Science.gov

Sample records for anomaly detection system

  1. 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.

  2. 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.

  3. An Immunity-Based Anomaly Detection System with Sensor Agents

    PubMed Central

    Okamoto, Takeshi; Ishida, Yoshiteru

    2009-01-01

    This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems. PMID:22291560

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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)

  12. 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.

  13. Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy.

    PubMed

    Kittler, Josef; Christmas, William; de Campos, Teófilo; Windridge, David; Yan, Fei; Illingworth, John; Osman, Magda

    2014-05-01

    We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.

  14. NADIR (Network Anomaly Detection and Intrusion Reporter): A prototype network intrusion detection system

    SciTech Connect

    Jackson, K.A.; DuBois, D.H.; Stallings, C.A.

    1990-01-01

    The Network Anomaly Detection and Intrusion Reporter (NADIR) is an expert system which is intended to provide real-time security auditing for intrusion and misuse detection at Los Alamos National Laboratory's Integrated Computing Network (ICN). It is based on three basic assumptions: that statistical analysis of computer system and user activities may be used to characterize normal system and user behavior, and that given the resulting statistical profiles, behavior which deviates beyond certain bounds can be detected, that expert system techniques can be applied to security auditing and intrusion detection, and that successful intrusion detection may take place while monitoring a limited set of network activities such as user authentication and access control, file movement and storage, and job scheduling. NADIR has been developed to employ these basic concepts while monitoring the audited activities of more than 8000 ICN users.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. On selecting reference image models for anomaly detection in industrial systems

    NASA Astrophysics Data System (ADS)

    Xiao, Xinhua; Quan, Jin; Ferro, Andrew; Han, Chia Y.; Zhou, Xuefu; Wee, William G.

    2013-09-01

    Automatic X-ray inspection of industrial parts usually uses reference-based methods, in which a set of model images or statistics extracted from the model image set are selected as the benchmark. Based on these methods, many systems are developed and are used extensively for anomaly detection. However, the performance of these systems relies heavily on the model image set. Thus, the selection of the model images is very important. This paper presents an approach for automatically selecting a set of model images to be used in a reference-based assisted defect recognition (ADR) system for anomaly detection of turbine blades of jet engines. The proposed approach to generating a model image set is based on feature extraction. Features are extracted from callout images of ADR, including potential defect indication type, size and location. Experimental results show that the proposed approach is fast and a low false alarm rate with acceptable detection rate is ensured. Moreover, the approach is applicable to different blade types and varied views of the blade. Further validation shows that the approach can be applied to the update of the model image set, when more images are generated from new blades and the model becomes inaccurate for anomaly detection in the new images.

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

    SciTech Connect

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

    1984-03-13

    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.

  6. 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.

  7. Fetal Central Nervous System Anomalies Detected by Magnetic Resonance Imaging: A Two-Year Experience

    PubMed Central

    Sefidbakht, Sepideh; Dehghani, Sakineh; Safari, Maryam; Vafaei, Homeira; Kasraeian, Maryam

    2016-01-01

    Background Magnetic resonance imaging (MRI) is gradually becoming more common for thorough visualization of the fetus than ultrasound (US), especially for neurological anomalies, which are the most common indications for fetal MRI and are a matter of concern for both families and society. Objectives We investigated fetal MRIs carried out in our center for frequency of central nervous system anomalies. This is the first such report in southern Iran. Materials and Methods One hundred and seven (107) pregnant women with suspicious fetal anomalies in prenatal ultrasound entered a cross-sectional retrospective study from 2011 to 2013. A 1.5 T Siemens Avanto scanner was employed for sequences, including T2 HASTE and Trufisp images in axial, coronal, and sagittal planes to mother’s body, T2 HASTE and Trufisp relative to the specific fetal body part being evaluated, and T1 flash images in at least one plane based on clinical indication. We investigated any abnormality in the central nervous system and performed descriptive analysis to achieve index of frequency. Results Mean gestational age ± standard deviation (SD) for fetuses was 25.54 ± 5.22 weeks, and mean maternal age ± SD was 28.38 ± 5.80 years Eighty out of 107 (74.7%) patients who were referred with initial impression of borderline ventriculomegaly. A total of 18 out of 107 (16.82%) patients were found to have fetuses with CNS anomalies and the remainder were neurologically normal. Detected anomalies were as follow: 3 (16.6%) fetuses each had the Dandy-Walker variant and Arnold-Chiari II (with myelomeningocele). Complete agenesis of corpus callosum, partial agenesis of corpus callosum, and aqueductal stenosis were each seen in 2 (11.1%) fetuses. Arnold-Chiari II without myelomeningocele, anterior spina bifida associated with neurenteric cyst, arachnoid cyst, lissencephaly, and isolated enlarged cisterna magna each presented in one (5.5%) fetus. One fetus had concomitant schizencephaly and complete agenesis of

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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

  13. 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.

  14. The Detection of Abundance Anomalies in the Infrared Spectra of Cataclysmic Variables: Shorter Period Systems

    NASA Astrophysics Data System (ADS)

    Harrison, Thomas E.; Osborne, Heather L.; Howell, Steve B.

    2005-05-01

    We present K-band spectra for 12 cataclysmic variables (CVs) with orbital periods under 6 hr. We confidently detect the secondary stars in nine of these systems and may have detected them in the other three. Nine of the 12 CVs clearly have CO first-overtone absorption features that are weaker than they should be for the derived spectral type. We demonstrate that, in general, the weak CO features are due to a carbon deficiency in the secondary star. In the case of U Gem, UU Aql, and TW Vir the carbon abundance in the secondary star appears to be very low, likely only a few percent of the solar value. Deficits of carbon, when combined with the detection of 13CO and the ultraviolet detections of enhanced levels of nitrogen in other CV systems, imply that material that has been processed through the CNO cycle is finding its way into the photospheres of CV secondary stars. While several plausible models exist to explain unusual levels of CNO species in CV secondary stars, they do not detail how such species as aluminum, magnesium, or silicon (elements that show abundance anomalies in our spectra) will behave. It appears that the standard model for the formation and evolution of CVs needs substantial revision.

  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. 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.

  17. 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.

  18. Alberta Congenital Anomalies Surveillance System.

    PubMed Central

    Lowry, R B; Thunem, N Y; Anderson-Redick, S

    1989-01-01

    The Alberta Congenital Anomalies Surveillance System was started in 1966 in response to the thalidomide tragedy earlier in the decade. It was one of four provincial surveillance systems on which the federal government relied for baseline statistics of congenital anomalies. The government now collects data from six provinces and one territory. The Alberta Congenital Anomaly Surveillance System originally depended on three types of notification to the Division of Vital Statistics, Department of Health, Government of Alberta: birth notice and certificates of death and stillbirth; increased sources of ascertainment have greatly improved data quality. We present the data for 1980-86 and compare the prevalence rates of selected anomalies with the rates from three other surveillance systems. Surveillance systems do not guarantee that a new teratogen will be detected, but they are extremely valuable for testing hypotheses regarding causation. At the very least they provide baseline data with which to compare any deviation or trend. For many, if not most, congenital anomalies total prevention is not possible; however, surveillance systems can be used to measure progress in prevention. PMID:2819634

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Credibility of anomaly detection in nuclear reactors using neural networks

    SciTech Connect

    Kozma, R.; Kitamura, Masaharu; Sakuma, M.; Hoogenboom, J.E.

    1994-12-31

    The detection of anomalies in nuclear reactors in an incipient phase is an important safety issue. Artificial neural network (ANN) models can be trained to identify various anomaly types and associate the actual system state with one of the anomaly classes. The ANNs have a clear advantage over the usual statistical methods in detecting anomalies at an early stage. This advantage becomes apparent in the case of short-term analysis when the uncertainty of the statistical discriminators is large. In this paper, the signals generated by ANNs are analyzed from the viewpoint of the credibility of the judgement of the network about the presence or absence of anomaly in the system. The results have been applied to analyze the boiling anomaly induced in the Interfaculty Reactor Institute (IRI) research reactor in the Netherlands.

  7. 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.

  8. Taming anomaly detection for industrial applications through spatial ponderation

    NASA Astrophysics Data System (ADS)

    Feller, Sebastian; Todorov, Yavor; Jaroszewski, Daniel; Chevalier, Roger

    2013-10-01

    In recent years, an abundant number of applications have been developed for anomaly detection methods. Anomaly detection algorithms offer an easy and interpretable possibility to monitor the health state of virtually any technical system and industrial process that can be described by periodic measurements. But one major caveat remains: As all state-space methods, anomaly detection algorithms rely on measures of distance and these distances are distorted by any kind of irregularity in the data. The introduction of a spatial ponderation promises to cure this illness, but no mathematical foundation has been built to support this intuition. In this paper, first steps are introduced towards a stringent description of this approach.

  9. 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.

  10. An enhanced stream mining approach for network anomaly detection

    NASA Astrophysics Data System (ADS)

    Bellaachia, Abdelghani; Bhatt, Rajat

    2005-03-01

    Network anomaly detection is one of the hot topics in the market today. Currently, researchers are trying to find a way in which machines could automatically learn both normal and anomalous behavior and thus detect anomalies if and when they occur. Most important applications which could spring out of these systems is intrusion detection and spam mail detection. In this paper, the primary focus on the problem and solution of "real time" network intrusion detection although the underlying theory discussed may be used for other applications of anomaly detection (like spam detection or spy-ware detection) too. Since a machine needs a learning process on its own, data mining has been chosen as a preferred technique. The object of this paper is to present a real time clustering system; we call Enhanced Stream Mining (ESM) which could analyze packet information (headers, and data) to determine intrusions.

  11. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    SciTech Connect

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  12. 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.

  13. Artificial immune system via Euclidean Distance Minimization for anomaly detection in bearings

    NASA Astrophysics Data System (ADS)

    Montechiesi, L.; Cocconcelli, M.; Rubini, R.

    2016-08-01

    In recent years new diagnostics methodologies have emerged, with particular interest into machinery operating in non-stationary conditions. In fact continuous speed changes and variable loads make non-trivial the spectrum analysis. A variable speed means a variable characteristic fault frequency related to the damage that is no more recognizable in the spectrum. To overcome this problem the scientific community proposed different approaches listed in two main categories: model-based approaches and expert systems. In this context the paper aims to present a simple expert system derived from the mechanisms of the immune system called Euclidean Distance Minimization, and its application in a real case of bearing faults recognition. The proposed method is a simplification of the original process, adapted by the class of Artificial Immune Systems, which proved to be useful and promising in different application fields. Comparative results are provided, with a complete explanation of the algorithm and its functioning aspects.

  14. Attention focussing and anomaly detection in real-time systems monitoring

    NASA Astrophysics Data System (ADS)

    Doyle, Richard J.; Chien, Steve A.; Fayyad, Usama M.; Porta, Harry J.

    1993-02-01

    In real-time monitoring situations, more information is not necessarily better. When faced with complex emergency situations, operators can experience information overload and a compromising of their ability to react quickly and correctly. We describe an approach to focusing operator attention in real-time systems monitoring based on a set of empirical and model-based measures for determining the relative importance of sensor data.

  15. Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher

    1994-01-01

    Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.

  16. Attention focussing and anomaly detection in real-time systems monitoring

    NASA Technical Reports Server (NTRS)

    Doyle, Richard J.; Chien, Steve A.; Fayyad, Usama M.; Porta, Harry J.

    1993-01-01

    In real-time monitoring situations, more information is not necessarily better. When faced with complex emergency situations, operators can experience information overload and a compromising of their ability to react quickly and correctly. We describe an approach to focusing operator attention in real-time systems monitoring based on a set of empirical and model-based measures for determining the relative importance of sensor data.

  17. 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.

  18. 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.

  19. Development of a Computer Architecture to Support the Optical Plume Anomaly Detection (OPAD) System

    NASA Technical Reports Server (NTRS)

    Katsinis, Constantine

    1996-01-01

    The NASA OPAD spectrometer system relies heavily on extensive software which repetitively extracts spectral information from the engine plume and reports the amounts of metals which are present in the plume. The development of this software is at a sufficiently advanced stage where it can be used in actual engine tests to provide valuable data on engine operation and health. This activity will continue and, in addition, the OPAD system is planned to be used in flight aboard space vehicles. The two implementations, test-stand and in-flight, may have some differing requirements. For example, the data stored during a test-stand experiment are much more extensive than in the in-flight case. In both cases though, the majority of the requirements are similar. New data from the spectrograph is generated at a rate of once every 0.5 sec or faster. All processing must be completed within this period of time to maintain real-time performance. Every 0.5 sec, the OPAD system must report the amounts of specific metals within the engine plume, given the spectral data. At present, the software in the OPAD system performs this function by solving the inverse problem. It uses powerful physics-based computational models (the SPECTRA code), which receive amounts of metals as inputs to produce the spectral data that would have been observed, had the same metal amounts been present in the engine plume. During the experiment, for every spectrum that is observed, an initial approximation is performed using neural networks to establish an initial metal composition which approximates as accurately as possible the real one. Then, using optimization techniques, the SPECTRA code is repetitively used to produce a fit to the data, by adjusting the metal input amounts until the produced spectrum matches the observed one to within a given level of tolerance. This iterative solution to the original problem of determining the metal composition in the plume requires a relatively long period of time

  20. Use of color lights for the detection of anomalies in quality systems.

    PubMed

    Báez, G; De la Vega, E; Castro, C; Elizarraras, R

    2012-01-01

    The importance of eye care in the industry is a first level topic, due to most of the assembly and manufacturing aimed companies of various products that require direct health care of their employees, specially eye care. The lighting system, the lamp features and job tasks are factors that impact over the visual performance of the worker. Each of these factors, either by themselves or in conjunction, influences the visual performance of the employee, and therefore its safety and efficacy. Some of the reported symptoms are: problem of visual fixation, eye redness, tearing, headache, blurred vision, eyelids heaviness and dry eyes, [7]. The research was developed with 48 people, 27 male and 21 female, in the range of ages of 17 to 58 years old. In the experiment were used illumination system base on Diode Emitting lights (LED's) of five different colors (White, Blue, Green, Red and Yellow), the reason of use of LED's it is because are source of monochromatic light, also it is also saving power light and low heating dissipation.

  1. 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.

  2. 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.

  3. Astrometric solar-system anomalies

    NASA Astrophysics Data System (ADS)

    Anderson, John D.; Nieto, Michael Martin

    2010-01-01

    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 reportedly increasing by about 15 cm yr-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, including us, 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 prudent 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.

  4. 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.

  5. Hyperspectral anomaly detection using enhanced global factors

    NASA Astrophysics Data System (ADS)

    Paciencia, Todd J.; Bauer, Kenneth W.

    2016-05-01

    Dimension reduction techniques have become one popular unsupervised approach used towards detecting anomalies in hyperspectral imagery. Although demonstrating promising results in the literature on specific images, these methods can become difficult to directly interpret and often require tuning of their parameters to achieve high performance on a specific set of images. This lack of generality is also compounded by the need to remove noise and atmospheric absorption spectral bands from the image prior to detection. Without a process for this band selection and to make the methods adaptable to different image compositions, performance becomes difficult to maintain across a wider variety of images. Here, we present a framework that uses factor analysis to provide a robust band selection and more meaningful dimension reduction with which to detect anomalies in the imagery. Measurable characteristics of the image are used to create an automated decision process that allows the algorithm to adjust to a particular image, while maintaining high detection performance. The framework and its algorithms are detailed, and results are shown for forest, desert, sea, rural, urban, anomaly-sparse, and anomaly-dense imagery types from different sensors. Additionally, the method is compared to current state-of-the-art methods and is shown to be computationally efficient.

  6. 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…

  7. 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

  8. Applications of TOPS Anomaly Detection Framework to Amazon Drought Analysis

    NASA Astrophysics Data System (ADS)

    Votava, P.; Nemani, R. R.; Ganguly, S.; Michaelis, A.; Hashimoto, H.

    2011-12-01

    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. 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. 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. We have integrated this knowledge base with a framework for deploying an ensemble of anomaly detection algorithms on large volumes of Earth science datasets and applied it to specific scientific applications that support research conducted by our group. In one early application, we were able to process large number of MODIS, TRMM, CERES data along with ground-based weather and river flow observations to detect the evolution of 2010 drought in the Amazon, identify the affected area, and publish the results in three weeks. A similar analysis of the 2005 drought using the same data sets took nearly 2 years, highlighting the potential contribution of our anomaly framework in accelerating scientific discoveries.

  9. 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.

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

    PubMed Central

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

    2014-01-01

    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

  11. 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

  12. 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.

  13. 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

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

    PubMed

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

    2015-01-27

    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.

  15. Astrometric Solar-System Anomalies

    NASA Astrophysics Data System (ADS)

    Anderson, John D.

    2009-05-01

    There are 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 experiences a gain in total orbital energy per unit mass (Anderson et al., Phys. Rev. Lett. 100, 091102). This amounts to a net velocity increase of 13.5 mm/s for the NEAR spacecraft at a closest approach of 539 km, 3.9 mm/s for the Galileo spacecraft at 960 km, and 1.8 mm/s for the Rosetta spacecraft at 1956 km. Next, I suggest the change in the astronomical unit AU is definitely a concern. It is increasing by about 15 cm/yr (Krasinsky and Brumberg, Celes. Mech. & Dynam. Astron. 90, 267). 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 (Anderson et al., Phys. Rev. D 65, 082004). Some, including me, are convinced this effect is of concern, but many 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 increase that is about three times larger than expected (J. G. Williams, DDA/AAS Brouwer Award Lecture, Halifax, Nova Scotia 2006). We suspect that all four anomalies have mundane explanations. However, the possibility that they will be explained by a new theory of gravitation is not ruled out, perhaps analogous to Einstein's 1916 explanation of the excess precession of Mercury's perihelion.

  16. 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

  17. Maintaining defender's reputation in anomaly detection against insider attacks.

    PubMed

    Zhang, Nan; Yu, Wei; Fu, Xinwen; Das, Sajal K

    2010-06-01

    We address issues related to establishing a defender's reputation in anomaly detection against two types of attackers: 1) smart insiders, who learn from historic attacks and adapt their strategies to avoid detection/punishment, and 2) naïve attackers, who blindly launch their attacks without knowledge of the history. In this paper, we propose two novel algorithms for reputation establishment--one for systems solely consisting of smart insiders and the other for systems in which both smart insiders and naïve attackers are present. The theoretical analysis and performance evaluation show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

  18. 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.

  19. 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.

  20. 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.

  1. Research on immune storage anomaly detection via user access behavior

    NASA Astrophysics Data System (ADS)

    Huang, Jianzhong; Chen, Yunliang; Fang, Yunfu

    2008-12-01

    If an intruder uses a stolen account, the authentication sub-system will regard the intruder as a legitimate user. In order to filter out such illegal users, the storage system should be capable of the user activity diagnosis. This paper presents a novel anomaly detection scheme to monitor the user access activities using the artificial immune technique. When an access request violates the access control rule, it is regarded as Non-self, so as to provide some early warning tips to the storage security sub-system. Compared with the NIDS, the proposed scheme targets the anomaly detection at storage level and focuses on the read/write data requests. In the prophase of simulation, a set of optimal parameters of algorithm are fitted according to the mean convergence speed and detection efficiency. The simulation shows the proposed scheme can reach rather high detection rate and low false alarm rate, further validating its feasibility. Thus the storage anomaly detection would strengthen the storage early warning and improve the storage security.

  2. 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.

  3. 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.

  4. Anomalies

    NASA Astrophysics Data System (ADS)

    Deo, Nivedita

    1988-12-01

    This thesis studies the structure of local and global anomalies in certain systems and examines the conditions for their cancellation. Gauge anomalies-abelian and non -albelian-antisymmetric tensor, and gravitational anomalies in simple spinor theories with background fields have been analyzed by perturbative methods and local counterterms have been constructed to cancel the anomalies wherever possible. Anomalies occurring in supersymmetric theories in (2 + 1)-dimensions have also been calculated using both perturbative and heat kernel techniques, here again counterterms have been constructed to cancel these parity violating anomalies for certain gauge field configurations. (i) For gauge theories in four dimensions which contain couplings of fermions to a non-abelian antisymmetric tensor field, the contribution of the later to anomalies in the non-abelian chiral Ward identity is computed. It is shown by explicit construction of suitable counterterms that these anomalies can all be cancelled. (ii) The gauge anomalies associated with the gravitational fields in abelian gauge theories can be completely removed provided torsion is nonzero. This is shown by constructing a counterterm associated with the gravitational Goldstone-Wilczek current which cancels the anomalous gravitational contribution to the chiral Ward identity without introducing anomalies in the Lorentz or Einstein Ward identities. (iii) Using perturbative BPHZ renormalization techniques the parity odd part of the effective action has been extracted and explicitly determined for abitrary non-abelian gauge superfields in odd dimensions and shown to be the supersymmetric Chern -Simons secondary topological invariant. (iv) Schwinger's proper time technique is generalized to supersymmetric theories in odd dimensions. The effective action for supersymmetric QED is exactly found for space-time constant superfield. The parity violating anomaly induced in the effective action can be cancelled by adding a local

  5. 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.

  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 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

  8. 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.

  9. Mobile gamma-ray scanning system for detecting radiation anomalies associated with /sup 226/Ra-bearing materials

    SciTech Connect

    Myrick, T.E.; Blair, M.S.; Doane, R.W.; Goldsmith, W.A.

    1982-11-01

    A mobile gamma-ray scanning system has been developed by Oak Ridge National Laboratory for use in the Department of Energy's remedial action survey programs. The unit consists of a NaI(T1) detection system housed in a specially-equipped van. The system is operator controlled through an on-board mini-computer, with data output provided on the computer video screen, strip chart recorders, and an on-line printer. Data storage is provided by a floppy disk system. Multichannel analysis capabilities are included for qualitative radionuclide identification. A /sup 226/Ra-specific algorithm is employed to identify locations containing residual radium-bearing materials. This report presents the details of the system description, software development, and scanning methods utilized with the ORNL system. Laboratory calibration and field testing have established the system sensitivity, field of view, and other performance characteristics, the results of which are also presented. Documentation of the instrumentation and computer programs are included.

  10. Discovering System Health Anomalies Using Data Mining Techniques

    NASA Technical Reports Server (NTRS)

    Sriastava, Ashok, N.

    2005-01-01

    We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

  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. 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.…

  13. 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.

  14. 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.

  15. Recent Advances in Ionospheric Anomalies detection

    NASA Astrophysics Data System (ADS)

    Titov, Anton; Vyacheslav, Khattatov

    2016-07-01

    The variability of the parameters of the ionosphere and ionospheric anomalies are the subject of intensive research. It is widely known and studied in the literature ionospheric disturbances caused by solar activity, the passage of the terminator, artificial heating of high-latitude ionosphere, as well as seismic events. Each of the above types of anomalies is the subject of study and analysis. Analysis of these anomalies will provide an opportunity to improve our understanding of the mechanisms of ionospheric disturbances. To solve this problem are encouraged to develop a method of modeling the ionosphere, based on the assimilation of large amounts of observational data.

  16. 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

  17. 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.

  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.

  19. 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

  20. 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

  1. 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.

  2. Embedded GPU implementation of anomaly detection for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Yang, Bin; Chen, Zhengchao

    2015-10-01

    Anomaly detection is one of the most important techniques for remotely sensed hyperspectral data interpretation. Developing fast processing techniques for anomaly detection has received considerable attention in recent years, especially in analysis scenarios with real-time constraints. In this paper, we develop an embedded graphics processing units based parallel computation for streaming background statistics anomaly detection algorithm. The streaming background statistics method can simulate real-time anomaly detection, which refer to that the processing can be performed at the same time as the data are collected. The algorithm is implemented on NVIDIA Jetson TK1 development kit. The experiment, conducted with real hyperspectral data, indicate the effectiveness of the proposed implementations. This work shows the embedded GPU gives a promising solution for high-performance with low power consumption hyperspectral image applications.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. Table of hyperfine anomaly in atomic systems

    SciTech Connect

    Persson, J.R.

    2013-01-15

    This table is a compilation of experimental values of magnetic hyperfine anomaly in atomic and ionic systems. The last extensive compilation was published in 1984 by Büttgenbach [S. Büttgenbach, Hyperfine Int. 20 (1984) 1] and the aim here is to make an up to date compilation. The literature search covers the period up to January 2011.

  8. 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.

  9. 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

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

    PubMed

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

    2016-04-29

    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.

  11. 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

  12. 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.

  13. 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,…

  14. Identifying Threats Using Graph-based Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Eberle, William; Holder, Lawrence; Cook, Diane

    Much of the data collected during the monitoring of cyber and other infrastructures is structural in nature, consisting of various types of entities and relationships between them. The detection of threatening anomalies in such data is crucial to protecting these infrastructures. We present an approach to detecting anomalies in a graph-based representation of such data that explicitly represents these entities and relationships. The approach consists of first finding normative patterns in the data using graph-based data mining and then searching for small, unexpected deviations to these normative patterns, assuming illicit behavior tries to mimic legitimate, normative behavior. The approach is evaluated using several synthetic and real-world datasets. Results show that the approach has high truepositive rates, low false-positive rates, and is capable of detecting complex structural anomalies in real-world domains including email communications, cellphone calls and network traffic.

  15. 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.

  16. 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.

  17. Learning patterns of human activity for anomaly detection

    NASA Astrophysics Data System (ADS)

    Gutchess, Daniel; Checka, Neal; Snorrason, Magnús S.

    2007-04-01

    Commercial security and surveillance systems offer advanced sensors, optics, and display capabilities but lack intelligent processing. This necessitates human operators who must closely monitor video for situational awareness and threat assessment. For instance, urban environments are typically in a state of constant activity, which generates numerous visual cues, each of which must be examined so that potential security breaches do not go unnoticed. We are building a prototype system called BALDUR (Behavior Adaptive Learning during Urban Reconnaissance) that learns probabilistic models of activity for a given site using online and unsupervised training techniques. Once a camera system is set up, no operator intervention is required for the system to begin learning patterns of activity. Anomalies corresponding to unusual or suspicious behavior are automatically detected in real time. All moving object tracks (pedestrians, vehicles, etc.) are efficiently stored in a relational database for use in training. The database is also well suited for answering human- initiated queries. An example of such a query is, "Display all pedestrians who approached the door of the building between the hours of 9:00pm and 11:00pm." This forensic analysis tool complements the system's real-time situational awareness capabilities. Several large datasets have been collected for the evaluation of the system, including one database containing an entire month of activity from a commercial parking lot.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  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. 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.

  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. An expert system for diagnosing anomalies of spacecraft

    NASA Technical Reports Server (NTRS)

    Lauriente, Michael; Durand, Rick; Vampola, AL; Koons, Harry C.; Gorney, David

    1994-01-01

    Although the analysis of anomalous behavior of satellites is difficult because it is a very complex process, it is important to be able to make an accurate assessment in a timely manner when the anomaly is observed. Spacecraft operators may have to take corrective action or to 'safe' the spacecraft; space-environment forecasters may have to assess the environmental situation and issue warnings and alerts regarding hazardous conditions, and scientists and engineers may want to gain knowledge for future designs to mitigate the problems. Anomalies can be hardware problems, software errors, environmentally induced, or even the cause of workmanship. Spacecraft anomalies attributable to electrostatic discharges have been known to cause command errors. A goal is to develop an automated system based on this concept to reduce the number of personnel required to operate large programs or missions such as Hubble Space Telescope (HST) and Mission to Planet Earth (MTPE). Although expert systems to detect anomalous behavior of satellites during operations are established, diagnosis of the anomaly is a complex procedure and is a new development.

  6. 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.

  7. 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.

  8. Parameter estimation for support vector anomaly detection in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Meth, Reuven; Ahn, James; Banerjee, Amit; Juang, Radford; Burlina, Philippe

    2012-06-01

    Hyperspectral Image (HSI) anomaly detectors typically employ local background modeling techniques to facilitate target detection from surrounding clutter. Global background modeling has been challenging due to the multi-modal content that must be automatically modeled to enable target/background separation. We have previously developed a support vector based anomaly detector that does not impose an a priori parametric model on the data and enables multi-modal modeling of large background regions with inhomogeneous content. Effective application of this support vector approach requires the setting of a kernel parameter that controls the tightness of the model fit to the background data. Estimation of the kernel parameter has typically considered Type I / false-positive error optimization due to the availability of background samples, but this approach has not proven effective for general application since these methods only control the false alarm level, without any optimization for maximizing detection. Parameter optimization with respect to Type II / false-negative error has remained elusive due to the lack of sufficient target training exemplars. We present an approach that optimizes parameter selection based on both Type I and Type II error criteria by introducing outliers based on existing hypercube content to guide parameter estimation. The approach has been applied to hyperspectral imagery and has demonstrated automatic estimation of parameters consistent with those that were found to be optimal, thereby providing an automated method for general anomaly detection applications.

  9. Real-time anomaly detection in full motion video

    NASA Astrophysics Data System (ADS)

    Konowicz, Glenn; Li, Jiang

    2012-06-01

    Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects within a video sequence and attempts to cluster each object's trajectory into a database of existing trajectories. Objects are tracked by first differentiating them from a Gaussian background model and then tracked over subsequent frames based on a combination of size and color. Once an object is tracked over several frames, its trajectory is calculated and compared with other trajectories earlier in the video sequence. Anomalous trajectories are differentiated by their failure to cluster with other well-known movement patterns. Adding the proposed algorithm to an existing surveillance system could increase the likelihood of identifying an anomaly and allow for more efficient collection of intelligence data. Additionally, by operating in real-time, our algorithm allows for the reallocation of sensing equipment to those areas most likely to contain movement that is valuable for situational awareness.

  10. 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

  11. Progressive anomaly detection in medical data using vital sign signals

    NASA Astrophysics Data System (ADS)

    Gao, Cheng; Lee, Li-Chien; Li, Yao; Chang, Chein-I.; Hu, Peter; Mackenzie, Colin

    2016-05-01

    Vital Sign Signals (VSSs) have been widely used for medical data analysis. One classic approach is to use Logistic Regression Model (LRM) to describe data to be analyzed. There are two challenging issues from this approach. One is how many VSSs needed to be used in the model since there are many VSSs can be used for this purpose. Another is that once the number of VSSs is determined, the follow-up issue what these VSSs are. Up to date these two issues are resolved by empirical selection. This paper addresses these two issues from a hyperspectral imaging perspective. If we view a patient with collected different vital sign signals as a pixel vector in hyperspectral image, then each vital sign signal can be considered as a particular band. In light of this interpretation each VSS can be ranked by band prioritization commonly used by band selection in hyperspectral imaging. In order to resolve the issue of how many VSSs should be used for data analysis we further develop a Progressive Band Processing of Anomaly Detection (PBPAD) which allows users to detect anomalies in medical data using prioritized VSSs one after another so that data changes between bands can be dictated by profiles provided by PBPAD. As a result, there is no need of determining the number of VSSs as well as which VSS should be used because all VSSs are used in their prioritized orders. To demonstrate the utility of PBPAD in medical data analysis anomaly detection is implemented as PBP to find anomalies which correspond to abnormal patients. The data to be used for experiments are data collected in University of Maryland, School of Medicine, Shock Trauma Center (STC). The results will be evaluated by the results obtained by Logistic Regression Model (LRM).

  12. BEARS: a multi-mission anomaly response system

    NASA Astrophysics Data System (ADS)

    Roberts, Bryce A.

    2009-05-01

    The Mission Operations Group at UC Berkeley's Space Sciences Laboratory operates a highly automated ground station and presently a fleet of seven satellites, each with its own associated command and control console. However, the requirement for prompt anomaly detection and resolution is shared commonly between the ground segment and all spacecraft. The efficient, low-cost operation and "lights-out" staffing of the Mission Operations Group requires that controllers and engineers be notified of spacecraft and ground system problems around the clock. The Berkeley Emergency Anomaly and Response System (BEARS) is an in-house developed web- and paging-based software system that meets this need. BEARS was developed as a replacement for an existing emergency reporting software system that was too closedsource, platform-specific, expensive, and antiquated to expand or maintain. To avoid these limitations, the new system design leverages cross-platform, open-source software products such as MySQL, PHP, and Qt. Anomaly notifications and responses make use of the two-way paging capabilities of modern smart phones.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Anomalies detection in hyperspectral imagery using projection pursuit algorithm

    NASA Astrophysics Data System (ADS)

    Achard, Veronique; Landrevie, Anthony; Fort, Jean Claude

    2004-11-01

    Hyperspectral imagery provides detailed spectral information on the observed scene which enhances detection possibility, in particular for subpixel targets. In this context, we have developed and compared several anomaly detection algorithms based on a projection pursuit approach. The projection pursuit is performed either on the ACP or on the MNF (Minimum Noise Fraction) components. Depending on the method, the best axes of the eigenvectors basis are directly selected, or a genetic algorithm is used in order to optimize the projections. Two projection index (PI) have been tested: the kurtosis and the skewness. These different approaches have been tested on Aviris and Hymap hyperspectral images, in which subpixel targets have been included by simulation. The proportion of target in pixels varies from 50% to 10% of the surface. The results are presented and discussed. The performance of our detection algorithm is very satisfactory for target surfaces until 10% of the pixel.

  19. 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.

  20. 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.

  1. 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.

  2. A High-Order Statistical Tensor Based Algorithm for Anomaly Detection in Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

    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.

  3. 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

  4. 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.

  5. 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.

  6. Low-rank decomposition-based anomaly detection

    NASA Astrophysics Data System (ADS)

    Chen, Shih-Yu; Yang, Shiming; Kalpakis, Konstantinos; Chang, Chein-I.

    2013-05-01

    With high spectral resolution hyperspectral imaging is capable of uncovering many subtle signal sources which cannot be known a priori or visually inspected. Such signal sources generally appear as anomalies in the data. Due to high correlation among spectral bands and sparsity of anomalies, a hyperspectral image can be e decomposed into two subspaces: a background subspace specified by a matrix with low rank dimensionality and an anomaly subspace specified by a sparse matrix with high rank dimensionality. This paper develops an approach to finding such low-high rank decomposition to identify anomaly subspace. Its idea is to formulate a convex constrained optimization problem that minimizes the nuclear norm of the background subspace and little ι1 norm of the anomaly subspace subject to a decomposition of data space into background and anomaly subspaces. By virtue of such a background-anomaly decomposition the commonly used RX detector can be implemented in the sense that anomalies can be separated in the anomaly subspace specified by a sparse matrix. Experimental results demonstrate that the background-anomaly subspace decomposition can actually improve and enhance RXD performance.

  7. Extending TOPS: A Prototype MODIS Anomaly Detection Architecture

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

    The management and processing of Earth science data has been gaining importance over the last decade due to higher data volumes generated by a larger number of instruments, and due to the increase in complexity of Earth science models that use this data. The volume of data itself is often a limiting factor in obtaining the information needed by the scientists; without more sophisticated data volume reduction technologies, possible key information may not be discovered. We are especially interested in automatic identification of disturbances within the ecosystems (e,g, wildfires, droughts, floods, insect/pest damage, wind damage, logging), and focusing our analysis efforts on the identified areas. There are dozens of variables that define the health of our ecosystem and both long-term and short-term changes in these variables can serve as early indicators of natural disasters and shifts in climate and ecosystem health. These changes can have profound socio-economic impacts and we need to develop capabilities for identification, analysis and response to these changes in a timely manner. Because the ecosystem consists of a large number of variables, there can be a disturbance that is only apparent when we examine relationships among multiple variables despite the fact that none of them is by itself alarming. We have to be able to extract information from multiple sensors and observations and discover these underlying relationships. As the data volumes increase, there is also potential for large number of anomalies to "flood" the system, so we need to provide ability to automatically select the most likely ones and the most important ones and the ability to analyze the anomaly with minimal involvement of scientists. We describe a prototype architecture for anomaly driven data reduction for both near-real-time and archived surface reflectance data from the MODIS instrument collected over Central California and test it using Orca and One-Class Support Vector Machines

  8. A divide and conquer approach to anomaly detection, localization and diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Jianbo; Djurdjanovic, Dragan; Marko, Kenneth A.; Ni, Jun

    2009-11-01

    With the growing complexity of dynamic control systems, the effective diagnosis of all possible failures has become increasingly difficult and time consuming. The virtually infinite variety of behavior patterns of such systems due to control inputs and environmental influences further complicates system characterization and fault diagnosis. To circumvent these difficulties, we propose a new diagnostic method, consisting of three elements: the first, based on anomaly detection, identifies any performance deviation from normal operation; the second, based on anomaly/fault localization, localizes the problem, as best as possible, to the specific component or subsystem that does not operate properly and the third, fault diagnosis, discriminates known and unknown faults and identifies the type of the fault if it is previously known. Our prescriptive method for diagnostic design relies on the use of self-organizing maps (SOMs) for regionalization of the system operating conditions, followed by the performance assessment module based on time-frequency distributions (TFDs) and principal component analysis (PCA) for anomaly detection and fault diagnosis. The complete procedure is described in detail and demonstrated with an example of automotive engine control system.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Hyperspectral anomaly detection using sparse kernel-based ensemble learning

    NASA Astrophysics Data System (ADS)

    Gurram, Prudhvi; Han, Timothy; Kwon, Heesung

    2011-06-01

    In this paper, sparse kernel-based ensemble learning for hyperspectral anomaly detection is proposed. The proposed technique is aimed to optimize an ensemble of kernel-based one class classifiers, such as Support Vector Data Description (SVDD) classifiers, by estimating optimal sparse weights. In this method, hyperspectral signatures are first randomly sub-sampled into a large number of spectral feature subspaces. An enclosing hypersphere that defines the support of spectral data, corresponding to the normalcy/background data, in the Reproducing Kernel Hilbert Space (RKHS) of each respective feature subspace is then estimated using regular SVDD. The enclosing hypersphere basically represents the spectral characteristics of the background data in the respective feature subspace. The joint hypersphere is learned by optimally combining the hyperspheres from the individual RKHS, while imposing the l1 constraint on the combining weights. The joint hypersphere representing the most optimal compact support of the local hyperspectral data in the joint feature subspaces is then used to test each pixel in hyperspectral image data to determine if it belongs to the local background data or not. The outliers are considered to be targets. The performance comparison between the proposed technique and the regular SVDD is provided using the HYDICE hyperspectral images.

  15. Multiprobe in-situ measurement of magnetic field in a minefield via a distributed network of miniaturized low-power integrated sensor systems for detection of magnetic field anomalies

    NASA Astrophysics Data System (ADS)

    Javadi, Hamid H. S.; Bendrihem, David; Blaes, B.; Boykins, Kobe; Cardone, John; Cruzan, C.; Gibbs, J.; Goodman, W.; Lieneweg, U.; Michalik, H.; Narvaez, P.; Perrone, D.; Rademacher, Joel D.; Snare, R.; Spencer, Howard; Sue, Miles; Weese, J.

    1998-09-01

    Based on technologies developed for the Jet Propulsion Laboratory (JPL) Free-Flying-Magnetometer (FFM) concept, we propose to modify the present design of FFMs for detection of mines and arsenals with large magnetic signature. The result will be an integrated miniature sensor system capable of identifying local magnetic field anomaly caused by a magnetic dipole moment. Proposed integrated sensor system is in line with the JPL technology road-map for development of autonomous, intelligent, networked, integrated systems with a broad range of applications. In addition, advanced sensitive magnetic sensors (e.g., silicon micromachined magnetometer, laser pumped helium magnetometer) are being developed for future NASA space plasma probes. It is envisioned that a fleet of these Integrated Sensor Systems (ISS) units will be dispersed on a mine-field via an aerial vehicle (a low-flying airplane or helicopter). The number of such sensor systems in each fleet and the corresponding in-situ probe-grid cell size is based on the strength of magnetic anomaly of the target and ISS measurement resolution of magnetic field vector. After a specified time, ISS units will transmit the measured magnetic field and attitude data to an air-borne platform for further data processing. The cycle of data acquisition and transmission will be continued until batteries run out. Data analysis will allow a local deformation of the Earth's magnetic field vector by a magnetic dipole moment to be detected. Each ISS unit consists of miniaturized sensitive 3- axis magnetometer, high resolution analog-to-digital converter (ADC), Field Programmable Gate Array (FPGA)-based data subsystem, Li-batteries and power regulation circuitry, memory, S-band transmitter, single-patch antenna, and a sun angle sensor. ISS unit is packaged with non-magnetic components and the electronic design implements low-magnetic signature circuits. Care is undertaken to guarantee no corruption of magnetometer sensitivity as a result

  16. 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.

  17. 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.

  18. 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.

  19. 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

  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. 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.

  2. Jamming anomaly in PT-symmetric systems

    NASA Astrophysics Data System (ADS)

    Barashenkov, I. V.; Zezyulin, D. A.; Konotop, V. V.

    2016-07-01

    The Schrödinger equation with a { P }{ T }-symmetric potential is used to model an optical structure consisting of an element with gain coupled to an element with loss. At low gain–loss amplitudes γ, raising the amplitude results in the energy flux from the active to the leaky element being boosted. We study the anomalous behaviour occurring for larger γ, where the increase of the amplitude produces a drop of the flux across the gain–loss interface. We show that this jamming anomaly is either a precursor of the exceptional point, where two real eigenvalues coalesce and acquire imaginary parts, or precedes the eigenvalue's immersion in the continuous spectrum.

  3. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.

    PubMed

    Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

    2012-01-01

    Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient's clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among

  4. A Healthcare Utilization Analysis Framework for Hot Spotting and Contextual Anomaly Detection

    PubMed Central

    Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

    2012-01-01

    Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient’s clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among

  5. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.

    PubMed

    Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

    2012-01-01

    Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient's clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among

  6. Detecting anomalies in CMB maps: a new method

    SciTech Connect

    Neelakanta, Jayanth T.

    2015-10-01

    Ever since WMAP announced its first results, different analyses have shown that there is weak evidence for several large-scale anomalies in the CMB data. While the evidence for each anomaly appears to be weak, the fact that there are multiple seemingly unrelated anomalies makes it difficult to account for them via a single statistical fluke. So, one is led to considering a combination of these anomalies. But, if we ''hand-pick'' the anomalies (test statistics) to consider, we are making an a posteriori choice. In this article, we propose two statistics that do not suffer from this problem. The statistics are linear and quadratic combinations of the a{sub ℓ m}'s with random co-efficients, and they test the null hypothesis that the a{sub ℓ m}'s are independent, normally-distributed, zero-mean random variables with an m-independent variance. The motivation for considering multiple modes is this: because most physical models that lead to large-scale anomalies result in coupling multiple ℓ and m modes, the ''coherence'' of this coupling should get enhanced if a combination of different modes is considered. In this sense, the statistics are thus much more generic than those that have been hitherto considered in literature. Using fiducial data, we demonstrate that the method works and discuss how it can be used with actual CMB data to make quite general statements about the incompatibility of the data with the null hypothesis.

  7. Structural chromosomal anomalies detected by prenatal genetic diagnosis: our experience.

    PubMed

    Farcaş, Simona; Crişan, C D; Andreescu, Nicoleta; Stoian, Monica; Motoc, A G M

    2013-01-01

    The prenatal diagnosis is currently widely spread and facilitates the acquiring of important genetic information about the fetus by a rate extremely accelerate and considered without precedent. In this paper, we like to present our experience concerning the genetic diagnosis and counseling offered for pregnancies in which a structural chromosomal aberration was found. The study group is formed by 528 prenatal samples of amniotic fluid and chorionic villi, received by our laboratory from 2006 through October 2012 for cytogenetic diagnosis. The appropriate genetic investigation was selected based on the indications for prenatal diagnosis. The cases with structural chromosomal anomalies and polymorphic variants were analyzed as regard to the maternal age, gestational age, referral indications and type of chromosomal anomaly found. A total number of 21 structural chromosomal anomalies and polymorphic variants were identified in the study group. Out of 21 structural chromosomal anomalies and polymorphic variants, six deletions and microdeletions, four situations with abnormal long "p" arm of acrocentric chromosomes, two duplications, two reciprocal translocations, two inversions, two additions, one Robertsonian translocation associating trisomy 13, one 9q heteromorphism and one complex chromosome rearrangement were noticed. To the best of our knowledge, this is the first Romanian study in which the diagnostic strategies and the management of the prenatal cases with structural rearrangements are presented. The data provided about the diagnosis strategy and the management of the prenatal cases with structural chromosomal anomalies represents a useful tool in genetic counseling of pregnancies diagnosed with rare structural chromosomal anomalies. PMID:23771085

  8. A mobile device system for early warning of ECG anomalies.

    PubMed

    Szczepański, Adam; Saeed, Khalid

    2014-01-01

    With the rapid increase in computational power of mobile devices the amount of ambient intelligence-based smart environment systems has increased greatly in recent years. A proposition of such a solution is described in this paper, namely real time monitoring of an electrocardiogram (ECG) signal during everyday activities for identification of life threatening situations. The paper, being both research and review, describes previous work of the authors, current state of the art in the context of the authors' work and the proposed aforementioned system. Although parts of the solution were described in earlier publications of the authors, the whole concept is presented completely for the first time along with the prototype implementation on mobile device-a Windows 8 tablet with Modern UI. The system has three main purposes. The first goal is the detection of sudden rapid cardiac malfunctions and informing the people in the patient's surroundings, family and friends and the nearest emergency station about the deteriorating health of the monitored person. The second goal is a monitoring of ECG signals under non-clinical conditions to detect anomalies that are typically not found during diagnostic tests. The third goal is to register and analyze repeatable, long-term disturbances in the regular signal and finding their patterns. PMID:24955946

  9. A Mobile Device System for Early Warning of ECG Anomalies

    PubMed Central

    Szczepański, Adam; Saeed, Khalid

    2014-01-01

    With the rapid increase in computational power of mobile devices the amount of ambient intelligence-based smart environment systems has increased greatly in recent years. A proposition of such a solution is described in this paper, namely real time monitoring of an electrocardiogram (ECG) signal during everyday activities for identification of life threatening situations. The paper, being both research and review, describes previous work of the authors, current state of the art in the context of the authors' work and the proposed aforementioned system. Although parts of the solution were described in earlier publications of the authors, the whole concept is presented completely for the first time along with the prototype implementation on mobile device—a Windows 8 tablet with Modern UI. The system has three main purposes. The first goal is the detection of sudden rapid cardiac malfunctions and informing the people in the patient's surroundings, family and friends and the nearest emergency station about the deteriorating health of the monitored person. The second goal is a monitoring of ECG signals under non-clinical conditions to detect anomalies that are typically not found during diagnostic tests. The third goal is to register and analyze repeatable, long-term disturbances in the regular signal and finding their patterns. PMID:24955946

  10. 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.

  11. 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

  12. Min-max hyperellipsoidal clustering for anomaly detection in network security.

    PubMed

    Sarasamma, Suseela T; Zhu, Qiuming A

    2006-08-01

    A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified.

  13. 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.

  14. 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.

  15. 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.

  16. Classification of SD-OCT volumes for DME detection: an anomaly detection approach

    NASA Astrophysics Data System (ADS)

    Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

    2016-03-01

    Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

  17. Discovering Recurring Anomalies in Text Reports Regarding Complex Space Systems

    NASA Technical Reports Server (NTRS)

    Zane-Ulman, Brett; Srivastava, Ashok N.

    2005-01-01

    Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. For some platforms, these reports may be encoded as scanned images rather than even searchable text. The problem that we address in this paper is the discovery of recurring anomalies and relationships between different problem reports that may indicate larger systemic problems. We will illustrate our techniques on data from discrepancy reports regarding software anomalies in the Space Shuttle. These free text reports are written by a number of different penp!e, thus the emphasis and wording varies considerably.

  18. 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.

  19. 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.

  20. Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

    NASA Technical Reports Server (NTRS)

    Srivastava, Ashok, N.; Akella, Ram; Diev, Vesselin; Kumaresan, Sakthi Preethi; McIntosh, Dawn M.; Pontikakis, Emmanuel D.; Xu, Zuobing; Zhang, Yi

    2006-01-01

    This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

  1. Multivariate diagnostics and anomaly detection for nuclear safeguards

    SciTech Connect

    Burr, T.; Jones, J.; Wangen, L.

    1994-08-01

    For process control and other reasons, new and future nuclear reprocessing plants are expected to be increasingly more automated than older plants. As a consequence of this automation, the quantity of data potentially available for safeguards may be much greater in future reprocessing plants than in current plants. The authors first review recent literature that applies multivariate Shewhart and multivariate cumulative sum (Cusum) tests to detect anomalous data. These tests are used to evaluate residuals obtained from a simulated three-tank problem in which five variables (volume, density, and concentrations of uranium, plutonium, and nitric acid) in each tank are modeled and measured. They then present results from several simulations involving transfers between the tanks and between the tanks and the environment. Residuals from a no-fault problem in which the measurements and model predictions are both correct are used to develop Cusum test parameters which are then used to test for faults for several simulated anomalous situations, such as an unknown leak or diversion of material from one of the tanks. The leak can be detected by comparing measurements, which estimate the true state of the tank system, with the model predictions, which estimate the state of the tank system as it ``should`` be. The no-fault simulation compares false alarm behavior for the various tests, whereas the anomalous problems allow one to compare the power of the various tests to detect faults under possible diversion scenarios. For comparison with the multivariate tests, univariate tests are also applied to the residuals.

  2. Isotopic anomalies and proton irradiation in the early solar system

    NASA Technical Reports Server (NTRS)

    Clayton, D. D.; Dwek, E.; Woosley, S. E.

    1977-01-01

    Nuclear cross sections relevant to the various isotopic-abundance anomalies found in solar-system objects are evaluated in an attempt to set constraints on the hypothesized mechanism of irradiation of forming planetesimals by energetic protons from the young sun. A power-law proton spectrum is adopted, attention is restricted to proton energies less than about 20 MeV, and average cross sections are calculated for several reactions that might be expected to lead to the observed anomalies. The following specific anomalies are examined in detail: Al-26, Na-22, Xe-126, I-129, Kr-80, V-50, Nb-92, La-138, Ta-180, Hg-196, K-40, Ar-36, O-17, O-18, N-15, C-13, Li, Be, and B. It is suggested that the picture of presolar-grain carriers accounts for the facts more naturally than do irradiation models.

  3. 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.

  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. Lunar magnetic anomalies detected by the Apollo substatellite magnetometers

    USGS Publications Warehouse

    Hood, L.L.; Coleman, P.J.; 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

  6. A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

    DOE PAGES

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

    2016-10-20

    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 node probabilities, and these 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 facilitatesmore » 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. Furthermore, 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.« less

  7. 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.

  8. 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.

  9. 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.

  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. 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

  12. 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.

  13. Electronic systems failures and anomalies attributed to electromagnetic interference

    NASA Technical Reports Server (NTRS)

    Leach, R. D. (Editor); Alexander, M. B. (Editor)

    1995-01-01

    The effects of electromagnetic interference can be very detrimental to electronic systems utilized in space missions. Assuring that subsystems and systems are electrically compatible is an important engineering function necessary to assure mission success. This reference publication will acquaint the reader with spacecraft electronic systems failures and anomalies caused by electromagnetic interference and will show the importance of electromagnetic compatibility activities in conjunction with space flight programs. It is also hoped that the report will illustrate that evolving electronic systems are increasingly sensitive to electromagnetic interference and that NASA personnel must continue to diligently pursue electromagnetic compatibility on space flight systems.

  14. Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations. Chapter 8

    NASA Technical Reports Server (NTRS)

    Martin, Rodney Alexander

    2010-01-01

    This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed. The other two types of algorithms being deployed include a "nile-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rule-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions), a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," can be found in. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies. Robust

  15. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Sun, Weiwei; Liu, Chun; Li, Jialin; Lai, Yenming Mark; Li, Weiyue

    2014-01-01

    A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to detect anomalies in hyperspectral imagery (HSI). The detector assumes background images are low-rank while anomalies are gross errors that are sparsely distributed throughout the image scene. By solving a constrained convex optimization problem, the LRaSMD detector separates the anomalies from the background. This protects the background model from corruption. An anomaly value for each pixel is calculated using the Euclidean distance, and anomalies are determined by thresholding the anomaly value. Four groups of experiments on three widely used HSI datasets are designed to completely analyze the performances of the new detector. Experimental results show that the LRaSMD detector outperforms the global Reed-Xiaoli (GRX), the orthogonal subspace projection-GRX, and the cluster-based detectors. Moreover, the results show that LRaSMD achieves equal or better detection performance than the local support vector data description detector within a shorter computational time.

  16. 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

    The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu

  17. CTS TEP thermal anomalies: Heat pipe system performance

    NASA Technical Reports Server (NTRS)

    Marcus, B. D.

    1977-01-01

    A part of the investigation is summarized of the thermal anomalies of the transmitter experiment package (TEP) on the Communications Technology Satellite (CTS) which were observed on four occasions in 1977. Specifically, the possible failure modes of the variable conductance heat pipe system (VCHPS) used for principal thermal control of the high-power traveling wave tube in the TEP are considered. Further, the investigation examines how those malfunctions may have given rise to the TEP thermal anomalies. Using CTS flight data information, ground test results, analysis conclusions, and other relevant information, the investigation concentrated on artery depriming as the most likely VCHPS failure mode. Included in the study as possible depriming mechanisms were freezing of the working fluid, Marangoni flow, and gas evolution within the arteries. The report concludes that while depriming of the heat pipe arteries is consistent with the bulk of the observed data, the factors which cause the arteries to deprime have yet to be identified.

  18. Stillbirth Risk Among Fetuses With Ultrasound-Detected Isolated Congenital Anomalies

    PubMed Central

    Frey, Heather A.; Odibo, Anthony O.; Dicke, Jeffrey M.; Shanks, Anthony L.; Macones, George A.; Cahill, Alison G.

    2014-01-01

    Objective To estimate the risk of stillbirth among pregnancies complicated by a major isolated congenital anomaly detected by antenatal ultrasound, and the influence of incidental growth restriction. Methods A retrospective cohort study of all consecutive singleton pregnancies undergoing routine anatomic survey between 1990 and 2009 was performed. Stillbirth rates among fetuses with an ultrasound-detected isolated major congenital anomaly were compared to fetuses without major anomalies. Stillbirth rates were calculated per 1,000 ongoing pregnancies. Exclusion criteria included delivery prior to 24 weeks of gestation, multiple fetal anomalies, minor anomalies and chromosomal abnormalities. Analyses were stratified by gestational age at delivery (prior to 32 weeks vs. 32 weeks of gestation or after) and birth weight less than the 10th percentile. We adjusted for confounders using logistic regression. Results Among 65,308 singleton pregnancies delivered at 24 weeks of gestation or after, 873 pregnancies with an isolated major congenital anomaly (1.3%) were identified. The overall stillbirth rate among fetuses with a major anomaly was 55/1,000 compared to 4/1,000 in nonanomalous fetuses (aOR 15.17, 95% CI 11.03–20.86). Stillbirth risk in anomalous fetuses was similar prior to 32 weeks of gestation (26/1,000) and 32 weeks of gestation or after (31/1,000). Among growth-restricted fetuses, the stillbirth rate increased among anomalous (127/1,000) and nonanomalous fetuses (18/1,000), and congenital anomalies remained associated with higher rates of stillbirth (aOR 8.20, 95% CI 5.27–12.74). Conclusion The stillbirth rate is increased in anomalous fetuses regardless of incidental growth restriction. These risks can assist practitioners designing care plans for anomalous fetuses who have elevated and competing risks of stillbirth and neonatal death. PMID:24901272

  19. 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.

  20. 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.

  1. Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

    PubMed

    Sivaraks, Haemwaan; Ratanamahatana, Chotirat Ann

    2015-01-01

    Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

  2. 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.

  3. 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

    diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens. PMID:27532264

  4. 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

    diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens. PMID:27532264

  5. Improvements in the method of radiation anomaly detection by spectral comparison ratios.

    PubMed

    Pfund, D M; Anderson, K K; Detwiler, R S; Jarman, K D; McDonald, B S; Milbrath, B D; Myjak, M J; Paradis, N C; Robinson, S M; Woodring, M L

    2016-04-01

    We present a new procedure for configuring the Nuisance-rejection Spectral Comparison Ratio Anomaly Detection (N-SCRAD) method. The procedure minimizes detectable count rates of source spectra at a specified false positive rate using simulated annealing. We also present a new method for correcting the estimates of background variability used in N-SCRAD to current conditions of the total count rate. The correction lowers detection thresholds for a specified false positive rate, enabling greater sensitivity to targets. PMID:26807839

  6. 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

  7. 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

  8. [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.

  9. [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

  10. 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.

  11. Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Goudail, François; Roux, Nicolas; Baarstad, Ivar; Løke, Trond; Kaspersen, Peter; Alouini, Mehdi; Normandin, Xavier

    2006-07-01

    We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are unknown. We show that, in real hyperspectral images, use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs), but, as they are prone to false alarms, one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized.

  12. Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images.

    PubMed

    Goudail, François; Roux, Nicolas; Baarstad, Ivar; Løke, Trond; Kaspersen, Peter; Alouini, Mehdi; Normandin, Xavier

    2006-07-20

    We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are unknown. We show that, in real hyperspectral images, use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs), but, as they are prone to false alarms, one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized. PMID:16826261

  13. 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…

  14. 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.

  15. Underwater magnetic gradiometer for magnetic anomaly detection, localization, and tracking

    NASA Astrophysics Data System (ADS)

    Kumar, S.; Sulzberger, G.; Bono, J.; Skvoretz, D.; Allen, G. I.; Clem, T. R.; Ebbert, M.; Bennett, S. L.; Ostrom, R. K.; Tzouris, A.

    2007-04-01

    GE Security and the Naval Surface Warfare Center, Panama City (NSWC-PC) have collaborated to develop a magnetic gradiometer, called the Real-time Tracking Gradiometer or RTG that is mounted inside an unmanned underwater vehicle (UUV). The RTG is part of a buried mine hunting platform being developed by the United States Navy. The RTG has been successfully used to make test runs on mine-like targets buried off the coast of Florida. We will present a general description of the system and latest results describing system performance. This system can be also potentially used for other applications including those in the area of Homeland Security.

  16. Radio signal anomalies detected with MEXART in 2012 during the recovery phase of geomagnetic storms

    NASA Astrophysics Data System (ADS)

    Carrillo-Vargas, Armando; Pérez-Enríquez, Román; López-Montes, Rebeca; Rodríguez-Martínez, Mario; Ugalde-Calvillo, Luis Gerardo

    2016-11-01

    In this work we present MEXART observations in 2012 from 17 radio sources in which we detected anomalies in the radio signal of these sources occurring during the recovery phase of some geomagnetic storms. We performed FFT and wavelet analysis of the radio signals during these periods and found that rather than IPS the anomalies seem to originate in the ionosphere, especially because of the frequencies at which they are observed. We discuss this results under the view that the source of the geomagnetic storm is no longer in the interplanetary medium.

  17. 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

    Spatial data are periodically collected and processed to monitor, analyze and interpret developments in our changing environment. Remote sensing is a modern way of data collecting and has seen an enormous growth since launching of modern satellites and development of airborne sensors. In particular, the recent availability of high spatial resolution hyperspectral imagery (spatial resolution of less than 5 meters and including data collected over 64 or more bands of electromagnetic radiation for each pixel offers a great potential to significantly enhance environmental mapping and our ability to model spatial systems. High spatial resolution imagery contains a remarkable quantity of information that could be used to analyze spatial breaks (boundaries), areas of similarity (clusters), and spatial autocorrelation (associations) across the landscape. This paper addresses the specific issue of soil disturbance detection, which could indicate the presence of land mines or recent movements of troop and heavy equipment. A challenge presented by soil detection is to retain the measurement of fine-scale features (i.e. mineral soil changes, organic content changes, vegetation disturbance related changes, aspect changes) while still covering proportionally large spatial areas. An additional difficulty is that no ground data might be available for the calibration of spectral signatures, and little might be known about the size of patches of disturbed soils to be detected. This paper describes a new technique for automatic target detection which capitalizes on both spatial and across spectral bands correlation, does not require any a priori information on the target spectral signature but does not allow discrimination between targets. This approach involves successively a multivariate statistical analysis (principal component analysis) of all spectral bands, a geostatistical filtering of noise and regional background in the first principal components using factorial kriging, and

  18. 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.

  19. 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.

  20. 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.

  1. Automated Detection of Volcanic Thermal Anomalies: Detailed Analysis of the 2004 - 2005 Mt. Etna, Italy Eruption

    NASA Astrophysics Data System (ADS)

    Steffke, A. M.; Harris, A.; Garbeil, H.; Wright, R.; Dehn, J.

    2007-05-01

    Use of thermal infrared satellite data to detect, characterize and track volcanic thermal emissions is an appealing method for monitoring volcanoes for a number of reasons. It provides a synoptic perspective, with satellites sensors such as AVHRR and MODIS allowing global coverage at-least 4 times/day. At the same time, direct reception of calibrated digital data in a standard and stable format allows automation, enabling near-real time analysis of many volcanoes over large regions, including volcanoes where other geophysical instruments are not deployed. In addition, extracted thermal data can be use to convert to heat and volume flux estimates/time series. The development of an automated algorithm to detect volcanic thermal anomalies using thermal satellite data was first attempted over a decade ago (VAST). Subsequently several attempts have been made to create an effective way to automatically detect thermal anomalies at volcanoes using such high-temporal resolution satellite data (e.g. Okmok, MODVOLC and RAT). The underlying motivation has been to allow automated, routine and timely hot spot detection for volcanic monitoring purposes. In this study we review four algorithms that have been implemented to date, specifically: VAST, Okmok, MODVOLC and RAT. To test how VAST and MODVOLC performed we tested them on the 2004 - 2005 effusive eruption of Mount Etna (Sicily, Italy). These results were then compared with manually detected and picked thermal anomalies. Each algorithm is designed for different purposes, thus they perform differently. MODVOLC, for example, must run efficiently, up to 4 times a day, on a full global data set. Thus the number of algorithm steps are minimal and the detection threshold is high, meaning that the incidence of false positives are low, but so too is its sensitivity. In contrast, VAST is designed to run on a single volcano and has the added advantage of some user input. Thus, a greater incidence of false positives occurs, but more

  2. 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.

  3. 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.

  4. 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.

  5. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

    NASA Astrophysics Data System (ADS)

    Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

    2013-11-01

    A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

  6. A Distance Measure for Attention Focusing and Anaomaly Detection in Systems Monitoring

    NASA Technical Reports Server (NTRS)

    Doyle, R. 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.

  7. System for detection of hazardous events

    DOEpatents

    Kulesz, James J.; Worley, Brian A.

    2006-05-23

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

  8. System For Detection Of Hazardous Events

    DOEpatents

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

    2005-08-16

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

  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. An expert system for diagnosing environmentally induced spacecraft anomalies

    NASA Technical Reports Server (NTRS)

    Rolincik, Mark; Lauriente, Michael; Koons, Harry C.; Gorney, David

    1992-01-01

    A new rule-based, machine independent analytical tool was designed for diagnosing spacecraft anomalies using an expert system. Expert systems provide an effective method for saving knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms, which allow approximate reasoning and inference and the ability to attack problems not rigidly defined. The knowledge base consists of over two-hundred (200) rules and provides links to historical and environmental databases. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information and allows the user to input partial information (varying degrees of confidence in an answer) or 'unknown' to any question. The modularity of the expert system allows for easy updates and modifications. It not only provides scientists with needed risk analysis and confidence not found in algorithmic programs, but is also an effective learning tool, and the window implementation makes it very easy to use. The system currently runs on a Micro VAX II at Goddard Space Flight Center (GSFC). The inference engine used is NASA's C Language Integrated Production System (CLIPS).

  11. 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.

  12. 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.

  13. Epsilon-optimal non-Bayesian anomaly detection for parametric tomography.

    PubMed

    Fillatre, Lionel; Nikiforov, Igor; Retraint, Florent

    2008-11-01

    The non-Bayesian detection of an anomaly from a single or a few noisy tomographic projections is considered as a statistical hypotheses testing problem. It is supposed that a radiography is composed of an imaged nonanomalous background medium, considered as a deterministic nuisance parameter, with a possibly hidden anomaly. Because the full voxel-by-voxel reconstruction is impossible, an original tomographic method based on the parametric models of the nonanomalous background medium and radiographic process is proposed to fill up the gap in the missing data. Exploiting this "parametric tomography," a new detection scheme with a limited loss of optimality is proposed as an alternative to the nonlinear generalized likelihood ratio test, which is untractable in the context of nondestructive testing for the objects with uncertainties in their physical/geometrical properties. The theoretical results are illustrated by the processing of real radiographies for the nuclear fuel rod inspection.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. Unmixing and anomaly detection in hyperspectral data due to cluster variation and local information

    NASA Astrophysics Data System (ADS)

    Maerker, Jochen M.; Huber, Johannes; Middelmann, Wolfgang

    2010-04-01

    This paper presents a novel method for anomaly detection based on a cluster unmixing approach. Several algorithms for endmember extraction and unmixing have been reported in literature. Endmember extraction algorithms search for pure materials which constitute the significant structure of the environment. For abundance estimation in hyperspectral imagery, various physically motivated least squares methods are considered. In real hyperspectral data, signatures of each pure material vary with physical texture and perspective. In this work, clustering of data is performed and normal distributions - instead of constant signatures - are used to represent the endmembers. This representation allows determination of class membership by means of unmixing. Furthermore, a parameter optimization is performed. Using only endmembers in a focal window around each pixel better fits the physical model. As result of this local approach, the residual of the reconstruction indicates the magnitude of anomalies. The results obtained with the new approach is called 'Cluster Mixing' (CM). The performance of Cluster Mixing is illustrated by a comparison with other anomaly detection algorithms.

  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. 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.

  2. 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.

  3. 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.

  4. Optimal Index Policies for Anomaly Localization in Resource-Constrained Cyber Systems

    NASA Astrophysics Data System (ADS)

    Cohen, Kobi; Zhao, Qing; Swami, Ananthram

    2014-08-01

    The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observations from a probed component are realizations drawn from two different distributions depending on whether the component is normal or anomalous. The objective is a probing strategy that minimizes the total expected cost, incurred by all the components during the detection process, under reliability constraints. We consider both independent and exclusive models. In the former, each component can be abnormal with a certain probability independent of other components. In the latter, one and only one component is abnormal. We develop optimal simple index policies under both models. The proposed index policies apply to a more general case where a subset (more than one) of the components can be probed simultaneously and have strong performance as demonstrated by simulation examples. The problem under study also finds applications in spectrum scanning in cognitive radio networks and event detection in sensor networks.

  5. Development of an expert system for analysis of Shuttle atmospheric revitalization and pressure control subsystem anomalies

    NASA Technical Reports Server (NTRS)

    Lafuse, Sharon A.

    1991-01-01

    The paper describes the Shuttle Leak Management Expert System (SLMES), a preprototype expert system developed to enable the ECLSS subsystem manager to analyze subsystem anomalies and to formulate flight procedures based on flight data. The SLMES combines the rule-based expert system technology with the traditional FORTRAN-based software into an integrated system. SLMES analyzes the data using rules, and, when it detects a problem that requires simulation, it sets up the input for the FORTRAN-based simulation program ARPCS2AT2, which predicts the cabin total pressure and composition as a function of time. The program simulates the pressure control system, the crew oxygen masks, the airlock repress/depress valves, and the leakage. When the simulation has completed, other SLMES rules are triggered to examine the results of simulation contrary to flight data and to suggest methods for correcting the problem. Results are then presented in form of graphs and tables.

  6. VISAD: an interactive and visual analytical tool for the detection of behavioral anomalies in maritime traffic data

    NASA Astrophysics Data System (ADS)

    Riveiro, Maria; Falkman, Göran; Ziemke, Tom; Warston, Håkan

    2009-05-01

    Monitoring the surveillance of large sea areas normally involves the analysis of huge quantities of heterogeneous data from multiple sources (radars, cameras, automatic identification systems, reports, etc.). The rapid identification of anomalous behavior or any threat activity in the data is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world. There are two main reasons: (1) the detection of anomalous behavior is normally not a well-defined and structured problem and therefore, automatic data mining approaches do not work well and (2) the difficulties that these systems have regarding the representation and employment of the prior knowledge that the users bring to their tasks. In order to overcome these limitations, we believe that human involvement in the entire discovery process is crucial. Using a visual analytics process model as a framework, we present VISAD: an interactive, visual knowledge discovery tool for supporting the detection and identification of anomalous behavior in maritime traffic data. VISAD supports the insertion of human expert knowledge in (1) the preparation of the system, (2) the establishment of the normal picture and (3) in the actual detection of rare events. For each of these three modules, VISAD implements different layers of data mining, visualization and interaction techniques. Thus, the detection procedure becomes transparent to the user, which increases his/her confidence and trust in the system and overall, in the whole discovery process.

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. Subspace based non-parametric approach for hyperspectral anomaly detection in complex scenarios

    NASA Astrophysics Data System (ADS)

    Matteoli, Stefania; Acito, Nicola; Diani, Marco; Corsini, Giovanni

    2014-10-01

    Recent studies on global anomaly detection (AD) in hyperspectral images have focused on non-parametric approaches that seem particularly suitable to detect anomalies in complex backgrounds without the need of assuming any specific model for the background distribution. Among these, AD algorithms based on the kernel density estimator (KDE) benefit from the flexibility provided by KDE, which attempts to estimate the background probability density function (PDF) regardless of its specific form. The high computational burden associated with KDE requires KDE-based AD algorithms be preceded by a suitable dimensionality reduction (DR) procedure aimed at identifying the subspace where most of the useful signal lies. In most cases, this may lead to a degradation of the detection performance due to the leakage of some anomalous target components to the subspace orthogonal to the one identified by the DR procedure. This work presents a novel subspace-based AD strategy that combines the use of KDE with a simple parametric detector performed on the orthogonal complement of the signal subspace, in order to benefit of the non-parametric nature of KDE and, at the same time, avoid the performance loss that may occur due to the DR procedure. Experimental results indicate that the proposed AD strategy is promising and deserves further investigation.

  14. 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.

  15. 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.

  16. Development of newly designed VHF interferometer system for observing earthquake-related atmospheric anomalies

    PubMed Central

    Yamamoto, Isao; Fujiwara, Hironobu; Kamogawa, Masashi; Iyono, Atsushi; Kroumov, Valeri; Azakami, Takashi

    2009-01-01

    Temporal correlation between atmospheric anomalies and earthquakes has recently been verified statistically through measuring VHF FM radio waves transmitted beyond the line-of-sight. In order to locate the sources of such atmospheric anomalies, we developed a VHF interferometer system (bistatic-radar type) capable of finding the arrival direction of FM radio waves scattered possibly by earthquake-related atmospheric anomalies. In general, frequency modulation of FM radio waves produces ambiguity of arrival direction. However, our system, employing high-sampling rates of the order of kHz, can precisely measure the arrival direction of FM radio waves by stacking received signals. PMID:20009381

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. Structure and dynamics of decadal anomalies in the wintertime midlatitude North Pacific ocean-atmosphere system

    NASA Astrophysics Data System (ADS)

    Fang, Jiabei; Yang, Xiu-Qun

    2015-12-01

    The structure and dynamics of decadal anomalies in the wintertime midlatitude North Pacific ocean-atmosphere system are examined in this study, using the NCEP/NCAR atmospheric reanalysis, HadISST SST and Simple Ocean Data Assimilation data for 1960-2010. The midlatitude decadal anomalies associated with the Pacific Decadal Oscillation are identified, being characterized by an equivalent barotropic atmospheric low (high) pressure over a cold (warm) oceanic surface. Such a unique configuration of decadal anomalies can be maintained by an unstable ocean-atmosphere interaction mechanism in the midlatitudes, which is hypothesized as follows. Associated with a warm PDO phase, an initial midlatitude surface westerly anomaly accompanied with intensified Aleutian low tends to force a negative SST anomaly by increasing upward surface heat fluxes and driving southward Ekman current anomaly. The SST cooling tends to increase the meridional SST gradient, thus enhancing the subtropical oceanic front. As an adjustment of the atmospheric boundary layer to the enhanced oceanic front, the low-level atmospheric meridional temperature gradient and thus the low-level atmospheric baroclinicity tend to be strengthened, inducing more active transient eddy activities that increase transient eddy vorticity forcing. The vorticity forcing that dominates the total atmospheric forcing tends to produce an equivalent barotropic atmospheric low pressure north of the initial westerly anomaly, intensifying the initial anomalies of the midlatitude surface westerly and Aleutian low. Therefore, it is suggested that the midlatitude ocean-atmosphere interaction can provide a positive feedback mechanism for the development of initial anomaly, in which the oceanic front and the atmospheric transient eddy are the indispensable ingredients. Such a positive ocean-atmosphere feedback mechanism is fundamentally responsible for the observed decadal anomalies in the midlatitude North Pacific ocean

  2. Structure and dynamics of decadal anomalies in the wintertime midlatitude North Pacific ocean-atmosphere system

    NASA Astrophysics Data System (ADS)

    Fang, Jiabei; Yang, Xiu-Qun

    2016-09-01

    The structure and dynamics of decadal anomalies in the wintertime midlatitude North Pacific ocean-atmosphere system are examined in this study, using the NCEP/NCAR atmospheric reanalysis, HadISST SST and Simple Ocean Data Assimilation data for 1960-2010. The midlatitude decadal anomalies associated with the Pacific Decadal Oscillation are identified, being characterized by an equivalent barotropic atmospheric low (high) pressure over a cold (warm) oceanic surface. Such a unique configuration of decadal anomalies can be maintained by an unstable ocean-atmosphere interaction mechanism in the midlatitudes, which is hypothesized as follows. Associated with a warm PDO phase, an initial midlatitude surface westerly anomaly accompanied with intensified Aleutian low tends to force a negative SST anomaly by increasing upward surface heat fluxes and driving southward Ekman current anomaly. The SST cooling tends to increase the meridional SST gradient, thus enhancing the subtropical oceanic front. As an adjustment of the atmospheric boundary layer to the enhanced oceanic front, the low-level atmospheric meridional temperature gradient and thus the low-level atmospheric baroclinicity tend to be strengthened, inducing more active transient eddy activities that increase transient eddy vorticity forcing. The vorticity forcing that dominates the total atmospheric forcing tends to produce an equivalent barotropic atmospheric low pressure north of the initial westerly anomaly, intensifying the initial anomalies of the midlatitude surface westerly and Aleutian low. Therefore, it is suggested that the midlatitude ocean-atmosphere interaction can provide a positive feedback mechanism for the development of initial anomaly, in which the oceanic front and the atmospheric transient eddy are the indispensable ingredients. Such a positive ocean-atmosphere feedback mechanism is fundamentally responsible for the observed decadal anomalies in the midlatitude North Pacific ocean

  3. 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.

  4. Cfetool: A General Purpose Tool for Anomaly Detection in Periodic Data

    SciTech Connect

    Wachsmann, Alf; Cassell, Elizabeth; /UC, Santa Barbara

    2007-03-06

    Cfengine's environment daemon ''cfenv'' has a limited and fixed set of metrics it measures on a computer. The data is assumed to be periodic in nature and cfenvd reports any data points that fall too far out of the pattern it has learned from past measurements. This is used to detect ''anomalies'' on computers. We introduce a new standalone tool, ''cfetool'', that allows arbitrary periodic data to be stored and evaluated. The user interface is modeled after rrdtool, another widely used tool to store measured data. Because a standalone tool can be used not only for computer related data, we have extended the built-in mathematics to apply to yearly data as well.

  5. 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.

  6. 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.

  7. The 2014-2015 warming anomaly in the Southern California Current System observed by underwater gliders

    NASA Astrophysics Data System (ADS)

    Zaba, Katherine D.; Rudnick, Daniel L.

    2016-02-01

    Large-scale patterns of positive temperature anomalies persisted throughout the surface waters of the North Pacific Ocean during 2014-2015. In the Southern California Current System, measurements by our sustained network of underwater gliders reveal the coastal effects of the recent warming. Regional upper ocean temperature anomalies were greatest since the initiation of the glider network in 2006. Additional observed physical anomalies included a depressed thermocline, high stratification, and freshening; induced biological consequences included changes in the vertical distribution of chlorophyll fluorescence. Contemporaneous surface heat flux and wind strength perturbations suggest that local anomalous atmospheric forcing caused the unusual oceanic conditions.

  8. Interior intrusion detection systems

    SciTech Connect

    Rodriguez, J.R.; Matter, J.C. ); Dry, B. )

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs.

  9. Paternal psychological response after ultrasonographic detection of structural fetal anomalies with a comparison to maternal response: a cohort study

    PubMed Central

    2013-01-01

    Background In Norway almost all pregnant women attend one routine ultrasound examination. Detection of fetal structural anomalies triggers psychological stress responses in the women affected. Despite the frequent use of ultrasound examination in pregnancy, little attention has been devoted to the psychological response of the expectant father following the detection of fetal anomalies. This is important for later fatherhood and the psychological interaction within the couple. We aimed to describe paternal psychological responses shortly after detection of structural fetal anomalies by ultrasonography, and to compare paternal and maternal responses within the same couple. Methods A prospective observational study was performed at a tertiary referral centre for fetal medicine. Pregnant women with a structural fetal anomaly detected by ultrasound and their partners (study group,n=155) and 100 with normal ultrasound findings (comparison group) were included shortly after sonographic examination (inclusion period: May 2006-February 2009). Gestational age was >12 weeks. We used psychometric questionnaires to assess self-reported social dysfunction, health perception, and psychological distress (intrusion, avoidance, arousal, anxiety, and depression): Impact of Event Scale. General Health Questionnaire and Edinburgh Postnatal Depression Scale. Fetal anomalies were classified according to severity and diagnostic or prognostic ambiguity at the time of assessment. Results Median (range) gestational age at inclusion in the study and comparison group was 19 (12–38) and 19 (13–22) weeks, respectively. Men and women in the study group had significantly higher levels of psychological distress than men and women in the comparison group on all psychometric endpoints. The lowest level of distress in the study group was associated with the least severe anomalies with no diagnostic or prognostic ambiguity (p < 0.033). Men had lower scores than women on all psychometric

  10. 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.

  11. 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

  12. 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.

  13. ISHM Anomaly Lexicon for Rocket Test

    NASA Technical Reports Server (NTRS)

    Schmalzel, John L.; Buchanan, Aubri; Hensarling, Paula L.; Morris, Jonathan; Turowski, Mark; Figueroa, Jorge F.

    2007-01-01

    Integrated Systems Health Management (ISHM) is a comprehensive capability. An ISHM system must detect anomalies, identify causes of such anomalies, predict future anomalies, help identify consequences of anomalies for example, suggested mitigation steps. The system should also provide users with appropriate navigation tools to facilitate the flow of information into and out of the ISHM system. Central to the ability of the ISHM to detect anomalies is a clearly defined catalog of anomalies. Further, this lexicon of anomalies must be organized in ways that make it accessible to a suite of tools used to manage the data, information and knowledge (DIaK) associated with a system. In particular, it is critical to ensure that there is optimal mapping between target anomalies and the algorithms associated with their detection. During the early development of our ISHM architecture and approach, it became clear that a lexicon of anomalies would be important to the development of critical anomaly detection algorithms. In our work in the rocket engine test environment at John C. Stennis Space Center, we have access to a repository of discrepancy reports (DRs) that are generated in response to squawks identified during post-test data analysis. The DR is the tool used to document anomalies and the methods used to resolve the issue. These DRs have been generated for many different tests and for all test stands. The result is that they represent a comprehensive summary of the anomalies associated with rocket engine testing. Fig. 1 illustrates some of the data that can be extracted from a DR. Such information includes affected transducer channels, narrative description of the observed anomaly, and the steps used to correct the problem. The primary goal of the anomaly lexicon development efforts we have undertaken is to create a lexicon that could be used in support of an associated health assessment database system (HADS) co-development effort. There are a number of significant

  14. Para-GMRF: parallel algorithm for anomaly detection of hyperspectral image

    NASA Astrophysics Data System (ADS)

    Dong, Chao; Zhao, Huijie; Li, Na; Wang, Wei

    2007-12-01

    The hyperspectral imager is capable of collecting hundreds of images corresponding to different wavelength channels for the observed area simultaneously, which make it possible to discriminate man-made objects from natural background. However, the price paid for the wealthy information is the enormous amounts of data, usually hundreds of Gigabytes per day. Turning the huge volume data into useful information and knowledge in real time is critical for geoscientists. In this paper, the proposed parallel Gaussian-Markov random field (Para-GMRF) anomaly detection algorithm is an attempt of applying parallel computing technology to solve the problem. Based on the locality of GMRF algorithm, we partition the 3-D hyperspectral image cube in spatial domain and distribute data blocks to multiple computers for concurrent detection. Meanwhile, to achieve load balance, a work pool scheduler is designed for task assignment. The Para-GMRF algorithm is organized in master-slave architecture, coded in C programming language using message passing interface (MPI) library and tested on a Beowulf cluster. Experimental results show that Para-GMRF algorithm successfully conquers the challenge and can be used in time sensitive areas, such as environmental monitoring and battlefield reconnaissance.

  15. The architecture of a network level intrusion detection system

    SciTech Connect

    Heady, R.; Luger, G.; Maccabe, A.; Servilla, M.

    1990-08-15

    This paper presents the preliminary architecture of a network level intrusion detection system. The proposed system will monitor base level information in network packets (source, destination, packet size, and time), learning the normal patterns and announcing anomalies as they occur. The goal of this research is to determine the applicability of current intrusion detection technology to the detection of network level intrusions. In particular, the authors are investigating the possibility of using this technology to detect and react to worm programs.

  16. Possibility of detecting triple gluon coupling and Adler-Bell-Jackiw anomaly in polarized deep inelastic scattering

    SciTech Connect

    Lam, C.S.; Li, B.A.

    1980-05-01

    A way to detect experimentally the existence of triple gluon coupling and the Adler-Bell-Jackiw anomaly is to measure the Q/sup 2/-dependence of polarized deep inelastic scattering. These effects lead to a ln ln Q/sup 2/ term which we calculate by introducing a new gluon operator in the Wilson expansion.

  17. Rapid detection and classification of airborne time-domain electromagnetic anomalies using weighted multi-linear regression

    NASA Astrophysics Data System (ADS)

    Claprood, Maxime; Chouteau, Michel; Cheng, Li Zhen

    2008-10-01

    We propose a rapid and efficient methodology for the detection and interpretation of airborne time-domain electromagnetic anomalies generated by thin sheet-like volcanogenic massive sulphides (VMS) deposits in a resistive environment, which are representative of VMS deposits in the Canadian Shield.

  18. 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.

  19. Dysplasia of the atrioventricular valves associated with conduction system anomalies.

    PubMed Central

    Daliento, L; Nava, A; Fasoli, G; Mazzucco, A; Thiene, G

    1984-01-01

    Clinical, vectorcardiographic, and echocardiographic data from two siblings with atrial septal defects and dysplasia of the mitral and tricuspid valves are reported. Vectorcardiograms showed that both siblings had abnormal ventricular activation with initial electrical forces directed posteriorly. One sibling died after surgery, and necropsy showed incomplete differentiation of the leaflets and tensor apparatus producing anomalies resembling "mitral arcade." Serial histological examination of the conducting tissue showed that the atrioventricular node was located on the left side of the atrial septum, that the central fibrous body and the membranous septum were hypoplastic, and that an accessory nodoventricular pathway originating in the compact node joined the left side of the ventricular septum. This accessory pathway was probably the cause of the unusual ventricular activation. Dysplasia of the mitral and tricuspid valves together with hypoplasia of the central fibrous body and the presence of accessory pathways are probably part of a malformative complex caused by incomplete differentiation of both the cardiac atrioventricular valves and the junctional area. Images PMID:6696801

  20. 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.

  1. 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

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

    PubMed

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

    2016-06-13

    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%.

  3. 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

  4. 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.

  5. 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.

  6. Acoustic leak detection system

    SciTech Connect

    Peacock, M.J.

    1993-08-03

    An acoustic leak detection system is described for determining the location of leaks in storage tanks, comprising: (a) sensor means for detecting a leak signal; (b) data acquisition means for digitizing and storing leak signals meeting preset criterion; and (c) analysis means for analyzing the digitized signals and computing the location of the source of the leak signals.

  7. Idaho Explosive Detection System

    ScienceCinema

    Klinger, Jeff

    2016-07-12

    Learn how INL researchers are making the world safer by developing an explosives detection system that can inspect cargo. For more information about INL security research, visit http://www.facebook.com/idahonationallaboratory

  8. Idaho Explosive Detection System

    SciTech Connect

    Klinger, Jeff

    2011-01-01

    Learn how INL researchers are making the world safer by developing an explosives detection system that can inspect cargo. For more information about INL security research, visit http://www.facebook.com/idahonationallaboratory

  9. Underwater laser detection system

    NASA Astrophysics Data System (ADS)

    Gomaa, Walid; El-Sherif, Ashraf F.; El-Sharkawy, Yasser H.

    2015-02-01

    The conventional method used to detect an underwater target is by sending and receiving some form of acoustic energy. But the acoustic systems have limitations in the range resolution and accuracy; while, the potential benefits of a laserbased underwater target detection include high directionality, high response, and high range accuracy. Lasers operating in the blue-green region of the light spectrum(420 : 570nm)have a several applications in the area of detection and ranging of submersible targets due to minimum attenuation through water ( less than 0.1 m-1) and maximum laser reflection from estimated target (like mines or submarines) to provide a long range of detection. In this paper laser attenuation in water was measured experimentally by new simple method by using high resolution spectrometer. The laser echoes from different targets (metal, plastic, wood, and rubber) were detected using high resolution CCD camera; the position of detection camera was optimized to provide a high reflection laser from target and low backscattering noise from the water medium, digital image processing techniques were applied to detect and discriminate the echoes from the metal target and subtract the echoes from other objects. Extraction the image of target from the scattering noise is done by background subtraction and edge detection techniques. As a conclusion, we present a high response laser imaging system to detect and discriminate small size, like-mine underwater targets.

  10. Bro Intrusion Detection System

    SciTech Connect

    Paxson, Vern; Campbell, Scott; leres, Craig; Lee, Jason

    2006-01-25

    Bro is a Unix-based Network Intrusion Detection System (IDS). Bro monitors network traffic and detects intrusion attempts based on the traffic characteristics and content. Bro detects intrusions by comparing network traffic against rules describing events that are deemed troublesome. These rules might describe activities (e.g., certain hosts connecting to certain services), what activities are worth alerting (e.g., attempts to a given number of different hosts constitutes a "scan"), or signatures describing known attacks or access to known vulnerabilities. If Bro detects something of interest, it can be instructed to either issue a log entry or initiate the execution of an operating system command. Bro targets high-speed (Gbps), high-volume intrusion detection. By judiciously leveraging packet filtering techniques, Bro is able to achieve the performance necessary to do so while running on commercially available PC hardware, and thus can serve as a cost effective means of monitoring a site’s Internet connection.

  11. 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

  12. 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.

  13. Portable pathogen detection system

    SciTech Connect

    Colston, Billy W.; Everett, Matthew; Milanovich, Fred P.; Brown, Steve B.; Vendateswaran, Kodumudi; Simon, Jonathan N.

    2005-06-14

    A portable pathogen detection system that accomplishes on-site multiplex detection of targets in biological samples. The system includes: microbead specific reagents, incubation/mixing chambers, a disposable microbead capture substrate, and an optical measurement and decoding arrangement. The basis of this system is a highly flexible Liquid Array that utilizes optically encoded microbeads as the templates for biological assays. Target biological samples are optically labeled and captured on the microbeads, which are in turn captured on an ordered array or disordered array disposable capture substrate and then optically read.

  14. Solar system fault detection

    NASA Astrophysics Data System (ADS)

    Farrington, R. B.; Pruett, J. C., Jr.

    1984-05-01

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combing the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  15. Solar system fault detection

    DOEpatents

    Farrington, Robert B.; Pruett, Jr., James C.

    1986-01-01

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  16. Solar system fault detection

    DOEpatents

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  17. 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.

  18. Detecting low Velocity Anomalies Combining Seismic Reflection With First Arrival Seismic Tomography

    NASA Astrophysics Data System (ADS)

    Flecha, I.; Marti, D.; Carbonell, R.

    2002-12-01

    In the present study seismic reflection techniques and high resolution seismic tomography are combined to determine location and geometry of shallow low velocity anomalies. Underground cavities (mines), water flows (formation with loose sand), etc. are geologic features characterized by slow seismic velocities and are targets of considerable social interest. Theoretical considerations (Snell's law) suggest that low velocity anomalies are undersampled and therefore badly resolved by ray tracing methods. A series of synthetics simulations have been carried out to asses the resolving power of the different methodologies. A 400mx50m two dimensional velocity model consisting of a background velocity gradient in depth from 3000 to 4000 m/s which included a rectangular low velocity anomaly (300 m/s). This anomaly was placed between 10m and 30m in depth and between 180m and 220m in length. The synthetic data calculation and the tomographic inversion have been done with absolutely independent programs. The data has been created using a 2D finite differences wave propagation acoustic algorithm. The tomographic inversion has been performed using two different software packages. The first one uses a combination of ray tracing a finite differences schemes to estimate the forward problem and an iterative conjugate gradient matrix solver to calculate the inverse. The second software package uses a modified Vidale scheme (Eikonal equation) to solve the forward problem and a LSQR to solve the inverse problem. The synthetic data were used for the inversions and for the generation of a conventional stacked section simulating a high resolution seismic reflection transect along the velocity model. The conventional stack images the diffractions caused by the velocity anomaly, which provided the location and extent of the low velocity anomaly. The inversions schemes provided estimates of the velocities, however, the tomograms and the ray tracing diagrams indicated a low resolution for

  19. Idaho Explosives Detection System

    SciTech Connect

    Edward L. Reber; J. Keith Jewell; Larry G. Blackwood; Andrew J. Edwards; Kenneth W. Rohde; Edward H. Seabury

    2004-10-01

    The Idaho Explosives Detection System (IEDS) was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-minute measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004.

  20. Damage detection in initially nonlinear systems

    SciTech Connect

    Bornn, Luke; Farrar, Charles; Park, Gyuhae

    2009-01-01

    The primary goal of Structural Health Monitoring (SHM) is to detect structural anomalies before they reach a critical level. Because of the potential life-safety and economic benefits, SHM has been widely studied over the past decade. In recent years there has been an effort to provide solid mathematical and physical underpinnings for these methods; however, most focus on systems that behave linearly in their undamaged state - a condition that often does not hold in complex 'real world' systems and systems for which monitoring begins mid-lifecycle. In this work, we highlight the inadequacy of linear-based methodology in handling initially nonlinear systems. We then show how the recently developed autoregressive support vector machine (AR-SVM) approach to time series modeling can be used for detecting damage in a system that exhibits initially nonlinear response. This process is applied to data acquired from a structure with induced nonlinearity tested in a laboratory environment.

  1. Detecting Anomalous Insiders in Collaborative Information Systems

    PubMed Central

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients’ records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients’ diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent. PMID:24489520

  2. Detecting Anomalous Insiders in Collaborative Information Systems.

    PubMed

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-05-01

    Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients' records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients' diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent.

  3. 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.

  4. 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.

  5. Water system virus detection

    NASA Technical Reports Server (NTRS)

    Fraser, A. S.; Wells, A. F.; Tenoso, H. J.

    1975-01-01

    A monitoring system developed to test the capability of a water recovery system to reject the passage of viruses into the recovered water is described. A nonpathogenic marker virus, bacteriophage F2, is fed into the process stream before the recovery unit and the reclaimed water is assayed for its presence. Detection of the marker virus consists of two major components, concentration and isolation of the marker virus, and detection of the marker virus. The concentration system involves adsorption of virus to cellulose acetate filters in the presence of trivalent cations and low pH with subsequent desorption of the virus using volumes of high pH buffer. The detection of the virus is performed by a passive immune agglutination test utilizing specially prepared polystyrene particles. An engineering preliminary design was performed as a parallel effort to the laboratory development of the marker virus test system. Engineering schematics and drawings of a fully functional laboratory prototype capable of zero-G operation are presented. The instrument consists of reagent pump/metering system, reagent storage containers, a filter concentrator, an incubation/detector system, and an electronic readout and control system.

  6. 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.

  7. 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

  8. Transmission resonances anomaly in one-dimensional disordered quantum systems

    NASA Astrophysics Data System (ADS)

    Eisenbach, A.; Bliokh, Y.; Freilkher, V.; Kaveh, M.; Berkovits, R.

    2016-07-01

    Connections between the electronic eigenstates and conductivity of one-dimensional (1D) disordered systems is studied in the framework of the tight-binding model. We show that for weak disorder only part of the states exhibit resonant transmission and contribute to the conductivity. The rest of the eigenvalues are not associated with peaks in transmission and the amplitudes of their wave functions do not exhibit a significant maxima within the sample. Moreover, unlike ordinary states, the lifetimes of these "hidden" modes either remain constant or even decrease (depending on the coupling with the leads) as the disorder becomes stronger. In a wide range of the disorder strengths, the averaged ratio of the number of transmission peaks to the total number of the eigenstates is independent of the degree of disorder and is close to the value √{2 /5 }, which was derived analytically in the weak-scattering approximation. These results are in perfect analogy to the spectral and transport properties of light in one-dimensional randomly inhomogeneous media [Y. P. Bliokh et al., New J. Phys. 17, 113009 (2015), 10.1088/1367-2630/17/11/113009], which provides strong grounds to believe that the existence of hidden, nonconducting modes is a general phenomenon inherent to 1D open random systems, and their fraction of the total density of states is the same for quantum particles and classical waves.

  9. DETECTION OR WARNING SYSTEM

    DOEpatents

    Tillman, J E

    1953-10-20

    This patent application describes a sensitive detection or protective system capable of giving an alarm or warning upon the entrance or intrusion of any body into a defined area or zone protected by a radiation field of suitable direction or extent.

  10. Bro Intrusion Detection System

    2006-01-25

    Bro is a Unix-based Network Intrusion Detection System (IDS). Bro monitors network traffic and detects intrusion attempts based on the traffic characteristics and content. Bro detects intrusions by comparing network traffic against rules describing events that are deemed troublesome. These rules might describe activities (e.g., certain hosts connecting to certain services), what activities are worth alerting (e.g., attempts to a given number of different hosts constitutes a "scan"), or signatures describing known attacks or accessmore » to known vulnerabilities. If Bro detects something of interest, it can be instructed to either issue a log entry or initiate the execution of an operating system command. Bro targets high-speed (Gbps), high-volume intrusion detection. By judiciously leveraging packet filtering techniques, Bro is able to achieve the performance necessary to do so while running on commercially available PC hardware, and thus can serve as a cost effective means of monitoring a site’s Internet connection.« less

  11. Gasometric anomalies in bottom sediments of the Barents Sea as instrument of Modern Petroleum System study

    NASA Astrophysics Data System (ADS)

    Fokina, A.; Akhmanov, G.; Andreassen, K.; Yurchenko, A.

    2014-12-01

    Southern Barents Sea no gas anomalies were detected: low gas concentrations, the gas is of biogenic origin. Geochemical survey within North- Kildinsk field and Fedynskii high were unsuccessful. Petroleum system in the surface geochemical field practically do not manifest due to the low permeability of dense clay silts.

  12. Radiation detection system

    DOEpatents

    Nelson, Melvin A.; Davies, Terence J.; Morton, III, John R.

    1976-01-01

    A radiation detection system which utilizes the generation of Cerenkov light in and the transmission of that light longitudinally through fiber optic wave guides in order to transmit intelligence relating to the radiation to a remote location. The wave guides are aligned with respect to charged particle radiation so that the Cerenkov light, which is generated at an angle to the radiation, is accepted by the fiber for transmission therethrough. The Cerenkov radiation is detected, recorded, and analyzed at the other end of the fiber.

  13. 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.

  14. Persistent left superior vena cava, absence of the innominate vein, and upper sinus venosus defect : a rare anomaly detected using bubbles.

    PubMed

    Akpinar, I; Sayin, M R; Karabag, T; Dogan, S M; Sen, S T; Gudul, N E; Aydin, M

    2013-05-01

    Superior vena cava anomalies are rare malformations that are typically seen with other congenital cardiac defects. Although a persistent left superior vena cava is the most common anomaly of the systemic venous return in the thorax, its combination with an upper sinus venosus defect and absence of the innominate vein is extremely rare. Here, we report a patient diagnosed with these anomalies based on a bubble study and confirmed with magnetic resonance imaging.

  15. Mesoscale convective system surface pressure anomalies responsible for meteotsunamis along the U.S. East Coast on June 13th, 2013

    PubMed Central

    Wertman, Christina A.; Yablonsky, Richard M.; Shen, Yang; Merrill, John; Kincaid, Christopher R.; Pockalny, Robert A.

    2014-01-01

    Two destructive high-frequency sea level oscillation events occurred on June 13th, 2013 along the U.S. East Coast. Seafloor processes can be dismissed as the sources, as no concurrent offshore earthquakes or landslides were detected. Here, we present evidence that these tsunami-like events were generated by atmospheric mesoscale convective systems (MCSs) propagating from inland to offshore. The USArray Transportable Array inland and NOAA tide gauges along the coast recorded the pressure anomalies associated with the MCSs. Once offshore, the pressure anomalies generated shallow water waves, which were amplified by the resonance between the water column and atmospheric forcing. Analysis of the tidal data reveals that these waves reflected off the continental shelf break and reached the coast, where bathymetry and coastal geometry contributed to their hazard potential. This study demonstrates that monitoring MCS pressure anomalies in the interior of the U.S. provides important observations for early warnings of MCS-generated tsunamis. PMID:25420958

  16. Mesoscale convective system surface pressure anomalies responsible for meteotsunamis along the U.S. East Coast on June 13th, 2013.

    PubMed

    Wertman, Christina A; Yablonsky, Richard M; Shen, Yang; Merrill, John; Kincaid, Christopher R; Pockalny, Robert A

    2014-01-01

    Two destructive high-frequency sea level oscillation events occurred on June 13th, 2013 along the U.S. East Coast. Seafloor processes can be dismissed as the sources, as no concurrent offshore earthquakes or landslides were detected. Here, we present evidence that these tsunami-like events were generated by atmospheric mesoscale convective systems (MCSs) propagating from inland to offshore. The USArray Transportable Array inland and NOAA tide gauges along the coast recorded the pressure anomalies associated with the MCSs. Once offshore, the pressure anomalies generated shallow water waves, which were amplified by the resonance between the water column and atmospheric forcing. Analysis of the tidal data reveals that these waves reflected off the continental shelf break and reached the coast, where bathymetry and coastal geometry contributed to their hazard potential. This study demonstrates that monitoring MCS pressure anomalies in the interior of the U.S. provides important observations for early warnings of MCS-generated tsunamis. PMID:25420958

  17. A local proton irradiation model for isotopic anomalies in the solar system

    NASA Technical Reports Server (NTRS)

    Lee, T.

    1978-01-01

    An attempt is made to explain the O-16 and Al-26 anomalies observed in solar-system bodies in the framework of a local irradiation model wherein a small amount of solar system matter of normal isotopic composition was irradiated by energetic protons from the primeval sun. Several isotopic constraints are summarized with which the model should be consistent, and a proton energy distribution and fluence and a target elemental composition are chosen such that the extraordinary component produced by irradiation satisfies the constraints. Detailed attention is given to the relevant oxygen reactions, Al-26 production, and effects of proton irradiation on isotopes of Mg, Ca, and Ba. A scenario is outlined which satisfies all the constraints. Consequences of the model are discussed with respect to the isotopic anomalies observed in Allende inclusions.

  18. 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.

  19. Low-field anomaly of the hall effect in disordered two-dimensional systems

    SciTech Connect

    Germanenko, A. V.; Minkov, G. M.; Rut, O. E.; Soldatov, I. V.; Sherstobitov, A. A.

    2010-11-15

    This study is devoted to investigation of the nonlinear behavior of the Hall resistance in low magnetic fields. When investigating two-dimensional electron gas in single GaAs/In{sub x}Ga{sub 1-x}As/GaAs quantum wells, it is shown that the anomaly of the Hall effect in disordered systems can be described taking into account the second-order quantum corrections to conductivity.

  20. Water system virus detection

    NASA Technical Reports Server (NTRS)

    Fraser, A. S.; Wells, A. F.; Tenoso, H. J. (Inventor)

    1978-01-01

    The performance of a waste water reclamation system is monitored by introducing a non-pathogenic marker virus, bacteriophage F2, into the waste-water prior to treatment and, thereafter, testing the reclaimed water for the presence of the marker virus. A test sample is first concentrated by absorbing any marker virus onto a cellulose acetate filter in the presence of a trivalent cation at low pH and then flushing the filter with a limited quantity of a glycine buffer solution to desorb any marker virus present on the filter. Photo-optical detection of indirect passive immune agglutination by polystyrene beads indicates the performance of the water reclamation system in removing the marker virus. A closed system provides for concentrating any marker virus, initiating and monitoring the passive immune agglutination reaction, and then flushing the system to prepare for another sample.

  1. Ultrasonic Leak Detection System

    NASA Technical Reports Server (NTRS)

    Youngquist, Robert C. (Inventor); Moerk, J. Steven (Inventor)

    1998-01-01

    A system for detecting ultrasonic vibrations. such as those generated by a small leak in a pressurized container. vessel. pipe. or the like. comprises an ultrasonic transducer assembly and a processing circuit for converting transducer signals into an audio frequency range signal. The audio frequency range signal can be used to drive a pair of headphones worn by an operator. A diode rectifier based mixing circuit provides a simple, inexpensive way to mix the transducer signal with a square wave signal generated by an oscillator, and thereby generate the audio frequency signal. The sensitivity of the system is greatly increased through proper selection and matching of the system components. and the use of noise rejection filters and elements. In addition, a parabolic collecting horn is preferably employed which is mounted on the transducer assembly housing. The collecting horn increases sensitivity of the system by amplifying the received signals. and provides directionality which facilitates easier location of an ultrasonic vibration source.

  2. 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.

  3. Lessons Learned from the Space Shuttle Engine Cutoff System (ECO) Anomalies

    NASA Technical Reports Server (NTRS)

    Martinez, Hugo E.; Welzyn, Ken

    2011-01-01

    The Space Shuttle Orbiter's main engine cutoff (ECO) system first failed ground checkout in April, 2005 during a first tanking test prior to Return-to-Flight. Despite significant troubleshooting and investigative efforts that followed, the root cause could not be found and intermittent anomalies continued to plague the Program. By implementing hardware upgrades, enhancing monitoring capability, and relaxing the launch rules, the Shuttle fleet was allowed to continue flying in spite of these unexplained failures. Root cause was finally determined following the launch attempts of STS-122 in December, 2007 when the anomalies repeated, which allowed drag-on instrumentation to pinpoint the fault (the ET feedthrough connector). The suspect hardware was removed and provided additional evidence towards root cause determination. Corrective action was implemented and the system has performed successfully since then. This white paper presents the lessons learned from the entire experience, beginning with the anomalies since Return-to-Flight through discovery and correction of the problem. To put these lessons in better perspective for the reader, an overview of the ECO system is presented first. Next, a chronological account of the failures and associated investigation activities is discussed. Root cause and corrective action are summarized, followed by the lessons learned.

  4. 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

  5. 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.

  6. Arc fault detection system

    DOEpatents

    Jha, K.N.

    1999-05-18

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

  7. Arc fault detection system

    DOEpatents

    Jha, Kamal N.

    1999-01-01

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

  8. Mesosiderite clasts with the most extreme positive europium anomalies among solar system rocks.

    PubMed

    Mittlefehldt, D W; Rubin, A E; Davis, A M

    1992-08-21

    Pigeonite-plagioclase gabbros that occur as clasts in mesosiderites (brecciated stony-iron meteorites) show extreme fractionations of the rare-earth elements (REEs) with larger positive europium anomalies than any previously known for igneous rocks from the Earth, moon, or meteorite parent bodies and greater depletions of light REEs relative to heavy REEs than known for comparable cumulate gabbros. The REE pattern for merrillite in one of these clasts is depleted in light REEs and has a large positive europium anomaly as a result of metamorphic equilibration with the silicates. The extreme REE ratios exhibited by the mesosiderite clasts demonstrate that multistage igneous processes must have occurred on some asteroids in the early solar system. Melting of the crust by large-scale impacts or electrical induction from an early T-Tauri-phase sun may be responsible for these processes.

  9. Mesosiderite clasts with the most extreme positive europium anomalies among solar system rocks

    NASA Technical Reports Server (NTRS)

    Mittlefehldt, David W.; Rubin, Alan E.; Davis, Andrew M.

    1992-01-01

    Pigeonite-plagioclase gabbros that occur as clasts in mesosiderites (brecciated stony-iron meteorites) show extreme fractionations of the rare-earth elements (REEs) with larger positive europium anomalies than any previously known for igneous rocks from the earth, moon, or meteorite parent bodies and greater depletions of light REEs relative to heavy REEs than known for comparable cumulate gabbros. The REE pattern for merrillite in one of these clasts is depleted in light REEs and has a large positive europium anomaly as a result of metamorphic equilibration with the silicates. The extreme REE ratios exhibited by the mesosiderite clasts demonstrate that multistage igneous processes must have occurred on some asteroids in the early solar system. Melting of the crust by large-scale impacts or electrical induction from an early T-Tauri-phase sun may be responsible for these processes.

  10. Neonatal head ultrasound: systematic approach to congenital Central Nervous System anomalies. A pictorial essay.

    PubMed

    Yoon, Hye-Kyung; Cho, Seong Whi

    2016-09-01

    Brain ultrasound is widely used for the screening of prematurely born babies. Although the best imaging modality for the central nervous system anomaly is brain MRI, the first imaging study in the post-natal period is brain ultrasonography in most cases. Anomalies could be found incidentally on screening ultrasound, or in those cases already suspected on prenatal ultrasound. In order not to miss congenital structural abnormalities of the brain on screening ultrasound, systematic approaches would be very helpful. The ventricles and sylvian fissures are very important structures to suspect central nervous system anomalies: they are symmetric structures so we should look for any asymmetry or maldevelopment. And then, on sagittal images, the midline structures including the corpus callosum and cerebellar vermis should be observed carefully. Finally, we should look for any abnormality in gyration or cortical development. Skull defect with herniation of intracranial contents, a spectrum of encephalo-meningocele, could be also identified on ultrasound. Congenital infections such as cytomegalovirus infection may show ventriculomegaly and malformation of the cortical development on imaging studies.

  11. Neonatal head ultrasound: systematic approach to congenital Central Nervous System anomalies. A pictorial essay.

    PubMed

    Yoon, Hye-Kyung; Cho, Seong Whi

    2016-09-01

    Brain ultrasound is widely used for the screening of prematurely born babies. Although the best imaging modality for the central nervous system anomaly is brain MRI, the first imaging study in the post-natal period is brain ultrasonography in most cases. Anomalies could be found incidentally on screening ultrasound, or in those cases already suspected on prenatal ultrasound. In order not to miss congenital structural abnormalities of the brain on screening ultrasound, systematic approaches would be very helpful. The ventricles and sylvian fissures are very important structures to suspect central nervous system anomalies: they are symmetric structures so we should look for any asymmetry or maldevelopment. And then, on sagittal images, the midline structures including the corpus callosum and cerebellar vermis should be observed carefully. Finally, we should look for any abnormality in gyration or cortical development. Skull defect with herniation of intracranial contents, a spectrum of encephalo-meningocele, could be also identified on ultrasound. Congenital infections such as cytomegalovirus infection may show ventriculomegaly and malformation of the cortical development on imaging studies. PMID:27622417

  12. Quantitative Integration of Multiple Geophysical Techniques for Reducing Uncertainty in Discrete Anomaly Detection

    NASA Astrophysics Data System (ADS)

    Carr, M. C.; Baker, G. S.; Herrmann, N.; Yerka, S.; Angst, M.

    2008-12-01

    The objectives of this project are to (1) utilize quantitative integration of multiple geophysical techniques, (2) determine geophysical anomalies that may indicate locations of various archaeological structures, and (3) develop techniques of quantifying causes of uncertainty. Two sites are used to satisfy these objectives. The first, representing a site with unknown target features, is an archaeological site on the Tennessee River floodplain. The area is divided into 437 (20 x 20 m) plots with 0.5 m spacing where magnetic gradiometry profiles were collected in a zig-zag pattern, resulting in 350 km of line data. Once anomalies are identified in the magnetics data, potential excavation sites for archeological features are determined and other geophysical techniques are utilized to gain confidence in choosing which anomalies to excavate. Several grids are resurveyed using Ground Penetrating Radar (GPR) and EM-31 with a 0.25 m spacing in a grid pattern. A quantitative method of integrating data into one comprehensive set is developed, enhancing interpretation because each geophysical technique utilized within this study produced a unique response to noise and the targets. Spatial visualization software is used to interpolate irregularly spaced XYZ data into a regularly spaced grid and display the geophysical data in 3D representations. Once all data are exported from each individual instrument, grid files are created for quantitative merging of the data and to create grid-based maps including contour, image, shaded relief, and surface maps. Statistics were calculated from anomaly classification in the data and excavated features present. To study this methodology in a more controlled setting, a second site is used. This site is analogous to the first in that it is along the Tennessee River floodplain on the same bedrock units. However, this analog site contains known targets (previously buried and accurately located) including size, shape, and orientation. Four

  13. Hydrocarbon anomaly in soil gas as near-surface expressions of upflows and outflows in geothermal systems

    SciTech Connect

    Ong, H.L.; Higashihara, M.; Klusman, R.W.; Voorhees, K.J.; Pudjianto, R.; Ong, J

    1996-01-24

    A variety of hydrocarbons, C1 - C12, have been found in volcanic gases (fumarolic) and in geothermal waters and gases. The hydrocarbons are thought to have come from products of pyrolysis of kerogen in sedimentary rocks or they could be fed into the geothermal system by the recharging waters which may contain dissolved hydrocarbons or hydrocarbons extracted by the waters from the rocks. In the hot geothermal zone, 300°+ C, many of these hydrocarbons are in their critical state. It is thought that they move upwards due to buoyancy and flux up with the upflowing geothermal fluids in the upflow zones together with the magmatic gases. Permeability which could be provided by faults, fissures, mini and micro fractures are thought to provide pathways for the upward flux. A sensitive technique (Petrex) utilizing passive integrative adsorption of the hydrocarbons in soil gas on activated charcoal followed by desorption and analysis of the hydrocarbons by direct introduction mass spectrometry allows mapping of the anomalous areas. Surveys for geothermal resources conducted in Japan and in Indonesia show that the hydrocarbon anomaly occur over known fields and over areas strongly suspected of geothermal potential. The hydrocarbons found and identified were n-paraffins (C7-C9) and aromatics (C7-C8). Detection of permeable, i.e. active or open faults, parts of older faults which have been reactivated, e.g. by younger intersecting faults, and the area surrounding these faulted and permeable region is possible. The mechanism leading to the appearance of the hydrocarbon in the soil gas over upflow zones of the geothermal reservoir is proposed. The paraffins seems to be better pathfinders for the location of upflows than the aromatics. However the aromatics may, under certain circumstances, give better indications of the direction of the outflow of the geothermal system. It is thought that an upflow zone can be

  14. Pattern of central nervous system anomalies in a population with a high rate of consanguineous marriages.

    PubMed

    Al-Gazali, L I; Sztriha, L; Dawodu, A; Bakir, M; Varghese, M; Varady, E; Scorer, J; Abdulrazzaq, Y M; Bener, A; Padmanabhan, R

    1999-02-01

    Nine thousand six hundred and ten births were prospectively studied in the three major hospitals in Al-Ain, United Arab Emirates (UAE) between October 1995 and January 1997. Babies suspected of, or diagnosed, as having central nervous system (CNS) abnormalities were evaluated by a neonatologist, a clinical geneticist and a pediatric neurologist. Brain computerized tomography/magnetic resonance imaging (CT/MRI) was performed on all babies suspected of having CNS abnormalities. In addition, metabolic screening and chromosome analysis were also performed when indicated. Of the 225 babies with congenital anomalies identified, 31 had CNS abnormalities (3.2/1000). Syndromic abnormalities of the CNS were present in 13 cases (42%), chromosomal abnormalities in one case (3.2%) and the rest included: neural tube defect (NTD) in 11 cases (36%), holoprosencephaly in two cases (6.4%) and hydrocephalus in four cases (12.9%). Detailed analysis of the syndromic types revealed that out of the 13 cases, 12 were inherited as autosomal recessive (AR) and in one case the inheritance was undetermined. Consanguinity with high level of inbreeding was present in 12 cases and the majority of the syndromes identified were extremely rare. The study indicates that CNS anomalies are fairly common in the UAE, particularly, the recessive syndromic types. Careful and detailed analysis of such anomalies is required so that accurate genetic advice can be given. PMID:10189086

  15. Anomalies of cardiac venous drainage associated with abnormalities of cardiac conduction system.

    PubMed

    Morgan, D R; Hanratty, C G; Dixon, L J; Trimble, M; O'Keeffe, D B

    2002-07-01

    The embryological development of the superior vena cava (SVC) is complex. If the left common cardinal vein fails to occlude it can, along with the left duct of Cuvier form a left SVC, which frequently drains into the coronary sinus. This may result in abnormalities in the anatomy of this structure. A persistent left SVC occurs in 0.5% of the normal population, and 3% to 4.3% of patients with congenital heart anomalies. The pacemaking tissue of the heart is derived from two sites near the progenitors of the superior vena cava. The right-sided site forms the sinoatrial node, the left-sided site is normally carried down to an area near the coronary sinus. Out of 300 patients with cardiac rhythm abnormalities, who have undergone electrophysiological studies (EPS), or permanent pacemaker insertion (PPI), we identified 12 patients with cardiac conduction abnormalities and anomalies of venous drainage. Anomalies of the coronary sinus may be associated with abnormalities of the conduction system of the heart. This may be due to the close proximity of the coronary sinus to the final position of the left-sided primitive pacemaking tissue. In our series of 300 patients, 4% had an associated left SVC, a similar incidence to that found in previous studies of congenital heart disease.

  16. Unified Mars detection system. [life detection

    NASA Technical Reports Server (NTRS)

    Martin, J. P.; Kok, B.; Radmer, R.; Johnson, R. D.

    1976-01-01

    A life-detection system is described which is designed to detect and characterize possible Martian biota and to gather information about the chemical environment of Mars, especially the water and amino acid contents of the soil. The system is organized around a central mass spectrometer that can sensitively analyze trace gases from a variety of different experiments. Some biological assays and soil-chemistry tests that have been performed in the laboratory as typical experiment candidates for the system are discussed, including tests for soil-organism metabolism, measurements of soil carbon contents, and determinations of primary aliphatic amines (amino acids and protein) in soils. Two possible test strategies are outlined, and the operational concept of the detection system is illustrated. Detailed descriptions are given for the mass spectrometer, gas inlet, incubation box, test cell modules, seal drive mechanism, soil distribution assembly, and electronic control system.

  17. Decreased Perifoveal Sensitivity Detected by Microperimetry in Patients Using Hydroxychloroquine and without Visual Field and Fundoscopic Anomalies

    PubMed Central

    Molina-Martín, A.; Piñero, D. P.; Pérez-Cambrodí, R. J.

    2015-01-01

    Purpose. To evaluate the usefulness of microperimetry in the early detection of the ocular anomalies associated with the use of hydroxychloroquine. Methods. Prospective comparative case series study comprising 14 healthy eyes of 7 patients (group A) and 14 eyes of 7 patients under treatment with hydroxychloroquine for the treatment of rheumatologic diseases and without fundoscopic or perimetric anomalies (group B). A comprehensive ophthalmological examination including microperimetry (MP) and spectral-domain optical coherence tomography was performed in both groups. Results. No significant differences were found in mean MP foveal sensitivity between groups (P = 0.18). However, mean MP overall sensitivity was significantly higher in group A (29.05 ± 0.57 dB versus group B, 26.05 ± 2.75 dB; P < 0.001). Significantly higher sensitivity values were obtained in group A in comparison to group B for the three eccentric loci evaluated (P < 0.001). Conclusion. Microperimetry seems to be a useful tool for the early detection of retinal damage in patients treated with hydroxychloroquine. PMID:25861463

  18. 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.

  19. First integrals of motion in a gauge covariant framework, Killing-Maxwell system and quantum anomalies

    SciTech Connect

    Visinescu, M.

    2012-10-15

    Hidden symmetries in a covariant Hamiltonian framework are investigated. The special role of the Stackel-Killing and Killing-Yano tensors is pointed out. The covariant phase-space is extended to include external gauge fields and scalar potentials. We investigate the possibility for a higher-order symmetry to survive when the electromagnetic interactions are taken into account. Aconcrete realization of this possibility is given by the Killing-Maxwell system. The classical conserved quantities do not generally transfer to the quantized systems producing quantum gravitational anomalies. As a rule the conformal extension of the Killing vectors and tensors does not produce symmetry operators for the Klein-Gordon operator.

  20. Neonatal Jaundice Detection System.

    PubMed

    Aydın, Mustafa; Hardalaç, Fırat; Ural, Berkan; Karap, Serhat

    2016-07-01

    Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %. PMID:27229489

  1. 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.

  2. Magnetic and gravity anomalies of the slow-spreading system in the Gulf of Aden

    NASA Astrophysics Data System (ADS)

    Nakanishi, M.; Fujimoto, H.; Tamaki, K.; Okino, K.

    2002-12-01

    The spreading system in the Gulf of Aden between Somalia, NE Africa, and Arabia has an ENE-WSW trend and its half spreading rate is about 1.0 cm/yr (e.g., Jestin et al., 1994). Previous studies (e.g., Tamsett and Searle, 1988) provided the general morphology of the spreading system. To reveal detailed morphology and tectonics of the spreading system in the Gulf of Aden, geophysical investigation was conducted along the spreading system between 45°30OE and 50°20OE by the R/V Hakuho-maru from December 2000 to January 2001. Bathymetric data were collected using an echo sounder SEA BEAM 2120 aboard R/V Hakuho-maru. Magnetic and gravity data were collected by towed proton magnetometer and shipboard gravimeter, respectively. The strike of the spreading centers east of 46°30OE is N65°W. The topographic expression of the spreading centers east of N46°30OE is an axial rift valley offset by transform faults siilar to that observed at slow spreading centers in other areas. The bathymetric feature of the spreading centers between 45°50OE and 46°30OE with a strike N80°E is N65°W trending en-echelon basins. The spreading center west of 45°50OE with a strike E-W is bouned by linear ridges and its bathymetric expression is N65°W trending en-echelon ridges. The axial rift valley west of N46°30OE is not offset by any prominent transform faults. Negative magnetic anomaly is dominant over the axial valleys. Its amplitude is about 500 nT and the wavelength is about 30 km. Prominent linear negative magnetic anomaly, which is more than 1000 nT, exists west of N46°30OE. The strike of the linear magnetic anomaly correlates with that of axial valleys west of N46°30OE. Mantle Bouguer gravity anomaly of the spreading centers increases eastward. This trend correlates with the eastward deepening of spreading centers.

  3. 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).

  4. An anomaly detector applied to a materials control and accounting system

    SciTech Connect

    Whiteson, R.; Kelso, F.; Baumgart, C.; Tunnell, T.W.

    1994-08-01

    Large amounts of safeguards data are automatically gathered and stored by monitoring instruments used in nuclear chemical processing plants, nuclear material storage facilities, and nuclear fuel fabrication facilities. An integrated safeguards approach requires the ability to identify anomalous activities or states in these data. Anomalies in the data could be indications of error, theft, or diversion of material. The large volume of the data makes analysis and evaluation by human experts very tedious, and the complex and diverse nature of the data makes these tasks difficult to automate. This paper describes the early work in the development of analysis tools to automate the anomaly detection process. Using data from accounting databases, the authors are modeling the normal behavior of processes. From these models they hope to be able to identify activities or data that deviate from that norm. Such tools would be used to reveal trends, identify errors, and recognize unusual data. Thus the expert`s attention can be focused directly on significant phenomena.

  5. Intelligent Leak Detection System

    SciTech Connect

    Mohaghegh, Shahab D.

    2014-10-27

    apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO2. This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise

  6. Intelligent Leak Detection System

    2014-10-27

    apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or nearmore » surface monitoring of the sequestered CO2. This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift

  7. 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

  8. 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

  9. 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.

  10. Incipient fire detection system

    DOEpatents

    Brooks, Jr., William K.

    1999-01-01

    A method and apparatus for an incipient fire detection system that receives gaseous samples and measures the light absorption spectrum of the mixture of gases evolving from heated combustibles includes a detector for receiving gaseous samples and subjecting the samples to spectroscopy and determining wavelengths of absorption of the gaseous samples. The wavelengths of absorption of the gaseous samples are compared to predetermined absorption wavelengths. A warning signal is generated whenever the wavelengths of absorption of the gaseous samples correspond to the predetermined absorption wavelengths. The method includes receiving gaseous samples, subjecting the samples to light spectroscopy, determining wavelengths of absorption of the gaseous samples, comparing the wavelengths of absorption of the gaseous samples to predetermined absorption wavelengths and generating a warning signal whenever the wavelengths of absorption of the gaseous samples correspond to the predetermined absorption wavelengths. In an alternate embodiment, the apparatus includes a series of channels fluidically connected to a plurality of remote locations. A pump is connected to the channels for drawing gaseous samples into the channels. A detector is connected to the channels for receiving the drawn gaseous samples and subjecting the samples to spectroscopy. The wavelengths of absorption are determined and compared to predetermined absorption wavelengths is provided. A warning signal is generated whenever the wavelengths correspond.

  11. Coronary artery anomalies.

    PubMed

    Earls, James P

    2006-12-01

    Coronary artery anomalies are uncommon findings but can be of significant clinical importance in a small number of individuals. Clinical presentation depends on the specific anomaly. Most coronary artery anomalies are benign and clinically insignificant, however, some anomalies are potentially significant and can lead to heart failure and even death. Noninvasive imaging has emerged as the preferred way to image coronary anomalies. Both electron beam computed tomography (EBCT) and magnetic resonance angiography (MRA) are useful for the diagnosis of anomalous coronary arteries. Recently, MDCT has also proven to be very useful in the detection and characterization of anomalous coronary arteries. This chapter will review the appearance of the most commonly encountered coronary anomalies on MDCT. PMID:17709086

  12. Analyzing the drainage system anomaly of Zagros basins: Implications for active tectonics

    NASA Astrophysics Data System (ADS)

    Bahrami, Shahram

    2013-11-01

    Morphometric analysis of hierarchical arrangement of drainage networks allows to evaluate the effects of external controls especially tectonics on basin development. In this study, a quantitative method for calculation of stream's hierarchical anomaly number is introduced. Morphometric parameters such as hierarchal anomaly index (∆a), percent of asymmetry factor (PAF), basin Shape (Bs), basin length to mean width ratio (Bl/Bmw), stream's bifurcation ratio (Rb), bifurcation index (R), drainage density (Dd), drainage frequency (Df) and anticline's hinge spacing (Hs) of 15 basins in Zagros Mountains were examined. Results show that the strong correlations exist between pairs ∆a-PAF (r = 0.844), ∆a-Bs (r = 0.732), ∆a-Bl/Bmw (r = 0.775), ∆a-R (r = 0.517), PAF-Bl/Bmw (r = 0.519), Bs-R (r = 0.659), Bl/Bmw-R (r = 0.703), Hs-∆a (r = - 0.708), Hs-PAF (r = - 0.529) and Hs-Bs (r = - 0.516). The variations in trend of anticlines control the shape of basins so that where anticlines hinges become closer to each other in the downstream direction, basin become narrower downward and hence the ∆a increases. The more uplifted northeastern anticlines cause the trunk river of the basins to migrate toward the younger anticlines in southwest and hence ∆a increases because the trunk river receives a lot of first order streams. Data reveal that the rate of ∆a is higher in elongated synclinal basins. Due to the decrease in the intensity of deformation from northeast toward southwest of Zagros, the hinge spacing of anticlines increases southwestwards. Data reveal that the variation in hinge spacing of anticlines strongly controls the basin's shape and tilting as well as the hierarchical anomaly of drainage system. Since the elongation and tilting of basins are associated with the variations in rates of folding, uplift and hinge spacing of anticlines, it can be concluded that the hierarchical anomaly of drainages in studied basins is controlled by the intensity of Zagros

  13. Can residuals of the solar system foreground explain low multipole anomalies of the CMB?

    SciTech Connect

    Hansen, M.; Kim, J.; Frejsel, A.M.; Ramazanov, S.; Naselsky, P.; Zhao, W.; Burigana, C. E-mail: jkim@nbi.dk E-mail: sabir_ra@nbi.dk E-mail: wzhao7@nbi.ku.dk

    2012-10-01

    The low multipole anomalies of the Cosmic Microwave Background has received much attention during the last few years. It is still not ascertained whether these anomalies are indeed primordial or the result of systematics or foregrounds. An example of a foreground, which could generate some non-Gaussian and statistically anisotropic features at low multipole range, is the very symmetric Kuiper Belt in the outer solar system. In this paper, expanding upon the methods presented in [1], we investigate the contributions from the Kuiper Belt objects (KBO) to the WMAP ILC 7 map, whereby we can minimize the contrast in power between even and odd multipoles in the CMB, discussed in [2,3]. We submit our KBO de-correlated CMB signal to several tests, to analyze its validity, and find that incorporation of the KBO emission can decrease the quadrupole-octupole alignment and parity asymmetry problems, provided that the KBO signals has a non-cosmological dipole modulation, associated with the statistical anisotropy of the ILC 7 map. Additionally, we show that the amplitude of the dipole modulation, within a 2σ interval, is in agreement with the corresponding amplitudes, discussed in [4].

  14. Spacecraft System Failures and Anomalies Attributed to the Natural Space Environment

    NASA Technical Reports Server (NTRS)

    Bedingfield, Keith, L.; Leach, Richard D.; Alexander, Margaret B. (Editor)

    1996-01-01

    The natural space environment is characterized by many complex and subtle phenomena hostile to spacecraft. The effects of these phenomena impact spacecraft design, development, and operations. Space systems become increasingly susceptible to the space environment as use of composite materials and smaller, faster electronics increases. This trend makes an understanding of the natural space environment essential to accomplish overall mission objectives, especially in the current climate of better/cheaper/faster. This primer provides a brief overview of the natural space environment - definition, related programmatic issues, and effects on various spacecraft subsystems. The primary focus, however, is to catalog, through representative case histories, spacecraft failures and anomalies attributed to the natural space environment. This primer is one in a series of NASA Reference Publications currently being developed by the Electromagnetics and Aerospace Environments Branch, Systems Analysis and Integration Laboratory, Marshall Space Flight Center (MSFC), National Aeronautics and Space Administration (NASA).

  15. An expert system application for network intrusion detection

    SciTech Connect

    Jackson, K.A.; Dubois, D.H.; Stallings, C.A.

    1991-01-01

    The paper describes the design of a prototype intrusion detection system for the Los Alamos National Laboratory's Integrated Computing Network (ICN). The Network Anomaly Detection and Intrusion Reporter (NADIR) differs in one respect from most intrusion detection systems. It tries to address the intrusion detection problem on a network, as opposed to a single operating system. NADIR design intent was to copy and improve the audit record review activities normally done by security auditors. We wished to replace the manual review of audit logs with a near realtime expert system. NADIR compares network activity, as summarized in user profiles, against expert rules that define network security policy, improper or suspicious network activities, and normal network and user activity. When it detects deviant (anomalous) behavior, NADIR alerts operators in near realtime, and provides tools to aid in the investigation of the anomalous event. 15 refs., 2 figs.

  16. 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.

  17. 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…

  18. Detection of Landmines by Neutron Backscattering: Effects of Soil Moisture on the Detection System

    SciTech Connect

    Baysoy, D. Y.; Subasi, M.

    2010-01-21

    Detection of buried land mines by using neutron backscattering technique (NBS) is a well established method. It depends on detecting a hydrogen anomaly in dry soil. Since a landmine and its plastic casing contain much more hydrogen atoms than the dry soil, this anomaly can be detected by observing a rise in the number of neutrons moderated to thermal or epithermal energy. But, the presence of moisture in the soil limits the effectiveness of the measurements. In this work, a landmine detection system using the NBS technique was designed. A series of Monte Carlo calculations was carried out to determine the limits of the system due to the moisture content of the soil. In the simulations, an isotropic fast neutron source ({sup 252}Cf, 100 mug) and a neutron detection system which consists of five {sup 3}He detectors were used in a practicable geometry. In order to see the effects of soil moisture on the efficiency of the detection system, soils with different water contents were tested.

  19. Hand held explosives detection system

    DOEpatents

    Conrad, Frank J.

    1992-01-01

    The present invention is directed to a sensitive hand-held explosives detection device capable of detecting the presence of extremely low quantities of high explosives molecules, and which is applicable to sampling vapors from personnel, baggage, cargo, etc., as part of an explosives detection system.

  20. Heat capacity anomaly in a self-aggregating system: Triblock copolymer 17R4 in water

    NASA Astrophysics Data System (ADS)

    Dumancas, Lorenzo V.; Simpson, David E.; Jacobs, D. T.

    2015-05-01

    The reverse Pluronic, triblock copolymer 17R4 is formed from poly(propylene oxide) (PPO) and poly(ethylene oxide) (PEO): PPO14 - PEO24 - PPO14, where the number of monomers in each block is denoted by the subscripts. In water, 17R4 has a micellization line marking the transition from a unimer network to self-aggregated spherical micelles which is quite near a cloud point curve above which the system separates into copolymer-rich and copolymer-poor liquid phases. The phase separation has an Ising-like, lower consolute critical point with a well-determined critical temperature and composition. We have measured the heat capacity as a function of temperature using an adiabatic calorimeter for three compositions: (1) the critical composition where the anomaly at the critical point is analyzed, (2) a composition much less than the critical composition with a much smaller spike when the cloud point curve is crossed, and (3) a composition near where the micellization line intersects the cloud point curve that only shows micellization. For the critical composition, the heat capacity anomaly very near the critical point is observed for the first time in a Pluronic/water system and is described well as a second-order phase transition resulting from the copolymer-water interaction. For all compositions, the onset of micellization is clear, but the formation of micelles occurs over a broad range of temperatures and never becomes complete because micelles form differently in each phase above the cloud point curve. The integrated heat capacity gives an enthalpy that is smaller than the standard state enthalpy of micellization given by a van't Hoff plot, a typical result for Pluronic systems.

  1. Methods and Systems for Characterization of an Anomaly Using Infrared Flash Thermography

    NASA Technical Reports Server (NTRS)

    Koshti, Ajay M. (Inventor)

    2013-01-01

    A method for characterizing an anomaly in a material comprises (a) extracting contrast data; (b) measuring a contrast evolution; (c) filtering the contrast evolution; (d) measuring a peak amplitude of the contrast evolution; (d) determining a diameter and a depth of the anomaly, and (e) repeating the step of determining the diameter and the depth of the anomaly until a change in the estimate of the depth is less than a set value. The step of determining the diameter and the depth of the anomaly comprises estimating the depth using a diameter constant C.sub.D equal to one for the first iteration of determining the diameter and the depth; estimating the diameter; and comparing the estimate of the depth of the anomaly after each iteration of estimating to the prior estimate of the depth to calculate the change in the estimate of the depth of the anomaly.

  2. Antigen detection systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Infectious agents or their constituent parts (antigens or nucleic acids) can be detected in fresh, frozen, or fixed tissues or other specimens, using a variety of direct or indirect assays. The assays can be modified to yield the greatest sensitivity and specificity but in most cases a particular m...

  3. Remote detection of metal anomalies on Pilot Mountain, Randolph County, North Carolina

    USGS Publications Warehouse

    Milton, N.M.; Collins, William; Chang, S.-H.; Schmidt, R.G.

    1982-01-01

    A biogeophysical technique used successfully to delineate mineralized zones under coniferous forests has been extended to a deciduous region in the Piedmont physiographic province of North Carolina. Pilot Mountain, a hydrothermally altered monadnock within the Carolina slate belt, contains areas of anomalously high amounts of Cu, Mo, and Sn in the soils. Leaves of canopy trees in the mineralized zone also contain significant amounts of Cu. Spectral data acquired from a high-resolution airborne spectroradiometer were processed using a waveform analysis technique to minimize background noise caused by canopy variations and slope effects. Areas containing anomalous metals were detected by spectral changes in the chlorophyll absorption region.

  4. 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.

  5. On the origin of the flux ratio anomaly in quadruple lens systems

    NASA Astrophysics Data System (ADS)

    Inoue, Kaiki Taro

    2016-09-01

    We explore the origin of the flux ratio anomaly in quadruple lens systems. Using a semi-analytic method based on N-body simulations, we estimate the effect of a possible magnification perturbation caused by subhaloes with a mass scale of ≲109 h-1 M⊙ in lensing galaxy haloes. Taking into account astrometric shifts and assuming that the primary lens is described by a singular isothermal ellipsoid, the expected change to the flux ratios for a multiply lensed image is just a few per cent and the mean of the expected convergence perturbation at the effective Einstein radius of the lensing galaxy halo is <δκsub> = 0.003, corresponding to the mean of the ratio of a projected dark matter mass fraction in subhaloes at the effective Einstein radius = 0.006. In contrast, the expected change to the flux ratio caused by line-of-sight structures is typically ˜10 per cent and the mean of the convergence perturbation is <|δκlos|> = 0.008, corresponding to = 0.017. The contribution of the magnification perturbation caused by subhaloes is ˜40 per cent of the total at a source redshift zS = 0.7 and decreases monotonically in zS to ˜20 per cent at zS = 3.6. Assuming statistical isotropy, the convergence perturbation estimated from 11 observed quadruple lens systems has a positive correlation with the source redshift zS, which is much stronger than that with the lens redshift zL. This feature also supports that the flux ratio anomaly is caused mainly by line-of-sight structures rather than subhaloes. We also discuss a possible imprint of line-of-sight structures in the demagnification of minimum images due to locally underdense structures in the line of sight.

  6. An on-line expert system for diagnosing environmentally induced spacecraft anomalies using CLIPS

    NASA Technical Reports Server (NTRS)

    Lauriente, Michael; Rolincik, Mark; Koons, Harry C; Gorney, David

    1993-01-01

    A new rule-based, expert system for diagnosing spacecraft anomalies is under development. The knowledge base consists of over two-hundred rules and provide links to historical and environmental databases. Environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information (varying degrees of confidence in an answer) or 'unknown' to any question. The expert system not only provides scientists with needed risk analysis and confidence estimates not available in standard numerical models or databases, but it is also an effective learning tool. In addition, the architecture of the expert system allows easy additions to the knowledge base and the database. For example, new frames concerning orbital debris and ionospheric scintillation are being considered. The system currently runs on a MicroVAX and uses the C Language Integrated Production System (CLIPS).

  7. Protein detection system

    DOEpatents

    Fruetel, Julie A.; Fiechtner, Gregory J.; Kliner, Dahv A. V.; McIlroy, Andrew

    2009-05-05

    The present embodiment describes a miniature, microfluidic, absorption-based sensor to detect proteins at sensitivities comparable to LIF but without the need for tagging. This instrument utilizes fiber-based evanescent-field cavity-ringdown spectroscopy, in combination with faceted prism microchannels. The combination of these techniques will increase the effective absorption path length by a factor of 10.sup.3 to 10.sup.4 (to .about.1-m), thereby providing unprecedented sensitivity using direct absorption. The coupling of high-sensitivity absorption with high-performance microfluidic separation will enable real-time sensing of biological agents in aqueous samples (including aerosol collector fluids) and will provide a general method with spectral fingerprint capability for detecting specific bio-agents.

  8. Rapid deployment intrusion detection system

    SciTech Connect

    Graham, R.H.

    1997-08-01

    A rapidly deployable security system is one that provides intrusion detection, assessment, communications, and annunciation capabilities; is easy to install and configure; can be rapidly deployed, and is reusable. A rapidly deployable intrusion detection system (RADIDS) has many potential applications within the DOE Complex: back-up protection for failed zones in a perimeter intrusion detection and assessment system, intrusion detection and assessment capabilities in temporary locations, protection of assets during Complex reconfiguration, and protection in hazardous locations, protection of assets during Complex reconfiguration, and protection in hazardous locations. Many DOE user-need documents have indicated an interest in a rapidly deployable intrusion detection system. The purpose of the RADIDS project is to design, develop, and implement such a system. 2 figs.

  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. Beware of Venous Anomalies in Young Patients with Sick Sinus Syndrome: A Report of Two Cases of Sick Sinus Syndrome with Systemic Venous Anomalies

    PubMed Central

    Rathakrishnan, Shanmuga Sundaram; Kaliappan, Tamilarasu; Gopalan, Rajendiran

    2015-01-01

    We report two young patients with symptomatic sick sinus syndrome admitted for permanent pacemaker implantation (PPI). On evaluation with echocardiography, one of them was found to have persistent left superior vena cava and venography showed absent right superior vena cava also. He underwent PPI with leads inserted via left superior vena cava, coronary sinus, right atrium and right ventricle. The other patient was incidentally found to have interrupted inferior vena cava with azygos continuation while being planned for temporary pacemaker implantation. She underwent successful PPI. We would like to stress the importance of having a high suspicion for these systemic venous anomalies in patients presenting with sick sinus syndrome especially at young age. If we could diagnose preoperatively, we can avoid on table surprises. PMID:27326354

  11. An automated computer misuse detection system for UNICOS

    SciTech Connect

    Jackson, K.A.; Neuman, M.C.; Simmonds, D.D.; Stallings, C.A.; Thompson, J.L.; Christoph, G.G.

    1994-09-27

    An effective method for detecting computer misuse is the automatic monitoring and analysis of on-line user activity. This activity is reflected in the system audit record, in the system vulnerability posture, and in other evidence found through active testing of the system. During the last several years we have implemented an automatic misuse detection system at Los Alamos. This is the Network Anomaly Detection and Intrusion Reporter (NADIR). We are currently expanding NADIR to include processing of the Cray UNICOS operating system. This new component is called the UNICOS Realtime NADIR, or UNICORN. UNICORN summarizes user activity and system configuration in statistical profiles. It compares these profiles to expert rules that define security policy and improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations. The first phase of UNICORN development is nearing completion, and will be operational in late 1994.

  12. Particle detection systems and methods

    DOEpatents

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine

    2016-08-01

    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.

  18. 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.

  19. Randomness fault detection system

    NASA Technical Reports Server (NTRS)

    Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

    1996-01-01

    A method and apparatus are provided for detecting a fault on a power line carrying a line parameter such as a load current. The apparatus monitors and analyzes the load current to obtain an energy value. The energy value is compared to a threshold value stored in a buffer. If the energy value is greater than the threshold value a counter is incremented. If the energy value is greater than a high value threshold or less than a low value threshold then a second counter is incremented. If the difference between two subsequent energy values is greater than a constant then a third counter is incremented. A fault signal is issued if the counter is greater than a counter limit value and either the second counter is greater than a second limit value or the third counter is greater than a third limit value.

  20. 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.

  1. Thermal neutron detection system

    DOEpatents

    Peurrung, Anthony J.; Stromswold, David C.

    2000-01-01

    According to the present invention, a system for measuring a thermal neutron emission from a neutron source, has a reflector/moderator proximate the neutron source that reflects and moderates neutrons from the neutron source. The reflector/moderator further directs thermal neutrons toward an unmoderated thermal neutron detector.

  2. System and Method for Outlier Detection via Estimating Clusters

    NASA Technical Reports Server (NTRS)

    Iverson, David J. (Inventor)

    2016-01-01

    An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

  3. Centrifugal unbalance detection system

    DOEpatents

    Cordaro, Joseph V.; Reeves, George; Mets, Michael

    2002-01-01

    A system consisting of an accelerometer sensor attached to a centrifuge enclosure for sensing vibrations and outputting a signal in the form of a sine wave with an amplitude and frequency that is passed through a pre-amp to convert it to a voltage signal, a low pass filter for removing extraneous noise, an A/D converter and a processor and algorithm for operating on the signal, whereby the algorithm interprets the amplitude and frequency associated with the signal and once an amplitude threshold has been exceeded the algorithm begins to count cycles during a predetermined time period and if a given number of complete cycles exceeds the frequency threshold during the predetermined time period, the system shuts down the centrifuge.

  4. Power line detection system

    DOEpatents

    Latorre, Victor R.; Watwood, Donald B.

    1994-01-01

    A short-range, radio frequency (RF) transmitting-receiving system that provides both visual and audio warnings to the pilot of a helicopter or light aircraft of an up-coming power transmission line complex. Small, milliwatt-level narrowband transmitters, powered by the transmission line itself, are installed on top of selected transmission line support towers or within existing warning balls, and provide a continuous RF signal to approaching aircraft. The on-board receiver can be either a separate unit or a portion of the existing avionics, and can also share an existing antenna with another airborne system. Upon receipt of a warning signal, the receiver will trigger a visual and an audio alarm to alert the pilot to the potential power line hazard.

  5. Power line detection system

    DOEpatents

    Latorre, V.R.; Watwood, D.B.

    1994-09-27

    A short-range, radio frequency (RF) transmitting-receiving system that provides both visual and audio warnings to the pilot of a helicopter or light aircraft of an up-coming power transmission line complex. Small, milliwatt-level narrowband transmitters, powered by the transmission line itself, are installed on top of selected transmission line support towers or within existing warning balls, and provide a continuous RF signal to approaching aircraft. The on-board receiver can be either a separate unit or a portion of the existing avionics, and can also share an existing antenna with another airborne system. Upon receipt of a warning signal, the receiver will trigger a visual and an audio alarm to alert the pilot to the potential power line hazard. 4 figs.

  6. Radiation detection system

    DOEpatents

    Whited, R.C.

    A system for obtaining improved resolution in relatively thick semiconductor radiation detectors, such as HgI/sub 2/, which exhibit significant hole trapping. Two amplifiers are used: the first measures the charge collected and the second the contribution of the electrons to the charge collected. The outputs of the two amplifiers are utilized to unfold the total charge generated within the detector in response to a radiation event.

  7. 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.

  8. The Association between Autism Spectrum Disorders and Congenital Anomalies by Organ Systems in a Finnish National Birth Cohort

    ERIC Educational Resources Information Center

    Timonen-Soivio, Laura; Sourander, Andre; Malm, Heli; Hinkka-Yli-Salomäki, Susanna; Gissler, Mika; Brown, Alan; Vanhala, Raija

    2015-01-01

    The aim of this study was to evaluate the association between autism spectrum disorders (ASD) with and without intellectual disability (ID) and congenital anomalies (CAs) by organ system. The sample included all children diagnosed with ASD (n = 4441) from the Finnish Hospital Discharge Register during 1987-2000 and a total of four controls per…

  9. 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.

  10. Early snow melt anomalies: their influence on peak discharge timing and use as an indicator of climate change in high latitude freshwater systems

    NASA Astrophysics Data System (ADS)

    Semmens, K. A.; Ramage, J. M.

    2010-12-01

    High latitude freshwater systems have and will continue to experience significant change due to warming trends higher than the global average. These systems are directly affected by climate change through alterations in snow melt timing, permafrost extent, and ice cover duration and timing, including shorter ice duration, earlier break-ups in spring and later freeze-ups in fall. Such changes affect the magnitudes and cycles of streamflow, discharge, and flooding, and thus impact the ecosystems in surrounding river basins. Therefore it is critical to be able to understand and model these processes and forecast future trends, especially for ungauged basins with little to no meteorological data. Early melt anomalies, short-lived melt events before melt onset, may be an indicator of climate change and of an area’s sensitivity in response to warming. These anomalies are defined here as short melt events where brightness temperatures (Tb) are above the melt threshold for less than three out of five consecutive days, meaning that melt is not sustained. Tb encompasses both physical temperature and emissivity with wet snow easily detected due to its abrupt increase in emissivity. Once melt is sustained, it is deemed melt onset. Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 37GHz vertically polarized data are used to determine when Tb is greater than 252K and when diurnal amplitude variations (DAV) are greater than 18K for the Stewart and Pelly subbasins of the Yukon River Basin for 2003 to 2009. Melt onset and data are used as input into a modified version of SWEHydro (Yan et al. 2009) to determine peak discharge and timing of peak and freshet. This model does not require any meteorological data, an advantage for use in remote northern areas. The number of early melt anomalies are analyzed in relation to the other cryosphere cycle variables. Early melt anomalies are found to correlate strongest with the timing of the end of high DAV later in the year but the

  11. APDS: Autonomous Pathogen Detection System

    SciTech Connect

    Langlois, R G; Brown, S; Burris, L; Colston, B; Jones, L; Makarewicz, T; Mariella, R; Masquelier, D; McBride, M; Milanovich, F; Masarabadi, S; Venkateswaran, K; Marshall, G; Olson, D; Wolcott, D

    2002-02-14

    An early warning system to counter bioterrorism, the Autonomous Pathogen Detection System (APDS) continuously monitors the environment for the presence of biological pathogens (e.g., anthrax) and once detected, it sounds an alarm much like a smoke detector warns of a fire. Long before September 11, 2001, this system was being developed to protect domestic venues and events including performing arts centers, mass transit systems, major sporting and entertainment events, and other high profile situations in which the public is at risk of becoming a target of bioterrorist attacks. Customizing off-the-shelf components and developing new components, a multidisciplinary team developed APDS, a stand-alone system for rapid, continuous monitoring of multiple airborne biological threat agents in the environment. The completely automated APDS samples the air, prepares fluid samples in-line, and performs two orthogonal tests: immunoassay and nucleic acid detection. When compared to competing technologies, APDS is unprecedented in terms of flexibility and system performance.

  12. Diversified transmission multichannel detection system

    SciTech Connect

    Tournois, P.; Engelhard, P.

    1984-07-03

    A detection system for imaging by sonar or radar signals. The system associates diversified transmissions with an interferometric base. This base provides an angular channel formation means and each signal formed in this way is processed by matched filtering in a circuit containing copy signals characterizing the space coloring obtained by the diversified transmission means. The invention is particularly applicable to side or front looking detection sonars.

  13. Incipient-signature identification of mechanical anomalies in a ship-borne satellite antenna system using an ensemble multiwavelet

    NASA Astrophysics Data System (ADS)

    He, Shuilong; Zi, Yanyang; Chen, Jinglong; Zhao, Chenlu; Chen, Binqiang; Yuan, Jing; He, Zhengjia

    2014-10-01

    The instrumented tracking and telemetry ship with a ship-borne satellite antenna (SSA) is the critical device to ensure high quality of space exploration work. To effectively detect mechanical anomalies that can lead to unexpected downtime of the SSA, an ensemble multiwavelet (EM) is presented for identifying the anomaly related incipient-signatures within the measured dynamic signals. Rather than using a predetermined basis as in a conventional multiwavelet, an EM optimizes the matching basis which satisfactorily adapts to the anomaly related incipient-signatures. The construction technique of an EM is based on the conjunction of a two-scale similarity transform (TST) and lifting scheme (LS). For the technique above, the TST improves the regularity by increasing the approximation order of multiscaling functions, while subsequently the LS enhances the smoothness and localizability via utilizing the vanishing moment of multiwavelet functions. Moreover, combining the Hilbert transform with EM decomposition, we identify the incipient-signatures induced by the mechanical anomalies from the measured dynamic signals. A numerical simulation and two successful applications of diagnosis cases (a planetary gearbox and a roller bearing) demonstrate that the proposed technique is capable of dealing with the challenging incipient-signature identification task even though spectral complexity, as well as the strong amplitude/frequency modulation effect, is present in the dynamic signals.

  14. 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.

  15. Heavy metal anomalies in the Tinto and Odiel River and estuary system, Spain

    USGS Publications Warehouse

    Nelson, C.H.; Lamothe, P.J.

    1993-01-01

    The Tinto and Odiel rivers drain 100 km from the Rio Tinto sulphide mining district, and join at a 20-km long estuary entering the Atlantic Ocean. A reconnaissance study of heavy metal anomalies in channel sand and overbank mud of the river and estuary by semi-quantitative emission dc-arc spectrographic analysis shows the following upstream to downstream ranges in ppm (??g g-1): As 3,000 to <200, Cd 30 to <0.1, Cu 1,500 to 10, Pb 2,000 to <10, Sb 3000 to <150, and Zn 3,000 to <200. Organic-rich (1.3-2.6% total organic carbon, TOC), sandysilty overbank clay has been analyzed to represent suspended load materials. The high content of heavy metals in the overbank clay throughout the river and estuary systems indicates the importance of suspended sediment transport for dispersing heavy metals from natural erosion and anthropogenic mining activities of the sulfide deposit. The organic-poor (0.21-0.37% TOC) river bed sand has been analyzed to represent bedload transport of naturally-occurring sulfide minerals. The sand has high concentrations of metals upstream but these decrease an order of magnitude in the lower estuary. Although heavy metal contamination of estuary mouth beach sand has been diluted to background levels estuary mud exhibits increased contamination apparently related to finer grain size, higher organic carbon content, precipitation of river-borne dissolved solids, and input of anthropogenic heavy metals from industrial sources. The contaminated estuary mud disperses to the inner shelf mud belt and offshore suspended sediment, which exhibit metal anomalies from natural erosion and mining of upstream Rio Tinto sulphide lode sources (Pb, Cu, Zn) and industrial activities within the estuary (Fe, Cr, Ti). Because heavy metal contamination of Tinto-Odiel river sediment reaches or exceeds the highest levels encountered in other river sediments of Spain and Europe, a detailed analysis of metals in water and suspended sediment throughout the system, and

  16. 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.

  17. Competing Orders and Anomalies.

    PubMed

    Moon, Eun-Gook

    2016-08-08

    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.

  18. 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

  19. 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

  20. Variations of Cloud and Radiative Properties of Boundary-layer and Deep Convective Systems with Sea Surface Temperature Anomalies

    NASA Technical Reports Server (NTRS)

    Xu, Kuan-Man

    2010-01-01

    Gridded monthly-mean satellite data contain compositing information from different cloud system types and clear-sky environments. To isolate the variations of cloud physical properties of an individual cloud system type with its environment, orbital data are needed. In this study, we will analyze the variations of cloud and radiative properties of boundary-layer clouds and deep convective cloud systems with sea surface temperature (SST) anomalies. We use Terra-CERES (Clouds and the Earth s Radiant Energy System) Level 2 data to classify distinct cloud objects defined by cloud-system types (deep convection, boundary-layer cumulus, stratocumulus and overcast clouds), sizes, geographic locations, and matched large-scale environments. This analysis method identifies a cloud object as a contiguous region of the Earth with a single dominant cloud-system type. It determines the shape and size of the cloud object from the satellite data and the cloud-system selection criteria. The statistical properties of the identified cloud objects are analyzed in terms of probability density functions (PDFs) of a single property or joint PDFs between two properties. The SST anomalies are defined as the differences from five-year annual-cycle means. Individual cloud objects are sorted into one of five equal size subsets, with the matched SST anomalies ranging from the most negative to the most positive values, for a given size category of deep convective cloud objects, boundary-layer cumulus, stratocumulus and overcast cloud objects. The PDFs of cloud and radiative properties for deep convective cloud objects (between 30 S and 30 N) are found to largely similar among the five SST anomaly subsets except for the lowest SST anomaly subset. The different characteristics from this SST anomaly subset may be related to some cloud objects resulting from equatorward movement of extratropical cloud systems. This result holds true for all three different size categories (measured by equivalent

  1. An airborne real-time hyperspectral target detection system

    NASA Astrophysics Data System (ADS)

    Skauli, Torbjorn; Haavardsholm, Trym V.; Kåsen, Ingebjørg; Arisholm, Gunnar; Kavara, Amela; Opsahl, Thomas Olsvik; Skaugen, Atle

    2010-04-01

    An airborne system for hyperspectral target detection is described. The main sensor is a HySpex pushbroom hyperspectral imager for the visible and near-infrared spectral range with 1600 pixels across track, supplemented by a panchromatic line imager. An optional third sensor can be added, either a SWIR hyperspectral camera or a thermal camera. In real time, the system performs radiometric calibration and georeferencing of the images, followed by image processing for target detection and visualization. The current version of the system implements only spectral anomaly detection, based on normal mixture models. Image processing runs on a PC with a multicore Intel processor and an Nvidia graphics processing unit (GPU). The processing runs in a software framework optimized for large sustained data rates. The platform is a Cessna 172 aircraft based close to FFI, modified with a camera port in the floor.

  2. Congenital anomalies of the genitourinary system can help in diagnosis of the primary site of metastatic cancer: a case report and a review of the literature

    PubMed Central

    Deptala, Andrzej; Romanowicz, Agnieszka; Czerw, Aleksandra; Walecki, Jerzy; Rogowski, Wojciech; Nasierowska-Guttmejer, Anna

    2016-01-01

    Objective To analyze whether the presence of congenital anomalies of the genitourinary system that are accompanied by specific types of cancer and predispose patients to many complications, including infection, obstruction, stasis, calculus formation, and impaired renal function, could help in the diagnosis of the primary site of a metastatic tumor. Case presentation We report a case of a 58-year-old man with metastatic adenocarcinoma, in whom congenital anomalies of the genitourinary system proved helpful for the diagnosis of the primary site of cancer originating in the seminal vesicles. Conclusion We report an extremely rare case of primary adenocarcinoma arising probably from the left seminal vesicle associated with ipsilateral renal agenesis. The lesion was detected on ultrasound and contrast-enhanced computed tomography and confirmed histologically with ultrasound-guided biopsy. Serum markers, ie, CA19-9 and CA125, were elevated, while prostate-specific antigen and carcinoembryonic antigen were within normal limits. Such a constellation of markers strengthened the diagnosis. Our patient unfortunately presented very late in the course of the disease. Hence, we decided to initiate antiandrogen therapy and best supportive care in a hospice setting. Only early detection seems to be the key factor that may result in improved cure rates for cancer of the seminal vesicles. We also performed a literature search for current concepts related to the diagnosis and clinical management of primary adenocarcinoma of seminal vesicles. PMID:27499637

  3. A vehicle threat detection system using correlation analysis and synthesized x-ray images

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Elmaghraby, Adel

    2013-06-01

    The goal of the proposed research is to automate the vehicle threat detection with X-ray images when a vehicle crosses the country border or the gateway of a secured facility (military base). The proposed detection system requires two inputs: probe images (from X-ray machine) and gallery images (from database). For each vehicle, the gallery images include the X-ray images of fully-loaded (with typical cargo) and unloaded (empty) vehicle. The proposed system produces two types of outputs for threat detection: the detected anomalies and the synthesized images (e.g., grayscale fusion, color fusion, and differential images). The anomalies are automatically detected with the block-wise correlation analysis between two temporally aligned images (probe versus gallery). The locations of detected anomalies can be marked with small rectangles on the probe X-ray images. The several side-view images can be combined into one fused image in gray scale and in colors (color fusion) that provides more comprehensive information to the operator. The fused images are suitable for human analysis and decision. We analyzed a set of vehicle X-ray images, which consists of 4 images generated from AS and E OmniView Gantry™. The preliminary results of detected anomalies and synthesized images are very promising; meanwhile the processing speed is very fast.

  4. 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

  5. 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

  6. Hearing aid malfunction detection system

    NASA Technical Reports Server (NTRS)

    Kessinger, R. L. (Inventor)

    1977-01-01

    A malfunction detection system for detecting malfunctions in electrical signal processing circuits is disclosed. Malfunctions of a hearing aid in the form of frequency distortion and/or inadequate amplification by the hearing aid amplifier, as well as weakening of the hearing aid power supply are detectable. A test signal is generated and a timed switching circuit periodically applies the test signal to the input of the hearing aid amplifier in place of the input signal from the microphone. The resulting amplifier output is compared with the input test signal used as a reference signal. The hearing aid battery voltage is also periodically compared to a reference voltage. Deviations from the references beyond preset limits cause a warning system to operate.

  7. Portable Microleak-Detection System

    NASA Technical Reports Server (NTRS)

    Rivers, H. Kevin; Sikora, Joseph G.; Sankaran, Sankara N.

    2007-01-01

    The figure schematically depicts a portable microleak-detection system that has been built especially for use in testing hydrogen tanks made of polymer-matrix composite materials. (As used here, microleak signifies a leak that is too small to be detectable by the simple soap-bubble technique.) The system can also be used to test for microleaks in tanks that are made of other materials and that contain gases other than hydrogen. Results of calibration tests have shown that measurement errors are less than 10 percent for leak rates ranging from 0.3 to 200 cm3/min. Like some other microleak-detection systems, this system includes a vacuum pump and associated plumbing for sampling the leaking gas, and a mass spectrometer for analyzing the molecular constituents of the gas. The system includes a flexible vacuum chamber that can be attached to the outer surface of a tank or other object of interest that is to be tested for leakage (hereafter denoted, simply, the test object). The gas used in a test can be the gas or vapor (e.g., hydrogen in the original application) to be contained by the test object. Alternatively, following common practice in leak testing, helium can be used as a test gas. In either case, the mass spectrometer can be used to verify that the gas measured by the system is the test gas rather than a different gas and, hence, that the leak is indeed from the test object.

  8. Semi autonomous mine detection system

    SciTech Connect

    Douglas Few; Roelof Versteeg; Herman Herman

    2010-04-01

    CMMAD is a risk reduction effort for the AMDS program. As part of CMMAD, multiple instances of semi autonomous robotic mine detection systems were created. Each instance consists of a robotic vehicle equipped with sensors required for navigation and marking, a countermine sensors and a number of integrated software packages which provide for real time processing of the countermine sensor data as well as integrated control of the robotic vehicle, the sensor actuator and the sensor. These systems were used to investigate critical interest functions (CIF) related to countermine robotic systems. To address the autonomy CIF, the INL developed RIK was extended to allow for interaction with a mine sensor processing code (MSPC). In limited field testing this system performed well in detecting, marking and avoiding both AT and AP mines. Based on the results of the CMMAD investigation we conclude that autonomous robotic mine detection is feasible. In addition, CMMAD contributed critical technical advances with regard to sensing, data processing and sensor manipulation, which will advance the performance of future fieldable systems. As a result, no substantial technical barriers exist which preclude – from an autonomous robotic perspective – the rapid development and deployment of fieldable systems.

  9. Semi autonomous mine detection system

    NASA Astrophysics Data System (ADS)

    Few, Doug; Versteeg, Roelof; Herman, Herman

    2010-04-01

    CMMAD is a risk reduction effort for the AMDS program. As part of CMMAD, multiple instances of semi autonomous robotic mine detection systems were created. Each instance consists of a robotic vehicle equipped with sensors required for navigation and marking, countermine sensors and a number of integrated software packages which provide for real time processing of the countermine sensor data as well as integrated control of the robotic vehicle, the sensor actuator and the sensor. These systems were used to investigate critical interest functions (CIF) related to countermine robotic systems. To address the autonomy CIF, the INL developed RIK was extended to allow for interaction with a mine sensor processing code (MSPC). In limited field testing this system performed well in detecting, marking and avoiding both AT and AP mines. Based on the results of the CMMAD investigation we conclude that autonomous robotic mine detection is feasible. In addition, CMMAD contributed critical technical advances with regard to sensing, data processing and sensor manipulation, which will advance the performance of future fieldable systems. As a result, no substantial technical barriers exist which preclude - from an autonomous robotic perspective - the rapid development and deployment of fieldable systems.

  10. Tape Cassette Bacteria Detection System

    NASA Technical Reports Server (NTRS)

    1973-01-01

    The design, fabrication, and testing of an automatic bacteria detection system with a zero-g capability and based on the filter-capsule approach is described. This system is intended for monitoring the sterility of regenerated water in a spacecraft. The principle of detection is based on measuring the increase in chemiluminescence produced by the action of bacterial porphyrins (i.e., catalase, cytochromes, etc.) on a luminol-hydrogen peroxide mixture. Since viable as well as nonviable organisms initiate this luminescence, viable organisms are detected by comparing the signal of an incubated water sample with an unincubated control. Higher signals for the former indicate the presence of viable organisms. System features include disposable sealed sterile capsules, each containing a filter membrane, for processing discrete water samples and a tape transport for moving these capsules through a processing sequence which involves sample concentration, nutrient addition, incubation, a 4 Molar Urea wash and reaction with luminol-hydrogen peroxide in front of a photomultiplier tube. Liquids are introduced by means of a syringe needle which pierces a rubber septum contained in the wall of the capsule. Detection thresholds obtained with this unit towards E. coli and S. marcescens assuming a 400 ml water sample are indicated.

  11. SADM potentiometer anomaly investigations

    NASA Astrophysics Data System (ADS)

    Wood, Brian; Mussett, David; Cattaldo, Olivier; Rohr, Thomas

    2005-07-01

    During the last 3 years Contraves Space have been developing a Low Power (1-2kW) Solar Array Drive Mechanism (SADM) aimed at small series production. The mechanism was subjected to two test programmes in order to qualify the SADM to acceptable levels. During the two test programmes, anomalies were experienced with the Potentiometers provided by Eurofarad SA and joint investigations were undertaken to resolve why these anomalies had occurred. This paper deals with the lessons learnt from the failure investigation on the two Eurofarad (rotary) Potentiometer anomaly. The Rotary Potentiometers that were used were fully redundant; using two back to back mounted "plastic tracks". It is a pancake configuration mounted directly to the shaft of the Slip Ring Assembly at the extreme in-board end of the SADM. It has no internal bearings. The anomaly initially manifested itself as a loss of performance in terms of linearity, which was first detected during Thermal Vacuum testing. A subsequent anomaly manifested itself by the complete failure of the redundant potentiometer again during thermal vacuum testing. This paper will follow and detail the chain of events following this anomaly and identifies corrective measures to be applied to the potentiometer design and assembly process.

  12. 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.

  13. Practical comparison of aberration detection algorithms for biosurveillance systems.

    PubMed

    Zhou, Hong; Burkom, Howard; Winston, Carla A; Dey, Achintya; Ajani, Umed

    2015-10-01

    National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data. PMID:26334478

  14. Networked gamma radiation detection system for tactical deployment

    NASA Astrophysics Data System (ADS)

    Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ronald; Smith, Ethan; Guss, Paul; Mitchell, Stephen

    2015-08-01

    A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2″ × 4″ × 16″ sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

  15. Microbial mine detection system (MMDS)

    NASA Astrophysics Data System (ADS)

    Fliermans, Carl B.; Lopez-de-Victoria, Geralyne

    1998-09-01

    The Savannah River Technology Center (SRTC) is developing the Microbial Mine Detection System (MMDS), a cost-effective, safe and reliable method to detect land mines using microorganisms as the primary biosensor detector. SRTC research has shown that various naturally occurring microbial species are stimulated by nitrogen, trinitrotoluene (TNT), dinitrotoluene (DNT), nitrates, nitrites, nitrous oxide, and the chemical components found in explosive materials. Several of the 10,000 indigenous bacteria already existing in the SRTC Subsurface Microbiology Culture Collection (SMCC) possess characteristics that would support discrete detection of land mines during metabolic activity or growth. SRTC scientists are screening and identifying bacteria residing in the SMCC, and other collections associated with specific land mines, for their attraction to explosive off-gasses. After contacting explosives or off-gasses, the micro-organisms will activate via bioluminescence and identify the location of the land mines. Once identified, down selected and mesocosmly defined, the micro-organisms can then be prepared for field deployment. This deployment process requires minimal user training and is envisioned to be administered in hand-held, vehicular mounted and airborne platforms. Microbial detection systems are a renewable resource, easy to preserve, inexpensive to maintain under field conditions, and provide a high-probability response recognition technology.

  16. AB086. Chromosomal microarray analysis—detection of both duplication and deletion in patients with multiple congenital anomalies and/or developmental delay

    PubMed Central

    Ee, Hui Jing; Yon, Hui Yi; Tan, Mui Li; Roch, Robin; Brett, Maggie; Yong, Min Hwee; Law, Hai Yang; Lai, Angeline

    2015-01-01

    Background and objective Chromosomal microarray analysis (CMA) is recommended as first-tier genetic testing for patients with multiple congenital anomalies, developmental delay/intellectual disability and/or autism spectrum disorder. It detects chromosomal imbalance at a higher resolution than conventional chromosomal analysis. CMA diagnostic service was launched in our hospital in February 2014. The aim of this report is to review the incidence of detecting both duplication and deletion in patients referred for this test. Methods DNA was extracted using Gentra Puregene Blood Kit. CMA was performed using the Agilent 4×180 K CGH + SNP array and analysed with Agilent CytoGenomics. G-banding analysis was carried out on stimulated lymphocytes culture. Targeted fluorescence in-situ hybridization (FISH) was performed using locus specific probes. Results From 1 February 2014 to 31 May 2015, a total of 205 patients were tested. Seven (3.4%) were identified to have both duplication and deletion of chromosomal segments that were pathogenic [5] or of uncertain clinical significance [2]. We present a case of a 1-day-old Chinese girl with oligohydramnios, prematurity (35+5 weeks) and multiple congenital anomalies including heart defect, cleft palate, ear anomalies, microcephaly, vaginal skin tag, bilateral clinodactyly and wide anterior fontanelle. Karyotyping and FISH analysis for 22q11 deletion were normal. CMA revealed a pathogenic gain of 2.143 Mb at 16p13.3 and a pathogenic loss of 0.271 Mb at 16q24.2q24.3. The gain at 16p13.3 affects 67 genes including CREBBP. The 16p13.3 duplication syndrome is a contiguous gene syndrome characterized by normal to moderate intellectual disability, normal growth, mild arthrogryposis, frequently small and proximally implanted thumbs, characteristic facial features and occasionally, developmental defects of the heart, genitalia, palate or eyes. The 0.271 Mb deletion at 16q24.3 affects four genes including ANKRD11 and CDH15. The clinical

  17. [Fetal ocular anomalies: the advantages of prenatal magnetic resonance imaging].

    PubMed

    Brémond-Gignac, D; Copin, H; Elmaleh, M; Milazzo, S

    2010-05-01

    Congenital ocular malformations are uncommon and require prenatal diagnosis. Severe anomalies are more often detected by trained teams and minor anomalies are more difficult to identify and must be systematically sought, particularly when multiple malformations or a family and maternal history is known. The prenatal diagnosis-imaging tool most commonly used is ultrasound but it can be completed by magnetic resonance imaging (MRI), which contributes crucial information. Fetal dysmorphism can occur in various types of dysfunction and prenatal diagnosis must recognize fetal ocular anomalies. After systematic morphologic ultrasound imaging, different abnormalities detected by MRI are studied. Classical parameters such as binocular and interorbital measurements are used to detect hypotelorism and hypertelorism. Prenatal ocular anomalies such as cataract microphthalmia, anophthalmia, and coloboma have been described. Fetal MRI added to prenatal sonography is essential in detecting cerebral and general anomalies and can give more information on the size and morphology of the eyeball. Fetal abnormality detection includes a detailed family and maternal history, an amniotic fluid sample for karyotype, and other analyses for a better understanding of the images. Each pregnancy must be discussed with all specialists for genetic counseling. With severe malformations, termination of pregnancy is proposed because of risk of blindness and associated cerebral or systemic anomalies. Early prenatal diagnosis of ocular malformations can also detect associated abnormalities, taking congenital cataracts that need surgical treatment into account as early as possible. Finally, various associated syndromes need a pediatric check-up that could lead to emergency treatment.

  18. The Autonomous Pathogen Detection System

    SciTech Connect

    Dzenitis, J M; Makarewicz, A J

    2009-01-13

    We developed, tested, and now operate a civilian biological defense capability that continuously monitors the air for biological threat agents. The Autonomous Pathogen Detection System (APDS) collects, prepares, reads, analyzes, and reports results of multiplexed immunoassays and multiplexed PCR assays using Luminex{copyright} xMAP technology and flow cytometer. The mission we conduct is particularly demanding: continuous monitoring, multiple threat agents, high sensitivity, challenging environments, and ultimately extremely low false positive rates. Here, we introduce the mission requirements and metrics, show the system engineering and analysis framework, and describe the progress to date including early development and current status.

  19. On strontium isotopic anomalies and odd-A p-process abundances. [in solar system

    NASA Technical Reports Server (NTRS)

    Clayton, D. D.

    1978-01-01

    Several aspects of the nucleosynthesis of Sr isotopes are considered in an attempt to shed light on the problem of the Sr isotopic anomalies discovered in an inclusion of the Allende meteorite. Decomposition of the Sr isotopes into average r-, s-, and p-process nucleosynthetic classes is performed. It is suggested that the Allende inclusion most likely has an excess of s-process Sr and that the initial Sr-87/Sr-86 isotopic ratio is probably slightly more primitive than basaltic achondrites. The results also show that Sn-115 is mostly due to the r-process and that odd-A yields are very small. It is concluded that if the Sr anomaly in the inclusion is an average s enhancement, it argues somewhat in favor of a model of gas/dust fractionation of s and r isotopes during accumulation of the inclusion parent in the protosolar cloud.

  20. A prototype implementation of a network-level intrusion detection system. Technical report number CS91-11

    SciTech Connect

    Heady, R.; Luger, G.F.; Maccabe, A.B.; Servilla, M.; Sturtevant, J.

    1991-05-15

    This paper presents the implementation of a prototype network level intrusion detection system. The prototype system monitors base level information in network packets (source, destination, packet size, time, and network protocol), learning the normal patterns and announcing anomalies as they occur. The goal of this research is to determine the applicability of current intrusion detection technology to the detection of network level intrusions. In particular, the authors are investigating the possibility of using this technology to detect and react to worm programs.

  1. Imaging of facial anomalies.

    PubMed

    Castillo, M; Mukherji, S K

    1995-01-01

    Anomalies of the face may occur in its lower or middle segments. Anomalies of the lower face generally involve the derivatives of the branchial apparatus and therefore manifest as defects in the mandible, pinnae, external auditory canals, and portions of the middle ears. These anomalies are occasionally isolated, but most of them occur in combination with systemic syndromes. These anomalies generally do not occur with respiratory compromise. Anomalies of the midface may extend from the upper lip to the forehead, reflecting the complex embryology of this region. Most of these deformities are isolated, but some patients with facial clefts, notably the midline cleft syndrome and holoprosencephaly, have anomalies in other sites. This is important because these patients will require detailed imaging of the face and brain. Anomalies of the midface tend to involve the nose and its air-conducting passages. We prefer to divide these anomalies into those with and without respiratory obstruction. The most common anomalies that result in airway compromise include posterior choanal stenoses and atresias, bilateral cysts (mucoceles) of the distal lacrimal ducts, and stenosis of the pyriform (anterior) nasal aperture. These may be optimally evaluated with computed tomography (CT) and generally require immediate treatment to ensure adequate ventilation. Rare nasal anomalies that also result in airway obstruction are agenesis of the pharynx, agenesis of the nose, and hypoplasia of the nasal alae. Agenesis of the nasopharynx and nose are complex anomalies that require both CT and magnetic resonance imaging (MRI). The diagnosis of hypoplasia of the nasal alae is a clinical one; these anomalies do not require imaging studies. Besides facial clefts, anomalies of the nose without respiratory obstruction tend to be centered around the nasofrontal region. This is the site of the most common sincipital encephaloceles. Patients with frontonasal and nasoethmoidal encephaloceles require both

  2. 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

  3. Detecting transition in agricultural systems

    NASA Technical Reports Server (NTRS)

    Neary, P. J.; Coiner, J. C.

    1979-01-01

    Remote sensing of agricultural phenomena has been largely concentrated on analysis of agriculture at the field level. Concern has been to identify crop status, crop condition, and crop distribution, all of which are spatially analyzed on a field-by-field basis. A more general level of abstraction is the agricultural system, or the complex of crops and other land cover that differentiate various agricultural economies. The paper reports on a methodology to assist in the analysis of the landscape elements of agricultural systems with Landsat digital data. The methodology involves tracing periods of photosynthetic activity for a fixed area. Change from one agricultural system to another is detected through shifts in the intensity and periodicity of photosynthetic activity as recorded in the radiometric return to Landsat. The Landsat-derived radiometric indicator of photosynthetic activity appears to provide the ability to differentiate agricultural systems from each other as well as from conterminous natural vegetation.

  4. 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)…

  5. Uhl's anomaly.

    PubMed Central

    Vecht, R J; Carmichael, D J; Gopal, R; Philip, G

    1979-01-01

    Uhl's anomaly of the heart is a rare condition. Another well-documented case is presented with a review of the published reports outlining the main clinical features and the bad overall prognosis. Right atriotomy should be avoided if closure of the atrial septal defect is attempted. Images PMID:465242

  6. Semiclassical anomalies of the quantum mechanical systems and their modifications for the asymptotic matching

    SciTech Connect

    Deniz, Coskun

    2011-08-15

    JWKB solutions to the Initial Value Problems (IVPs) of the Time Independent Schrodinger's Equation (TISE) for the Simple Linear Potentials (SLPs) with a turning point parameter have been studied according to the turning points by graphical analysis to test the results of the JWKB solutions and suggested modifications. The anomalies happening in the classically inaccessible region where the SLP function is smaller than zero and the results of the suggested modifications, which are in consistent with the quantum mechanical theories, to remove these anomalies in this region have been presented. The origins of the anomalies and verifications of the suggested modifications showing a great success in the results have also been studied in terms of a suggested M{sub ij}=S{sup {approx}}{sub i-1,j} matrix elements made up of the JWKB expansion terms, S{sub i-1,j} (where i = 1, 2, 3 and j 1, 2). The results of the modifications for the IVPs and their application to the Bound State Problems (BSPs) with an example application of the Harmonic Oscillator (HO) have been presented and their generalization for any potential function have been discussed and classified accordingly.

  7. Geoelectrical Characterization of the Punta Banda System: A Possible Structural Control for the Geothermal Anomalies

    NASA Astrophysics Data System (ADS)

    Arango-Galvan, C.; Flores-Marquez, E.; Prol-Ledesma, R.; Working Group, I.

    2007-05-01

    The lack of sufficient drinking water in México has become a very serious problem, especially in the northern desert regions of the country. In order to give a real solution to this phenomenon the IMPULSA research program has been created to develope novel technologies based on desalination of sea and brackish water using renewable sources of energy to face the problem. The Punta Banda geothermal anomaly is located towards the northern part of Baja California Peninsula (Mexico). High water temperatures in some wells along the coast depicted a geothermal anomaly. An audiomagnetotelluric survey was carried out in the area as a preliminary study, both to understand the process generating these anomalous temperatures and to assess its potential exploitation to supply hot water to desalination plants. Among the electromagnetic methods, the audiomagnetotellurics (AMT) method is appropriated for deep groundwater and geothermal studies. The survey consisted of 27 AMT stations covering a 5 km profile along the Agua Blanca Fault. The employed array allowed us to characterize the geoelectrical properties of the main structures up to 500 m depth. Two main geoelectrical zones were identified: 1) a shallow low resistivity media located at the central portion of the profile, coinciding with the Maneadero valley and 2) two high resitivity structures bordering the conductive zone possibly related to NS faulting, already identified by previous geophysical studies. These results suggest that the main geothermal anomalies are controlled by the dominant structural regime in the zone.

  8. Nucleic acid detection system and method for detecting influenza

    DOEpatents

    Cai, Hong; Song, Jian

    2015-03-17

    The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

  9. Electrochemical anomalies of protic ionic liquid - Water systems: A case study using ethylammonium nitrate - Water system

    NASA Astrophysics Data System (ADS)

    Abe, Hiroshi; Nakama, Kazuya; Hayashi, Ryotaro; Aono, Masami; Takekiyo, Takahiro; Yoshimura, Yukihiro; Saihara, Koji; Shimizu, Akio

    2016-08-01

    Electrochemical impedance spectroscopy was used to evaluate protic ionic liquid (pIL)-water mixtures in the temperature range of -35-25 °C. The pIL used in this study was ethylammonium nitrate (EAN). At room temperature, the resonant mode of conductivity was observed in the high frequency region. The anomalous conductivity disappeared once solidification occurred at low temperatures. The kinetic pH of the EAN-water system was investigated at a fixed temperature. Rhythmic pH oscillations in the EAN-H2O mixtures were induced at 70 < x < 90 mol% H2O. The electrochemical instabilities in a EAN-water mixture are caused in an intermediate state between pIL and bulk water. From the ab initio calculations, it was observed that the dipole moment of the EAN-water complex shows a discrete jump at around 85 mol% H2O. Water-mediated hydrogen bonding network drastically changes at the crossover concentration.

  10. Capillary Electrophoresis - Optical Detection Systems

    SciTech Connect

    Sepaniak, M. J.

    2001-08-06

    Molecular recognition systems are developed via molecular modeling and synthesis to enhance separation performance in capillary electrophoresis and optical detection methods for capillary electrophoresis. The underpinning theme of our work is the rational design and development of molecular recognition systems in chemical separations and analysis. There have been, however, some subtle and exciting shifts in our research paradigm during this period. Specifically, we have moved from mostly separations research to a good balance between separations and spectroscopic detection for separations. This shift is based on our perception that the pressing research challenges and needs in capillary electrophoresis and electrokinetic chromatography relate to the persistent detection and flow rate reproducibility limitations of these techniques (see page 1 of the accompanying Renewal Application for further discussion). In most of our work molecular recognition reagents are employed to provide selectivity and enhance performance. Also, an emerging trend is the use of these reagents with specially-prepared nano-scale materials. Although not part of our DOE BES-supported work, the modeling and synthesis of new receptors has indirectly supported the development of novel microcantilevers-based MEMS for the sensing of vapor and liquid phase analytes. This fortuitous overlap is briefly covered in this report. Several of the more significant publications that have resulted from our work are appended. To facilitate brevity we refer to these publications liberally in this progress report. Reference is also made to very recent work in the Background and Preliminary Studies Section of the Renewal Application.

  11. Compensated intruder-detection systems

    DOEpatents

    McNeilly, David R.; Miller, William R.

    1984-01-01

    Intruder-detection systems in which intruder-induced signals are transmitted through a medium also receive spurious signals induced by changes in a climatic condition affecting the medium. To combat this, signals received from the detection medium are converted to a first signal. The system also provides a reference signal proportional to climate-induced changes in the medium. The first signal and the reference signal are combined for generating therefrom an output signal which is insensitive to the climatic changes in the medium. An alarm is energized if the output signal exceeds a preselected value. In one embodiment, an acoustic cable is coupled to a fence to generate a first electrical signal proportional to movements thereof. False alarms resulting from wind-induced movements of the fence (detection medium) are eliminated by providing an anemometer-driven voltage generator to provide a reference voltage proportional to the velocity of wind incident on the fence. An analog divider receives the first electrical signal and the reference signal as its numerator and denominator inputs, respectively, and generates therefrom an output signal which is insensitive to the wind-induced movements in the fence.

  12. Classification of congenital anomalies of the hand and upper limb: development and assessment of a new system.

    PubMed

    Tonkin, Michael A; Tolerton, Sarah K; Quick, Tom J; Harvey, Isaac; Lawson, Richard D; Smith, Nicholas C; Oberg, Kerby C

    2013-09-01

    The Oberg, Manske, and Tonkin (OMT) classification of congenital hand and upper limb anomalies was proposed in 2010 as a replacement for the Swanson International Federation of Societies for Surgery of the Hand classification system, which has been the accepted system of classification for the international surgical community since 1976. The OMT system separates malformations from deformations and dysplasias. Malformations are subdivided according to the axis of formation and differentiation that is primarily affected and whether the anomalies involve the whole limb or the hand plate. This review outlines the development of classification systems and explores the difficulty of incorporating our current knowledge of limb embryogenesis at a molecular level into current systems. An assessment of the efficacy of the OMT classification demonstrates acceptable inter- and intraobserver reliability. A prospective review of 101 patients confirms that all diagnoses could be classified within the OMT system. Consensus expert opinion allowed classification of those conditions for which there is not a clear understanding of the mechanism of dysmorphology. A refined and expanded OMT classification is presented.

  13. Ionization detection system for aerosols

    DOEpatents

    Jacobs, Martin E.

    1977-01-01

    This invention relates to an improved smoke-detection system of the ionization-chamber type. In the preferred embodiment, the system utilizes a conventional detector head comprising a measuring ionization chamber, a reference ionization chamber, and a normally non-conductive gas triode for discharging when a threshold concentration of airborne particulates is present in the measuring chamber. The improved system utilizes a measuring ionization chamber which is modified to minimize false alarms and reductions in sensitivity resulting from changes in ambient temperature. In the preferred form of the modification, an annular radiation shield is mounted about the usual radiation source provided to effect ionization in the measuring chamber. The shield is supported by a bimetallic strip which flexes in response to changes in ambient temperature, moving the shield relative to the source so as to vary the radiative area of the source in a manner offsetting temperature-induced variations in the sensitivity of the chamber.

  14. Autonomous pathogen detection system 2001

    SciTech Connect

    Langlois, R G; Wang, A; Colston, B; Masquelier, D; Jones, L; Venkateswaran, K S; Nasarabadi, S; Brown, S; Ramponi, A; Milanovich, F P

    2001-01-09

    The objective of this project is to design, fabricate and field-demonstrate a fully Autonomous Pathogen Detector (identifier) System (APDS). This will be accomplished by integrating a proven flow cytometer and real-time polymerase chain reaction (PCR) detector with sample collection, sample preparation and fluidics to provide a compact, autonomously operating instrument capable of simultaneously detecting multiple pathogens and/or toxins. The APDS will be designed to operate in fixed locations, where it continuously monitors air samples and automatically reports the presence of specific biological agents. The APDS will utilize both multiplex immuno and nucleic acid assays to provide ''quasi-orthogonal'', multiple agent detection approaches to minimize false positives and increase the reliability of identification. Technical advancements across several fronts must first be made in order to realize the full extent of the APDS. Commercialization will be accomplished through three progressive generations of instruments. The APDS is targeted for domestic applications in which (1) the public is at high risk of exposure to covert releases of bioagent such as in major subway systems and other transportation terminals, large office complexes, and convention centers; and (2) as part of a monitoring network of sensors integrated with command and control systems for wide area monitoring of urban areas and major gatherings (e.g., inaugurations, Olympics, etc.). In this latter application there is potential that a fully developed APDS could add value to Defense Department monitoring architectures.

  15. Enterprise network intrusion detection and prevention system (ENIDPS)

    NASA Astrophysics Data System (ADS)

    Akujuobi, C. M.; Ampah, N. K.

    2007-04-01

    Securing enterprise networks comes under two broad topics: Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). The right combination of selected algorithms/techniques under both topics produces better security for a given network. This approach leads to using layers of physical, administrative, electronic, and encrypted systems to protect valuable resources. So far, there is no algorithm, which guarantees absolute protection for a given network from intruders. Intrusion Prevention Systems like IPSec, Firewall, Sender ID, Domain Keys Identified Mail (DKIM) etc. do not guarantee absolute security just like existing Intrusion Detection Systems. Our approach focuses on developing an IDS, which will detect all intruders that bypass the IPS and at the same time will be used in updating the IPS, since the IPS fail to prevent some intruders from entering a given network. The new IDS will employ both signature-based detection and anomaly detection as its analysis strategy. It should therefore be able to detect known and unknown intruders or attacks and further isolate those sources of attack within the network. Both real-time and off-line IDS predictions will be applied under the analysis and response stages. The basic IDS architecture will involve both centralized and distributed/heterogeneous architecture to ensure effective detection. Pro-active responses and corrective responses will be employed. The new security system, which will be made up of both IDS and IPS, should be less expensive to implement compared to existing ones. Finally, limitations of existing security systems have to be eliminated with the introduction of the new security system.

  16. Pulsed helium ionization detection system

    DOEpatents

    Ramsey, Roswitha S.; Todd, Richard A.

    1987-01-01

    A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

  17. Pulsed helium ionization detection system

    DOEpatents

    Ramsey, R.S.; Todd, R.A.

    1985-04-09

    A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

  18. Optical fibre gas detections systems

    NASA Astrophysics Data System (ADS)

    Culshaw, Brian

    2016-05-01

    This tutorial review covers the principles of and prospects for fibre optic sensor technology in gas detection. Many of the potential benefits common to fibre sensor technology also apply in the context of gas sensing - notably long distance - many km - access to multiple remote measurement points; invariably intrinsic safety; access to numerous important gas species and often uniquely high levels of selectivity and/or sensitivity. Furthermore, the range of fibre sensor network architectures - single point, multiple point and distributed - enable unprecedented flexibility in system implementation. Additionally, competitive technologies and regulatory issues contribute to final application potential.

  19. Infrared trace element detection system

    DOEpatents

    Bien, F.; Bernstein, L.S.; Matthew, M.W.

    1988-11-15

    An infrared trace element detection system includes an optical cell into which the sample fluid to be examined is introduced and removed. Also introduced into the optical cell is a sample beam of infrared radiation in a first wavelength band which is significantly absorbed by the trace element and a second wavelength band which is not significantly absorbed by the trace element for passage through the optical cell through the sample fluid. The output intensities of the sample beam of radiation are selectively detected in the first and second wavelength bands. The intensities of a reference beam of the radiation are similarly detected in the first and second wavelength bands. The sensed output intensity of the sample beam in one of the first and second wavelength bands is normalized with respect to the other and similarly, the intensity of the reference beam of radiation in one of the first and second wavelength bands is normalized with respect to the other. The normalized sample beam intensity and normalized reference beam intensity are then compared to provide a signal from which the amount of trace element in the sample fluid can be determined. 11 figs.

  20. Infrared trace element detection system

    DOEpatents

    Bien, Fritz; Bernstein, Lawrence S.; Matthew, Michael W.

    1988-01-01

    An infrared trace element detection system including an optical cell into which the sample fluid to be examined is introduced and removed. Also introduced into the optical cell is a sample beam of infrared radiation in a first wavelength band which is significantly absorbed by the trace element and a second wavelength band which is not significantly absorbed by the trace element for passage through the optical cell through the sample fluid. The output intensities of the sample beam of radiation are selectively detected in the first and second wavelength bands. The intensities of a reference beam of the radiation are similarly detected in the first and second wavelength bands. The sensed output intensity of the sample beam in one of the first and second wavelength bands is normalized with respect to the other and similarly, the intensity of the reference beam of radiation in one of the first and second wavelength bands is normalized with respect to the other. The normalized sample beam intensity and normalized reference beam intensity are then compared to provide a signal from which the amount of trace element in the sample fluid can be determined.

  1. The isotopic homogeneity in the early solar system: Revisiting the CAI oxygen isotopic anomaly

    NASA Astrophysics Data System (ADS)

    Ozima, M.; Yamada, A.

    2009-12-01

    Since the first discovery of the mass-independently fractionated oxygen isotopes in anhydrous, high temperature Ca-Al rich inclusion minerals in carbonaceous meteorites (CAIs) by Clayton et al. (1), their common occurrence in primitive meteorites has generally been regarded to reflect some fundamental process prevalent in the early solar nebula. The CAI oxygen isotopic composition is uniquely characterized by (i) large mass independent isotopic fractionation and (ii) their isotopic data in an oxygen three isotope plot (δ17O - δ18O (δ17O ≡ {(17O/16O)/(17O/16O)SMOW - 1} × 1000) yield nearly a straight line with a slope 1.0. In establishing these characteristics, ion microprobe analyses has played a central role, especially an isotopic mapping technique (isotopography) was crucial (e.g., 2). The extraordinary oxygen isotopic ratio in CAIs is widely attributed to the self-shielding absorption of UV radiation in CO, one of the dominant chemical compounds in the early solar nebula (3). However, the self-shielding scenario necessarily leads to the unusual prediction that a mean solar oxygen isotopic composition differs from most of planetary bodies including Earth, Moon, and Mars. If the self-shielding process were indeed responsible to the CAI oxygen isotopic anomaly, this would require a fundamental revision of the current theory of the origin of the solar system, which generally assumes the initial total vaporization of nebula material to give rise to isotopic homogenization. The GENESIS mission launched in 2001(4), which collected oxygen in the solar wind was hoped to resolve the isotopic composition of the Sun. However, because of difficulties in correcting for instrumental and more importantly for intrinsic isotopic fractionation between the SW and the Sun, a final answer is yet to be seen (5). Here, we show on the basis of the oxygen isotopic fractionation systematics that the self shielding hypothesis cannot explain the key characteristics of the CAI oxygen

  2. Explosives detection system and method

    DOEpatents

    Reber, Edward L.; Jewell, James K.; Rohde, Kenneth W.; Seabury, Edward H.; Blackwood, Larry G.; Edwards, Andrew J.; Derr, Kurt W.

    2007-12-11

    A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

  3. 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

  4. Recent advances in microfluidic detection systems

    PubMed Central

    Baker, Christopher A; Duong, Cindy T; Grimley, Alix; Roper, Michael G

    2009-01-01

    There are numerous detection methods available for methods are being put to use for detection on these miniaturized systems, with the analyte of interest driving the choice of detection method. In this article, we summarize microfluidic 2 years. More focus is given to unconventional approaches to detection routes and novel strategies for performing high-sensitivity detection. PMID:20414455

  5. A Portable Infrasonic Detection System

    NASA Technical Reports Server (NTRS)

    Shams, Qamar A.; Burkett, Cecil G.; Zuckerwar, Allan J.; Lawrenson, Christopher C.; Masterman, Michael

    2008-01-01

    During last couple of years, NASA Langley has designed and developed a portable infrasonic detection system which can be used to make useful infrasound measurements at a location where it was not possible previously. The system comprises an electret condenser microphone, having a 3-inch membrane diameter, and a small, compact windscreen. Electret-based technology offers the lowest possible background noise, because Johnson noise generated in the supporting electronics (preamplifier) is minimized. The microphone features a high membrane compliance with a large backchamber volume, a prepolarized backplane and a high impedance preamplifier located inside the backchamber. The windscreen, based on the high transmission coefficient of infrasound through matter, is made of a material having a low acoustic impedance and sufficiently thick wall to insure structural stability. Close-cell polyurethane foam has been found to serve the purpose well. In the proposed test, test parameters will be sensitivity, background noise, signal fidelity (harmonic distortion), and temporal stability. The design and results of the compact system, based upon laboratory and field experiments, will be presented.

  6. Photoelectric detection system. [manufacturing automation

    NASA Technical Reports Server (NTRS)

    Currie, J. R.; Schansman, R. R. (Inventor)

    1982-01-01

    A photoelectric beam system for the detection of the arrival of an object at a discrete station wherein artificial light, natural light, or no light may be present is described. A signal generator turns on and off a signal light at a selected frequency. When the object in question arrives on station, ambient light is blocked by the object, and the light from the signal light is reflected onto a photoelectric sensor which has a delayed electrical output but is of the frequency of the signal light. Outputs from both the signal source and the photoelectric sensor are fed to inputs of an exclusively OR detector which provides as an output the difference between them. The difference signal is a small width pulse occurring at the frequency of the signal source. By filter means, this signal is distinguished from those responsive to sunlight, darkness, or 120 Hz artificial light. In this fashion, the presence of an object is positively established.

  7. Planetary system detection by POINTS

    NASA Technical Reports Server (NTRS)

    Reasenberg, Robert D.

    1993-01-01

    The final report and semiannual reports 1, 2, and 3 in response to the study of 'Planetary System Detection by POINTS' is presented. The grant covered the period from 15 Jun. 1988 through 31 Dec. 1989. The work during that period comprised the further development and refinement of the POINTS concept. The status of the POINTS development at the end of the Grant period was described by Reasenberg in a paper given at the JPL Workshop on Space Interferometry, 12-13 Mar. 1990, and distributed as CfA Preprint 3138. That paper, 'POINTS: a Small Astrometric Interferometer,' follows as Appendix-A. Our proposal P2276-7-09, dated July 1990, included a more detailed description of the state of the development of POINTS at the end of the tenure of Grant NAGW-1355. That proposal, which resulted in Grant NAGW-2497, is included by reference.

  8. Controls on Martian Hydrothermal Systems: Application to Valley Network and Magnetic Anomaly Formation

    NASA Technical Reports Server (NTRS)

    Harrison, Keith P.; Grimm, Robert E.

    2002-01-01

    Models of hydrothermal groundwater circulation can quantify limits to the role of hydrothermal activity in Martian crustal processes. We present here the results of numerical simulations of convection in a porous medium due to the presence of a hot intruded magma chamber. The parameter space includes magma chamber depth, volume, aspect ratio, and host rock permeability and porosity. A primary goal of the models is the computation of surface discharge. Discharge increases approximately linearly with chamber volume, decreases weakly with depth (at low geothermal gradients), and is maximized for equant-shaped chambers. Discharge increases linearly with permeability until limited by the energy available from the intrusion. Changes in the average porosity are balanced by changes in flow velocity and therefore have little effect. Water/rock ratios of approximately 0.1, obtained by other workers from models based on the mineralogy of the Shergotty meteorite, imply minimum permeabilities of 10(exp -16) sq m2 during hydrothermal alteration. If substantial vapor volumes are required for soil alteration, the permeability must exceed 10(exp -15) sq m. The principal application of our model is to test the viability of hydrothermal circulation as the primary process responsible for the broad spatial correlation of Martian valley networks with magnetic anomalies. For host rock permeabilities as low as 10(exp -17) sq m and intrusion volumes as low as 50 cu km, the total discharge due to intrusions building that part of the southern highlands crust associated with magnetic anomalies spans a comparable range as the inferred discharge from the overlying valley networks.

  9. Analysis of the Nuevo Leon magnetic anomaly and its possible relation to the Cerro Prieto magmatic-hydrothermal system

    SciTech Connect

    Goldstein, N.E.; Wilt, M.J.; Corrigan, D.J.

    1982-10-01

    The broad dipolar magnetic anomaly whose positive peak is centered near Ejido Nuevo Leon, some 5 km east of the Cerro Prieto I Power Plant, has long been suspected to have a genetic relationship to the thermal source of the Cerro Prieto geothermal system. This suspicion was reinforced after several deep geothermal wells, drilled to depths of 3 to 3.5 km over the anomaly, intersected an apparent dike-sill complex consisting mainly of diabase but with minor rhyodacite. A detailed fit of the observed magnetic field to a computer model indicates that the source may be approximated by a tabular block 4 by 6 km in area, 3.7 km in depth, 2.3 km thick, and dipping slightly to the north. Mafic dike chips from one well, NL-1, were analyzed by means of electron microprobe analyses which showed tham to contain a titanomagnetite that is paramagnetic at in-situ temperature conditions. As the dike mineralogy does not account for the magnetic anomaly, the magnetic source is believed to be a deeper, magnetite-rich assemblage of peridotite-gabbro plutons. the suite of igneous rocks was probably passively emplaced at a shallow depth in response to crustal extension and thinning brought on by strike-slip faulting. The bottom of the magnetic source body, at an estimated depth of 6 km, is presumed to be at or near that of the Curie isotherm (575/sup 0/C) for magnetite, the principal ferromagnetic mineral in peridotitic-gabbroic rocks. The geological model derived from the magnetic study is generally supported by other geophysical data. In particular, earthquake data suggest dike injection is occurring at depths of 6 to 11 km in an area beneath the magnetic source. Thus, it is possible that heat for the geothermal field is being maintained by continuing crustal extension and magmatic activity.

  10. Analysis of the Nuevo Leon Magnetic Anomaly and its possible relation to the Cerro Prieto magmatic-hydrothermal system

    SciTech Connect

    Goldstein, N.E.; Corrigan, D.J.; Wilt, M.J.

    1984-01-01

    The broad dipolar magnetic anomaly whose positive peak is centered near Ejido Nuevo Leon, some 5 km east of the Cerro Prieto I power plant, has long been suspected to have a genetic relationship to the thermal source of the Cerro Prieto geothermal system. This suspicion was reinforced after several deep geothermal wells, drilled to depths of 3-3.5 km over the anomaly, intersected an apparent dike-sill complex consisting mainly of diabase but with minor rhyodacite. A detailed fit of the observed magnetic field to a computer model indicates that the source may be approximated by a tabular block 4 x 6 km in area, 3.7 km in depth, 2.3 km thick, and dipping slightly to the north. Mafic dike chips from one well, NL-1, were analysed by means of electron microprobe analyses which showed them to contain a titanomagnetite that is paramagnetic at in situ temperature conditions. As the dike mineralogy does not account for the magnetic anomaly, the magnetic source is believed to be a deeper, magnetite-rich assemblage of peridotite-gabbro plutons. The suite of igneous rocks was probably emplaced at a shallow depth in response to crustal extension and thinning brought on by en echelon strike-slip faulting. The bottom of the magnetic source body, at an estimated depth of 6 km, is presumed to be at or near that of the Curie isotherm (575/sup 0/C) for magnetite, the principal ferromagnetic mineral in peridotiticgabbroic rocks. The geological model derived from the magnetic study is generally supported by other geophysical data. In particular, earthquake data suggest dike injection is occurring at depths of 6-11 km in an area beneath the magnetic source. Thus, it is possible that heat for the geothermal field is being maintained by continuing crustal extension and magmatic activity.

  11. New Peak Temperature Constraints Using RSCM Geothermometry on Lucia Subterrane in Franciscan Complex (California, USA): Detection of Thermal Anomalies in Gold-Bearing Quartz Veins Surrounding.

    NASA Astrophysics Data System (ADS)

    Lahfid, A.; Delchini, S.; Lacroix, B.

    2015-12-01

    The occurrence of deposits hosted by carbonaceous materials-rich metasediments is widespread. Therefore, we aims in this study to investigate the potential of the Raman Spectroscopy of Carbonaceous Material (RSCM) geothermometry to detect thermal anomalies in hydrothermal ore deposits environment and to demonstrate the ability of warm fluids, migrating through the sedimentary sequence to locally disturb the thermal gradient and associated peak temperatures. For this purpose, we have chosen the Lucia subterrane in the Franciscan Complex (California, USA), which includes gold-bearing quartz veins that witness a hydrothermal overprint (Underwood et al., 1995).The sediments in this zone essentially comprise greywacke and shale-matrix mélange (e.g. Frey and Robinson, 1999), which have undergone high-pressure, low-temperature metamorphism. The thermal history of the Lucia subterrane has been previously proposed by Underwood et al. (1995), essentially using vitrinite reflectance method (Rm). Rm values increase from the south to the north; they vary between 0.9 and 3.7 % (~150-280°C). All these results suggest that the Lucia subterrane underwent a regional increase of thermal gradient toward the north. Anomalous Rm values from 4.5% to 4.9% (~305-315°C) are recorded near Cape San Martin. These highest temperatures estimated are likely, associated with a late hydrothermal event (Underwood et al., 1995). Estimated Raman temperatures 1) confirmed the increase in the metamorphic grade towards the north already shown by Underwood et al. (1995), using classical methods like mineralogy and vitrinite reflectance and 2) exhibit anomalous values (temperatures reach 350°C). These anomalies are probably due to the later hydrothermal event. This result suggests that RSCM could be used as a reliable tool to determine thermal anomalies caused by hot fluid-flow.

  12. An automatic LCD panel quality detection system

    NASA Astrophysics Data System (ADS)

    Guo, Bianfang; Hou, Wenguang; Ding, Mingyue

    2009-10-01

    Automatic detection using computer vision expands rapidly along with the development of image processing technology. In this paper, we developed a rapid LCD quality detection system for automobile instrument panel production, which has wide range of usage and good stability.Our automatic detection system consists of four parts: panel fixture, signal generator module, image acquisition module and image processing software. Experiments demonstrated that our system is feasible, efficient and fast compared to manual detection.

  13. ISOTOPIC ANOMALIES IN PRIMITIVE SOLAR SYSTEM MATTER: SPIN-STATE-DEPENDENT FRACTIONATION OF NITROGEN AND DEUTERIUM IN INTERSTELLAR CLOUDS

    SciTech Connect

    Wirstroem, Eva S.; Cordiner, Martin A.; Charnley, Steven B.; Milam, Stefanie N.

    2012-09-20

    Organic material found in meteorites and interplanetary dust particles is enriched in D and {sup 15}N. This is consistent with the idea that the functional groups carrying these isotopic anomalies, nitriles and amines, were formed by ion-molecule chemistry in the protosolar nebula. Theoretical models of interstellar fractionation at low temperatures predict large enrichments in both D and {sup 15}N and can account for the largest isotopic enrichments measured in carbonaceous meteorites. However, more recent measurements have shown that, in some primitive samples, a large {sup 15}N enrichment does not correlate with one in D, and that some D-enriched primitive material displays little, if any, {sup 15}N enrichment. By considering the spin-state dependence in ion-molecule reactions involving the ortho and para forms of H{sub 2}, we show that ammonia and related molecules can exhibit such a wide range of fractionation for both {sup 15}N and D in dense cloud cores. We also show that while the nitriles, HCN and HNC, contain the greatest {sup 15}N enrichment, this is not expected to correlate with extreme D enrichment. These calculations therefore support the view that solar system {sup 15}N and D isotopic anomalies have an interstellar heritage. We also compare our results to existing astronomical observations and briefly discuss future tests of this model.

  14. Isotopic Anomalies in Primitive Solar System Matter: Spin-State Dependent Fractionation of Nitrogen and Deuterium in Interstellar Clouds

    NASA Technical Reports Server (NTRS)

    Wirstrom, Eva S.; Charnley, Steven B.; Cordiner, Martin A.; Milan, Stefanie N.

    2012-01-01

    Organic material found in meteorites and interplanetary dust particles is enriched in D and N-15, This is consistent with the idea that the functional groups carrying these isotopic anomalies, nitriles and amines, were formed by ion-molecule chemistry in the protosolar core. Theoretical models of interstellar fractionation at low temperatures predict large enrichments in both D and N-15 and can account for the largest isotop c enrichments measured in carbonaceous meteorites, However, more recent measurements have shown that, in some primitive samples, a large N-15 enrichment does not correlate with one in D, and that some D-enriched primitive material displays little, if any, N-15 enrichment. By considering the spin-state dependence in ion-molecule reactions involving the ortho and para forms of H2, we show that ammonia and related molecules can exhibit such a wide range of fractionation for both N-15 and D in dense cloud cores, We also show that while the nitriles, HCN and HNC, contain the greatest N-15 enrichment, this is not expected to correlate with extreme D emichment. These calculations therefore support the view that Solar System N-15 and D isotopic anomalies have an interstellar heritage, We also compare our results to existing astronomical observations and briefly discuss future tests of this model.

  15. Isotopic Anomalies in Primitive Solar System Matter: Spin-State-Dependent Fractionation of Nitrogen and Deuterium in Interstellar Clouds

    NASA Technical Reports Server (NTRS)

    Wirstrom, Eva S.; Charnley, Steven B.; Cordiner, Martin A.; Milam, Stefanie N.

    2012-01-01

    Organic material found in meteorites and interplanetary dust particles is enriched in D and N-15. This is consistent with the idea that the functional groups carrying these isotopic anomalies, nitriles and amines, were formed by ion-molecule chemistry in the protosolar nebula, Theoretical models of interstellar fractionation at low temperatures predict large enrichments in both D and N-15 and can account for the largest isotopic enrichments measured in carbonaceous meteorites. However, more recent measurements have shown that, in some primitive samples, a large N-15 enrichment does not correlate with one in D, and that some D-enriched primitive material displays little, if any, N-15 enrichment. By considering the spin-state dependence in ion-molecule reactions involving the ortho and para forms of H2, we show that ammonia and related molecules can exhibit such a wide range of fractionation for both N-15 and D in dense cloud cores. We also show that while the nitriles, HCN and HNC, contain the greatest N=15 enrichment, this is not expected to correlate with extreme D enrichment. These calculations therefore support the view that solar system N-15 and D isotopic anomalies have an interstellar heritage. We also compare our results to existing astronomical observations and briefly discuss future tests of this model.

  16. Trouble Brewing: Using Observations of Invariant Behavior to Detect Malicious Agency in Distributed Control Systems

    NASA Astrophysics Data System (ADS)

    McEvoy, Thomas Richard; Wolthusen, Stephen D.

    Recent research on intrusion detection in supervisory data acquisition and control (SCADA) and DCS systems has focused on anomaly detection at protocol level based on the well-defined nature of traffic on such networks. Here, we consider attacks which compromise sensors or actuators (including physical manipulation), where intrusion may not be readily apparent as data and computational states can be controlled to give an appearance of normality, and sensor and control systems have limited accuracy. To counter these, we propose to consider indirect relations between sensor readings to detect such attacks through concurrent observations as determined by control laws and constraints.

  17. A population-based case-control study of drinking-water nitrate and congenital anomalies using Geographic Information Systems (GIS) to develop individual-level exposure estimates.

    PubMed

    Holtby, Caitlin E; Guernsey, Judith R; Allen, Alexander C; Vanleeuwen, John A; Allen, Victoria M; Gordon, Robert J

    2014-02-01

    Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured nitrate concentrations from 140 temporally monitored private wells and 6 municipal water supplies. Cases of major congenital anomalies in Kings County, Nova Scotia, Canada, between 1988 and 2006 were selected from province-wide population-based perinatal surveillance databases and matched to controls from the same databases. Unconditional multivariable logistic regression was performed to test for an association between drinking-water nitrate exposure and congenital anomalies after adjusting for clinically relevant risk factors. Employing all nitrate data there was a trend toward increased risk of congenital anomalies for increased nitrate exposure levels though this was not statistically significant. After stratification of the data by conception before or after folic acid supplementation, an increased risk of congenital anomalies for nitrate exposure of 1.5-5.56 mg/L (2.44; 1.05-5.66) and a trend toward increased risk for >5.56 mg/L (2.25; 0.92-5.52) was found. Though the study is likely underpowered, these results suggest that drinking-water nitrate exposure may contribute to increased risk of congenital anomalies at levels below the current Canadian maximum allowable concentration. PMID:24503976

  18. A population-based case-control study of drinking-water nitrate and congenital anomalies using Geographic Information Systems (GIS) to develop individual-level exposure estimates.

    PubMed

    Holtby, Caitlin E; Guernsey, Judith R; Allen, Alexander C; Vanleeuwen, John A; Allen, Victoria M; Gordon, Robert J

    2014-02-05

    Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured nitrate concentrations from 140 temporally monitored private wells and 6 municipal water supplies. Cases of major congenital anomalies in Kings County, Nova Scotia, Canada, between 1988 and 2006 were selected from province-wide population-based perinatal surveillance databases and matched to controls from the same databases. Unconditional multivariable logistic regression was performed to test for an association between drinking-water nitrate exposure and congenital anomalies after adjusting for clinically relevant risk factors. Employing all nitrate data there was a trend toward increased risk of congenital anomalies for increased nitrate exposure levels though this was not statistically significant. After stratification of the data by conception before or after folic acid supplementation, an increased risk of congenital anomalies for nitrate exposure of 1.5-5.56 mg/L (2.44; 1.05-5.66) and a trend toward increased risk for >5.56 mg/L (2.25; 0.92-5.52) was found. Though the study is likely underpowered, these results suggest that drinking-water nitrate exposure may contribute to increased risk of congenital anomalies at levels below the current Canadian maximum allowable concentration.

  19. A Population-Based Case-Control Study of Drinking-Water Nitrate and Congenital Anomalies Using Geographic Information Systems (GIS) to Develop Individual-Level Exposure Estimates

    PubMed Central

    Holtby, Caitlin E.; Guernsey, Judith R.; Allen, Alexander C.; VanLeeuwen, John A.; Allen, Victoria M.; Gordon, Robert J.

    2014-01-01

    Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured nitrate concentrations from 140 temporally monitored private wells and 6 municipal water supplies. Cases of major congenital anomalies in Kings County, Nova Scotia, Canada, between 1988 and 2006 were selected from province-wide population-based perinatal surveillance databases and matched to controls from the same databases. Unconditional multivariable logistic regression was performed to test for an association between drinking-water nitrate exposure and congenital anomalies after adjusting for clinically relevant risk factors. Employing all nitrate data there was a trend toward increased risk of congenital anomalies for increased nitrate exposure levels though this was not statistically significant. After stratification of the data by conception before or after folic acid supplementation, an increased risk of congenital anomalies for nitrate exposure of 1.5–5.56 mg/L (2.44; 1.05–5.66) and a trend toward increased risk for >5.56 mg/L (2.25; 0.92–5.52) was found. Though the study is likely underpowered, these results suggest that drinking-water nitrate exposure may contribute to increased risk of congenital anomalies at levels below the current Canadian maximum allowable concentration. PMID:24503976

  20. 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.

  1. A Rare Anomaly of Biliary System: MRCP Evidence of a Cystic Duct Cyst.

    PubMed

    Goya, Cemil; Arslan, Mehmet Serif; Yavuz, Alpaslan; Hamidi, Cihad; Kuday, Suzan; Okur, Mehmet Hanifi; Aydogdu, Bahattin

    2014-01-01

    Cystic duct cysts are a rare congenital anomaly. While the other bile duct cysts (choledochus and the intrahepatic bile ducts) are classified according to the classification described by Tadoni, there is no classification method described by the cystic duct cysts, although it is claimed that the cystic duct cysts may constitute a new "Type 6" category. Only a limited number of patients with cystic duct cysts have been reported in the literature. The diagnosis is usually made in the neonatal period or during childhood. The clinical symptoms are nonspecific and usually include pain in the right upper quadrant and jaundice. The condition may also present with biliary colic, cholangitis, cholelithiasis, or pancreatitis. In our case, the abdominal ultrasonography (US) performed on a 6-year-old female patient who presented with pain in the right upper quadrant pointed out an anechoic cyst at the neck of the gall bladder. Based on the magnetic resonance cholangiopancreatography (MRCP) results, a cystic dilatation was diagnosed in the cystic duct. The aim of this case-report presentation was to discuss the US and MRCP findings of the cystic dilatation of cystic duct, which is an extremely rare condition, in the light of the literature information. PMID:24987540

  2. Damage-detection system for LNG carriers

    NASA Technical Reports Server (NTRS)

    Mastandrea, J. R.; Scherb, M. V.

    1978-01-01

    System utilizes array of acoustical transducers to detect cracks and leaks in liquefied natural gas (LNG) containers onboard ships. In addition to detecting leaks, device indicates location and leak rate.

  3. Neutron Interrogation System For Underwater Threat Detection And Identification

    SciTech Connect

    Barzilov, Alexander P.; Novikov, Ivan S.; Womble, Phil C.

    2009-03-10

    Wartime and terrorist activities, training and munitions testing, dumping and accidents have generated significant munitions contamination in the coastal and inland waters in the United States and abroad. Although current methods provide information about the existence of the anomaly (for instance, metal objects) in the sea bottom, they fail to identify the nature of the found objects. Field experience indicates that often in excess of 90% of objects excavated during the course of munitions clean up are found to be non-hazardous items (false alarm). The technology to detect and identify waterborne or underwater threats is also vital for protection of critical infrastructures (ports, dams, locks, refineries, and LNG/LPG). We are proposing a compact neutron interrogation system, which will be used to confirm possible threats by determining the chemical composition of the suspicious underwater object. The system consists of an electronic d-T 14-MeV neutron generator, a gamma detector to detect the gamma signal from the irradiated object and a data acquisition system. The detected signal then is analyzed to quantify the chemical elements of interest and to identify explosives or chemical warfare agents.

  4. Neutron Interrogation System For Underwater Threat Detection And Identification

    NASA Astrophysics Data System (ADS)

    Barzilov, Alexander P.; Novikov, Ivan S.; Womble, Phil C.

    2009-03-01

    Wartime and terrorist activities, training and munitions testing, dumping and accidents have generated significant munitions contamination in the coastal and inland waters in the United States and abroad. Although current methods provide information about the existence of the anomaly (for instance, metal objects) in the sea bottom, they fail to identify the nature of the found objects. Field experience indicates that often in excess of 90% of objects excavated during the course of munitions clean up are found to be non-hazardous items (false alarm). The technology to detect and identify waterborne or underwater threats is also vital for protection of critical infrastructures (ports, dams, locks, refineries, and LNG/LPG). We are proposing a compact neutron interrogation system, which will be used to confirm possible threats by determining the chemical composition of the suspicious underwater object. The system consists of an electronic d-T 14-MeV neutron generator, a gamma detector to detect the gamma signal from the irradiated object and a data acquisition system. The detected signal then is analyzed to quantify the chemical elements of interest and to identify explosives or chemical warfare agents.

  5. Tectonic history of the north portion of the San Andreas fault system, California, inferred from gravity and magnetic anomalies

    USGS Publications Warehouse

    Griscom, A.; Jachens, R.C.

    1989-01-01

    Geologic and geophysical data for the San Andreas fault system north of San Francisco suggest that the eastern boundary of the Pacific plate migrated eastward from its presumed original position at the base of the continental slope to its present position along the San Andreas transform fault by means of a series of eastward jumps of the Mendocino triple junction. These eastward jumps total a distance of about 150 km since 29 Ma. Correlation of right-laterally displaced gravity and magnetic anomalies that now have components at San Francisco and on the shelf north of Point Arena indicates that the presently active strand of the San Andreas fault north of the San Francisco peninsula formed recently at about 5 Ma when the triple junction jumped eastward a minimum of 100 km to its present location at the north end of the San Andreas fault. -from Authors

  6. Nondestructive Crack Detection in a Fuel System Component

    NASA Technical Reports Server (NTRS)

    Koshti, Ajay; Wincheski, Russell; Prosser, William; Russell, Richard; Devries, Robert; Engel, James; Ruffino, Norman

    2010-01-01

    The paper discusses development of various NDE techniques to detect cracks in A40 steel poppets used in a valve of the fuel system of the Space Shuttle Orbiter. The valve assembly experiences a severe high cycle fatigue environment during its operation. Cracks were discovered at the radius of the poppet flange. Experience shows that very small cracks or material anomalies do not cause failure in a single operation event. While the design is being modified to eliminate the issue, NDE has been used to screen the poppets for cracks before every use. Several surface flaw detection techniques were considered and a few NDE techniques were developed to provide NDE screening for the flaw detection. The primary method used was the eddy current testing. In the eddy current technique, the X-Y channel test data from the eddy current instrument was recorded as computer files. A Matlab data review and plotting application was developed to analyze the data files. The Matlab application provides much higher resolution than the eddy current instrument that was used to acquire the data. Other techniques that were used included ultrasonic surface wave and magnetic particle testing. A probability of detection (POD) study was undertaken to determine the 90/95 size for the eddy current technique. This study used specimens with same geometry and material as the poppet. Fatigue cracks were grown in these specimens. Information on results of the NDE techniques and results of the POD study are provided.

  7. 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.

  8. Idaho National Laboratory Supervisory Control and Data Acquisition Intrusion Detection System (SCADA IDS)

    SciTech Connect

    Jared Verba; Michael Milvich

    2008-05-01

    Current Intrusion Detection System (IDS) technology is not suited to be widely deployed inside a Supervisory, Control and Data Acquisition (SCADA) environment. Anomaly- and signature-based IDS technologies have developed methods to cover information technology-based networks activity and protocols effectively. However, these IDS technologies do not include the fine protocol granularity required to ensure network security inside an environment with weak protocols lacking authentication and encryption. By implementing a more specific and more intelligent packet inspection mechanism, tailored traffic flow analysis, and unique packet tampering detection, IDS technology developed specifically for SCADA environments can be deployed with confidence in detecting malicious activity.

  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. 13q Deletion and central nervous system anomalies: further insights from karyotype–phenotype analyses of 14 patients

    PubMed Central

    Ballarati, Lucia; Rossi, Elena; Bonati, Maria Teresa; Gimelli, Stefania; Maraschio, Paola; Finelli, Palma; Giglio, Sabrina; Lapi, Elisabetta; Bedeschi, Maria Francesca; Guerneri, Silvana; Arrigo, Giulia; Patricelli, Maria Grazia; Mattina, Teresa; Guzzardi, Oriana; Pecile, Vanna; Police, Adalgisa; Scarano, Gioacchino; Larizza, Lidia; Zuffardi, Orsetta; Giardino, Daniela

    2007-01-01

    Background Chromosome 13q deletion is associated with varying phenotypes, which seem to depend on the location of the deleted segment. Although various attempts have been made to link the 13q deletion intervals to distinct phenotypes, there is still no acknowledged consensus correlation between the monosomy of distinct 13q regions and specific clinical features. Methods 14 Italian patients carrying partial de novo 13q deletions were studied. Molecular–cytogenetic characterisation was carried out by means of array‐comparative genomic hybridisation (array‐CGH) or fluorescent in situ hybridisation (FISH). Results Our 14 patients showed mental retardation ranging from profound–severe to moderate–mild: eight had central nervous system (CNS) anomalies, including neural tube defects (NTDs), six had eye abnormalities, nine had facial dysmorphisms and 10 had hand or feet anomalies. The size of the deleted regions varied from 4.2 to 75.7 Mb. Conclusion This study is the first systematic molecular characterisation of de novo 13q deletions, and offers a karyotype–phenotype correlation based on detailed clinical studies and molecular determinations of the deleted regions. Analyses confirm that patients lacking the 13q32 band are the most seriously affected, and critical intervals have been preliminarily assigned for CNS malformations. Dose‐sensitive genes proximal to q33.2 may be involved in NTDs. The minimal deletion interval associated with the Dandy–Walker malformation (DWM) was narrowed to the 13q32.2–33.2 region, in which the ZIC2 and ZIC5 genes proposed as underlying various CNS malformations are mapped. PMID:17209130

  11. Discriminating ultrasonic proximity detection system

    DOEpatents

    Annala, Wayne C.

    1989-01-01

    This invention uses an ultrasonic transmitter and receiver and a microprocessor to detect the presence of an object. In the reset mode the invention uses a plurality of echoes from each ultrasonic burst to create a reference table of the echo-burst-signature of the empty monitored environment. The invention then processes the reference table so that it only uses the most reliable data. In the detection mode the invention compares the echo-burst-signature of the present environment with the reference table, detecting an object if there is a consistent difference between the echo-burst-signature of the empty monitored environment recorded in the reference table and the echo-burst-signature of the present environment.

  12. 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.

  13. Airborne change detection system for the detection of route mines

    NASA Astrophysics Data System (ADS)

    Donzelli, Thomas P.; Jackson, Larry; Yeshnik, Mark; Petty, Thomas E.

    2003-09-01

    The US Army is interested in technologies that will enable it to maintain the free flow of traffic along routes such as Main Supply Routes (MSRs). Mines emplaced in the road by enemy forces under cover of darkness represent a major threat to maintaining a rapid Operational Tempo (OPTEMPO) along such routes. One technique that shows promise for detecting enemy mining activity is Airborne Change Detection, which allows an operator to detect suspicious day-to-day changes in and around the road that may be indicative of enemy mining. This paper presents an Airborne Change Detection that is currently under development at the US Army Night Vision and Electronic Sensors Directorate (NVESD). The system has been tested using a longwave infrared (LWIR) sensor on a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) and a midwave infrared (MWIR) sensor on a fixed wing aircraft. The system is described and results of the various tests conducted to date are presented.

  14. Thermal systems for landmine detection

    NASA Astrophysics Data System (ADS)

    D'Angelo, Marco; Del Vecchio, Luca; Esposito, Salvatore; Balsi, Marco; Jankowski, Stanislaw

    2009-06-01

    This paper presents new techniques of landmine detection and localization using thermal methods. Described methods use both dynamical and static analysis. The work is based on datasets obtained from the Humanitarian Demining Laboratory of Università La Sapienza di Roma, Italy.

  15. Expandable coating cocoon leak detection system

    NASA Technical Reports Server (NTRS)

    Hauser, R. L.; Kochansky, M. C.

    1972-01-01

    Development of system and materials for detecting leaks in cocoon protective coatings are discussed. Method of applying materials for leak determination is presented. Pressurization of system following application of materials will cause formation of bubble if leak exists.

  16. Development of a Global Agricultural Hotspot Detection and Early Warning System

    NASA Astrophysics Data System (ADS)

    Lemoine, G.; Rembold, F.; Urbano, F.; Csak, G.

    2015-12-01

    The number of web based platforms for crop monitoring has grown rapidly over the last years and anomaly maps and time profiles of remote sensing derived indicators can be accessed online thanks to a number of web based portals. However, while these systems make available a large amount of crop monitoring data to the agriculture and food security analysts, there is no global platform which provides agricultural production hotspot warning in a highly automatic and timely manner. Therefore a web based system providing timely warning evidence as maps and short narratives is currently under development by the Joint Research Centre. The system (called "HotSpot Detection System of Agriculture Production Anomalies", HSDS) will focus on water limited agricultural systems worldwide. The automatic analysis of relevant meteorological and vegetation indicators at selected administrative units (Gaul 1 level) will trigger warning messages for the areas where anomalous conditions are observed. The level of warning (ranging from "watch" to "alert") will depend on the nature and number of indicators for which an anomaly is detected. Information regarding the extent of the agricultural areas concerned by the anomaly and the progress of the agricultural season will complement the warning label. In addition, we are testing supplementary detailed information from other sources for the areas triggering a warning. These regard the automatic web-based and food security-tailored analysis of media (using the JRC Media Monitor semantic search engine) and the automatic detection of active crop area using Sentinel 1, upcoming Sentinel-2 and Landsat 8 imagery processed in Google Earth Engine. The basic processing will be fully automated and updated every 10 days exploiting low resolution rainfall estimates and satellite vegetation indices. Maps, trend graphs and statistics accompanied by short narratives edited by a team of crop monitoring experts, will be made available on the website on a

  17. Forward Obstacle Detection System by Stereo Vision

    NASA Astrophysics Data System (ADS)

    Iwata, Hiroaki; Saneyoshi, Keiji

    Forward obstacle detection is needed to prevent car accidents. We have developed forward obstacle detection system which has good detectability and the accuracy of distance only by using stereo vision. The system runs in real time by using a stereo processing system based on a Field-Programmable Gate Array (FPGA). Road surfaces are detected and the space to drive can be limited. A smoothing filter is also used. Owing to these, the accuracy of distance is improved. In the experiments, this system could detect forward obstacles 100 m away. Its error of distance up to 80 m was less than 1.5 m. It could immediately detect cutting-in objects.

  18. Toward detecting deception in intelligent systems

    NASA Astrophysics Data System (ADS)

    Santos, Eugene, Jr.; Johnson, Gregory, Jr.

    2004-08-01

    Contemporary decision makers often must choose a course of action using knowledge from several sources. Knowledge may be provided from many diverse sources including electronic sources such as knowledge-based diagnostic or decision support systems or through data mining techniques. As the decision maker becomes more dependent on these electronic information sources, detecting deceptive information from these sources becomes vital to making a correct, or at least more informed, decision. This applies to unintentional disinformation as well as intentional misinformation. Our ongoing research focuses on employing models of deception and deception detection from the fields of psychology and cognitive science to these systems as well as implementing deception detection algorithms for probabilistic intelligent systems. The deception detection algorithms are used to detect, classify and correct attempts at deception. Algorithms for detecting unexpected information rely upon a prediction algorithm from the collaborative filtering domain to predict agent responses in a multi-agent system.

  19. Flowthrough Bacteria-Detection System

    NASA Technical Reports Server (NTRS)

    Grana, D. C.; Wilkins, J. R.

    1983-01-01

    Online system allows repetitive cycling of sample intake, bacteria counting and sterilization. System measures bacteria count by using sample/incubate/ measure cycle. Steps in cycle are on/off operations to cycle automated easily.

  20. Association rule mining in intrusion detection systems

    NASA Astrophysics Data System (ADS)

    Zhao, Dong; Lu, Yan-sheng

    2004-04-01

    In a modern computer system, intrusion detection has become an essential and critical component. Data mining generally refers to the process of extracting models from large stores of data. The intrusion detection system first apply data mining programs to audit data to compute frequent patterns, extract features, and then use classification algorithms to compute detection models. The most important step of this process is to determine relations between fields in the database records to construct features. The standard association rules have not enough expressiveness. Intrusion detection system can extract the association rule with negations and with varying support thresholds to get better performance rather than extract the standard association rule.

  1. Canadian pipeline installs leak-detection system

    SciTech Connect

    Yoon, M.S.; Mensik, M.; Luk, W.Y.

    1988-05-30

    Site-acceptance tests for a recently installed leak-detection system on a pipeline in southern Alberta indicated that the system will reduce spillage because of leaks. The tests on the Porcupine Hills Pipeline also indicated that pipeline isolation and spill containment are enhanced by the use of the system. Covered here are the selection, design, and implementation of the real time leak-detection system and its extension to offshore and arctic applications.

  2. 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

  3. Hydrogen Fire Detection System Features Sharp Discrimination

    NASA Technical Reports Server (NTRS)

    Bright, C. S.

    1966-01-01

    Hydrogen fire detection system discovers fires by detecting the flickering ultraviolet radiation emitted by the OH molecule, a short-lived intermediate combustion product found in hydrogen-air flames. In a space application, the system discriminates against false signals from sunlight and rocket engine exhaust plume radiation.

  4. Online Monitoring System for Performance Fault Detection

    SciTech Connect

    Gioiosa, Roberto; Kestor, Gokcen; Kerbyson, Darren J.

    2014-05-19

    To achieve the exaFLOPS performance within a contain power budget, next supercomputers will feature hundreds of millions of components operating at low- and near-threshold voltage. As the probability that at least one of these components fails during the execution of an application approaches certainty, it seems unrealistic to expect that any run of a scientific application will not experience some performance faults. We believe that there is need of a new generation of light-weight performance and debugging tools that can be used online even during production runs of parallel applications and that can identify performance anomalies during the application execution. In this work we propose the design and implementation of a monitoring system that continuously inspects the evolution of run

  5. Online Monitoring System for Performance Fault Detection

    SciTech Connect

    Gioiosa, Roberto; Kestor, Gokcen; Kerbyson, Darren J.

    2014-12-31

    To achieve the exaFLOPS performance within a contained power budget, next generation supercomputers will feature hundreds of millions of components operating at low- and near-threshold voltage. As the probability that at least one of these components fails during the execution of an application approaches certainty, it seems unrealistic to expect that any run of a scientific application will not experience some performance faults. We believe that there is need of a new generation of light-weight performance and debugging tools that can be used online even during production runs of parallel applications and that can identify performance anomalies during the application execution. In this work we propose the design and implementation of such a monitoring system.

  6. Congenital anomalies in the baboon (Papio spp.)

    PubMed Central

    Fox, Benjamin; Owston, Michael A.; Kumar, Shyamesh; Dick, Edward J.

    2011-01-01

    Background A comprehensive survey of the prevalence of congenital anomalies in baboons has not been previously reported. We report the congenital anomalies observed over a 26-year period in a large captive baboon colony. Methods A computer search was performed for all baboon congenital anomalies identified at necropsy and recorded on necropsy submissions. Results We identified 198 congenital anomalies in 166 baboons from 9,972 necropsies (1.66% of total necropsies). The nervous, urogenital, musculoskeletal, and cardiovascular systems were most commonly affected. The most common organs affected were the brain, bone, heart, testicle, kidney, penis, aorta, and skeletal muscle. The most frequent congenital anomalies were blindness, seizures, and hydrocephalus. Conclusions The baboon has an overall frequency of congenital anomalies similar to humans and other nonhuman primates. Although the most frequently affected systems are similar, congenital anomalies involving the digestive system appear to be less common in the baboon. PMID:21332757

  7. An expert system for satellite and instrument data anomaly and fault isolation

    NASA Technical Reports Server (NTRS)

    Busse, Carl

    1988-01-01

    A prototype Generic Payload Operations Control System (GPOCC) is being developed at the NASA Jet Propulsion Laboratory to provide a low-cost command and control processing center for science instruments and small payloads. The GPOCC supports the difficult transition from integration and test to flight operations. The prototype will incorporate four expert systems to perform telemetry, command, and mission planning functions as well as telecommunications scheduling. The first of these expert systems to be developed will perform telemetry data analysis and fault isolation, as well as propose corrective action. This Data Analysis Module (DAM) will monitor telemetry data and perform continual data monitoring and trend analysis based on a knowledge base and historic data archived on an optical disk storage device. The system maintains a continuous knowledge database of past system performance characteristics. The goal of the Data Analysis Module is to achieve consistent, dependable and validatable performance, to demonstrate thorough, reliable and fast reasoning, and to reduce the concentration demanded of flight analysis personnel.

  8. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks.

    PubMed

    Amin, Syed Obaid; Siddiqui, Muhammad Shoaib; Hong, Choong Seon; Lee, Sungwon

    2009-01-01

    The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the "Internet of things". By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  9. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks.

    PubMed

    Amin, Syed Obaid; Siddiqui, Muhammad Shoaib; Hong, Choong Seon; Lee, Sungwon

    2009-01-01

    The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the "Internet of things". By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components. PMID:22412321

  10. The Price of Failure - A Study of System Test Anomaly Trends

    NASA Astrophysics Data System (ADS)

    Arnheim, B.; Chang, P. S.

    2004-08-01

    One challenge with system level testing is that most failures at this level result in impacting the critical path. This paper presents analysis of system test failures and their impact on the critical path schedule for a production program. The paper focuses on a multiple vehicle program where it is possible to explore the effects of schedule slip and learning curve across a production line. Another feature of this type of program is that problems encountered on any particular satellite can be addressed on all other vehicles that have not yet flown. However, reachback of this nature comes with an impact to the program. This paper will also explore that impact.

  11. Multisensor system for mine detection

    NASA Astrophysics Data System (ADS)

    Duvoisin, Herbert A., III; Steinway, William J.; Tomassi, Mark S.; Thomas, James E.; Morris, Carl A.; Kahn, Barry A.; Stern, Peter H.; Krywick, Scott; Johnson, Kevin; Dennis, Kevin; Betts, George; Blood, Ben; Simoneaux, Wanda; Miller, John L.

    1998-10-01

    A multi-sensor approach to buried object discrimination has been developed by Coleman Research Corporation (CRC) as a practical successor to currently prevalent metal detectors. The CRC multi-sensor unit integrates with and complements standard metal detectors to enable the detection of low- metallic and non-metallic anti-tank and anti-personnel mines as well as the older metallic-jacketed mines. The added sensors include Ground Penetration Radar (GPR) and Infrared (IR). The GPR consists of a lightweight (less than 1 LB) snap on antenna unit, a belt attached electronics unit (less than 5 LB) and batteries. The IR consists of a lightweight (less than 3 LB) head mounted camera, a heads-up virtual display, and a belt attached processing unit (Figure 1.1). The output from Automatic Target Recognition algorithms provide the detection of metallic and non-metallic mines in real-time on the IR display and as an audio alert from the GPR and MD.

  12. Experimental evidence for spring and autumn windows for the detection of geobotanical anomalies through the remote sensing of overlying vegetation

    NASA Technical Reports Server (NTRS)

    Labovitz, M. L.; Masuoka, E. J.; Bell, R.; Nelson, R. F.; Larsen, C. A.; Hooker, L. K.; Troensegaard, K. W.

    1985-01-01

    It is pointed out that in many regions of the world, vegetation is the predominant factor influencing variation in reflected energy in the 0.4-2.5 micron region of the spectrum. Studies have, therefore, been conducted regarding the utility of remote sensing for detecting changes in vegetation which could be related to the presence of mineralization. The present paper provides primarily a report on the results of the second year of a multiyear study of geobotanical-remote-sensing relationships as developed over areas of sulfide mineralization. The field study has a strong experimental design basis. It is proceeded by first delineating the boundaries of a large geographic region which satisfied a set of previously enumerated field-site criteria. Within this region, carefully selected pairs of mineralized and nonmineralized test sites were examined over the growing season. The experiment is to provide information about the spectral and temporal resolutions required for remote-sensing-geobotanical exploration. The obtained results are evaluated.

  13. Electrometrical Methods Application for Detection of Heating System Pipeline Corrosion

    NASA Astrophysics Data System (ADS)

    Vetrov, A.; Ilyin, Y.; Isaev, V.; Rondel, A.; Shapovalov, N.

    2004-12-01

    Coated steel underground pipelines are widely used for the petroleum and gaze transportation, for the water and heat supply. The soils, where the pipelines are placed, are usually highly corrosive for pipe's metal. In the places of crippling of external coating the corrosion processes begin, and this can provoke a pipe breakage. To ensure the pipeline survivability it is necessary to carry out the control of pipeline conditions. The geophysical methods are used to provide such diagnostic. Authors have studied the corrosion processes of the municipal heating system pipelines in Saint-Petersburg (Russia) using the air thermal imaging method, the investigation of electromagnetic fields and spontaneous polarization, measurements of electrode potentials of metal tubes. The pipeline reparation works, which have been provided this year, allowed us to make the visual observation of pipes. The investigation object comprises a pipeline composed of two parallel tubes, which are placed 1-2 meters deep. The fact that the Russian Federation and CIS countries still use the direct heat supply system makes impossible any addition of anticorrosion components to circulating water. Pipelines operate under high pressure (up to 5 atm) and high temperature (designed temperature is 150°C). Tube's isolation is meant for heat loss minimization, and ordinary has poor hydro-isolation. Some pipeline construction elements (sliding and fixed bearings, pressure compensators, heat enclosures) are often non-isolated, and tube's metal contacts with soil. Hard usage condition, ingress of technical contamination cause, stray currents etc. cause high accidental rate. Realization of geophysical diagnostics, including electrometry, is hampered in a city by underground communication systems, power lines, isolating ground cover (asphalt), limitation of the working area with buildings. These restrictions form the investigation conditions. In order to detect and localize isolation (coat) defects authors

  14. Inertial navigation sensor integrated obstacle detection system

    NASA Technical Reports Server (NTRS)

    Bhanu, Bir (Inventor); Roberts, Barry A. (Inventor)

    1992-01-01

    A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

  15. 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.).

  16. Voice activity detection for speaker verification systems

    NASA Astrophysics Data System (ADS)

    Borowski, Filip

    2008-01-01

    Complex algorithm for speech activity detection was presented in this article. It is based on speech enhancement, features extraction and final detection algorithm. The first one was published in ETSI standard as a module of "Advanced front-end feature extraction algorithm" in distributed speech recognition system. It consists of two main parts, noise estimatiom and Wiener filtering. For the final detection modified linear prediction coefficients and spectral entropy features are extracted form denoised signal.

  17. Chiral anomalies and differential geometry

    SciTech Connect

    Zumino, B.

    1983-10-01

    Some properties of chiral anomalies are described from a geometric point of view. Topics include chiral anomalies and differential forms, transformation properties of the anomalies, identification and use of the anomalies, and normalization of the anomalies. 22 references. (WHK)

  18. 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.

  19. CN ANOMALIES IN THE HALO SYSTEM AND THE ORIGIN OF GLOBULAR CLUSTERS IN THE MILKY WAY

    SciTech Connect

    Carollo, Daniela; Martell, Sarah L.; Beers, Timothy C.; Freeman, Ken C. E-mail: smartell@aao.gov.au E-mail: kcf@mso.anu.edu.au

    2013-06-01

    We explore the kinematics and orbital properties of a sample of red giants in the halo system of the Milky Way that are thought to have formed in globular clusters based on their anomalously strong UV/blue CN bands. The orbital parameters of the CN-strong halo stars are compared to those of the inner- and outer-halo populations as described by Carollo et al., and to the orbital parameters of globular clusters with well-studied Galactic orbits. The CN-strong field stars and the globular clusters both exhibit kinematics and orbital properties similar to the inner-halo population, indicating that stripped or destroyed globular clusters could be a significant source of inner-halo field stars, and suggesting that both the CN-strong stars and the majority of globular clusters are primarily associated with this population.

  20. Multisensor cargo bay fire detection system

    NASA Astrophysics Data System (ADS)

    Snyder, Brian L.; Anderson, Kaare J.; Renken, Christopher H.; Socha, David M.; Miller, Mark S.

    2004-08-01

    Current aircraft cargo bay fire detection systems are generally based on smoke detection. Smoke detectors in modern aircraft are predominately photoelectric particle detectors that reliably detect smoke, but also detect dust, fog, and most other small particles. False alarms caused by these contaminants can be very costly to the airlines because they can cause flights to be diverted needlessly. To minimize these expenses, a new approach to cargo bay fire detection is needed. This paper describes a novel fire detection system developed by the Goodrich Advanced Sensors Technical Center. The system uses multiple sensors of different technologies to provide a way of discriminating between real fire events and false triggers. The system uses infrared imaging along with multiple, distributed chemical sensors and smoke detectors, all feeding data to a digital signal processor. The processor merges data from the chemical sensors, smoke detectors, and processed images to determine if a fire (or potential fire) is present. Decision algorithms look at all this data in real-time and make the final decision about whether a fire is present. In the paper, we present a short background of the problem we are solving, the reasons for choosing the technologies used, the design of the system, the signal processing methods and results from extensive system testing. We will also show that multiple sensing technologies are crucial to reducing false alarms in such systems.