is often used along with other types of sensors, like sonar and close-circuit television (CCTV). These measurements can be used for pipe deterioration modeling and condition assessment. Geometric information needs to be extracted to characterize anomalies in the pipe profile. Since the laser scanning measures the distance, segmentation with a threshold is a straightforward way to isolate the anomalies. However, threshold with a fixed distance value does not work well for the laser range image due to the intensity inhomogeneity, which is caused the uncontrollable factors during the inspection. Thus, a local binary fitting (LBF) active contour model is employed in this work to process the laser range image and an image phase congruency algorithm is adopted to provide the initial contour as required by the LBF method. The combination of these two approaches can successfully detect the anomalies from a laser range image.

  5. Detection of chiral anomaly and valley transport in Dirac semimetals

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng; Zhang, Enze; Liu, Yanwen; Chen, Zhigang; Liang, Sihang; Cao, Junzhi; Yuan, Xiang; Tang, Lei; Li, Qian; Gu, Teng; Wu, Yizheng; Zou, Jin; Xiu, Faxian

    Chiral anomaly is a non-conservation of chiral charge pumped by the topological nontrivial gauge field, which has been predicted to exist in the emergent quasiparticle excitations in Dirac and Weyl semimetals. However, so far, such pumping process hasn't been clearly demonstrated and lacks a convincing experimental identification. Here, we report the detection of the charge pumping effect and the related valley transport in Cd3As2 driven by external electric and magnetic fields (EB). We find that the chiral imbalance leads to a non-zero gyrotropic coefficient, which can be confirmed by the EB-generated Kerr effect. By applying B along the current direction, we observe a negative magnetoresistance despite the giant positive one at other directions, a clear indication of the chiral anomaly. Remarkably, a robust nonlocal response in valley diffusion originated from the chiral anomaly is persistent up to room temperature when B is parallel to E. The ability to manipulate the valley polarization in Dirac semimetal opens up a brand-new route to understand its fundamental properties through external fields and utilize the chiral fermions in valleytronic applications.

  6. New models for hyperspectral anomaly detection and un-mixing

    NASA Astrophysics Data System (ADS)

    Bernhardt, M.; Heather, J. P.; Smith, M. I.

    2005-06-01

    It is now established that hyperspectral images of many natural backgrounds have statistics with fat-tails. In spite of this, many of the algorithms that are used to process them appeal to the multivariate Gaussian model. In this paper we consider biologically motivated generative models that might explain observed mixtures of vegetation in natural backgrounds. The degree to which these models match the observed fat-tailed distributions is investigated. Having shown how fat-tailed statistics arise naturally from the generative process, the models are put to work in new anomaly detection and un-mixing algorithms. The performance of these algorithms is compared with more traditional approaches.

  7. Inductive inference model of anomaly and misuse detection

    SciTech Connect

    Helman, P.

    1997-01-01

    Further consequences of the inductive inference model of anomaly and misuse detection are presented. The results apply to the design of both probability models for the inductive inference framework and to the design of W&S rule bases. The issues considered include: the role of misuse models M{sub A}, the selection of relevant sets of attributes and the aggregation of their values, the effect on a rule base of nonmaximal rules, and the partitioning of a set of attributes into a left hand and right hand side.

  8. System for Anomaly and Failure Detection (SAFD) system development

    NASA Technical Reports Server (NTRS)

    Oreilly, D.

    1993-01-01

    The System for Anomaly and Failure Detection (SAFD) algorithm was developed as an improvement over the current redline system used in the Space Shuttle Main Engine Controller (SSMEC). Simulation tests and execution against previous hot fire tests demonstrated that the SAFD algorithm can detect engine failures as much as tens of seconds before the redline system recognized the failure. Although the current algorithm only operates during steady state conditions (engine not throttling), work is underway to expand the algorithm to work during transient conditions. This task assignment originally specified developing a platform for executing the algorithm during hot fire tests at Technology Test Bed (TTB) and installing the SAFD algorithm on that platform. Two units were built and installed in the Hardware Simulation Lab and at the TTB in December 1991. Since that time, the task primarily entailed improvement and maintenance of the systems, additional testing to prove the feasibility of the algorithm, and support of hot fire testing. This document addresses the work done since the last report of June 1992. The work on the System for Anomaly and Failure Detection during this period included improving the platform and the algorithm, testing the algorithm against previous test data and in the Hardware Simulation Lab, installing other algorithms on the system, providing support for operations at the Technology Test Bed, and providing routine maintenance.

  9. Anomaly depth detection in trans-admittance mammography: a formula independent of anomaly size or admittivity contrast

    NASA Astrophysics Data System (ADS)

    Zhang, Tingting; Lee, Eunjung; Seo, Jin Keun

    2014-04-01

    Trans-admittance mammography (TAM) is a bioimpedance technique for breast cancer detection. It is based on the comparison of tissue conductivity: cancerous tissue is identified by its higher conductivity in comparison with the surrounding normal tissue. In TAM, the breast is compressed between two electrical plates (in a similar architecture to x-ray mammography). The bottom plate has many sensing point electrodes that provide two-dimensional images (trans-admittance maps) that are induced by voltage differences between the two plates. Multi-frequency admittance data (Neumann data) are measured over the range 50 Hz-500 kHz. TAM aims to determine the location and size of any anomaly from the multi-frequency admittance data. Various anomaly detection algorithms can be used to process TAM data to determine the transverse positions of anomalies. However, existing methods cannot reliably determine the depth or size of an anomaly. Breast cancer detection using TAM would be improved if the depth or size of an anomaly could also be estimated, properties that are independent of the admittivity contrast. A formula is proposed here that can estimate the depth of an anomaly independent of its size and the admittivity contrast. This depth estimation can also be used to derive an estimation of the size of the anomaly. The proposed estimations are verified rigorously under a simplified model. Numerical simulation shows that the proposed method also works well in general settings.

  10. 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.

  11. System for Anomaly and Failure Detection (SAFD) system development

    NASA Astrophysics Data System (ADS)

    Oreilly, D.

    1992-07-01

    This task specified developing the hardware and software necessary to implement the System for Anomaly and Failure Detection (SAFD) algorithm, developed under Technology Test Bed (TTB) Task 21, on the TTB engine stand. This effort involved building two units; one unit to be installed in the Block II Space Shuttle Main Engine (SSME) Hardware Simulation Lab (HSL) at Marshall Space Flight Center (MSFC), and one unit to be installed at the TTB engine stand. Rocketdyne personnel from the HSL performed the task. The SAFD algorithm was developed as an improvement over the current redline system used in the Space Shuttle Main Engine Controller (SSMEC). Simulation tests and execution against previous hot fire tests demonstrated that the SAFD algorithm can detect engine failure as much as tens of seconds before the redline system recognized the failure. Although the current algorithm only operates during steady state conditions (engine not throttling), work is underway to expand the algorithm to work during transient condition.

  12. System for Anomaly and Failure Detection (SAFD) system development

    NASA Technical Reports Server (NTRS)

    Oreilly, D.

    1992-01-01

    This task specified developing the hardware and software necessary to implement the System for Anomaly and Failure Detection (SAFD) algorithm, developed under Technology Test Bed (TTB) Task 21, on the TTB engine stand. This effort involved building two units; one unit to be installed in the Block II Space Shuttle Main Engine (SSME) Hardware Simulation Lab (HSL) at Marshall Space Flight Center (MSFC), and one unit to be installed at the TTB engine stand. Rocketdyne personnel from the HSL performed the task. The SAFD algorithm was developed as an improvement over the current redline system used in the Space Shuttle Main Engine Controller (SSMEC). Simulation tests and execution against previous hot fire tests demonstrated that the SAFD algorithm can detect engine failure as much as tens of seconds before the redline system recognized the failure. Although the current algorithm only operates during steady state conditions (engine not throttling), work is underway to expand the algorithm to work during transient condition.

  13. Log Summarization and Anomaly Detection for TroubleshootingDistributed Systems

    SciTech Connect

    Gunter, Dan; Tierney, Brian L.; Brown, Aaron; Swany, Martin; Bresnahan, John; Schopf, Jennifer M.

    2007-08-01

    Today's system monitoring tools are capable of detectingsystem failures such as host failures, OS errors, and network partitionsin near-real time. Unfortunately, the same cannot yet be said of theend-to-end distributed softwarestack. Any given action, for example,reliably transferring a directory of files, can involve a wide range ofcomplex and interrelated actions across multiple pieces of software:checking user certificates and permissions, getting details for allfiles, performing third-party transfers, understanding re-try policydecisions, etc. We present an infrastructure for troubleshooting complexmiddleware, a general purpose technique for configurable logsummarization, and an anomaly detection technique that works in near-realtime on running Grid middleware. We present results gathered using thisinfrastructure from instrumented Grid middleware and applications runningon the Emulab testbed. From these results, we analyze the effectivenessof several algorithms at accurately detecting a variety of performanceanomalies.

  14. Identification and detection of anomalies through SSME data analysis

    NASA Technical Reports Server (NTRS)

    Pereira, Lisa; Ali, Moonis

    1990-01-01

    The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

  15. Anomaly Detection in Test Equipment via Sliding Mode Observers

    NASA Technical Reports Server (NTRS)

    Solano, Wanda M.; Drakunov, Sergey V.

    2012-01-01

    Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control

  16. Anomaly detection of microstructural defects in continuous fiber reinforced composites

    NASA Astrophysics Data System (ADS)

    Bricker, Stephen; Simmons, J. P.; Przybyla, Craig; Hardie, Russell

    2015-03-01

    Ceramic matrix composites (CMC) with continuous fiber reinforcements have the potential to enable the next generation of high speed hypersonic vehicles and/or significant improvements in gas turbine engine performance due to their exhibited toughness when subjected to high mechanical loads at extreme temperatures (2200F+). Reinforced fiber composites (RFC) provide increased fracture toughness, crack growth resistance, and strength, though little is known about how stochastic variation and imperfections in the material effect material properties. In this work, tools are developed for quantifying anomalies within the microstructure at several scales. The detection and characterization of anomalous microstructure is a critical step in linking production techniques to properties, as well as in accurate material simulation and property prediction for the integrated computation materials engineering (ICME) of RFC based components. It is desired to find statistical outliers for any number of material characteristics such as fibers, fiber coatings, and pores. Here, fiber orientation, or `velocity', and `velocity' gradient are developed and examined for anomalous behavior. Categorizing anomalous behavior in the CMC is approached by multivariate Gaussian mixture modeling. A Gaussian mixture is employed to estimate the probability density function (PDF) of the features in question, and anomalies are classified by their likelihood of belonging to the statistical normal behavior for that feature.

  17. Anomaly detection applied to a materials control and accounting database

    SciTech Connect

    Whiteson, R.; Spanks, L.; Yarbro, T.

    1995-09-01

    An important component of the national mission of reducing the nuclear danger includes accurate recording of the processing and transportation of nuclear materials. Nuclear material storage facilities, nuclear chemical processing plants, and nuclear fuel fabrication facilities collect and store large amounts of data describing transactions that involve nuclear materials. To maintain confidence in the integrity of these data, it is essential to identify anomalies in the databases. Anomalous data could indicate error, theft, or diversion of material. Yet, because of the complex and diverse nature of the data, analysis and evaluation are extremely tedious. This paper describes the authors work in the development of analysis tools to automate the anomaly detection process for the Material Accountability and Safeguards System (MASS) that tracks and records the activities associated with accountable quantities of nuclear material at Los Alamos National Laboratory. Using existing guidelines that describe valid transactions, the authors have created an expert system that identifies transactions that do not conform to the guidelines. Thus, this expert system can be used to focus the attention of the expert or inspector directly on significant phenomena.

  18. A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery.

    PubMed

    Geng, Xiurui; Sun, Kang; Ji, Luyan; Zhao, Yongchao

    2014-01-01

    Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm. PMID:25366706

  19. Near-Real Time Anomaly Detection for Scientific Sensor Data

    NASA Astrophysics Data System (ADS)

    Gallegos, I.; Gates, A.; Tweedie, C. E.; goswami, S.; Jaimes, A.; Gamon, J. A.

    2011-12-01

    Verification (SDVe) prototype tool identified anomalies detected by the expert-specified data properties over the EC data. Scientists using DaProS and SDVe were able to detect environmental variability, instrument malfunctioning, and seasonal and diurnal variability in EC and hyperspectral datasets. The results of the experiment also yielded insights regarding the practices followed by scientists to specify data properties, and it exposed new data properties challenges and a potential method for capturing data quality confidence levels.

  20. Apparatus for detecting a magnetic anomaly contiguous to remote location by squid gradiometer and magnetometer systems

    DOEpatents

    Overton, Jr., William C.; Steyert, Jr., William A.

    1984-01-01

    A superconducting quantum interference device (SQUID) magnetic detection apparatus detects magnetic fields, signals, and anomalies at remote locations. Two remotely rotatable SQUID gradiometers may be housed in a cryogenic environment to search for and locate unambiguously magnetic anomalies. The SQUID magnetic detection apparatus can be used to determine the azimuth of a hydrofracture by first flooding the hydrofracture with a ferrofluid to create an artificial magnetic anomaly therein.

  1. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

    PubMed Central

    Brodley, Carla; Slonim, Donna

    2011-01-01

    Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

  2. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

    PubMed Central

    Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

    2015-01-01

    Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

  3. Online anomaly detection in crowd scenes via structure analysis.

    PubMed

    Yuan, Yuan; Fang, Jianwu; Wang, Qi

    2015-03-01

    Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural context descriptor (SCD) for describing the crowd individual, which originally introduces the potential energy function of particle's interforce in solid-state physics to intuitively conduct vision contextual cueing. For computing the crowd SCD variation effectively, we then design a robust multi-object tracker to associate the targets in different frames, which employs the incremental analytical ability of the 3-D discrete cosine transform (DCT). By online spatial-temporal analyzing the SCD variation of the crowd, the abnormality is finally localized. Our contribution mainly lies on three aspects: 1) the new exploration of abnormal detection from structure modeling where the motion difference between individuals is computed by a novel selective histogram of optical flow that makes the proposed method can deal with more kinds of anomalies; 2) the SCD description that can effectively represent the relationship among the individuals; and 3) the 3-D DCT multi-object tracker that can robustly associate the limited number of (instead of all) targets which makes the tracking analysis in high density crowd situation feasible. Experimental results on several publicly available crowd video datasets verify the effectiveness of the proposed method. PMID:24988603

  4. Sequential detection of a weak target in a hostile ocean environment

    SciTech Connect

    Candy, J V; Sullivan, E J

    2005-03-14

    When the underlying physical phenomenology (medium, sediment, bottom, etc.) is space-time varying along with corresponding nonstationary statistics characterizing noise and uncertainties, then sequential methods must be applied to capture the underlying processes. Sequential detection and estimation techniques offer distinct advantages over batch methods. A reasonable signal processing approach to solve this class of problem is to employ adaptive or parametrically adaptive signal models and noise to capture these phenomena. In this paper, we develop a sequential approach to solve the signal detection problem in a nonstationary environment.

  5. Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

    NASA Astrophysics Data System (ADS)

    Piatyszek, E.; Voignier, P.; Graillot, D.

    2000-05-01

    One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

  6. A Bayesian group sequential approach to safety signal detection.

    PubMed

    Chen, Wenfeng; Zhao, Naiqing; Qin, Guoyou; Chen, Jie

    2013-01-01

    Clinical safety data, usually reported as clinically manifested adverse events (AEs) according to the Medical Dictionary for Regulatory Activities (MedDRA), are routinely collected during the course of a clinical trial involving comparative groups, and periodical monitoring of the safety events is often required to determine whether excessive occurrence of a set of AEs is associated with treatment. To accommodate the structure of reported AEs with the MedDRA system, a Bayesian hierarchical model has been proposed for the analysis of clinical safety data. However, the characteristics of sequential use of the Bayesian method has not been studied. In this paper the Bayesian hierarchical model is applied in a group sequential manner for multiple interim analyses of safety events. A decision-theoretic approach is employed to determine threshold values in the safety signaling process. The proposed approach is illustrated through simulations and a real example. PMID:23331232

  7. Study of Southern Tyrrhenian and Sicilian regions by a sequential procedure to integrate WAM seismic tomographies and Bouguer anomaly data

    NASA Astrophysics Data System (ADS)

    Panepinto, S.; Calo, M. M.; Luzio, D.; Dorbath, C.

    2009-12-01

    A procedure to obtain 3D velocity-density models and earthquake relocation by integrated inversion of P and S wave traveltimes and Bouguer anomaly distribution was applied to a large dataset concerning the Southern Tyrrhenian and Sicilian areas. The seismic dataset was subdivided into two subsets for separate inversions, whose results were later on joined by the WAM (Weighted Average Model) technique. This is a post-processing technique proposed by Calò et al. (2009) by which preliminary tomographic models are unified in a common 3D grid. The first dataset concerns 28873 P and 9990 S arrival times of 1800 earthquakes located in the area 14°30‧ E - 17°E, 37°N - 41°N while the second dataset contains 31250 P and 13588 S arrival-times related to 1951 events located in the area 11° E - 15°48‧ E, 36°30‧N - 39°N. The selected events were recorded at least by 10 stations in the period 1981-2005 and marked by RMS < 0.50 s. The second dataset was integrated with P-wave traveltimes picked in several sesmic profiles carried out in the study region. The Bouguer anomaly measurements were interpolated in the nodes of a 8x8 km regular grid covering the area 12° E - 16°01‧ E, 36°13‧ N - 38°31‧ N. The proposed procedure allows to invert seismic and gravimetric data with a sequential technique to avoid the problematic optimization of the relative weights to assign to the different type of data. A first WAM provides a preliminary Vp, Vs and Vp/Vs models and a first ipocentral relocation. Since the obtained Vs model seems poorly constrained by the S wave arrival times, the Vp model is converted in a new Vs model, through a Vs-Vp correlation law proposed by T.M. Brocher (2005), and used, jointly to the Vp model, as input for a second WAM. The results of this second step are used to derive, by the empirical Brocher’s equations, 2 density distributions associated to the Vp and Vs models. These density models are statistically compared and the distribution of

  8. SCADA Protocol Anomaly Detection Utilizing Compression (SPADUC) 2013

    SciTech Connect

    Gordon Rueff; Lyle Roybal; Denis Vollmer

    2013-01-01

    There is a significant need to protect the nation’s energy infrastructures from malicious actors using cyber methods. Supervisory, Control, and Data Acquisition (SCADA) systems may be vulnerable due to the insufficient security implemented during the design and deployment of these control systems. This is particularly true in older legacy SCADA systems that are still commonly in use. The purpose of INL’s research on the SCADA Protocol Anomaly Detection Utilizing Compression (SPADUC) project was to determine if and how data compression techniques could be used to identify and protect SCADA systems from cyber attacks. Initially, the concept was centered on how to train a compression algorithm to recognize normal control system traffic versus hostile network traffic. Because large portions of the TCP/IP message traffic (called packets) are repetitive, the concept of using compression techniques to differentiate “non-normal” traffic was proposed. In this manner, malicious SCADA traffic could be identified at the packet level prior to completing its payload. Previous research has shown that SCADA network traffic has traits desirable for compression analysis. This work investigated three different approaches to identify malicious SCADA network traffic using compression techniques. The preliminary analyses and results presented herein are clearly able to differentiate normal from malicious network traffic at the packet level at a very high confidence level for the conditions tested. Additionally, the master dictionary approach used in this research appears to initially provide a meaningful way to categorize and compare packets within a communication channel.

  9. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

    PubMed

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

  10. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

    PubMed Central

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

  11. Remote detection of geobotanical anomalies associated with hydrocarbon microseepage

    NASA Technical Reports Server (NTRS)

    Rock, B. N.

    1985-01-01

    As part of the continuing study of the Lost River, West Virginia NASA/Geosat Test Case Site, an extensive soil gas survey of the site was conducted during the summer of 1983. This soil gas survey has identified an order of magnitude methane, ethane, propane, and butane anomaly that is precisely coincident with the linear maple anomaly reported previously. This and other maple anomalies were previously suggested to be indicative of anaerobic soil conditions associated with hydrocarbon microseepage. In vitro studies support the view that anomalous distributions of native tree species tolerant of anaerobic soil conditions may be useful indicators of methane microseepage in heavily vegetated areas of the United States characterized by deciduous forest cover. Remote sensing systems which allow discrimination and mapping of native tree species and/or species associations will provide the exploration community with a means of identifying vegetation distributional anomalies indicative of microseepage.

  12. Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

    NASA Astrophysics Data System (ADS)

    Sun, Hao; Zou, Huanxin; Zhou, Shilin

    2016-03-01

    Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

  13. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    NASA Astrophysics Data System (ADS)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  14. Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions.

    PubMed

    Fiedler, Klaus; Kareev, Yaakov; Avrahami, Judith; Beier, Susanne; Kutzner, Florian; Hütter, Mandy

    2016-01-01

    Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience. PMID:26179055

  15. Inverse sequential detection of parameter changes in developing time series

    NASA Technical Reports Server (NTRS)

    Radok, Uwe; Brown, Timothy J.

    1992-01-01

    Progressive values of two probabilities are obtained for parameter estimates derived from an existing set of values and from the same set enlarged by one or more new values, respectively. One probability is that of erroneously preferring the second of these estimates for the existing data ('type 1 error'), while the second probability is that of erroneously accepting their estimates for the enlarged test ('type 2 error'). A more stable combined 'no change' probability which always falls between 0.5 and 0 is derived from the (logarithmic) width of the uncertainty region of an equivalent 'inverted' sequential probability ratio test (SPRT, Wald 1945) in which the error probabilities are calculated rather than prescribed. A parameter change is indicated when the compound probability undergoes a progressive decrease. The test is explicitly formulated and exemplified for Gaussian samples.

  16. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation.

    PubMed

    Cheng, Kai-Wen; Chen, Yie-Tarng; Fang, Wen-Hsien

    2015-12-01

    This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden. PMID:26394423

  17. Numerical study on the sequential Bayesian approach for radioactive materials detection

    NASA Astrophysics Data System (ADS)

    Qingpei, Xiang; Dongfeng, Tian; Jianyu, Zhu; Fanhua, Hao; Ge, Ding; Jun, Zeng

    2013-01-01

    A new detection method, based on the sequential Bayesian approach proposed by Candy et al., offers new horizons for the research of radioactive detection. Compared with the commonly adopted detection methods incorporated with statistical theory, the sequential Bayesian approach offers the advantages of shorter verification time during the analysis of spectra that contain low total counts, especially in complex radionuclide components. In this paper, a simulation experiment platform implanted with the methodology of sequential Bayesian approach was developed. Events sequences of γ-rays associating with the true parameters of a LaBr3(Ce) detector were obtained based on an events sequence generator using Monte Carlo sampling theory to study the performance of the sequential Bayesian approach. The numerical experimental results are in accordance with those of Candy. Moreover, the relationship between the detection model and the event generator, respectively represented by the expected detection rate (Am) and the tested detection rate (Gm) parameters, is investigated. To achieve an optimal performance for this processor, the interval of the tested detection rate as a function of the expected detection rate is also presented.

  18. Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

    NASA Technical Reports Server (NTRS)

    Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

    2012-01-01

    This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

  19. Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

  20. Energy Detection Based on Undecimated Discrete Wavelet Transform and Its Application in Magnetic Anomaly Detection

    PubMed Central

    Nie, Xinhua; Pan, Zhongming; Zhang, Dasha; Zhou, Han; Chen, Min; Zhang, Wenna

    2014-01-01

    Magnetic anomaly detection (MAD) is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise) with a power spectral density of 1/fa (0detection method based on undecimated discrete wavelet transform (UDWT) is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI) magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT), the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method. PMID:25343484

  1. Lunar magnetic anomalies detected by the Apollo subsatellite magnetometers

    NASA Technical Reports Server (NTRS)

    Hood, L. L.; Coleman, P. J., Jr.; Russell, C. T.; Wilhelms, D. E.

    1979-01-01

    Properties of lunar crustal magnetization thus far deduced from Apollo subsatellite magnetometer data are reviewed using two of the most accurate available magnetic anomaly maps, one covering a portion of the lunar near side and the other a part of the far side. The largest single anomaly found within the region of coverage on the near-side map correlates exactly with a conspicuous light-colored marking in western Oceanus Procellarum called Reiner Gamma. This feature is interpreted as an unusual deposit of ejecta from secondary craters of the large nearby primary impact crater Cavalerius. The mean altitude of the far-side anomaly gap is much higher than that of the near side map and the surface geology is more complex; individual anomaly sources have therefore not yet been identified. The mechanism of magnetization and the origin of the magnetizing field remain unresolved, but the uniformity with which the Reiner Gamma deposit is apparently magnetized, and the north-south depletion of magnetization intensity across a substantial portion of the far side, seem to require the existence of an ambient field, perhaps of global or larger extent.

  2. A novel approach for detection of anomalies using measurement data of the Ironton-Russell bridge

    NASA Astrophysics Data System (ADS)

    Zhang, Fan; Norouzi, Mehdi; Hunt, Victor; Helmicki, Arthur

    2015-04-01

    Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.

  3. Detection of nucleic acids by multiple sequential invasive cleavages

    DOEpatents

    Hall, Jeff G.; Lyamichev, Victor I.; Mast, Andrea L.; Brow, Mary Ann D.

    1999-01-01

    The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

  4. Detection of nucleic acids by multiple sequential invasive cleavages 02

    DOEpatents

    Hall, Jeff G.; Lyamichev, Victor I.; Mast, Andrea L.; Brow, Mary Ann D.

    2002-01-01

    The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

  5. Detection of nucleic acids by multiple sequential invasive cleavages

    DOEpatents

    Hall, Jeff G; Lyamichev, Victor I; Mast, Andrea L; Brow, Mary Ann D

    2012-10-16

    The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The structure-specific nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based on charge. The present invention also provides methods for the detection of non-target cleavage products via the formation of a complete and activated protein binding region. The invention further provides sensitive and specific methods for the detection of human cytomegalovirus nucleic acid in a sample.

  6. Off-line experiments on radionuclide detection based on the sequential Bayesian approach

    NASA Astrophysics Data System (ADS)

    Qingpei, Xiang; Dongfeng, Tian; Fanhua, Hao; Ge, Ding; Jun, Zeng; Fei, Luo

    2013-11-01

    The sequential Bayesian approach proposed by Candy et al. for radioactive materials detection has aroused increasing interest in radiation detection research and is potentially a useful tool for prevention of the transportation of radioactive materials by terrorists. In our previous work, the performance of the sequential Bayesian approach was studied numerically through a simulation experiment platform. In this paper, a sequential Bayesian processor incorporating a LaBr3(Ce) detector, and using the energy, decay rate and emission probability of the radionuclide, is fully developed. Off-line experiments for the performance of the sequential Bayesian approach in radionuclide detection are developed by placing 60Co, 137Cs, 133Ba and 152Eu at various distances from the front face of the LaBr3(Ce) detector. The off-line experiment results agree well with the results of previous numerical experiments. The maximum detection distance is introduced to evaluate the processor‧s ability to detect radionuclides with a specific level of activity.

  7. Anomaly Detection in Multiple Scale for Insider Threat Analysis

    SciTech Connect

    Kim, Yoohwan; Sheldon, Frederick T; Hively, Lee M

    2012-01-01

    We propose a method to quantify malicious insider activity with statistical and graph-based analysis aided with semantic scoring rules. Different types of personal activities or interactions are monitored to form a set of directed weighted graphs. The semantic scoring rules assign higher scores for the events more significant and suspicious. Then we build personal activity profiles in the form of score tables. Profiles are created in multiple scales where the low level profiles are aggregated toward more stable higherlevel profiles within the subject or object hierarchy. Further, the profiles are created in different time scales such as day, week, or month. During operation, the insider s current activity profile is compared to the historical profiles to produce an anomaly score. For each subject with a high anomaly score, a subgraph of connected subjects is extracted to look for any related score movement. Finally the subjects are ranked by their anomaly scores to help the analysts focus on high-scored subjects. The threat-ranking component supports the interaction between the User Dashboard and the Insider Threat Knowledge Base portal. The portal includes a repository for historical results, i.e., adjudicated cases containing all of the information first presented to the user and including any additional insights to help the analysts. In this paper we show the framework of the proposed system and the operational algorithms.

  8. Software Tool Support to Specify and Verify Scientific Sensor Data Properties to Improve Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Gallegos, I.; Gates, A. Q.; Tweedie, C.; Cybershare

    2010-12-01

    Advancements in scientific sensor data acquisition technologies, such as wireless sensor networks and robotic trams equipped with sensors, are increasing the amount of data being collected at field sites . This elevates the challenges of verifying the quality of streamed data and monitoring the correct operation of the instrumentation. Without the ability to evaluate the data collection process at near real-time, scientists can lose valuable time and data. In addition, scientists have to rely on their knowledge and experience in the field to evaluate data quality. Such knowledge is rarely shared or reused by other scientists mostly because of the lack of a well-defined methodology and tool support. Numerous scientific projects address anomaly detection, mostly as part of the verification system’s source code; however, anomaly detection properties, which often are embedded or hard-coded in the source code, are difficult to refine. In addition, a software developer is required to modify the source code every time a new anomaly detection property or a modification to an existing property is needed. This poster describes the tool support that has been developed, based on software engineering techniques, to address these challenges. The overall tool support allows scientists to specify and reuse anomaly detection properties generated using the specification tool and to use the specified properties to conduct automated anomaly detection at near-real time. The anomaly-detection mechanism is independent of the system used to collect the sensor data. With guidance provided by a classification and categorization of anomaly-detection properties, the user specifies properties on scientific sensor data. The properties, which can be associated with particular field sites or instrumentation, document knowledge about data anomalies that otherwise would have limited availability to the scientific community.

  9. Lunar magnetic anomalies detected by the Apollo substatellite magnetometers

    USGS Publications Warehouse

    Hood, L.L.; Coleman, P.J., Jr.; Russell, C.T.; Wilhelms, D.E.

    1979-01-01

    Properties of lunar crustal magnetization thus far deduced from Apollo subsatellite magnetometer data are reviewed using two of the most accurate presently available magnetic anomaly maps - one covering a portion of the lunar near side and the other a part of the far side. The largest single anomaly found within the region of coverage on the near-side map correlates exactly with a conspicuous, light-colored marking in western Oceanus Procellarum called Reiner Gamma. This feature is interpreted as an unusual deposit of ejecta from secondary craters of the large nearby primary impact crater Cavalerius. An age for Cavalerius (and, by implication, for Reiner Gamma) of 3.2 ?? 0.2 ?? 109 y is estimated. The main (30 ?? 60 km) Reiner Gamma deposit is nearly uniformly magnetized in a single direction, with a minimum mean magnetization intensity of ???7 ?? 10-2 G cm3/g (assuming a density of 3 g/cm3), or about 700 times the stable magnetization component of the most magnetic returned samples. Additional medium-amplitude anomalies exist over the Fra Mauro Formation (Imbrium basin ejecta emplaced ???3.9 ?? 109 y ago) where it has not been flooded by mare basalt flows, but are nearly absent over the maria and over the craters Copernicus, Kepler, and Reiner and their encircling ejecta mantles. The mean altitude of the far-side anomaly gap is much higher than that of the near-side map and the surface geology is more complex, so individual anomaly sources have not yet been identified. However, it is clear that a concentration of especially strong sources exists in the vicinity of the craters Van de Graaff and Aitken. Numerical modeling of the associated fields reveals that the source locations do not correspond with the larger primary impact craters of the region and, by analogy with Reiner Gamma, may be less conspicuous secondary crater ejecta deposits. The reason for a special concentration of strong sources in the Van de Graaff-Aitken region is unknown, but may be indirectly

  10. Sequential capillary electrophoresis analysis using optically gated sample injection and UV/vis detection.

    PubMed

    Liu, Xiaoxia; Tian, Miaomiao; Camara, Mohamed Amara; Guo, Liping; Yang, Li

    2015-10-01

    We present sequential CE analysis of amino acids and L-asparaginase-catalyzed enzyme reaction, by combing the on-line derivatization, optically gated (OG) injection and commercial-available UV-Vis detection. Various experimental conditions for sequential OG-UV/vis CE analysis were investigated and optimized by analyzing a standard mixture of amino acids. High reproducibility of the sequential CE analysis was demonstrated with RSD values (n = 20) of 2.23, 2.57, and 0.70% for peak heights, peak areas, and migration times, respectively, and the LOD of 5.0 μM (for asparagine) and 2.0 μM (for aspartic acid) were obtained. With the application of the OG-UV/vis CE analysis, sequential online CE enzyme assay of L-asparaginase-catalyzed enzyme reaction was carried out by automatically and continuously monitoring the substrate consumption and the product formation every 12 s from the beginning to the end of the reaction. The Michaelis constants for the reaction were obtained and were found to be in good agreement with the results of traditional off-line enzyme assays. The study demonstrated the feasibility and reliability of integrating the OG injection with UV/vis detection for sequential online CE analysis, which could be of potential value for online monitoring various chemical reaction and bioprocesses. PMID:26040711

  11. A hyperspectral imagery anomaly detection algorithm based on local three-dimensional orthogonal subspace projection

    NASA Astrophysics Data System (ADS)

    Zhang, Xing; Wen, Gongjian

    2015-10-01

    Anomaly detection (AD) becomes increasingly important in hyperspectral imagery analysis with many practical applications. Local orthogonal subspace projection (LOSP) detector is a popular anomaly detector which exploits local endmembers/eigenvectors around the pixel under test (PUT) to construct background subspace. However, this subspace only takes advantage of the spectral information, but the spatial correlat ion of the background clutter is neglected, which leads to the anomaly detection result sensitive to the accuracy of the estimated subspace. In this paper, a local three dimensional orthogonal subspace projection (3D-LOSP) algorithm is proposed. Firstly, under the jointly use of both spectral and spatial information, three directional background subspaces are created along the image height direction, the image width direction and the spectral direction, respectively. Then, the three corresponding orthogonal subspaces are calculated. After that, each vector along three direction of the local cube is projected onto the corresponding orthogonal subspace. Finally, a composite score is given through the three direction operators. In 3D-LOSP, the anomalies are redefined as the target not only spectrally different to the background, but also spatially distinct. Thanks to the addition of the spatial information, the robustness of the anomaly detection result has been improved greatly by the proposed 3D-LOSP algorithm. It is noteworthy that the proposed algorithm is an expansion of LOSP and this ideology can inspire many other spectral-based anomaly detection methods. Experiments with real hyperspectral images have proved the stability of the detection result.

  12. Multi-Level Anomaly Detection on Time-Varying Graph Data

    SciTech Connect

    Bridges, Robert A; Collins, John P; Ferragut, Erik M; Laska, Jason A; Sullivan, Blair D

    2015-01-01

    This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

  13. Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

    NASA Technical Reports Server (NTRS)

    Dominguez, Jesus A.; Klinko, Steve; Ferrell, Bob; Steinrock, Todd (Technical Monitor)

    2001-01-01

    The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

  14. Analyzing Global Climate System Using Graph Based Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Das, K.; Agrawal, S.; Atluri, G.; Liess, S.; Steinbach, M.; Kumar, V.

    2014-12-01

    Climate networks have been studied for understanding complex relationships between different spatial locations such as community structures and teleconnections. Analysis of time-evolving climate networks reveals changes that occur in those relationships over time and can provide insights for discovering new and complex climate phenomena. We have recently developed a novel data mining technique to discover anomalous relationships from dynamic climate networks. The algorithms efficiently identifies anomalous changes in relationships that cause significant structural changes in the climate network from one time instance to the next. Using this technique we investigated the presence of anomalies in precipitation networks that were constructed based on monthly averages of precipitation recorded at .5 degree resolution during the time period 1982 to 2002. The precipitation network consisted of 10-nearest neighbor graphs for every month's data. Preliminary results on this data set indicate that we were able to discover several anomalies that have been verified to be related to or as the outcome of well known climate phenomena. For instance, one such set of anomalies corresponds to transition from January 1994 (normal conditions) to January 1995 (El-Nino conditions) and include events like worst droughts of the 20th century in Australian Plains, very high rainfall in southeast Asian islands, and drought-like conditions in Peru, Chile, and eastern equatorial Africa during that time period. We plan to further apply our technique to networks constructed out of different climate variables such as sea-level pressure, surface air temperature, wind velocity, 500 geo-potential height etc. at different resolutions. Using this method we hope to develop deeper insights regarding the interactions of multiple climate variables globally over time, which might lead to discovery of previously unknown climate phenomena involving heterogeneous data sources.

  15. Sequential detection of temporal communities by estrangement confinement.

    PubMed

    Kawadia, Vikas; Sreenivasan, Sameet

    2012-01-01

    Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Here, motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables one to find meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement thus provides a principled approach to uncovering temporal communities in evolving networks. PMID:23145317

  16. Detection of anomaly in human retina using Laplacian Eigenmaps and vectorized matched filtering

    NASA Astrophysics Data System (ADS)

    Yacoubou Djima, Karamatou A.; Simonelli, Lucia D.; Cunningham, Denise; Czaja, Wojciech

    2015-03-01

    We present a novel method for automated anomaly detection on auto fluorescent data provided by the National Institute of Health (NIH). This is motivated by the need for new tools to improve the capability of diagnosing macular degeneration in its early stages, track the progression over time, and test the effectiveness of new treatment methods. In previous work, macular anomalies have been detected automatically through multiscale analysis procedures such as wavelet analysis or dimensionality reduction algorithms followed by a classification algorithm, e.g., Support Vector Machine. The method that we propose is a Vectorized Matched Filtering (VMF) algorithm combined with Laplacian Eigenmaps (LE), a nonlinear dimensionality reduction algorithm with locality preserving properties. By applying LE, we are able to represent the data in the form of eigenimages, some of which accentuate the visibility of anomalies. We pick significant eigenimages and proceed with the VMF algorithm that classifies anomalies across all of these eigenimages simultaneously. To evaluate our performance, we compare our method to two other schemes: a matched filtering algorithm based on anomaly detection on single images and a combination of PCA and VMF. LE combined with VMF algorithm performs best, yielding a high rate of accurate anomaly detection. This shows the advantage of using a nonlinear approach to represent the data and the effectiveness of VMF, which operates on the images as a data cube rather than individual images.

  17. Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

    PubMed

    Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

    2016-01-01

    In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

  18. Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

    PubMed Central

    Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

    2016-01-01

    In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

  19. Addressing the Challenges of Anomaly Detection for Cyber Physical Energy Grid Systems

    SciTech Connect

    Ferragut, Erik M; Laska, Jason A; Melin, Alexander M; Czejdo, Bogdan

    2013-01-01

    The consolidation of cyber communications networks and physical control systems within the energy smart grid introduces a number of new risks. Unfortunately, these risks are largely unknown and poorly understood, yet include very high impact losses from attack and component failures. One important aspect of risk management is the detection of anomalies and changes. However, anomaly detection within cyber security remains a difficult, open problem, with special challenges in dealing with false alert rates and heterogeneous data. Furthermore, the integration of cyber and physical dynamics is often intractable. And, because of their broad scope, energy grid cyber-physical systems must be analyzed at multiple scales, from individual components, up to network level dynamics. We describe an improved approach to anomaly detection that combines three important aspects. First, system dynamics are modeled using a reduced order model for greater computational tractability. Second, a probabilistic and principled approach to anomaly detection is adopted that allows for regulation of false alerts and comparison of anomalies across heterogeneous data sources. Third, a hierarchy of aggregations are constructed to support interactive and automated analyses of anomalies at multiple scales.

  20. Physics-based, Bayesian sequential detection method and system for radioactive contraband

    DOEpatents

    Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E

    2014-03-18

    A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

  1. A Distance Measure for Attention Focusing and Anomaly Detection in Systems Monitoring

    NASA Technical Reports Server (NTRS)

    Doyle, R.

    1994-01-01

    Any attempt to introduce automation into the monitoring of complex physical systems must start from a robust anomaly detection capability. This task is far from straightforward, for a single definition of what constitutes an anomaly is difficult to come by. In addition, to make the monitoring process efficient, and to avoid the potential for information overload on human operators, attention focusing must also be addressed. When an anomaly occurs, more often than not several sensors are affected, and the partially redundant information they provide can be confusing, particularly in a crisis situation where a response is needed quickly. Previous results on extending traditional anomaly detection techniques are summarized. The focus of this paper is a new technique for attention focusing.

  2. Dual-Functional Nanoparticles for In Situ Sequential Detection and Imaging of ATP and H2 O2.

    PubMed

    Ren, Hong; Long, Zi; Cui, Mengchao; Shao, Kang; Zhou, Kaixiang; Ouyang, Jin; Na, Na

    2016-08-01

    Within a complex biological sample, the in situ sequential detection of multiple molecules without any interference is greatly desirable. Dual-functional nanoparticles are constructed, with the enzyme-based core-shell structures, for the in situ sequential detection of ATP and H2 O2 within the same biological system. PMID:27337683

  3. Security inspection in ports by anomaly detection using hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Rivera, Javier; Valverde, Fernando; Saldaña, Manuel; Manian, Vidya

    2013-05-01

    Applying hyperspectral imaging technology in port security is crucial for the detection of possible threats or illegal activities. One of the most common problems that cargo suffers is tampering. This represents a danger to society because it creates a channel to smuggle illegal and hazardous products. If a cargo is altered, security inspections on that cargo should contain anomalies that reveal the nature of the tampering. Hyperspectral images can detect anomalies by gathering information through multiple electromagnetic bands. The spectrums extracted from these bands can be used to detect surface anomalies from different materials. Based on this technology, a scenario was built in which a hyperspectral camera was used to inspect the cargo for any surface anomalies and a user interface shows the results. The spectrum of items, altered by different materials that can be used to conceal illegal products, is analyzed and classified in order to provide information about the tampered cargo. The image is analyzed with a variety of techniques such as multiple features extracting algorithms, autonomous anomaly detection, and target spectrum detection. The results will be exported to a workstation or mobile device in order to show them in an easy -to-use interface. This process could enhance the current capabilities of security systems that are already implemented, providing a more complete approach to detect threats and illegal cargo.

  4. Advancements of data anomaly detection research in wireless sensor networks: a survey and open issues.

    PubMed

    Rassam, Murad A; Zainal, Anazida; Maarof, Mohd Aizaini

    2013-01-01

    Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept "Internet of Things" has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed. PMID:23966182

  5. Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

    PubMed Central

    Rassam, Murad A.; Zainal, Anazida; Maarof, Mohd Aizaini

    2013-01-01

    Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed. PMID:23966182

  6. Improving Cyber-Security of Smart Grid Systems via Anomaly Detection and Linguistic Domain Knowledge

    SciTech Connect

    Ondrej Linda; Todd Vollmer; Milos Manic

    2012-08-01

    The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this work. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies the previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. Furthermore, the developed system dynamically adjusts the sensitivity threshold of each anomaly detection algorithm based on domain knowledge about the specific network system. It is proposed to model this domain knowledge using Interval Type-2 Fuzzy Logic rules, which linguistically describe the relationship between various features of the network communication and the possibility of a cyber attack. The proposed method was tested on experimental smart grid system demonstrating enhanced cyber-security.

  7. Sequential Model Selection based Segmentation to Detect DNA Copy Number Variation

    PubMed Central

    Hu, Jianhua; Zhang, Liwen; Wang, Huixia Judy

    2016-01-01

    Summary Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation. PMID:26954760

  8. Incremental classification learning for anomaly detection in medical images

    NASA Astrophysics Data System (ADS)

    Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

    2009-02-01

    Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

  9. Fuzzy neural networks for classification and detection of anomalies.

    PubMed

    Meneganti, M; Saviello, F S; Tagliaferri, R

    1998-01-01

    In this paper, a new learning algorithm for the Simpson's fuzzy min-max neural network is presented. It overcomes some undesired properties of the Simpson's model: specifically, in it there are neither thresholds that bound the dimension of the hyperboxes nor sensitivity parameters. Our new algorithm improves the network performance: in fact, the classification result does not depend on the presentation order of the patterns in the training set, and at each step, the classification error in the training set cannot increase. The new neural model is particularly useful in classification problems as it is shown by comparison with some fuzzy neural nets cited in literature (Simpson's min-max model, fuzzy ARTMAP proposed by Carpenter, Grossberg et al. in 1992, adaptive fuzzy systems as introduced by Wang in his book) and the classical multilayer perceptron neural network with backpropagation learning algorithm. The tests were executed on three different classification problems: the first one with two-dimensional synthetic data, the second one with realistic data generated by a simulator to find anomalies in the cooling system of a blast furnace, and the third one with real data for industrial diagnosis. The experiments were made following some recent evaluation criteria known in literature and by using Microsoft Visual C++ development environment on personal computers. PMID:18255771

  10. Anomaly detection of turbopump vibration in Space Shuttle Main Engine using statistics and neural networks

    NASA Astrophysics Data System (ADS)

    Lo, C. F.; Wu, K.; Whitehead, B. A.

    1993-06-01

    The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.

  11. Anomaly detection of turbopump vibration in Space Shuttle Main Engine using statistics and neural networks

    NASA Technical Reports Server (NTRS)

    Lo, C. F.; Wu, K.; Whitehead, B. A.

    1993-01-01

    The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.

  12. Adaptive sequential algorithms for detecting targets in a heavy IR clutter

    NASA Astrophysics Data System (ADS)

    Tartakovsky, Alexander G.; Kligys, Skirmantas; Petrov, Anton

    1999-10-01

    Cruise missiles over land and sea cluttered background are serious threats to search and track systems. In general, these threats are stealth in both the IR and radio frequency bands. That is, their thermal IR signature and their radar cross section can be quite small. This paper discusses adaptive sequential detection methods which exploit 'track- before-detect' technology for detection glow-SNR targets in IR search and track (IRST) systems. Despite the fact that we focus on an IRST against cruise missiles over land and sea cluttered backgrounds, the results are applicable to other sensors and other kinds of targets.

  13. Apparatus and method for detecting a magnetic anomaly contiguous to remote location by SQUID gradiometer and magnetometer systems

    DOEpatents

    Overton, W.C. Jr.; Steyert, W.A. Jr.

    1981-05-22

    A superconducting quantum interference device (SQUID) magnetic detection apparatus detects magnetic fields, signals, and anomalies at remote locations. Two remotely rotatable SQUID gradiometers may be housed in a cryogenic environment to search for and locate unambiguously magnetic anomalies. The SQUID magnetic detection apparatus can be used to determine the azimuth of a hydrofracture by first flooding the hydrofracture with a ferrofluid to create an artificial magnetic anomaly therein.

  14. A novel anomaly detection approach based on clustering and decision-level fusion

    NASA Astrophysics Data System (ADS)

    Zhong, Shengwei; Zhang, Ye

    2015-09-01

    In hyperspectral image processing, anomaly detection is a valuable way of searching targets whose spectral characteristics are not known, and the estimation of background signals is the key procedure. On account of the high dimensionality and complexity of hyperspectral image, dimensionality reduction and background suppression is necessary. In addition, the complementarity of different anomaly detection algorithms can be utilized to improve the effectiveness of anomaly detection. In this paper, we propose a novel method of anomaly detection, which is based on clustering of optimized K-means and decision-level fusion. In our proposed method, pixels with similar features are firstly clustered using an optimized k-means method. Secondly, dimensionality reduction is conducted using principle component analysis to reduce the amount of calculation. Then, to increase the accuracy of detection and decrease the false-alarm ratio, both Reed-Xiaoli (RX) and Kernel RX algorithm are used on processed image. Lastly, a decision-level fusion is processed on the detection results. A simulated hyperspectral image and a real hyperspectral one are both used to evaluate the performance of our proposed method. Visual analysis and quantative analysis of receiver operating characteristic (ROC) curves show that our algorithm can achieve better performance when compared with other classic approaches and state-of-the-art approaches.

  15. Low frequency of Y anomaly detected in Australian Brahman cow-herds.

    PubMed

    de Camargo, Gregório M F; Porto-Neto, Laercio R; Fortes, Marina R S; Bunch, Rowan J; Tonhati, Humberto; Reverter, Antonio; Moore, Stephen S; Lehnert, Sigrid A

    2015-02-01

    Indicine cattle have lower reproductive performance in comparison to taurine. A chromosomal anomaly characterized by the presence Y markers in females was reported and associated with infertility in cattle. The aim of this study was to investigate the occurrence of the anomaly in Brahman cows. Brahman cows (n = 929) were genotyped for a Y chromosome specific region using real time-PCR. Only six out of 929 cows had the anomaly (0.6%). The anomaly frequency was much lower in Brahman cows than in the crossbred population, in which it was first detected. It also seems that the anomaly doesn't affect pregnancy in the population. Due to the low frequency, association analyses couldn't be executed. Further, SNP signal of the pseudoautosomal boundary region of the Y chromosome was investigated using HD SNP chip. Pooled DNA of "non-pregnant" and "pregnant" cows were compared and no difference in SNP allele frequency was observed. Results suggest that the anomaly had a very low frequency in this Australian Brahman population and had no effect on reproduction. Further studies comparing pregnant cows and cows that failed to conceive should be executed after better assembly and annotation of the Y chromosome in cattle. PMID:25750859

  16. Low frequency of Y anomaly detected in Australian Brahman cow-herds

    PubMed Central

    de Camargo, Gregório M.F.; Porto-Neto, Laercio R.; Fortes, Marina R.S.; Bunch, Rowan J.; Tonhati, Humberto; Reverter, Antonio; Moore, Stephen S.; Lehnert, Sigrid A.

    2015-01-01

    Indicine cattle have lower reproductive performance in comparison to taurine. A chromosomal anomaly characterized by the presence Y markers in females was reported and associated with infertility in cattle. The aim of this study was to investigate the occurrence of the anomaly in Brahman cows. Brahman cows (n = 929) were genotyped for a Y chromosome specific region using real time-PCR. Only six out of 929 cows had the anomaly (0.6%). The anomaly frequency was much lower in Brahman cows than in the crossbred population, in which it was first detected. It also seems that the anomaly doesn't affect pregnancy in the population. Due to the low frequency, association analyses couldn't be executed. Further, SNP signal of the pseudoautosomal boundary region of the Y chromosome was investigated using HD SNP chip. Pooled DNA of “non-pregnant” and “pregnant” cows were compared and no difference in SNP allele frequency was observed. Results suggest that the anomaly had a very low frequency in this Australian Brahman population and had no effect on reproduction. Further studies comparing pregnant cows and cows that failed to conceive should be executed after better assembly and annotation of the Y chromosome in cattle. PMID:25750859

  17. Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

    NASA Astrophysics Data System (ADS)

    Koeppen, W. C.; Pilger, E.; Wright, R.

    2011-07-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

  18. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    PubMed

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency. PMID:26904830

  19. Using new edges for anomaly detection in computer networks

    DOEpatents

    Neil, Joshua Charles

    2015-05-19

    Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

  20. Detection of anomalies in radio tomography of asteroids: Source count and forward errors

    NASA Astrophysics Data System (ADS)

    Pursiainen, S.; Kaasalainen, M.

    2014-09-01

    The purpose of this study was to advance numerical methods for radio tomography in which asteroid's internal electric permittivity distribution is to be recovered from radio frequency data gathered by an orbiter. The focus was on signal generation via multiple sources (transponders) providing one potential, or even essential, scenario to be implemented in a challenging in situ measurement environment and within tight payload limits. As a novel feature, the effects of forward errors including noise and a priori uncertainty of the forward (data) simulation were examined through a combination of the iterative alternating sequential (IAS) inverse algorithm and finite-difference time-domain (FDTD) simulation of time evolution data. Single and multiple source scenarios were compared in two-dimensional localization of permittivity anomalies. Three different anomaly strengths and four levels of total noise were tested. Results suggest, among other things, that multiple sources can be necessary to obtain appropriate results, for example, to distinguish three separate anomalies with permittivity less or equal than half of the background value, relevant in recovery of internal cavities.

  1. Radiation detection method and system using the sequential probability ratio test

    DOEpatents

    Nelson, Karl E.; Valentine, John D.; Beauchamp, Brock R.

    2007-07-17

    A method and system using the Sequential Probability Ratio Test to enhance the detection of an elevated level of radiation, by determining whether a set of observations are consistent with a specified model within a given bounds of statistical significance. In particular, the SPRT is used in the present invention to maximize the range of detection, by providing processing mechanisms for estimating the dynamic background radiation, adjusting the models to reflect the amount of background knowledge at the current point in time, analyzing the current sample using the models to determine statistical significance, and determining when the sample has returned to the expected background conditions.

  2. Dynamic analysis methods for detecting anomalies in asynchronously interacting systems

    SciTech Connect

    Kumar, Akshat; Solis, John Hector; Matschke, Benjamin

    2014-01-01

    Detecting modifications to digital system designs, whether malicious or benign, is problematic due to the complexity of the systems being analyzed. Moreover, static analysis techniques and tools can only be used during the initial design and implementation phases to verify safety and liveness properties. It is computationally intractable to guarantee that any previously verified properties still hold after a system, or even a single component, has been produced by a third-party manufacturer. In this paper we explore new approaches for creating a robust system design by investigating highly-structured computational models that simplify verification and analysis. Our approach avoids the need to fully reconstruct the implemented system by incorporating a small verification component that dynamically detects for deviations from the design specification at run-time. The first approach encodes information extracted from the original system design algebraically into a verification component. During run-time this component randomly queries the implementation for trace information and verifies that no design-level properties have been violated. If any deviation is detected then a pre-specified fail-safe or notification behavior is triggered. Our second approach utilizes a partitioning methodology to view liveness and safety properties as a distributed decision task and the implementation as a proposed protocol that solves this task. Thus the problem of verifying safety and liveness properties is translated to that of verifying that the implementation solves the associated decision task. We develop upon results from distributed systems and algebraic topology to construct a learning mechanism for verifying safety and liveness properties from samples of run-time executions.

  3. Dual Use Corrosion Inhibitor and Penetrant for Anomaly Detection in Neutron/X Radiography

    NASA Technical Reports Server (NTRS)

    Hall, Phillip B. (Inventor); Novak, Howard L. (Inventor)

    2004-01-01

    A dual purpose corrosion inhibitor and penetrant composition sensitive to radiography interrogation is provided. The corrosion inhibitor mitigates or eliminates corrosion on the surface of a substrate upon which the corrosion inhibitor is applied. In addition, the corrosion inhibitor provides for the attenuation of a signal used during radiography interrogation thereby providing for detection of anomalies on the surface of the substrate.

  4. A comparison of algorithms for anomaly detection in safeguards and computer security systems using neural networks

    SciTech Connect

    Howell, J.A.; Whiteson, R.

    1992-08-01

    Detection of anomalies in nuclear safeguards and computer security systems is a tedious and time-consuming task. It typically requires the examination of large amounts of data for unusual patterns of activity. Neural networks provide a flexible pattern-recognition capability that can easily be adapted for these purposes. In this paper, we discuss architectures for accomplishing this task.

  5. A comparison of algorithms for anomaly detection in safeguards and computer security systems using neural networks

    SciTech Connect

    Howell, J.A.; Whiteson, R.

    1992-01-01

    Detection of anomalies in nuclear safeguards and computer security systems is a tedious and time-consuming task. It typically requires the examination of large amounts of data for unusual patterns of activity. Neural networks provide a flexible pattern-recognition capability that can easily be adapted for these purposes. In this paper, we discuss architectures for accomplishing this task.

  6. Anomaly Detection in the Right Hemisphere: The Influence of Visuospatial Factors

    ERIC Educational Resources Information Center

    Smith, Stephen D.; Dixon, Michael J.; Tays, William J.; Bulman-Fleming, M. Barbara

    2004-01-01

    Previous research with both brain-damaged and neurologically intact populations has demonstrated that the right cerebral hemisphere (RH) is superior to the left cerebral hemisphere (LH) at detecting anomalies (or incongruities) in objects (Ramachandran, 1995; Smith, Tays, Dixon, & Bulman-Fleming, 2002). The current research assesses whether the RH…

  7. Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

    NASA Astrophysics Data System (ADS)

    Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

    2012-04-01

    This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

  8. Magnetic anomaly detection (MAD) of ferromagnetic pipelines using principal component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Sheinker, Arie; Moldwin, Mark B.

    2016-04-01

    The magnetic anomaly detection (MAD) method is used for detection of visually obscured ferromagnetic objects. The method exploits the magnetic field originating from the ferromagnetic object, which constitutes an anomaly in the ambient earth’s magnetic field. Traditionally, MAD is used to detect objects with a magnetic field of a dipole structure, where far from the object it can be considered as a point source. In the present work, we expand MAD to the case of a non-dipole source, i.e. a ferromagnetic pipeline. We use principal component analysis (PCA) to calculate the principal components, which are then employed to construct an effective detector. Experiments conducted in our lab with real-world data validate the above analysis. The simplicity, low computational complexity, and the high detection rate make the proposed detector attractive for real-time, low power applications.

  9. A New Curb Detection Method for Unmanned Ground Vehicles Using 2D Sequential Laser Data

    PubMed Central

    Liu, Zhao; Wang, Jinling; Liu, Daxue

    2013-01-01

    Curb detection is an important research topic in environment perception, which is an essential part of unmanned ground vehicle (UGV) operations. In this paper, a new curb detection method using a 2D laser range finder in a semi-structured environment is presented. In the proposed method, firstly, a local Digital Elevation Map (DEM) is built using 2D sequential laser rangefinder data and vehicle state data in a dynamic environment and a probabilistic moving object deletion approach is proposed to cope with the effect of moving objects. Secondly, the curb candidate points are extracted based on the moving direction of the vehicle in the local DEM. Finally, the straight and curved curbs are detected by the Hough transform and the multi-model RANSAC algorithm, respectively. The proposed method can detect the curbs robustly in both static and typical dynamic environments. The proposed method has been verified in real vehicle experiments. PMID:23325170

  10. A new curb detection method for unmanned ground vehicles using 2D sequential laser data.

    PubMed

    Liu, Zhao; Wang, Jinling; Liu, Daxue

    2013-01-01

    Curb detection is an important research topic in environment perception, which is an essential part of unmanned ground vehicle (UGV) operations. In this paper, a new curb detection method using a 2D laser range finder in a semi-structured environment is presented. In the proposed method, firstly, a local Digital Elevation Map (DEM) is built using 2D sequential laser rangefinder data and vehicle state data in a dynamic environment and a probabilistic moving object deletion approach is proposed to cope with the effect of moving objects. Secondly, the curb candidate points are extracted based on the moving direction of the vehicle in the local DEM. Finally, the straight and curved curbs are detected by the Hough transform and the multi-model RANSAC algorithm, respectively. The proposed method can detect the curbs robustly in both static and typical dynamic environments. The proposed method has been verified in real vehicle experiments. PMID:23325170

  11. Towards spatial localisation of harmful algal blooms; statistics-based spatial anomaly detection

    NASA Astrophysics Data System (ADS)

    Shutler, J. D.; Grant, M. G.; Miller, P. I.

    2005-10-01

    Harmful algal blooms are believed to be increasing in occurrence and their toxins can be concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested. As a result fisheries and beaches in the vicinity of blooms may need to be closed and the local population informed. For this avoidance planning timely information on the existence of a bloom, its species and an accurate map of its extent would be prudent. Current research to detect these blooms from space has mainly concentrated on spectral approaches towards determining species. We present a novel statistics-based background-subtraction technique that produces improved descriptions of an anomaly's extent from remotely-sensed ocean colour data. This is achieved by extracting bulk information from a background model; this is complemented by a computer vision ramp filtering technique to specifically detect the perimeter of the anomaly. The complete extraction technique uses temporal-variance estimates which control the subtraction of the scene of interest from the time-weighted background estimate, producing confidence maps of anomaly extent. Through the variance estimates the method learns the associated noise present in the data sequence, providing robustness, and allowing generic application. Further, the use of the median for the background model reduces the effects of anomalies that appear within the time sequence used to generate it, allowing seasonal variations in the background levels to be closely followed. To illustrate the detection algorithm's application, it has been applied to two spectrally different oceanic regions.

  12. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

    SciTech Connect

    Chen, Yan

    2013-12-05

    Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm is significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.

  13. Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

    NASA Technical Reports Server (NTRS)

    Budalakoti, Suratna; Srivastava, Ashok N.; Akella, Ram; Turkov, Eugene

    2006-01-01

    This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the paper discusses existing algorithms, such as the Hunt-Szymanski algorithm, that have low time-complexity. We then discuss why these algorithms often do not work well in practice and present a new hybrid algorithm for computing the LCS that, in our tests, outperforms the Hunt-Szymanski algorithm by a factor of five. The second part of the paper presents new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence, compared to more normal sequences. The algorithms we present are general and domain-independent, so we discuss applications in related areas such as anomaly detection.

  14. Structural Anomaly Detection Using Fiber Optic Sensors and Inverse Finite Element Method

    NASA Technical Reports Server (NTRS)

    Quach, Cuong C.; Vazquez, Sixto L.; Tessler, Alex; Moore, Jason P.; Cooper, Eric G.; Spangler, Jan. L.

    2005-01-01

    NASA Langley Research Center is investigating a variety of techniques for mitigating aircraft accidents due to structural component failure. One technique under consideration combines distributed fiber optic strain sensing with an inverse finite element method for detecting and characterizing structural anomalies anomalies that may provide early indication of airframe structure degradation. The technique identifies structural anomalies that result in observable changes in localized strain but do not impact the overall surface shape. Surface shape information is provided by an Inverse Finite Element Method that computes full-field displacements and internal loads using strain data from in-situ fiberoptic sensors. This paper describes a prototype of such a system and reports results from a series of laboratory tests conducted on a test coupon subjected to increasing levels of damage.

  15. Detection of Surface Temperature Anomalies in the Coso Geothermal Field Using Thermal Infrared Remote Sensing

    NASA Astrophysics Data System (ADS)

    Coolbaugh, M.; Eneva, M.; Bjornstad, S.; Combs, J.

    2007-12-01

    We use thermal infrared (TIR) data from the spaceborne ASTER instrument to detect surface temperature anomalies in the Coso geothermal field in eastern California. The identification of such anomalies in a known geothermal area serves as an incentive to search for similar markers to areas of unknown geothermal potential. We carried out field measurements concurrently with the collection of ASTER images. The field data included reflectance, subsurface and surface temperatures, and radiosonde atmospheric profiles. We apply techniques specifically targeted to correct for thermal artifacts caused by topography, albedo, and thermal inertia. This approach has the potential to reduce data noise and to reveal thermal anomalies which are not distinguishable in the uncorrected imagery. The combination of remote sensing and field data can be used to evaluate the performance of TIR remote sensing as a cost-effective geothermal exploration tool.

  16. Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM

    NASA Astrophysics Data System (ADS)

    Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

    Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it is unable to detect unknown attacks, i.e., 0-day attacks, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack by an automated manner. Over the past few years, several studies on solving these problems have been made on anomaly detection using unsupervised learning techniques such as clustering, one-class support vector machine (SVM), etc. Although they enable one to construct intrusion detection models at low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we propose a new anomaly detection method based on clustering and multiple one-class SVM in order to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that our approach outperforms the existing algorithms reported in the literature; especially in detection of unknown attacks.

  17. Sequential detection of influenza epidemics by the Kolmogorov-Smirnov test

    PubMed Central

    2012-01-01

    Background Influenza is a well known and common human respiratory infection, causing significant morbidity and mortality every year. Despite Influenza variability, fast and reliable outbreak detection is required for health resource planning. Clinical health records, as published by the Diagnosticat database in Catalonia, host useful data for probabilistic detection of influenza outbreaks. Methods This paper proposes a statistical method to detect influenza epidemic activity. Non-epidemic incidence rates are modeled against the exponential distribution, and the maximum likelihood estimate for the decaying factor λ is calculated. The sequential detection algorithm updates the parameter as new data becomes available. Binary epidemic detection of weekly incidence rates is assessed by Kolmogorov-Smirnov test on the absolute difference between the empirical and the cumulative density function of the estimated exponential distribution with significance level 0 ≤ α ≤ 1. Results The main advantage with respect to other approaches is the adoption of a statistically meaningful test, which provides an indicator of epidemic activity with an associated probability. The detection algorithm was initiated with parameter λ0 = 3.8617 estimated from the training sequence (corresponding to non-epidemic incidence rates of the 2008-2009 influenza season) and sequentially updated. Kolmogorov-Smirnov test detected the following weeks as epidemic for each influenza season: 50−10 (2008-2009 season), 38−50 (2009-2010 season), weeks 50−9 (2010-2011 season) and weeks 3 to 12 for the current 2011-2012 season. Conclusions Real medical data was used to assess the validity of the approach, as well as to construct a realistic statistical model of weekly influenza incidence rates in non-epidemic periods. For the tested data, the results confirmed the ability of the algorithm to detect the start and the end of epidemic periods. In general, the proposed test could be applied to other data

  18. Application of Artificial Bee Colony algorithm in TEC seismo-ionospheric anomalies detection

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2015-09-01

    In this study, the efficiency of Artificial Bee Colony (ABC) algorithm is investigated to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of some strong earthquakes including Chile (27 February 2010; 01 April 2014), Varzeghan (11 August 2012), Saravan (16 April 2013) and Papua New Guinea (29 March 2015). In comparison with other anomaly detection algorithms, ABC has a number of advantages which can be numerated as (1) detection of discord patterns in a large non linear data during a short time, (2) simplicity, (3) having less control parameters and (4) efficiently for solving multimodal and multidimensional optimization problems. Also the results of this study acknowledge the TEC time-series as a robust earthquake precursor.

  19. A new approach for structural health monitoring by applying anomaly detection on strain sensor data

    NASA Astrophysics Data System (ADS)

    Trichias, Konstantinos; Pijpers, Richard; Meeuwissen, Erik

    2014-03-01

    Structural Health Monitoring (SHM) systems help to monitor critical infrastructures (bridges, tunnels, etc.) remotely and provide up-to-date information about their physical condition. In addition, it helps to predict the structure's life and required maintenance in a cost-efficient way. Typically, inspection data gives insight in the structural health. The global structural behavior, and predominantly the structural loading, is generally measured with vibration and strain sensors. Acoustic emission sensors are more and more used for measuring global crack activity near critical locations. In this paper, we present a procedure for local structural health monitoring by applying Anomaly Detection (AD) on strain sensor data for sensors that are applied in expected crack path. Sensor data is analyzed by automatic anomaly detection in order to find crack activity at an early stage. This approach targets the monitoring of critical structural locations, such as welds, near which strain sensors can be applied during construction and/or locations with limited inspection possibilities during structural operation. We investigate several anomaly detection techniques to detect changes in statistical properties, indicating structural degradation. The most effective one is a novel polynomial fitting technique, which tracks slow changes in sensor data. Our approach has been tested on a representative test structure (bridge deck) in a lab environment, under constant and variable amplitude fatigue loading. In both cases, the evolving cracks at the monitored locations were successfully detected, autonomously, by our AD monitoring tool.

  20. Sequential structural damage diagnosis algorithm using a change point detection method

    NASA Astrophysics Data System (ADS)

    Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

    2013-11-01

    This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

  1. Detecting errors and anomalies in computerized materials control and accountability databases

    SciTech Connect

    Whiteson, R.; Hench, K.; Yarbro, T.; Baumgart, C.

    1998-12-31

    The Automated MC and A Database Assessment project is aimed at improving anomaly and error detection in materials control and accountability (MC and A) databases and increasing confidence in the data that they contain. Anomalous data resulting in poor categorization of nuclear material inventories greatly reduces the value of the database information to users. Therefore it is essential that MC and A data be assessed periodically for anomalies or errors. Anomaly detection can identify errors in databases and thus provide assurance of the integrity of data. An expert system has been developed at Los Alamos National Laboratory that examines these large databases for anomalous or erroneous data. For several years, MC and A subject matter experts at Los Alamos have been using this automated system to examine the large amounts of accountability data that the Los Alamos Plutonium Facility generates. These data are collected and managed by the Material Accountability and Safeguards System, a near-real-time computerized nuclear material accountability and safeguards system. This year they have expanded the user base, customizing the anomaly detector for the varying requirements of different groups of users. This paper describes the progress in customizing the expert systems to the needs of the users of the data and reports on their results.

  2. Millimeter Wave Detection of Localized Anomalies in the Space Shuttle External Fuel Tank Insulating Foam

    NASA Technical Reports Server (NTRS)

    Kharkovsky, S.; Case, J. T.; Abou-Khousa, M. A.; Zoughi, R.; Hepburn, F.

    2006-01-01

    The Space Shuttle Columbia's catastrophic accident emphasizes the growing need for developing and applying effective, robust and life-cycle oriented nondestructive testing (NDT) methods for inspecting the shuttle external fuel tank spray on foam insulation (SOFI). Millimeter wave NDT techniques were one of the methods chosen for evaluating their potential for inspecting these structures. Several panels with embedded anomalies (mainly voids) were produced and tested for this purpose. Near-field and far-field millimeter wave NDT methods were used for producing images of the anomalies in these panels. This paper presents the results of an investigation for the purpose of detecting localized anomalies in several SOFI panels. To this end, reflectometers at a relatively wide range of frequencies (Ka-band (26.5 - 40 GHz) to W-band (75 - 110 GHz)) and utilizing different types of radiators were employed. The resulting raw images revealed a significant amount of information about the interior of these panels. However, using simple image processing techniques the results were improved in particular as it relate s to detecting the smaller anomalies. This paper presents the results of this investigation and a discussion of these results.

  3. Conformal prediction for anomaly detection and collision alert in space surveillance

    NASA Astrophysics Data System (ADS)

    Chen, Huimin; Chen, Genshe; Blasch, Erik; Pham, Khanh

    2013-05-01

    Anomaly detection has been considered as an important technique for detecting critical events in a wide range of data rich applications where a majority of the data is inconsequential and/or uninteresting. We study the detection of anomalous behaviors among space objects using the theory of conformal prediction for distribution-independent on-line learning to provide collision alerts with a desirable confidence level. We exploit the fact that conformal predictors provide valid forecasted sets at specified confidence levels under the relatively weak assumption that the normal training data, together with the normal testing data, are generated from the same distribution. If the actual observation is not included in the conformal prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the false alarm rate without having to find the application specific threshold. The proposed conformal prediction method was evaluated for a space surveillance application using the open source North American Aerospace Defense Command (NORAD) catalog data. The validity of the prediction sets is justified by the empirical error rate that matches the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity with conformal prediction is superior to that of the existing methods in declaring potential collision events.

  4. [Multi-DSP parallel processing technique of hyperspectral RX anomaly detection].

    PubMed

    Guo, Wen-Ji; Zeng, Xiao-Ru; Zhao, Bao-Wei; Ming, Xing; Zhang, Gui-Feng; Lü, Qun-Bo

    2014-05-01

    To satisfy the requirement of high speed, real-time and mass data storage etc. for RX anomaly detection of hyperspectral image data, the present paper proposes a solution of multi-DSP parallel processing system for hyperspectral image based on CPCI Express standard bus architecture. Hardware topological architecture of the system combines the tight coupling of four DSPs sharing data bus and memory unit with the interconnection of Link ports. On this hardware platform, by assigning parallel processing task for each DSP in consideration of the spectrum RX anomaly detection algorithm and the feature of 3D data in the spectral image, a 4DSP parallel processing technique which computes and solves the mean matrix and covariance matrix of the whole image by spatially partitioning the image is proposed. The experiment result shows that, in the case of equivalent detective effect, it can reach the time efficiency 4 times higher than single DSP process with the 4-DSP parallel processing technique of RX anomaly detection algorithm proposed by this paper, which makes a breakthrough in the constraints to the huge data image processing of DSP's internal storage capacity, meanwhile well meeting the demands of the spectral data in real-time processing. PMID:25095443

  5. A sequential enzymatic microreactor system for ethanol detection of gasohol mixtures.

    PubMed

    Alhadeff, Eliana M; Salgado, Andréa M; Pereira, Nei; Valdman, Belkis

    2005-01-01

    A sequential enzymatic double microreactor system with dilution line was developed for quantifying ethanol from gasohol mixtures, using a colorimetric detection method, as a new proposal to the single micro reactor system used in previous work. Alcohol oxidase (AOD) and horseradish peroxidase (HRP) immobilized on glass beads, one in each microreactor, were used with phenol and 4-aminophenazone and the red-colored product was detected with a spectrophotometer at 555 nm. Good results were obtained with the immobilization technique used for both AOD and HRP enzymes, with best retention efficiencies of 95.3 +/- 2.3% and 63.2 +/- 7.0%, respectively. The two microreactors were used to analyze extracted ethanol from gasohol blends in the range 1-30 % v/v (10.0-238.9 g ethanol/L), with and without an on-line dilution sampling line. A calibration curve was obtained in the range 0.0034-0.087 g ethanol/L working with the on-line dilution integrated to the biosensor FIA system proposed. The diluted sample concentrations were also determined by gas chromatography (GC) and high-pressure liquid chromatography (HPLC) methods and the results compared with the proposed sequential system measurements. The effect of the number of analysis performed with the same system was also investigated. PMID:15917613

  6. Complexity-Measure-Based Sequential Hypothesis Testing for Real-Time Detection of Lethal Cardiac Arrhythmias

    NASA Astrophysics Data System (ADS)

    Chen, Szi-Wen

    2006-12-01

    A novel approach that employs a complexity-based sequential hypothesis testing (SHT) technique for real-time detection of ventricular fibrillation (VF) and ventricular tachycardia (VT) is presented. A dataset consisting of a number of VF and VT electrocardiogram (ECG) recordings drawn from the MIT-BIH database was adopted for such an analysis. It was split into two smaller datasets for algorithm training and testing, respectively. Each ECG recording was measured in a 10-second interval. For each recording, a number of overlapping windowed ECG data segments were obtained by shifting a 5-second window by a step of 1 second. During the windowing process, the complexity measure (CM) value was calculated for each windowed segment and the task of pattern recognition was then sequentially performed by the SHT procedure. A preliminary test conducted using the database produced optimal overall predictive accuracy of[InlineEquation not available: see fulltext.]. The algorithm was also implemented on a commercial embedded DSP controller, permitting a hardware realization of real-time ventricular arrhythmia detection.

  7. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    SciTech Connect

    Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D; Reed, Joel W; Goodall, John R

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  8. Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding

    NASA Astrophysics Data System (ADS)

    Krasichkov, Alexander S.; Grigoriev, Eugene B.; Bogachev, Mikhail I.; Nifontov, Eugene M.

    2015-10-01

    We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.

  9. Capacitance probe for detection of anomalies in non-metallic plastic pipe

    DOEpatents

    Mathur, Mahendra P.; Spenik, James L.; Condon, Christopher M.; Anderson, Rodney; Driscoll, Daniel J.; Fincham, Jr., William L.; Monazam, Esmail R.

    2010-11-23

    The disclosure relates to analysis of materials using a capacitive sensor to detect anomalies through comparison of measured capacitances. The capacitive sensor is used in conjunction with a capacitance measurement device, a location device, and a processor in order to generate a capacitance versus location output which may be inspected for the detection and localization of anomalies within the material under test. The components may be carried as payload on an inspection vehicle which may traverse through a pipe interior, allowing evaluation of nonmetallic or plastic pipes when the piping exterior is not accessible. In an embodiment, supporting components are solid-state devices powered by a low voltage on-board power supply, providing for use in environments where voltage levels may be restricted.

  10. Parallel implementation of RX anomaly detection on multi-core processors: impact of data partitioning strategies

    NASA Astrophysics Data System (ADS)

    Molero, Jose M.; Garzón, Ester M.; García, Inmaculada; Plaza, Antonio

    2011-11-01

    Anomaly detection is an important task for remotely sensed hyperspectral data exploitation. One of the most widely used and successful algorithms for anomaly detection in hyperspectral images is the Reed-Xiaoli (RX) algorithm. Despite its wide acceptance and high computational complexity when applied to real hyperspectral scenes, few documented parallel implementations of this algorithm exist, in particular for multi-core processors. The advantage of multi-core platforms over other specialized parallel architectures is that they are a low-power, inexpensive, widely available and well-known technology. A critical issue in the parallel implementation of RX is the sample covariance matrix calculation, which can be approached in global or local fashion. This aspect is crucial for the RX implementation since the consideration of a local or global strategy for the computation of the sample covariance matrix is expected to affect both the scalability of the parallel solution and the anomaly detection results. In this paper, we develop new parallel implementations of the RX in multi-core processors and specifically investigate the impact of different data partitioning strategies when parallelizing its computations. For this purpose, we consider both global and local data partitioning strategies in the spatial domain of the scene, and further analyze their scalability in different multi-core platforms. The numerical effectiveness of the considered solutions is evaluated using receiver operating characteristics (ROC) curves, analyzing their capacity to detect thermal hot spots (anomalies) in hyperspectral data collected by the NASA's Airborne Visible Infra- Red Imaging Spectrometer system over the World Trade Center in New York, five days after the terrorist attacks of September 11th, 2001.

  11. A new morphological anomaly detection algorithm for hyperspectral images and its GPU implementation

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2011-10-01

    Anomaly detection is considered a very important task for hyperspectral data exploitation. It is now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we develop a new morphological algorithm for anomaly detection in hyperspectral images along with an efficient GPU implementation of the algorithm. The algorithm is implemented on latest-generation GPU architectures, and evaluated with regards to other anomaly detection algorithms using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex. The proposed GPU implementation achieves real-time performance in the considered case study.

  12. Can we detect regional methane anomalies? A comparison between three observing systems

    NASA Astrophysics Data System (ADS)

    Cressot, Cindy; Pison, Isabelle; Rayner, Peter J.; Bousquet, Philippe; Fortems-Cheiney, Audrey; Chevallier, Frédéric

    2016-07-01

    A Bayesian inversion system is used to evaluate the capability of the current global surface network and of the space-borne GOSAT/TANSO-FTS and IASI instruments to quantify surface flux anomalies of methane at various spatial (global, semi-hemispheric and regional) and time (seasonal, yearly, 3-yearly) scales. The evaluation is based on a signal-to-noise ratio analysis, the signal being the methane fluxes inferred from the surface-based inversion from 2000 to 2011 and the noise (i.e., precision) of each of the three observing systems being computed from the Bayesian equation. At the global and semi-hemispheric scales, all observing systems detect flux anomalies at most of the tested timescales. At the regional scale, some seasonal flux anomalies are detected by the three observing systems, but year-to-year anomalies and longer-term trends are only poorly detected. Moreover, reliably detected regions depend on the reference surface-based inversion used as the signal. Indeed, tropical flux inter-annual variability, for instance, can be attributed mostly to Africa in the reference inversion or spread between tropical regions in Africa and America. Our results show that inter-annual analyses of methane emissions inferred by atmospheric inversions should always include an uncertainty assessment and that the attribution of current trends in atmospheric methane to particular regions' needs increased effort, for instance, gathering more observations (in the future) and improving transport models. At all scales, GOSAT generally shows the best performance of the three observing systems.

  13. A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

    NASA Astrophysics Data System (ADS)

    Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Inoue, Daisuke; Eto, Masashi; Nakao, Koji

    Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

  14. Developing a new, passive diffusion sampling array to detect helium anomalies associated with volcanic unrest

    USGS Publications Warehouse

    Dame, Brittany E; Solomon, D Kip; Evans, William C.; Ingebritsen, Steven E.

    2015-01-01

    Helium (He) concentration and 3 He/ 4 He anomalies in soil gas and spring water are potentially powerful tools for investigating hydrothermal circulation associated with volca- nism and could perhaps serve as part of a hazards warning system. However, in operational practice, He and other gases are often sampled only after volcanic unrest is detected by other means. A new passive diffusion sampler suite, intended to be collected after the onset of unrest, has been developed and tested as a relatively low-cost method of determining He- isotope composition pre- and post-unrest. The samplers, each with a distinct equilibration time, passively record He concen- tration and isotope ratio in springs and soil gas. Once collected and analyzed, the He concentrations in the samplers are used to deconvolve the time history of the He concentration and the 3 He/ 4 He ratio at the collection site. The current suite consisting of three samplers is sufficient to deconvolve both the magnitude and the timing of a step change in in situ con- centration if the suite is collected within 100 h of the change. The effects of temperature and prolonged deployment on the suite ’ s capability of recording He anomalies have also been evaluated. The suite has captured a significant 3 He/ 4 He soil gas anomaly at Horseshoe Lake near Mammoth Lakes, California. The passive diffusion sampler suite appears to be an accurate and affordable alternative for determining He anomalies associated with volcanic unrest.

  15. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2010-12-01

    Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets) searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over theWorld Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  16. 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.

  17. 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.

  18. Detection and Origin of Hydrocarbon Seepage Anomalies in the Barents Sea

    NASA Astrophysics Data System (ADS)

    Polteau, Stephane; Planke, Sverre; Stolze, Lina; Kjølhamar, Bent E.; Myklebust, Reidun

    2016-04-01

    We have collected more than 450 gravity cores in the Barents Sea to detect hydrocarbon seepage anomalies and for seismic-stratigraphic tie. The cores are from the Hoop Area (125 samples) and from the Barents Sea SE (293 samples). In addition, we have collected cores near seven exploration wells. The samples were analyzed using three different analytical methods; (1) the standard organic geochemical analyzes of Applied Petroleum Technologies (APT), (2) the Amplified Geochemical Imaging (AGI) method, and (3) the Microbial Prospecting for Oil and Gas (MPOG) method. These analytical approaches can detect trace amounts of thermogenic hydrocarbons in the sediment samples, and may provide additional information about the fluid phases and the depositional environment, maturation, and age of the source rocks. However, hydrocarbon anomalies in seabed sediments may also be related to shallow sources, such as biogenic gas or reworked source rocks in the sediments. To better understand the origin of the hydrocarbon anomalies in the Barents Sea we have studied 35 samples collected approximately 200 m away from seven exploration wells. The wells included three boreholes associated with oil discoveries, two with gas discoveries, one dry well with gas shows, and one dry well. In general, the results of this case study reveal that the oil wells have an oil signature, gas wells show a gas signature, and dry wells have a background signature. However, differences in results from the three methods may occur and have largely been explained in terms of analytical measurement ranges, method sensitivities, and bio-geochemical processes in the seabed sediments. The standard geochemical method applied by APT relies on measuring the abundance of compounds between C1 to C5 in the headspace gas and between C11 to C36 in the sediment extracts. The anomalies detected in the sediment samples from this study were in the C16 to C30 range. Since the organic matter yields were mostly very low, the

  19. 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.

  20. Small sample training and test selection method for optimized anomaly detection algorithms in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.

    2012-01-01

    There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques provide an avenue to select robust settings capable of operating consistently across a large variety of image scenes. Many researchers in this area are faced with a paucity of data. Unfortunately, there are no data splitting methods for model validation of datasets with small sample sizes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research has developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. We have developed method for selecting hyperspectral image training and test subsets that yields consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. The small sample training and test selection method is contrasted with randomly selected training sets as well as training sets chosen from the CADEX and DUPLEX algorithms for the well known Reed-Xiaoli anomaly detector.

  1. Wavelet-RX anomaly detection for dual-band forward-looking infrared imagery.

    PubMed

    Mehmood, Asif; Nasrabadi, Nasser M

    2010-08-20

    This paper describes a new wavelet-based anomaly detection technique for a dual-band forward-looking infrared (FLIR) sensor consisting of a coregistered longwave (LW) with a midwave (MW) sensor. The proposed approach, called the wavelet-RX (Reed-Xiaoli) algorithm, consists of a combination of a two-dimensional (2D) wavelet transform and a well-known multivariate anomaly detector called the RX algorithm. In our wavelet-RX algorithm, a 2D wavelet transform is first applied to decompose the input image into uniform subbands. A subband-image cube is formed by concatenating together a number of significant subbands (high-energy subbands). The RX algorithm is then applied to the subband-image cube obtained from a wavelet decomposition of the LW or MW sensor data. In the case of the dual band, the RX algorithm is applied to a subband-image cube constructed by concatenating together the high-energy subbands of the LW and MW subband-image cubes. Experimental results are presented for the proposed wavelet-RX and the classical constant false alarm rate (CFAR) algorithm for detecting anomalies (targets) in a single broadband FLIR (LW or MW) or in a coregistered dual-band FLIR sensor. The results show that the proposed wavelet-RX algorithm outperforms the classical CFAR detector for both single-band and dual-band FLIR sensors. PMID:20733634

  2. On-road anomaly detection by multimodal sensor analysis and multimedia processing

    NASA Astrophysics Data System (ADS)

    Orhan, Fatih; Eren, P. E.

    2014-03-01

    The use of smartphones in Intelligent Transportation Systems is gaining popularity, yet many challenges exist in developing functional applications. Due to the dynamic nature of transportation, vehicular social applications face complexities such as developing robust sensor management, performing signal and image processing tasks, and sharing information among users. This study utilizes a multimodal sensor analysis framework which enables the analysis of sensors in multimodal aspect. It also provides plugin-based analyzing interfaces to develop sensor and image processing based applications, and connects its users via a centralized application as well as to social networks to facilitate communication and socialization. With the usage of this framework, an on-road anomaly detector is being developed and tested. The detector utilizes the sensors of a mobile device and is able to identify anomalies such as hard brake, pothole crossing, and speed bump crossing. Upon such detection, the video portion containing the anomaly is automatically extracted in order to enable further image processing analysis. The detection results are shared on a central portal application for online traffic condition monitoring.

  3. Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition

    NASA Astrophysics Data System (ADS)

    Cui, Xiaoguang; Tian, Yuan; Weng, Lubin; Yang, Yiping

    2014-01-01

    This paper presents a novel low-rank and sparse decomposition (LSD) based model for anomaly detection in hyperspectral images. In our model, a local image region is represented as a low-rank matrix plus spares noises in the spectral space, where the background can be explained by the low-rank matrix, and the anomalies are indicated by the sparse noises. The detection of anomalies in local image regions is formulated as a constrained LSD problem, which can be solved efficiently and robustly with a modified "Go Decomposition" (GoDec) method. To enhance the validity of this model, we adapts a "simple linear iterative clustering" (SLIC) superpixel algorithm to efficiently generate homogeneous local image regions i.e. superpixels in hyperspectral imagery, thus ensures that the background in local image regions satisfies the condition of low-rank. Experimental results on real hyperspectral data demonstrate that, compared with several known local detectors including RX detector, kernel RX detector, and SVDD detector, the proposed model can comfortably achieves better performance in satisfactory computation time.

  4. Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

    NASA Astrophysics Data System (ADS)

    Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

    2011-06-01

    Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

  5. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

    PubMed Central

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

  6. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

    PubMed

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

  7. Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

    NASA Technical Reports Server (NTRS)

    Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

    2010-01-01

    In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

  8. Radiation anomaly detection algorithms for field-acquired gamma energy spectra

    NASA Astrophysics Data System (ADS)

    Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ron; Guss, Paul; Mitchell, Stephen

    2015-08-01

    The Remote Sensing Laboratory (RSL) is developing a tactical, networked radiation detection system that will be agile, reconfigurable, and capable of rapid threat assessment with high degree of fidelity and certainty. Our design is driven by the needs of users such as law enforcement personnel who must make decisions by evaluating threat signatures in urban settings. The most efficient tool available to identify the nature of the threat object is real-time gamma spectroscopic analysis, as it is fast and has a very low probability of producing false positive alarm conditions. Urban radiological searches are inherently challenged by the rapid and large spatial variation of background gamma radiation, the presence of benign radioactive materials in terms of the normally occurring radioactive materials (NORM), and shielded and/or masked threat sources. Multiple spectral anomaly detection algorithms have been developed by national laboratories and commercial vendors. For example, the Gamma Detector Response and Analysis Software (GADRAS) a one-dimensional deterministic radiation transport software capable of calculating gamma ray spectra using physics-based detector response functions was developed at Sandia National Laboratories. The nuisance-rejection spectral comparison ratio anomaly detection algorithm (or NSCRAD), developed at Pacific Northwest National Laboratory, uses spectral comparison ratios to detect deviation from benign medical and NORM radiation source and can work in spite of strong presence of NORM and or medical sources. RSL has developed its own wavelet-based gamma energy spectral anomaly detection algorithm called WAVRAD. Test results and relative merits of these different algorithms will be discussed and demonstrated.

  9. Motivating Complex Dependence Structures in Data Mining: A Case Study with Anomaly Detection in Climate

    SciTech Connect

    Kao, Shih-Chieh; Ganguly, Auroop R; Steinhaeuser, Karsten J K

    2009-01-01

    While data mining aims to identify hidden knowledge from massive and high dimensional datasets, the importance of dependence structure among time, space, and between different variables is less emphasized. Analogous to the use of probability density functions in modeling individual variables, it is now possible to characterize the complete dependence space mathematically through the application of copulas. By adopting copulas, the multivariate joint probability distribution can be constructed without constraint to specific types of marginal distributions. Some common assumptions, like normality and independence between variables, can also be relieved. This study provides fundamental introduction and illustration of dependence structure, aimed at the potential applicability of copulas in general data mining. The case study in hydro-climatic anomaly detection shows that the frequency of multivariate anomalies is affected by the dependence level between variables. The appropriate multivariate thresholds can be determined through a copula-based approach.

  10. Using the sequential regression (SER) algorithm for long-term signal processing. [Intrusion detection

    SciTech Connect

    Soldan, D. L.; Ahmed, N.; Stearns, S. D.

    1980-01-01

    The use of the sequential regression (SER) algorithm (Electron. Lett., 14, 118(1978); 13, 446(1977)) for long-term processing applications is limited by two problems that can occur when an SER predictor has more weights than required to predict the input signal. First, computational difficulties related to updating the autocorrelation matrix inverse could arise, since no unique least-squares solution exists. Second, the predictor strives to remove very low-level components in the input, and hence could implement a gain function that is essentially zero over the entire passband. The predictor would then tend to become a no-pass filter which is undesirable in certain applications, e.g., intrusion detection (SAND--78-1032). Modifications to the SER algorithm that overcome the above problems are presented, which enable its use for long-term signal processing applications. 3 figures.

  11. Automated determinations of selenium in thermal power plant wastewater by sequential hydride generation and chemiluminescence detection.

    PubMed

    Ezoe, Kentaro; Ohyama, Seiichi; Hashem, Md Abul; Ohira, Shin-Ichi; Toda, Kei

    2016-02-01

    After the Fukushima disaster, power generation from nuclear power plants in Japan was completely stopped and old coal-based power plants were re-commissioned to compensate for the decrease in power generation capacity. Although coal is a relatively inexpensive fuel for power generation, it contains high levels (mgkg(-1)) of selenium, which could contaminate the wastewater from thermal power plants. In this work, an automated selenium monitoring system was developed based on sequential hydride generation and chemiluminescence detection. This method could be applied to control of wastewater contamination. In this method, selenium is vaporized as H2Se, which reacts with ozone to produce chemiluminescence. However, interference from arsenic is of concern because the ozone-induced chemiluminescence intensity of H2Se is much lower than that of AsH3. This problem was successfully addressed by vaporizing arsenic and selenium individually in a sequential procedure using a syringe pump equipped with an eight-port selection valve and hot and cold reactors. Oxidative decomposition of organoselenium compounds and pre-reduction of the selenium were performed in the hot reactor, and vapor generation of arsenic and selenium were performed separately in the cold reactor. Sample transfers between the reactors were carried out by a pneumatic air operation by switching with three-way solenoid valves. The detection limit for selenium was 0.008 mg L(-1) and calibration curve was linear up to 1.0 mg L(-1), which provided suitable performance for controlling selenium in wastewater to around the allowable limit (0.1 mg L(-1)). This system consumes few chemicals and is stable for more than a month without any maintenance. Wastewater samples from thermal power plants were collected, and data obtained by the proposed method were compared with those from batchwise water treatment followed by hydride generation-atomic fluorescence spectrometry. PMID:26653491

  12. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery.

    PubMed

    Marapareddy, Ramakalavathi; Aanstoos, James V; Younan, Nicolas H

    2016-01-01

    Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ₁, λ₂, and λ₃), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. PMID:27322270

  13. Automatic, Real-Time Algorithms for Anomaly Detection in High Resolution Satellite Imagery

    NASA Astrophysics Data System (ADS)

    Srivastava, A. N.; Nemani, R. R.; Votava, P.

    2008-12-01

    Earth observing satellites are generating data at an unprecedented rate, surpassing almost all other data intensive applications. However, most of the data that arrives from the satellites is not analyzed directly. Rather, multiple scientific teams analyze only a small fraction of the total data available in the data stream. Although there are many reasons for this situation one paramount concern is developing algorithms and methods that can analyze the vast, high dimensional, streaming satellite images. This paper describes a new set of methods that are among the fastest available algorithms for real-time anomaly detection. These algorithms were built to maximize accuracy and speed for a variety of applications in fields outside of the earth sciences. However, our studies indicate that with appropriate modifications, these algorithms can be extremely valuable for identifying anomalies rapidly using only modest computational power. We review two algorithms which are used as benchmarks in the field: Orca, One-Class Support Vector Machines and discuss the anomalies that are discovered in MODIS data taken over the Central California region. We are especially interested in automatic identification of disturbances within the ecosystems (e,g, wildfires, droughts, floods, insect/pest damage, wind damage, logging). We show the scalability of the algorithms and demonstrate that with appropriately adapted technology, the dream of real-time analysis can be made a reality.

  14. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

    PubMed Central

    Marapareddy, Ramakalavathi; Aanstoos, James V.; Younan, Nicolas H.

    2016-01-01

    Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ1, λ2, and λ3), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. PMID:27322270

  15. Volcanic activity and satellite-detected thermal anomalies at Central American volcanoes

    NASA Technical Reports Server (NTRS)

    Stoiber, R. E. (Principal Investigator); Rose, W. I., Jr.

    1973-01-01

    The author has identified the following significant results. A large nuee ardente eruption occurred at Santiaguito volcano, within the test area on 16 September 1973. Through a system of local observers, the eruption has been described, reported to the international scientific community, extent of affected area mapped, and the new ash sampled. A more extensive report on this event will be prepared. The eruption is an excellent example of the kind of volcanic situation in which satellite thermal imagery might be useful. The Santiaguito dome is a complex mass with a whole series of historically active vents. It's location makes access difficult, yet its activity is of great concern to large agricultural populations who live downslope. Santiaguito has produced a number of large eruptions with little apparent warning. In the earlier ground survey large thermal anomalies were identified at Santiaguito. There is no way of knowing whether satellite monitoring could have detected changes in thermal anomaly patterns related to this recent event, but the position of thermal anomalies on Santiaguito and any changes in their character would be relevant information.

  16. A function approximation approach to anomaly detection in propulsion system test data

    NASA Astrophysics Data System (ADS)

    Whitehead, Bruce A.; Hoyt, W. A.

    1993-06-01

    Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

  17. Ferromagnetic eddy current probe having eccentric magnetization for detecting anomalies in a tube

    SciTech Connect

    Cecco, V.S.; Carter, J.R.

    1993-08-17

    An eddy current probe is described for detecting anomalies in a tube made of a ferromagnetic material, comprising: a probe housing made of a non-ferromagnetic material and shaped to be introduced into the tube for inspection, said housing having a central axis substantially coinciding with the axis of the tube to be inspected when the probe is in use; at least two eddy current measuring assemblies provided in said housing, each said assembly including magnetization means for generating a magnetic field in the tube under inspection to magnetize said tube, said magnetization means producing a maximum magnetization at an area of said tube and a minimum magnetization at a diametrically opposite area of said tube and at least one eddy current measuring coil associated with said magnetization means to measure the eddy current generated in the said tube and which has a relatively high sensitivity to an anomaly at said maximum magnetization area; and said eddy current measuring assemblies being spaced apart axially within said housing and rotated about said central axis from each other by a predetermined angle so that each assembly is sensitive to anomalies differently depending upon their location in said housing.

  18. A function approximation approach to anomaly detection in propulsion system test data

    NASA Technical Reports Server (NTRS)

    Whitehead, Bruce A.; Hoyt, W. A.

    1993-01-01

    Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

  19. GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2010-08-01

    Automatic target and anomaly detection are considered very important tasks for hyperspectral data exploitation. These techniques are now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly and target detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we describe several new GPU-based implementations of target and anomaly detection algorithms for hyperspectral data exploitation. The parallel algorithms are implemented on latest-generation Tesla C1060 GPU architectures, and quantitatively evaluated using hyperspectral data collected by NASA's AVIRIS system over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex.

  20. Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

    NASA Technical Reports Server (NTRS)

    Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

    2010-01-01

    The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

  1. Fiber Optic Bragg Grating Sensors for Thermographic Detection of Subsurface Anomalies

    NASA Technical Reports Server (NTRS)

    Allison, Sidney G.; Winfree, William P.; Wu, Meng-Chou

    2009-01-01

    Conventional thermography with an infrared imager has been shown to be an extremely viable technique for nondestructively detecting subsurface anomalies such as thickness variations due to corrosion. A recently developed technique using fiber optic sensors to measure temperature holds potential for performing similar inspections without requiring an infrared imager. The structure is heated using a heat source such as a quartz lamp with fiber Bragg grating (FBG) sensors at the surface of the structure to detect temperature. Investigated structures include a stainless steel plate with thickness variations simulated by small platelets attached to the back side using thermal grease. A relationship is shown between the FBG sensor thermal response and variations in material thickness. For comparison, finite element modeling was performed and found to agree closely with the fiber optic thermography results. This technique shows potential for applications where FBG sensors are already bonded to structures for Integrated Vehicle Health Monitoring (IVHM) strain measurements and can serve dual-use by also performing thermographic detection of subsurface anomalies.

  2. Molecular Detection of Human Cytomegalovirus (HCMV) Among Infants with Congenital Anomalies in Khartoum State, Sudan

    PubMed Central

    Ebrahim, Maha G.; Ali, Aisha S.; Mustafa, Mohamed O.; Musa, Dalal F.; El Hussein, Abdel Rahim M.; Elkhidir, Isam M.; Enan, Khalid A.

    2015-01-01

    Human Cytomegalovirus (HCMV) infection still represents the most common potentially serious viral complication in humans and is a major cause of congenital anomalies in infants. This study is aimed to detect HCMV in infants with congenital anomalies. Study subjects consisted of infants born with neural tube defect, hydrocephalus and microcephaly. Fifty serum specimens (20 males, 30 females) were collected from different hospitals in Khartoum State. The sera were investigated for cytomegalovirus specific immunoglobin M (IgM) antibodies using enzyme-linked immunosorbent assay (ELISA), and for Cytomegalovirus DNA using polymerase chain reaction (PCR). Out of the 50 sera tested, one patient’s (2%) sample showed HCMV IgM, but with no detectable DNA, other 4(8.2 %) sera were positive for HCMV DNA but with no detectable IgM. Various diagnostic techniques should be considered to evaluate HCMV disease and routine screening for HCMV should be introduced for pregnant women in this setting. It is vital to initiate further research work with many samples from different area to assess prevalence and characterize HCMV and evaluate its maternal health implications. PMID:26862356

  3. The Frog-Boiling Attack: Limitations of Anomaly Detection for Secure Network Coordinate Systems

    NASA Astrophysics Data System (ADS)

    Chan-Tin, Eric; Feldman, Daniel; Hopper, Nicholas; Kim, Yongdae

    A network coordinate system assigns Euclidean “virtual” coordinates to every node in a network to allow easy estimation of network latency between pairs of nodes that have never contacted each other. These systems have been implemented in a variety of applications, most notably the popular Azureus/Vuze BitTorrent client. Zage and Nita-Rotaru (CCS 2007) and independently, Kaafar et al. (SIGCOMM 2007), demonstrated that several widely-cited network coordinate systems are prone to simple attacks, and proposed mechanisms to defeat these attacks using outlier detection to filter out adversarial inputs. We propose a new attack, Frog-Boiling, that defeats anomaly-detection based defenses in the context of network coordinate systems, and demonstrate empirically that Frog-Boiling is more disruptive than the previously known attacks. Our results suggest that a new approach is needed to solve this problem: outlier detection alone cannot be used to secure network coordinate systems.

  4. 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

  5. Feasibility of anomaly detection and characterization using trans-admittance mammography with 60 × 60 electrode array

    NASA Astrophysics Data System (ADS)

    Zhao, Mingkang; Wi, Hun; Lee, Eun Jung; Woo, Eung Je; In Oh, Tong

    2014-10-01

    Electrical impedance imaging has the potential to detect an early stage of breast cancer due to higher admittivity values compared with those of normal breast tissues. The tumor size and extent of axillary lymph node involvement are important parameters to evaluate the breast cancer survival rate. Additionally, the anomaly characterization is required to distinguish a malignant tumor from a benign tumor. In order to overcome the limitation of breast cancer detection using impedance measurement probes, we developed the high density trans-admittance mammography (TAM) system with 60 × 60 electrode array and produced trans-admittance maps obtained at several frequency pairs. We applied the anomaly detection algorithm to the high density TAM system for estimating the volume and position of breast tumor. We tested four different sizes of anomaly with three different conductivity contrasts at four different depths. From multifrequency trans-admittance maps, we can readily observe the transversal position and estimate its volume and depth. Specially, the depth estimated values were obtained accurately, which were independent to the size and conductivity contrast when applying the new formula using Laplacian of trans-admittance map. The volume estimation was dependent on the conductivity contrast between anomaly and background in the breast phantom. We characterized two testing anomalies using frequency difference trans-admittance data to eliminate the dependency of anomaly position and size. We confirmed the anomaly detection and characterization algorithm with the high density TAM system on bovine breast tissue. Both results showed the feasibility of detecting the size and position of anomaly and tissue characterization for screening the breast cancer.

  6. Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection

    NASA Astrophysics Data System (ADS)

    Zhang, Lili; Zhao, Chunhui

    2016-04-01

    Hyperspectral imagery (HSI) has high spectral and spatial resolutions, which are essential for anomaly detection (AD). Many anomaly detectors assume that the spectrum signature of HSI pixels can be modeled with a Gaussian distribution, which is actually not accurate and often leads to many false alarms. Therefore, a sparsity model without any distribution hypothesis is usually employed. Dimensionality reduction (DR) as a preprocessing step for HSI is important. Principal component analysis as a conventional DR method is a linear projection and cannot exploit the nonlinear properties in hyperspectral data, whereas locally linear embedding (LLE) as a local, nonlinear manifold learning algorithm works well for DR of HSI. A modified algorithm of sparsity divergence index based on locally linear embedding (SDI-LLE) is thus proposed. First, kernel collaborative representation detection is adopted to calculate the sparse dictionary matrix of local reconstruction weights in LLE. Then, SDI is obtained both in the spectral and spatial domains, where spatial SDI is computed after DR by LLE. Finally, joint SDI, combining spectral SDI and spatial SDI, is computed, and the optimal SDI is performed for AD. Experimental results demonstrate that the proposed algorithm significantly improves the performance, when compared with its counterparts.

  7. System and method for the detection of anomalies in an image

    DOEpatents

    Prasad, Lakshman; Swaminarayan, Sriram

    2013-09-03

    Preferred aspects of the present invention can include receiving a digital image at a processor; segmenting the digital image into a hierarchy of feature layers comprising one or more fine-scale features defining a foreground object embedded in one or more coarser-scale features defining a background to the one or more fine-scale features in the segmentation hierarchy; detecting a first fine-scale foreground feature as an anomaly with respect to a first background feature within which it is embedded; and constructing an anomalous feature layer by synthesizing spatially contiguous anomalous fine-scale features. Additional preferred aspects of the present invention can include detecting non-pervasive changes between sets of images in response at least in part to one or more difference images between the sets of images.

  8. Anomaly Detection Techniques with Real Test Data from a Spinning Turbine Engine-Like Rotor

    NASA Technical Reports Server (NTRS)

    Abdul-Aziz, Ali; Woike, Mark R.; Oza, Nikunj C.; Matthews, Bryan L.

    2012-01-01

    Online detection techniques to monitor the health of rotating engine components are becoming increasingly attractive to aircraft engine manufacturers in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenge to easily implement, especially in the presence of scattered loading conditions, crack size, component geometry, and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini-cracks before any catastrophic event occurs. These techniques go further to evaluate material discontinuities and other anomalies that have grown to the level of critical defects that can lead to failure. Generally, health monitoring is highly dependent on sensor systems capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system.

  9. A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-06-01

    Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor's domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.The duration of TEC time series dataset is 120 days at time resolution of 2 h. The results show that the MLP presents anomalies better than referenced and conventional methods such as Auto-Regressive Integrated Moving Average (ARIMA) technique. In this study, also the detected TEC anomalies using the proposed method, are compared to the previous results (Akhoondzadeh, 2012) dealing with the observed TEC anomalies by applying the mean, median, wavelet and Kalman filter methods. The MLP detected anomalies are similar to those detected using the previous methods applied on the same case study. The results indicate that a MLP feed-forward neural network can be a suitable non-parametric method to detect changes of a non linear time series such as variations of earthquake precursors.

  10. MedMon: securing medical devices through wireless monitoring and anomaly detection.

    PubMed

    Zhang, Meng; Raghunathan, Anand; Jha, Niraj K

    2013-12-01

    Rapid advances in personal healthcare systems based on implantable and wearable medical devices promise to greatly improve the quality of diagnosis and treatment for a range of medical conditions. However, the increasing programmability and wireless connectivity of medical devices also open up opportunities for malicious attackers. Unfortunately, implantable/wearable medical devices come with extreme size and power constraints, and unique usage models, making it infeasible to simply borrow conventional security solutions such as cryptography. We propose a general framework for securing medical devices based on wireless channel monitoring and anomaly detection. Our proposal is based on a medical security monitor (MedMon) that snoops on all the radio-frequency wireless communications to/from medical devices and uses multi-layered anomaly detection to identify potentially malicious transactions. Upon detection of a malicious transaction, MedMon takes appropriate response actions, which could range from passive (notifying the user) to active (jamming the packets so that they do not reach the medical device). A key benefit of MedMon is that it is applicable to existing medical devices that are in use by patients, with no hardware or software modifications to them. Consequently, it also leads to zero power overheads on these devices. We demonstrate the feasibility of our proposal by developing a prototype implementation for an insulin delivery system using off-the-shelf components (USRP software-defined radio). We evaluate its effectiveness under several attack scenarios. Our results show that MedMon can detect virtually all naive attacks and a large fraction of more sophisticated attacks, suggesting that it is an effective approach to enhancing the security of medical devices. PMID:24473551

  11. 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

  12. 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

  13. Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling.

    PubMed

    Raghuram, Jayaram; Miller, David J; Kesidis, George

    2014-07-01

    We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates. PMID:25685511

  14. Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

    PubMed Central

    Raghuram, Jayaram; Miller, David J.; Kesidis, George

    2014-01-01

    We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates. PMID:25685511

  15. Detection of submicron scale cracks and other surface anomalies using positron emission tomography

    DOEpatents

    Cowan, Thomas E.; Howell, Richard H.; Colmenares, Carlos A.

    2004-02-17

    Detection of submicron scale cracks and other mechanical and chemical surface anomalies using PET. This surface technique has sufficient sensitivity to detect single voids or pits of sub-millimeter size and single cracks or fissures of millimeter size; and single cracks or fissures of millimeter-scale length, micrometer-scale depth, and nanometer-scale length, micrometer-scale depth, and nanometer-scale width. This technique can also be applied to detect surface regions of differing chemical reactivity. It may be utilized in a scanning or survey mode to simultaneously detect such mechanical or chemical features over large interior or exterior surface areas of parts as large as about 50 cm in diameter. The technique involves exposing a surface to short-lived radioactive gas for a time period, removing the excess gas to leave a partial monolayer, determining the location and shape of the cracks, voids, porous regions, etc., and calculating the width, depth, and length thereof. Detection of 0.01 mm deep cracks using a 3 mm detector resolution has been accomplished using this technique.

  16. Detection of subpixel anomalies in multispectral infrared imagery using an adaptive Bayesian classifier

    SciTech Connect

    Ashton, E.A.

    1998-03-01

    The detection of subpixel targets with unknown spectral signatures and cluttered backgrounds in multispectral imagery is a topic of great interest for remote surveillance applications. Because no knowledge of the target is assumed, the only way to accomplish such a detection is through a search for anomalous pixels. Two approaches to this problem are examined in this paper. The first is to separate the image into a number of statistical clusters by using an extension of the well-known {kappa}-means algorithm. Each bin of resultant residual vectors is then decorrelated, and the results are thresholded to provide detection. The second approach requires the formation of a probabilistic background model by using an adaptive Bayesian classification algorithm. This allows the calculation of a probability for each pixel, with respect to the model. These probabilities are then thresholded to provide detection. Both algorithms are shown to provide significant improvement over current filtering techniques for anomaly detection in experiments using multispectral IR imagery with both simulated and actual subpixel targets.

  17. Classification of radar data by detecting and identifying spatial and temporal anomalies

    NASA Astrophysics Data System (ADS)

    Väilä, Minna; Venäläinen, Ilkka; Jylhä, Juha; Ruotsalainen, Marja; Perälä, Henna; Visa, Ari

    2010-04-01

    For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.

  18. Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS).

    PubMed

    Sankaran, Sindhuja; Ehsani, Reza; Morgan, Kelly T

    2015-08-01

    Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing. PMID:26163130

  19. Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection.

    PubMed

    Wang, Kun; Langevin, Stanley; O'Hern, Corey S; Shattuck, Mark D; Ogle, Serenity; Forero, Adriana; Morrison, Juliet; Slayden, Richard; Katze, Michael G; Kirby, Michael

    2016-01-01

    Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical

  20. Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection

    PubMed Central

    O’Hern, Corey S.; Shattuck, Mark D.; Ogle, Serenity; Forero, Adriana; Morrison, Juliet; Slayden, Richard; Katze, Michael G.

    2016-01-01

    Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical

  1. Characterization of normality of chaotic systems including prediction and detection of anomalies

    NASA Astrophysics Data System (ADS)

    Engler, Joseph John

    Accurate prediction and control pervades domains such as engineering, physics, chemistry, and biology. Often, it is discovered that the systems under consideration cannot be well represented by linear, periodic nor random data. It has been shown that these systems exhibit deterministic chaos behavior. Deterministic chaos describes systems which are governed by deterministic rules but whose data appear to be random or quasi-periodic distributions. Deterministically chaotic systems characteristically exhibit sensitive dependence upon initial conditions manifested through rapid divergence of states initially close to one another. Due to this characterization, it has been deemed impossible to accurately predict future states of these systems for longer time scales. Fortunately, the deterministic nature of these systems allows for accurate short term predictions, given the dynamics of the system are well understood. This fact has been exploited in the research community and has resulted in various algorithms for short term predictions. Detection of normality in deterministically chaotic systems is critical in understanding the system sufficiently to able to predict future states. Due to the sensitivity to initial conditions, the detection of normal operational states for a deterministically chaotic system can be challenging. The addition of small perturbations to the system, which may result in bifurcation of the normal states, further complicates the problem. The detection of anomalies and prediction of future states of the chaotic system allows for greater understanding of these systems. The goal of this research is to produce methodologies for determining states of normality for deterministically chaotic systems, detection of anomalous behavior, and the more accurate prediction of future states of the system. Additionally, the ability to detect subtle system state changes is discussed. The dissertation addresses these goals by proposing new representational

  2. Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

    NASA Astrophysics Data System (ADS)

    Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

    2016-04-01

    /index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

  3. Recursive spectral similarity measure-based band selection for anomaly detection in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    He, Yuanlei; Liu, Daizhi; Yi, Shihua

    2011-01-01

    Band selection has been widely used in hyperspectral image processing for dimension reduction. In this paper, a recursive spectral similarity measure-based band selection (RSSM-BBS) method is presented. Unlike most of the existing image-based band selection techniques, it is for two hyperspectral signatures with its main focus on their spectral separability. Furthermore, it is unsupervised and based on the recursive calculation of the spectral similarity measure with an additional band. In order to demonstrate the utility of the proposed method, an anomaly detection algorithm is developed, which first extracts the anomalous target spectrum from the image using the automatic target detection and classification algorithm (ATDCA), followed by the maximum spectral screening (MSS) to obtain a good estimate of the background, and then implements RSSM-BBS to select bands that participate in the subsequent adaptive cosine/coherence estimator (ACE) target detection. As shown in the experimental result on the AVIRIS dataset, the detection performance of the ACE has been improved greatly with the bands selected by RSSM-BBS over that using full bands.

  4. Adaptive hidden Markov model with anomaly States for price manipulation detection.

    PubMed

    Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, Thomas Martin

    2015-02-01

    Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models. PMID:25608293

  5. Anomaly Detection using Multi-channel FLAC for Supporting Diagnosis of ECG

    NASA Astrophysics Data System (ADS)

    Ye, Jiaxing; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Otsu, Nobuyuki

    In this paper, we propose an approach for abnormality detection in multi-channel ECG signals. This system serves as front end to detect the irregular sections in ECG signals, where symptoms may be observed. Thereby, the doctor can focus on only the detected suspected symptom sections, ignoring the disease-free parts. Hence the workload of the inspection by the doctors is significantly reduced and the diagnosis efficiency can be sharply improved. For extracting the predominant characteristics of multi-channel ECG signals, we propose multi-channel Fourier local auto-correlations (m-FLAC) features on multi-channel complex spectrograms. The method characterizes the amplitude and phase information as well as temporal dynamics of the multi-channel ECG signal. At the anomaly detection stage, we employ complex subspace method for statistically modeling the normal (healthy) ECG patterns as in one-class learning. Then, we investigate the input ECG signals by measuring its deviation distance to the trained subspace. The ECG sections with disordered spectral distributions can be effectively discerned based on such distance metric. To validate the proposed approach, we conducted experiments on ECG dataset. The experimental results demonstrated the effectiveness of the proposed approach including promising performance and high efficiency, compared to conventional methods.

  6. Scalable Algorithms for Unsupervised Classification and Anomaly Detection in Large Geospatiotemporal Data Sets

    NASA Astrophysics Data System (ADS)

    Mills, R. T.; Hoffman, F. M.; Kumar, J.

    2015-12-01

    The increasing availability of high-resolution geospatiotemporal datasets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of ecological data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe some unsupervised knowledge discovery and anomaly detection approaches based on highly scalable parallel algorithms for k-means clustering and singular value decomposition, consider a few practical applications thereof to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.

  7. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  8. Operator based integration of information in multimodal radiological search mission with applications to anomaly detection

    NASA Astrophysics Data System (ADS)

    Benedetto, J.; Cloninger, A.; Czaja, W.; Doster, T.; Kochersberger, K.; Manning, B.; McCullough, T.; McLane, M.

    2014-05-01

    Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.

  9. Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease

    NASA Astrophysics Data System (ADS)

    Jansson, Daniel; Medvedev, Alexander; Axelson, Hans; Nyholm, Dag

    2013-10-01

    Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson's disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener-type model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patientts with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects attempting to track visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson's disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson's disease.

  10. Realization and detection of Weyl semimetals and the chiral anomaly in cold atomic systems

    NASA Astrophysics Data System (ADS)

    He, Wen-Yu; Zhang, Shizhong; Law, K. T.

    2016-07-01

    In this work, we describe a method to realize a three-dimensional Weyl semimetal by coupling multilayers of a honeycomb optical lattice in the presence of a pair of Raman lasers. The Raman lasers render each isolated honeycomb layer a Chern insulator. With finite interlayer coupling, the bulk gap of the system closes at certain out-of-plane momenta due to Raman assisted tunneling and results in the Weyl semimetal phase. Using experimentally relevant parameters, we show that both one pair and two pairs of Weyl points can be realized by tuning the interlayer coupling strength. We suggest that Landau-Zener tunneling can be used to detect Weyl points and show that the transition probability increases dramatically when the Weyl point emerges. The realization of chiral anomaly by using a magnetic-field gradient is also discussed.

  11. Bayesian anomaly detection in heterogeneous media with applications to geophysical tomography

    NASA Astrophysics Data System (ADS)

    Simon, Martin

    2014-11-01

    In this paper, we consider the problem of detecting a parameterized anomaly in an isotropic, stationary and ergodic conductivity random field via electrical impedance tomography. A homogenization result for a stochastic forward problem built on the complete electrode model is derived, which serves as the basis for a two-stage numerical method in the framework of Bayesian inverse problems. The novelty of this method lies in the introduction of an enhanced error model accounting for the approximation errors that result from reducing the full forward model to a homogenized one. In the first stage, a MAP estimate for the reduced forward model equipped with the enhanced error model is computed. Then, in the second stage, a bootstrap prior based on the first stage results is defined and the resulting posterior distribution is sampled via Markov chain Monte Carlo. We provide the theoretical foundation of the proposed method, discuss different aspects of a numerical implementation and present numerical experiments to support our findings.

  12. Detection, identification and mapping of iron anomalies in brain tissue using X-ray absorption spectroscopy

    SciTech Connect

    Mikhaylova, A.; Davidson, M.; Toastmann, H.; Channell, J.E.T.; Guyodo, Y.; Batich, C.; Dobson, J.

    2008-06-16

    This work describes a novel method for the detection, identification and mapping of anomalous iron compounds in mammalian brain tissue using X-ray absorption spectroscopy. We have located and identified individual iron anomalies in an avian tissue model associated with ferritin, biogenic magnetite and haemoglobin with a pixel resolution of less than 5 {micro}m. This technique represents a breakthrough in the study of both intra- and extra-cellular iron compounds in brain tissue. The potential for high-resolution iron mapping using microfocused X-ray beams has direct application to investigations of the location and structural form of iron compounds associated with human neurodegenerative disorders - a problem which has vexed researchers for 50 years.

  13. Detection, identification and mapping of iron anomalies in brain tissue using X-ray absorption spectroscopy

    PubMed Central

    Mikhaylova, A; Davidson, M; Toastmann, H; Channell, J.E.T; Guyodo, Y; Batich, C; Dobson, J

    2005-01-01

    This work describes a novel method for the detection, identification and mapping of anomalous iron compounds in mammalian brain tissue using X-ray absorption spectroscopy. We have located and identified individual iron anomalies in an avian tissue model associated with ferritin, biogenic magnetite and haemoglobin with a pixel resolution of less than 5 μm. This technique represents a breakthrough in the study of both intra- and extra-cellular iron compounds in brain tissue. The potential for high-resolution iron mapping using microfocused X-ray beams has direct application to investigations of the location and structural form of iron compounds associated with human neurodegenerative disorders—a problem which has vexed researchers for 50 years. PMID:16849161

  14. Seismological detection of low-velocity anomalies surrounding the mantle transition zone in Japan subduction zone

    NASA Astrophysics Data System (ADS)

    Liu, Zhen; Park, Jeffrey; Karato, Shun-ichiro

    2016-03-01

    In the Japan subduction zone, a locally depressed 660 discontinuity has been observed beneath northeast Asia, suggesting downwelling of materials from the mantle transition zone (MTZ). Vertical transport of water-rich MTZ materials across the major mineral phase changes could lead to water release and to partial melting in surrounding mantle regions, causing seismic low-velocity anomalies. Melt layers implied by low-velocity zones (LVZs) above the 410 discontinuity have been detected in many regions, but seismic evidence for partial melting below the 660 discontinuity has been limited. High-frequency migrated Ps receiver functions indicate LVZs below the depressed 660 discontinuity and above the 410 discontinuity in the deep Japan subduction zone, suggesting dehydration melting induced by water transport out of the MTZ. Our results provide insights into water circulation associated with dynamic interactions between the subducted slab and surrounding mantle.

  15. Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

    SciTech Connect

    Dumidu Wijayasekara; Ondrej Linda; Milos Manic; Craig Rieger

    2014-08-01

    Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD) based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.

  16. Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation

    NASA Astrophysics Data System (ADS)

    Ribeiro, Sara; Caineta, Júlio; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

    2016-05-01

    Climate data homogenisation is of major importance in climate change monitoring, validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. The reason is that non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on a geostatistical simulation technique (DSS - direct sequential simulation), where local probability density functions (pdfs) are calculated at candidate monitoring stations using spatial and temporal neighbouring observations, which then are used for the detection of inhomogeneities. Such approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). That study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneity detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall, Wald-Wolfowitz runs, Von Neumann ratio, Pettitt, Buishand range test, and standard normal homogeneity test (SNHT) for a single break). Moreover, a sensitivity analysis is performed to investigate the number of simulated realisations which should be used to infer the local pdfs with more accuracy. Accordingly, the number of simulations per iteration was increased from 50 to 500, which resulted in a more representative local pdf. As in the previous study, the results are compared with those from the

  17. Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

    NASA Astrophysics Data System (ADS)

    Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

    2014-05-01

    Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help

  18. Detection of Local Anomalies in High Resolution Hyperspectral Imagery Using Geostatistical Filtering and Local Spatial Statistics

    NASA Astrophysics Data System (ADS)

    Goovaerts, P.; Jacquez, G. M.; Marcus, A. W.

    2004-12-01

    finally the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values as well as anomalies. The approach is illustrated using one meter resolution data collected in Yellowstone National Park. Ground validation data demonstrate the ability of the filtering procedure to reduce the proportion of false alarms, and its robustness under low signal to noise ratios. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e. RXD) and fewer false alarms were obtained when using statistic S2 (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, leading to more false alarms, in particular for the identification of the least pure pixels. Results from the tailings site demonstrated that the approach still performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.

  19. Approaches for detecting behavioural anomalies in public areas using video surveillance data

    NASA Astrophysics Data System (ADS)

    Brax, Christoffer; Laxhammar, Rikard; Niklasson, Lars

    2008-10-01

    In many surveillance missions information from a large number of interconnected sensors must be analysed in real time. When using visual sensors like CCTV cameras, it is not uncommon that an operator simultaneously has to survey the information from as many as fifty to a hundred cameras. It is obvious that the probability that the operator finds interesting observations is quite low when surveying information from that many cameras. In this paper we evaluate two different approaches for automatically detecting anomalies in data from visual surveillance sensors. Using the approaches suggested here the system can automatically direct the operator to the cameras where some possibly interesting activities take place. The approaches include creating structures for representing data, building "normal models" by filling the structures with data for the situation at hand, and finally detecting deviations in new data. One approach allows detections based on the incorporation of a priori knowledge about the situation combined with data-driven analysis. The other approach makes as few assumptions as possible about the situation at hand and builds almost entirely on data-driven analysis. The proposed approaches are evaluated off-line using real-world data and the results shows that the approaches can be used in real-time applications to support operators in civil and military surveillance applications.

  20. Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs

    PubMed Central

    Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

    2011-01-01

    Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10−6. Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful. PMID:22195129

  1. Reliable detection of fluence anomalies in EPID-based IMRT pretreatment quality assurance using pixel intensity deviations

    SciTech Connect

    Gordon, J. J.; Gardner, J. K.; Wang, S.; Siebers, J. V.

    2012-08-15

    Purpose: This work uses repeat images of intensity modulated radiation therapy (IMRT) fields to quantify fluence anomalies (i.e., delivery errors) that can be reliably detected in electronic portal images used for IMRT pretreatment quality assurance. Methods: Repeat images of 11 clinical IMRT fields are acquired on a Varian Trilogy linear accelerator at energies of 6 MV and 18 MV. Acquired images are corrected for output variations and registered to minimize the impact of linear accelerator and electronic portal imaging device (EPID) positioning deviations. Detection studies are performed in which rectangular anomalies of various sizes are inserted into the images. The performance of detection strategies based on pixel intensity deviations (PIDs) and gamma indices is evaluated using receiver operating characteristic analysis. Results: Residual differences between registered images are due to interfraction positional deviations of jaws and multileaf collimator leaves, plus imager noise. Positional deviations produce large intensity differences that degrade anomaly detection. Gradient effects are suppressed in PIDs using gradient scaling. Background noise is suppressed using median filtering. In the majority of images, PID-based detection strategies can reliably detect fluence anomalies of {>=}5% in {approx}1 mm{sup 2} areas and {>=}2% in {approx}20 mm{sup 2} areas. Conclusions: The ability to detect small dose differences ({<=}2%) depends strongly on the level of background noise. This in turn depends on the accuracy of image registration, the quality of the reference image, and field properties. The longer term aim of this work is to develop accurate and reliable methods of detecting IMRT delivery errors and variations. The ability to resolve small anomalies will allow the accuracy of advanced treatment techniques, such as image guided, adaptive, and arc therapies, to be quantified.

  2. Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

    SciTech Connect

    Burr, T.L.; Mullen, M.F.; Wangen, L.E.

    1994-02-01

    In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect loss of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.

  3. A Stochastic-entropic Approach to Detect Persistent Low-temperature Volcanogenic Thermal Anomalies

    NASA Astrophysics Data System (ADS)

    Pieri, D. C.; Baxter, S.

    2011-12-01

    Eruption prediction is a chancy idiosyncratic affair, as volcanoes often manifest waxing and/or waning pre-eruption emission, geodetic, and seismic behavior that is unsystematic. Thus, fundamental to increased prediction accuracy and precision are good and frequent assessments of the time-series behavior of relevant precursor geophysical, geochemical, and geological phenomena, especially when volcanoes become restless. The Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), in orbit since 1999 on the NASA Terra Earth Observing System satellite is an important capability for detection of thermal eruption precursors (even subtle ones) and increased passive gas emissions. The unique combination of ASTER high spatial resolution multi-spectral thermal IR imaging data (90m/pixel; 5 bands in the 8-12um region), combined with simultaneous visible and near-IR imaging data, and stereo-photogrammetric capabilities make it a useful, especially thermal, precursor detection tool. The JPL ASTER Volcano Archive consisting of 80,000+ASTER volcano images allows systematic analysis of (a) baseline thermal emissions for 1550+ volcanoes, (b) important aspects of the time-dependent thermal variability, and (c) the limits of detection of temporal dynamics of eruption precursors. We are analyzing a catalog of the magnitude, frequency, and distribution of ASTER-documented volcano thermal signatures, compiled from 2000 onward, at 90m/pixel. Low contrast thermal anomalies of relatively low apparent absolute temperature (e.g., summit lakes, fumarolically altered areas, geysers, very small sub-pixel hotspots), for which the signal-to-noise ratio may be marginal (e.g., scene confusion due to clouds, water and water vapor, fumarolic emissions, variegated ground emissivity, and their combinations), are particularly important to discern and monitor. We have developed a technique to detect persistent hotspots that takes into account in-scene observed pixel joint frequency

  4. Sequential acquisition of multi-dimensional heteronuclear chemical shift correlation spectra with 1H detection

    PubMed Central

    Bellstedt, Peter; Ihle, Yvonne; Wiedemann, Christoph; Kirschstein, Anika; Herbst, Christian; Görlach, Matthias; Ramachandran, Ramadurai

    2014-01-01

    RF pulse schemes for the simultaneous acquisition of heteronuclear multi-dimensional chemical shift correlation spectra, such as {HA(CA)NH & HA(CACO)NH}, {HA(CA)NH & H(N)CAHA} and {H(N)CAHA & H(CC)NH}, that are commonly employed in the study of moderately-sized protein molecules, have been implemented using dual sequential 1H acquisitions in the direct dimension. Such an approach is not only beneficial in terms of the reduction of experimental time as compared to data collection via two separate experiments but also facilitates the unambiguous sequential linking of the backbone amino acid residues. The potential of sequential 1H data acquisition procedure in the study of RNA is also demonstrated here. PMID:24671105

  5. Value of Ultrasound in Detecting Urinary Tract Anomalies After First Febrile Urinary Tract Infection in Children.

    PubMed

    Ghobrial, Emad E; Abdelaziz, Doaa M; Sheba, Maha F; Abdel-Azeem, Yasser S

    2016-05-01

    Background Urinary tract infection (UTI) is an infection that affects part of the urinary tract. Ultrasound is a noninvasive test that can demonstrate the size and shape of kidneys, presence of dilatation of the ureters, and the existence of anatomic abnormalities. The aim of the study is to estimate the value of ultrasound in detecting urinary tract anomalies after first attack of UTI. Methods This study was conducted at the Nephrology Clinic, New Children's Hospital, Faculty of Medicine, Cairo University, from August 2012 to March 2013, and included 30 children who presented with first attack of acute febrile UTI. All patients were subjected to urine analysis, urine culture and sensitivity, serum creatinine, complete blood count, and imaging in the form of renal ultrasound, voiding cysto-urethrography, and renal scan. Results All the patients had fever with a mean of 38.96°C ± 0.44°C and the mean duration of illness was 6.23 ± 5.64 days. Nineteen patients (63.3%) had an ultrasound abnormality. The commonest abnormalities were kidney stones (15.8%). Only 2 patients who had abnormal ultrasound had also vesicoureteric reflux on cystourethrography. Sensitivity of ultrasound was 66.7%, specificity was 37.5%, positive predictive value was 21.1%, negative predictive value was 81.8%, and total accuracy was 43.33%. Conclusion We concluded that ultrasound alone was not of much value in diagnosing and putting a plan of first attack of febrile UTI. It is recommended that combined investigations are the best way to confirm diagnosis of urinary tract anomalies. PMID:26084536

  6. GNSS reflectometry aboard the International Space Station: phase-altimetry simulation to detect ocean topography anomalies

    NASA Astrophysics Data System (ADS)

    Semmling, Maximilian; Leister, Vera; Saynisch, Jan; Zus, Florian; Wickert, Jens

    2016-04-01

    An ocean altimetry experiment using Earth reflected GNSS signals has been proposed to the European Space Agency (ESA). It is part of the GNSS Reflectometry Radio Occultation Scatterometry (GEROS) mission that is planned aboard the International Space Station (ISS). Altimetric simulations are presented that examine the detection of ocean topography anomalies assuming GNSS phase delay observations. Such delay measurements are well established for positioning and are possible due to a sufficient synchronization of GNSS receiver and transmitter. For altimetric purpose delays of Earth reflected GNSS signals can be observed similar to radar altimeter signals. The advantage of GNSS is the synchronized separation of transmitter and receiver that allow a significantly increased number of observation per receiver due to more than 70 GNSS transmitters currently in orbit. The altimetric concept has already been applied successfully to flight data recorded over the Mediterranean Sea. The presented altimetric simulation considers anomalies in the Agulhas current region which are obtained from the Region Ocean Model System (ROMS). Suitable reflection events in an elevation range between 3° and 30° last about 10min with ground track's length >3000km. Typical along-track footprints (1s signal integration time) have a length of about 5km. The reflection's Fresnel zone limits the footprint of coherent observations to a major axis extention between 1 to 6km dependent on the elevation. The altimetric performance depends on the signal-to-noise ratio (SNR) of the reflection. Simulation results show that precision is better than 10cm for SNR of 30dB. Whereas, it is worse than 0.5m if SNR goes down to 10dB. Precision, in general, improves towards higher elevation angles. Critical biases are introduced by atmospheric and ionospheric refraction. Corresponding correction strategies are still under investigation.

  7. Airborne detection of magnetic anomalies associated with soils on the Oak Ridge Reservation, Tennessee

    SciTech Connect

    Doll, W.E.; Beard, L.P.; Helm, J.M.

    1995-04-01

    Reconnaissance airborne geophysical data acquired over the 35,000-acre Oak Ridge Reservation (ORR), TN, show several magnetic anomalies over undisturbed areas mapped as Copper Ridge Dolomite (CRD). The anomalies of interest are most apparent in magnetic gradient maps where they exceed 0.06 nT/m and in some cases exceed 0.5 nT/m. Anomalies as large as 25nT are seen on maps. Some of the anomalies correlate with known or suspected karst, or with apparent conductivity anomalies calculated from electromagnetic data acquired contemporaneously with the magnetic data. Some of the anomalies have a strong correlation with topographic lows or closed depressions. Surface magnetic data have been acquired over some of these sites and have confirmed the existence of the anomalies. Ground inspections in the vicinity of several of the anomalies has not led to any discoveries of manmade surface materials of sufficient size to generate the observed anomalies. One would expect an anomaly of approximately 1 nT for a pickup truck from 200 ft altitude. Typical residual magnetic anomalies have magnitudes of 5--10 nT, and some are as large as 25nT. The absence of roads or other indications of culture (past or present) near the anomalies and the modeling of anomalies in data acquired with surface instruments indicate that man-made metallic objects are unlikely to be responsible for the anomaly. The authors show that observed anomalies in the CRD can reasonably be associated with thickening of the soil layer. The occurrence of the anomalies in areas where evidences of karstification are seen would follow because sediment deposition would occur in topographic lows. Linear groups of anomalies on the maps may be associated with fracture zones which were eroded more than adjacent rocks and were subsequently covered with a thicker blanket of sediment. This study indicates that airborne magnetic data may be of use in other sites where fracture zones or buried collapse structures are of interest.

  8. Hypergraph-based anomaly detection of high-dimensional co-occurrences.

    PubMed

    Silva, Jorge; Willett, Rebecca

    2009-03-01

    This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods. PMID:19147882

  9. Insider threat detection enabled by converting user applications into fractal fingerprints and autonomously detecting anomalies

    NASA Astrophysics Data System (ADS)

    Jaenisch, Holger M.; Handley, James

    2012-06-01

    We demonstrate insider threat detection for determining when the behavior of a computer user is suspicious or different from his or her normal behavior. This is accomplished by combining features extracted from text, emails, and blogs that are associated with the user. These sources can be characterized using QUEST, DANCER, and MenTat to extract features; however, some of these features are still in text form. We show how to convert these features into numerical form and characterize them using parametric and non-parametric statistics. These features are then used as input into a Random Forest classifier that is trained to recognize whenever the user's behavior is suspicious or different from normal (off-nominal). Active authentication (user identification) is also demonstrated using the features and classifiers derived in this work. We also introduce a novel concept for remotely monitoring user behavior indicator patterns displayed as an infrared overlay on the computer monitor, which the user is unaware of, but a narrow pass-band filtered webcam can clearly distinguish. The results of our analysis are presented.

  10. Sequential strand displacement beacon for detection of DNA coverage on functionalized gold nanoparticles.

    PubMed

    Paliwoda, Rebecca E; Li, Feng; Reid, Michael S; Lin, Yanwen; Le, X Chris

    2014-06-17

    Functionalizing nanomaterials for diverse analytical, biomedical, and therapeutic applications requires determination of surface coverage (or density) of DNA on nanomaterials. We describe a sequential strand displacement beacon assay that is able to quantify specific DNA sequences conjugated or coconjugated onto gold nanoparticles (AuNPs). Unlike the conventional fluorescence assay that requires the target DNA to be fluorescently labeled, the sequential strand displacement beacon method is able to quantify multiple unlabeled DNA oligonucleotides using a single (universal) strand displacement beacon. This unique feature is achieved by introducing two short unlabeled DNA probes for each specific DNA sequence and by performing sequential DNA strand displacement reactions. Varying the relative amounts of the specific DNA sequences and spacing DNA sequences during their coconjugation onto AuNPs results in different densities of the specific DNA on AuNP, ranging from 90 to 230 DNA molecules per AuNP. Results obtained from our sequential strand displacement beacon assay are consistent with those obtained from the conventional fluorescence assays. However, labeling of DNA with some fluorescent dyes, e.g., tetramethylrhodamine, alters DNA density on AuNP. The strand displacement strategy overcomes this problem by obviating direct labeling of the target DNA. This method has broad potential to facilitate more efficient design and characterization of novel multifunctional materials for diverse applications. PMID:24848126

  11. An Approach to Detecting Crowd Anomalies for Entrance and Checkpoint Security

    NASA Astrophysics Data System (ADS)

    Zelnio, Holly

    This thesis develops an approach for detecting behavioral anomalies using tracks of pedestrians, including specified threat tracks. The application area is installation security with focus on monitoring the entrances of these installations. The approach specifically allows operator interaction to specify threats and to interactively adjust the system parameters depending on the context of the situation. This research has discovered physically meaningful features that are developed and organized in a manner so that features can be systematically added or deleted depending on the situation and operator preference. The features can be used with standard classifiers such as the one class support vector machine that is used in this research. The one class support vector machine is very stable for this application and provides significant insight into the nature of its decision boundary. Its stability and ease of system use stems from a unique automatic tuning approach that is computationally efficient and compares favorable with competing approaches. This automatic tuning approach is believed to be novel and was developed as part of this research. Results are provided using both measured and synthetic data.

  12. Experiments to Detect Clandestine Graves from Interpreted High Resolution Geophysical Anomalies

    NASA Astrophysics Data System (ADS)

    Molina, C. M.; Hernandez, O.; Pringle, J.

    2013-05-01

    This project refers to the search for clandestine sites where possibly missing people have been buried based on interpreted near surface high resolution geophysical anomalies. Nowadays, there are thousands of missing people around the world that could have been tortured and killed and buried in clandestine graves. This is a huge problem for their families and governments that are responsible to warranty the human rights for everybody. These people need to be found and the related crime cases need to be resolved. This work proposes to construct a series of graves where all the conditions of the grave, human remains and related objects are known. It is expected to detect contrasting physical properties of soil to identify the known human remains and objects. The proposed geophysical methods will include electrical tomography, magnetic and ground penetrating radar, among others. Two geographical sites will be selected to located and build standard graves with contrasting weather, soil, vegetation, geographic and geologic conditions. Forward and inverse modeling will be applied to locate and enhance the geophysical response of the known graves and to validate the methodology. As a result, an integrated geophysical program will be provided to support the search for clandestine graves helping to find missing people that have been illegally buried. Optionally, the methodology will be tested to search for real clandestine graves.

  13. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

    PubMed Central

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-01-01

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%. PMID:27304957

  14. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks.

    PubMed

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-01-01

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens' quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%. PMID:27304957

  15. Anomaly Identification from Super-Low Frequency Electromagnetic Data for the Coalbed Methane Detection

    NASA Astrophysics Data System (ADS)

    Zhao, S. S.; Wang, N.; Hui, J.; Ye, X.; Qin, Q.

    2016-06-01

    Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

  16. Extraction of oil slicks on the sea surface from optical satellite images by using an anomaly detection technique

    NASA Astrophysics Data System (ADS)

    Chen, Chi-Farn; Chang, Li-Yu

    2010-12-01

    Many methods for the detection of oil pollution on the sea surface from remotely sensed images have been developed in recent years. However, because of the diverse physical properties of oil on the sea surface in the visible wavelengths, such images are easily affected by the surrounding environment. This is a common difficulty encountered when optical satellite images are used as data sources for observing oil slicks on the sea surface. However, provided the spectral interference generated by the surrounding environment can be regarded as noise and properly modeled, the spectral anomalies caused by an oil slick on normal sea water may be observed after the suppression of this noise. In this study, sea surface oil slicks are extracted by detecting spectral anomalies in multispectral optical satellite images. First, assuming that the sea water and oil slick comprise the dominant background and target anomaly, respectively, an RX algorithm is used to enhance the oil slick anomaly. The oil slick can be distinguished from the sea water background after modeling and suppression of inherent noise. Next, a Gaussian mixture model is used to characterize the statistical distributions of the background and anomaly, respectively. The expectation maximization (EM) algorithm is used to obtain the parameters needed for the Gaussian mixture model. Finally, according to the Bayesian decision rule of minimum error, an optimized threshold can be obtained to extract the oil slick areas from the source image. Furthermore, with the obtained Gaussian distributions and optimized threshold, a theoretical false alarm level can be established to evaluate the quality of the extracted oil slicks. Experimental results show that the proposed method can not only successfully detect oil slicks from multispectral optical satellite images, but also provide a quantitative accuracy evaluation of the detected image.

  17. A Diagnoser Algorithm for Anomaly Detection in DEDS under Partial Unreliable Observations: Characterization and Inclusion in Sensor Configuration Optimizaton

    SciTech Connect

    Wen-Chiao Lin; Humberto Garcia; Tae-Sic Yoo

    2013-03-01

    Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.

  18. DNA sequencing by a single molecule detection of labeled nucleotides sequentially cleaved from a single strand of DNA

    SciTech Connect

    Goodwin, P.M.; Schecker, J.A.; Wilkerson, C.W.; Hammond, M.L.; Ambrose, W.P.; Jett, J.H.; Martin, J.C.; Marrone, B.L.; Keller, R.A. ); Haces, A.; Shih, P.J.; Harding, J.D. )

    1993-01-01

    We are developing a laser-based technique for the rapid sequencing of large DNA fragments (several kb in size) at a rate of 100 to 1000 bases per second. Our approach relies on fluorescent labeling of the bases in a single fragment of DNA, attachment of this labeled DNA fragment to a support, movement of the supported DNA into a flowing sample stream, sequential cleavage of the end nucleotide from the DNA fragment with an exonuclease, and detection of the individual fluorescently labeled bases by laser-induced fluorescence.

  19. DNA sequencing by a single molecule detection of labeled nucleotides sequentially cleaved from a single strand of DNA

    SciTech Connect

    Goodwin, P.M.; Schecker, J.A.; Wilkerson, C.W.; Hammond, M.L.; Ambrose, W.P.; Jett, J.H.; Martin, J.C.; Marrone, B.L.; Keller, R.A.; Haces, A.; Shih, P.J.; Harding, J.D.

    1993-02-01

    We are developing a laser-based technique for the rapid sequencing of large DNA fragments (several kb in size) at a rate of 100 to 1000 bases per second. Our approach relies on fluorescent labeling of the bases in a single fragment of DNA, attachment of this labeled DNA fragment to a support, movement of the supported DNA into a flowing sample stream, sequential cleavage of the end nucleotide from the DNA fragment with an exonuclease, and detection of the individual fluorescently labeled bases by laser-induced fluorescence.

  20. Detection of aeromagnetic anomaly change associated with volcanic activity: An application of the generalized mis-tie control method

    NASA Astrophysics Data System (ADS)

    Nakatsuka, Tadashi; Utsugi, Mitsuru; Okuma, Shigeo; Tanaka, Yoshikazu; Hashimoto, Takeshi

    2009-12-01

    Repeat aeromagnetic surveys may assist in mapping and monitoring long-term changes associated with volcanic activity. However, when dealing with repeat aeromagnetic survey data, the problem of how to extract the real change of magnetic anomalies from a limited set of observations arises, i.e. the problem of spatial aliasing. Recent development of the generalized mis-tie control method for aeromagnetic surveys flown at variable elevations enables us to statistically extract the errors from ambiguous noise sources. This technique can be applied to overcome the spatial alias effect when detecting magnetic anomaly changes between aeromagnetic surveys flown at different times. We successfully apply this technique to Asama Volcano, one of the active volcanoes in Japan, which erupted in 2004. Following the volcanic activity in 2005, we conducted a helicopter-borne aeromagnetic survey, which we compare here to the result from a previous survey flown in 1992. To discuss small changes in magnetic anomalies induced by volcanic activity, it is essential to estimate the accuracy of the reference and the repeat aeromagnetic measurements and the probable errors induced by data processing. In our case, the positioning inaccuracy of the 1992 reference survey was the most serious factor affecting the estimation of the magnetic anomaly change because GPS was still in an early stage at that time. However, our analysis revealed that the magnetic anomaly change over the Asama Volcano area from 1992 to 2005 exceeded the estimated error at three locations, one of which is interpreted as a loss of magnetization induced by volcanic activity. In this study, we suffered from the problem of positioning inaccuracy in the 1992 survey data, and it was important to evaluate its effect when deriving the magnetic anomaly change.

  1. Reasoning about anomalies: a study of the analytical process of detecting and identifying anomalous behavior in maritime traffic data

    NASA Astrophysics Data System (ADS)

    Riveiro, Maria; Falkman, Göran; Ziemke, Tom; Kronhamn, Thomas

    2009-05-01

    The goal of visual analytical tools is to support the analytical reasoning process, maximizing human perceptual, understanding and reasoning capabilities in complex and dynamic situations. Visual analytics software must be built upon an understanding of the reasoning process, since it must provide appropriate interactions that allow a true discourse with the information. In order to deepen our understanding of the human analytical process and guide developers in the creation of more efficient anomaly detection systems, this paper investigates how is the human analytical process of detecting and identifying anomalous behavior in maritime traffic data. The main focus of this work is to capture the entire analysis process that an analyst goes through, from the raw data to the detection and identification of anomalous behavior. Three different sources are used in this study: a literature survey of the science of analytical reasoning, requirements specified by experts from organizations with interest in port security and user field studies conducted in different marine surveillance control centers. Furthermore, this study elaborates on how to support the human analytical process using data mining, visualization and interaction methods. The contribution of this paper is twofold: (1) within visual analytics, contribute to the science of analytical reasoning with practical understanding of users tasks in order to develop a taxonomy of interactions that support the analytical reasoning process and (2) within anomaly detection, facilitate the design of future anomaly detector systems when fully automatic approaches are not viable and human participation is needed.

  2. MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

    NASA Astrophysics Data System (ADS)

    Koeppen, W. C.; Wright, R.; Pilger, E.

    2009-12-01

    We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found

  3. Discrete shearlet transform on GPU with applications in anomaly detection and denoising

    NASA Astrophysics Data System (ADS)

    Gibert, Xavier; Patel, Vishal M.; Labate, Demetrio; Chellappa, Rama

    2014-12-01

    Shearlets have emerged in recent years as one of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localized functions defined not only over a range of scales and locations, but also over a range of orientations and with highly anisotropic supports. As a result, shearlets are much more effective than traditional wavelets in handling the geometry of multidimensional data, and this was exploited in a wide range of applications from image and signal processing. However, despite their desirable properties, the wider applicability of shearlets is limited by the computational complexity of current software implementations. For example, denoising a single 512 × 512 image using a current implementation of the shearlet-based shrinkage algorithm can take between 10 s and 2 min, depending on the number of CPU cores, and much longer processing times are required for video denoising. On the other hand, due to the parallel nature of the shearlet transform, it is possible to use graphics processing units (GPU) to accelerate its implementation. In this paper, we present an open source stand-alone implementation of the 2D discrete shearlet transform using CUDA C++ as well as GPU-accelerated MATLAB implementations of the 2D and 3D shearlet transforms. We have instrumented the code so that we can analyze the running time of each kernel under different GPU hardware. In addition to denoising, we describe a novel application of shearlets for detecting anomalies in textured images. In this application, computation times can be reduced by a factor of 50 or more, compared to multicore CPU implementations.

  4. Finding Cardinality Heavy-Hitters in Massive Traffic Data and Its Application to Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Ishibashi, Keisuke; Mori, Tatsuya; Kawahara, Ryoichi; Hirokawa, Yutaka; Kobayashi, Atsushi; Yamamoto, Kimihiro; Sakamoto, Hitoaki; Asano, Shoichiro

    introduce an application of our algorithm to anomaly detection. With actual traffic data, our method could successfully detect a sudden network scan.

  5. A reversible fluorescence "off-on-off" sensor for sequential detection of aluminum and acetate/fluoride ions.

    PubMed

    Gupta, Vinod Kumar; Mergu, Naveen; Kumawat, Lokesh Kumar; Singh, Ashok Kumar

    2015-11-01

    A new rhodamine functionalized fluorogenic Schiff base CS was synthesized and its colorimetric and fluorescence responses toward various metal ions were explored. The sensor exhibited highly selective and sensitive colorimetric and "off-on" fluorescence response towards Al(3+) in the presence of other competing metal ions. These spectral changes are large enough in the visible region of the spectrum and thus enable naked-eye detection. Studies proved that the formation of CS-Al(3+) complex is fully reversible and can sense to AcO(-)/F(-) via dissociation. The results revealed that the sensor provides fluorescence "off-on-off" strategy for the sequential detection of Al(3+) and AcO(-)/F(-). PMID:26452794

  6. Detection of heteromerization of more than two proteins by sequential BRET-FRET.

    PubMed

    Carriba, Paulina; Navarro, Gemma; Ciruela, Francisco; Ferré, Sergi; Casadó, Vicent; Agnati, Luigi; Cortés, Antoni; Mallol, Josefa; Fuxe, Kjell; Canela, Enric I; Lluís, Carmen; Franco, Rafael

    2008-08-01

    Identification of higher-order oligomers in the plasma membrane is essential to decode the properties of molecular networks controlling intercellular communication. We combined bioluminescence resonance energy transfer (BRET) and fluorescence resonance energy transfer (FRET) in a technique called sequential BRET-FRET (SRET) that permits identification of heteromers formed by three different proteins. In SRET, the oxidation of a Renilla luciferase (Rluc) substrate by an Rluc fusion protein triggers acceptor excitation of a second fusion protein by BRET and subsequent FRET to a third fusion protein. We describe two variations of SRET that use different Rluc substrates with appropriately paired acceptor fluorescent proteins. Using SRET, we identified complexes of cannabinoid CB(1), dopamine D(2) and adenosine A(2A) receptors in living cells. SRET is an invaluable technique to identify heteromeric complexes of more than two neurotransmitter receptors, which will allow us to better understand how signals are integrated at the molecular level. PMID:18587404

  7. Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (Mw = 8.8) earthquake of 27 February 2010

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, Mehdi

    2016-06-01

    In this paper for the first time ensemble methods including Decision Tree, Bagging and Random Forest have been proposed in the field of earthquake precursors to detect GPS-TEC (Total Electron Content) seismo-ionospheric anomalies around the time and location of Chile earthquake of 27 February 2010. All of the implemented ensemble methods detected a striking anomaly in time series of TEC data, 1 day after the earthquake at 14:00 UTC. The results indicate that the proposed methods due to their performance, speed and simplicity are quite promising and deserve serious attention as a new predictor tools for seismo-ionospheric anomalies detection.

  8. Digital speckle pattern interferometry based anomaly detection in breast mimicking phantoms: a pilot study

    NASA Astrophysics Data System (ADS)

    Udayakumar, K.; Sujatha, N.; Ganesan, A. R.

    2015-03-01

    Early screening of subsurface anomalies in breast can improve the patient survival rate. Clinically approved breast screening modalities may either have body ionizing effect/cause pain to the body parts/ involves body contact/ increased cost. In this paper, a non-invasive, whole field Digital Speckle Pattern Interferometry (DSPI) is used to study normal and abnormal breast mimicking tissue phantoms. While uniform fringes were obtained for a normal phantom in the out of plane speckle pattern interferometry configuration, the non uniformity in the observed fringes clearly showed the anomaly location in the abnormal phantom. The results are compared with deformation profiles using finite element analysis of the sample under similar loading conditions.

  9. An earthquake from space: detection of precursory magnetic anomalies from Swarm satellites before the 2015 M8 Nepal Earthquake

    NASA Astrophysics Data System (ADS)

    De Santis, A.; Balasis, G.; Pavón-Carrasco, F. J.; Cianchini, G.; Mandea, M.

    2015-12-01

    A large earthquake of around 8 magnitude occurred on 25 April 2015, 06:26 UTC, with epicenter in Nepal, causing more than 9000 fatalities and devastating destruction. The contemporary orbiting in the topside ionosphere of the three Swarm satellites by ESA makes it possible to look for possible pre-earthquake magnetic anomalous signals, likely due to some lithosphere-atmosphere-ionosphere (LAI) coupling. First, a wavelet analysis has been performed during the same day of the earthquake (from the external magnetic point of view, an exceptionally quiet day) with the result that a ULF anomalous and persisting signal (from around 3 to 6 UTC), is clearly detected before the earthquake. After this single-spot analysis, we performed a more extensive analysis for two months around the earthquake occurrence, to confirm or refute the cause-effect relationship. From the series of the detected magnetic anomalies (during night and magnetically quiet times) from Swarm satellites, we show that the cumulative numbers of anomalies follows the same typical power-law behavior of a critical system approaching its critical time, in our case, the large seismic event of 25 April, 2015, and then it recovers as the typical recovery phase after a large earthquake. The impressive similarity of this behavior with the analogous of seismic data analysis, provides strong support to the lithospheric origin of the satellite magnetic anomalies, as due to the LAI coupling during the preparation phase of the Nepal earthquake.

  10. Anomaly detection in radiographic images of composite materials via crosshatch regression

    NASA Astrophysics Data System (ADS)

    Lockard, Colin D.

    The development and testing of new composite materials is an important area of research supporting advances in aerospace engineering. Understanding the properties of these materials requires the analysis of material samples to identify damage. Given the significant time and effort required from human experts to analyze computed tomography (CT) scans related to the non-destructive evaluation of carbon fiber materials, it is advantageous to develop an automated system for identifying anomalies in these images. This thesis introduces a regression-based algorithm for identifying anomalies in grayscale images, with a particular focus on its application for the analysis of CT scan images of carbon fiber. The algorithm centers around a "crosshatch regression" approach in which each two-dimensional image is divided into a series of one-dimensional signals, each representing a single line of pixels. A robust multiple linear regression model is fitted to each signal and outliers are identified. Smoothing and quality control techniques help better define anomaly boundaries and remove noise, and multiple crosshatch regression runs are combined to generate the final result. A ground truth set was created and the algorithm was run against these images for testing. The experimental results support the efficacy of the technique, locating 92% of anomalies with an average recall of 88%, precision of 78%, and root mean square deviation of 11.2 pixels.

  11. Early India-Australia Spreading History Revealed by Newly Detected Magnetic Anomalies

    NASA Astrophysics Data System (ADS)

    Williams, S.; Whittaker, J. M.; Granot, R.; Müller, D.

    2013-12-01

    The seafloor within the Perth Abyssal Plain (PAP), offshore Western Australia, is the only section of crust that directly records the early spreading history between India and Australia during the Mesozoic breakup of Gondwana. However, this early spreading has been poorly constrained due to an absence of data, including marine magnetic anomalies and data constraining the crustal nature of key tectonic features. Here, we present new magnetic anomaly data from the PAP that shows that the crust in the western part of the basin was part of the Indian Plate - the conjugate flank to the oceanic crust immediately offshore the Perth margin, Australia. We identify a sequence of M2 and older anomalies in the west PAP within crust that initially moved with the Indian Plate, formed at intermediate half-spreading rates (35 mm/yr) consistent with the conjugate sequence on the Australian Plate. More speculatively, we reinterpret the youngest anomalies in the east PAP, finding that the M0-age crust initially formed on the Indian Plate was transferred to the Australian Plate by a westward jump or propagation of the spreading ridge shortly after M0 time. Samples dredged from the Gulden Draak and Batavia Knolls (at the western edge of the PAP) reveal that these bathymetric features are continental fragments rather than igneous plateaus related to Broken Ridge. These microcontinents rifted away from Australia with Greater India during initial breakup at ~130 Ma, then rifted from India following the cessation of spreading in the PAP (~101-103 Ma).

  12. Processing forward-looking data for anomaly detection: single-look, multi-look, and spatial classification

    NASA Astrophysics Data System (ADS)

    Malof, Jordan M.; Morton, Kenneth D., Jr.; Collins, Leslie M.; Torrione, Peter A.

    2012-06-01

    Many effective buried threat detection systems rely on close proximity and near vertical deployment over subsurface objects before reasonable performance can be obtained. A forward-looking sensor configuration, where an object can be detected from much greater distances, allows for safer detection of buried explosive threats, and increased rates of advance. Forward-looking configurations also provide an additional advantage of yielding multiple perspectives and looks at each subsurface area, and data from these multiple pose angles can be potentially exploited for improved detection. This work investigates several aspects of detection algorithms that can be applied to forward-looking imagery. Previous forward-looking detection algorithms have employed several anomaly detection algorithms, such as the RX algorithm. In this work the performance of the RX algorithm is compared to a scale-space approach based on Laplcaian of Gaussian filtering. This work also investigates methods to combine the detection output from successive frames to aid detection performance. This is done by exploiting the spatial colocation of detection alarms after they are mapped from image coordinates into world coordinates. The performance of the resulting algorithms are measured on data from a forward-looking vehicle mounted optical sensor system collected over several lanes at a western U.S. test facility. Results indicate that exploiting the spatial colocation of detections made in successive frames can yield improved performance.

  13. Detection of receptor heteromers involving dopamine receptors by the sequential BRET-FRET technology.

    PubMed

    Navarro, Gemma; McCormick, Peter J; Mallol, Josefa; Lluís, Carme; Franco, Rafael; Cortés, Antoni; Casadó, Vicent; Canela, Enric I; Ferré, Sergi

    2013-01-01

    Until very recently, dopamine receptors, like other G-protein-coupled receptors, were believed to function as individual units on the cell surface. Now it has been described by several groups including ours that dopamine receptors not only function as homomers but also form heteromers with other receptors at the membrane level. Bioluminescence and fluorescence resonance energy transfer (BRET and FRET) based techniques have been very useful to determine the interaction between two receptors, but to demonstrate the existence of higher-order complexes involving more than two molecules requires more sophisticated techniques. Combining BRET and FRET in the Sequential BRET-FRET (SRET) technique permits heteromers formed by three different proteins to be identified. In SRET experiments, the oxidation of a Renilla Luciferase substrate triggers acceptor excitation by BRET and subsequent energy transfer to a FRET acceptor. Using this methodology here we describe the heteromerization between adenosine A(2A), dopamine D(2), and cannabinoids CB(1) receptors in living cells. PMID:23296780

  14. Sequential extraction of heavy metals in river sediments of an abandoned pyrite mining area: pollution detection and affinity series.

    PubMed

    Pagnanelli, F; Moscardini, E; Giuliano, V; Toro, L

    2004-11-01

    In this paper heavy metal pollution at an abandoned Italian pyrite mine has been investigated by comparing total concentrations and speciation of heavy metals (Fe, Cu, Mn, Zn, Pb and As) in a red mud sample and a river sediment. Acid digestions show that all the investigated heavy metals present larger concentrations in the sediment than in the tailing. A modified Tessier's procedure has been used to discriminate heavy metal bound to organic fraction from those originally present in the mineral sulphide matrix and to detect a possible trend of metal mobilisation from red mud to river sediment. Sequential extractions on bulk and size fractionated samples denote that sediment samples present larger percent concentrations of the investigated heavy metals in the first extractive steps (I-IV) especially in lower dimension size fractionated samples suggesting that heavy metals in the sediment are significantly bound by superficial adsorption mechanisms. PMID:15312934

  15. Millimeter Wave Detection of Localized Anomalies in the Space Shuttle External Fuel Tank Insulating Foam and Acreage Heat Tiles

    NASA Technical Reports Server (NTRS)

    Kharkovsky, S.; Case, J. T.; Zoughi, R.; Hepburn, F.

    2005-01-01

    The Space Shuttle Columbia's catastrophic accident emphasizes the growing need for developing and applying effective, robust and life-cycle oriented nondestructive testing (NDT) methods for inspecting the shuttle external fuel tank spray on foam insulation (SOFI) and its protective acreage heat tiles. Millimeter wave NDT techniques were one of the methods chosen for evaluating their potential for inspecting these structures. Several panels with embedded anomalies (mainly voids) were produced and tested for this purpose. Near-field and far-field millimeter wave NDT methods were used for producing millimeter wave images of the anomalies in SOFI panel and heat tiles. This paper presents the results of an investigation for the purpose of detecting localized anomalies in two SOFI panels and a set of heat tiles. To this end, reflectometers at a relatively wide range of frequencies (Ka-band (26.5 - 40 GHz) to W-band (75 - 110 GHz)) and utilizing different types of radiators were employed. The results clearly illustrate the utility of these methods for this purpose.

  16. Detection of Characteristic Precipitation Anomaly Patterns of El Nino / La Nina in Time- variable Gravity Fields by GRACE

    NASA Astrophysics Data System (ADS)

    Heki, K.; Morishita, Y.

    2007-12-01

    GRACE (Gravity Recovery and Climate Experiment) satellites, launched in March 2002, have been mapping monthly gravity fields of the Earth, allowing us to infer changes in surface mass, e.g. water and ice. Past findings include the ice mass loss in southern Greenland (Luthcke et al., 2006) and its acceleration in 2004 (Velicogna and Wahr, 2006), crustal dilatation by the 2004 Sumatra Earthquake (Han et al., 2006) and the postseismic movement of water in mantle (Ogawa and Heki, 2007). ENSO (El Nino and Southern Oscillation) brings about global climate impacts, together with its opposite phenomenon, La Nina. Ropelewski and Halpert (1987) showed typical precipitation patterns in ENSO years; characteristic regional-scale precipitation anomalies occur in India, tropical and southern Africa and South America. Nearly opposite precipitation anomalies are shown to occur in La Nina years (Ropelewski and Halpert, 1988). Here we report the detection of such precipitation anomaly patterns in the GRACE monthly gravity data 2002 - 2007, which includes both La Nina (2005 fall - 2006 spring) and El Nino (2006 fall - 2007 spring) periods. We modeled the worldwide gravity time series with constant trends and seasonal changes, and extracted deviations of gravity values at two time epochs, i.e. February 2006 and 2007, and converted them into the changes in equivalent surface water mass. East Africa showed negative gravity deviation (-20.5 cm in water) in 2006 February (La Nina), which reversed to positive (18.7 cm) in 2007 February (El Nino). Northern and southern parts of South America also showed similar see-saw patterns. Such patterns closely resemble to those found meteorologically (Ropelewski and Halpert, 1987; 1988), suggesting the potential of GRACE as a sensor of inter-annual precipitation anomalies through changes in continental water storage. We performed numerical simulations of soil moisture changes at grid points in land area incorporating the CMAP precipitation data, NCEP

  17. Response-time evidence for mixed memory states in a sequential-presentation change-detection task.

    PubMed

    Nosofsky, Robert M; Donkin, Chris

    2016-02-01

    Response-time (RT) and choice-probability data were obtained in a rapid visual sequential-presentation change-detection task in which memory set size, study-test lag, and objective change probabilities were manipulated. False "change" judgments increased dramatically with increasing lag, consistent with the idea that study items with long lags were ejected from a discrete-slots buffer. Error RTs were nearly invariant with set size and lag, consistent with the idea that the errors were produced by a stimulus-independent guessing process. The patterns of error and RT data could not be explained in terms of encoding limitations, but were consistent with the hypothesis that long retention lags produced a zero-stimulus-information state that required guessing. Formal modeling of the change-detection RT and error data pointed toward a hybrid model of visual working memory. The hybrid model assumed mixed states involving a combination of memory and guessing, but with higher memory resolution for items with shorter retention lags. The work raises new questions concerning the nature of the memory representations that are produced across the closely related tasks of change detection and visual memory search. PMID:26706291

  18. Interpretation of Magnetic Anomalies in Salihli (Turkey) Geothermal Area Using 3-D Inversion and Edge Detection Techniques

    NASA Astrophysics Data System (ADS)

    Timur, Emre

    2016-04-01

    There are numerous geophysical methods used to investigate geothermal areas. The major purpose of this magnetic survey is to locate the boudaries of active hydrothermal system in the South of Gediz Graben in Salihli (Manisa/Turkey). The presence of the hydrothermal system had already been inferred from surface evidence of hydrothermal activity and drillings. Firstly, 3-D prismatic models were theoretically investigated and edge detection methods were utilized with an iterative inversion method to define the boundaries and the parameters of the structure. In the first step of the application, it was necessary to convert the total field anomaly into a pseudo-gravity anomaly map. Then the geometric boudaries of the structures were determined by applying a MATLAB based software with 3 different edge detection algorithms. The exact location of the structures were obtained by using these boundary coordinates as initial geometric parameters in the inversion process. In addition to these methods, reduction to pole and horizontal gradient methods were applied to the data to achieve more information about the location and shape of the possible reservoir. As a result, the edge detection methods were found to be successful, both in the field and as theoretical data sets for delineating the boundaries of the possible geothermal reservoir structure. The depth of the geothermal reservoir was determined as 2,4 km from 3-D inversion and 2,1 km from power spectrum methods.

  19. A Hybrid Positive-and-Negative Curvature Approach for Detection of the Edges of Magnetic Anomalies, and Its Application in the South China Sea

    NASA Astrophysics Data System (ADS)

    Guo, Lianghui; Gao, Rui; Meng, Xiaohong; Zhang, Guoli

    2015-10-01

    In work discussed in this paper the characteristics of both the most positive and most negative curvatures of a magnetic anomaly were analyzed, and a new approach for detection of the edges of magnetic anomalies is proposed. The new approach, called the hybrid positive-and-negative curvature approach, combines the most positive and most negative curvatures into one curvature by formula adjustments and weighted summation, combining the advantages of the two curvatures to improve edge detection. This approach is suitable for vertically magnetized or reduction-to-pole anomalies, which avoids the complexity of magnetic anomalies caused by oblique magnetization. Testing on synthetic vertically magnetized magnetic anomalies data demonstrated that the hybrid approach traces the edges of magnetic source bodies effectively, discriminates between high and low magnetism intuitively, and is better than approaches based solely on use of the most positive or most negative curvature. Testing on reduced-to-pole magnetic anomalies data around the ocean basin of the South China Sea showed that the hybrid approach enables better edge detection than the most positive or most negative curvatures. On the basis of the features of the reduced-to-pole magnetic anomalies and their hybrid curvature, we suggest the tectonic boundary between the southwestern subbasin and the eastern subbasin of the South China Sea ranges from the northeastern edge of the Zhongsha Islands in the southeast direction to the northeastern edge of the Reed Bank.

  20. Structural features of sequential weak measurements

    NASA Astrophysics Data System (ADS)

    Diósi, Lajos

    2016-07-01

    We discuss the abstract structure of sequential weak measurement (WM) of general observables. In all orders, the sequential WM correlations without postselection yield the corresponding correlations of the Wigner function, offering direct quantum tomography through the moments of the canonical variables. Correlations in spin-1/2 sequential weak measurements coincide with those in strong measurements, they are constrained kinematically, and they are equivalent with single measurements. In sequential WMs with postselection, an anomaly occurs, different from the weak value anomaly of single WMs. In particular, the spread of polarization σ ̂ as measured in double WMs of σ ̂ will diverge for certain orthogonal pre- and postselected states.

  1. 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.

  2. Space Shuttle Main Engine Propellant Path Leak Detection Using Sequential Image Processing

    NASA Technical Reports Server (NTRS)

    Smith, L. Montgomery; Malone, Jo Anne; Crawford, Roger A.

    1995-01-01

    Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.

  3. Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

    NASA Astrophysics Data System (ADS)

    Miller, David J.; Natraj, Aditya; Hockenbury, Ryler; Dunn, Katherine; Sheffler, Michael; Sullivan, Kevin

    2012-06-01

    We describe a comprehensive system for learning to identify suspicious vehicle tracks from wide-area motion (WAMI) video. First, since the road network for the scene of interest is assumed unknown, agglomerative hierarchical clustering is applied to all spatial vehicle measurements, resulting in spatial cells that largely capture individual road segments. Next, for each track, both at the cell (speed, acceleration, azimuth) and track (range, total distance, duration) levels, extreme value feature statistics are both computed and aggregated, to form summary (p-value based) anomaly statistics for each track. Here, to fairly evaluate tracks that travel across different numbers of spatial cells, for each cell-level feature type, a single (most extreme) statistic is chosen, over all cells traveled. Finally, a novel active learning paradigm, applied to a (logistic regression) track classifier, is invoked to learn to distinguish suspicious from merely anomalous tracks, starting from anomaly-ranked track prioritization, with ground-truth labeling by a human operator. This system has been applied to WAMI video data (ARGUS), with the tracks automatically extracted by a system developed in-house at Toyon Research Corporation. Our system gives promising preliminary results in highly ranking as suspicious aerial vehicles, dismounts, and traffic violators, and in learning which features are most indicative of suspicious tracks.

  4. Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

    NASA Astrophysics Data System (ADS)

    Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.

    2011-06-01

    There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques have been applied to some of these algorithms in an attempt to choose robust settings capable of operating consistently across a large variety of image scenes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research developed a frameworkfor optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. This paper describes a method for selecting hyperspectral image training and test subsets yielding consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. Several different mathematical models representing the value of a training and test set based on such measures as the D-optimal score and various distance norms are tested in a simulation experiment.

  5. Anomaly discrimination in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Chen, Shih-Yu; Paylor, Drew; Chang, Chein-I.

    2014-05-01

    Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it cannot discriminate the anomalies it detected one from another. In order to accomplish this task it requires a way of measuring spectral similarity such as spectral angle mapper (SAM) or spectral information divergence (SID) to determine if a detected anomaly is different from another. However, this arises in a challenging issue of how to find an appropriate thresholding value for this purpose. Interestingly, this issue has not received much attention in the past. This paper investigates the issue of anomaly discrimination which can differentiate detected anomalies without using any spectral measure. The ideas are to makes use unsupervised target detection algorithms, Automatic Target Generation Process (ATGP) coupled with an anomaly detector to distinguish detected anomalies. Experimental results show that the proposed methods are indeed very effective in anomaly discrimination.

  6. Detecting Medusahead (Taeniatherum caput-medusae (L.) Nevski) Using High Frequency, Sequential, Globally Positioned Digital Images

    NASA Astrophysics Data System (ADS)

    Ndzeidze, Stephen Koghan

    Invasive plant species are expanding and transforming vegetative communities across Oregon and throughout the United States. Over the past three decades remote sensing, geographic information system (GIS), and Global Positioning System (GPS) technologies have been integrated to detect and map the distribution of noxious rangeland plants. This study developed low-cost protocols to detect and map Medusahead (Taeniatherum caput-medusae (L.) Nevski) weed infestations using GPS loggers to track aircraft/camera position, altitude, and bearing, as well as Aerial Image Positioning Tool software to geographically rectify and project each aerial image. We then mapped the extent of medusahead in target areas and evaluated patterns of infestation. Flying in a single engine fixed-wing aircraft, images were collected every five seconds, with a total of 10,362 images obtained. All of the aerial images were processed and, on average, 23.9 % of the area was classified as medusahead infested, with 76.1 % without infestation. Each image covered 215 ha (531 acres), with 60% overlap, at a cost of $ 0.54/km2. Our study also employed mobile mapping technology to map medusahead on the ground by digitizing infestations using a laptop computer equipped with a GPS antenna and GIS software. Mobile mapping was also done from aircraft, but yielded coarser infestation maps, as the observation distance was greater. These maps covered the full study area. Aerial reconnaissance and mobile survey is cost effective, because thousands of digital images were collected, automatically positioned, and stored.

  7. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition

    PubMed Central

    Hedwig, Berthold G.

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song. PMID:26941647

  8. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition.

    PubMed

    Hedwig, Berthold G

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song. PMID:26941647

  9. Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

    SciTech Connect

    Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic

    2011-04-01

    Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.

  10. Using anomaly detection method and multi-temporal Radarsat images for short-term land use/land cover change detection

    NASA Astrophysics Data System (ADS)

    Qian, JunPing; Chen, XiaoYue; Li, Xia; Yeh, Anthony Gar-On; Ai, Bin

    2008-10-01

    Rapid urbanization took place in the Pearl River Delta of south China since 1980. Although drastic land use change took place in very short interval within this area, hardly any research has been done on this phenomenon for lacking of available data. Remote sensing is presently the most favorable observation method for land use and land cover change (LUCC) researches. While located in the south of China, the Pearl River Delta suffers from heavy cloud cover for more than half of the year. This makes real-time LUCC monitoring and change detection almost impossible with commonly used optical remote sensing data. In this paper, the orbital highest resolution SAR (Synthetic Aperture Radar) data - Fine Mode Radarsat data was used for trail of short-term land use change detection. Three scenes of repeat-pass Radarsat data was collected over the study area. Although repeat-pass Radarsat enable continuous land use monitoring under all weather condition, images acquired during different time are inevitably affected by seasonal land cover change and variable environmental status such as air humidity and raining. Besides, some significant observation bias might be induced because of the platform and sensor instability. All these variations and instability made short-term land use change detection quite a perplex problem. In this paper, short-term land use change caused by human activity was considered as abnormal phenomena in both spatial and temporal domain in time series images. And a Density-based Anomaly Detection (DBAD) algorithm was designed to detect abnormally changed land parcels in time series Radarsat images. Firstly, totally 3 scenes of fine mode Radarsat images were collected in the study area from January 1st to May 3rd, 2006. Simply stacked temporal images reveal apparent backscattering variation between the three scenes of images, which mainly owes to the fast vegetable growth during the observation period. Then image segmentation was done on the multi

  11. Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw = 7.7) earthquake of 16 April 2013

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2014-02-01

    A powerful earthquake of Mw = 7.7 struck the Saravan region (28.107° N, 62.053° E) in Iran on 16 April 2013. Up to now nomination of an automated anomaly detection method in a non linear time series of earthquake precursor has been an attractive and challenging task. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) have revealed strong potentials in accurate time series prediction. This paper presents the first study of an integration of ANN and PSO method in the research of earthquake precursors to detect the unusual variations of the thermal and total electron content (TEC) seismo-ionospheric anomalies induced by the strong earthquake of Saravan. In this study, to overcome the stagnation in local minimum during the ANN training, PSO as an optimization method is used instead of traditional algorithms for training the ANN method. The proposed hybrid method detected a considerable number of anomalies 4 and 8 days preceding the earthquake. Since, in this case study, ionospheric TEC anomalies induced by seismic activity is confused with background fluctuations due to solar activity, a multi-resolution time series processing technique based on wavelet transform has been applied on TEC signal variations. In view of the fact that the accordance in the final results deduced from some robust methods is a convincing indication for the efficiency of the method, therefore the detected thermal and TEC anomalies using the ANN + PSO method were compared to the results with regard to the observed anomalies by implementing the mean, median, Wavelet, Kalman filter, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM) and Genetic Algorithm (GA) methods. The results indicate that the ANN + PSO method is quite promising and deserves serious attention as a new tool for thermal and TEC seismo anomalies detection.

  12. Principle of indirect comparison (PIC): simulation and analysis of PIC-based anomaly detection in multispectral data

    NASA Astrophysics Data System (ADS)

    Rosario, Dalton

    2006-05-01

    The Army has gained a renewed interest in hyperspectral (HS) imagery for military surveillance. As a result, a HS research team has been established at the Army Research Lab (ARL) to focus exclusively on the design of innovative algorithms for target detection in natural clutter. In 2005 at this symposium, we presented comparison performances between a proposed anomaly detector and existing ones testing real HS data. Herein, we present some insightful results on our general approach using analyses of statistical performances of an additional ARL anomaly detector testing 1500 simulated realizations of model-specific data to shed some light on its effectiveness. Simulated data of increasing background complexity will be used for the analysis, where highly correlated multivariate Gaussian random samples will model homogeneous backgrounds and mixtures of Gaussian will model non-homogeneous backgrounds. Distinct multivariate random samples will model targets, and targets will be added to backgrounds. The principle that led to the design of our detectors employs an indirect sample comparison to test the likelihood that local HS random samples belong to the same population. Let X and Y denote two random samples, and let Z = X U Y, where U denotes the union. We showed that X can be indirectly compared to Y by comparing, instead, Z to Y (or to X). Mathematical implementations of this simple idea have shown a remarkable ability to preserve performance of meaningful detections (e.g., full-pixel targets), while significantly reducing the number of meaningless detections (e.g., transitions of background regions in the scene).

  13. A Clustering Method for Improving Performance of Anomaly-Based Intrusion Detection System

    NASA Astrophysics Data System (ADS)

    Song, Jungsuk; Ohira, Kenji; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

    Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion detection employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched to the signatures, and their methods consume huge amounts of cost and time to acquire the signatures. In order to cope with the problems, many researchers have proposed various kinds of methods that are based on unsupervised learning techniques. Although they enable one to construct intrusion detection model with low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we present a new clustering method to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that superiority of our approach to other existing algorithms reported in the literature.

  14. Selecting Observation Platforms for Optimized Anomaly Detectability under Unreliable Partial Observations

    SciTech Connect

    Wen-Chiao Lin; Humberto E. Garcia; Tae-Sic Yoo

    2011-06-01

    Diagnosers for keeping track on the occurrences of special events in the framework of unreliable partially observed discrete-event dynamical systems were developed in previous work. This paper considers observation platforms consisting of sensors that provide partial and unreliable observations and of diagnosers that analyze them. Diagnosers in observation platforms typically perform better as sensors providing the observations become more costly or increase in number. This paper proposes a methodology for finding an observation platform that achieves an optimal balance between cost and performance, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, an observation platform optimization algorithm is utilized that uses two greedy heuristics, one myopic and another based on projected performances. These heuristics are sequentially executed in order to find best observation platforms. The developed algorithm is then applied to an observation platform optimization problem for a multi-unit-operation system. Results show that improved observation platforms can be found that may significantly reduce the observation platform cost but still yield acceptable performance for correctly inferring the occurrences of special events.

  15. Isolation and detection of single molecules on paramagnetic beads using sequential fluid flows in microfabricated polymer array assemblies.

    PubMed

    Kan, Cheuk W; Rivnak, Andrew J; Campbell, Todd G; Piech, Tomasz; Rissin, David M; Mösl, Matthias; Peterça, Andrej; Niederberger, Hans-Peter; Minnehan, Kaitlin A; Patel, Purvish P; Ferrell, Evan P; Meyer, Raymond E; Chang, Lei; Wilson, David H; Fournier, David R; Duffy, David C

    2012-03-01

    We report a method for isolating individual paramagnetic beads in arrays of femtolitre-sized wells and detecting single enzyme-labeled proteins on these beads using sequential fluid flows in microfabricated polymer array assemblies. Arrays of femtolitre-sized wells were fabricated in cyclic olefin polymer (COP) using injection moulding based on DVD manufacturing. These arrays were bonded to a complementary fluidic structure that was also moulded in COP to create an enclosed device to allow delivery of liquids to the arrays. Enzyme-associated, paramagnetic beads suspended in aqueous solutions of enzyme substrate were delivered fluidically to the array such that one bead per well was loaded by gravity. A fluorocarbon oil was then flowed into the device to remove excess beads from the surface of the array, and to seal and isolate the femtolitre-sized wells containing beads and enzyme substrate. The device was then imaged using standard fluorescence imaging to determine which wells contained single enzyme molecules. The analytical performance of this device as the detector for digital ELISA compared favourably to the standard method, i.e., glass arrays mechanically sealed against a silicone gasket; prostate specific antigen (PSA) could be detected from 0.011 pg mL(-1) up to 100 pg mL(-1). The use of an enclosed fluidic device to isolate beads in single-molecule arrays offers a multitude of advantages for low-cost manufacturing, ease of automation, and instrument development to enable applications in biomarker validation and medical diagnosis. PMID:22179487

  16. Detection of oxygen isotopic anomaly in terrestrial atmospheric carbonates and its implications to Mars.

    PubMed

    Shaheen, R; Abramian, A; Horn, J; Dominguez, G; Sullivan, R; Thiemens, Mark H

    2010-11-23

    The debate of life on Mars centers around the source of the globular, micrometer-sized mineral carbonates in the ALH84001 meteorite; consequently, the identification of Martian processes that form carbonates is critical. This paper reports a previously undescribed carbonate formation process that occurs on Earth and, likely, on Mars. We identified micrometer-sized carbonates in terrestrial aerosols that possess excess (17)O (0.4-3.9‰). The unique O-isotopic composition mechanistically describes the atmospheric heterogeneous chemical reaction on aerosol surfaces. Concomitant laboratory experiments define the transfer of ozone isotopic anomaly to carbonates via hydrogen peroxide formation when O(3) reacts with surface adsorbed water. This previously unidentified chemical reaction scenario provides an explanation for production of the isotopically anomalous carbonates found in the SNC (shergottites, nakhlaites, chassignites) Martian meteorites and terrestrial atmospheric carbonates. The anomalous hydrogen peroxide formed on the aerosol surfaces may transfer its O-isotopic signature to the water reservoir, thus producing mass independently fractionated secondary mineral evaporites. The formation of peroxide via heterogeneous chemistry on aerosol surfaces also reveals a previously undescribed oxidative process of utility in understanding ozone and oxygen chemistry, both on Mars and Earth. PMID:21059939

  17. Detection of oxygen isotopic anomaly in terrestrial atmospheric carbonates and its implications to Mars

    PubMed Central

    Shaheen, R.; Abramian, A.; Horn, J.; Dominguez, G.; Sullivan, R.; Thiemens, Mark H.

    2010-01-01

    The debate of life on Mars centers around the source of the globular, micrometer-sized mineral carbonates in the ALH84001 meteorite; consequently, the identification of Martian processes that form carbonates is critical. This paper reports a previously undescribed carbonate formation process that occurs on Earth and, likely, on Mars. We identified micrometer-sized carbonates in terrestrial aerosols that possess excess 17O (0.4–3.9‰). The unique O-isotopic composition mechanistically describes the atmospheric heterogeneous chemical reaction on aerosol surfaces. Concomitant laboratory experiments define the transfer of ozone isotopic anomaly to carbonates via hydrogen peroxide formation when O3 reacts with surface adsorbed water. This previously unidentified chemical reaction scenario provides an explanation for production of the isotopically anomalous carbonates found in the SNC (shergottites, nakhlaites, chassignites) Martian meteorites and terrestrial atmospheric carbonates. The anomalous hydrogen peroxide formed on the aerosol surfaces may transfer its O-isotopic signature to the water reservoir, thus producing mass independently fractionated secondary mineral evaporites. The formation of peroxide via heterogeneous chemistry on aerosol surfaces also reveals a previously undescribed oxidative process of utility in understanding ozone and oxygen chemistry, both on Mars and Earth. PMID:21059939

  18. Subsurface faults detection based on magnetic anomalies investigation: A field example at Taba protectorate, South Sinai

    NASA Astrophysics Data System (ADS)

    Khalil, Mohamed H.

    2016-08-01

    Quantitative interpretation of the magnetic data particularly in a complex dissected structure necessitates using of filtering techniques. In Taba protectorate, Sinai synthesis of different filtering algorithms was carried out to distinct and verifies the subsurface structure and estimates the depth of the causative magnetic sources. In order to separate the shallow-seated structure, filters of the vertical derivatives (VDR), Butterworth high-pass (BWHP), analytic signal (AS) amplitude, and total horizontal derivative of the tilt derivative (TDR_THDR) were conducted. While, filters of the apparent susceptibility and Butterworth low-pass (BWLP) were conducted to identify the deep-seated structure. The depths of the geological contacts and faults were calculated by the 3D Euler deconvolution. Noteworthy, TDR_THDR was independent of geomagnetic inclination, significantly less susceptible to noise, and more sensitive to the details of the shallow superimposed structures. Whereas, the BWLP proved high resolution capabilities in attenuating the shorter wavelength of the near surface anomalies and emphasizing the longer wavelength derived from deeper causative structure. 3D Euler deconvolution (SI = 0) was quite amenable to estimate the depths of superimposed subsurface structure. The pattern, location, and trend of the deduced shallow and deep faults were conformed remarkably to the addressed fault system.

  19. Application of the LMC algorithm to anomaly detection using the Wichmann/NIITEK ground-penetrating radar

    NASA Astrophysics Data System (ADS)

    Torrione, Peter A.; Collins, Leslie M.; Clodfelter, Fred; Frasier, Shane; Starnes, Ian

    2003-09-01

    This paper describes the application of a 2-dimensional (2-D) lattice LMS algorithm for anomaly detection using the Wichmann/Niitek ground penetrating radar (GPR) system. Sets of 3-dimensional (3-D) data are collected from the GPR system and these are processed in separate 2-D slices. Those 2-D slices that are spatially correlated in depth are combined into separate "depth segments" and these are processed independently. When target/no target declarations need to be made, the individual depth segments are combined to yield a 2-D confidence map. The 2-D confidence map is then thresholded and alarms are placed at the centroids of the remaining 8-connected data points. Calibration lane results are presented for data collected over several soil types under several weather conditions. Results show a false alarm rate improvement of at least an order of magnitude over other GPR systems, as well as significant improvement over other adaptive algorithms operating on the same data.

  20. DEVELOPMENT AND TESTING OF PROCEDURES FOR CARRYING OUT EMERGENCY PHYSICAL INVENTORY TAKING AFTER DETECTING ANOMALY EVENTS CONCERNING NM SECURITY.

    SciTech Connect

    VALENTE,J.FISHBONE,L.ET AL.

    2003-07-13

    In the State Scientific Center of Russian Federation - Institute of Physics and Power Engineering (SSC RF-IPPE, Obninsk), which is under Minatom jurisdiction, the procedures for carrying out emergency physical inventory taking (EPIT) were developed and tested in cooperation with the Brookhaven National Laboratory (USA). Here the emergency physical inventory taking means the PIT, which is carried out in case of symptoms indicating a possibility of NM loss (theft). Such PIT often requires a verification of attributes and quantitative characteristics for all the NM items located in a specific Material Balance Area (MBA). In order to carry out the exercise, an MBA was selected where many thousands of NM items containing highly enriched uranium are used. Three clients of the computerized material accounting system (CMAS) are installed in this MBA. Labels with unique (within IPPE site) identification numbers in the form of digit combinations and an appropriate bar code have been applied on the NM items, containers and authorized locations. All the data to be checked during the EPIT are stored in the CMAS database. Five variants of anomalies initiating EPIT and requiring different types of activities on EPIT organization are considered. Automatic working places (AWP) were created on the basis of the client computers in order to carry out a large number of measurements within a reasonable time. In addition to a CMAS client computer, the main components of an AWP include a bar-code reader, an electronic scale and an enrichment meter with NaI--detector--the lMCA Inspector (manufactured by the Canberra Company). All these devices work together with a client computer in the on-line mode. Special computer code (Emergency Inventory Software-EIS) was developed. All the algorithms of interaction between the operator and the system, as well as algorithms of data exchange during the measurements and data comparison, are implemented in this software. Registration of detected

  1. On the possibility of detecting large-scale crustal remanent magnetization with Magsat vector magnetic anomaly data

    NASA Technical Reports Server (NTRS)

    Galliher, S. C.; Mayhew, M. A.

    1982-01-01

    Magnetic anomaly component data measured by Magsat is compared with synthetic anomaly component fields arising from an equivalent source dipole array at the earth's surface generated from total field anomaly data alone. It is found that the synthetic components fit the component data regardless of the dipole orientation assigned to the equivalent sources and of the dipole spacing. Tentative conclusions are: (1) over the U.S., vector anomaly fields can be determined to the accuracy of the measurements from the total field anomaly data alone; and (2) the equivalent source technique is not useful for determining the direction of large-scale crustal magnetization.

  2. Evolutionary neural networks for anomaly detection based on the behavior of a program.

    PubMed

    Han, Sang-Jun; Cho, Sung-Bae

    2006-06-01

    The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection. PMID:16761810

  3. Spatial scanning for anomaly detection in acoustic emission testing of an aerospace structure

    NASA Astrophysics Data System (ADS)

    Hensman, James; Worden, Keith; Eaton, Mark; Pullin, Rhys; Holford, Karen; Evans, Sam

    2011-10-01

    Acoustic emission (AE) monitoring of engineering structures potentially provides a convenient, cost-effective means of performing structural health monitoring. Networks of AE sensors can be easily and unobtrusively installed upon structures, giving the ability to detect and locate damage-related strain releases ('events') in the structure. Use of the technique is not widespread due to the lack of a simple and effective method for detecting abnormal activity levels: the sensitivity of AE sensor networks is such that events unrelated to damage are prevalent in most applications. In this publication, we propose to monitor AE activity in a structure using a spatial scanning statistic, developed and used effectively in the field of epidemiology. The technique is demonstrated on an aerospace structure - an Airbus A320 main landing gear fitting - undergoing fatigue loading, and the method is compared to existing techniques. Despite its simplicity, the scanning statistic proves to be an extremely effective tool in detecting the onset of damage in the structure: it requires little to no user intervention or expertise, is inexpensive to compute and has an easily interpretable output. Furthermore, the generic nature of the method allows the technique to be used in a variety of monitoring scenarios, to detect damage in a wide range of structures.

  4. Behavioral Anomaly Detection: A Socio-Technical Study of Trustworthiness in Virtual Organizations

    ERIC Educational Resources Information Center

    Ho, Shuyuan Mary

    2009-01-01

    This study examines perceptions of human "trustworthiness" as a key component in countering insider threats. The term "insider threat" refers to situations where a critical member of an organization behaves against the interests of the organization, in an illegal and/or unethical manner. Identifying and detecting how an individual's behavior…

  5. Least Square Support Vector Machine for Detection of - Ionospheric Anomalies Associated with the Powerful Nepal Earthquake (Mw = 7.5) of 25 April 2015

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2016-06-01

    Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth = 15.0 km) a powerful earthquake of Mw = 7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

  6. Recent Results on "Approximations to Optimal Alarm Systems for Anomaly Detection"

    NASA Technical Reports Server (NTRS)

    Martin, Rodney Alexander

    2009-01-01

    An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario.

  7. Concept for Inclusion of Analytical and Computational Capability in Optical Plume Anomaly Detection (OPAD) for Measurement of Neutron Flux

    NASA Technical Reports Server (NTRS)

    Patrick, M. Clinton; Cooper, Anita E.; Powers, W. T.

    2004-01-01

    Researchers are working on many konts to make possible high speed, automated classification and quantification of constituent materials in numerous environments. NASA's Marshall Space Flight Center has implemented a system for rocket engine flow fields/plumes; the Optical Plume Anomaly Detection (OPAD) system was designed to utilize emission and absorption spectroscopy for monitoring molecular and atomic particulates in gas plasma. An accompanying suite of tools and analytical package designed to utilize information collected by OPAD is known as the Engine Diagnostic Filtering System (EDIFIS). The current combination of these systems identifies atomic and molecular species and quantifies mass loss rates in H2/O2 rocket plumes. Additionally, efforts are being advanced to hardware encode components of the EDIFIS in order to address real-time operational requirements for health monitoring and management. This paper addresses the OPAD with its tool suite, and discusses what is considered a natural progression: a concept for migrating OPAD towards detection of high energy particles, including neutrons and gamma rays. The integration of these tools and capabilities will provide NASA with a systematic approach to monitor space vehicle internal and external environment.

  8. Cyber-Critical Infrastructure Protection Using Real-Time Payload-Based Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Düssel, Patrick; Gehl, Christian; Laskov, Pavel; Bußer, Jens-Uwe; Störmann, Christof; Kästner, Jan

    With an increasing demand of inter-connectivity and protocol standardization modern cyber-critical infrastructures are exposed to a multitude of serious threats that may give rise to severe damage for life and assets without the implementation of proper safeguards. Thus, we propose a method that is capable to reliably detect unknown, exploit-based attacks on cyber-critical infrastructures carried out over the network. We illustrate the effectiveness of the proposed method by conducting experiments on network traffic that can be found in modern industrial control systems. Moreover, we provide results of a throughput measuring which demonstrate the real-time capabilities of our system.

  9. Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (Mw = 8.0) earthquake of 06 February 2013

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-08-01

    On 6 February 2013, at 12:12:27 local time (01:12:27 UTC) a seismic event registering Mw 8.0 struck the Solomon Islands, located at the boundaries of the Australian and Pacific tectonic plates. Time series prediction is an important and widely interesting topic in the research of earthquake precursors. This paper describes a new computational intelligence approach to detect the unusual variations of the total electron content (TEC) seismo-ionospheric anomalies induced by the powerful Solomon earthquake using genetic algorithm (GA). The GA detected a considerable number of anomalous occurrences on earthquake day and also 7 and 8 days prior to the earthquake in a period of high geomagnetic activities. In this study, also the detected TEC anomalies using the proposed method are compared to the results dealing with the observed TEC anomalies by applying the mean, median, wavelet, Kalman filter, ARIMA, neural network and support vector machine methods. The accordance in the final results of all eight methods is a convincing indication for the efficiency of the GA method. It indicates that GA can be an appropriate non-parametric tool for anomaly detection in a non linear time series showing the seismo-ionospheric precursors variations.

  10. Anomaly detection using simulated MTI data cubes derived from HYDICE data

    SciTech Connect

    Moya, M.M.; Taylor, J.G.; Stallard, B.R.; Motomatsu, S.E.

    1998-07-01

    The US Department of Energy is funding the development of the Multi-spectral Thermal Imager (MTI), a satellite-based multi-spectral (MS) thermal imaging sensor scheduled for launch in October 1999. MTI is a research and development (R and D) platform to test the applicability of multispectral and thermal imaging technology for detecting and monitoring signs of proliferation of weapons of mass destruction. During its three-year mission, MTI will periodically record images of participating government, industrial and natural sites in fifteen visible and infrared spectral bands to provide a variety of image data associated with weapons production activities. The MTI satellite will have spatial resolution in the visible bands that is five times better than LANDSAT TM in each dimension and will have five thermal bands. In this work, the authors quantify the separability between specific materials and the natural background by applying Receiver Operating Curve (ROC) analysis to the residual errors from a linear unmixing. The authors apply the ROC analysis to quantify performance of the MTI. They describe the MTI imager and simulate its data by filtering HYDICE hyperspectral imagery both spatially and spectrally and by introducing atmospheric effects corresponding to the MTI satellite altitude. They compare and contrast the individual effects on performance of spectral resolution, spatial resolution, atmospheric corrections, and varying atmospheric conditions.

  11. A computational tool to highlight anomalies on shearographic images in optical flaw detection

    NASA Astrophysics Data System (ADS)

    Fantin, A. V.; Willemann, D. P.; Viotti, M.; Albertazzi, A.

    2013-04-01

    Shearography is an optical and nondestructive technique that has been largely used for damage detection in layered composite materials where delaminations and debondings are found among the most common flaws. Shearography is a relative measurement on which two images are recorded for different loading conditions of the sample. The applied loading induces some deformations into the sample generating a displacement field on its surface. The absolute difference between two phase maps recorded at two different loading instances produces an interference fringe pattern which is directly correlated to the displacements produced on the material surface. In some cases, depending on the loading level and mainly on the sample geometry, interference patterns will contain fringes resulting from geometry changes. This will mask those fringes correlated to flaws presented into the material, resulting in an image misinterpretation. This phenomenon takes place mainly when the sample has curved geometries, as for example pipe or vessel surfaces. This paper presents an algorithm which uses a mathematical processing to improve the visualization of flaws in shearographic images. The mathematical processing is based on divergent calculation. This algorithm highlights defected regions and eliminates fringes caused by geometry changes, providing an easier interpretation for complex shearographic images. This paper also shows the principle and the algorithm used for the processing. Results, advantages and difficulties of the method are presented and discussed by using simulated fringe maps as well as real ones.

  12. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition

    PubMed Central

    Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine

    2016-01-01

    We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks. PMID:27535466

  13. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

    PubMed

    Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

    2016-01-01

    We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks. PMID:27535466

  14. Regional and residual anomaly separation in microgravity maps for cave detection: The case study of Gruta de las Maravillas (SW Spain)

    NASA Astrophysics Data System (ADS)

    Martínez-Moreno, F. J.; Galindo-Zaldívar, J.; Pedrera, A.; Teixidó, T.; Peña, J. A.; González-Castillo, L.

    2015-03-01

    Gravity can be considered an optimal geophysical method for cave detection, given the high density contrast between an empty cavity and the surrounding materials. A number of methods can be used for regional and residual gravity anomaly separation, although they have not been tested in natural scenarios. With the purpose of comparing the different methods, we calculate the residual anomalies associated with the karst system of Gruta de las Maravillas whose cave morphology and dimensions are well-known. A total of 1857 field measurements, mostly distributed in a regular grid of 10 × 10 m, cover the studied area. The microgravity data were acquired using a Scintrex CG5 gravimeter and topography control was carried out with a differential GPS. Regional anomaly maps were calculated by means of several algorithms to generate the corresponding residual gravimetric maps: polynomial first-order fitting, fast Fourier transformation with an upward continuation filter, moving average, minimum curvature and kriging methods. Results are analysed and discussed in terms of resolution, implying the capacity to detect shallow voids. We propose that polynomial fitting is the best technique when microgravity data are used to obtain the residual anomaly maps for cave detection.

  15. Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution.

    PubMed

    Hou, Dibo; Zhang, Jian; Yang, Zheling; Liu, Shu; Huang, Pingjie; Zhang, Guangxin

    2015-06-29

    The issue of distribution water quality security ensuring is recently attracting global attention due to the potential threat from harmful contaminants. The real-time monitoring based on ultraviolet optical sensors is a promising technique. This method is of reagent-free, low maintenance cost, rapid analysis and wide cover range. However, the ultraviolet absorption spectra are of large size and easily interfered. While within the on-site application, there is almost no prior knowledge like spectral characteristics of potential contaminants before determined. Meanwhile, the concept of normal water quality is also varying due to the operating condition. In this paper, a procedure based on multivariate statistical analysis is proposed to detect distribution water quality anomaly based on ultraviolet optical sensors. Firstly, the principal component analysis is employed to capture the main variety features from the spectral matrix and reduce the dimensionality. A new statistical variable is then constructed and used for evaluating the local outlying degree according to the chi-square distribution in the principal component subspace. The possibility of anomaly of the latest observation is calculated by the accumulation of the outlying degrees from the adjacent previous observations. To develop a more reliable anomaly detection procedure, several key parameters are discussed. By utilizing the proposed methods, the distribution water quality anomalies and the optical abnormal changes can be detected. The contaminants intrusion experiment is conducted in a pilot-scale distribution system by injecting phenol solution. The effectiveness of the proposed procedure is finally testified using the experimental spectral data. PMID:26191757

  16. Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Srivastava, Askok N.; Matthews, Bryan; Das, Santanu

    2008-01-01

    The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

  17. Structural Anomalies Detected in Ceramic Matrix Composites Using Combined Nondestructive Evaluation and Finite Element Analysis (NDE and FEA)

    NASA Technical Reports Server (NTRS)

    Abdul-Aziz, Ali; Baaklini, George Y.; Bhatt, Ramakrishna T.

    2003-01-01

    and the experimental data. Furthermore, modeling of the voids collected via NDE offered an analytical advantage that resulted in more accurate assessments of the material s structural strength. The top figure shows a CT scan image of the specimen test section illustrating various hidden structural entities in the material and an optical image of the test specimen considered in this study. The bottom figure represents the stress response predicted from the finite element analyses (ref .3 ) for a selected CT slice where it clearly illustrates the correspondence of the high stress risers due to voids in the material with those predicted by the NDE. This study is continuing, and efforts are concentrated on improving the modeling capabilities to imitate the structural anomalies as detected.

  18. Holonomy anomalies

    SciTech Connect

    Bagger, J.; Nemeschansky, D.; Yankielowicz, S.

    1985-05-01

    A new type of anomaly is discussed that afflicts certain non-linear sigma models with fermions. This anomaly is similar to the ordinary gauge and gravitational anomalies since it reflects a topological obstruction to the reparametrization invariance of the quantum effective action. Nonlinear sigma models are constructed based on homogeneous spaces G/H. Anomalies arising when the fermions are chiral are shown to be cancelled sometimes by Chern-Simons terms. Nonlinear sigma models are considered based on general Riemannian manifolds. 9 refs. (LEW)

  19. Sequential hypothesis testing for automatic detection of task-related changes in cerebral perfusion in a brain-computer interface.

    PubMed

    Faulkner, Hayley G; Myrden, Andrew; Li, Michael; Mamun, Khondaker; Chau, Tom

    2015-11-01

    Evidence suggests that the cerebral blood flow patterns accompanying cognitive activity are retained in many locked-in patients. These patterns can be monitored using transcranial Doppler ultrasound (TCD), a medical imaging technique that measures bilateral cerebral blood flow velocities. Recently, TCD has been proposed as an alternative imaging modality for brain-computer interfaces (BCIs). However, most previous TCD-BCI studies have performed offline analyses with impractically lengthy tasks. In this study, we designed a BCI that automatically differentiates between counting and verbal fluency tasks using sequential hypothesis testing to make decisions as quickly as possible. Ten able-bodied participants silently alternated between counting and verbal fluency tasks within the paradigm of a simulated on-screen keyboard. During this experiment, blood flow velocities were recorded within the left and right middle cerebral arteries using bilateral TCD. Twelve features were used to characterize TCD signals. In a simulated online analysis, sequential hypothesis testing was used to update estimates of class probability every 250 ms as TCD data were processed. Classification was terminated once a threshold level of certainty was reached. Mean classification accuracy across all participants was 72% after an average of 23s, compared to an offline analysis which obtained a classification accuracy of 80% after 45 s. This represents a substantial gain in data transmission rate, while maintaining classification accuracies exceeding 70%. Furthermore, a range of decision times between 19 and 28s was observed, suggesting that the ability of sequential hypothesis testing to adapt the task duration for each individual participant is critical to achieving consistent performance across participants. These results indicate that sequential hypothesis testing is a promising alternative for online TCD-BCIs. PMID:26163771

  20. The role of noninvasive and invasive diagnostic imaging techniques for detection of extra-cranial venous system anomalies and developmental variants

    PubMed Central

    2013-01-01

    The extra-cranial venous system is complex and not well studied in comparison to the peripheral venous system. A newly proposed vascular condition, named chronic cerebrospinal venous insufficiency (CCSVI), described initially in patients with multiple sclerosis (MS) has triggered intense interest in better understanding of the role of extra-cranial venous anomalies and developmental variants. So far, there is no established diagnostic imaging modality, non-invasive or invasive, that can serve as the “gold standard” for detection of these venous anomalies. However, consensus guidelines and standardized imaging protocols are emerging. Most likely, a multimodal imaging approach will ultimately be the most comprehensive means for screening, diagnostic and monitoring purposes. Further research is needed to determine the spectrum of extra-cranial venous pathology and to compare the imaging findings with pathological examinations. The ability to define and reliably detect noninvasively these anomalies is an essential step toward establishing their incidence and prevalence. The role for these anomalies in causing significant hemodynamic consequences for the intra-cranial venous drainage in MS patients and other neurologic disorders, and in aging, remains unproven. PMID:23806142

  1. Anomaly Monitoring Method for Key Components of Satellite

    PubMed Central

    Fan, Linjun; Xiao, Weidong; Tang, Jun

    2014-01-01

    This paper presented a fault diagnosis method for key components of satellite, called Anomaly Monitoring Method (AMM), which is made up of state estimation based on Multivariate State Estimation Techniques (MSET) and anomaly detection based on Sequential Probability Ratio Test (SPRT). On the basis of analysis failure of lithium-ion batteries (LIBs), we divided the failure of LIBs into internal failure, external failure, and thermal runaway and selected electrolyte resistance (R e) and the charge transfer resistance (R ct) as the key parameters of state estimation. Then, through the actual in-orbit telemetry data of the key parameters of LIBs, we obtained the actual residual value (R X) and healthy residual value (R L) of LIBs based on the state estimation of MSET, and then, through the residual values (R X and R L) of LIBs, we detected the anomaly states based on the anomaly detection of SPRT. Lastly, we conducted an example of AMM for LIBs, and, according to the results of AMM, we validated the feasibility and effectiveness of AMM by comparing it with the results of threshold detective method (TDM). PMID:24587703

  2. Bangui Anomaly

    NASA Technical Reports Server (NTRS)

    Taylor, Patrick T.

    2004-01-01

    Bangui anomaly is the name given to one of the Earth s largest crustal magnetic anomalies and the largest over the African continent. It covers two-thirds of the Central African Republic and therefore the name derives from the capitol city-Bangui that is also near the center of this feature. From surface magnetic survey data Godivier and Le Donche (1962) were the first to describe this anomaly. Subsequently high-altitude world magnetic surveying by the U.S. Naval Oceanographic Office (Project Magnet) recorded a greater than 1000 nT dipolar, peak-to-trough anomaly with the major portion being negative (figure 1). Satellite observations (Cosmos 49) were first reported in 1964, these revealed a 40nT anomaly at 350 km altitude. Subsequently the higher altitude (417-499km) POGO (Polar Orbiting Geomagnetic Observatory) satellite data recorded peak-to-trough anomalies of 20 nT these data were added to Cosmos 49 measurements by Regan et al. (1975) for a regional satellite altitude map. In October 1979, with the launch of Magsat, a satellite designed to measure crustal magnetic anomalies, a more uniform satellite altitude magnetic map was obtained. These data, computed at 375 km altitude recorded a -22 nT anomaly (figure 2). This elliptically shaped anomaly is approximately 760 by 1000 km and is centered at 6%, 18%. The Bangui anomaly is composed of three segments; there are two positive anomalies lobes north and south of a large central negative field. This displays the classic pattern of a magnetic anomalous body being magnetized by induction in a zero inclination field. This is not surprising since the magnetic equator passes near the center of this body.

  3. Space-borne detection of volcanic carbon dioxide anomalies: The importance of ground-based validation networks

    NASA Astrophysics Data System (ADS)

    Schwandner, F. M.; Carn, S. A.; Corradini, S.; Merucci, L.; Salerno, G.; La Spina, A.

    2012-04-01

    We have investigated the feasibility of space-borne detection of volcanic carbon dioxide (CO2) anomalies, and their integration with ground-based observations. Three goals provide motivation to their integration: (a) development of new volcano monitoring techniques, with better spatial and temporal coverage, because pre-eruptive volcanic CO2 emissions are potentially the earliest available indicators of volcanic unrest; (b) improvement the currently very poor global CO2 source strength inventory for volcanoes, and (c) use of volcanic CO2 emissions for high altitude strong point source emission and dispersion studies. (1) Feasibility of space-borne detection of volcanic CO2 anomalies. Volcanoes are highly variable but continuous CO2 emitters, distributed globally, and emissions often occur at high altitudes. To detect strong point sources of CO2 from space, several hurdles have to be overcome: orographic clouds, unknown dispersion behavior, a high CO2 background in the troposphere, and sparse data coverage from existing satellite sensors. These obstacles can be overcome by a small field of view, enhanced spectral resolving power, and by employing repeat target mode observation strategies. The Japanese GOSAT instrument has been operational since January 2009, producing CO2 total column measurements with a repeat cycle of 3 days and a field of view of 10km. GOSAT thus has the potential to provide spatially integrated data for entire volcanic edifices, especially in target mode. Since summer 2010 we have conducted repeated target mode observations of over 20 persistently active global volcanoes including Etna (Italy), Erta Ale (Ethiopia), and Ambrym (Vanuatu), using L2 GOSAT FTS SWIR data. One of our best-studied test cases is Mt. Etna on Sicily (Italy), which reawakened in 2011 after a period of quiescence and produced a sequence of eruptive activities including lava fountaining events, coinciding with target-mode GOSAT observations conducted there since 2010. For the

  4. Early India-Australia spreading history revealed by newly detected Mesozoic magnetic anomalies in the Perth Abyssal Plain

    NASA Astrophysics Data System (ADS)

    Williams, Simon E.; Whittaker, Joanne M.; Granot, Roi; Müller, Dietmar R.

    2013-07-01

    seafloor within the Perth Abyssal Plain (PAP), offshore Western Australia, is the only section of crust that directly records the early spreading history between India and Australia during the Mesozoic breakup of Gondwana. However, this early spreading has been poorly constrained due to an absence of data, including marine magnetic anomalies and data constraining the crustal nature of key tectonic features. Here, we present new magnetic anomaly data from the PAP that shows that the crust in the western part of the basin was part of the Indian Plate—the conjugate flank to the oceanic crust immediately offshore the Perth margin, Australia. We identify a sequence of M2 and older anomalies in the west PAP within crust that initially moved with the Indian Plate, formed at intermediate half-spreading rates (35 mm/yr) consistent with the conjugate sequence on the Australian Plate. More speculatively, we reinterpret the youngest anomalies in the east PAP, finding that the M0-age crust initially formed on the Indian Plate was transferred to the Australian Plate by a westward jump or propagation of the spreading ridge shortly after M0 time. Samples dredged from the Gulden Draak and Batavia Knolls (at the western edge of the PAP) reveal that these bathymetric features are continental fragments rather than igneous plateaus related to Broken Ridge. These microcontinents rifted away from Australia with Greater India during initial breakup at ~130 Ma, then rifted from India following the cessation of spreading in the PAP (~101-103 Ma).

  5. A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4)

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-04-01

    In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

  6. A colorimetric sensor for the sequential detection of Cu(2+) and CN(-) in fully aqueous media: practical performance of Cu(2+).

    PubMed

    You, Ga Rim; Park, Gyeong Jin; Lee, Jae Jun; Kim, Cheal

    2015-05-21

    A new highly selective colorimetric chemosensor 1 (E)-9-(((5-mercapto-1,3,4-thiadiazol-2-yl)imino)methyl)-2,3,6,7-tetrahydro-1H,5H-pyrido[3,2,1-ij]quinolin-8-ol was designed and synthesized for the sequential detection of Cu(2+) and CN(-). This sensor 1 exhibited an obvious color change from yellow to orange in the presence of Cu(2+) in a fully aqueous solution. The detection limit (0.9 μM) of 1 for Cu(2+) is far lower than the WHO limit (31.5 μM) for drinking water. In addition, the resulting Cu(2+)-2· 1 complex can be further used to detect toxic cyanide through a color change from orange to yellow, indicating the recovery of 1 from Cu(2+)-2·1. Importantly, chemosensor 1 could be used to detect and quantify Cu(2+) in water samples, and a colorimetric test strip of 1 for the detection of Cu(2+) could be useful for all practical purposes. PMID:25900000

  7. Sequential on-line C-terminal sequencing of peptides based on carboxypeptidase Y digestion and optically gated capillary electrophoresis with laser-induced fluorescence detection.

    PubMed

    Tian, Miaomiao; Zhang, Ning; Liu, Xiaoxia; Guo, Liping; Yang, Li

    2016-08-12

    We report a novel method for sequential on-line C-terminal sequencing of peptides, which combines carboxypeptidase Y (CPY) digestion with on-line derivatization and optically gated capillary electrophoresis with laser-induced fluorescence detection (OGCE-LIF). Various factors that may affect the C-terminal sequencing were investigated and optimized. High repeatability of on-line derivatization and the sequential OGCE-LIF assay of amino acids (AAs) was achieved with relative standard deviation (RSD) (n=20) less than 1.5% and 3.2% for migration time and peak height, respectively. A total of 13 AAs was efficiently separated in the present study, indicating that the method can be used for sequencing of peptides consisting of the 13 AAs studied. Using two synthesized N-terminally blocked peptides as test examples, we show that the present method can on-line monitor the released AAs with a temporal resolution of 50s during the entire CPY digestion process. The rates of AA release as a function of digestion time were easily measured; thus, the AA sequence of the peptide was determined with just one OGCE assay. Our study indicates the present approach is an effective, reliable, and convenient method for rapid analysis of the C-terminal sequence of peptides, with potential application in peptide analysis and proteome research. PMID:27425760

  8. Sequential injection analysis of lead using time-based colorimetric detection and preconcentration on an anionic-exchange resin.

    PubMed

    Aracama, Nestor Zárate; Araújo, Alberto N; Perez-Olmos, Ricardo

    2004-04-01

    The development of a sequential injection analysis manifold for the colorimetric determination of lead in water samples is described The concentration of lead was assessed from its catalytic effect on the reaction of resazurine reduction caused by sulfide in an alkali medium. To that effect, the reaction zone was stopped at the detector, and the time interval required for the attainment of an absorbance decrease of 0.800 at the wavelength of 610 nm was estimated. Interference of other transition metals of the samples was minimized by adding potassium iodide to the sample and retaining the iodocomplexes formed in an on-line anionic resin (AGI X8). Elution was made with a 2 mol/L sodium hydroxide solution. The relationship [SIA] microg/L = 0.99 (+/- 0.11) x [ETAAS] microg/L + 0 (+/- 4) was obtained upon comparing the results given by the proposed system and by electrothermal atomization atomic absorption spectrometry (ETAAS) after the analysis of ten water samples. PMID:15116968

  9. Enzyme leaching of surficial geochemical samples for detecting hydromorphic trace-element anomalies associated with precious-metal mineralized bedrock buried beneath glacial overburden in northern Minnesota

    USGS Publications Warehouse

    Clark, Robert J.; Meier, A.L.; Riddle, G.

    1990-01-01

    One objective of the International Falls and Roseau, Minnesota, CUSMAP projects was to develop a means of conducting regional-scale geochemical surveys in areas where bedrock is buried beneath complex glacially derived overburden. Partial analysis of B-horizon soils offered hope for detecting subtle hydromorphic trace-element dispersion patterns. An enzyme-based partial leach selectively removes metals from oxide coatings on the surfaces of soil materials without attacking their matrix. Most trace-element concentrations in the resulting solutions are in the part-per-trillion to low part-per-billion range, necessitating determinations by inductively coupled plasma/mass spectrometry. The resulting data show greater contrasts for many trace elements than with other techniques tested. Spatially, many trace metal anomalies are locally discontinuous, but anomalous trends within larger areas are apparent. In many instances, the source for an anomaly seems to be either basal till or bedrock. Ground water flow is probably the most important mechanism for transporting metals toward the surface, although ionic diffusion, electrochemical gradients, and capillary action may play a role in anomaly dispersal. Sample sites near the Rainy Lake-Seine River fault zone, a regional shear zone, often have anomalous concentrations of a variety of metals, commonly including Zn and/or one or more metals which substitute for Zn in sphalerite (Cd, Ge, Ga, and Sn). Shifts in background concentrations of Bi, Sb, and As show a trend across the area indicating a possible regional zoning of lode-Au mineralization. Soil anomalies of Ag, Co, and Tl parallel basement structures, suggesting areas that may have potential for Cobalt/Thunder Baytype silver viens. An area around Baudette, Minnesota, which is underlain by quartz-chlorite-carbonate-altered shear zones, is anomalous in Ag, As, Bi, Co, Mo, Te, Tl, and W. Anomalies of Ag, As, Bi, Te, and W tend to follow the fault zones, suggesting potential

  10. DOWN'S ANOMALY.

    ERIC Educational Resources Information Center

    PENROSE, L.S.; SMITH, G.F.

    BOTH CLINICAL AND PATHOLOGICAL ASPECTS AND MATHEMATICAL ELABORATIONS OF DOWN'S ANOMALY, KNOWN ALSO AS MONGOLISM, ARE PRESENTED IN THIS REFERENCE MANUAL FOR PROFESSIONAL PERSONNEL. INFORMATION PROVIDED CONCERNS (1) HISTORICAL STUDIES, (2) PHYSICAL SIGNS, (3) BONES AND MUSCLES, (4) MENTAL DEVELOPMENT, (5) DERMATOGLYPHS, (6) HEMATOLOGY, (7)…

  11. Diagnostic accuracy of VIA and HPV detection as primary and sequential screening tests in a cervical cancer screening demonstration project in India.

    PubMed

    Basu, Partha; Mittal, Srabani; Banerjee, Dipanwita; Singh, Priyanka; Panda, Chinmay; Dutta, Sankhadeep; Mandal, Ranajit; Das, Pradip; Biswas, Jaydip; Muwonge, Richard; Sankaranarayanan, Rengaswamy

    2015-08-15

    Visual inspection after acetic acid application (VIA) and human papillomavirus (HPV) detection tests have been recommended to screen women for cervical cancer in low and middle income countries. A demonstration project in rural India screened 39,740 women with both the tests to compare their accuracies in real population setting. The project also evaluated the model of screening women in the existing primary health care facilities, evaluating the screen positive women with colposcopy (and biopsy) in the same setup and recalling the women diagnosed to have disease for treatment at tertiary center. Accuracy of VIA and HPV test used sequentially was also studied. VIA was performed by trained health workers and Hybrid Capture II (HC II) assay was used for oncogenic HPV detection. Test positivity was 7.1% for VIA and 4.7% for HC II. Detection rate of CIN 3+ disease was significantly higher with HC II than VIA. Sensitivities of VIA and HC II to detect 162 histology proved CIN 3+ lesions were 67.9 and 91.2%, respectively after adjusting for verification bias. Specificity for the same disease outcome and verification bias correction was 93.2% for VIA and 96.9% for HC II. Triaging of VIA positive women with HPV test would have considerably improved the positive predictive value (4.0 to 37.5% to detect CIN 3+) without significant drop in sensitivity. All VIA positive women and 74.0% of HC II positive women had colposcopy. There was high compliance to treatment and significant stage-shift of the screen-detected cancers towards more early stage. PMID:25631198

  12. Sequential (step-by-step) detection, identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles.

    PubMed

    Capote, F Priego; Jiménez, J Ruiz; de Castro, M D Luque

    2007-08-01

    An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria. PMID:17611742

  13. Synthesis and Application of an Aldazine-Based Fluorescence Chemosensor for the Sequential Detection of Cu2+ and Biological Thiols in Aqueous Solution and Living Cells

    PubMed Central

    Jia, Hongmin; Yang, Ming; Meng, Qingtao; He, Guangjie; Wang, Yue; Hu, Zhizhi; Zhang, Run; Zhang, Zhiqiang

    2016-01-01

    A fluorescence chemosensor, 2-hydroxy-1-naphthaldehyde azine (HNA) was designed and synthesized for sequential detection of Cu2+ and biothiols. It was found that HNA can specifically bind to Cu2+ with 1:1 stoichiometry, accompanied with a dramatic fluorescence quenching and a remarkable bathochromic-shift of the absorbance peak in HEPES buffer. The generated HNA-Cu2+ ensemble displayed a “turn-on” fluorescent response specific for biothiols (Hcy, Cys and GSH) based on the displacement approach, giving a remarkable recovery of fluorescence and UV-Vis spectra. The detection limits of HNA-Cu2+ to Hcy, Cys and GSH were estimated to be 1.5 μM, 1.0 μM and 0.8 μM, respectively, suggesting that HNA-Cu2+ is sensitive enough for the determination of thiols in biological systems. The biocompatibility of HNA towards A549 human lung carcinoma cell, was evaluated by an MTT assay. The capability of HNA-Cu2+ to detect biothiols in live A549 cells was then demonstrated by a microscopy fluorescence imaging assay. PMID:26761012

  14. Using a combination of MLPA kits to detect chromosomal imbalances in patients with multiple congenital anomalies and mental retardation is a valuable choice for developing countries.

    PubMed

    Jehee, Fernanda Sarquis; Takamori, Jean Tetsuo; Medeiros, Paula F Vasconcelos; Pordeus, Ana Carolina B; Latini, Flavia Roche M; Bertola, Débora Romeo; Kim, Chong Ae; Passos-Bueno, Maria Rita

    2011-01-01

    Conventional karyotyping detects anomalies in 3-15% of patients with multiple congenital anomalies and mental retardation (MCA/MR). Whole-genome array screening (WGAS) has been consistently suggested as the first choice diagnostic test for this group of patients, but it is very costly for large-scale use in developing countries. We evaluated the use of a combination of Multiplex Ligation-dependent Probe Amplification (MLPA) kits to increase the detection rate of chromosomal abnormalities in MCA/MR patients. We screened 261 MCA/MR patients with two subtelomeric and one microdeletion kits. This would theoretically detect up to 70% of all submicroscopic abnormalities. Additionally we scored the de Vries score for 209 patients in an effort to find a suitable cut-off for MLPA screening. Our results reveal that chromosomal abnormalities were present in 87 (33.3%) patients, but only 57 (21.8%) were considered causative. Karyotyping detected 15 abnormalities (6.9%), while MLPA identified 54 (20.7%). Our combined MLPA screening raised the total detection number of pathogenic imbalances more than three times when compared to conventional karyotyping. We also show that using the de Vries score as a cut-off for this screening would only be suitable under financial restrictions. A decision analytic model was constructed with three possible strategies: karyotype, karyotype + MLPA and karyotype + WGAS. Karyotype + MLPA strategy detected anomalies in 19.8% of cases which account for 76.45% of the expected yield for karyotype + WGAS. Incremental Cost Effectiveness Ratio (ICER) of MLPA is three times lower than that of WGAS, which means that, for the same costs, we have three additional diagnoses with MLPA but only one with WGAS. We list all causative alterations found, including rare findings, such as reciprocal duplications of regions deleted in Sotos and Williams-Beuren syndromes. We also describe imbalances that were considered polymorphisms or rare variants, such as the new SNP

  15. Assessment of portal contribution to liver perfusion by quantitative sequential scintigraphy and Doppler ultrasound in alcoholic cirrhosis. Diagnostic value in the detection of portal hypertension.

    PubMed

    Dao, T; Elfadel, S; Bouvard, G; Bouvard, N; Lecointe, I; Jardin-Grimaux, I; Verwaerde, J C; Valla, A

    1993-03-01

    To assess the portal contribution to liver perfusion, we carried out quantitative sequential scintigraphy in 110 patients with alcoholic cirrhosis (22 Child-Pugh class A, 39 class B, 49 class C) and 15 normal subjects. Duplex Doppler ultrasound found a type of intrahepatic circulation that made the standard scintigraphic procedure inaccurate in four cases of cirrhosis, which were reevaluated. Portal contribution to liver perfusion was lower in cirrhotics than in normal subjects (48.7 +/- 29% versus 78.4 +/- 6%; p < 0.001). The sensitivity of scintigraphy in detecting portal hypertension, based on portal contribution < or = 66%, was 61.8% (with a 100% specificity) compared with 66.7% for endoscopy (diagnosis based on existence of varices). The overall sensitivity of the two tests together was 86.1%. Portal contribution to liver perfusion was inversely correlated to Child-Pugh score (r = 0.53; p < 0.001), to prothrombin time (r = 0.52; p < 0.001), and to hepatic venous pressure gradient (r = 0.43; p < 0.001) and positively correlated to albuminemia (r = 0.42; p < 0.001). Concurrent alcoholic hepatitis and the existence of large portosystemic collaterals were related to a decrease in portal contribution to liver perfusion. We conclude that quantitative sequential scintigraphy, which shows a direct relationship between portal contribution to liver perfusion, on the one hand, and the amount of portosystemic shunting, the progression of liver disease, and/or acute liver injury, on the other, could serve as a diagnostic test for portal hypertension. The addition of scintigraphy improves the overall sensitivity of endoscopy. PMID:8463621

  16. Polyclonal infections due to Mycobacterium avium complex in patients with AIDS detected by pulsed-field gel electrophoresis of sequential clinical isolates.

    PubMed Central

    Slutsky, A M; Arbeit, R D; Barber, T W; Rich, J; von Reyn, C F; Pieciak, W; Barlow, M A; Maslow, J N

    1994-01-01

    Invasive infection with organisms of the Mycobacterium avium complex (MAC) is common among patients with advanced human immunodeficiency virus infection. In previous studies, we analyzed multiple individual colonies of MAC isolated from specimens obtained at the same time and observed that 14 to 20% of patients are simultaneously infected with more than one strain. In this study, we examined sequential isolates from 12 patients with AIDS who had two or more MAC isolates available from clinical specimens collected more than 1 week apart; the intervals between the first and last specimens ranged from 8 to 192 (median, 46) days. For each isolate, restriction digests of genomic DNA were analyzed by pulsed-field gel electrophoresis; DNA was prepared by using a protocol, described here in detail, which had been optimized for conditions of bacterial growth and lysis. The pulsed-field gel electrophoresis analysis identified four patients (33%) infected with two different MAC strains. Both M. avium and M. intracellulare were cultured from blood specimens from two patients. In each of the four patients, the second strain was identified from a culture taken within 14 days of the initial study isolate, and in three of these patients, the first strain was detected again in a subsequent culture. These observations suggest that the presence of two different strains among isolates from sequential cultures may reflect ongoing polyclonal infection. We conclude that polyclonal infection with MAC is common among patients with AIDS. The identification of such infections may be critical in the development of effective treatments. Images PMID:7929773

  17. Reliable VLSI sequential controllers

    NASA Technical Reports Server (NTRS)

    Whitaker, S.; Maki, G.; Shamanna, M.

    1990-01-01

    A VLSI architecture for synchronous sequential controllers is presented that has attractive qualities for producing reliable circuits. In these circuits, one hardware implementation can realize any flow table with a maximum of 2(exp n) internal states and m inputs. Also all design equations are identical. A real time fault detection means is presented along with a strategy for verifying the correctness of the checking hardware. This self check feature can be employed with no increase in hardware. The architecture can be modified to achieve fail safe designs. With no increase in hardware, an adaptable circuit can be realized that allows replacement of faulty transitions with fault free transitions.

  18. A sequential method for passive detection, characterization, and localization of multiple low probability of intercept LFMCW signals

    NASA Astrophysics Data System (ADS)

    Hamschin, Brandon M.

    A method for passive Detection, Characterization, and Localization (DCL) of multiple low power, Linear Frequency Modulated Continuous Wave (LFMCW) (i.e., Low Probability of Intercept (LPI)) signals is proposed. We demonstrate, via simulation, laboratory, and outdoor experiments, that the method is able to detect and correctly characterize the parameters that define two simultaneous LFMCW signals with probability greater than 90% when the signal to noise ratio is -10 dB or greater. While this performance is compelling, it is far from the Cramer-Rao Lower Bound (CRLB), which we derive, and the performance of the Maximum Likelihood Estimator (MLE), whose performance we simulate. The loss in performance relative to the CRLB and the MLE is the price paid for computational tractability. The LFMCW signal is the focus of this work because of its common use in modern, low-cost radar systems. In contrast to other detection and characterization approaches, such as the MLE and those based on the Wigner-Ville Transform (WVT) or the Wigner-Ville Hough Transform (WVHT), our approach does not begin with a parametric model of the received signal that is specified directly in terms of its LFMCW constituents. Rather, we analyze the signal over time intervals that are short, non-overlapping, and contiguous by modeling it within these intervals as a sum of a small number sinusoidal (i.e., harmonic) components with unknown frequencies, deterministic but unknown amplitudes, unknown order (i.e., number of harmonic components), and unknown noise autocorrelation function. It is this model of the data that makes the solution computationally feasible, but also what leads to a degradation in performance since estimates are not based on the full time series. By modeling the signal in this way, we reliably detect the presence of multiple LFMCW signals in colored noise without the need for prewhitening, efficiently estimate (i.e. , characterize) their parameters, provide estimation error

  19. An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-09-01

    Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

  20. Anomaly Detection and Comparative Analysis of Hydrothermal Alteration Materials Trough Hyperspectral Multisensor Data in the Turrialba Volcano

    NASA Astrophysics Data System (ADS)

    Rejas, J. G.; Martínez-Frías, J.; Bonatti, J.; Martínez, R.; Marchamalo, M.

    2012-07-01

    The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica) in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS), short wave infrared (SWIR) and thermal infrared (TIR). We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

  1. Investigation of the collision line broadening problem as applicable to the NASA Optical Plume Anomaly Detection (OPAD) system, phase 1

    NASA Astrophysics Data System (ADS)

    Dean, Timothy C.; Ventrice, Carl A.

    1995-05-01

    As a final report for phase 1 of the project, the researchers are submitting to the Tennessee Tech Office of Research the following two papers (reprinted in this report): 'Collision Line Broadening Effects on Spectrometric Data from the Optical Plume Anomaly System (OPAD),' presented at the 30th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, 27-29 June 1994, and 'Calculation of Collision Cross Sections for Atomic Line Broadening in the Plume of the Space Shuttle Main Engine (SSME),' presented at the IEEE Southeastcon '95, 26-29 March 1995. These papers fully state the problem and the progress made up to the end of NASA Fiscal Year 1994. The NASA OPAD system was devised to predict concentrations of anomalous species in the plume of the Space Shuttle Main Engine (SSME) through analysis of spectrometric data. The self absorption of the radiation of these plume anomalies is highly dependent on the line shape of the atomic transition of interest. The Collision Line Broadening paper discusses the methods used to predict line shapes of atomic transitions in the environment of a rocket plume. The Voigt profile is used as the line shape factor since both Doppler and collisional line broadening are significant. Methods used to determine the collisional cross sections are discussed and the results are given and compared with experimental data. These collisional cross sections are then incorporated into the current self absorbing radiative model and the predicted spectrum is compared to actual spectral data collected from the Stennis Space Center Diagnostic Test Facility rocket engine. The second paper included in this report investigates an analytical method for determining the cross sections for collision line broadening by molecular perturbers, using effective central force interaction potentials. These cross sections are determined for several atomic species with H2, one of the principal constituents of the SSME plume environment, and compared with experimental data.

  2. Investigation of the collision line broadening problem as applicable to the NASA Optical Plume Anomaly Detection (OPAD) system, phase 1

    NASA Technical Reports Server (NTRS)

    Dean, Timothy C.; Ventrice, Carl A.

    1995-01-01

    As a final report for phase 1 of the project, the researchers are submitting to the Tennessee Tech Office of Research the following two papers (reprinted in this report): 'Collision Line Broadening Effects on Spectrometric Data from the Optical Plume Anomaly System (OPAD),' presented at the 30th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, 27-29 June 1994, and 'Calculation of Collision Cross Sections for Atomic Line Broadening in the Plume of the Space Shuttle Main Engine (SSME),' presented at the IEEE Southeastcon '95, 26-29 March 1995. These papers fully state the problem and the progress made up to the end of NASA Fiscal Year 1994. The NASA OPAD system was devised to predict concentrations of anomalous species in the plume of the Space Shuttle Main Engine (SSME) through analysis of spectrometric data. The self absorption of the radiation of these plume anomalies is highly dependent on the line shape of the atomic transition of interest. The Collision Line Broadening paper discusses the methods used to predict line shapes of atomic transitions in the environment of a rocket plume. The Voigt profile is used as the line shape factor since both Doppler and collisional line broadening are significant. Methods used to determine the collisional cross sections are discussed and the results are given and compared with experimental data. These collisional cross sections are then incorporated into the current self absorbing radiative model and the predicted spectrum is compared to actual spectral data collected from the Stennis Space Center Diagnostic Test Facility rocket engine. The second paper included in this report investigates an analytical method for determining the cross sections for collision line broadening by molecular perturbers, using effective central force interaction potentials. These cross sections are determined for several atomic species with H2, one of the principal constituents of the SSME plume environment, and compared with experimental data.

  3. Detection of High-Potential Oil and Gas Fields Using Normalized Full Gradient of Gravity Anomalies: A Case Study in the Tabas Basin, Eastern Iran

    NASA Astrophysics Data System (ADS)

    Aghajani, Hamid; Moradzadeh, Ali; Zeng, Hualin

    2011-10-01

    The normalized full gradient (NFG) represents the full gradient of the gravity anomaly at a point divided by the average of the full gradient at the same point. The NFG minimum between two maxima in an NFG section or a closed minimum surrounded by closed maxima on an NFG map may indicate density-deficient anomalies closely related to possible oil-gas reservoirs. On a cross-section, closed minima can be used to estimate the depth to centers of possible hydrocarbon reservoirs. The NFG map can also be used to locate oil-gas exploratory wells for estimation of the depth of possible reservoirs. The objective of this paper is to use two and three-dimensional (2D and 3D) NFG on gravity data of the Tabas basin in Yazd province, eastern Iran. A hypothetical model is first considered to explore the NFG characteristics and their relationship with the geometry of the model. The physical properties of the model are then studied to simplify the interpretation of real data. Finally 2D and 3D NFG models are developed for real gravity data to predict the location of any possible high potential oil-gas reservoirs. The results obtained indicate two zones in the northern and central parts of the Tabas basin suitable for hydrocarbon prospecting. However, the favorable zone located in the middle of the basin in which anticline E is detected at a depth of 5-7 km is more important for the purpose of hydrocarbon exploration.

  4. Concept for Inclusion of Analytical and Computational Capability in Optical Plume Anomaly Detection (OPAD) for Measurement of Neutron Flux

    NASA Technical Reports Server (NTRS)

    Patrick, Marshall Clint; Cooper, Anita E.; Powers, W. T.

    2004-01-01

    Researchers are working on many fronts to make possible high-speed, automated classification and quantification of constituent materials in numerous environments. NASA's Marshall Space Flight Center has implemented a system for rocket engine flowfields/plumes. The Optical Plume Anomaly Detector (OPAD) system was designed to utilize emission and absorption spectroscopy for monitoring molecular and atomic particulates in gas plasma. An accompanying suite of tools and analytical package designed to utilize information collected by OPAD is known as the Engine Diagnostic Filtering System (EDiFiS). The current combination of these systems identifies atomic and molecular species and quantifies mass loss rates in H2/O2 rocket plumes. Capabilities for real-time processing are being advanced on several fronts, including an effort to hardware encode components of the EDiFiS for health monitoring and management. This paper addresses the OPAD with its tool suites, and discusses what is considered a natural progression: a concept for taking OPAD to the next logical level of high energy physics, incorporating fermion and boson particle analyses in measurement of neutron flux.

  5. Detection of a tropospheric ozone anomaly using a newly developed ozone retrieval algorithm for an up-looking infrared interferometer

    NASA Astrophysics Data System (ADS)

    Lightner, K. J.; McMillan, W. W.; McCann, K. J.; Hoff, R. M.; Newchurch, M. J.; Hintsa, E. J.; Barnet, C. D.

    2009-03-01

    On 2 June 2003, the Baltimore Bomem Atmospheric Emitted Radiance Interferometer (BBAERI) recorded an infrared spectral time series indicating the presence of a tropospheric ozone anomaly. The measurements were collected during an Atmospheric Infrared Sounder (AIRS) validation campaign called the 2003 AIRS BBAERI Ocean Validation Experiment (ABOVE03) conducted at the United States Coast Guard Chesapeake Light station located 14 miles due east of Virginia Beach, Virginia (36.91°N, 75.71°W). Ozone retrievals were performed with the Kurt Lightner Ozone BBAERI Retrieval (KLOBBER) algorithm, which retrieves tropospheric column ozone, surface to 300 mbar, from zenith-viewing atmospheric thermal emission spectra. KLOBBER is modeled after the AIRS retrieval algorithm consisting of a synthetic statistical regression followed by a physical retrieval. The physical retrieval is implemented using the k-Compressed Atmospheric Radiative Transfer Algorithm (kCARTA) to compute spectra. The time series of retrieved integrated ozone column on 2 June 2003 displays spikes of about 10 Dobson units, well above the error of the KLOBBER algorithm. Using instrumentation at Chesapeake Light, satellite imaging, trace gas retrievals from satellites, and Potential Vorticity (PV) computations, it was determined that these sudden increases in column ozone likely were caused by a combination of midtropospheric biomass burning products from forest fires in Siberia, Russia, and stratospheric intrusion by a tropopause fold occurring over central Canada and the midwestern United States.

  6. Delay test generation for synchronous sequential circuits

    NASA Astrophysics Data System (ADS)

    Devadas, Srinivas

    1989-05-01

    We address the problem of generating tests for delay faults in non-scan synchronous sequential circuits. Delay test generation for sequential circuits is a considerably more difficult problem than delay testing of combinational circuits and has received much less attention. In this paper, we present a method for generating test sequences to detect delay faults in sequential circuits using the stuck-at fault sequential test generator STALLION. The method is complete in that it will generate a delay test sequence for a targeted fault given sufficient CPU time, if such a sequence exists. We term faults for which no delay test sequence exists, under out test methodology, sequentially delay redundant. We describe means of eliminating sequential delay redundancies in logic circuits. We present a partial-scan methodology for enhancing the testability of difficult-to-test of untestable sequential circuits, wherein a small number of flip-flops are selected and made controllable/observable. The selection process guarantees the elimination of all sequential delay redundancies. We show that an intimate relationship exists between state assignment and delay testability of a sequential machine. We describe a state assignment algorithm for the synthesis of sequential machines with maximal delay fault testability. Preliminary experimental results using the test generation, partial-scan and synthesis algorithm are presented.

  7. Rapid determination of plutonium isotopes in environmental samples using sequential injection extraction chromatography and detection by inductively coupled plasma mass spectrometry.

    PubMed

    Qiao, Jixin; Hou, Xiaolin; Roos, Per; Miró, Manuel

    2009-10-01

    This article presents an automated method for the rapid determination of 239Pu and 240Pu in various environmental samples. The analytical method involves the in-line separation of Pu isotopes using extraction chromatography (TEVA) implemented in a sequential injection (SI) network followed by detection of isolated analytes with inductively coupled plasma mass spectrometry (ICP-MS). The method has been devised for the determination of Pu isotopes at environmentally relevant concentrations, whereby it has been successfully applied to the analyses of large volumes/amounts of samples, for example, 100-200 g of soil and sediment, 20 g of seaweed, and 200 L of seawater following analyte preconcentration. The investigation of the separation capability of the assembled SI system revealed that up to 200 g of soil or sediment can be treated using a column containing about 0.70 g of TEVA resin. The analytical results of Pu isotopes in the reference materials showed good agreement with the certified or reference values at the 0.05 significance level. Chemical yields of Pu ranged from 80 to 105%, and the decontamination factors for uranium, thorium, mercury and lead were all above 10(4). The duration of the in-line extraction chromatographic run was <1.5 h, and the proposed setup was able to handle up to 20 samples (14 mL each) in a fully automated mode using a single chromatographic column. The SI manifold is thus suitable for rapid and automated determination of Pu isotopes in environmental risk assessment and emergency preparedness scenarios. PMID:19722516

  8. Gauge anomalies, gravitational anomalies, and superstrings

    SciTech Connect

    Bardeen, W.A.

    1985-08-01

    The structure of gauge and gravitational anomalies will be reviewed. The impact of these anomalies on the construction, consistency, and application of the new superstring theories will be discussed. 25 refs.

  9. ANOMALY STRUCTURE OF SUPERGRAVITY AND ANOMALY CANCELLATION

    SciTech Connect

    Butter, Daniel; Gaillard, Mary K.

    2009-06-10

    We display the full anomaly structure of supergravity, including new D-term contributions to the conformal anomaly. This expression has the super-Weyl and chiral U(1){sub K} transformation properties that are required for implementation of the Green-Schwarz mechanism for anomaly cancellation. We outline the procedure for full anomaly cancellation. Our results have implications for effective supergravity theories from the weakly coupled heterotic string theory.

  10. Prenatal Detection of Cardiac Anomalies in Fetuses with Single Umbilical Artery: Diagnostic Accuracy Comparison of Maternal-Fetal-Medicine and Pediatric Cardiologist

    PubMed Central

    Tasha, Ilir; Brook, Rachel; Frasure, Heidi

    2014-01-01

    Aim. To determine agreement of cardiac anomalies between maternal fetal medicine (MFM) physicians and pediatric cardiologists (PC) in fetuses with single umbilical artery (SUA). Methods. A retrospective review of all fetuses with SUA between 1999 and 2008. Subjects were studied by MFM and PC, delivered at our institution, and had confirmation of SUA and cardiac anomaly by antenatal and neonatal PC follow-up. Subjects were divided into four groups: isolated SUA, SUA and isolated cardiac anomaly, SUA and multiple anomalies without heart anomalies, and SUA and multiple malformations including cardiac anomaly. Results. 39,942 cases were studied between 1999 and 2008. In 376 of 39,942 cases (0.94%), SUA was diagnosed. Only 182 (48.4%) met inclusion criteria. Cardiac anomalies were found in 21% (38/182). Agreement between MFM physicians and PC in all groups combined was 94% (171/182) (95% CI [89.2, 96.8]). MFM physicians overdiagnosed cardiac anomalies in 4.4% (8/182). MFM physicians and PC failed to antenatally diagnose cardiac anomaly in the same two cases. Conclusions. Good agreement was noted between MFM physicians and PC in our institution. Studies performed antenatally by MFM physicians and PC are less likely to uncover the entire spectrum of cardiac abnormalities and thus neonatal follow-up is suggested. PMID:24719766

  11. The elliptic anomaly

    NASA Technical Reports Server (NTRS)

    Janin, G.; Bond, V. R.

    1980-01-01

    An independent variable different from the time for elliptic orbit integration is used. Such a time transformation provides an analytical step-size regulation along the orbit. An intermediate anomaly (an anomaly intermediate between the eccentric and the true anomaly) is suggested for optimum performances. A particular case of an intermediate anomaly (the elliptic anomaly) is defined, and its relation with the other anomalies is developed.

  12. New tool to detect operation anomalies on automatic voltage regulator equipment of large power units; Generator simulator (GS)

    SciTech Connect

    Blanchet, P. )

    1990-01-01

    When large generating plants are installed on site remote from the consumer areas, the operation of network with correct margins of stability is conditioned by adjustment of automatic voltage regulator (AVR). Any spoiled deviation in normal operation or especially in abnormal run must be detected at first overhaul or first shutdown. Then, without delay, this new tool which is the generator simulator (GS) contributes to minimize the time necessary for failures investigation and to qualify again AVR equipment after repair. The two main objectives of this paper are: to qualify the AVR performances of power unit during the scheduled overhaul; and to lighten failures research into AVR system, avoiding faulty dismantling during the unit fortuitous shutdown.

  13. Measuring anomaly with algorithmic entropy

    NASA Astrophysics Data System (ADS)

    Solano, Wanda M.

    Anomaly detection refers to the identification of observations that are considered outside of normal. Since they are unknown to the system prior to training and rare, the anomaly detection problem is particularly challenging. Model based techniques require large quantities of existing data are to build the model. Statistically based techniques result in the use of statistical metrics or thresholds for determining whether a particular observation is anomalous. I propose a novel approach to anomaly detection using wavelet based algorithmic entropy that does not require modeling or large amounts of data. My method embodies the concept of information distance that rests on the fact that data encodes information. This distance is large when little information is shared, and small when there is greater information sharing. I compare my approach with several techniques in the literature using data obtained from testing of NASA's Space Shuttle Main Engines (SSME)

  14. Sequentially Executed Model Evaluation Framework

    SciTech Connect

    2015-10-20

    Provides a message passing framework between generic input, model and output drivers, and specifies an API for developing such drivers. Also provides batch and real-time controllers which step the model and I/O through the time domain (or other discrete domain), and sample I/O drivers. This is a library framework, and does not, itself, solve any problems or execute any modeling. The SeMe framework aids in development of models which operate on sequential information, such as time-series, where evaluation is based on prior results combined with new data for this iteration. Has applications in quality monitoring, and was developed as part of the CANARY-EDS software, where real-time water quality data is being analyzed for anomalies.

  15. Sequentially Executed Model Evaluation Framework

    Energy Science and Technology Software Center (ESTSC)

    2015-10-20

    Provides a message passing framework between generic input, model and output drivers, and specifies an API for developing such drivers. Also provides batch and real-time controllers which step the model and I/O through the time domain (or other discrete domain), and sample I/O drivers. This is a library framework, and does not, itself, solve any problems or execute any modeling. The SeMe framework aids in development of models which operate on sequential information, such asmore » time-series, where evaluation is based on prior results combined with new data for this iteration. Has applications in quality monitoring, and was developed as part of the CANARY-EDS software, where real-time water quality data is being analyzed for anomalies.« less

  16. Deployment of a sequential two-photon laser-induced fluorescence sensor for the detection of gaseous elemental mercury at ambient levels: fast, specific, ultrasensitive detection with parts-per-quadrillion sensitivity

    NASA Astrophysics Data System (ADS)

    Bauer, D.; Everhart, S.; Remeika, J.; Tatum Ernest, C.; Hynes, A. J.

    2014-12-01

    The operation of a laser-based sensor for gas-phase elemental mercury, Hg(0), is described. It utilizes sequential two-photon laser excitation with detection of blue-shifted laser-induced fluorescence (LIF) to provide a highly specific detection scheme that precludes detection of anything other than atomic mercury. It has high sensitivity, fast temporal resolution, and can be deployed for in situ measurements in the open atmosphere with essentially no perturbation of the environment. An ambient sample can also be pulled through a fluorescence cell, allowing for standard addition calibrations of the concentration. No type of preconcentration is required and there appears to be no significant interferences from other atmospheric constituents, including gas-phase oxidized mercury species. As a consequence, it is not necessary to remove oxidized mercury, commonly referred to as reactive gaseous mercury (RGM), from the air sample. The instrument has been deployed as part of an instrument intercomparison and compares well with conventional instrumentation that utilizes preconcentration on gold followed by analysis using cold-vapor atomic fluorescence spectroscopy (CVAFS). Currently, the achievable detection sensitivity is ~ 15 pg m-3 (~ 5 × 104 atoms cm-3, ~ 2 ppq) at a sampling rate of 0.1 Hz, i.e., averaging 100 shots with a 10 Hz laser system. Preliminary results are described for a 50 Hz instrument that utilizes a modified excitation sequence and has monitored ambient elemental mercury with an effective sampling rate of 10 Hz. Additional work is required to produce the precision necessary to perform eddy correlation measurements. Addition of a pyrolysis channel should allow for the measurement of total gaseous mercury (TGM) and hence RGM (by difference) with good sensitivity and time resolution.

  17. Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam.

    PubMed

    Richards, K K; Hazelton, M L; Stevenson, M A; Lockhart, C Y; Pinto, J; Nguyen, L

    2014-10-01

    The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006-2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but exceedance probabilities identified provinces in which the number of reported FMD outbreaks was unexpectedly low. This finding is interesting given that these provinces are on major cattle movement pathways through Viet Nam. PMID:25457601

  18. Aeromagnetic anomalies over faulted strata

    USGS Publications Warehouse

    Grauch, V.J.S.; Hudson, Mark R.

    2011-01-01

    High-resolution aeromagnetic surveys are now an industry standard and they commonly detect anomalies that are attributed to faults within sedimentary basins. However, detailed studies identifying geologic sources of magnetic anomalies in sedimentary environments are rare in the literature. Opportunities to study these sources have come from well-exposed sedimentary basins of the Rio Grande rift in New Mexico and Colorado. High-resolution aeromagnetic data from these areas reveal numerous, curvilinear, low-amplitude (2–15 nT at 100-m terrain clearance) anomalies that consistently correspond to intrasedimentary normal faults (Figure 1). Detailed geophysical and rock-property studies provide evidence for the magnetic sources at several exposures of these faults in the central Rio Grande rift (summarized in Grauch and Hudson, 2007, and Hudson et al., 2008). A key result is that the aeromagnetic anomalies arise from the juxtaposition of magnetically differing strata at the faults as opposed to chemical processes acting at the fault zone. The studies also provide (1) guidelines for understanding and estimating the geophysical parameters controlling aeromagnetic anomalies at faulted strata (Grauch and Hudson), and (2) observations on key geologic factors that are favorable for developing similar sedimentary sources of aeromagnetic anomalies elsewhere (Hudson et al.).

  19. Graph anomalies in cyber communications

    SciTech Connect

    Vander Wiel, Scott A; Storlie, Curtis B; Sandine, Gary; Hagberg, Aric A; Fisk, Michael

    2011-01-11

    Enterprises monitor cyber traffic for viruses, intruders and stolen information. Detection methods look for known signatures of malicious traffic or search for anomalies with respect to a nominal reference model. Traditional anomaly detection focuses on aggregate traffic at central nodes or on user-level monitoring. More recently, however, traffic is being viewed more holistically as a dynamic communication graph. Attention to the graph nature of the traffic has expanded the types of anomalies that are being sought. We give an overview of several cyber data streams collected at Los Alamos National Laboratory and discuss current work in modeling the graph dynamics of traffic over the network. We consider global properties and local properties within the communication graph. A method for monitoring relative entropy on multiple correlated properties is discussed in detail.

  20. Detecting the Anomaly Zone in Species Trees and Evidence for a Misleading Signal in Higher-Level Skink Phylogeny (Squamata: Scincidae).

    PubMed

    Linkem, Charles W; Minin, Vladimir N; Leaché, Adam D

    2016-05-01

    The anomaly zone, defined by the presence of gene tree topologies that are more probable than the true species tree, presents a major challenge to the accurate resolution of many parts of the Tree of Life. This discrepancy can result from consecutive rapid speciation events in the species tree. Similar to the problem of long-branch attraction, including more data via loci concatenation will only reinforce the support for the incorrect species tree. Empirical phylogenetic studies often employ coalescent-based species tree methods to avoid the anomaly zone, but to this point these studies have not had a method for providing any direct evidence that the species tree is actually in the anomaly zone. In this study, we use 16 species of lizards in the family Scincidae to investigate whether nodes that are difficult to resolve place the species tree within the anomaly zone. We analyze new phylogenomic data (429 loci), using both concatenation and coalescent-based species tree estimation, to locate conflicting topological signal. We then use the unifying principle of the anomaly zone, together with estimates of ancestral population sizes and species persistence times, to determine whether the observed phylogenetic conflict is a result of the anomaly zone. We identify at least three regions of the Scincidae phylogeny that provide demographic signatures consistent with the anomaly zone, and this new information helps reconcile the phylogenetic conflict in previously published studies on these lizards. The anomaly zone presents a real problem in phylogenetics, and our new framework for identifying anomalous relationships will help empiricists leverage their resources appropriately for investigating and overcoming this challenge. PMID:26738927

  1. INVESTIGATION OF ARSENIC SPECIATION ON DRINKING WATER TREATMENT MEDIA UTILIZING AUTOMATED SEQUENTIAL CONTINUOUS FLOW EXTRACTION WITH IC-ICP-MS DETECTION

    EPA Science Inventory

    Three treatment media, used for the removal of arsenic from drinking water, were sequentially extracted using 10mM MgCl2 (pH 8), 10mM NaH2PO4 (pH 7) followed by 10mM (NH4)2C2O4 (pH 3). The media were extracted using an on-line automated continuous extraction system which allowed...

  2. Cross correlation anomaly detection system

    NASA Technical Reports Server (NTRS)

    Micka, E. Z. (Inventor)

    1975-01-01

    This invention provides a method for automatically inspecting the surface of an object, such as an integrated circuit chip, whereby the data obtained by the light reflected from the surface, caused by a scanning light beam, is automatically compared with data representing acceptable values for each unique surface. A signal output provided indicated of acceptance or rejection of the chip. Acceptance is based on predetermined statistical confidence intervals calculated from known good regions of the object being tested, or their representative values. The method can utilize a known good chip, a photographic mask from which the I.C. was fabricated, or a computer stored replica of each pattern being tested.

  3. Partition algebraic design of asynchronous sequential circuits

    NASA Technical Reports Server (NTRS)

    Maki, Gary K.; Chen, Kristen Q.; Gopalakrishnan, Suresh K.

    1993-01-01

    Tracey's Theorem has long been recognized as essential in generating state assignments for asynchronous sequential circuits. This paper shows that partitioning variables derived from Tracey's Theorem also has a significant impact in generating the design equations. Moreover, this theorem is important to the fundamental understanding of asynchronous sequential operation. The results of this work simplify asynchronous logic design. Moreover, detection of safe circuits is made easier.

  4. Considerations in the Interpretation of Cosmological Anomalies

    NASA Astrophysics Data System (ADS)

    Peiris, Hiranya V.

    2014-05-01

    Anomalies drive scientific discovery - they are associated with the cutting edge of the research frontier, and thus typically exploit data in the low signal-to-noise regime. In astronomy, the prevalence of systematics - both ``known unknowns'' and ``unknown unknowns'' - combined with increasingly large datasets, the widespread use of ad hoc estimators for anomaly detection, and the ``look-elsewhere'' effect, can lead to spurious false detections. In this informal note, I argue that anomaly detection leading to discoveries of new physics requires a combination of physical understanding, careful experimental design to avoid confirmation bias, and self-consistent statistical methods. These points are illustrated with several concrete examples from cosmology.

  5. Adaptive sequential controller

    DOEpatents

    El-Sharkawi, Mohamed A.; Xing, Jian; Butler, Nicholas G.; Rodriguez, Alonso

    1994-01-01

    An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.

  6. Angular distributions of sequentially emitted particles and gamma rays in deep inelastic processes

    SciTech Connect

    Moretto, L.G.

    1981-01-01

    A general theory for the angular distribution of sequentially emitted particles and gamma rays is developed. Comparison with experimental data allows one to obtain information on the fragment spin and misalignment. Angular distributions of sequentially emitted gammas, alphas, and fission fragments are discussed in detail. It is shown that the experimental data are consistent with the thermal excitation of angular momentum-bearing modes. The anomaly of sequential fission suggests the presence of a prompt or direct fission component. 13 figures.

  7. Lymphatic Anomalies Registry

    ClinicalTrials.gov

    2016-07-26

    Lymphatic Malformation; Generalized Lymphatic Anomaly (GLA); Central Conducting Lymphatic Anomaly; CLOVES Syndrome; Gorham-Stout Disease ("Disappearing Bone Disease"); Blue Rubber Bleb Nevus Syndrome; Kaposiform Lymphangiomatosis; Kaposiform Hemangioendothelioma/Tufted Angioma; Klippel-Trenaunay Syndrome; Lymphangiomatosis

  8. Dual left anterior descending artery with anomalous origin of long LAD from pulmonary artery - rare coronary anomaly detected on computed tomography coronary angiography

    PubMed Central

    Vohra, Aditi; Narula, Harneet

    2016-01-01

    Dual left anterior descending artery is a rare coronary artery anomaly showing two left anterior descending arteries. Short anterior descending artery usually arises from the left coronary artery, while long anterior descending artery has anomalous origin and course. Dual left anterior descending artery with origin of long anterior descending artery from the pulmonary artery (ALCAPA) is a very rare coronary artery anomaly which has not been reported previously in the literature. We present the computed tomography coronary angiographic findings of this rare case in a young female patient who presented with atypical chest pain. PMID:27413266

  9. Identification of mineral resources in Afghanistan-Detecting and mapping resource anomalies in prioritized areas using geophysical and remote sensing (ASTER and HyMap) data

    USGS Publications Warehouse

    : King, Trude V. V., (Edited By); Johnson, Michaela R.; Hubbard, Bernard E.; Drenth, Benjamin J.

    2011-01-01

    During the independent analysis of the geophysical, ASTER, and imaging spectrometer (HyMap) data by USGS scientists, previously unrecognized targets of potential mineralization were identified using evaluation criteria most suitable to the individual dataset. These anomalous zones offer targets of opportunity that warrant additional field verification. This report describes the standards used to define the anomalies, summarizes the results of the evaluations for each type of data, and discusses the importance and implications of regions of anomaly overlap between two or three of the datasets.

  10. Analysis of spacecraft anomalies

    NASA Technical Reports Server (NTRS)

    Bloomquist, C. E.; Graham, W. C.

    1976-01-01

    The anomalies from 316 spacecraft covering the entire U.S. space program were analyzed to determine if there were any experimental or technological programs which could be implemented to remove the anomalies from future space activity. Thirty specific categories of anomalies were found to cover nearly 85 percent of all observed anomalies. Thirteen experiments were defined to deal with 17 of these categories; nine additional experiments were identified to deal with other classes of observed and anticipated anomalies. Preliminary analyses indicate that all 22 experimental programs are both technically feasible and economically viable.

  11. Sequential ranging: How it works

    NASA Technical Reports Server (NTRS)

    Baugh, Harold W.

    1993-01-01

    This publication is directed to the users of data from the Sequential Ranging Assembly (SRA), and to others who have a general interest in range measurements. It covers the hardware, the software, and the processes used in acquiring range data; it does not cover analytical aspects such as the theory of modulation, detection, noise spectral density, and other highly technical subjects. In other words, it covers how ranging is done, but not the details of why it works. The publication also includes an appendix that gives a brief discussion of PN ranging, a capability now under development.

  12. Glassy carbon electrodes sequentially modified by cysteamine-capped gold nanoparticles and poly(amidoamine) dendrimers generation 4.5 for detecting uric acid in human serum without ascorbic acid interference.

    PubMed

    Ramírez-Segovia, A S; Banda-Alemán, J A; Gutiérrez-Granados, S; Rodríguez, A; Rodríguez, F J; Godínez, Luis A; Bustos, E; Manríquez, J

    2014-02-17

    Glassy carbon electrodes (GCE) were sequentially modified by cysteamine-capped gold nanoparticles (AuNp@cysteamine) and PAMAM dendrimers generation 4.5 bearing 128-COOH peripheral groups (GCE/AuNp@cysteamine/PAMAM), in order to explore their capabilities as electrochemical detectors of uric acid (UA) in human serum samples at pH 2. The results showed that concentrations of UA detected by cyclic voltammetry with GCE/AuNp@cysteamine/PAMAM were comparable (deviation <±10%; limits of detection (LOD) and quantification (LOQ) were 1.7×10(-4) and 5.8×10(-4) mg dL(-1), respectively) to those concentrations obtained using the uricase-based enzymatic-colorimetric method. It was also observed that the presence of dendrimers in the GCE/AuNp@cysteamine/PAMAM system minimizes ascorbic acid (AA) interference during UA oxidation, thus improving the electrocatalytic activity of the gold nanoparticles. PMID:24491759

  13. Multiple and sequential data acquisition method: an improved method for fragmentation and detection of cross-linked peptides on a hybrid linear trap quadrupole Orbitrap Velos mass spectrometer.

    PubMed

    Rudashevskaya, Elena L; Breitwieser, Florian P; Huber, Marie L; Colinge, Jacques; Müller, André C; Bennett, Keiryn L

    2013-02-01

    The identification and validation of cross-linked peptides by mass spectrometry remains a daunting challenge for protein-protein cross-linking approaches when investigating protein interactions. This includes the fragmentation of cross-linked peptides in the mass spectrometer per se and following database searching, the matching of the molecular masses of the fragment ions to the correct cross-linked peptides. The hybrid linear trap quadrupole (LTQ) Orbitrap Velos combines the speed of the tandem mass spectrometry (MS/MS) duty circle with high mass accuracy, and these features were utilized in the current study to substantially improve the confidence in the identification of cross-linked peptides. An MS/MS method termed multiple and sequential data acquisition method (MSDAM) was developed. Preliminary optimization of the MS/MS settings was performed with a synthetic peptide (TP1) cross-linked with bis[sulfosuccinimidyl] suberate (BS(3)). On the basis of these results, MSDAM was created and assessed on the BS(3)-cross-linked bovine serum albumin (BSA) homodimer. MSDAM applies a series of multiple sequential fragmentation events with a range of different normalized collision energies (NCE) to the same precursor ion. The combination of a series of NCE enabled a considerable improvement in the quality of the fragmentation spectra for cross-linked peptides, and ultimately aided in the identification of the sequences of the cross-linked peptides. Concurrently, MSDAM provides confirmatory evidence from the formation of reporter ions fragments, which reduces the false positive rate of incorrectly assigned cross-linked peptides. PMID:23301806

  14. Sequential inductive learning

    SciTech Connect

    Gratch, J.

    1996-12-31

    This article advocates a new model for inductive learning. Called sequential induction, it helps bridge classical fixed-sample learning techniques (which are efficient but difficult to formally characterize), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). Learning proceeds as a sequence of decisions which are informed by training data. By analyzing induction at the level of these decisions, and by utilizing the only enough data to make each decision, sequential induction provides statistical guarantees but with substantially less data than worst-case methods require. The sequential inductive model is also useful as a method for determining a sufficient sample size for inductive learning and as such, is relevant to learning problems where the preponderance of data or the cost of gathering data precludes the use of traditional methods.

  15. Towards chemiluminescence detection in micro-sequential injection lab-on-valve format: a proof of concept based on the reaction between Fe(II) and luminol in seawater.

    PubMed

    Oliveira, Hugo M; Grand, Maxime M; Ruzicka, Jaromir; Measures, Christopher I

    2015-02-01

    Micro-sequential injection lab-on-valve (µSI-LOV) is a well-established analytical platform for absorbance and fluorescence based assays but its applicability to chemiluminescence detection remains largely unexplored. In this work, we describe a novel fluidic protocol and two distinct strategies for photon collection that enable chemiluminescence detection using µSI-LOV for the first time. To illustrate this proof of concept, we selected the reaction between Fe(II) and luminol and developed a preliminary protocol for Fe(II) determinations in acidified seawater. The optimized fluidic strategy consists of holding 100 µL of the luminol reagent in a confined zone of the LOV and then displacing it with 50 µL of sample while monitoring the chemiluminescent product. Detection is achieved using two strategies: one based on a bifurcated optical fiber and the other based on a customized detection window created by mounting a photomultiplier tube atop of the LOV device. We show that detection is possible using both strategies but that the window strategy yields significantly enhanced sensitivity (355×) due to the larger detection area. In our final experimental conditions and using window detection, it was possible to achieve a limit of detection (LOD) of 1 nmol L(-1) and to quantify Fe(II) in acidified seawater samples up to 20.00 nmol L(-1) with high precision (RSD<6%). These analytical features combined with the long-term stability of luminol solution and the full automation and low reagent consumption make this approach a promising analytical tool for shipboard analysis of Fe(II). The intrinsic capacity of the LOV to operate at a low microliter level and to handle solid phases also opens up a new avenue for chemiluminescence applications. Moreover, this contribution shows that LOV can be a universal platform for optical detection, capable of absorbance, fluorescence and luminescence measurements in a single instrument setup. PMID:25435235

  16. Sequential elution process

    DOEpatents

    Kingsley, Ilse S.

    1987-01-01

    A process and apparatus for the separation of complex mixtures of carbonaceous material by sequential elution with successively stronger solvents. In the process, a column containing glass beads is maintained in a fluidized state by a rapidly flowing stream of a weak solvent, and the sample is injected into this flowing stream such that a portion of the sample is dissolved therein and the remainder of the sample is precipitated therein and collected as a uniform deposit on the glass beads. Successively stronger solvents are then passed through the column to sequentially elute less soluble materials.

  17. Sequential elution process

    DOEpatents

    Kingsley, I.S.

    1987-01-06

    A process and apparatus are disclosed for the separation of complex mixtures of carbonaceous material by sequential elution with successively stronger solvents. In the process, a column containing glass beads is maintained in a fluidized state by a rapidly flowing stream of a weak solvent, and the sample is injected into this flowing stream such that a portion of the sample is dissolved therein and the remainder of the sample is precipitated therein and collected as a uniform deposit on the glass beads. Successively stronger solvents are then passed through the column to sequentially elute less soluble materials. 1 fig.

  18. [Kimmerle's anomaly and stroke].

    PubMed

    Barsukov, S F; Antonov, G I

    1992-10-01

    The anomaly of cranio-vertebral area can frequently be the reason of acute cerebrovascular disorders in vertebro-basilar field. The frequent C1 pathology in the Kimmerle's anomaly. The anatomic studies has shown that 30% of people had this type of anomaly. This pathology can lead to severe vascular diseases of cerebrum because of the squeezing effect upon vertebral arteries in the zone of osteal ponticulus of the rear arch of atlas. PMID:1481402

  19. Taussig-Bing Anomaly

    PubMed Central

    Konstantinov, Igor E.

    2009-01-01

    Taussig-Bing anomaly is a rare congenital heart malformation that was first described in 1949 by Helen B. Taussig (1898–1986) and Richard J. Bing (1909–). Although substantial improvement has since been achieved in surgical results of the repair of the anomaly, management of the Taussig-Bing anomaly remains challenging. A history of the original description of the anomaly, the life stories of the individuals who first described it, and the current outcomes of its surgical management are reviewed herein. PMID:20069085

  20. Spatio-Temporal Sensitivity of MODIS Land Surface Temperature Anomalies Indicates High Potential for Large-Scale Land Cover Change Detection in Permafrost Landscapes

    NASA Astrophysics Data System (ADS)

    Muster, S.; Langer, M.; Abnizova, A.; Young, K. L.; Boike, J.

    2014-12-01

    The accelerated warming Arctic climate may alter the surface energy balance locally and regionally of which a changing land surface temperature (LST) is a key indicator. Modelling current and anticipated changes of the surface energy balance requires an understanding of the spatio-temporal interactions between LST and land cover. This paper investigated the accuracy of MODIS LST V5 1 km level 3 product and its spatio-temporal sensitivity to land cover properties in a Canadian High Arctic permafrost landscape. Land cover ranged from fully vegetated moss/segde grass tundra to sparsely vegetated bare soil and barren areas. Daily mean MODIS LST were compared to in-situ radiometer measurements over wet tundra for three summers and two winters in 2008, 2009, and 2010. MODIS LST showed an accuracy of 1.8°C and a RMSE of 3.8°C in the total observation period including both summer and winter. Agreement was lowest during summer 2009 and freeze-back periods which were associated with prevailing overcast conditions. A multi-year anomaly analysis revealed robust spatio-temporal patterns taking into account the found uncertainty and different atmospheric conditions. Summer periods with regional mean LST larger than 5°C showed highest spatial diversity with four distinct anomaly classes. Dry ridge areas heated up most whereas wetland areas and dry barren surfaces with high albedo were coolest. Mean inter-annual differences of LST anomalies for different land cover classes were less than 1°C. However, spatial pattern showed fewer positive anomalies in 2010 suggesting differences in surface moisture due to inter­annual differences in the amount of end-of-winter snow. Presented summer LST anomalies might serve as a baseline against which to evaluate past and future changes in land surface properties with regard to the surface energy balance. Sub-temporal heterogeneity due to snow or ice on/off as well as the effect of subpixel water bodies has to be taken into account. A multi

  1. NADINE: new approaches to detecting breast cancer by sequential μm-wavelength imaging with the aid of novel frequency analysis techniques.

    PubMed

    Joro, R; Dastidar, P; Iivonen, V; Ylänen, H; Soimakallio, S

    2012-07-01

    The study focuses on 12 breasts of six breast cancer patients sequential µm-wavelength imaging, taken by two different 3-5 μm wavelength area indium antimony (InSb) photovoltaic cameras. The aim of the study was to compare the functionality of area and pixel-based frequency analyses. Comparisons between these frequency analysis methods were made according to their relevancy to mammographic findings. Another objective of the study was to find reliable imaging conditions by specifying the border conditions for the patient stabilizing imaging bed and managing the imaging situation. According to the results, the match of pixel based frequency analysis to the mammography findings is better than using area frequency analysis. The results also indicate that when the optical axis of the camera in relation to the surface of the breast to be imaged grows to more than 40°, the emissivity changes dramatically and at that point reliable results will not be obtained. Consequently the analysis of the imagined breast requires more images to be fused into one analysis image to cover the whole breast. PMID:22512737

  2. Rapid isolation of plutonium in environmental solid samples using sequential injection anion exchange chromatography followed by detection with inductively coupled plasma mass spectrometry.

    PubMed

    Qiao, Jixin; Hou, Xiaolin; Roos, Per; Miró, Manuel

    2011-01-31

    This paper reports an automated analytical method for rapid determination of plutonium isotopes ((239)Pu and (240)Pu) in environmental solid extracts. Anion exchange chromatographic columns were incorporated in a sequential injection (SI) system to undertake the automated separation of plutonium from matrix and interfering elements. The analytical results most distinctly demonstrated that the crosslinkage of the anion exchanger is a key parameter controlling the separation efficiency. AG 1-×4 type resin was selected as the most suitable sorbent material for analyte separation. Investigation of column size effect upon the separation efficiency revealed that small-sized (2 mL) columns sufficed to handle up to 50 g of environmental soil samples. Under the optimum conditions, chemical yields of plutonium exceeded 90% and the decontamination factors for uranium, thorium and lead ranged from 10(3) to 10(4). The determination of plutonium isotopes in three standard/certified reference materials (IAEA-375 soil, IAEA-135 sediment and NIST-4359 seaweed) and two reference samples (Irish Sea sediment and Danish soil) revealed a good agreement with reference/certified values. The SI column-separation method is straightforward and less labor intensive as compared with batch-wise anion exchange chromatographic procedures. Besides, the automated method features low consumption of ion-exchanger and reagents for column washing and elution, with the consequent decrease in the generation of acidic waste, thus bearing green chemical credentials. PMID:21168558

  3. Sequential Dependencies in Driving

    ERIC Educational Resources Information Center

    Doshi, Anup; Tran, Cuong; Wilder, Matthew H.; Mozer, Michael C.; Trivedi, Mohan M.

    2012-01-01

    The effect of recent experience on current behavior has been studied extensively in simple laboratory tasks. We explore the nature of sequential effects in the more naturalistic setting of automobile driving. Driving is a safety-critical task in which delayed response times may have severe consequences. Using a realistic driving simulator, we find…

  4. Sequential memory: Binding dynamics

    NASA Astrophysics Data System (ADS)

    Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

    2015-10-01

    Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

  5. Sequential memory: Binding dynamics.

    PubMed

    Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

    2015-10-01

    Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories-episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities. PMID:26520084

  6. Implications of Tracey's theorem to asynchronous sequential circuit design

    NASA Technical Reports Server (NTRS)

    Gopalakrishnan, S.; Kim, G.; Maki, G.

    1990-01-01

    Tracey's Theorem has long been recognized as essential in generating state assignments for asynchronous sequential circuits. This paper shows that Tracey's Theorem also has a significant impact in generating the design equations. Moreover, this theorem is important to the fundamental understanding of asynchronous sequential operation. The results of this work simplify asynchronous logic design. Moreover, detection of safe circuits is made easier.

  7. Competing Orders and Anomalies.

    PubMed

    Moon, Eun-Gook

    2016-01-01

    A conservation law is one of the most fundamental properties in nature, but a certain class of conservation "laws" could be spoiled by intrinsic quantum mechanical effects, so-called quantum anomalies. Profound properties of the anomalies have deepened our understanding in quantum many body systems. Here, we investigate quantum anomaly effects in quantum phase transitions between competing orders and striking consequences of their presence. We explicitly calculate topological nature of anomalies of non-linear sigma models (NLSMs) with the Wess-Zumino-Witten (WZW) terms. The non-perturbative nature is directly related with the 't Hooft anomaly matching condition: anomalies are conserved in renormalization group flow. By applying the matching condition, we show massless excitations are enforced by the anomalies in a whole phase diagram in sharp contrast to the case of the Landau-Ginzburg-Wilson theory which only has massive excitations in symmetric phases. Furthermore, we find non-perturbative criteria to characterize quantum phase transitions between competing orders. For example, in 4D, we show the two competing order parameter theories, CP(1) and the NLSM with WZW, describe different universality class. Physical realizations and experimental implication of the anomalies are also discussed. PMID:27499184

  8. Competing Orders and Anomalies

    NASA Astrophysics Data System (ADS)

    Moon, Eun-Gook

    2016-08-01

    A conservation law is one of the most fundamental properties in nature, but a certain class of conservation “laws” could be spoiled by intrinsic quantum mechanical effects, so-called quantum anomalies. Profound properties of the anomalies have deepened our understanding in quantum many body systems. Here, we investigate quantum anomaly effects in quantum phase transitions between competing orders and striking consequences of their presence. We explicitly calculate topological nature of anomalies of non-linear sigma models (NLSMs) with the Wess-Zumino-Witten (WZW) terms. The non-perturbative nature is directly related with the ’t Hooft anomaly matching condition: anomalies are conserved in renormalization group flow. By applying the matching condition, we show massless excitations are enforced by the anomalies in a whole phase diagram in sharp contrast to the case of the Landau-Ginzburg-Wilson theory which only has massive excitations in symmetric phases. Furthermore, we find non-perturbative criteria to characterize quantum phase transitions between competing orders. For example, in 4D, we show the two competing order parameter theories, CP(1) and the NLSM with WZW, describe different universality class. Physical realizations and experimental implication of the anomalies are also discussed.

  9. Competing Orders and Anomalies

    PubMed Central

    Moon, Eun-Gook

    2016-01-01

    A conservation law is one of the most fundamental properties in nature, but a certain class of conservation “laws” could be spoiled by intrinsic quantum mechanical effects, so-called quantum anomalies. Profound properties of the anomalies have deepened our understanding in quantum many body systems. Here, we investigate quantum anomaly effects in quantum phase transitions between competing orders and striking consequences of their presence. We explicitly calculate topological nature of anomalies of non-linear sigma models (NLSMs) with the Wess-Zumino-Witten (WZW) terms. The non-perturbative nature is directly related with the ’t Hooft anomaly matching condition: anomalies are conserved in renormalization group flow. By applying the matching condition, we show massless excitations are enforced by the anomalies in a whole phase diagram in sharp contrast to the case of the Landau-Ginzburg-Wilson theory which only has massive excitations in symmetric phases. Furthermore, we find non-perturbative criteria to characterize quantum phase transitions between competing orders. For example, in 4D, we show the two competing order parameter theories, CP(1) and the NLSM with WZW, describe different universality class. Physical realizations and experimental implication of the anomalies are also discussed. PMID:27499184

  10. The resolution of a magnetic anomaly map expected from GRM data

    NASA Technical Reports Server (NTRS)

    Strangway, D. W.; Arkani-Hamed, J.; Teskey, D. J.; Hood, P. J.

    1985-01-01

    Data from the MAGSAT mission were used to derive a global scalar magnetic anomaly map at an average altitude of about 400 km. It was possible to work with 2 data sets corresponding to dawn and dusk. The anomalies which were repeatable at dawn and at dusk was identified and the error limits of these anomalies were estimated. The repeatable anomalies were downward continued to about 10 km altitude. The anomalies over Canada were correlated quantitatively with bandpass filtered magnetic anomalies derived from aeromagnetic surveys. The close correlation indicates that the repeatable anomalies detected from orbit are due to geological causes. This correlation supports the geological significance of the global anomaly map.

  11. Detecting ecosystem performance anomalies for land management in the upper colorado river basin using satellite observations, climate data, and ecosystem models

    USGS Publications Warehouse

    Gu, Y.; Wylie, B.K.

    2010-01-01

    This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005-2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using "percentage of bare soil" ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005-2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions. ?? 2010 by the authors.

  12. Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

    USGS Publications Warehouse

    Gu, Yingxin; Wylie, Bruce K.

    2010-01-01

    This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005–2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using “percentage of bare soil” ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005–2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

  13. Sequential cloning of chromosomes

    SciTech Connect

    Lacks, S.A.

    1991-12-31

    A method for sequential cloning of chromosomal DNA and chromosomal DNA cloned by this method are disclosed. The method includes the selection of a target organism having a segment of chromosomal DNA to be sequentially cloned. A first DNA segment, having a first restriction enzyme site on either side. homologous to the chromosomal DNA to be sequentially cloned is isolated. A first vector product is formed by ligating the homologous segment into a suitably designed vector. The first vector product is circularly integrated into the target organism`s chromosomal DNA. The resulting integrated chromosomal DNA segment includes the homologous DNA segment at either end of the integrated vector segment. The integrated chromosomal DNA is cleaved with a second restriction enzyme and ligated to form a vector-containing plasmid, which is replicated in a host organism. The replicated plasmid is then cleaved with the first restriction enzyme. Next, a DNA segment containing the vector and a segment of DNA homologous to a distal portion of the previously isolated DNA segment is isolated. This segment is then ligated to form a plasmid which is replicated within a suitable host. This plasmid is then circularly integrated into the target chromosomal DNA. The chromosomal DNA containing the circularly integrated vector is treated with a third, retrorestriction enzyme. The cleaved DNA is ligated to give a plasmid that is used to transform a host permissive for replication of its vector. The sequential cloning process continues by repeated cycles of circular integration and excision. The excision is carried out alternately with the second and third enzymes.

  14. Sequential cloning of chromosomes

    DOEpatents

    Lacks, Sanford A.

    1995-07-18

    A method for sequential cloning of chromosomal DNA of a target organism is disclosed. A first DNA segment homologous to the chromosomal DNA to be sequentially cloned is isolated. The first segment has a first restriction enzyme site on either side. A first vector product is formed by ligating the homologous segment into a suitably designed vector. The first vector product is circularly integrated into the target organism's chromosomal DNA. The resulting integrated chromosomal DNA segment includes the homologous DNA segment at either end of the integrated vector segment. The integrated chromosomal DNA is cleaved with a second restriction enzyme and ligated to form a vector-containing plasmid, which is replicated in a host organism. The replicated plasmid is then cleaved with the first restriction enzyme. Next, a DNA segment containing the vector and a segment of DNA homologous to a distal portion of the previously isolated DNA segment is isolated. This segment is then ligated to form a plasmid which is replicated within a suitable host. This plasmid is then circularly integrated into the target chromosomal DNA. The chromosomal DNA containing the circularly integrated vector is treated with a third, retrorestriction (class IIS) enzyme. The cleaved DNA is ligated to give a plasmid that is used to transform a host permissive for replication of its vector. The sequential cloning process continues by repeated cycles of circular integration and excision. The excision is carried out alternately with the second and third enzymes.

  15. Sequential cloning of chromosomes

    DOEpatents

    Lacks, S.A.

    1995-07-18

    A method for sequential cloning of chromosomal DNA of a target organism is disclosed. A first DNA segment homologous to the chromosomal DNA to be sequentially cloned is isolated. The first segment has a first restriction enzyme site on either side. A first vector product is formed by ligating the homologous segment into a suitably designed vector. The first vector product is circularly integrated into the target organism`s chromosomal DNA. The resulting integrated chromosomal DNA segment includes the homologous DNA segment at either end of the integrated vector segment. The integrated chromosomal DNA is cleaved with a second restriction enzyme and ligated to form a vector-containing plasmid, which is replicated in a host organism. The replicated plasmid is then cleaved with the first restriction enzyme. Next, a DNA segment containing the vector and a segment of DNA homologous to a distal portion of the previously isolated DNA segment is isolated. This segment is then ligated to form a plasmid which is replicated within a suitable host. This plasmid is then circularly integrated into the target chromosomal DNA. The chromosomal DNA containing the circularly integrated vector is treated with a third, retrorestriction (class IIS) enzyme. The cleaved DNA is ligated to give a plasmid that is used to transform a host permissive for replication of its vector. The sequential cloning process continues by repeated cycles of circular integration and excision. The excision is carried out alternately with the second and third enzymes. 9 figs.

  16. Müllerian anomalies.

    PubMed

    Gell, Jennifer S

    2003-11-01

    The reproductive organs in both males and females consist of gonads, internal ductal structures, and external genitalia. Normal sexual differentiation is dependent on the genetic sex determined by the presence or absence of the Y chromosome at fertilization. Testes develop under the influence of the Y chromosome and ovaries develop when no Y chromosome is present. In the absence of testes and their normal hormonal products, sexual differentiation proceeds along the female pathway, resulting in a normal female phenotype. Anatomic gynecologic anomalies occur when there is failure of normal embryologic ductal development. These anomalies include congenital absence of the vagina as well as defects in lateral and vertical fusion of the Müllerian ducts. Treatment of müllerian anomalies begins with the correct identification of the anomaly and an understanding of the embryologic origin. This includes evaluation for other associated anomalies such as renal or skeletal abnormalities. After correct identification, treatment options include nonsurgical as well as surgical intervention. This chapter serves to review the embryology and development of the reproductive system and to describe common genital tract anomalies. Details of surgical or nonsurgical correction of these anomalies are presented. PMID:14724770

  17. Congenital basis of posterior fossa anomalies

    PubMed Central

    Cotes, Claudia; Bonfante, Eliana; Lazor, Jillian; Jadhav, Siddharth; Caldas, Maria; Swischuk, Leonard

    2015-01-01

    The classification of posterior fossa congenital anomalies has been a controversial topic. Advances in genetics and imaging have allowed a better understanding of the embryologic development of these abnormalities. A new classification schema correlates the embryologic, morphologic, and genetic bases of these anomalies in order to better distinguish and describe them. Although they provide a better understanding of the clinical aspects and genetics of these disorders, it is crucial for the radiologist to be able to diagnose the congenital posterior fossa anomalies based on their morphology, since neuroimaging is usually the initial step when these disorders are suspected. We divide the most common posterior fossa congenital anomalies into two groups: 1) hindbrain malformations, including diseases with cerebellar or vermian agenesis, aplasia or hypoplasia and cystic posterior fossa anomalies; and 2) cranial vault malformations. In addition, we will review the embryologic development of the posterior fossa and, from the perspective of embryonic development, will describe the imaging appearance of congenital posterior fossa anomalies. Knowledge of the developmental bases of these malformations facilitates detection of the morphological changes identified on imaging, allowing accurate differentiation and diagnosis of congenital posterior fossa anomalies. PMID:26246090

  18. Behavioral economics without anomalies.

    PubMed Central

    Rachlin, H

    1995-01-01

    Behavioral economics is often conceived as the study of anomalies superimposed on a rational system. As research has progressed, anomalies have multiplied until little is left of rationality. Another conception of behavioral economics is based on the axiom that value is always maximized. It incorporates so-called anomalies either as conflicts between temporal patterns of behavior and the individual acts comprising those patterns or as outcomes of nonexponential time discounting. This second conception of behavioral economics is both empirically based and internally consistent. PMID:8551195

  19. The P300-based, complex trial protocol for concealed information detection resists any number of sequential countermeasures against up to five irrelevant stimuli.

    PubMed

    Labkovsky, Elena; Rosenfeld, J Peter

    2012-03-01

    We previously tested the P300-based Complex Trial protocol for deception detection against 2 and 4 countered of 4 irrelevant stimuli. The protocol detected 90-100% of these subjects with <10% false positives. We have also shown that Reaction Time (RT) to the first trial stimulus is increased (group effect) with countermeasure use. We also reported a new P900 component associated with countermeasure use when 2 of 4 irrelevants are countered. In the present study we report data from 4 new groups and re-present for comparison previously collected data to have 7 groups: an innocent control, a guilty group not using countermeasures, and 5 guilty/countermeasure groups who counter from 1 to all 5 stimuli presented (4 irrelevants plus a probe). Subjects were detected at rates varying from 92 to 100% in the 6 guilty groups (n = 12 or 13 per group). There was 1 false positive in 13 innocent subjects. Additionally, 41 of 60 CM users were identified with RT as using countermeasures. P900 appeared in the 2 groups using 2 and 3 countermeasures. PMID:21965119

  20. Lunar magnetic anomalies and surface optical properties

    NASA Astrophysics Data System (ADS)

    Hood, L. L.; Schubert, G.

    1980-04-01

    Consideration is given to the influence of lunar magnetic anomalies on the darkening of the lunar surface by solar wind ion bombardment. It is shown that lunar magnetic anomalies with dipole moments much greater than 5 x 10 to the 13th gauss cu cm will strongly deflect the typical solar wind, producing local plasma voids at the lunar surface. Direct measurements of lunar magnetic fields have shown most lunar magnetic fields to have moments below this level, with the exception of anomalies detected in the areas of the Reiner Gamma albedo feature, the Van de Graaff-Aitken region and Mare Marginis. Such magnetic anomalies are shown to be capable of accounting for the higher albedo and swirl-like morphology f these features by the deflection and focusing incident solar wind ions, which tend to darken the surface upon impact.

  1. Design and Implementation of an Anomaly Detector

    SciTech Connect

    Bagherjeiran, A; Cantu-Paz, E; Kamath, C

    2005-07-11

    This paper describes the design and implementation of a general-purpose anomaly detector for streaming data. Based on a survey of similar work from the literature, a basic anomaly detector builds a model on normal data, compares this model to incoming data, and uses a threshold to determine when the incoming data represent an anomaly. Models compactly represent the data but still allow for effective comparison. Comparison methods determine the distance between two models of data or the distance between a model and a point. Threshold selection is a largely neglected problem in the literature, but the current implementation includes two methods to estimate thresholds from normal data. With these components, a user can construct a variety of anomaly detection schemes. The implementation contains several methods from the literature. Three separate experiments tested the performance of the components on two well-known and one completely artificial dataset. The results indicate that the implementation works and can reproduce results from previous experiments.

  2. Sequential products on effect algebras

    NASA Astrophysics Data System (ADS)

    Gudder, Stan; Greechie, Richard

    2002-02-01

    A sequential effect algebra (SEA) is an effect algebra on which a sequential product with natural properties is defined. The properties of sequential products on Hilbert space effect algebras are discussed. For a general SEA, relationships between sequential independence, coexistence and compatibility are given. It is shown that the sharp elements of a SEA form an orthomodular poset. The sequential center of a SEA is discussed and a characterization of when the sequential center is isomorphic to a fuzzy set system is presented. It is shown that the existence, of a sequential product is a strong restriction that eliminates many effect algebras from being SEA's. For example, there are no finite nonboolean SEA's, A measure of sharpness called the sharpness index is studied. The existence of horizontal sums of SEA's is characterized and examples of horizontal sums and tensor products are presented.

  3. An approach for quantitative analysis of vitamins D and B9 and their metabolites in human biofluids by on-line orthogonal sample preparation and sequential mass spectrometry detection.

    PubMed

    Ferreiro-Vera, Carlos; Priego-Capote, Feliciano; Luque de Castro, María Dolores

    2013-04-01

    An approach for quantitative analysis of two vitamins with different polarities (vitamins D and B9) and their metabolites is presented here. The approach is based on an experimental setup based on hyphenation of an automated workstation for preparation of liquid samples and an LC-MS/MS system with a triple quadrupole mass spectrometer. This configuration enabled development of an orthogonal protocol for sequential SPE retention of analytes with different polarities for subsequent elution and chromatographic separation prior to detection. The resulting method was validated by application to three human biofluids. Estimation of recovery factors in the SPE step led to values from 85.2 to 100% for vitamin D and metabolites and from 93.1 to 100% for vitamin B9 and metabolites (folic acid and folates). The influence of sample matrix variability by analysis of human serum, urine and breast milk was minimized with a complete optimization of the SPE step. The utility of the proposed configuration is shown by the sensitivity and precision of the method, expressed as limits of detection (between 0.2 and 0.30 ng mL(-1) or 4 and 60 pg on-column) and within-laboratory reproducibility (lower than 6.7%, as relative standard deviation). The present application represents an example of determination methods involving targeted analysis of compounds with different polarities using a single aliquot of the sample. PMID:23435066

  4. Dual diaphragmatic anomalies.

    PubMed

    Padmanabhan, Arjun; Thomas, Abin Varghese

    2016-01-01

    Although diaphragmatic anomalies such as an eventration and hiatus hernia are commonly encountered in incidental chest X-ray imaging, the presence of concomitant multiple anomalies is extremely rare. This is all the more true in adults. Herein, we present the case of a 75-year-old female, while undergoing a routine chest X-ray imaging, was found to have eventration of right hemidiaphragm along with a hiatus hernia as well. PMID:27625457

  5. Dual diaphragmatic anomalies

    PubMed Central

    Padmanabhan, Arjun; Thomas, Abin Varghese

    2016-01-01

    Although diaphragmatic anomalies such as an eventration and hiatus hernia are commonly encountered in incidental chest X-ray imaging, the presence of concomitant multiple anomalies is extremely rare. This is all the more true in adults. Herein, we present the case of a 75-year-old female, while undergoing a routine chest X-ray imaging, was found to have eventration of right hemidiaphragm along with a hiatus hernia as well.

  6. Anomalies and entanglement entropy

    NASA Astrophysics Data System (ADS)

    Nishioka, Tatsuma; Yarom, Amos

    2016-03-01

    We initiate a systematic study of entanglement and Rényi entropies in the presence of gauge and gravitational anomalies in even-dimensional quantum field theories. We argue that the mixed and gravitational anomalies are sensitive to boosts and obtain a closed form expression for their behavior under such transformations. Explicit constructions exhibiting the dependence of entanglement entropy on boosts is provided for theories on spacetimes with non-trivial magnetic fluxes and (or) non-vanishing Pontryagin classes.

  7. On isostatic geoid anomalies

    NASA Technical Reports Server (NTRS)

    Haxby, W. F.; Turcotte, D. L.

    1978-01-01

    In regions of slowly varying lateral density changes, the gravity and geoid anomalies may be expressed as power series expansions in topography. Geoid anomalies in isostatically compensated regions can be directly related to the local dipole moment of the density-depth distribution. This relationship is used to obtain theoretical geoid anomalies for different models of isostatic compensation. The classical Pratt and Airy models give geoid height-elevation relationships differing in functional form but predicting geoid anomalies of comparable magnitude. The thermal cooling model explaining ocean floor subsidence away from mid-ocean ridges predicts a linear age-geoid height relationship of 0.16 m/m.y. Geos 3 altimetry profiles were examined to test these theoretical relationships. A profile over the mid-Atlantic ridge is closely matched by the geoid curve derived from the thermal cooling model. The observed geoid anomaly over the Atlantic margin of North America can be explained by Airy compensation. The relation between geoid anomaly and bathymetry across the Bermuda Swell is consistent with Pratt compensation with a 100-km depth of compensation.

  8. Radioactive anomaly discrimination from spectral ratios

    DOEpatents

    Maniscalco, James; Sjoden, Glenn; Chapman, Mac Clements

    2013-08-20

    A method for discriminating a radioactive anomaly from naturally occurring radioactive materials includes detecting a first number of gamma photons having energies in a first range of energy values within a predetermined period of time and detecting a second number of gamma photons having energies in a second range of energy values within the predetermined period of time. The method further includes determining, in a controller, a ratio of the first number of gamma photons having energies in the first range and the second number of gamma photons having energies in the second range, and determining that a radioactive anomaly is present when the ratio exceeds a threshold value.

  9. Relationships between Rwandan seasonal rainfall anomalies and ENSO events

    NASA Astrophysics Data System (ADS)

    Muhire, I.; Ahmed, F.; Abutaleb, K.

    2015-10-01

    This study aims primarily at investigating the relationships between Rwandan seasonal rainfall anomalies and El Niño-South Oscillation phenomenon (ENSO) events. The study is useful for early warning of negative effects associated with extreme rainfall anomalies across the country. It covers the period 1935-1992, using long and short rains data from 28 weather stations in Rwanda and ENSO events resourced from Glantz (2001). The mean standardized anomaly indices were calculated to investigate their associations with ENSO events. One-way analysis of variance was applied on the mean standardized anomaly index values per ENSO event to explore the spatial correlation of rainfall anomalies per ENSO event. A geographical information system was used to present spatially the variations in mean standardized anomaly indices per ENSO event. The results showed approximately three climatic periods, namely, dry period (1935-1960), semi-humid period (1961-1976) and wet period (1977-1992). Though positive and negative correlations were detected between extreme short rains anomalies and El Niño events, La Niña events were mostly linked to negative rainfall anomalies while El Niño events were associated with positive rainfall anomalies. The occurrence of El Niño and La Niña in the same year does not show any clear association with rainfall anomalies. However, the phenomenon was more linked with positive long rains anomalies and negative short rains anomalies. The normal years were largely linked with negative long rains anomalies and positive short rains anomalies, which is a pointer to the influence of other factors other than ENSO events. This makes projection of seasonal rainfall anomalies in the country by merely predicting ENSO events difficult.

  10. "RCL-Pooling Assay": A Simplified Method for the Detection of Replication-Competent Lentiviruses in Vector Batches Using Sequential Pooling.

    PubMed

    Corre, Guillaume; Dessainte, Michel; Marteau, Jean-Brice; Dalle, Bruno; Fenard, David; Galy, Anne

    2016-02-01

    Nonreplicative recombinant HIV-1-derived lentiviral vectors (LV) are increasingly used in gene therapy of various genetic diseases, infectious diseases, and cancer. Before they are used in humans, preparations of LV must undergo extensive quality control testing. In particular, testing of LV must demonstrate the absence of replication-competent lentiviruses (RCL) with suitable methods, on representative fractions of vector batches. Current methods based on cell culture are challenging because high titers of vector batches translate into high volumes of cell culture to be tested in RCL assays. As vector batch size and titers are continuously increasing because of the improvement of production and purification methods, it became necessary for us to modify the current RCL assay based on the detection of p24 in cultures of indicator cells. Here, we propose a practical optimization of this method using a pairwise pooling strategy enabling easier testing of higher vector inoculum volumes. These modifications significantly decrease material handling and operator time, leading to a cost-effective method, while maintaining optimal sensibility of the RCL testing. This optimized "RCL-pooling assay" ameliorates the feasibility of the quality control of large-scale batches of clinical-grade LV while maintaining the same sensitivity. PMID:26886834

  11. Congenital Anomalies in Infant with Congenital Hypothyroidism

    PubMed Central

    Razavi, Zahra; Yavarikia, Alireza; Torabian, Saadat

    2012-01-01

    Objective Congenital hypothyroidism is characterized by inadequate thyroid hormone production in newborn infants. Many infants with CH have co-occurring congenital malformations. This is an investigation on the frequency and types of congenital anomalies in infants with congenital hypothyroidism born from May 2006-2010 in Hamadan, west province of Iran. Methods The Iranian neonatal screening program for congenital hypothyroidism was initiated in May 2005. This prospective descriptive study was conducted in infants diagnosed with congenital hypothyroidism being followed up in Pediatric Endocrinology Clinic of Besat Hospital, a tertiary care centre in Hamadan. Cases included all infants with congenital hypothyroidism diagnosed through newborn screening program or detected clinically. Anomalies were identified by clinical examination, echocardiography, and X-ray of the hip during the infant’s first year of life. Results A total of 150 infants with biochemically confirmed primary congenital hypothyroidism (72 females and 78 males) were recruited during the period between May 2006-2010. Overall, 30 (20%) infants had associated congenital anomalies. The most common type of anomaly was Down syndrome. Seven infants (3.1%) had congenital cardiac anomalies such as: ASD (n=3), VSD (n=2), PS (n =1), PDA (n=1). Three children (2.6%) had developmental dysplasia of the hip (n=3). Conclusion The overall frequency of Down syndrome, cardiac malformation and other birth defect was high in infants with CH. This reinforces the need to examine all infants with congenital hypothyroidism for the presence of associated congenital anomalies. PMID:23074545

  12. Quasi-periodic Fast-mode Wave Trains Within a Global EUV Wave and Sequential Transverse Oscillations Detected by SDO-AIA

    NASA Technical Reports Server (NTRS)

    Liu, Wei; Ofman, Leon; Nitta, Nariaki; Aschwanden, Markus J.; Schrijver, Carolus J.; Title, Alan M.; Tarbell, Theodore D.

    2012-01-01

    We present the first unambiguous detection of quasi-periodic wave trains within the broad pulse of a global EUV wave (so-called EIT wave) occurring on the limb. These wave trains, running ahead of the lateral coronal mass ejection (CME) front of 2-4 times slower, coherently travel to distances greater than approximately solar radius/2 along the solar surface, with initial velocities up to 1400 kilometers per second decelerating to approximately 650 kilometers per second. The rapid expansion of the CME initiated at an elevated height of 110 Mm produces a strong downward and lateral compression, which may play an important role in driving the primary EUV wave and shaping its front forwardly inclined toward the solar surface. The wave trains have a dominant 2 minute periodicity that matches the X-ray flare pulsations, suggesting a causal connection. The arrival of the leading EUV wave front at increasing distances produces an uninterrupted chain sequence of deflections and/or transverse (likely fast kink mode) oscillations of local structures, including a flux-rope coronal cavity and its embedded filament with delayed onsets consistent with the wave travel time at an elevated (by approximately 50%) velocity within it. This suggests that the EUV wave penetrates through a topological separatrix surface into the cavity, unexpected from CME-caused magnetic reconfiguration. These observations, when taken together, provide compelling evidence of the fast-mode MHD wave nature of the primary (outer) fast component of a global EUV wave, running ahead of the secondary (inner) slow component of CME-caused restructuring.

  13. QUASI-PERIODIC FAST-MODE WAVE TRAINS WITHIN A GLOBAL EUV WAVE AND SEQUENTIAL TRANSVERSE OSCILLATIONS DETECTED BY SDO/AIA

    SciTech Connect

    Liu Wei; Nitta, Nariaki V.; Aschwanden, Markus J.; Schrijver, Carolus J.; Title, Alan M.; Tarbell, Theodore D.; Ofman, Leon

    2012-07-01

    We present the first unambiguous detection of quasi-periodic wave trains within the broad pulse of a global EUV wave (so-called EIT wave) occurring on the limb. These wave trains, running ahead of the lateral coronal mass ejection (CME) front of 2-4 times slower, coherently travel to distances {approx}> R{sub Sun }/2 along the solar surface, with initial velocities up to 1400 km s{sup -1} decelerating to {approx}650 km s{sup -1}. The rapid expansion of the CME initiated at an elevated height of 110 Mm produces a strong downward and lateral compression, which may play an important role in driving the primary EUV wave and shaping its front forwardly inclined toward the solar surface. The wave trains have a dominant 2 minute periodicity that matches the X-ray flare pulsations, suggesting a causal connection. The arrival of the leading EUV wave front at increasing distances produces an uninterrupted chain sequence of deflections and/or transverse (likely fast kink mode) oscillations of local structures, including a flux-rope coronal cavity and its embedded filament with delayed onsets consistent with the wave travel time at an elevated (by {approx}50%) velocity within it. This suggests that the EUV wave penetrates through a topological separatrix surface into the cavity, unexpected from CME-caused magnetic reconfiguration. These observations, when taken together, provide compelling evidence of the fast-mode MHD wave nature of the primary (outer) fast component of a global EUV wave, running ahead of the secondary (inner) slow component of CME-caused restructuring.

  14. Astrometric solar system anomalies

    SciTech Connect

    Nieto, Michael Martin; Anderson, John D

    2009-01-01

    There are at least four unexplained anomalies connected with astrometric data. perhaps the most disturbing is the fact that when a spacecraft on a flyby trajectory approaches the Earth within 2000 km or less, it often experiences a change in total orbital energy per unit mass. next, a secular change in the astronomical unit AU is definitely a concern. It is increasing by about 15 cm yr{sup -1}. The other two anomalies are perhaps less disturbing because of known sources of nongravitational acceleration. The first is an apparent slowing of the two Pioneer spacecraft as they exit the solar system in opposite directions. Some astronomers and physicists are convinced this effect is of concern, but many others are convinced it is produced by a nearly identical thermal emission from both spacecraft, in a direction away from the Sun, thereby producing acceleration toward the Sun. The fourth anomaly is a measured increase in the eccentricity of the Moon's orbit. Here again, an increase is expected from tidal friction in both the Earth and Moon. However, there is a reported unexplained increase that is significant at the three-sigma level. It is produent to suspect that all four anomalies have mundane explanations, or that one or more anomalies are a result of systematic error. Yet they might eventually be explained by new physics. For example, a slightly modified theory of gravitation is not ruled out, perhaps analogous to Einstein's 1916 explanation for the excess precession of Mercury's perihelion.

  15. The structure of sequential effects.

    PubMed

    Gökaydin, Dinis; Navarro, Daniel J; Ma-Wyatt, Anna; Perfors, Amy

    2016-01-01

    There is a long history of research into sequential effects, extending more than one hundred years. The pattern of sequential effects varies widely with both experimental conditions as well as for different individuals performing the same experiment. Yet this great diversity of results is poorly understood, particularly with respect to individual variation, which save for some passing mentions has largely gone unreported in the literature. Here we seek to understand the way in which sequential effects vary by identifying the causes underlying the differences observed in sequential effects. In order to achieve this goal we perform principal component analysis on a dataset of 158 individual results from participants performing different experiments with the aim of identifying hidden variables responsible for sequential effects. We find a latent structure consisting of 3 components related to sequential effects-2 main and 1 minor. A relationship between the 2 main components and the separate processing of stimuli and of responses is proposed on the basis of previous empirical evidence. It is further speculated that the minor component of sequential effects arises as the consequence of processing delays. Independently of the explanation for the latent variables encountered, this work provides a unified descriptive model for a wide range of different types of sequential effects previously identified in the literature. In addition to explaining individual differences themselves, it is demonstrated how the latent structure uncovered here is useful in understanding the classical problem of the dependence of sequential effects on the interval between successive stimuli. PMID:26523425

  16. NRC PAGE. Sequential Analysis Materials Balance

    SciTech Connect

    Picard, R.

    1992-01-13

    NRCPAGE is used in safeguards applications to detect a recurring loss of special nuclear material by frequent evaluation (sequential analysis) of accountability data. Standard sequential testing procedures are traditionally based on sequences of independent and normally distributed measurements. This same approach can be applied to materials balance (MB) data. Here, the term materials balance has a meaning similar to inventory difference and represents a materials loss indicator localized in time and space. However, distinct MBs cannot be reasonably treated as statistically independent and may not always be reasonably treated as normally distributed. Furthermore, the covariance structure associated with a given MB sequence is not known and must be estimated. Nonindependence is treated by converting the MB sequence to the innovation sequence, sometimes called the ITMUF sequence or the sequence of MUF residuals, which are statistically independent and amenable to sequential test procedures. A one-sided page`s test, effective for a wide range of recurring loss scenarios, is applied to the standardized innovation sequence. The program can be easily modified to suit particular needs; the models for the assumption of multivariate normality for MBs when computing the innovation sequence or the test procedure can be changed as can the input/output format, dimensioning, local error checking, and simulation work. Input files can be sequentially constructed using local text editors to update existing files. Output files can be read by graphics, report writer, or other stand-alone utility routines.

  17. La detection des cyanobacteries en milieu lacustre par l'etude des anomalies des spectres de reflectance de l'eau

    NASA Astrophysics Data System (ADS)

    Constantin, Gabriel

    Proliferation of cyanobacteria is a growing problem in lacustrine environment that results in rapid degradation of water quality. Moreover, certain cyanobacteria species produce harmful toxins. Phycocyanin (PC) is a photosynthetic pigment typical of cyanobacteria and affects the water color: it is therefore possible to study them using remote sensing. At least three algorithms to estimate PC concentration ([PC]) have been published, but their relative errors are important, especially for lower concentration. In this study, we are presenting the results of a new algorithm that uses the second order variability (anomalies) of water's reflectance spectrum to estimate [PC]. This method has never been used in lacustrine environment. The dataset used to develop and validate the algorithm was obtained between 2001 and 2005 in 57 different lakes and reservoirs of the Netherlands and Spain. The performance of the second order algorithm is equivalent or better than the three previously published algorithms. For the subset were [PC] > 32 mg m-3, the contribution of the second order term (R2=0.68 and RMSE=0.25) seems to improve considerably the first order algorithm (R2=0.50 and RMSE=0.35). The accuracy of the second order algorithm for [PC] > 32 mg m-3 is superior to the one calculated for the whole dataset (R2=0.69 and RMSE=0.44). The algorithm can also be adapted to the. bands of satellite sensor MERIS for the study of cyanobacteria. The application of this algorithm to a MERIS image acquired the 29 August 2010 taken over the Missisquoi Bay (Quebec, Canada) demonstrates the potential of this new algorithm for a future cyanobacteria' monitoring system. Note that all the statistical results presented above are for the logarithm of [PC] and the units of the RMSE are log(mg/m 3).

  18. Sequential analysis of uncommon adverse outcomes.

    PubMed

    Morton, A; Mengersen, K; Waterhouse, M; Steiner, S; Looke, D

    2010-10-01

    Sequential analysis of uncommon adverse outcomes (AEs) such as surgical site infections (SSIs) is desirable. Short postoperative lengths of stay (LOS) result in many SSIs occurring after discharge and they are often superficial. Deep and organ space (complex) SSIs occur less frequently but are detected more reliably and are suitable for monitoring wound care. Those occurring post-discharge usually require readmissison and can be counted accurately. Sequential analysis of meticillin-resistant Staphylococcus aureus bacteraemia is also needed. The key to prevention is to implement systems based on evidence, e.g. using 'bundles' and checklists. Regular mortality and morbidity audit meetings are required and these may need to be followed by independent audits. Sequential statistical analysis is desirable for data presentation, to detect changes, and to discourage tampering with processes when occasional AEs occur in a reliable system. Tabulations and cumulative observed minus expected (O-E) charts and funnel plots are valuable, supplemented in the presence of apparent 'runs' of AEs by cumulative sum analysis. Used prospectively, they may enable staff to visualise and detect patterns or shifts in rates and counts that might not otherwise be apparent. PMID:20656377

  19. Multi-Attribute Sequential Search

    ERIC Educational Resources Information Center

    Bearden, J. Neil; Connolly, Terry

    2007-01-01

    This article describes empirical and theoretical results from two multi-attribute sequential search tasks. In both tasks, the DM sequentially encounters options described by two attributes and must pay to learn the values of the attributes. In the "continuous" version of the task the DM learns the precise numerical value of an attribute when she…

  20. Student Storytelling through Sequential Art

    ERIC Educational Resources Information Center

    Fay, David

    2007-01-01

    If you are interested in using sequential art forms such as comic books in your EFL classroom, this article is full of helpful advice. Reading sequential art is beneficial because students can work with authentic texts with real language and graphic support. Students can also apply research and cultural knowledge to the creation of their own…

  1. QCD trace anomaly

    SciTech Connect

    Andersen, Jens O.; Leganger, Lars E.; Strickland, Michael; Su, Nan

    2011-10-15

    In this brief report we compare the predictions of a recent next-to-next-to-leading order hard-thermal-loop perturbation theory (HTLpt) calculation of the QCD trace anomaly to available lattice data. We focus on the trace anomaly scaled by T{sup 2} in two cases: N{sub f}=0 and N{sub f}=3. When using the canonical value of {mu}=2{pi}T for the renormalization scale, we find that for Yang-Mills theory (N{sub f}=0) agreement between HTLpt and lattice data for the T{sup 2}-scaled trace anomaly begins at temperatures on the order of 8T{sub c}, while treating the subtracted piece as an interaction term when including quarks (N{sub f}=3) agreement begins already at temperatures above 2T{sub c}. In both cases we find that at very high temperatures the T{sup 2}-scaled trace anomaly increases with temperature in accordance with the predictions of HTLpt.

  2. Modeling the Pioneer anomaly

    NASA Astrophysics Data System (ADS)

    Leibovitz, Jacques

    2007-04-01

    Scientists continue their attempts to model the observed Pioneer anomaly (PA) as an artifact of measurement or of equipment operation. Scientists also explore ``new physics'' as a possible explanation, but they have eliminated dark matter (DM). Here, the main arguments used to eliminate DM are refuted and then the anomaly is modeled by application of Newton laws to the observed macroscopic properties of DM. Around a central mass M, the modeling predicts a DM distribution that produces the PA at short distances (R smaller than 188 AU) from a star like the Sun, and a flat rotation curve at sufficiently large distances from the center of a galaxy. Below about 188 AU from the Sun, the modeling predicts that the anomaly may be expressed as PA = 8.3E-8 [R̂(-2)] -- 1 cm (s)̂(-2). It shows that the anomaly remains fairly constant down to 5 AU, decreases significantly from 5 AU to 1 AU where it becomes zero and changes sign below a distance of 1 AU, then increases rapidly in magnitude as R decreases in that range. Verifiable tests are proposed. Some related topics for future research are proposed.

  3. Continental and oceanic magnetic anomalies: Enhancement through GRM

    NASA Technical Reports Server (NTRS)

    Vonfrese, R. R. B.; Hinze, W. J.

    1985-01-01

    In contrast to the POGO and MAGSAT satellites, the Geopotential Research Mission (GRM) satellite system will orbit at a minimum elevation to provide significantly better resolved lithospheric magnetic anomalies for more detailed and improved geologic analysis. In addition, GRM will measure corresponding gravity anomalies to enhance our understanding of the gravity field for vast regions of the Earth which are largely inaccessible to more conventional surface mapping. Crustal studies will greatly benefit from the dual data sets as modeling has shown that lithospheric sources of long wavelength magnetic anomalies frequently involve density variations which may produce detectable gravity anomalies at satellite elevations. Furthermore, GRM will provide an important replication of lithospheric magnetic anomalies as an aid to identifying and extracting these anomalies from satellite magnetic measurements. The potential benefits to the study of the origin and characterization of the continents and oceans, that may result from the increased GRM resolution are examined.

  4. Continuity of the sequential product of sequential quantum effect algebras

    NASA Astrophysics Data System (ADS)

    Lei, Qiang; Su, Xiaochao; Wu, Junde

    2016-04-01

    In order to study quantum measurement theory, sequential product defined by A∘B = A1/2BA1/2 for any two quantum effects A, B has been introduced. Physically motivated conditions ask the sequential product to be continuous with respect to the strong operator topology. In this paper, we study the continuity problems of the sequential product A∘B = A1/2BA1/2 with respect to other important topologies, such as norm topology, weak operator topology, order topology, and interval topology.

  5. Influence of ionospheric anomalies in the positioning

    NASA Astrophysics Data System (ADS)

    Rodriguez-Caderot, G.; Moreno, B.; de Lacy, M. C.

    2009-04-01

    GNSS observables depend on the satellite-receiver distance, atmospheric effects, satellite and receiver offsets and phase ambiguities, as well as satellite and receiver equipment delays. GNSS observations specific to a receiver and a satellite (undifferenced observations) can be used to estimate the ionospheric effect. In this study, different procedures are used to estimate the ionospheric delay from GNSS data belonging to permanent GPS stations. In particular, these tests intend to detect ionospheric anomalies under certain conditions in equatorial geographical latitudes. From the Slant Total Electron Content (STEC) estimated between one GPS station and several satellites the contribution of the anomalies is isolated and its amplitude and duration are computed. Finally, an analysis of the possible influence of these anomalies in the positioning estimation is carried out.

  6. High-throughput sequential injection method for simultaneous determination of plutonium and neptunium in environmental solids using macroporous anion-exchange chromatography, followed by inductively coupled plasma mass spectrometric detection.

    PubMed

    Qiao, Jixin; Hou, Xiaolin; Roos, Per; Miró, Manuel

    2011-01-01

    This paper reports an automated analytical method for rapid and simultaneous determination of plutonium and neptunium in soil, sediment, and seaweed, with detection via inductively coupled plasma mass spectrometry (ICP-MS). A chromatographic column packed with a macroporous anion exchanger (AG MP-1 M) was incorporated in a sequential injection (SI) system for the efficient retrieval of plutonium, along with neptunium, from matrix elements and potential interfering nuclides. The sorption and elution behavior of plutonium and neptunium onto AG MP-1 M resin was compared with a commonly utilized AG 1-gel-type anion exchanger. Experimental results reveal that the pore structure of the anion exchanger plays a pivotal role in ensuring similar separation behavior of plutonium and neptunium along the separation protocol. It is proven that plutonium-242 ((242)Pu) performs well as a tracer for monitoring the chemical yield of neptunium when using AG MP-1 M resin, whereby the difficulties in obtaining a reliable and practicable isotopic neptunium tracer are overcome. An important asset of the SI setup is the feasibility of processing up to 100 g of solid substrates using a small-sized (ca. 2 mL) column with chemical yields of neptunium and plutonium being ≥79%. Analytical results of three certified/standard reference materials and two solid samples from intercomparison exercises are in good agreement with the reference values at the 0.05 significance level. The overall on-column separation can be completed within 3.5 h for 10 g of soil samples. Most importantly, the anion-exchange mini-column suffices to be reused up to 10-fold with satisfactory chemical yields (>70%), as demanded in environmental monitoring and emergency scenarios, making the proposed automated assembly well-suited for unattended and high-throughput analysis. PMID:21121695

  7. Antler anomalies in tule elk

    USGS Publications Warehouse

    Gogan, Peter J.P.; Jessup, David A.; Barrett, Reginald H.

    1988-01-01

    Antler anomalies were evident in tule elk (Cervus elaphus nannodes) within 1 yr of reintroduction to Point Reyes, California (USA). These anomalies are consistent with previously described mineral deficiency-induced anomalies in cervids. The elk were judged deficient in copper. Low levels of copper in soils and vegetation at the release site, exacerbated by possible protein deficiency due to poor range conditions, are postulated as likely causes of the antler anomalies.

  8. Frequency of Foetal Anomalies in a Tertiary Care Centre

    PubMed Central

    Babu, Rameswarapu Suman; Pasula, Sujatha

    2013-01-01

    Objective: The present study was undertaken to explore the incidence of congenital foetal anomalies and the advantages of ultrasonography in detecting the foetal anomalies during the antenatal period. Method: We focused our study on 1000 consecutive pregnancies that came for check up in the second and third trimesters, with major or minor clinically relevant malformations which were detectable by ultrasonography. Results: The analysis revealed that they were 38 foetal anomalies in 37 foetuses. One had multiple anomalies, with the highest incidence of neural tube defects. There was also significant correlation with consanguinity. Conclusion: The overall incidence of congenital foetal anomalies in the present study was 3.8%.This might be probably due to environmental pollution, radiation, exposure to different chemicals and teratogenic drugs. PMID:23998044

  9. Mass Anomalies on Ganymede

    NASA Technical Reports Server (NTRS)

    Schubert, G.; Anderson, J. D.; Jacobson, R. A.; Lau, E. L.; Moore, W. B.; Palguta, J.

    2004-01-01

    Radio Doppler data from two Ganymede encounters (G1 and G2) on the first two orbits in the Galileo mission have been analyzed previously for gravity information . For a satellite in hydrostatic equilibrium, its gravitational field can be modeled adequately by a truncated spherical harmonic series of degree two. However, a fourth degree field is required in order to fit the second Galileo flyby (G2). This need for a higher degree field strongly suggests that Ganymede s gravitational field is perturbed by a gravity anomaly near the G2 closest approach point (79.29 latitude, 123.68 west longitude). In fact, a plot of the Doppler residuals , after removal of the best-fit model for the zero degree term (GM) and the second degree moments (J2 and C22), suggests that if an anomaly exists, it is located downtrack of the closest approach point, closer to the equator.

  10. Branching in the sequential folding pathway of cytochrome c.

    PubMed

    Krishna, Mallela M G; Maity, Haripada; Rumbley, Jon N; Englander, S Walter

    2007-09-01

    Previous results indicate that the folding pathways of cytochrome c and other proteins progressively build the target native protein in a predetermined stepwise manner by the sequential formation and association of native-like foldon units. The present work used native state hydrogen exchange methods to investigate a structural anomaly in cytochrome c results that suggested the concerted folding of two segments that have little structural relationship in the native protein. The results show that the two segments, an 18-residue omega loop and a 10-residue helix, are able to unfold and refold independently, which allows a branch point in the folding pathway. The pathway that emerges assembles native-like foldon units in a linear sequential manner when prior native-like structure can template a single subsequent foldon, and optional pathway branching is seen when prior structure is able to support the folding of two different foldons. PMID:17660254

  11. Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

    PubMed Central

    Murphy, Sean Patrick; Burkom, Howard

    2008-01-01

    Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

  12. Sequentially pulsed traveling wave accelerator

    DOEpatents

    Caporaso, George J.; Nelson, Scott D.; Poole, Brian R.

    2009-08-18

    A sequentially pulsed traveling wave compact accelerator having two or more pulse forming lines each with a switch for producing a short acceleration pulse along a short length of a beam tube, and a trigger mechanism for sequentially triggering the switches so that a traveling axial electric field is produced along the beam tube in synchronism with an axially traversing pulsed beam of charged particles to serially impart energy to the particle beam.

  13. DNA Sequential Logic Gate Using Two-Ring DNA.

    PubMed

    Zhang, Cheng; Shen, Linjing; Liang, Chao; Dong, Yafei; Yang, Jing; Xu, Jin

    2016-04-13

    Sequential DNA detection is a fundamental issue for elucidating the interactive relationships among complex gene systems. Here, a sequential logic DNA gate was achieved by utilizing the two-ring DNA structure, with the ability to recognize "before" and "after" triggering sequences of DNA signals. By taking advantage of a "loop-open" mechanism, separations of two-ring DNAs were controlled. Three triggering pathways with different sequential DNA treatments were distinguished by comparing fluorescent outputs. Programmed nanoparticle arrangement guided by "interlocked" two-ring DNA was also constructed to demonstrate the achievement of designed nanostrucutres. Such sequential logic DNA operation may guide future molecular sensors to monitor more complex gene network in biological systems. PMID:26990044

  14. Sequential injection immunoassay for environmental measurements.

    PubMed

    Soh, Nobuaki; Tanaka, Mayumi; Hirakawa, Koji; Zhang, RuiQi; Nakajima, Hizuru; Nakano, Koji; Imato, Toshihiko

    2011-01-01

    Sequential injection immunoassay systems for environmental measurements based on the selective immunoreaction between antigen and antibody were described. A sequential injection analysis (SIA) technique is suitable to be applied for the procedure of enzyme-linked immunosorbent assay (ELISA), because the washing and the addition of reagent solutions can be automated by using a computer-controlled syringe pump and switching valve. We selected vitellogenin (Vg), which is a biomarker for evaluating environmental risk caused by endocrine-disrupting chemicals in the hydrosphere, and linear alkylbenzene sulfonates (LAS) and alkylphenol polyethoxylates (APEO), which are versatile surfactants, as target analytes in the flow immunoassay systems. For Vg monitoring, SIA systems based on spectrophotometric, chemiluminescence, and electrochemical determinations were constructed. On the other hand, chemiluminescence determination was applied to the detection of LAS and APEO. For APEO, an SIA system combined with surface plasmon resonance (SPR) sensor was also developed. These new sequential injection immunoassay systems are expected to be useful systems for environmental analysis. PMID:22076332

  15. A Bayesian sequential processor approach to spectroscopic portal system decisions

    SciTech Connect

    Sale, K; Candy, J; Breitfeller, E; Guidry, B; Manatt, D; Gosnell, T; Chambers, D

    2007-07-31

    The development of faster more reliable techniques to detect radioactive contraband in a portal type scenario is an extremely important problem especially in this era of constant terrorist threats. Towards this goal the development of a model-based, Bayesian sequential data processor for the detection problem is discussed. In the sequential processor each datum (detector energy deposit and pulse arrival time) is used to update the posterior probability distribution over the space of model parameters. The nature of the sequential processor approach is that a detection is produced as soon as it is statistically justified by the data rather than waiting for a fixed counting interval before any analysis is performed. In this paper the Bayesian model-based approach, physics and signal processing models and decision functions are discussed along with the first results of our research.

  16. Physicochemical isotope anomalies

    SciTech Connect

    Esat, T.M.

    1988-06-01

    Isotopic composition of refractory elements can be modified, by physical processes such as distillation and sputtering, in unexpected patterns. Distillation enriches the heavy isotopes in the residue and the light isotopes in the vapor. However, current models appear to be inadequate to describe the detailed mass dependence, in particular for large fractionations. Coarse- and fine-grained inclusions from the Allende meteorite exhibit correlated isotope effects in Mg both as mass-dependent fractionation and residual anomalies. This isotope pattern can be duplicated by high temperature distillation in the laboratory. A ubiquitous property of meteoritic inclusions for Mg as well as for most of the other elements, where measurements exist, is mass-dependent fractionation. In contrast, terrestrial materials such as microtektites, tektite buttons as well as lunar orange and green glass spheres have normal Mg isotopic composition. A subset of interplanetary dust particles labelled as chondritic aggregates exhibit excesses in {sup 26}Mg and deuterium anomalies. Sputtering is expected to be a dominant mechanism in the destruction of grains within interstellar dust clouds. An active proto-sun as well as the present solar-wind and solar-flare flux are of sufficient intensity to sputter significant amounts of material. Laboratory experiments in Mg show widespread isotope effects including residual {sup 26}Mg excesses and mass dependent fractionation. It is possible that the {sup 26}Mg excesses in interplanetary dust is related to sputtering by energetic solar-wind particles. The implication if the laboratory distillation and sputtering effects are discussed and contrasted with the anomalies in meteoritic inclusions the other extraterrestrial materials the authors have access to.

  17. Hypercharged anomaly mediation.

    PubMed

    Dermísek, Radovan; Verlinde, Herman; Wang, Lian-Tao

    2008-04-01

    We show that, in string models with the minimal supersymmetric standard model residing on D-branes, the bino mass can be generated in a geometrically separated hidden sector. Hypercharge mediation thus naturally teams up with anomaly mediation. The mixed scenario predicts a distinctive yet viable superpartner spectrum, provided that the ratio alpha between the bino and gravitino mass lies in the range 0.05 < or = |alpha| < or = 0.25 and m(3/2) > or = 35 TeV. We summarize some of the experimental signatures of this scenario. PMID:18517937

  18. Satellite magnetic anomalies over subduction zones - The Aleutian Arc anomaly

    NASA Technical Reports Server (NTRS)

    Clark, S. C.; Frey, H.; Thomas, H. H.

    1985-01-01

    Positive magnetic anomalies seen in MAGSAT average scalar anomaly data overlying some subduction zones can be explained in terms of the magnetization contrast between the cold subducted oceanic slab and the surrounding hotter, nonmagnetic mantle. Three-dimensional modeling studies show that peak anomaly amplitude and location depend on slab length and dip. A model for the Aleutian Arc anomaly matches the general trend of the observed MAGSAT anomaly if a slab thickness of 7 km and a relatively high (induced plus viscous) magnetization contrast of 4 A/m are used. A second source body along the present day continental margin is required to match the observed anomaly in detail, and may be modeled as a relic slab from subduction prior to 60 m.y. ago.

  19. Sequential Syndrome Decoding of Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    The algebraic structure of convolutional codes are reviewed and sequential syndrome decoding is applied to those codes. These concepts are then used to realize by example actual sequential decoding, using the stack algorithm. The Fano metric for use in sequential decoding is modified so that it can be utilized to sequentially find the minimum weight error sequence.

  20. Prevalence of minor musculoskeletal anomalies in children with congenital hypothyroidism.

    PubMed

    El Kholy, Mohamed; Fahmi, Marwa E; Nassar, Ayman E; Selim, Samia; Elsedfy, Heba H

    2007-01-01

    In the last decade a high frequency of extrathyroidal congenital anomalies has been reported in infants with congenital hypothyroidism (CH) detected by neonatal screening. In the present study the occurrence of additional congenital malformations (CM) in a cohort of children with confirmed primary CH due to thyroid dysgenesis was investigated. A high prevalence of extrathyroidal major congenital anomalies (15.9%), more than 5-fold higher than that reported in the Egyptian population (2.7%), was found. The cardiac and musculoskeletal systems were the most commonly involved, comprising 9.09 and 47.72% of all anomalies, respectively. The high prevalence of musculoskeletal anomalies in this study was mostly due to minor anomalies as brachydactyly and digitalization of thumbs. The type of dysgenesis (i.e. aplastic, ectopic or hypoplastic) as well as the severity of hypothyroidism, as assessed by TSH and T(4) levels at diagnosis, had no relation with the occurrence of extrathyroidal abnormalities. PMID:17587855

  1. Systematic Screening for Subtelomeric Anomalies in a Clinical Sample of Autism

    ERIC Educational Resources Information Center

    Wassink, Thomas H.; Losh, Molly; Piven, Joseph; Sheffield, Val C.; Ashley, Elizabeth; Westin, Erik R.; Patil, Shivanand R.

    2007-01-01

    High-resolution karyotyping detects cytogenetic anomalies in 5-10% of cases of autism. Karyotyping, however, may fail to detect abnormalities of chromosome subtelomeres, which are gene rich regions prone to anomalies. We assessed whether panels of FISH probes targeted for subtelomeres could detect abnormalities beyond those identified by…

  2. The XXXXY Chromosome Anomaly

    PubMed Central

    Zaleski, Witold A.; Houston, C. Stuart; Pozsonyi, J.; Ying, K. L.

    1966-01-01

    The majority of abnormal sex chromosome complexes in the male have been considered to be variants of Klinefelter's syndrome but an exception should probably be made in the case of the XXXXY individual who has distinctive phenotypic features. Clinical, radiological and cytological data on three new cases of XXXXY syndrome are presented and 30 cases from the literature are reviewed. In many cases the published clinical and radiological data were supplemented and re-evaluated. Mental retardation, usually severe, was present in all cases. Typical facies was observed in many; clinodactyly of the fifth finger was seen in nearly all. Radiological examination revealed abnormalities in the elbows and wrists in all the 19 personally evaluated cases, and other skeletal anomalies were very frequent. Cryptorchism is very common and absence of Leydig's cells may differentiate the XXXXY chromosome anomaly from polysomic variants of Klinefelter's syndrome. The relationship of this syndrome to Klinefelter's syndrome and to Down's syndrome is discussed. ImagesFig. 1Fig. 2Fig. 3Fig. 4Fig. 5Fig. 6Fig. 7Fig. 8Fig. 9Fig. 10Fig. 11Fig. 12Fig. 13Fig. 14Fig. 15 PMID:4222822

  3. Trace anomaly driven inflation

    NASA Astrophysics Data System (ADS)

    Hawking, S. W.; Hertog, T.; Reall, H. S.

    2001-04-01

    This paper investigates Starobinsky's model of inflation driven by the trace anomaly of conformally coupled matter fields. This model does not suffer from the problem of contrived initial conditions that occurs in most models of inflation driven by a scalar field. The universe can be nucleated semiclassically by a cosmological instanton that is much larger than the Planck scale provided there are sufficiently many matter fields. There are two cosmological instantons: the four sphere and a new ``double bubble'' solution. This paper considers a universe nucleated by the four sphere. The AdS/CFT correspondence is used to calculate the correlation function for scalar and tensor metric perturbations during the ensuing de Sitter phase. The analytic structure of the scalar and tensor propagators is discussed in detail. Observational constraints on the model are discussed. Quantum loops of matter fields are shown to strongly suppress short scale metric perturbations, which implies that short distance modifications of gravity would probably not be observable in the cosmic microwave background. This is probably true for any model of inflation provided there are sufficiently many matter fields. This point is illustrated by a comparison of anomaly driven inflation in four dimensions and in a Randall-Sundrum brane-world model.

  4. Statistical significance of the gallium anomaly

    SciTech Connect

    Giunti, Carlo; Laveder, Marco

    2011-06-15

    We calculate the statistical significance of the anomalous deficit of electron neutrinos measured in the radioactive source experiments of the GALLEX and SAGE solar neutrino detectors, taking into account the uncertainty of the detection cross section. We found that the statistical significance of the anomaly is {approx}3.0{sigma}. A fit of the data in terms of neutrino oscillations favors at {approx}2.7{sigma} short-baseline electron neutrino disappearance with respect to the null hypothesis of no oscillations.

  5. Quantum anomalies in dense matter

    SciTech Connect

    Son, D.T.; Zhitnitsky, Ariel R.

    2004-10-01

    We consider the effects of quantum anomalies involving the baryon current for high-density matter. In the effective Lagrangian, the anomaly terms describe the interaction of three light fields: the electromagnetic photons A{sub {mu}}, neutral light Nambu-Goldstone bosons ({pi}, {eta}, {eta}{sup '}), and the superfluid phonon. The anomaly induced interactions lead to a number of interesting phenomena which may have phenomenological consequences observable in neutron stars.

  6. A bit serial sequential circuit

    NASA Technical Reports Server (NTRS)

    Hu, S.; Whitaker, S.

    1990-01-01

    Normally a sequential circuit with n state variables consists of n unique hardware realizations, one for each state variable. All variables are processed in parallel. This paper introduces a new sequential circuit architecture that allows the state variables to be realized in a serial manner using only one next state logic circuit. The action of processing the state variables in a serial manner has never been addressed before. This paper presents a general design procedure for circuit construction and initialization. Utilizing pass transistors to form the combinational next state forming logic in synchronous sequential machines, a bit serial state machine can be realized with a single NMOS pass transistor network connected to shift registers. The bit serial state machine occupies less area than other realizations which perform parallel operations. Moreover, the logical circuit of the bit serial state machine can be modified by simply changing the circuit input matrix to develop an adaptive state machine.

  7. Turtle Carapace Anomalies: The Roles of Genetic Diversity and Environment

    PubMed Central

    Velo-Antón, Guillermo; Becker, C. Guilherme; Cordero-Rivera, Adolfo

    2011-01-01

    Background Phenotypic anomalies are common in wild populations and multiple genetic, biotic and abiotic factors might contribute to their formation. Turtles are excellent models for the study of developmental instability because anomalies are easily detected in the form of malformations, additions, or reductions in the number of scutes or scales. Methodology/Principal Findings In this study, we integrated field observations, manipulative experiments, and climatic and genetic approaches to investigate the origin of carapace scute anomalies across Iberian populations of the European pond turtle, Emys orbicularis. The proportion of anomalous individuals varied from 3% to 69% in local populations, with increasing frequency of anomalies in northern regions. We found no significant effect of climatic and soil moisture, or climatic temperature on the occurrence of anomalies. However, lower genetic diversity and inbreeding were good predictors of the prevalence of scute anomalies among populations. Both decreasing genetic diversity and increasing proportion of anomalous individuals in northern parts of the Iberian distribution may be linked to recolonization events from the Southern Pleistocene refugium. Conclusions/Significance Overall, our results suggest that developmental instability in turtle carapace formation might be caused, at least in part, by genetic factors, although the influence of environmental factors affecting the developmental stability of turtle carapace cannot be ruled out. Further studies of the effects of environmental factors, pollutants and heritability of anomalies would be useful to better understand the complex origin of anomalies in natural populations. PMID:21533278

  8. Prevalence of Associated Anomalies in Cleft Lip and/or Palate Patients

    PubMed Central

    Abdollahi Fakhim, Shahin; Shahidi, Nikzad; Lotfi, Alireza

    2016-01-01

    Introduction: Orofacial clefts are among the most common congenital anomalies. Patients presenting with orofacial clefts often require surgery or other complex procedures. A cleft lip or palate can be a single anomaly or a part of multiple congenital anomalies. The reported prevalence of cleft disease and associated anomalies varies widely across the literature, and is dependent on the diagnostic procedure used. In this study we determined the prevalence of associated anomalies in patients with a cleft lip and/or palate, with a specific focus on cardiac anomalies. Materials and Methods: In this cross-sectional study, 526 patients with a cleft lip and /or palate admitted to the children’s referral hospital between 2006 and 2011 were evaluated. All associated anomalies were detected and recorded. Patient information collected included age, gender, type and side of cleft, craniofacial anomalies and presence of other anomalies, including cardiac anomalies. Data were analyzed using SPSS version 16. Results: Of the 526 patients enrolled in the study, 58% (305) were male and 42% (221) were female. In total, 75% of patients (396) were aged between 4 and 8 years and 25% (130) were aged less than 4 years. The most common cleft type in our study was bilateral cleft palate. The most commonly associated anomaly among cleft patients, in 12% of cleft patients, was a cardiac anomaly. The most common cardiac anomaly was atrial septal defect (ASD). Conclusion: The prevalence of associated anomalies among orofacial cleft patients is high. The most common associated anomaly is cardiac anomaly, with ASD being the most common cardiac anomaly. There are no significant relationships between type of cleft and associated cardiac anomalies. PMID:27280100

  9. Universality of sequential quantum measurements

    NASA Astrophysics Data System (ADS)

    Heinosaari, Teiko; Miyadera, Takayuki

    2015-02-01

    We show that any jointly measurable pair of quantum observables can be obtained in a sequential measurement scheme, even if the second observable will be decided after the first measurement. This means that it is possible to perform a measurement of any quantum observable in a way that does not disturb the subsequent measurements more than is dictated by joint measurability. Only measurements with a specific structure have this universality feature. As a supplementing result, we provide a characterization of all possible joint measurements obtained from a sequential measurement lacking universality.

  10. Rare Upper Airway Anomalies.

    PubMed

    Windsor, Alanna; Clemmens, Clarice; Jacobs, Ian N

    2016-01-01

    A broad spectrum of congenital upper airway anomalies can occur as a result of errors during embryologic development. In this review, we will describe the clinical presentation, diagnosis, and management strategies for a few select, rare congenital malformations of this system. The diagnostic tools used in workup of these disorders range from prenatal tests to radiological imaging, swallowing evaluations, indirect or direct laryngoscopy, and rigid bronchoscopy. While these congenital defects can occur in isolation, they are often associated with disorders of other organ systems or may present as part of a syndrome. Therefore workup and treatment planning for patients with these disorders often involves a team of multiple specialists, including paediatricians, otolaryngologists, pulmonologists, speech pathologists, gastroenterologists, and geneticists. PMID:26277452

  11. Genetics of lymphatic anomalies

    PubMed Central

    Brouillard, Pascal; Boon, Laurence; Vikkula, Miikka

    2014-01-01

    Lymphatic anomalies include a variety of developmental and/or functional defects affecting the lymphatic vessels: sporadic and familial forms of primary lymphedema, secondary lymphedema, chylothorax and chylous ascites, lymphatic malformations, and overgrowth syndromes with a lymphatic component. Germline mutations have been identified in at least 20 genes that encode proteins acting around VEGFR-3 signaling but also downstream of other tyrosine kinase receptors. These mutations exert their effects via the RAS/MAPK and the PI3K/AKT pathways and explain more than a quarter of the incidence of primary lymphedema, mostly of inherited forms. More common forms may also result from multigenic effects or post-zygotic mutations. Most of the corresponding murine knockouts are homozygous lethal, while heterozygotes are healthy, which suggests differences in human and murine physiology and the influence of other factors. PMID:24590274

  12. Nolen-Schiffer anomaly

    SciTech Connect

    Pieper, S.C.; Wiringa, R.B.

    1995-08-01

    The Argonne v{sub 18} potential contains a detailed treatment of the pp, pn and nn electromagnetic potential, including Coulomb, vacuum polarization, Darwin Foldy and magnetic moment terms, all with suitable form factors and was fit to pp and pn data using the appropriate nuclear masses. In addition, it contains a nuclear charge-symmetry breaking (CSB) term adjusted to reproduce the difference in the experimental pp and nn scattering lengths. We have used these potential terms to compute differences in the binding energies of mirror isospin-1/2 nuclei (Nolen-Schiffer [NS] anomaly). Variational Monte Carlo calculations for the {sup 3}He-{sup 3}H system and cluster variational Monte Carlo for the {sup 15}O-{sup 15}N and {sup 17}F-{sup 17}O systems were made. In the first case, the best variational wave function for the A = 3 nuclei was used. However, because our {sup 16}O wave function does not reproduce accurately the {sup 16}O rms radius, to which the NS anomaly is very sensitive, we adjusted the A = 15 and A = 17 wave functions to reproduce the experimental density profiles. Our computed energy differences for these three systems are 0.757 {plus_minus} .001, 3.544 {plus_minus} .018 and 3.458 {plus_minus} .040 MeV respectively, which are to be compared with the experimental differences of 0.764, 3.537, and 3.544 MeV. Most of the theoretical uncertainties are due to uncertainties in the experimental rms radii. The nuclear CSB potential contributes 0.066, 0.188, and 0.090 MeV to these totals. We also attempted calculations for A = 39 and A = 41. However, in these cases, the experimental uncertainties in the rms radius make it impossible to extract useful information about the contribution of the nuclear CSB potential.

  13. Sequential Processes In Image Generation.

    ERIC Educational Resources Information Center

    Kosslyn, Stephen M.; And Others

    1988-01-01

    Results of three experiments are reported, which indicate that images of simple two-dimensional patterns are formed sequentially. The subjects included 48 undergraduates and 16 members of the Harvard University (Cambridge, Mass.) community. A new objective methodology indicates that images of complex letters require more time to generate. (TJH)

  14. Sequential Effects in Essay Ratings

    ERIC Educational Resources Information Center

    Attali, Yigal

    2011-01-01

    Contrary to previous research on sequential ratings of student performance, this study found that professional essay raters of a large-scale standardized testing program produced ratings that were drawn toward previous ratings, creating an assimilation effect. Longer intervals between the two adjacent ratings and higher degree of agreement with…

  15. Medical management of vascular anomalies.

    PubMed

    Trenor, Cameron C

    2016-03-01

    We have entered an exciting era in the care of patients with vascular anomalies. These disorders require multidisciplinary care and coordination and dedicated centers have emerged to address this need. Vascular tumors have been treated with medical therapies for many years, while malformations have been historically treated with endovascular and operative procedures. The recent serendipitous discoveries of propranolol and sirolimus for vascular anomalies have revolutionized this field. In particular, sirolimus responses are challenging the dogma that vascular malformations are not biologically active. While initially explored for lymphatic anomalies, sirolimus is now being used broadly throughout the spectrum of vascular anomalies. Whether medical therapies are reserved for refractory patients or used first line is currently dependent on the experience and availability of alternative therapies at each institution. On the horizon, we anticipate new drugs targeting genes and pathways involved in vascular anomalies to be developed. Also, combinations of medications and protocols combining medical and procedural approaches are in development for refractory patients. PMID:27607327

  16. Congenital Anomalies of the Nose.

    PubMed

    Funamura, Jamie L; Tollefson, Travis T

    2016-04-01

    Congenital anomalies of the nose range from complete aplasia of the nose to duplications and nasal masses. Nasal development is the result of a complex embryologic patterning and fusion of multiple primordial structures. Loss of signaling proteins or failure of migration or proliferation can result in structural anomalies with significant cosmetic and functional consequences. Congenital anomalies of the nose can be categorized into four broad categories: (1) aplastic or hypoplastic, (2) hyperplastic or duplications, (3) clefts, and (4) nasal masses. Our knowledge of the embryologic origin of these anomalies helps dictate subsequent work-up for associated conditions, and the appropriate treatment or surgical approach to manage newborns and children with these anomalies. PMID:27097134

  17. System for closure of a physical anomaly

    DOEpatents

    Bearinger, Jane P; Maitland, Duncan J; Schumann, Daniel L; Wilson, Thomas S

    2014-11-11

    Systems for closure of a physical anomaly. Closure is accomplished by a closure body with an exterior surface. The exterior surface contacts the opening of the anomaly and closes the anomaly. The closure body has a primary shape for closing the anomaly and a secondary shape for being positioned in the physical anomaly. The closure body preferably comprises a shape memory polymer.

  18. An investigation of thermal anomalies in the Central American volcanic chain and evaluation of the utility of thermal anomaly monitoring in the prediction of volcanic eruptions. [Central America

    NASA Technical Reports Server (NTRS)

    Stoiber, R. E. (Principal Investigator); Rose, W. I., Jr.

    1975-01-01

    The author has identified the following significant results. Ground truth data collection proves that significant anomalies exist at 13 volcanoes within the test site of Central America. The dimensions and temperature contrast of these ten anomalies are large enough to be detected by the Skylab 192 instrument. The dimensions and intensity of thermal anomalies have changed at most of these volcanoes during the Skylab mission.

  19. Multiplexed protein profiling by sequential affinity capture.

    PubMed

    Ayoglu, Burcu; Birgersson, Elin; Mezger, Anja; Nilsson, Mats; Uhlén, Mathias; Nilsson, Peter; Schwenk, Jochen M

    2016-04-01

    Antibody microarrays enable parallelized and miniaturized analysis of clinical samples, and have proven to provide novel insights for the analysis of different proteomes. However, there are concerns that the performance of such direct labeling and single antibody assays are prone to off-target binding due to the sample context. To improve selectivity and sensitivity while maintaining the possibility to conduct multiplexed protein profiling, we developed a multiplexed and semi-automated sequential capture assay. This novel bead-based procedure encompasses a first antigen capture, labeling of captured protein targets on magnetic particles, combinatorial target elution and a read-out by a secondary capture bead array. We demonstrate in a proof-of-concept setting that target detection via two sequential affinity interactions reduced off-target contribution, while lowered background and noise levels, improved correlation to clinical values compared to single binder assays. We also compared sensitivity levels with single binder and classical sandwich assays, explored the possibility for DNA-based signal amplification, and demonstrate the applicability of the dual capture bead-based antibody microarray for biomarker analysis. Hence, the described concept enhances the possibilities for antibody array assays to be utilized for protein profiling in body fluids and beyond. PMID:26935855

  20. Binning of satellite magnetic anomalies

    NASA Technical Reports Server (NTRS)

    Goyal, H. K.; Vonfrese, R. R. B.; Hinze, W. J.

    1985-01-01

    Crustal magnetic anomaly signals over satellite orbits were simulated to investigate numerical averaging as an anomaly estimator. Averaging as an anomaly estimator involves significant problems concerning spatial and amplitude smoothing of the satellite magnetic observations. The results of simulations suggest that the error of numerical averaging constitutes a small and relatively minor component of the total error-budget of higher orbital anomaly estimates, whereas for lower orbital estimates numerical averaging error increases substantially. As an alternative to numerical averaging, least-squares collocation was investigated and observed to produce substantially more accurate anomaly estimates, particularly as the orbital elevation of prediction was decreased towards the crustal sources. In contrast to averaging, collocation is a significantly more resource-intensive procedure to apply because of the practical, but surmountable problems related to establishing and inverting the covariance matrix for accurate anomaly prediction. However, collocation may be much more effectively used to exploit the anomaly details contained in the lower orbital satellite magnetic data for geologic analysis.