Science.gov

Sample records for real event detection

  1. Real-Time Event Detection for Monitoring Natural and Source ...

    EPA Pesticide Factsheets

    The use of event detection systems in finished drinking water systems is increasing in order to monitor water quality in both operational and security contexts. Recent incidents involving harmful algal blooms and chemical spills into watersheds have increased interest in monitoring source water quality prior to treatment. This work highlights the use of the CANARY event detection software in detecting suspected illicit events in an actively monitored watershed in South Carolina. CANARY is an open source event detection software that was developed by USEPA and Sandia National Laboratories. The software works with any type of sensor, utilizes multiple detection algorithms and approaches, and can incorporate operational information as needed. Monitoring has been underway for several years to detect events related to intentional or unintentional dumping of materials into the monitored watershed. This work evaluates the feasibility of using CANARY to enhance the detection of events in this watershed. This presentation will describe the real-time monitoring approach used in this watershed, the selection of CANARY configuration parameters that optimize detection for this watershed and monitoring application, and the performance of CANARY during the time frame analyzed. Further, this work will highlight how rainfall events impacted analysis, and the innovative application of CANARY taken in order to effectively detect the suspected illicit events. This presentation d

  2. Solar Demon: near real-time solar eruptive event detection on SDO/AIA images

    NASA Astrophysics Data System (ADS)

    Kraaikamp, Emil; Verbeeck, Cis

    Solar flares, dimmings and EUV waves have been observed routinely in extreme ultra-violet (EUV) images of the Sun since 1996. These events are closely associated with coronal mass ejections (CMEs), and therefore provide useful information for early space weather alerts. The Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) generates such a massive dataset that it becomes impossible to find most of these eruptive events manually. Solar Demon is a set of automatic detection algorithms that attempts to solve this problem by providing both near real-time warnings of eruptive events and a catalog of characterized events. Solar Demon has been designed to detect and characterize dimmings, EUV waves, as well as solar flares in near real-time on SDO/AIA data. The detection modules are running continuously at the Royal Observatory of Belgium on both quick-look data and synoptic science data. The output of Solar Demon can be accessed in near real-time on the Solar Demon website, and includes images, movies, light curves, and the numerical evolution of several parameters. Solar Demon is the result of collaboration between the FP7 projects AFFECTS and COMESEP. Flare detections of Solar Demon are integrated into the COMESEP alert system. Here we present the Solar Demon detection algorithms and their output. We will focus on the algorithm and its operational implementation. Examples of interesting flare, dimming and EUV wave events, and general statistics of the detections made so far during solar cycle 24 will be presented as well.

  3. Real-time gait event detection for normal subjects from lower trunk accelerations.

    PubMed

    González, Rafael C; López, Antonio M; Rodriguez-Uría, Javier; Alvarez, Diego; Alvarez, Juan C

    2010-03-01

    In this paper we report on a novel algorithm for the real-time detection and timing of initial (IC) and final contact (FC) gait events. We process the vertical and antero-posterior accelerations registered at the lower trunk (L3 vertebra). The algorithm is based on a set of heuristic rules extracted from a set of 1719 steps. An independent experiment was conducted to compare the results of our algorithms with those obtained from a Digimax force platform. The results show small deviations from times of occurrence of events recorded from the platform (13+/-35 ms for IC and 9+/-54 ms for FC). Results for the FC timing are especially relevant in this field, as no previous work has addressed its temporal location through the processing of lower trunk accelerations. The delay in the real-time detection of the IC is 117+/-39 ms and 34+/-72 ms for the FC, improving previously reported results for real-time detection of events from lower trunk accelerations.

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

    PubMed

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

    2012-01-01

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

  5. Real-time gait event detection for lower limb amputees using a single wearable sensor.

    PubMed

    Maqbool, H F; Husman, M A B; Awad, M I; Abouhossein, A; Mehryar, P; Iqbal, N; Dehghani-Sanij, A A

    2016-08-01

    This paper presents a rule-based real-time gait event/phase detection system (R-GEDS) using a shank mounted inertial measurement unit (IMU) for lower limb amputees during the level ground walking. Development of the algorithm is based on the shank angular velocity in the sagittal plane and linear acceleration signal in the shank longitudinal direction. System performance was evaluated with four control subjects (CS) and one transfemoral amputee (TFA) and the results were validated with four FlexiForce footswitches (FSW). The results showed a data latency for initial contact (IC) and toe off (TO) within a range of ± 40 ms for both CS and TFA. A delay of about 3.7 ± 62 ms for a foot-flat start (FFS) and an early detection of -9.4 ± 66 ms for heel-off (HO) was found for CS. Prosthetic side showed an early detection of -105 ± 95 ms for FFS whereas intact side showed a delay of 141 ±73 ms for HO. The difference in the kinematics of the TFA and CS is one of the potential reasons for high variations in the time difference. Overall, detection accuracy was 99.78% for all the events in both groups. Based on the validated results, the proposed system can be used to accurately detect the temporal gait events in real-time that leads to the detection of gait phase system and therefore, can be utilized in gait analysis applications and the control of lower limb prostheses.

  6. The HAWC Real-time Flare Monitor for Rapid Detection of Transient Events

    NASA Astrophysics Data System (ADS)

    Abeysekara, A. U.; Alfaro, R.; Alvarez, C.; Álvarez, J. D.; Arceo, R.; Arteaga-Velázquez, J. C.; Avila Rojas, D.; Ayala Solares, H. A.; Barber, A. S.; Bautista-Elivar, N.; Becerra Gonzalez, J.; Becerril, A.; Belmont-Moreno, E.; BenZvi, S. Y.; Bernal, A.; Braun, J.; Brisbois, C.; Caballero-Mora, K. S.; Capistrán, T.; Carramiñana, A.; Casanova, S.; Castillo, M.; Cotti, U.; Cotzomi, J.; Coutiño de León, S.; De la Fuente, E.; De León, C.; Díaz-Vélez, J. C.; Dingus, B. L.; DuVernois, M. A.; Ellsworth, R. W.; Engel, K.; Fiorino, D. W.; Fraija, N.; García-González, J. A.; Garfias, F.; Gerhardt, M.; González, M. M.; González Muñoz, A.; Goodman, J. A.; Hampel-Arias, Z.; Harding, J. P.; Hernandez, S.; Hernandez-Almada, A.; Hona, B.; Hui, C. M.; Hüntemeyer, P.; Iriarte, A.; Jardin-Blicq, A.; Joshi, V.; Kaufmann, S.; Kieda, D.; Lauer, R. J.; Lee, W. H.; Lennarz, D.; León Vargas, H.; Linnemann, J. T.; Longinotti, A. L.; López-Cámara, D.; López-Coto, R.; Raya, G. Luis; Luna-García, R.; Malone, K.; Marinelli, S. S.; Martinez, O.; Martinez-Castellanos, I.; Martínez-Castro, J.; Martínez-Huerta, H.; Matthews, J. A.; Miranda-Romagnoli, P.; Moreno, E.; Mostafá, M.; Nellen, L.; Newbold, M.; Nisa, M. U.; Noriega-Papaqui, R.; Pelayo, R.; Pérez-Pérez, E. G.; Pretz, J.; Ren, Z.; Rho, C. D.; Rivière, C.; Rosa-González, D.; Rosenberg, M.; Ruiz-Velasco, E.; Salazar, H.; Salesa Greus, F.; Sandoval, A.; Schneider, M.; Schoorlemmer, H.; Sinnis, G.; Smith, A. J.; Springer, R. W.; Surajbali, P.; Taboada, I.; Tibolla, O.; Tollefson, K.; Torres, I.; Ukwatta, T. N.; Vianello, G.; Weisgarber, T.; Westerhoff, S.; Wisher, I. G.; Wood, J.; Yapici, T.; Younk, P. W.; Zepeda, A.; Zhou, H.

    2017-07-01

    We present the development of a real-time flare monitor for the High Altitude Water Cherenkov (HAWC) observatory. The flare monitor has been fully operational since 2017 January and is designed to detect very high energy (VHE; E ≳ 100 GeV) transient events from blazars on timescales lasting from 2 minutes to 10 hr in order to facilitate multiwavelength and multimessenger studies. These flares provide information for investigations into the mechanisms that power the blazars’ relativistic jets and accelerate particles within them, and they may also serve as probes of the populations of particles and fields in intergalactic space. To date, the detection of blazar flares in the VHE range has relied primarily on pointed observations by imaging atmospheric Cherenkov telescopes. The recently completed HAWC observatory offers the opportunity to study VHE flares in survey mode, scanning two-thirds of the entire sky every day with a field of view of ∼1.8 steradians. In this work, we report on the sensitivity of the HAWC real-time flare monitor and demonstrate its capabilities via the detection of three high-confidence VHE events in the blazars Markarian 421 and Markarian 501.

  7. Visual and Real-Time Event-Specific Loop-Mediated Isothermal Amplification Based Detection Assays for Bt Cotton Events MON531 and MON15985.

    PubMed

    Randhawa, Gurinder Jit; Chhabra, Rashmi; Bhoge, Rajesh K; Singh, Monika

    2015-01-01

    Bt cotton events MON531 and MON15985 are authorized for commercial cultivation in more than 18 countries. In India, four Bt cotton events have been commercialized; more than 95% of total area under genetically modified (GM) cotton cultivation comprises events MON531 and MON15985. The present study reports on the development of efficient event-specific visual and real-time loop-mediated isothermal amplification (LAMP) assays for detection and identification of cotton events MON531 and MON15985. Efficiency of LAMP assays was compared with conventional and real-time PCR assays. Real-time LAMP assay was found time-efficient and most sensitive, detecting up to two target copies within 35 min. The developed real-time LAMP assays, when combined with efficient DNA extraction kit/protocol, may facilitate onsite GM detection to check authenticity of Bt cotton seeds.

  8. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

    PubMed Central

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-01-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses. PMID:27706086

  9. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

    PubMed

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-10-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

  10. Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France.

    PubMed

    Abat, Cédric; Chaudet, Hervé; Colson, Philippe; Rolain, Jean-Marc; Raoult, Didier

    2015-08-01

    Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved.

  11. Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France

    PubMed Central

    Abat, Cédric; Chaudet, Hervé; Colson, Philippe; Rolain, Jean-Marc

    2015-01-01

    Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data–based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved. PMID:26196165

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

    DOEpatents

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

    2016-04-19

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

  13. Real-Time Event Detection for Monitoring Natural and Source Waterways - Sacramento, CA

    EPA Science Inventory

    The use of event detection systems in finished drinking water systems is increasing in order to monitor water quality in both operational and security contexts. Recent incidents involving harmful algal blooms and chemical spills into watersheds have increased interest in monitori...

  14. A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits.

    PubMed

    Gouwanda, Darwin; Gopalai, Alpha Agape

    2015-02-01

    Gait events detection allows clinicians and biomechanics researchers to determine timing of gait events, to estimate duration of stance phase and swing phase and to segment gait data. It also aids biomedical engineers to improve the design of orthoses and FES (functional electrical stimulation) systems. In recent years, researchers have resorted to using gyroscopes to determine heel-strike (HS) and toe-off (TO) events in gait cycles. However, these methods are subjected to significant delays when implemented in real-time gait monitoring devices, orthoses, and FES systems. Therefore, the work presented in this paper proposes a method that addresses these delays, to ensure real-time gait event detection. The proposed algorithm combines the use of heuristics and zero-crossing method to identify HS and TO. Experiments involving: (1) normal walking; (2) walking with knee brace; and (3) walking with ankle brace for overground walking and treadmill walking were designed to verify and validate the identified HS and TO. The performance of the proposed method was compared against the established gait detection algorithms. It was observed that the proposed method produced detection rate that was comparable to earlier reported methods and recorded reduced time delays, at an average of 100 ms. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  15. Decaplex and real-time PCR based detection of MON531 and MON15985 Bt cotton events.

    PubMed

    Randhawa, Gurinder Jit; Chhabra, Rashmi; Singh, Monika

    2010-09-22

    The genetically modified (GM) Bt crops expressing delta-endotoxins from Bacillus thuringiensis provide protection against a wide range of lepidopteron insect pests throughout the growing season of the plant. Bt cotton is the only commercialized crop in India that is planted on an area of 7.6 million hectares. With the increase in development and commercialization of transgenic crops, it is necessary to develop appropriate qualitative and quantitative methods for detection of different transgenic events. The present study reports on the development of a decaplex polymerase chain reaction (PCR) assay for simultaneous detection of transgene sequences, specific transgene constructs, and endogenous stearoyl acyl desaturase (Sad1) gene in two events of Bt cotton, i.e., MON531 and MON15985. The decaplex PCR assay is an efficient tool to identify and discriminate the two major commercialized events of Bt cotton, i.e., MON531 and MON15985, in India. Real-time PCR assays were also developed for quantification of cry1Ac and cry2Ab genes being employed in these two events. The quantitative method was developed using seven serial dilutions containing different levels of Bt cotton DNA mixed with a non-Bt counterpart ranging from 0.01 to 100%. The results revealed that the biases from the true value and the relative standard deviations were all within the range of ±20%. The limit of quantification (LOQ) of the developed real-time PCR method has also been established up to 0.01%.

  16. homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring.

    PubMed

    Alsina-Pagès, Rosa Ma; Navarro, Joan; Alías, Francesc; Hervás, Marcos

    2017-04-13

    The consistent growth in human life expectancy during the recent years has driven governments and private organizations to increase the efforts in caring for the eldest segment of the population. These institutions have built hospitals and retirement homes that have been rapidly overfilled, making their associated maintenance and operating costs prohibitive. The latest advances in technology and communications envisage new ways to monitor those people with special needs at their own home, increasing their quality of life in a cost-affordable way. The purpose of this paper is to present an Ambient Assisted Living (AAL) platform able to analyze, identify, and detect specific acoustic events happening in daily life environments, which enables the medic staff to remotely track the status of every patient in real-time. Additionally, this tele-care proposal is validated through a proof-of-concept experiment that takes benefit of the capabilities of the NVIDIA Graphical Processing Unit running on a Jetson TK1 board to locally detect acoustic events. Conducted experiments demonstrate the feasibility of this approach by reaching an overall accuracy of 82% when identifying a set of 14 indoor environment events related to the domestic surveillance and patients' behaviour monitoring field. Obtained results encourage practitioners to keep working in this direction, and enable health care providers to remotely track the status of their patients in real-time with non-invasive methods.

  17. homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring

    PubMed Central

    Alsina-Pagès, Rosa Ma; Navarro, Joan; Alías, Francesc; Hervás, Marcos

    2017-01-01

    The consistent growth in human life expectancy during the recent years has driven governments and private organizations to increase the efforts in caring for the eldest segment of the population. These institutions have built hospitals and retirement homes that have been rapidly overfilled, making their associated maintenance and operating costs prohibitive. The latest advances in technology and communications envisage new ways to monitor those people with special needs at their own home, increasing their quality of life in a cost-affordable way. The purpose of this paper is to present an Ambient Assisted Living (AAL) platform able to analyze, identify, and detect specific acoustic events happening in daily life environments, which enables the medic staff to remotely track the status of every patient in real-time. Additionally, this tele-care proposal is validated through a proof-of-concept experiment that takes benefit of the capabilities of the NVIDIA Graphical Processing Unit running on a Jetson TK1 board to locally detect acoustic events. Conducted experiments demonstrate the feasibility of this approach by reaching an overall accuracy of 82% when identifying a set of 14 indoor environment events related to the domestic surveillance and patients’ behaviour monitoring field. Obtained results encourage practitioners to keep working in this direction, and enable health care providers to remotely track the status of their patients in real-time with non-invasive methods. PMID:28406459

  18. New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal.

    PubMed

    Lee, Hyoki; Park, Jonguk; Kim, Hojoong; Lee, Kyoung-Joung

    2016-12-01

    We developed a rule-based algorithm for automatic real-time detection of sleep apnea and hypopnea events using a nasal pressure signal. Our basic premise was that the performance of our new algorithm using the nasal pressure signal would be comparable to that using other sensors as well as manual annotation labeled by a technician on polysomnography study. We investigated fifty patients with sleep apnea-hypopnea syndrome (age: 56.8 ± 10.5 years, apnea-hypopnea index (AHI): 36.2 ± 18.1/h) during full night PSG recordings at the sleep center. The algorithm was comprised of pre-processing with a median filter, amplitude computation and apnea-hypopnea detection parts. We evaluated the performance of the algorithm a confusion matric for each event and statistical analyses for AHI. Our evaluation achieved a good performance, with a sensitivity of 86.4 %, and a positive predictive value of 84.5 % for detection of apnea and hypopnea regardless of AHI severity. Our results indicated a high correlation with the manually labeled apnea-hypopnea events during PSG, with a correlation coefficient of r = 0.94 (p < 0.0001) and a mean difference of -2.9 ± 11.6 per hour. The proposed new algorithm could provide significant clinical and computational insights to design a PSG analysis system and a continuous positive airway pressure (CPAP) device for screening sleep quality related in patients with sleep apnea-hypopnea syndrome.

  19. Characterization and event specific-detection by quantitative real-time PCR of T25 maize insert.

    PubMed

    Collonnier, Cécile; Schattner, Alexandra; Berthier, Georges; Boyer, Francine; Coué-Philippe, Géraldine; Diolez, Annick; Duplan, Marie-Noëlle; Fernandez, Sophie; Kebdani, Naïma; Kobilinsky, André; Romaniuk, Marcel; de Beuckeleer, Marc; de Loose, Marc; Windels, Pieter; Bertheau, Yves

    2005-01-01

    T25 is one of the 4 maize transformation events from which commercial lines have so far been authorized in Europe. It was created by polyethylene glycol-mediated transformation using a construct bearing one copy of the synthetic pat gene associated with both promoter and terminator of the 35S ribosomal gene from cauliflower mosaic virus. In this article, we report the sequencing of the whole T25 insert and the characterization of its integration site by using a genome walking strategy. Our results confirmed that one intact copy of the initial construct had been integrated in the plant genome. They also revealed, at the 5' junction of the insert, the presence of a second truncated 35S promoter, probably resulting from rearrangements which may have occurred before or during integration of the plasmid DNA. The analysis of the junction fragments showed that the integration site of the insert presented high homologies with the Huck retrotransposon family. By using one primer annealing in the maize genome and the other in the 5' end of the integrated DNA, we developed a reliable event-specific detection system for T25 maize. To provide means to comply with the European regulation, a real-time PCR test was designed for specific quantitation of T25 event by using Taqman chemistry.

  20. Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events

    NASA Astrophysics Data System (ADS)

    Javidi, Bahram; Yeom, Seokwon; Moon, Inkyu; Daneshpanah, Mehdi

    2006-05-01

    In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.

  1. Interlaboratory validation study of an event-specific real-time polymerase chain reaction detection method for genetically modified 55-1 papaya.

    PubMed

    Noguchi, Akio; Nakamura, Kosuke; Sakata, Kozue; Kobayashi, Tomoko; Akiyama, Hiroshi; Kondo, Kazunari; Teshima, Reiko; Ohmori, Kiyomi; Kasahara, Masaki; Takabatake, Reona; Kitta, Kazumi

    2013-01-01

    Genetically modified (GM) papaya line 55-1 (55-1) is resistant to papaya ringspot virus infection, and is commercially available in several countries. A specific detection method for 55-1 is required for mandatory labeling regulations. An event-specific real-time PCR method was developed by our laboratory. To validate the method, interlaboratory validation of event-specific qualitative real-time PCR analysis for 55-1 was performed in collaboration with 12 laboratories. DNA extraction and real-time PCR reaction methods were evaluated using 12 blind samples: six non-GM papayas and six GM papayas in each laboratory. Genomic DNA was highly purified from all papayas using an ion-exchange column, and the resulting DNA sample was analyzed using real-time PCR. Papaya endogenous reference gene chymopapain (CHY) and the event-specific 55-1 targeted sequence were detected in GM papayas whereas CHYalone was detected in non-GM papayas in all laboratories. The cycle threshold values of CHYand the 55-1 targeted sequence showed high repeatability (RSD, 0.6-0.8%) and reproducibility (RSDR 2.2-3.6%). This study demonstrates that the 55-1 real-time PCR detection method is a useful and reliable method to monitor 55-1 papaya in foods.

  2. Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

    PubMed

    Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

    2017-05-01

    The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T(2) statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

  3. Near-Real-Time Detection and Monitoring of Dust Events by Satellite (SeaWIFS, MODIS, and TOMS)

    NASA Technical Reports Server (NTRS)

    Hsu, N. Christina; Tsay, Si-Chee; Herman, Jay R.; Kaufman, Yoram

    2002-01-01

    Over the last few years satellites have given us increasingly detailed information on the size, location, and duration of dust events around the world. These data not only provide valuable feedback to the modelling community as to the fidelity of their aerosol models but are also finding increasing use in near real-time applications. In particular, the ability to locate and track the development of aerosol dust clouds on a near real-time basis is being used by scientists and government to provide warning of air pollution episodes over major urban area. This ability has also become a crucial component of recent coordinated campaigns to study the characteristics of tropospheric aerosols such as dust and their effect on climate. One such recent campaign was ACE-Asia, which was designed to obtain the comprehensive set of ground, aircraft, and satellite data necessary to provide a detailed understanding of atmospheric aerosol particles over the Asian-Pacific region. As part of ACE-Asia, we developed a near real-time data processing and access system to provide satellite data from the polar-orbiting instruments Earth Probe TOMS (in the form of absorbing aerosol index) and SeaWiFS (in the form of aerosol optical thickness, AOT, and Angstrom exponent). The results were available via web access. The location and movement information provided by these data were used both in support of the day-to-day flight planning of ACE-Asia and as input into aerosol transport models. While near real-time SeaWiFS data processing can be performed using either the normal global data product or data obtained via direct broadcast to receiving stations close to the area of interest, near real-time MODIS processing of data to provide aerosol retrievals is currently only available using its direct broadcast capability. In this paper, we will briefly discuss the algorithms used to generate these data. The retrieved aerosol optical thickness and Angstrom exponent from SeaWiFS will be compared with

  4. Event-Driven Collaboration through Publish/Subscribe Messaging Services for Near-Real- Time Environmental Sensor Anomaly Detection and Management

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Downey, S.; Minsker, B.; Myers, J. D.; Wentling, T.; Marini, L.

    2006-12-01

    One of the challenges in designing cyberinfrastructure for national environmental observatories is how to provide integrated cyberenvironment which not only provides a standardized pipeline for streaming data from sensors into the observatory for archiving and distribution, but also makes raw data and identified events available in real-time for use in individual and group research efforts. This aspect of observatories is critical for promoting efficient collaboration and innovation among scientists and engineers and enabling observatories to serve as a focus that directly supports the broad community. The National Center for Supercomputing Applications' Environmental Cyberinfrastructure Demo (ECID) project has adopted an event-driven architecture and developed a CyberCollaboratory to facilitate event-driven, near-real-time collaboration and management of sensors and workflows for bringing data from environmental observatories into local research contexts. The CyberCollaboratory's event broker uses publish-subscribe service powered by JMS (Java Messaging Service) with semantics-enhanced messages using RDF (Resource Description Framework) triples to allow exchange of contextual information about the event between the event generators and the event consumers. Non-scheduled, event-driven collaboration effectively reduces the barrier to collaboration for scientists and engineers and promotes much faster turn-around time for critical environmental events. This is especially useful for real-time adaptive monitoring and modeling of sensor data in environmental observatories. In this presentation, we illustrate our system using a sensor anomaly detection event as an example where near-real- time data streams from field sensor in Corpus Christi Bay, Texas, trigger monitoring/anomaly alerts in the CyberCollaboratory's CyberDashboard and collaborative activities in the CyberCollaboratory. The CyberDashboard is a Java application where users can monitor various events

  5. SPR and SPR Imaging: Recent Trends in Developing Nanodevices for Detection and Real-Time Monitoring of Biomolecular Events

    PubMed Central

    Puiu, Mihaela; Bala, Camelia

    2016-01-01

    In this paper we review the underlying principles of the surface plasmon resonance (SPR) technique, particularly emphasizing its advantages along with its limitations regarding the ability to discriminate between the specific binding response and the interfering effects from biological samples. While SPR sensors were developed almost three decades, SPR detection is not yet able to reduce the time-consuming steps of the analysis, and is hardly amenable for miniaturized, portable platforms required in point-of-care (POC) testing. Recent advances in near-field optics have emerged, resulting in the development of SPR imaging (SPRi) as a powerful optical, label-free monitoring tool for multiplexed detection and monitoring of biomolecular events. The microarrays design of the SPRi chips incorporating various metallic nanostructures make these optofluidic devices more suitable for diagnosis and near-patient testing than the traditional SPR sensors. The latest developments indicate SPRi detection as being the most promising surface plasmon-based technique fulfilling the demands for implementation in lab-on-a-chip (LOC) technologies. PMID:27314345

  6. A novel quadruplex real-time PCR method for simultaneous detection of Cry2Ae and two genetically modified cotton events (GHB119 and T304-40)

    PubMed Central

    2014-01-01

    Background To date, over 150 genetically modified (GM) crops are widely cultivated. To comply with regulations developed for genetically modified organisms (GMOs), including labeling policies, many detection methods for GMO identification and quantification have been developed. Results To detect the entrance and exit of unauthorized GM crop events in China, we developed a novel quadruplex real-time PCR method for simultaneous detection and quantification of GM cotton events GHB119 and T304-40 in cotton-derived products (based on the 5′-flanking sequence) and the insect-resistance gene Cry2Ae. The limit of detection was 10 copies for GHB119 and Cry2Ae and 25 copies for T304-40. The limit of quantification was 25 copies for GHB119 and Cry2Ae and 50 copies for T304-40. Moreover, low bias and acceptable standard deviation and relative standard deviation values were obtained in quantification analysis of six blind samples containing different GHB119 and T304-40 ingredients. Conclusions The developed quadruplex quantitative method could be used for quantitative detection of two GM cotton events (GHB119 and T304-40) and Cry2Ae gene ingredient in cotton derived products. PMID:24884946

  7. A real-time quantitative PCR detection method specific to widestrike transgenic cotton (event 281-24-236/3006-210-23).

    PubMed

    Baeumler, Stefan; Wulff, Dörte; Tagliani, Laura; Song, Ping

    2006-09-06

    In compliance with global regulations on transgenic crops, a real-time quantitative PCR method specific to Widestrike transgenic cotton (event 281-24-236/3006-210-23, OECD Unique Identifier DAS-24236-5/DAS-21023-5) was established on the basis of the DNA sequences in the junction between the transgene insert and cotton genome. The optimized method consists of a DNA extraction method for cotton seeds and three PCR systems corresponding to a cotton-specific endogenous reference DNA sequence SAH7 (Sinapis Arabidopsis Homolog 7) and specific detection of event 281-24-236 and event 3006-210-23. The method performance including specificity, sensitivity, accuracy, and precision was determined at a dynamic range of Widestrike DNA levels from 0.04% to 5.0%. The limits of detection (LOD) and quantification (LOQ) were < or =0.04% and < or =0.09%, respectively, at 100 ng of DNA sample per reaction. The quantification results using either the event 281-24-236 or 3006-210-23 system were consistent, and the relative deviation from the expected (true) value was in the range of +/-25%. The robustness of the method was demonstrated by a series of tests with deviations from the optimized assay parameters such as annealing temperature, extension time, PCR instrument, interlaboratory transferability, etc. All the measurements from these tests met the criteria set by EU JRC-CRL (European Commission Joint Research Centre-Community Reference Lab). This real-time quantitative PCR method is accurate and robust, and is recommended as a global benchmark method for the detection and quantification of Widestrike cotton. The method including description, protocol, and performance results is available on the JRC-CRL website (http://gmo-crl.jrc.it/statusofdoss.htm).

  8. Detection of anomalous events

    DOEpatents

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

    2016-06-07

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

  9. Detection of solar events

    DOEpatents

    Fischbach, Ephraim; Jenkins, Jere

    2013-08-27

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

  10. Event-specific detection of stacked genetically modified maize Bt11 x GA21 by UP-M-PCR and real-time PCR.

    PubMed

    Xu, Wentao; Yuan, Yanfang; Luo, Yunbo; Bai, Weibin; Zhang, Chunjiao; Huang, Kunlun

    2009-01-28

    More and more stacked GMOs have been developed for more improved functional properties and/or a stronger intended characteristic, such as antipest, improved product efficiency etc. Bt11 x GA21 is a new kind of stacked GM maize developed by Monsanto Company. Since there are no unique flanking sequences in stacked GMOs, up to now, no appropriate method has been reported to accurately detect them. In this passage, a novel universal primer multiplex PCR (UP-M-PCR) was developed and applied as a rapid screening method for the simultaneous detection of five target sequences (NOS, 35S, Bt11 event, GA21 event, and IVR) in maize Bt11 x GA21. This method overcame the disadvantages rooted deeply in conventional multiplex PCR such as complex manipulation, lower sensitivity, self-inhibition and amplification disparity resulting from different primers. What's more, it got a high specificity and had a detection limit of 0.1% (approximates to 38 haploid genome copies). Furthermore, real-time PCR combined with multivariate statistical analysis was used for accurate quantification of stacked GM maize Bt11 x GA21 in 100% GM maize mixture (Bt11 x GA21, Bt11 and GA21). Detection results showed that this method could accurately validate the content of Bt11, GA21 and Bt11 x GA21 in 100% GM mixture with a detection limit of 0.5% (approximates to 200 haploid genome copies) and a low relative standard deviation <5%. All the data proved that this method may be widely applied in event-specific detection of other stacked GMOs in GM-mixture.

  11. Microelectrode Arrays of Diamond-Insulated Graphitic Channels for Real-Time Detection of Exocytotic Events from Cultured Chromaffin Cells and Slices of Adrenal Glands.

    PubMed

    Picollo, Federico; Battiato, Alfio; Bernardi, Ettore; Marcantoni, Andrea; Pasquarelli, Alberto; Carbone, Emilio; Olivero, Paolo; Carabelli, Valentina

    2016-08-02

    A microstructured graphitic 4 × 4 multielectrode array was embedded in a single-crystal diamond substrate (4 × 4 μG-SCD MEA) for real-time monitoring of exocytotic events from cultured chromaffin cells and adrenal slices. The current approach relies on the development of a parallel ion beam lithographic technique, which assures the time-effective fabrication of extended arrays with reproducible electrode dimensions. The reported device is suitable for performing amperometric and voltammetric recordings with high sensitivity and temporal resolution, by simultaneously acquiring data from 16 rectangularly shaped microelectrodes (20 × 3.5 μm(2)) separated by 200 μm gaps. Taking advantage of the array geometry we addressed the following specific issues: (i) detect both the spontaneous and KCl-evoked secretion simultaneously from several chromaffin cells directly cultured on the device surface, (ii) resolve the waveform of different subsets of exocytotic events, and (iii) monitoring quantal secretory events from thin slices of the adrenal gland. The frequency of spontaneous release was low (0.12 and 0.3 Hz, respectively, for adrenal slices and cultured cells) and increased up to 0.9 Hz after stimulation with 30 mM KCl in cultured cells. The spike amplitude as well as rise and decay time were comparable with those measured by carbon fiber microelectrodes and allowed to identify three different subsets of secretory events associated with "full fusion" events, "kiss-and-run" and "kiss-and-stay" exocytosis, confirming that the device has adequate sensitivity and time resolution for real-time recordings. The device offers the significant advantage of shortening the time to collect data by allowing simultaneous recordings from cell populations either in primary cell cultures or in intact tissues.

  12. Infrasound ray tracing models for real events

    NASA Astrophysics Data System (ADS)

    Averbuch, Gil; Applbaum, David; Price, Colin; Ben Horin, Yochai

    2015-04-01

    Infrasound ray tracing models for real events C. Price1, G. Averbuch1, D. Applbaum1, Y. Ben Horin2 (1) Department of Geosciences, Tel Aviv University, Israel (2) Soreq Nuclear Research Center, Yavne, Israel Ray tracing models for infrasound propagation require two atmospheric parameters: the speed of sound profile and the wind profile. The usage of global atmospheric models for the speed of sound and wind profiles raises a fundamental question: can these models provide accurate results for modeling real events that have been detected by the infrasound arrays? Moreover, can these models provide accurate results for events that occurred during extreme weather conditions? We use 2D and 3D ray tracing models based on a modified Hamiltonian for a moving medium. Radiosonde measurements enable us to update the first 20 km of both speed of sound and wind profiles. The 2009 and 2011 Sayarim calibration experiments in Israel served us as a test for the models. In order to answer the question regarding the accuracy of the model during extreme weather conditions, we simulate infrasound sprite signals that were detected by the infrasound array in Mt. Meron, Israel. The results from modeling the Sayarim experiment provided us sufficient insight to conclude that ray tracing modeling can provide accurate results for real events that occurred during fair weather conditions. We conclude that the time delay in the model of the 2009 experiment is due to lack of accuracy in the wind and speed of sound profiles. Perturbed profiles provide accurate results. Earlier arrivals in 2011 are a result of the assumption that the earth is flat (no topography) and the use of local radiosonde measurements for the entire model. Using local radiosonde measurements only for part of the model and neglecting them on other parts prevents the early arrivals. We were able to determine which sprite is the one that got detected in the infrasound array as well as providing a height range for the sprite

  13. Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database.

    PubMed

    Khandelwal, Siddhartha; Wickström, Nicholas

    2017-01-01

    Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings. This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Real-time detection of the early event of cytotoxicity of herbal ingredients on single leukemia cells studied in a microfluidic biochip.

    PubMed

    Li, XiuJun; Xue, Xiaoyan; Li, Paul C H

    2009-01-01

    A microfluidic approach has been developed for the real-time detection of drug effects, based on the quantitative measurement of calibrated cytosolic calcium ([Ca(2+)](i)) on single cancer cells. This microfluidic method is rapid by detecting the early event of cytotoxicity of drug candidates on cancer cells, without waiting for a couple of days needed for cell seeding and drug treatment by conventional assays. The miniaturized biochip consists of a V-shaped structure for the single-cell selection and retention. Various test reagents such as the chemotherapy drug (daunorubicin), an ionophore (ionomycin), and herbal ingredients from licorice (isoliquiritigenin or IQ) were investigated for their abilities to stimulate sustained cellular [Ca(2+)](i) elevations. The microfluidic results obtained in hours have been confirmed by conventional cytotoxicity assays which take days to complete. Moreover, any color or chemical interference problems found in the conventional assays of herbal compounds could be resolved. Using the microfluidic approach, IQ (50 microM) has been found to cause a sustained [Ca(2+)](i) elevation and cytotoxic effects on leukemia cells. The microfluidic single-cell analysis not only reduces reagent cost, and demands less cells, but also reveals some phenomena due to cellular heterogeneity that cannot be observed in bulk analysis.

  15. Detection of Saharan dust and biomass burning events using near-real-time intensive aerosol optical properties in the north-western Mediterranean

    NASA Astrophysics Data System (ADS)

    Ealo, Marina; Alastuey, Andrés; Ripoll, Anna; Pérez, Noemí; Cruz Minguillón, María; Querol, Xavier; Pandolfi, Marco

    2016-10-01

    The study of Saharan dust events (SDEs) and biomass burning (BB) emissions are both topics of great scientific interest since they are frequent and important polluting scenarios affecting air quality and climate. The main aim of this work is evaluating the feasibility of using near-real-time in situ aerosol optical measurements for the detection of these atmospheric events in the western Mediterranean Basin (WMB). With this aim, intensive aerosol optical properties (SAE: scattering Ångström exponent, AAE: absorption Ångström exponent, SSAAE: single scattering albedo Ångström exponent and g: asymmetry parameter) were derived from multi-wavelength aerosol light scattering, hemispheric backscattering and absorption measurements performed at regional (Montseny; MSY, 720 m a.s.l.) and continental (Montsec; MSA, 1570 m a.s.l.) background sites in the WMB. A sensitivity study aiming at calibrating the measured intensive optical properties for SDEs and BB detection is presented and discussed. The detection of SDEs by means of the SSAAE parameter and Ångström matrix (made up by SAE and AAE) depended on the altitude of the measurement station and on SDE intensity. At MSA (mountain-top site) SSAAE detected around 85 % of SDEs compared with 50 % at the MSY station, where pollution episodes dominated by fine anthropogenic particles frequently masked the effect of mineral dust on optical properties during less intense SDEs. Furthermore, an interesting feature of SSAAE was its capability to detect the presence of mineral dust after the end of SDEs. Thus, resuspension processes driven by summer regional atmospheric circulations and dry conditions after SDEs favoured the accumulation of mineral dust at regional level having important consequences for air quality. On average, SAE, AAE and g ranged between -0.7 and 1, 1.3 and 2.5 and 0.5 and 0.75 respectively during SDEs. Based on the aethalometer model, BB contribution to equivalent black carbon (BC) accounted for 36 and 40

  16. The spectral absorption coefficient at 254nm as a real-time early warning proxy for detecting faecal pollution events at alpine karst water resources

    PubMed Central

    Stadler, H.; Klock, E.; Skritek, P.; Mach, R.L.; Zerobin, W.; Farnleitner, A.H.

    2011-01-01

    Because spring water quality from alpine karst aquifers can change very rapidly during event situations, water abstraction management has to be performed in near real-time. Four summer events (2005-2008) at alpine karst springs were investigated in detail in order to evaluate the spectral absorption coefficient at 254nm (SAC254) as a real-time early warning proxy for faecal pollution. For the investigation Low-Earth-Orbit (LEO) Satellite-based data communication between portable hydrometeorological measuring stations and an automated microbiological sampling device was used. The method for event triggered microbial sampling and analyzing was already established and described in a previous paper (Stadler et al., Wat. Sci. Technol. 58(4): 899-909, 2008). Data analysis including on-line event characterisation (i.e. precipitation, discharge, turbidity, SAC254) and comprehensive E. coli determination (n > 800) indicated that SAC254 is a useful early warning proxy. Irrespective of the studied event situations SAC254 always increased 3 to 6 hours earlier than the onset of faecal pollution, featuring different correlation phases. Furthermore, it seems also possible to use SAC254 as a real-time proxy parameter for estimating the extent of faecal pollution after establishing specific spring and event-type calibrations that take into consideration the variability of the occurrence and the transferability of faecal material It should be highlighted that diffuse faecal pollution from wildlife and live stock sources was responsible for spring water contamination at the investigated catchments. In this respect, the SAC254 can also provide useful information to support microbial source tracking efforts where different situations of infiltration have to be investigated. PMID:20962406

  17. The spectral absorption coefficient at 254 nm as a real-time early warning proxy for detecting faecal pollution events at alpine karst water resources.

    PubMed

    Stadler, H; Klock, E; Skritek, P; Mach, R L; Zerobin, W; Farnleitner, A H

    2010-01-01

    Because spring water quality from alpine karst aquifers can change very rapidly during event situations, water abstraction management has to be performed in near real-time. Four summer events (2005-2008) at alpine karst springs were investigated in detail in order to evaluate the spectral absorption coefficient at 254 nm (SAC254) as a real-time early warning proxy for faecal pollution. For the investigation Low-Earth-Orbit (LEO) Satellite-based data communication between portable hydrometeorological measuring stations and an automated microbiological sampling device was used. The method for event triggered microbial sampling and analyzing was already established and described in a previous paper. Data analysis including on-line event characterisation (i.e. precipitation, discharge, turbidity, SAC254) and comprehensive E. coli determination (n>800) indicated that SAC254 is a useful early warning proxy. Irrespective of the studied event situations SAC254 always increased 3 to 6 hours earlier than the onset of faecal pollution, featuring different correlation phases. Furthermore, it seems also possible to use SAC254 as a real-time proxy parameter for estimating the extent of faecal pollution after establishing specific spring and event-type calibrations that take into consideration the variability of the occurrence and the transferability of faecal material It should be highlighted that diffuse faecal pollution from wildlife and live stock sources was responsible for spring water contamination at the investigated catchments. In this respect, the SAC254 can also provide useful information to support microbial source tracking efforts where different situations of infiltration have to be investigated.

  18. Joint Attributes and Event Analysis for Multimedia Event Detection.

    PubMed

    Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

    2017-06-15

    Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.

  19. Development, optimization, and single laboratory validation of an event-specific real-time PCR method for the detection and quantification of Golden Rice 2 using a novel taxon-specific assay.

    PubMed

    Jacchia, Sara; Nardini, Elena; Savini, Christian; Petrillo, Mauro; Angers-Loustau, Alexandre; Shim, Jung-Hyun; Trijatmiko, Kurniawan; Kreysa, Joachim; Mazzara, Marco

    2015-02-18

    In this study, we developed, optimized, and in-house validated a real-time PCR method for the event-specific detection and quantification of Golden Rice 2, a genetically modified rice with provitamin A in the grain. We optimized and evaluated the performance of the taxon (targeting rice Phospholipase D α2 gene)- and event (targeting the 3' insert-to-plant DNA junction)-specific assays that compose the method as independent modules, using haploid genome equivalents as unit of measurement. We verified the specificity of the two real-time PCR assays and determined their dynamic range, limit of quantification, limit of detection, and robustness. We also confirmed that the taxon-specific DNA sequence is present in single copy in the rice genome and verified its stability of amplification across 132 rice varieties. A relative quantification experiment evidenced the correct performance of the two assays when used in combination.

  20. Real-time measurements, rare events and photon economics

    NASA Astrophysics Data System (ADS)

    Jalali, B.; Solli, D. R.; Goda, K.; Tsia, K.; Ropers, C.

    2010-07-01

    Rogue events otherwise known as outliers and black swans are singular, rare, events that carry dramatic impact. They appear in seemingly unconnected systems in the form of oceanic rogue waves, stock market crashes, evolution, and communication systems. Attempts to understand the underlying dynamics of such complex systems that lead to spectacular and often cataclysmic outcomes have been frustrated by the scarcity of events, resulting in insufficient statistical data, and by the inability to perform experiments under controlled conditions. Extreme rare events also occur in ultrafast physical sciences where it is possible to collect large data sets, even for rare events, in a short time period. The knowledge gained from observing rare events in ultrafast systems may provide valuable insight into extreme value phenomena that occur over a much slower timescale and that have a closer connection with human experience. One solution is a real-time ultrafast instrument that is capable of capturing singular and randomly occurring non-repetitive events. The time stretch technology developed during the past 13 years is providing a powerful tool box for reaching this goal. This paper reviews this technology and discusses its use in capturing rogue events in electronic signals, spectroscopy, and imaging. We show an example in nonlinear optics where it was possible to capture rare and random solitons whose unusual statistical distribution resemble those observed in financial markets. The ability to observe the true spectrum of each event in real time has led to important insight in understanding the underlying process, which in turn has made it possible to control soliton generation leading to improvement in the coherence of supercontinuum light. We also show a new class of fast imagers which are being considered for early detection of cancer because of their potential ability to detect rare diseased cells (so called rogue cells) in a large population of healthy cells.

  1. Automated Detection of Events of Scientific Interest

    NASA Technical Reports Server (NTRS)

    James, Mark

    2007-01-01

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

  2. Acoustic Event Detection and Classification

    NASA Astrophysics Data System (ADS)

    Temko, Andrey; Nadeu, Climent; Macho, Dušan; Malkin, Robert; Zieger, Christian; Omologo, Maurizio

    The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Indeed, speech is usually the most informative sound, but other kinds of AEs may also carry useful information, for example, clapping or laughing inside a speech, a strong yawn in the middle of a lecture, a chair moving or a door slam when the meeting has just started. Additionally, detection and classification of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.

  3. Detectability of Discrete Event Systems with Dynamic Event Observation

    PubMed Central

    Shu, Shaolong; Lin, Feng

    2009-01-01

    Our previous work considers detectability of discrete event systems which is to determine the current state and subsequent states of a system based on event observation. We assume that event observation is static, that is, if an event is observable, then all its occurrences are observable. However, in practical systems such as sensor networks, event observation often needs to be dynamic, that is, the occurrences of same events may or may not be observable, depending on the state of the system. In this paper, we generalize static event observation into dynamic event observation and consider the detectability problem under dynamic event observation. We define four types of detectabilities. To check detectabilities, we construct the observer with exponential complexity. To reduce computational complexity, we can also construct a detector with polynomial complexity to check strong detectabilities. Dynamic event observation can be implemented in two possible ways: a passive observation and an active observation. For the active observation, we discuss how to find minimal event observation policies that preserve four types of detectabilities respectively. PMID:20161618

  4. Phase-Space Detection of Cyber Events

    SciTech Connect

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

    2015-01-01

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

  5. Real-time prediction of the occurrence of GLE events

    NASA Astrophysics Data System (ADS)

    Núñez, Marlon; Reyes-Santiago, Pedro J.; Malandraki, Olga E.

    2017-07-01

    A tool for predicting the occurrence of Ground Level Enhancement (GLE) events using the UMASEP scheme is presented. This real-time tool, called HESPERIA UMASEP-500, is based on the detection of the magnetic connection, along which protons arrive in the near-Earth environment, by estimating the lag correlation between the time derivatives of 1 min soft X-ray flux (SXR) and 1 min near-Earth proton fluxes observed by the GOES satellites. Unlike current GLE warning systems, this tool can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min with respect to the first NM station's alert and 15 min to the GLE Alert Plus's warning. We have tested the model by replacing the GOES proton data with SOHO/EPHIN proton data, and the results are similar in terms of POD, FAR, and AWT for the same period. The paper also presents a comparison with a GLE warning system.

  6. Memory characteristics of recently imagined events and real events experienced previously.

    PubMed

    Stern, E R; Rotello, C M

    2000-01-01

    In two experiments, we evaluated the memory characteristics of real and imagined events as they changed over time. Memories of real events were richer than memories of imagined events, and memories of recent events were richer than of events from a week earlier. These differences interacted such that memories of real events performed in week 1 were very similar to memories of events that were imagined in week 2. Source monitoring was tested and implications for the repressed or recovered memory debate are considered.

  7. A Framework of Simple Event Detection in Surveillance Video

    NASA Astrophysics Data System (ADS)

    Xu, Weiguang; Zhang, Yafei; Lu, Jianjiang; Tian, Yulong; Wang, Jiabao

    Video surveillance is playing more and more important role in people's social life. Real-time alerting of threaten events and searching interesting content in stored large scale video footage needs human operator to pay full attention on monitor for long time. The labor intensive mode has limit the effectiveness and efficiency of the system. A framework of simple event detection is presented advance the automation of video surveillance. An improved inner key point matching approach is used to compensate motion of background in real-time; frame difference are used to detect foreground; HOG based classifiers are used to classify foreground object into people and car; mean-shift is used to tracking the recognized objects. Events are detected based on predefined rules. The maturity of the algorithms guarantee the robustness of the framework, and the improved approach and the easily checked rules enable the framework to work in real-time. Future works to be done are also discussed.

  8. Event oriented dictionary learning for complex event detection.

    PubMed

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

    2015-06-01

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

  9. Children's Eyewitness Memory for Multiple Real-Life Events

    ERIC Educational Resources Information Center

    Odegard, Timothy N.; Cooper, Crystal M.; Lampinen, James M.; Reyna, Valerie F.; Brainerd, Charles J.

    2009-01-01

    The present research examined the influence of prior knowledge on children's free recall, cued recall, recognition memory, and source memory judgments for a series of similar real-life events. Forty children (5-12 years old) attended 4 thematic birthday parties and were later interviewed about the events that transpired during the parties using…

  10. A new real-time tsunami detection algorithm

    NASA Astrophysics Data System (ADS)

    Chierici, Francesco; Embriaco, Davide; Pignagnoli, Luca

    2017-01-01

    Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of seabed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on 28 October 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard abyssal observatories, deployed in the Gulf of Cadiz and in the Western Ionian Sea.

  11. Efficient method for events detection in phonocardiographic signals

    NASA Astrophysics Data System (ADS)

    Martinez-Alajarin, Juan; Ruiz-Merino, Ramon

    2005-06-01

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

  12. Generalized Detectability for Discrete Event Systems

    PubMed Central

    Shu, Shaolong; Lin, Feng

    2011-01-01

    In our previous work, we investigated detectability of discrete event systems, which is defined as the ability to determine the current and subsequent states of a system based on observation. For different applications, we defined four types of detectabilities: (weak) detectability, strong detectability, (weak) periodic detectability, and strong periodic detectability. In this paper, we extend our results in three aspects. (1) We extend detectability from deterministic systems to nondeterministic systems. Such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems. (2) We develop polynomial algorithms to check strong detectability. The previous algorithms are based on observer whose construction is of exponential complexity, while the new algorithms are based on a new automaton called detector. (3) We extend detectability to D-detectability. While detectability requires determining the exact state of a system, D-detectability relaxes this requirement by asking only to distinguish certain pairs of states. With these extensions, the theory on detectability of discrete event systems becomes more applicable in solving many practical problems. PMID:21691432

  13. Real-time Detection of Locked Modes

    NASA Astrophysics Data System (ADS)

    Angelini, S.; Granetz, R. S.; Wolfe, S. M.

    2007-11-01

    Disruptions are one of the largest problems facing tokamaks. In a large-scale experiment such as ITER, disruptions would cause crippling damage and severe setbacks in experimentation. One method for disruption mitigation involves the use of a gas jet which has been tested on both normally running plasmas and vertical displacement events (VDEs) on Alcator C-Mod. In both cases, the jet was successful in mitigating disruption effects. The gas jet has not yet been tested on other types of disruptions. Locked-mode major disruptions are easily created in C-Mod and could be used to test the effectiveness of the gas jet as a mitigation method if the jet could be fired early enough. It has been empirically observed that the electron cyclotron emissions (ECE) signal displays a flattening of the normally-present sawteeth before the current quench occurs in certain locked-mode major disruptions. A procedure is being written which will detect the ECE flattening by reading changes in the standard deviation of the signal. This procedure will be programmed into the digital plasma control system (DPCS) for real-time testing.

  14. Detecting unitary events without discretization of time.

    PubMed

    Grün, S; Diesmann, M; Grammont, F; Riehle, A; Aertsen, A

    1999-12-15

    In earlier studies we developed the 'Unitary Events' analysis (Grün S. Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance and Interpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deutsch, 1996.) to detect the presence of conspicuous spike coincidences in multiple single unit recordings and to evaluate their statistical significance. The method enabled us to study the relation between spike synchronization and behavioral events (Riehle A, Grün S, Diesmann M, Aertsen A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 1997;278:1950-1953.). There is recent experimental evidence that the timing accuracy of coincident spiking events, which might be relevant for higher brain function, may be in the range of 1-5 ms. To detect coincidences on that time scale, we sectioned the observation interval into short disjunct time slices ('bins'). Unitary Events analysis of this discretized process demonstrated that coincident events can indeed be reliably detected. However, the method looses sensitivity for higher temporal jitter of the events constituting the coincidences (Grün S. Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance and Interpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deutsch, 1996.). Here we present a new approach, the 'multiple shift' method (MS), which overcomes the need for binning and treats the data in their (original) high time resolution (typically 1 ms, or better). Technically, coincidences are detected by shifting the spike trains against each other over the range of allowed coincidence width and integrating the number of exact coincidences (on the time resolution of the data) over all shifts. We found that the new method enhances the sensitivity for coincidences with temporal jitter. Both methods are outlined and compared on the basis of their analytical description and their application on

  15. Detecting Extreme Events in Gridded Climate Data

    SciTech Connect

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

    2016-01-01

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

  16. Object detection in real-time

    NASA Astrophysics Data System (ADS)

    Solder, Ulrich; Graefe, Volker

    1991-03-01

    An algorithm working on monocular gray-scale image sequences for object detection combined with a road tracker is presented. This algorithm appropriate for the real-time demands of an autonomous car driving with speeds over 40 km/h may be used for triggering obstacle avoidance maneuvers such as coming to a safe stop automatically in front of an obstacle or following another car. Moving and static objects have been detected in real-world experiments on various types of roads even under unfavorable weather conditions. . Morgenthaler and

  17. Detection of goal events in soccer videos

    NASA Astrophysics Data System (ADS)

    Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

    2004-12-01

    In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

  18. Detection of goal events in soccer videos

    NASA Astrophysics Data System (ADS)

    Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

    2005-01-01

    In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

  19. A real-time assessment of factors influencing medication events.

    PubMed

    Dollarhide, Adrian W; Rutledge, Thomas; Weinger, Matthew B; Fisher, Erin Stucky; Jain, Sonia; Wolfson, Tanya; Dresselhaus, Timothy R

    2014-01-01

    Reducing medical error is critical to improving the safety and quality of healthcare. Physician stress, fatigue, and excessive workload are performance-shaping factors (PSFs) that may influence medical events (actual administration errors and near misses), but direct relationships between these factors and patient safety have not been clearly defined. This study assessed the real-time influence of emotional stress, workload, and sleep deprivation on self-reported medication events by physicians in academic hospitals. During an 18-month study period, 185 physician participants working at four university-affiliated teaching hospitals reported medication events using a confidential reporting application on handheld computers. Emotional stress scores, perceived workload, patient case volume, clinical experience, total sleep, and demographic variables were also captured via the handheld computers. Medication event reports (n = 11) were then correlated with these demographic and PSFs. Medication events were associated with 36.1% higher perceived workload (p < .05), 38.6% higher inpatient caseloads (p < .01), and 55.9% higher emotional stress scores (p < .01). There was a trend for reported events to also be associated with less sleep (p = .10). These results confirm the effect of factors influencing medication events, and support attention to both provider and hospital environmental characteristics for improving patient safety.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  1. On event-based optical flow detection

    PubMed Central

    Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

    2015-01-01

    Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470

  2. Bi-Level Semantic Representation Analysis for Multimedia Event Detection.

    PubMed

    Chang, Xiaojun; Ma, Zhigang; Yang, Yi; Zeng, Zhiqiang; Hauptmann, Alexander G

    2017-05-01

    Multimedia event detection has been one of the major endeavors in video event analysis. A variety of approaches have been proposed recently to tackle this problem. Among others, using semantic representation has been accredited for its promising performance and desirable ability for human-understandable reasoning. To generate semantic representation, we usually utilize several external image/video archives and apply the concept detectors trained on them to the event videos. Due to the intrinsic difference of these archives, the resulted representation is presumable to have different predicting capabilities for a certain event. Notwithstanding, not much work is available for assessing the efficacy of semantic representation from the source-level. On the other hand, it is plausible to perceive that some concepts are noisy for detecting a specific event. Motivated by these two shortcomings, we propose a bi-level semantic representation analyzing method. Regarding source-level, our method learns weights of semantic representation attained from different multimedia archives. Meanwhile, it restrains the negative influence of noisy or irrelevant concepts in the overall concept-level. In addition, we particularly focus on efficient multimedia event detection with few positive examples, which is highly appreciated in the real-world scenario. We perform extensive experiments on the challenging TRECVID MED 2013 and 2014 datasets with encouraging results that validate the efficacy of our proposed approach.

  3. A new real-time tsunami detection algorithm

    NASA Astrophysics Data System (ADS)

    Chierici, F.; Embriaco, D.; Pignagnoli, L.

    2016-12-01

    Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of sea-bed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability, at low computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on October 28th, 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard the GEOSTAR stand-alone multidisciplinary abyssal observatory, deployed in the Gulf of Cadiz during the EC project NEAREST and on NEMO-SN1 cabled observatory deployed in the Western Ionian Sea, operational node of the European research infrastructure EMSO.

  4. Subnoise detection of a fast random event.

    PubMed

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

    2015-12-11

    Observation of random, nonrepetitive phenomena is of critical importance in astronomy, spectroscopy, biology, and remote sensing. Heralded by weak signals, hidden in noise, they pose basic detection challenges. In contrast to repetitive waveforms, a single-instance signal cannot be separated from noise through averaging. Here, we show that a fast, randomly occurring event can be detected and extracted from a noisy background without conventional averaging. An isolated 80-picosecond pulse was received with confidence level exceeding 99%, even when accompanied by noise. Our detector relies on instantaneous spectral cloning and a single-step, coherent field processor. The ability to extract fast, subnoise events is expected to increase detection sensitivity in multiple disciplines. Additionally, the new spectral-cloning receiver can potentially intercept communication signals that are presently considered secure. Copyright © 2015, American Association for the Advancement of Science.

  5. Developing Situational Learning Events: A Practical Merger of Real-Life Events with Content Instruction.

    ERIC Educational Resources Information Center

    Salyer, B. Keith; Thyfault, Alberta

    This paper discusses the value of merging real-life events with content instruction and provides six sample lessons to illustrate such instruction. A brief review of the literature notes historic recognition of the importance of applied learning, the issue of retention and transfer of learning, the approach of using content relevant experiences…

  6. Global grid of master events for waveform cross-correlation: from testing to real time processing

    NASA Astrophysics Data System (ADS)

    Bobrov, Dmitry; Rozhkov, Mikhail; Kitov, Ivan

    2014-05-01

    Seismic monitoring of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) requires a globally uniform detection threshold, which is provided by geographical distribution of the Primary Seismic Network of the International Monitoring System (IMS). This detection threshold has to be as low as allowed by the entire set of real time and historical data recorded by the IMS. The International Data Centre (IDC) analyzes all relevant data in automatic processing and interactive review to issue a Reviewed Event Bulletin (REB), which includes all qualified events as obtained for the purpose of nuclear test monitoring. Since 2000, raw data, individual detections, and created events are saved in the IDC archive currently reaching tens of terabyte. In order to effectively use this archive in global monitoring we introduced the waveform cross correlation (matched filter) technique. Cross correlation between real time records at IMS stations and template waveforms is calculated for a dense (spacing of ~ 140 km) and regular grid of master events uniformly covering the globe. There are approximately 25,000 master events with 3 to 10 templates at IMS stations. In seismically active zones, we populate masters with real waveforms. For aseismic zones, we develop an extended set of synthetic templates for virtual master events. For optimal performance of cross correlation, the Principal and Independent Component Analysis are applied to the historical (from earthquakes and underground nuclear tests) and synthetic waveforms. Real waveform templates and selected PCA/ICA components are used in automatic processing for the production of a tentative cross-correlation standard event list (XSEL).

  7. Real Time Detection of Foodborne Pathogens

    NASA Astrophysics Data System (ADS)

    Velusamy, V.; Arshak, K.; Korostynka, O.; Vaseashta, Ashok; Adley, C.

    Contamination of foods by harmful bacteria by natural events or malicious intent poses a serious threat to public health and safety. This review introduces current technologies in detecting pathogens in food and foodborne illnesses. Causes of foodborne diseases and trends impacting foodborne diseases such as globalization and changes in micro-organisms, human populations, lifestyles, and climates are addressed. In addition, a review of the limitations in detecting pathogens with conventional technologies is presented. Finally, a review of nanostructured and nanomaterials based sensing technologies by pathogen, detection limits, and advantages is described.

  8. Real-Time Multimission Event Notification System for Mars Relay

    NASA Technical Reports Server (NTRS)

    Wallick, Michael N.; Allard, Daniel A.; Gladden, Roy E.; Wang, Paul; Hy, Franklin H.

    2013-01-01

    As the Mars Relay Network is in constant flux (missions and teams going through their daily workflow), it is imperative that users are aware of such state changes. For example, a change by an orbiter team can affect operations on a lander team. This software provides an ambient view of the real-time status of the Mars network. The Mars Relay Operations Service (MaROS) comprises a number of tools to coordinate, plan, and visualize various aspects of the Mars Relay Network. As part of MaROS, a feature set was developed that operates on several levels of the software architecture. These levels include a Web-based user interface, a back-end "ReSTlet" built in Java, and databases that store the data as it is received from the network. The result is a real-time event notification and management system, so mission teams can track and act upon events on a moment-by-moment basis. This software retrieves events from MaROS and displays them to the end user. Updates happen in real time, i.e., messages are pushed to the user while logged into the system, and queued when the user is not online for later viewing. The software does not do away with the email notifications, but augments them with in-line notifications. Further, this software expands the events that can generate a notification, and allows user-generated notifications. Existing software sends a smaller subset of mission-generated notifications via email. A common complaint of users was that the system-generated e-mails often "get lost" with other e-mail that comes in. This software allows for an expanded set (including user-generated) of notifications displayed in-line of the program. By separating notifications, this can improve a user's workflow.

  9. Detection and recognition of indoor smoking events

    NASA Astrophysics Data System (ADS)

    Bien, Tse-Lun; Lin, Chang Hong

    2013-03-01

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

  10. Optimizing master event templates for CTBT monitoring with dimensionality reduction techniques: real waveforms vs. synthetics.

    NASA Astrophysics Data System (ADS)

    Rozhkov, Mikhail; Bobrov, Dmitry; Kitov, Ivan

    2014-05-01

    The Master Event technique is a powerful tool for Expert Technical Analysis within the CTBT framework as well as for real-time monitoring with the waveform cross-correlation (CC) (match filter) approach. The primary goal of CTBT monitoring is detection and location of nuclear explosions. Therefore, the cross-correlation monitoring should be focused on finding such events. The use of physically adequate waveform templates may significantly increase the number of valid, both natural and manmade, events in the Reviewed Event Bulletin (REB) of the International Data Centre. Inadequate templates for master events may increase the number of CTBT irrelevant events in REB and reduce the sensitivity of the CC technique to valid events. In order to cover the entire earth, including vast aseismic territories, with the CC based nuclear test monitoring we conducted a thorough research and defined the most appropriate real and synthetic master events representing underground explosion sources. A procedure was developed on optimizing the master event template simulation and narrowing the classes of CC templates used in detection and location process based on principal and independent component analysis (PCA and ICA). Actual waveforms and metadata from the DTRA Verification Database were used to validate our approach. The detection and location results based on real and synthetic master events were compared. The prototype of CC-based Global Grid monitoring system developed in IDC during last year was populated with different hybrid waveform templates (synthetics, synthetics components, and real components) and its performance was assessed with the world seismicity data flow, including the DPRK-2013 event. The specific features revealed in this study for the P-waves from the DPRK underground nuclear explosions (UNEs) can reduce the global detection threshold of seismic monitoring under the CTBT by 0.5 units of magnitude. This corresponds to the reduction in the test yield by a

  11. Facing a real person: an event-related potential study.

    PubMed

    Pönkänen, Laura M; Hietanen, Jari K; Peltola, Mikko J; Kauppinen, Pasi K; Haapalainen, Antti; Leppänen, Jukka M

    2008-03-05

    Although faces are typically perceived in the context of human interaction, face processing is commonly studied by displaying faces on a computer screen. This study on event-related potential examined whether the processing of faces differs depending on whether participants are viewing faces live or on a computer screen. In both the conditions, the participants were shown a real face, a dummy face, and a control object. N170 and early posterior negativity discriminated between faces and control object in both the conditions. Interestingly, early posterior negativity differentiated between the real face and the dummy face only in the live condition. The results indicate that a live face, as a potentially interacting stimulus, is processed differently than an inanimate face already at the early processing stages.

  12. Application of Kalman Filtering Techniques for Microseismic Event Detection

    NASA Astrophysics Data System (ADS)

    Baziw, E.; Weir-Jones, I.

    - Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during fluid injection or extraction) in oil exploitation, dam construction or fluid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identification of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-effective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital filtering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simplified. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital filters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded

  13. LAN attack detection using Discrete Event Systems.

    PubMed

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

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  14. WCEDS: A waveform correlation event detection system

    SciTech Connect

    Young, C.J.; Beiriger, J.I.; Trujillo, J.R.; Withers, M.M.; Aster, R.C.; Astiz, L.; Shearer, P.M.

    1995-08-01

    We have developed a working prototype of a grid-based global event detection system based on waveform correlation. The algorithm comes from a long-period detector but we have recast it in a full matrix formulation which can reduce the number of multiplications needed by better than two orders of magnitude for realistic monitoring scenarios. The reduction is made possible by eliminating redundant multiplications in the original formulation. All unique correlations for a given origin time are stored in a correlation matrix (C) which is formed by a full matrix product of a Master Image matrix (M) and a data matrix (D). The detector value at each grid point is calculated by following a different summation path through the correlation matrix. Master Images can be derived either empirically or synthetically. Our testing has used synthetic Master Images because their influence on the detector is easier to understand. We tested the system using the matrix formulation with continuous data from the IRIS (Incorporate Research Institutes for Seismology) broadband global network to monitor a 2 degree evenly spaced surface grid with a time discretization of 1 sps; we successfully detected the largest event in a two hour segment from October 1993. The output at the correct gridpoint was at least 33% larger than at adjacent grid points, and the output at the correct gridpoint at the correct origin time was more than 500% larger than the output at the same gridpoint immediately before or after. Analysis of the C matrix for the origin time of the event demonstrates that there are many significant ``false`` correlations of observed phases with incorrect predicted phases. These false correlations dull the sensitivity of the detector and so must be dealt with if our system is to attain detection thresholds consistent with a Comprehensive Test Ban Treaty (CTBT).

  15. Machine learning for real time remote detection

    NASA Astrophysics Data System (ADS)

    Labbé, Benjamin; Fournier, Jérôme; Henaff, Gilles; Bascle, Bénédicte; Canu, Stéphane

    2010-10-01

    Infrared systems are key to providing enhanced capability to military forces such as automatic control of threats and prevention from air, naval and ground attacks. Key requirements for such a system to produce operational benefits are real-time processing as well as high efficiency in terms of detection and false alarm rate. These are serious issues since the system must deal with a large number of objects and categories to be recognized (small vehicles, armored vehicles, planes, buildings, etc.). Statistical learning based algorithms are promising candidates to meet these requirements when using selected discriminant features and real-time implementation. This paper proposes a new decision architecture benefiting from recent advances in machine learning by using an effective method for level set estimation. While building decision function, the proposed approach performs variable selection based on a discriminative criterion. Moreover, the use of level set makes it possible to manage rejection of unknown or ambiguous objects thus preserving the false alarm rate. Experimental evidences reported on real world infrared images demonstrate the validity of our approach.

  16. Near Real Time Ship Detection Experiments

    NASA Astrophysics Data System (ADS)

    Brusch, S.; Lehner, S.; Schwarz, E.; Fritz, T.

    2010-04-01

    A new Near Real Time (NRT) ship detection processor SAINT (SAR AIS Integrated Toolbox) was developed in the framework of the ESA project MARISS. Data are received at DLRs ground segment DLR-BN (Neustrelitz, Germany). Results of the ship detection are available on ftp server within 30 min after the acquisition started. The detectability of ships on Synthetic Aperture Radar (SAR) ERS-2, ENVISAT ASAR and TerraSAR-X (TS-X) images is validated by coastal (live) AIS and space AIS. The monitoring areas chosen for surveillance are the North-, Baltic Sea, and Cape Town. The detectability in respect to environmental parameters like wind field, sea state, currents and changing coastlines due to tidal effects is investigated. In the South Atlantic a tracking experiment of the German research vessel Polarstern has been performed. Issues of piracy in particular in respect to ships hijacked at the Somali coast are discussed. Some examples using high resolution images from TerraSAR-X are given.

  17. Integrating event detection system operation characteristics into sensor placement optimization.

    SciTech Connect

    Hart, William Eugene; McKenna, Sean Andrew; Phillips, Cynthia Ann; Murray, Regan Elizabeth; Hart, David Blaine

    2010-05-01

    We consider the problem of placing sensors in a municipal water network when we can choose both the location of sensors and the sensitivity and specificity of the contamination warning system. Sensor stations in a municipal water distribution network continuously send sensor output information to a centralized computing facility, and event detection systems at the control center determine when to signal an anomaly worthy of response. Although most sensor placement research has assumed perfect anomaly detection, signal analysis software has parameters that control the tradeoff between false alarms and false negatives. We describe a nonlinear sensor placement formulation, which we heuristically optimize with a linear approximation that can be solved as a mixed-integer linear program. We report the results of initial experiments on a real network and discuss tradeoffs between early detection of contamination incidents, and control of false alarms.

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

    DOE PAGES

    Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; ...

    2016-01-01

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

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

    SciTech Connect

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

    2016-01-01

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

  20. Real-time detection of optical transients with RAPTOR

    SciTech Connect

    Borozdin, K. N.; Brumby, Steven P.; Galassi, M. C.; McGowan, K. E.; Starr, D. L.; Vestrand, W. T.; White, R. R.; Wozniak, P. R.; Wren, J.

    2002-01-01

    Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient, celestial events in the images is very important, for such study as it allows rapid follow-up with more sensitive instruments, We discuss an approach which we have chosen for the RAPTOR project which is a pioneering close-loop system combining real-time transient detection with rapid follow-up. Our data processing pipeline is able to identify and localize an optical transient within seconds after the observation. We describe the challenges we met, solutions we found and some results obtained in our search for fast optical transients. The software pipeline we have developed for RAPTOR can easily be applied to the data from other experiments.

  1. Near-real-time attribution of extreme weather events

    NASA Astrophysics Data System (ADS)

    Allen, M. R.; Pall, P.; Stone, D.; Stott, P.; Lohmann, D.

    2007-12-01

    As the impacts of global climate change become increasingly evident, there is growing demand for a quantitative and objective answer the the question of what is "to blame" for observed extreme weather phenomena. In addition to considerable public interest, understanding how external drivers, particularly secular trends such as anthropogenic greenhouse gas forcing, is important for the correct quantification of current weather-related risks for the insurance industry. We propose a method of quantifying the contribution of external drivers to weather-related risks based on a twinned ensemble design. Under this approach, a large ensemble of simulations with a forecast-resolution atmospheric model is driven with observed sea surface temperatures and atmospheric composition over the period of interest. A second ensemble is then generated with the influence of a particular external agent, such as anthropogenic greenhouse gases, removed through modification of composition and surface temperatures. Conventional detection and attribution techniques are used to allow for uncertainty in the magnitude and pattern of the signal removed. The frequency of occurrence of the weather event in question can then be compared between the two ensembles. For the exploration of changing risks of the most extreme events, very large ensembles (thousands of members, unprecedented for a model of this resolution) are needed, requiring a novel distributed computing approach, relying on computing resources donated by the general public: see http://attribution.cpdn.org. We focus as an example on the events of Autumn 2000 which brought widespread flooding to many regions of the UK. Precipitation from the twin ensembles is used to force an empirical run-off model to provide an estimate of its contribution to flood risk. Results are summarized in the form of an estimated fraction attributable risk for the anthropogenic contribution to the flooding events of that year.

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

    SciTech Connect

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

    2008-12-15

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

  3. Real-Time Detection of a Virus Using Detection Dogs.

    PubMed

    Angle, T Craig; Passler, Thomas; Waggoner, Paul L; Fischer, Terrence D; Rogers, Bart; Galik, Patricia K; Maxwell, Herris S

    2015-01-01

    Viral infections are ubiquitous in humans, animals, and plants. Real-time methods to identify viral infections are limited and do not exist for use in harsh or resource-constrained environments. Previous research identified that tissues produce unique volatile organic compounds (VOC) and demonstrated that VOC concentrations change during pathologic states, including infection, neoplasia, or metabolic disease. Patterns of VOC expression may be pathogen specific and may be associated with an odor that could be used for disease detection. We investigated the ability of two trained dogs to detect cell cultures infected with bovine viral diarrhea virus (BVDV) and to discriminate BVDV-infected cell cultures from uninfected cell cultures and from cell cultures infected with bovine herpes virus 1 (BHV 1) and bovine parainfluenza virus 3 (BPIV 3). Dogs were trained to recognize cell cultures infected with two different biotypes of BVDV propagated in Madin-Darby bovine kidney cells using one of three culture media. For detection trials, one target and seven distractors were presented on a scent wheel by a dog handler unaware of the location of targets and distractors. Detection of BVDV-infected cell cultures by Dog 1 had a diagnostic sensitivity of 0.850 (95% CI: 0.701-0.942), which was lower than Dog 2 (0.967, 95% CI: 0.837-0.994). Both dogs exhibited very high diagnostic specificity (0.981, 95% CI: 0.960-0.993) and (0.993, 95% CI: 0.975-0.999), respectively. These findings demonstrate that trained dogs can differentiate between cultured cells infected with BVDV, BHV1, and BPIV3 and are a realistic real-time mobile pathogen sensing technology for viral pathogens. The ability to discriminate between target and distractor samples plausibly results from expression of unique VOC patterns in virus-infected and -uninfected cells.

  4. Real-Time Detection of a Virus Using Detection Dogs

    PubMed Central

    Angle, T. Craig; Passler, Thomas; Waggoner, Paul L.; Fischer, Terrence D.; Rogers, Bart; Galik, Patricia K.; Maxwell, Herris S.

    2016-01-01

    Viral infections are ubiquitous in humans, animals, and plants. Real-time methods to identify viral infections are limited and do not exist for use in harsh or resource-constrained environments. Previous research identified that tissues produce unique volatile organic compounds (VOC) and demonstrated that VOC concentrations change during pathologic states, including infection, neoplasia, or metabolic disease. Patterns of VOC expression may be pathogen specific and may be associated with an odor that could be used for disease detection. We investigated the ability of two trained dogs to detect cell cultures infected with bovine viral diarrhea virus (BVDV) and to discriminate BVDV-infected cell cultures from uninfected cell cultures and from cell cultures infected with bovine herpes virus 1 (BHV 1) and bovine parainfluenza virus 3 (BPIV 3). Dogs were trained to recognize cell cultures infected with two different biotypes of BVDV propagated in Madin–Darby bovine kidney cells using one of three culture media. For detection trials, one target and seven distractors were presented on a scent wheel by a dog handler unaware of the location of targets and distractors. Detection of BVDV-infected cell cultures by Dog 1 had a diagnostic sensitivity of 0.850 (95% CI: 0.701–0.942), which was lower than Dog 2 (0.967, 95% CI: 0.837–0.994). Both dogs exhibited very high diagnostic specificity (0.981, 95% CI: 0.960–0.993) and (0.993, 95% CI: 0.975–0.999), respectively. These findings demonstrate that trained dogs can differentiate between cultured cells infected with BVDV, BHV1, and BPIV3 and are a realistic real-time mobile pathogen sensing technology for viral pathogens. The ability to discriminate between target and distractor samples plausibly results from expression of unique VOC patterns in virus-infected and -uninfected cells. PMID:26779494

  5. Real-time interpretation of novel events across childhood

    PubMed Central

    Borovsky, Arielle; Sweeney, Kim; Elman, Jeffrey L.; Fernald, Anne

    2014-01-01

    Despite extensive evidence that adults and children rapidly integrate world knowledge to generate expectancies for upcoming language, little work has explored how this knowledge is initially acquired and used. We explore this question in 3- to 10-year-old children and adults by measuring the degree to which sentences depicting recently learned connections between agents, actions and objects lead to anticipatory eye-movements to the objects. Combinatory information in sentences about agent and action elicited anticipatory eye-movements to the Target object in adults and older children. Our findings suggest that adults and school-aged children can quickly activate information about recently exposed novel event relationships in real-time language processing. However, there were important developmental differences in the use of this knowledge. Adults and school-aged children used the sentential agent and action to predict the sentence final theme, while preschool children’s fixations reflected a simple association to the currently spoken item. We consider several reasons for this developmental difference and possible extensions of this paradigm. PMID:24976677

  6. Machine learning for the automatic detection of anomalous events

    NASA Astrophysics Data System (ADS)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

  7. Multitarget real-time PCR-based system: monitoring for unauthorized genetically modified events in India.

    PubMed

    Randhawa, Gurinder Jit; Singh, Monika; Sood, Payal; Bhoge, Rajesh K

    2014-07-23

    A multitarget TaqMan real-time PCR (RTi-PCR) based system was developed to monitor unauthorized genetically modified (GM) events in India. Most of the GM events included in this study are either authorized for commercial cultivation or field trials, which were indigenously developed or imported for research purposes. The developed system consists of a 96-well prespotted plate with lyophilized primers and probes, for simultaneous detection of 47 targets in duplicate, including 21 event-specific sequences, 5 construct regions, 15 for transgenic elements, and 6 taxon-specific targets for cotton, eggplant, maize, potato, rice, and soybean. Limit of detection (LOD) of assays ranged from 0.1 to 0.01% GM content for different targets. Applicability, robustness, and practical utility of the developed system were verified with stacked GM cotton event, powdered samples of proficiency testing and two unknown test samples. This user-friendly multitarget approach can be efficiently utilized for monitoring the unauthorized GM events in an Indian context.

  8. Improving the prospects for detecting extrasolar planets in gravitational microlensing events in 2002

    NASA Astrophysics Data System (ADS)

    Bond, I. A.; Abe, F.; Dodd, R. J.; Hearnshaw, J. B.; Kilmartin, P. M.; Masuda, K.; Matsubara, Y.; Muraki, Y.; Noda, S.; Petterson, O. K. L.; Rattenbury, N. J.; Reid, M.; Saito, To.; Saito, Y.; Sako, T.; Skuljan, J.; Sullivan, D. J.; Sumi, T.; Wilkinson, S.; Yamada, R.; Yanagisawa, T.; Yock, P. C. M.

    2002-03-01

    Gravitational microlensing events of high magnification have been shown to be promising targets for detecting extrasolar planets. However, only a few events of high magnification have been found using conventional survey techniques. Here we demonstrate that high-magnification events can be readily found in microlensing surveys using a strategy that combines high-frequency sampling of target fields with on-line difference imaging analysis. We present 10 microlensing events with peak magnifications greater than 40 that were detected in real-time towards the Galactic bulge during 2001 by the Microlensing Observations in Astrophysics (MOA) project. We show that Earth-mass planets can be detected in future events such as these through intensive follow-up observations around the event peaks. We report this result with urgency as a similar number of such events are expected in 2002.

  9. Extensions to Real-time Hierarchical Mine Detection Algorithm

    DTIC Science & Technology

    2002-09-01

    Extensions to Real-Time Hierarchical Mine Detection Algorithm System Number: Patron Number: Requester: Notes: DSIS Use only: Deliver to: DK...Recherche et developpement pour Ia defense Canada Extensions to Real-Time Hierarchical Mine Detection Algorithm Final Report Sinh Duong and Mabo R. Ito...EXTENSIONS TO REAL-TIME HIERARCHICAL MINE DETECTION ALGORITHM FINAL REPORT by Smh Duong and Mabo R Ito The Univer~ity of Bntl~h Columbia Vancouver

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

    PubMed Central

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

    2016-01-01

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

  11. Detecting seismic events using Benford's Law

    NASA Astrophysics Data System (ADS)

    Diaz, Jordi; Gallart, Josep; Ruiz, Mario

    2015-04-01

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

  12. Real-time gesture interface based on event-driven processing from stereo silicon retinas.

    PubMed

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael; Park, Paul K J; Shin, Chang-Woo; Ryu, Hyunsurk Eric; Kang, Byung Chang

    2014-12-01

    We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of video frames, and allows significantly faster and more energy-efficient processing. In addition, the rate of input events depends on the observed movements, and thus provides an additional cue for solving the gesture spotting problem, i.e., finding the onsets and offsets of gestures. We propose a postprocessing framework based on spiking neural networks that can process the events received from the DVSs in real time, and provides an architecture for future implementation in neuromorphic hardware devices. The motion trajectories of moving hands are detected by spatiotemporally correlating the stereoscopically verged asynchronous events from the DVSs by using leaky integrate-and-fire (LIF) neurons. Adaptive thresholds of the LIF neurons achieve the segmentation of trajectories, which are then translated into discrete and finite feature vectors. The feature vectors are classified with hidden Markov models, using a separate Gaussian mixture model for spotting irrelevant transition gestures. The disparity information from stereovision is used to adapt LIF neuron parameters to achieve recognition invariant of the distance of the user to the sensor, and also helps to filter out movements in the background of the user. Exploiting the high dynamic range of DVSs, furthermore, allows gesture recognition over a 60-dB range of scene illuminance. The system achieves recognition rates well over 90% under a variety of variable conditions with static and dynamic backgrounds with naïve users.

  13. A Unified Framework for Event Summarization and Rare Event Detection from Multiple Views.

    PubMed

    Kwon, Junseok; Lee, Kyoung Mu

    2015-09-01

    A novel approach for event summarization and rare event detection is proposed. Unlike conventional methods that deal with event summarization and rare event detection independently, our method solves them in a single framework by transforming them into a graph editing problem. In our approach, a video is represented by a graph, each node of which indicates an event obtained by segmenting the video spatially and temporally. The edges between nodes describe the relationship between events. Based on the degree of relations, edges have different weights. After learning the graph structure, our method finds subgraphs that represent event summarization and rare events in the video by editing the graph, that is, merging its subgraphs or pruning its edges. The graph is edited to minimize a predefined energy model with the Markov Chain Monte Carlo (MCMC) method. The energy model consists of several parameters that represent the causality, frequency, and significance of events. We design a specific energy model that uses these parameters to satisfy each objective of event summarization and rare event detection. The proposed method is extended to obtain event summarization and rare event detection results across multiple videos captured from multiple views. For this purpose, the proposed method independently learns and edits each graph of individual videos for event summarization or rare event detection. Then, the method matches the extracted multiple graphs to each other, and constructs a single composite graph that represents event summarization or rare events from multiple views. Experimental results show that the proposed approach accurately summarizes multiple videos in a fully unsupervised manner. Moreover, the experiments demonstrate that the approach is advantageous in detecting rare transition of events.

  14. Diagnosis of delay-deadline failures in real time discrete event models.

    PubMed

    Biswas, Santosh; Sarkar, Dipankar; Bhowal, Prodip; Mukhopadhyay, Siddhartha

    2007-10-01

    In this paper a method for fault detection and diagnosis (FDD) of real time systems has been developed. A modeling framework termed as real time discrete event system (RTDES) model is presented and a mechanism for FDD of the same has been developed. The use of RTDES framework for FDD is an extension of the works reported in the discrete event system (DES) literature, which are based on finite state machines (FSM). FDD of RTDES models are suited for real time systems because of their capability of representing timing faults leading to failures in terms of erroneous delays and deadlines, which FSM-based ones cannot address. The concept of measurement restriction of variables is introduced for RTDES and the consequent equivalence of states and indistinguishability of transitions have been characterized. Faults are modeled in terms of an unmeasurable condition variable in the state map. Diagnosability is defined and the procedure of constructing a diagnoser is provided. A checkable property of the diagnoser is shown to be a necessary and sufficient condition for diagnosability. The methodology is illustrated with an example of a hydraulic cylinder.

  15. Children's judgments of televised events: the real versus pretend distinction.

    PubMed

    Downs, A C

    1990-06-01

    Previous research suggests that children's understanding that television is fictional may be dependent on various factors inherent in televised events. 36 children, 4- to 6-yr.-old, were asked to judge the reality of specific televised events shown via videotape. The events varied in aggressiveness, format, and type of character shown. While age and sex differences were absent, children's judgments were dependent on the format of the televised events (cartoon versus noncartoon) rather than other variables. Implications for research on children's learning of televised aggression and prosocial behaviors are discussed.

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

    NASA Astrophysics Data System (ADS)

    Pinsky, Vladimir; Shapira, Avi

    2017-01-01

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

  17. Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG

    PubMed Central

    Bigdely-Shamlo, Nima; Cockfield, Jeremy; Makeig, Scott; Rognon, Thomas; La Valle, Chris; Miyakoshi, Makoto; Robbins, Kay A.

    2016-01-01

    Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies. PMID:27799907

  18. Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG.

    PubMed

    Bigdely-Shamlo, Nima; Cockfield, Jeremy; Makeig, Scott; Rognon, Thomas; La Valle, Chris; Miyakoshi, Makoto; Robbins, Kay A

    2016-01-01

    Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies.

  19. Robust event detection scheme for complex scenes in video surveillance

    NASA Astrophysics Data System (ADS)

    Chen, Erkang; Xu, Yi; Yang, Xiaokang; Zhang, Wenjun

    2011-07-01

    Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

  20. Casi real-time surface-laid mine detection system

    NASA Astrophysics Data System (ADS)

    Ivanco, Tyler; Achal, Stephen B.; McFee, John E.; Anger, Clifford D.

    2001-10-01

    A ground vehicle-based, real-time, surface mine detection system, utilizing a Compact Airborne Spectrographic Image (casi), efficient mine detection algorithms, and real-time processing systems, was designed and tested. The combined real-time system was capable of 'learning' the in-situ spectra of various mines, thus providing a spectral library for the detection algorithms. The real-time processing of the casi data involved three steps. The first step was the radiometric correction of the raw data. The second step involved the application of the mine detection algorithms to the corrected data, referencing the spectral library. In the final step, the results of the real-time processes were stored and displayed, usually within a few frame times of the data acquisition. To the authors knowledge, this system represents the first hyperspectral imager to detect mines in real-time. This paper describes the generation of the in-situ mine spectral library, the collection of the scene data, the real-time processing of the scene data and the subsequent display and recording of the detection data. The limitation and expansion capabilities of the real-time system are discussed as well as various techniques that were implemented to achieve the goals. Planned future improvements that have been identified to create a more robust and higher performance, yet simpler processing systems are also discussed.

  1. Chapter 8: Spatiotemporal dynamics in bacterial cells: real-time studies with single-event resolution.

    PubMed

    Golding, Ido; Cox, Edward C

    2008-01-01

    To produce a quantitative picture of cellular life, one has to study the processes comprising it in individual living cells, quantifying intracellular dynamics with sufficient resolution to describe individual events in space and time. To perform such studies, we have recently developed a novel measurement approach, based on quantitative fluorescence microscopy, and applied it to the study of transcription in Escherichia coli and of the spatiotemporal dynamics of individual mRNA molecules in the cell (Golding and Cox, 2004, 2006a; Golding et al., 2005). The ability to detect individual events in real time depends on the engineering of an endogenous cellular process for amplifying the biological signal, in a way which allows signal detection to be independent of slow and highly stochastic cellular processes (Golding and Cox, 2006a). In this chapter, we describe the ingredients of our system and the way data is acquired and analyzed. We attempt to give general lessons for researchers who wish to implement a similar approach for the study of transcription in other organisms and, more generally, for the study of cellular processes with single-event resolution.

  2. Forecasting space weather: Using ACE data to provide real-time predictions of high-intensity energetic storm particle events

    NASA Astrophysics Data System (ADS)

    Wagstaff, K.; Ho, G. C.; Vandegriff, J.; Plauger, J.

    2003-04-01

    Geo-effective interplanetary (IP) shocks are often accompanied by Energetic Storm Particle (ESP) events, during which the intensity of charged particles can increase by several orders of magnitude. Such high intensities of incident ions present a radiation hazard to astronauts and electronics in Earth orbit. Observations by NASA's Advanced Composition Explorer (ACE) spacecraft indicate that these events are usually preceded by characteristic signatures in the ion intensities, thus providing an opportunity for predicting the events before they arrive. We have developed an algorithm that can forecast the arrival of ESP events. Using historical ion data from ACE, we trained an artificial neural network to detect the characteristic signals that warn of an impending event. The network predicts the time remaining until the maximum intensity is reached. We trained the network on 37 events, from 1997 to 2002, and tested it on a separate set of 18 events from the same time period. Initial performance of the network is very encouraging; the average uncertainty in predictions made 24 hours in advance is 9.4 hours, while the uncertainty improves to 4.9 hours when the event is 12 hours away. Recently, we have integrated our predictive algorithm in a system that uses real-time ACE data provided by the NOAA Space Environment Center. This system continually processes the latest ACE data and reports whether or not there is an impending ESP event. After detecting an event, our algorithm predicts the time remaining until the peak intensity occurs. For example, on November 25, 2002, our real-time system successfully detected an upcoming event and steadily produced predictions until the corresponding IP shock hit, at 9:45 p.m. on November 26, 2002. By providing a significant amount of lead-time, as well updated predictions every five minutes, this system can be a crucial source of information to mission planners, satellite operations controllers, and scientists.

  3. Real-time and reliable human detection in clutter scene

    NASA Astrophysics Data System (ADS)

    Tan, Yumei; Luo, Xiaoshu; Xia, Haiying

    2013-10-01

    To solve the problem that traditional HOG approach for human detection can not achieve real-time detection due to its time-consuming detection, an efficient algorithm based on first segmentation then identify method for real-time human detection is proposed to achieve real-time human detection in clutter scene. Firstly, the ViBe algorithm is used to segment all possible human target regions quickly, and more accurate moving objects is obtained by using the YUV color space to eliminate the shadow; secondly, using the body geometry knowledge can help to found the valid human areas by screening the regions of interest; finally, linear support vector machine (SVM) classifier and HOG are applied to train for human body classifier, to achieve accurate positioning of human body's locations. The results of our comparative experiments demonstrated that the approach proposed can obtain high accuracy, good real-time performance and strong robustness.

  4. Detecting plastic events in emulsions simulations

    NASA Astrophysics Data System (ADS)

    Lulli, Matteo; Matteo Lulli, Massimo Bernaschi, Mauro Sbragaglia Team

    2016-11-01

    Emulsions are complex systems which are formed by a number of non-coalescing droplets dispersed in a solvent leading to non-trivial effects in the overall flowing dynamics. Such systems possess a yield stress below which an elastic response to an external forcing occurs, while above the yield stress the system flows as a non-Newtonian fluid, i.e. the stress is not proportional to the shear. In the solid-like regime the network of the droplets interfaces stores the energy coming from the work exerted by an external forcing, which can be used to move the droplets in a non-reversible way, i.e. causing plastic events. The Kinetic-Elasto-Plastic (KEP) theory is an effective theory describing some features of the flowing regime relating the rate of plastic events to a scalar field called fluidity f =γ˙/σ , i.e. the inverse of an effective viscosity. Boundary conditions have a non-trivial role not captured by the KEP description. In this contribution we will compare numerical results against experiments concerning the Poiseuille flow of emulsions in microchannels with complex boundary geometries. Using an efficient computational tool we can show non-trivial results on plastic events for different realizations of the rough boundaries. The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007- 2013)/ERC Grant Agreement no. [279004].

  5. Facial landmark detection in real-time with correlation filtering

    NASA Astrophysics Data System (ADS)

    Contreras, Viridiana; Díaz-Ramírez, Víctor H.

    2016-09-01

    An algorithm for facial landmark detection based on template matched filtering is presented. The algorithm is able to detect and estimate the position of a set of prespecified landmarks by employing a bank of linear filters. Each filter in the bank is trained to detect a single landmark that is located in a small region of the input face image. The filter bank is implemented in parallel on a graphics processing unit to perform facial landmark detection in real-time. Computer simulation results obtained with the proposed algorithm are presented and discussed in terms of detection rate, accuracy of landmark location estimation, and real-time efficiency.

  6. Real time viability detection of bacterial spores

    DOEpatents

    Vanderberg, Laura A.; Herdendorf, Timothy J.; Obiso, Richard J.

    2003-07-29

    This invention relates to a process for detecting the presence of viable bacterial spores in a sample and to a spore detection system, the process including placing a sample in a germination medium for a period of time sufficient for commitment of any present viable bacterial spores to occur, mixing the sample with a solution of a lanthanide capable of forming a fluorescent complex with dipicolinic acid, and, measuring the sample for the presence of dipicolinic acid, and the system including a germination chamber having inlets from a sample chamber, a germinant chamber and a bleach chamber, the germination chamber further including an outlet through a filtering means, the outlet connected to a detection chamber, the detection chamber having an inlet from a fluorescence promoting metal chamber and the detection chamber including a spectral excitation source and a means of measuring emission spectra from a sample, the detection chamber further connected to a waste chamber. A germination reaction mixture useful for promoting commitment of any viable bacterial spores in a sample including a combination of L-alanine, L-asparagine and D-glucose is also described.

  7. Empirical study of crowd behavior during a real mass event

    NASA Astrophysics Data System (ADS)

    Zhang, X. L.; Weng, W. G.; Yuan, H. Y.

    2012-08-01

    The study of crowd behavior is essential for the safe organization of mass events. However, precise quantitative empirical data are insufficient due to the lack of mass event scenarios suitable for observation. In this paper, crowd behavior during a mass event in which many people go through a door and then pass a bridge is studied by a new method based on a flow field visualization algorithm widely used in fluid experiments. Two important movement phases, laminar flow on a bridge and stop-and-go waves in a bottleneck area, are investigated. The results show that the velocity profile on the bridge is similar to that of fully developed laminar flow in a pipe. Quantitative analysis of the stop-and-go wave in the bottleneck area shows that the dominant fluctuation frequencies are mainly below 0.1 Hz and the peak frequency is around 0.05 Hz the wave propagation speed is about - 0.69 m s-1. The absolute decrease in speed as the wave propagates is also indicated. By a combination of shock wave theory and a fundamental diagram, an analytical model of a shock wave in a crowd is established to theoretically investigate the stop-and-go wave, and the model can be used to explain the measurement results. This study provides a new method and fundamental data for understanding crowd behavior. The results are also expected to be useful for the design of crowd management strategies during mass events.

  8. Detection and Real Time Spectroscopy of Charged Particles with the TimePix Pixel Detector

    NASA Astrophysics Data System (ADS)

    Granja, Carlos; Jakubek, Jan; Platkevic, Michal; Pospisil, Stanislav; Vykydal, Zdenek

    2010-01-01

    We tested the position—, spectral— and time—resolution capability of the TimePix semiconductor detector together with the USB readout interface and Pixelman control and DAQ software tool for detection and visualization of particles. Event—by—event spectroscopy can be achieved by real time analysis of the characteristic tracks and specific response of different radiation in the pixel detector.

  9. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

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

    2012-01-01

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

  10. Saliency-based abnormal event detection in crowded scenes

    NASA Astrophysics Data System (ADS)

    Shi, Yanjiao; Liu, Yunxiang; Zhang, Qing; Yi, Yugen; Li, Wenju

    2016-11-01

    Abnormal event detection plays a critical role for intelligent video surveillance, and detection in crowded scenes is a challenging but more practical task. We present an abnormal event detection method for crowded video. Region-wise modeling is proposed to address the inconsistent detected motion of the same object due to different depths of field. Comparing to traditional block-wise modeling, the region-wise method not only can reduce heavily the number of models to be built but also can enrich the samples for training the normal events model. In order to reduce the computational burden and make the region-based anomaly detection feasible, a saliency detection technique is adopted in this paper. By identifying the salient parts of the image sequences, the irrelevant blocks are ignored, which removes the disturbance and improves the detection performance further. Experiments on the benchmark dataset and comparisons with the state-of-the-art algorithms validate the advantages of the proposed method.

  11. Modeling Concept Dependencies for Event Detection

    DTIC Science & Technology

    2014-04-04

    approach to model concept dependencies. An MRF is commonly used in the machine learning domain and is an undirected graph- ical model. It models the...Repairing an appliance, (E015) Working on a sewing project. The EC collection has 2,062 videos each of which is relevant to an event. The DevT...30.4 34.7 29.7 E014 63.6 43.2 37.5 75.0 34.1 28.4 36.4 26.1 32.9 23.2 E015 80.5 68.3 58.5 80.5 52.4 43.9 43.9 45.1 48.7 42.1 sewing project”. When we

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

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

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

    PubMed

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

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    PubMed Central

    Cheng, Tao; Wicks, Thomas

    2014-01-01

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

  15. A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.

    PubMed

    Oliker, Nurit; Ostfeld, Avi

    2014-03-15

    This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. SSME propellant path leak detection real-time

    NASA Technical Reports Server (NTRS)

    Crawford, R. A.; Smith, L. M.

    1994-01-01

    Included are four documents that outline the technical aspects of the research performed on NASA Grant NAG8-140: 'A System for Sequential Step Detection with Application to Video Image Processing'; 'Leak Detection from the SSME Using Sequential Image Processing'; 'Digital Image Processor Specifications for Real-Time SSME Leak Detection'; and 'A Color Change Detection System for Video Signals with Applications to Spectral Analysis of Rocket Engine Plumes'.

  17. Ontologies to capture adverse events following immunisation (AEFI) from real world health data.

    PubMed

    Liyanage, Harshana; de Lusignan, Simon

    2014-01-01

    Immunisation is an important part of health care and adverse events following immunisation (AEFI) are relatively rare. AEFI can be detected through long term follow up of a cohort or from looking for signals from real world, routine data; from different health systems using a variety of clinical coding systems. Mapping these is a challenging aspect of integrating data across borders. Ontological representations of clinical concepts provide a method to map similar concepts, in this case AEFI across different coding systems. We describe a method using ontologies to be flag definite, probable or possible cases. We use Guillain-Barre syndrome (GBS) as an AEFI to illustrate this method, and the Brighton collaboration's case definition of GBS as the gold standard. Our method can be used to flag definite, probable or possible cases of GBS. Whilst there has been much research into the use of ontologies in immunisation these have focussed on database interrogation; where ours looks to identify varying signal strength.

  18. Real-time head motion detection system

    NASA Astrophysics Data System (ADS)

    Mase, Kenji; Watanabe, Yasuhiko; Suenaga, Yasuhito

    1990-01-01

    We present a three-dimensional head motion detection system called a realtime headreader. This headreader analyzes the head motion picture sequences taken by a TV-camera, and extracts the motion parameters in realtime, i.e. 3-d rotations and translations. We used a simple but very fast algorithm, which exploits the contrast of hair and face to recognize face orientation. The system extracts the head and face area, then estimates the head motion parameters from the change in position of each area's centroids. The head motion is computed at nearly 10 frames per second on a SUN4 workstation and the motion parameters are sent to an IRIS workstation at a 2.5 Kbps. The IRIS generates a head motion sequence that duplicates the original head motion. The entire motion detection program is written in C language. No special image processing hardware is used, except for a video digitizer. Our head motion detection system will enhance man-machine interactions by providing a new visual eue. An operator will be able to point to a target by just looking at it thus a mouse or 3-d tracking device is not needed. The eventual goal of this research is to build an intelligent video communication system that codes the information in terms of high level language rather than compressed video signals.

  19. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data.

    PubMed

    Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

    2016-01-01

    This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.

  20. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

    PubMed Central

    Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

    2015-01-01

    This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

  1. Service oriented architecture to support real-time implementation of artifact detection in critical care monitoring.

    PubMed

    Nizami, Shermeen; Green, James Robert; McGregor, Carolyn

    2011-01-01

    The quality of automated real-time critical care monitoring is impacted by the degree of signal artifact present in clinical data. This is further complicated when different clinical rules applied for disease detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time implementation of clinical artifact detection in critical care settings. The framework is instantiated through a Neonatal Intensive Care case study which assesses signal quality of physiological data streams prior to detection of late-onset neonatal sepsis. In this case study requirements and provisions of artifact and clinical event detection are determined for real-time clinical implementation, which forms the second important contribution of this paper.

  2. Intelligent fuzzy controller for event-driven real time systems

    NASA Technical Reports Server (NTRS)

    Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

    1992-01-01

    Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

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

    ERIC Educational Resources Information Center

    Park, Dong-Jun

    2011-01-01

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

  4. Characteristics of near-death experiences memories as compared to real and imagined events memories.

    PubMed

    Thonnard, Marie; Charland-Verville, Vanessa; Brédart, Serge; Dehon, Hedwige; Ledoux, Didier; Laureys, Steven; Vanhaudenhuyse, Audrey

    2013-01-01

    Since the dawn of time, Near-Death Experiences (NDEs) have intrigued and, nowadays, are still not fully explained. Since reports of NDEs are proposed to be imagined events, and since memories of imagined events have, on average, fewer phenomenological characteristics than real events memories, we here compared phenomenological characteristics of NDEs reports with memories of imagined and real events. We included three groups of coma survivors (8 patients with NDE as defined by the Greyson NDE scale, 6 patients without NDE but with memories of their coma, 7 patients without memories of their coma) and a group of 18 age-matched healthy volunteers. Five types of memories were assessed using Memory Characteristics Questionnaire (MCQ--Johnson et al., 1988): target memories (NDE for NDE memory group, coma memory for coma memory group, and first childhood memory for no memory and control groups), old and recent real event memories and old and recent imagined event memories. Since NDEs are known to have high emotional content, participants were requested to choose the most emotionally salient memories for both real and imagined recent and old event memories. Results showed that, in NDE memories group, NDE memories have more characteristics than memories of imagined and real events (p<0.02). NDE memories contain more self-referential and emotional information and have better clarity than memories of coma (all ps<0.02). The present study showed that NDE memories contained more characteristics than real event memories and coma memories. Thus, this suggests that they cannot be considered as imagined event memories. On the contrary, their physiological origins could lead them to be really perceived although not lived in the reality. Further work is needed to better understand this phenomenon.

  5. Characteristics of Near-Death Experiences Memories as Compared to Real and Imagined Events Memories

    PubMed Central

    Brédart, Serge; Dehon, Hedwige; Ledoux, Didier; Laureys, Steven; Vanhaudenhuyse, Audrey

    2013-01-01

    Since the dawn of time, Near-Death Experiences (NDEs) have intrigued and, nowadays, are still not fully explained. Since reports of NDEs are proposed to be imagined events, and since memories of imagined events have, on average, fewer phenomenological characteristics than real events memories, we here compared phenomenological characteristics of NDEs reports with memories of imagined and real events. We included three groups of coma survivors (8 patients with NDE as defined by the Greyson NDE scale, 6 patients without NDE but with memories of their coma, 7 patients without memories of their coma) and a group of 18 age-matched healthy volunteers. Five types of memories were assessed using Memory Characteristics Questionnaire (MCQ – Johnson et al., 1988): target memories (NDE for NDE memory group, coma memory for coma memory group, and first childhood memory for no memory and control groups), old and recent real event memories and old and recent imagined event memories. Since NDEs are known to have high emotional content, participants were requested to choose the most emotionally salient memories for both real and imagined recent and old event memories. Results showed that, in NDE memories group, NDE memories have more characteristics than memories of imagined and real events (p<0.02). NDE memories contain more self-referential and emotional information and have better clarity than memories of coma (all ps<0.02). The present study showed that NDE memories contained more characteristics than real event memories and coma memories. Thus, this suggests that they cannot be considered as imagined event memories. On the contrary, their physiological origins could lead them to be really perceived although not lived in the reality. Further work is needed to better understand this phenomenon. PMID:23544039

  6. Semantic Concept Discovery for Large Scale Zero Shot Event Detection

    DTIC Science & Technology

    2015-07-25

    NO. 0704-0188 3. DATES COVERED (From - To) - UU UU UU UU 18-08-2015 Approved for public release; distribution is unlimited. Semantic Concept Discovery ...Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 zero shot event detection, semantic concept discovery REPORT DOCUMENTATION PAGE 11...Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 -3815 ABSTRACT Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection Report

  7. Pipeline Implementation of Real Time Event Cross Correlation for Nuclear Treaty Monitoring

    NASA Astrophysics Data System (ADS)

    Junek, W. N.; Wehlen, J. A., III

    2014-12-01

    The United States National Data Center (US NDC) is responsible for monitoring international compliance to nuclear test ban treaties. This mission is performed through real time acquisition, processing, and evaluation of data acquired by a global network of seismic, hydroacoustic, and infrasonic sensors. Automatic and human reviewed event solutions are stored in a data warehouse which contains over 15 years of alphanumeric information and waveform data. A significant effort is underway to employ the data warehouse in real time processing to improve the quality of automatic event solutions, reduce analyst burden, and supply decision makers with information regarding relevant historic events. To this end, the US NDC processing pipeline has been modified to automatically recognize events built in the past. Event similarity information and the most relevant historic solution are passed to the human analyst to assist their evaluation of automatically formed events. This is achieved through real time cross correlation of selected seismograms from automatically formed events against those stored in the data warehouse. Historic events used in correlation analysis are selected based on a set of user defined parameters, which are tuned to maintain pipeline timeliness requirements. Software architecture and database infrastructure were modified using a multithreaded design for increased processing speed, database connection pools for parallel queries, and Oracle spatial indexing to enhance query efficiency. This functionality allows the human analyst to spend more time studying anomalous events and less time rebuilding routine events.

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

    PubMed

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

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

  9. Preferential Inspection of Recent Real-World Events Over Future Events: Evidence from Eye Tracking during Spoken Sentence Comprehension.

    PubMed

    Knoeferle, Pia; Carminati, Maria Nella; Abashidze, Dato; Essig, Kai

    2011-01-01

    Eye-tracking findings suggest people prefer to ground their spoken language comprehension by focusing on recently seen events more than anticipating future events: When the verb in NP1-VERB-ADV-NP2 sentences was referentially ambiguous between a recently depicted and an equally plausible future clipart action, listeners fixated the target of the recent action more often at the verb than the object that hadn't yet been acted upon. We examined whether this inspection preference generalizes to real-world events, and whether it is (vs. isn't) modulated by how often people see recent and future events acted out. In a first eye-tracking study, the experimenter performed an action (e.g., sugaring pancakes), and then a spoken sentence either referred to that action or to an equally plausible future action (e.g., sugaring strawberries). At the verb, people more often inspected the pancakes (the recent target) than the strawberries (the future target), thus replicating the recent-event preference with these real-world actions. Adverb tense, indicating a future versus past event, had no effect on participants' visual attention. In a second study we increased the frequency of future actions such that participants saw 50/50 future and recent actions. During the verb people mostly inspected the recent action target, but subsequently they began to rely on tense, and anticipated the future target more often for future than past tense adverbs. A corpus study showed that the verbs and adverbs indicating past versus future actions were equally frequent, suggesting long-term frequency biases did not cause the recent-event preference. Thus, (a) recent real-world actions can rapidly influence comprehension (as indexed by eye gaze to objects), and (b) people prefer to first inspect a recent action target (vs. an object that will soon be acted upon), even when past and future actions occur with equal frequency. A simple frequency-of-experience account cannot accommodate these findings.

  10. Monitoring Natural Events Globally in Near Real-Time Using NASA's Open Web Services and Tools

    NASA Technical Reports Server (NTRS)

    Boller, Ryan A.; Ward, Kevin Alan; Murphy, Kevin J.

    2015-01-01

    Since 1960, NASA has been making global measurements of the Earth from a multitude of space-based missions, many of which can be useful for monitoring natural events. In recent years, these measurements have been made available in near real-time, making it possible to use them to also aid in managing the response to natural events. We present the challenges and ongoing solutions to using NASA satellite data for monitoring and managing these events.

  11. Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

    PubMed

    Housh, Mashor; Ohar, Ziv

    2017-03-01

    The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Real-time iris detection on rotated faces

    NASA Astrophysics Data System (ADS)

    Perez, Claudio A.; Lazcano, Vanel A.; Estevez, Pablo A.; Held, Claudio M.

    2003-10-01

    Real-time face and iris detection on video sequences has been used to study the eye function and in diverse applications such as drowsiness detection, virtual keyboard interfaces, face recognition and multimedia retrieval. A non-invasive real time iris detection method was developed and consists of three stages: coarse face detection, fine face detection and iris detection. Anthropometric templates are used in these three stages. Elliptical templates are used to locate the coarse face center. A set of anthropometric templates which are probabilistic maps for the eyebrows, nose and mouth are used to perform the fine face detection. Face rotations are considered by rotating the anthropometric templates in fixed angles in steps of 10 degrees. Iris position is then determined within the eye region using another template with concentric semi-circles to compute a line integral in the boundary iris-sclera. The position with the maximum value indicates the iris center. The new method was applied on 10 video sequences, with a total of 6470 frames, from different people rotating their faces in the coronal axis. Results of correct face detection on 8 video sequences was 100%, one reached 99.9% and one 98.2%. Results on correct iris detection are above 96% in 9 of the video sequences and one reached 77.8%. The method was implemented in real-time (30 frames per second) with a PC 1.8 GHz.

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

    PubMed

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

    2015-12-15

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

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

    PubMed Central

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

    2015-01-01

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

  15. Performance evaluation for three pollution detection methods using data from a real contamination accident.

    PubMed

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

    2015-09-15

    Early warning systems have been widely deployed to safeguard water security. Many contamination detection methods have been developed and evaluated in the past decades. Although encouraging detection performance has been obtained and reported, these evaluations mainly used artificial or laboratory data. The evaluation of detection performance with data from real contamination accidents has rarely been conducted. Implementation of contamination event methods without full assessment using field data might lead to failure of an early warning system. In this paper, the detection performance of three contamination detection methods, a Pearson correlation Euclidean distance (PE) based detection method, a multivariate Euclidean distance (MED) method and a linear prediction filter (LPF) method, was evaluated using data from a real contamination accident. Results improve understanding of the implementation of detection methods to field situations and show that all methods are prone to yielding worse detection performance when applied to data from a real contamination accident. They also revealed that the Pearson correlation Euclidean distance based method is more capable of differentiating between equipment noise and presence of contamination and has greater potential to be used in real field situations than the MED and LPF methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Real Time Data Mining-Based Intrusion Detection

    DTIC Science & Technology

    2001-06-01

    In this paper, we present an overview of our research in real time data mining -based intrusion detection systems (IDSs). We focus on issues related...to deploying a data mining -based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and...usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from

  17. Respiratory Event Detection by a Positive Airway Pressure Device

    PubMed Central

    Berry, Richard B.; Kushida, Clete A.; Kryger, Meir H.; Soto-Calderon, Haideliza; Staley, Bethany; Kuna, Samuel T.

    2012-01-01

    Study Objectives: Compare automatic event detection (AED) of respiratory events using a positive airway pressure (PAP) device with manual scoring of polysomnography (PSG) during PAP treatment of obstructive sleep apnea (OSA). Design: Prospective PSGs of patients using a PAP device. Setting: Six academic and private sleep disorders centers. Patients: A total of 148 PSGs from 115 participants with OSA (apnea-hypopnea index [AHI] ≥ 15 events/hr) were analyzed. Interventions: A signal generated by the PAP device identifying the AED of respiratory events based on airflow was recorded during PSG. Measurements and Results: The PSGs were manually scored without visualization of the AED signal and scoring of a hypopnea required a ≥ 4% oxygen desaturation. The apnea index (AI), hypopnea index (HI), and AHI by manual score and PAP AED were compared. A customized computer program compared individual events by manual scoring and AED to determine the true positive, false positive, false negative, or true negative events and found a sensitivity of 0.58 and a specificity of 0.98. The AHI, AI, and HI by the two methods were highly correlated. Bland-Altman analysis showed better agreement for AI than HI. Using a manually scored AHI of ≥ 10 events/hr to denote inadequate treatment, an AED AHI ≥ 10 events/hr had a sensitivity of 0.58 and a specificity of 0.94. Conclusions: An AHI < 10 events/hr by PAP AED is usually associated with good treatment efficacy. Differences between manually scored and AED events were primarily due to different criteria for hypopnea detection. Citation: Berry RB; Kushida CA; Kryger MH; Soto-Calderon H; Staley B; Kuna ST. Respiratory event detection by a positive airway pressure device. SLEEP 2012;35(3):361-367. PMID:22379242

  18. Verifying Ptolemy II Discrete-Event Models Using Real-Time Maude

    NASA Astrophysics Data System (ADS)

    Bae, Kyungmin; Ölveczky, Peter Csaba; Feng, Thomas Huining; Tripakis, Stavros

    This paper shows how Ptolemy II discrete-event (DE) models can be formally analyzed using Real-Time Maude. We formalize in Real-Time Maude the semantics of a subset of hierarchical Ptolemy II DE models, and explain how the code generation infrastructure of Ptolemy II has been used to automatically synthesize a Real-Time Maude verification model from a Ptolemy II design model. This enables a model-engineering process that combines the convenience of Ptolemy II DE modeling and simulation with formal verification in Real-Time Maude.

  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. Near Real-Time Optimal Prediction of Adverse Events in Aviation Data

    NASA Technical Reports Server (NTRS)

    Martin, Rodney Alexander; Das, Santanu

    2010-01-01

    The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we demonstrate how to recast the anomaly prediction problem into a form whose solution is accessible as a level-crossing prediction problem. The level-crossing prediction problem has an elegant, optimal, yet untested solution under certain technical constraints, and only when the appropriate modeling assumptions are made. As such, we will thoroughly investigate the resilience of these modeling assumptions, and show how they affect final performance. Finally, the predictive capability of this method will be assessed by quantitative means, using both validation and test data containing anomalies or adverse events from real aviation data sets that have previously been identified as operationally significant by domain experts. It will be shown that the formulation proposed yields a lower false alarm rate on average than competing methods based on similarly advanced concepts, and a higher correct detection rate than a standard method based upon exceedances that is commonly used for prediction.

  1. Virtual and Real World Adaptation for Pedestrian Detection.

    PubMed

    Vázquez, David; López, Antonio M; Marín, Javier; Ponsa, Daniel; Gerónimo, David

    2014-04-01

    Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in real-world images? Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the data set shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.

  2. Detection of earth-approaching asteroids in near real time

    NASA Technical Reports Server (NTRS)

    Rabinowitz, D. L.

    1991-01-01

    Computer software, called the Moving Object Detection Program (MODP), is described which detects earth-approaching asteroids in near real time. The software runs on a workstation linked to the output of the drift-scanning CCD camera of the Spacewatch Telescope. MOPD recognizes trailed images, detects motion, and accurately determines angular positions and rates of motion for moving objects in the scan images. The results are obtained a few seconds after the image signals are shifted out of the CCD. During 2 months of trial observations with this system, 304 asteroids were detected down to a limiting apparent magnitude for untrailed images of V = 20.5.

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

    NASA Astrophysics Data System (ADS)

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

    2003-09-01

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

  4. Abnormal events detection in crowded scenes by trajectory cluster

    NASA Astrophysics Data System (ADS)

    Zhou, Shifu; Zhang, Zhijiang; Zeng, Dan; Shen, Wei

    2015-02-01

    Abnormal events detection in crowded scenes has been a challenge due to volatility of the definitions for both normality and abnormality, the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. A novel framework was proposed for the detection of unusual events in crowded scenes using trajectories produced by moving pedestrians based on an intuition that the motion patterns of usual behaviors are similar to these of group activity, whereas unusual behaviors are not. First, spectral clustering is used to group trajectories with similar spatial patterns. Different trajectory clusters represent different activities. Then, unusual trajectories can be detected using these patterns. Furthermore, behavior of a mobile pedestrian can be defined by comparing its direction with these patterns, such as moving in the opposite direction of the group or traversing the group. Experimental results indicated that the proposed algorithm could be used to reliably locate the abnormal events in crowded scenes.

  5. Virtual and Real World Adaptation for Pedestrian Detection.

    PubMed

    Vazquez, David; Lopez, Antonio M; Marin, Javier; Ponsa, Daniel; Geronimo, David

    2013-08-23

    Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing performance in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection performance than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.

  6. Data mining for signal detection of adverse event safety data.

    PubMed

    Chen, Hung-Chia; Tsong, Yi; Chen, James J

    2013-01-01

    The Adverse Event Reporting System (AERS) is the primary database designed to support the Food and Drug Administration (FDA) postmarketing safety surveillance program for all approved drugs and therapeutic biologic products. Most current disproportionality analysis focuses on the detection of potential adverse events (AE) involving a single drug and a single AE only. In this paper, we present a data mining biclustering technique based on the singular value decomposition to extract local regions of association for a safety study. The analysis consists of collection of biclusters, each representing an association between a set of drugs with the corresponding set of adverse events. Significance of each bicluster can be tested using disproportionality analysis. Individual drug-event combination can be further tested. A safety data set consisting of 193 drugs with 8453 adverse events is analyzed as an illustration.

  7. Method for early detection of cooling-loss events

    DOEpatents

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

    2015-12-22

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

  8. Method for early detection of cooling-loss events

    DOEpatents

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

    2015-06-30

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

  9. Respiratory event detection by a positive airway pressure device.

    PubMed

    Berry, Richard B; Kushida, Clete A; Kryger, Meir H; Soto-Calderon, Haideliza; Staley, Bethany; Kuna, Samuel T

    2012-03-01

    Compare automatic event detection (AED) of respiratory events using a positive airway pressure (PAP) device with manual scoring of polysomnography (PSG) during PAP treatment of obstructive sleep apnea (OSA). Prospective PSGs of patients using a PAP device. Six academic and private sleep disorders centers. A total of 148 PSGs from 115 participants with OSA (apnea-hypopnea index [AHI] ≥ 15 events/hr) were analyzed. A signal generated by the PAP device identifying the AED of respiratory events based on airflow was recorded during PSG. The PSGs were manually scored without visualization of the AED signal and scoring of a hypopnea required a ≥ 4% oxygen desaturation. The apnea index (AI), hypopnea index (HI), and AHI by manual score and PAP AED were compared. A customized computer program compared individual events by manual scoring and AED to determine the true positive, false positive, false negative, or true negative events and found a sensitivity of 0.58 and a specificity of 0.98. The AHI, AI, and HI by the two methods were highly correlated. Bland-Altman analysis showed better agreement for AI than HI. Using a manually scored AHI of ≥ 10 events/hr to denote inadequate treatment, an AED AHI ≥ 10 events/hr had a sensitivity of 0.58 and a specificity of 0.94. An AHI < 10 events/hr by PAP AED is usually associated with good treatment efficacy. Differences between manually scored and AED events were primarily due to different criteria for hypopnea detection.

  10. DETECTION OF FECAL ENTEROCOCCI USING A REAL TIME PCR METHOD

    EPA Science Inventory

    In spite of their importance in public health, the detection of fecal enterococci is performed via culturing methods that are time consuming and that are subject to inaccuracies that relate to their culturable status. In order to address these problems, a real time PCR (TaqMan) ...

  11. DETECTION OF FECAL ENTEROCOCCI USING A REAL TIME PCR METHOD

    EPA Science Inventory

    In spite of their importance in public health, the detection of fecal enterococci is performed via culturing methods that are time consuming and that are subject to inaccuracies that relate to their culturable status. In order to address these problems, a real time PCR (TaqMan) ...

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

    PubMed

    Chen, Feng; Neill, Daniel B

    2015-03-01

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

  13. Preferential Inspection of Recent Real-World Events Over Future Events: Evidence from Eye Tracking during Spoken Sentence Comprehension

    PubMed Central

    Knoeferle, Pia; Carminati, Maria Nella; Abashidze, Dato; Essig, Kai

    2011-01-01

    Eye-tracking findings suggest people prefer to ground their spoken language comprehension by focusing on recently seen events more than anticipating future events: When the verb in NP1-VERB-ADV-NP2 sentences was referentially ambiguous between a recently depicted and an equally plausible future clipart action, listeners fixated the target of the recent action more often at the verb than the object that hadn’t yet been acted upon. We examined whether this inspection preference generalizes to real-world events, and whether it is (vs. isn’t) modulated by how often people see recent and future events acted out. In a first eye-tracking study, the experimenter performed an action (e.g., sugaring pancakes), and then a spoken sentence either referred to that action or to an equally plausible future action (e.g., sugaring strawberries). At the verb, people more often inspected the pancakes (the recent target) than the strawberries (the future target), thus replicating the recent-event preference with these real-world actions. Adverb tense, indicating a future versus past event, had no effect on participants’ visual attention. In a second study we increased the frequency of future actions such that participants saw 50/50 future and recent actions. During the verb people mostly inspected the recent action target, but subsequently they began to rely on tense, and anticipated the future target more often for future than past tense adverbs. A corpus study showed that the verbs and adverbs indicating past versus future actions were equally frequent, suggesting long-term frequency biases did not cause the recent-event preference. Thus, (a) recent real-world actions can rapidly influence comprehension (as indexed by eye gaze to objects), and (b) people prefer to first inspect a recent action target (vs. an object that will soon be acted upon), even when past and future actions occur with equal frequency. A simple frequency-of-experience account cannot accommodate these

  14. Detection of Abnormal Events via Optical Flow Feature Analysis

    PubMed Central

    Wang, Tian; Snoussi, Hichem

    2015-01-01

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

  15. Planning in the real world: preschool children's scripts and plans for familiar events.

    PubMed

    Hudson, J A; Shapiro, L R; Sosa, B B

    1995-08-01

    3-, 4-, and 5-year-olds reported either scripts or verbal plans for 2 familiar events, going grocery shopping and going to the beach, and also constructed plans to remedy and prevent mishaps that might occur for each event. With increasing age, children reported more information, focused more on onset activities, and mentioned more specific planning activities in their plans than in their scripts. Although children at all ages provided adequate remedy plans, only 5-year-olds provided adequate prevention plans. In general, children were better at planning for the beach than for grocery shopping. Results indicate that developmental differences in event knowledge, in the ability to reflect upon event knowledge, and the event that they are planning for affect preschoolers' planning for real-world events.

  16. Real-time distributed fiber optic sensor for security systems: Performance, event classification and nuisance mitigation

    NASA Astrophysics Data System (ADS)

    Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

    2012-09-01

    The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the nuisance alarm rate (NAR), and the false alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.

  17. A comparison of moving object detection methods for real-time moving object detection

    NASA Astrophysics Data System (ADS)

    Roshan, Aditya; Zhang, Yun

    2014-06-01

    Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

  18. Near real time detection of hazardous airborne substances.

    PubMed

    Leppert, J; Horner, G; Rietz, F; Ringer, J; Schulze Lammers, P; Boeker, P

    2012-11-15

    A fast near real-time monitoring system for hazardous airborne substances, such as chemical warfare agents (CWA) is presented and limits of detection (LOD) for five CW simulants are determined. A tandem thermal desorber (TTD) continuously collects and pre-concentrates air. The pre-concentrated samples are then separated in a fast gas chromatographic (GC) run of 6.9min. and detected by a time-of-flight mass spectrometer (TOFMS). The GC-TOFMS signals are evaluated using chemometric methods for deconvolution and target identification. The high toxicity of nerve agents requires extremely low detection limits; for some as low as 100 ng/m(3) (10 ppt). The combination of TTD, TOFMS and chemometric data evaluation methods enables the system to fulfill this requirement. Calibration measurements for five different CWA simulants show lower limits of detection in the range of 10 ng/m(3)-60 ng/m(3) (1-11 ppt). In addition, the ability to detect trace concentrations of real CWA is demonstrated with a measurement of 30 pg Sarin on column. Several other real CWA measurements are shown, like sulfur mustard in diesel, lewisite under humid conditions and VX. As part of this work the influence of stationary film thickness on peak tailing of organophosphates is investigated for peak shape optimization.

  19. Near real time vapor detection and enhancement using aerosol adsorption

    SciTech Connect

    Novick, Vincent J.; Johnson, Stanley A.

    1997-12-01

    A vapor sample detection method where the vapor sample contains vapor and ambient air and surrounding natural background particles. The vapor sample detection method includes the steps of generating a supply of aerosol that have a particular effective median particle size, mixing the aerosol with the vapor sample forming aerosol and adsorbed vapor suspended in an air stream, impacting the suspended aerosol and adsorbed vapor upon a reflecting element, alternatively directing infrared light to the impacted aerosol and adsorbed vapor, detecting and analyzing the alternatively directed infrared light in essentially real time using a spectrometer and a microcomputer and identifying the vapor sample.

  20. Near real time vapor detection and enhancement using aerosol adsorption

    DOEpatents

    Novick, V.J.; Johnson, S.A.

    1999-08-03

    A vapor sample detection method is described where the vapor sample contains vapor and ambient air and surrounding natural background particles. The vapor sample detection method includes the steps of generating a supply of aerosol that have a particular effective median particle size, mixing the aerosol with the vapor sample forming aerosol and adsorbed vapor suspended in an air stream, impacting the suspended aerosol and adsorbed vapor upon a reflecting element, alternatively directing infrared light to the impacted aerosol and adsorbed vapor, detecting and analyzing the alternatively directed infrared light in essentially real time using a spectrometer and a microcomputer and identifying the vapor sample. 13 figs.

  1. Near real time vapor detection and enhancement using aerosol adsorption

    DOEpatents

    Novick, Vincent J.; Johnson, Stanley A.

    1999-01-01

    A vapor sample detection method where the vapor sample contains vapor and ambient air and surrounding natural background particles. The vapor sample detection method includes the steps of generating a supply of aerosol that have a particular effective median particle size, mixing the aerosol with the vapor sample forming aerosol and adsorbed vapor suspended in an air stream, impacting the suspended aerosol and adsorbed vapor upon a reflecting element, alternatively directing infrared light to the impacted aerosol and adsorbed vapor, detecting and analyzing the alternatively directed infrared light in essentially real time using a spectrometer and a microcomputer and identifying the vapor sample.

  2. Real-time system for road following and obstacle detection

    NASA Astrophysics Data System (ADS)

    Denasi, Sandra; Lanzone, Claudio; Martinese, Paolo; Pettiti, Giuseppe; Quaglia, Giorgio; Viglione, Luca

    1994-10-01

    Path planning of a vehicle running in a structured environment requires road boundaries evaluation for mapping its position and reducing the search area for obstacle detection. This paper describes a real time system that has been developed in the framework of the EUREKA PROMETHEUS European project and is presently under test on a Mobile Laboratory (MOBLAB). The road boundaries are detected by highlighting the large homogeneous region that lies in the bottom of the image, in front of the vehicle. Edge detection, local thresholding and morphological filtering techniques are used to define this region. Its boundaries are tracked in the sequence, relying on hypotheses of continuity of color and shape of the road to overcome drawbacks due to shadows, intersections, hidden boundaries. The proposed technique has been implemented on an integrated system based on a real time imaging processor and a workstation.

  3. November 2004 space weather events: Real-time observations and forecasts

    NASA Astrophysics Data System (ADS)

    Trichtchenko, L.; Zhukov, A.; van der Linden, R.; Stankov, S. M.; Jakowski, N.; StanisłAwska, I.; Juchnikowski, G.; Wilkinson, P.; Patterson, G.; Thomson, A. W. P.

    2007-06-01

    Space weather events with their solar origin and their distribution through the heliosphere affect the whole magnetosphere-ionosphere-Earth system. Their real-time monitoring and forecasting are important for science and technology. Here we discuss one of the largest space weather events of Solar Cycle 23, in November 2004, which was also one of the most difficult periods to forecast. Nine halo coronal mass ejections (CMEs), interacting on their way through the interplanetary medium and forming two geoeffective interplanetary structures, exemplify the complexity of the event. Real-time and quasi-real-time observations of the ground geomagnetic field show rapid and extensive expansion of the auroral oval to 55° in geomagnetic latitude accompanied by great variability of the ionosphere. Geomagnetically induced currents (GICs) seen in ground networks, such as power grids and pipelines, were significant during the event, although no problems were reported. Forecasts of the CME propagation, global and local ground geomagnetic activity, and ionospheric parameters, issued by several regional warning centers, revealed certain deficiencies in predictions of the interplanetary characteristics of the CME, size of the geomagnetic disturbances, and complexity of the ionospheric variations produced by this event. This paper is a collective report based on the materials presented at the splinter session on November 2004 events during the first European Space Weather Week.

  4. Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence

    PubMed Central

    Ananthakrishnan, Sankaranarayanan; Narayanan, Shrikanth S.

    2008-01-01

    With the advent of prosody annotation standards such as tones and break indices (ToBI), speech technologists and linguists alike have been interested in automatically detecting prosodic events in speech. This is because the prosodic tier provides an additional layer of information over the short-term segment-level features and lexical representation of an utterance. As the prosody of an utterance is closely tied to its syntactic and semantic content in addition to its lexical content, knowledge of the prosodic events within and across utterances can assist spoken language applications such as automatic speech recognition and translation. On the other hand, corpora annotated with prosodic events are useful for building natural-sounding speech synthesizers. In this paper, we build an automatic detector and classifier for prosodic events in American English, based on their acoustic, lexical, and syntactic correlates. Following previous work in this area, we focus on accent (prominence, or “stress”) and prosodic phrase boundary detection at the syllable level. Our experiments achieved a performance rate of 86.75% agreement on the accent detection task, and 91.61% agreement on the phrase boundary detection task on the Boston University Radio News Corpus. PMID:19122857

  5. C4: a real-time object detection framework.

    PubMed

    Wu, Jianxin; Liu, Nini; Geyer, Christopher; Rehg, James M

    2013-10-01

    A real-time and accurate object detection framework, C(4), is proposed in this paper. C(4) achieves 20 fps speed and the state-of-the-art detection accuracy, using only one processing thread without resorting to special hardware such as GPU. The real-time accurate object detection is made possible by two contributions. First, we conjecture (with supporting experiments) that contour is what we should capture and signs of comparisons among neighboring pixels are the key information to capture contour cues. Second, we show that the CENTRIST visual descriptor is suitable for contour based object detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image preprocessing or feature vector normalization, and only requires O(1) steps to test an image patch. C(4) is also friendly to further hardware acceleration. It has been applied to detect objects such as pedestrians, faces, and cars on benchmark data sets. It has comparable detection accuracy with state-of-the-art methods, and has a clear advantage in detection speed.

  6. Automatic microseismic event detection by band-limited phase-only correlation

    NASA Astrophysics Data System (ADS)

    Wu, Shaojiang; Wang, Yibo; Zhan, Yi; Chang, Xu

    2016-12-01

    Identification and detection of microseismic events is a significant issue in source locations and source mechanism analysis. The number of the records is notably large, especially in the case of some real-time monitoring, and while the majority of microseismic events are highly weak and sparse, automatic algorithms are indispensable. In this study, we introduce an effective method for the identification and detection of microseismic events by judging whether the P-wave phase exists in a local segment from a single three-component microseismic records. The new judging algorithm consists primarily of the following key steps: 1) transform the waveform time series into time-varying spectral representations using the S-transform; 2) calculate the similarity of the frequency content in the time-frequency domain using the phase-only correlation function; and 3) identify the P-phase by the combination analysis between any two components. The proposed algorithm is compared to a similar approach using the cross-correlation in the time domain between any two components and later tested with synthetic microseismic datasets and real field-recorded datasets. The results indicate that the proposed algorithm is able to distinguish similar and dissimilar waveforms, even for low signal noise ratio and emergent events, which is important for accurate and rapid selection of microseismic events from a large number of records. This method can be applied to other geophysical analyses based on the waveform data.

  7. Real-time potentiometric detection of bacteria in complex samples.

    PubMed

    Zelada-Guillén, Gustavo A; Bhosale, Suryakant V; Riu, Jordi; Rius, F Xavier

    2010-11-15

    Detecting and identifying pathogen bacteria is essential to ensure quality at all stages of the food chain and to diagnose and control microbial infections. Traditional detection methods, including those based on cell culturing, are tedious and time-consuming, and their further application in real samples generally implies more complex pretreatment steps. Even though state-of-the-art techniques for detecting microorganisms enable the quantification of very low concentrations of bacteria, to date it has been difficult to obtain successful results in real samples in a simple, reliable, and rapid manner. In this Article, we demonstrate that the label-free detection and identification of living bacteria in real samples can be carried out in a couple of minutes and in a direct, simple, and selective way at concentration levels as low as 6 colony forming units/mL (CFU) in complex matrices such as milk or 26 CFU/mL in apple juice where the pretreatment step of samples is extremely easy. We chose Escherichia coli ( E. coli ) CECT 675 cells as a model organism as a nonpathogenic surrogate for pathogenic E. coli O157:H7 to test the effectiveness of a potentiometric aptamer-based biosensor. This biosensor uses single-walled carbon nanotubes (SWCNT) as excellent ion-to-electron transducers and covalently immobilized aptamers as biorecognition elements. The selective aptamer-target interaction significantly changes the electrical potential, thus allowing for both interspecies and interstrain selectivity and enabling the direct detection of the target. This technique is therefore a powerful tool for the immediate identification and detection of microorganisms. We demonstrate the highly selective detection of living bacteria with an immediate linear response of up to 10(4) CFU/mL. The biosensor can be easily built and used, is regenerated without difficulty, and can be used at least five times with no loss in the minimum amount of detected bacteria.

  8. Terahertz real-time imaging for nondestructive detection

    NASA Astrophysics Data System (ADS)

    Zhang, LiangLiang; Karpowicz, Nick; Zhang, CunLin; Zhao, YueJin; Zhang, XiCheng

    2008-03-01

    We present a real time imaging measurement in the terahertz (THz) frequency region. The dynamic subtraction technique is used to reduce long-term optical background drift. The reflective images of two targets, a Nikon camera's lens cap and a plastic toy gun, are obtained. For the lens cap, the image data were processed to be false color images. For the toy gun, we show that even under an optically opaque canvas bag, a clear terahertz image is obtained. It is shown that terahertz real time imaging can be used to nondestructively detect concealed objects.

  9. Approaching near real-time biosensing: microfluidic microsphere based biosensor for real-time analyte detection.

    PubMed

    Cohen, Noa; Sabhachandani, Pooja; Golberg, Alexander; Konry, Tania

    2015-04-15

    In this study we describe a simple lab-on-a-chip (LOC) biosensor approach utilizing well mixed microfluidic device and a microsphere-based assay capable of performing near real-time diagnostics of clinically relevant analytes such cytokines and antibodies. We were able to overcome the adsorption kinetics reaction rate-limiting mechanism, which is diffusion-controlled in standard immunoassays, by introducing the microsphere-based assay into well-mixed yet simple microfluidic device with turbulent flow profiles in the reaction regions. The integrated microsphere-based LOC device performs dynamic detection of the analyte in minimal amount of biological specimen by continuously sampling micro-liter volumes of sample per minute to detect dynamic changes in target analyte concentration. Furthermore we developed a mathematical model for the well-mixed reaction to describe the near real time detection mechanism observed in the developed LOC method. To demonstrate the specificity and sensitivity of the developed real time monitoring LOC approach, we applied the device for clinically relevant analytes: Tumor Necrosis Factor (TNF)-α cytokine and its clinically used inhibitor, anti-TNF-α antibody. Based on the reported results herein, the developed LOC device provides continuous sensitive and specific near real-time monitoring method for analytes such as cytokines and antibodies, reduces reagent volumes by nearly three orders of magnitude as well as eliminates the washing steps required by standard immunoassays.

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  11. Human visual system-based smoking event detection

    NASA Astrophysics Data System (ADS)

    Odetallah, Amjad D.; Agaian, Sos S.

    2012-06-01

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

  12. Discrete-event requirements model for sensor fusion to provide real-time diagnostic feedback

    NASA Astrophysics Data System (ADS)

    Rokonuzzaman, Mohd; Gosine, Raymond G.

    1998-06-01

    Minimally-invasive surgical techniques reduce the size of the access corridor and affected zones resulting in limited real-time perceptual information available to the practitioners. A real-time feedback system is required to offset deficiencies in perceptual information. This feedback system acquires data from multiple sensors and fuses these data to extract pertinent information within defined time windows. To perform this task, a set of computing components interact with each other resulting in a discrete event dynamic system. In this work, a new discrete event requirements model for sensor fusion has been proposed to ensure logical and temporal correctness of the operation of the real-time diagnostic feedback system. This proposed scheme models system requirements as a Petri net based discrete event dynamic machine. The graphical representation and quantitative analysis of this model has been developed. Having a natural graphical property, this Petri net based model enables the requirements engineer to communicate intuitively with the client to avoid faults in the early phase of the development process. The quantitative analysis helps justify the logical and temporal correctness of the operation of the system. It has been shown that this model can be analyzed to check the presence of deadlock, reachability, and repetitiveness of the operation of the sensor fusion system. This proposed novel technique to model the requirements of sensor fusion as a discrete event dynamic system has the potential to realize highly reliable real-time diagnostic feedback system for many applications, such as minimally invasive instrumentation.

  13. A microarray scanner for the real-time quantitative detection

    NASA Astrophysics Data System (ADS)

    Liu, Quanjun; Zhuang, Ying; Wu, Lingwei; Wu, Zhongwei; Hu, Song; Lu, Zuhong

    2007-05-01

    The real-time and quantitative detection assay is important for the gene detection. With the TaqMan probes for the detection based polymerase chain reaction (PCR), four targets could be checked in a single process in solution assay. A new method is developed to immobilize the TaqMan probes on a microarray, which could be used to the multi-target gene fragment quantitative detection with PCR. A new type microarray scanner is designed for the assay. A thermocycler system was built into the scanner platform. A new type of the vessel sealed with the gene amplification solution which could perform the thermo-cycling and scanning. To decrease the background intensity a confocal system was used as the fluorescent intensity detection in the scanner. To calculate the gene quantity, a standard liner graph was draw with the fluorescent intensity versus the cycles. From the standard liner, the quantity of the original gene fragment could be calculated in time with the cycles. This scanner offers the great advantage of real-time quantitative detection of DNA targets in a microarray.

  14. The early bird catches the term: combining twitter and news data for event detection and situational awareness.

    PubMed

    Thapen, Nicholas; Simmie, Donal; Hankin, Chris

    2016-10-07

    Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. They can therefore be highly useful for event detection and situational awareness applications. In this paper we apply customised filtering techniques to existing bio-surveillance algorithms to detect localised spikes in Twitter activity, showing that these correspond to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which best describe the event and by linking to highly relevant news articles. This news linkage is accomplished by identifying terms occurring more frequently in the event tweets than in a baseline of activity for the area concerned, and using these to search for news. We apply our methods to outbreaks of illness and events strongly affecting sentiment and are able to detect events verifiable by third party sources and produce high quality summaries. This study demonstrates linking event detection from Twitter with relevant online news to provide situational awareness. This builds on the existing studies that focus on Twitter alone, showing that integrating information from multiple online sources can produce useful analysis.

  15. Aseismic events in Southern California: Detection with InSAR

    NASA Astrophysics Data System (ADS)

    Lohman, R. B.; McGuire, J. J.; Lundgren, P.

    2007-05-01

    Aseismic slow slip events are usually studied using data types that have a dense temporal sampling rate, such as continuous GPS or tremor analysis using seismic data. However, even the sparser temporal coverage of InSAR data can further our understanding of these events in three significant ways - First, in areas where aseismic transients have been detected on geodetic arrays, InSAR may be able to provide a spatially denser image of the extent and magnitude of deformation. Second, InSAR observations are complementary to GPS because of the differing sensitivities to horizontal and vertical motions. Thirdly, in areas with no ground-based geodetic instrumentation, InSAR can be used in survey mode to detect deformation signals that are not associated with any observed seismicity. The temporal constraints on such signals may not be tight enough to allow for dynamics models of how aseismic transients occur, but InSAR-only detections can improve our understanding of the spatial extent of these types of events and can also identify key areas for future instrumentation and observation. Here, I summarize some of the contributions of InSAR observations of slow slip events, including data spanning the 2005 Obsidian Buttes swam in the Salton Trough, CA, and InSAR time-series results for the Salton Trough using both traditional interferometry and the persistent scatterer method.

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

    PubMed

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

    2007-01-10

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

  17. A methodology for detecting routing events in discrete flow networks.

    SciTech Connect

    Garcia, H. E.; Yoo, T.; Nuclear Technology

    2004-01-01

    A theoretical framework for formulating and implementing model-based monitoring of discrete flow networks is discussed. Possible flows of items are described as the sequence of discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path and visits tracking locations distributed within the monitored system. Given the set of possible discrete flows, a possible-behavior model - an interacting set of automata - is constructed, where each automaton models the discrete flow of items at each tracking location. Event labels or symbols contain all the information required to unambiguously distinguish each discrete flow. Within the possible behavior, there is a special sub-behavior whose occurrence is required to be detected. The special behavior may be specified by the occurrence of routing events, such as faults. These intermittent or non-persistent events may occur repeatedly. An observation mask is then defined, characterizing the actual observation configuration available for collecting item tracking data. The analysis task is then to determine whether this observation configuration is capable of detecting the identified special behavior. The assessment is accomplished by evaluating several observability notions, such as detectability and diagnosability. If the corresponding property is satisfied, associated formal observers are constructed to perform the monitoring task at hand. The synthesis of an optimal observation mask may also be conducted to suggest an appropriate observation configuration guaranteeing the detection of the special events and to construct associated monitoring agents. The proposed framework, modeling methodology, and supporting techniques for discrete flow networks monitoring are presented and illustrated with an example.

  18. Real-time EEG-based happiness detection system.

    PubMed

    Jatupaiboon, Noppadon; Pan-ngum, Setha; Israsena, Pasin

    2013-01-01

    We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.

  19. Real-Time EEG-Based Happiness Detection System

    PubMed Central

    2013-01-01

    We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness. PMID:24023532

  20. A coincidence detection system based on real-time software

    NASA Astrophysics Data System (ADS)

    Ayuso, Sindulfo; José Blanco, Juan; Medina, José; Gómez-Herrero, Raúl; García-Población, Oscar; García Tejedor, Ignacio

    2016-09-01

    Conventional real-time coincidence systems use electronic circuitry to detect coincident pulses (hardware coincidence). In this work, a new concept of coincidence system based on real-time software (software coincidence) is presented. This system is based on the recurrent supervision of the analogue-to-digital converters status, which is described in detail. A prototype has been designed and built using a low-cost development platform. It has been applied to two different experimental sets for cosmic ray muon detection. Experimental muon measurements recorded simultaneously using conventional hardware coincidence and our software coincidence system have been compared, yielding identical results. These measurements have also been validated using simultaneous neutron monitor observations. This new software coincidence system provides remarkable advantages such as higher simplicity of interconnection and adjusting. Thus, our system replaces, at least, three Nuclear Instrument Modules (NIMs) required by conventional coincidence systems, reducing its cost by a factor of 40 and eliminating pulse delay adjustments.

  1. Comparison of Event Detection Methods for Centralized Sensor Networks

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Pilger, Christoph; Koch, Karl; Ceranna, Lars

    2016-04-01

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

  4. Detection of and response to mid-ocean ridge magmatic events: Implications for the subsurface biosphere

    NASA Astrophysics Data System (ADS)

    Cowen, James P.; Baker, Edward T.; Embley, Robert W.

    Magmatic events are unpredictable dynamic processes that are integral to the evolution of mid-ocean ridges. Dikes and lava flows develop rapidly and instantly alter the local hydrothermal flow regime, initiating dramatic changes in hydrothermal discharge at the seafloor, and triggering geochemical and microbiological changes within the shallow crust, at the seafloor and within the overlying water column. Despite considerable logistical difficulties, real-time remote detection capabilities (SOSUS) along limited regions of the MOR system have allowed investigators to rapidly respond to significant seismic events. There have been more than 20 documented examples of seafloor volcanic/tectonic events, at both isolated volcanoes and mid-ocean ridges, but only a few of these have led to significant response efforts. The most rapid and thorough response efforts have been to the 1991 9° N EPR event and several events (1986,1993,1996, 1998,2001) on the Juan de Fuca and Gorda Ridges. Together these "SOSUS directed' responses plus the few serendipitous encounters have led to important discoveries (e.g., event plumes; `snow-blower' vents) and provided basic new constraints on presently immature models of submarine magmatic-hydrothermal systems (e.g., intrusive/extrusive diking; event plume formation; subsurface hydrothermal communities). The event response community has gained valuable experience in learning how to exploit these opportunities for scientific observation and is currently poised to continue such studies with increased speed and efficiency. However, our understanding of these geophysical, chemical and biological processes is only in their infancy.

  5. Automatic near-real-time detection of CMEs in Mauna Loa K-Cor coronagraph images

    NASA Astrophysics Data System (ADS)

    Thompson, William T.; St. Cyr, Orville Chris; Burkepile, Joan; Posner, Arik

    2017-08-01

    A simple algorithm has been developed to detect the onset of coronal mass ejections (CMEs), together with an estimate of their speed, in near-real-time using images of the linearly polarized white-light solar corona taken by the K-Cor telescope at the Mauna Loa Solar Observatory (MLSO). The algorithm used is a variation on the Solar Eruptive Event Detection System (SEEDS) developed at George Mason University. The algorithm was tested against K-Cor data taken between 29 April 2014 and 20 February 2017, on days which the MLSO website marked as containing CMEs. This resulted in testing of 139 days worth of data containing 171 CMEs. The detection rate varied from close to 80% in 2014-2015 when solar activity was high, down to as low as 20-30% in 2017 when activity was low. The difference in effectiveness with solar cycle is attributed to the difference in relative prevalance of strong CMEs between active and quiet periods. There were also twelve false detections during this time period, leading to an average false detection rate of 8.6% on any given day. However, half of the false detections were clustered into two short periods of a few days each when special conditions prevailed to increase the false detection rate. The K-Cor data were also compared with major Solar Energetic Particle (SEP) storms during this time period. There were three SEP events detected either at Earth or at one of the two STEREO spacecraft where K-Cor was observing during the relevant time period. The K-Cor CME detection algorithm successfully generated alerts for two of these events, with lead times of 1-3 hours before the SEP onset at 1 AU. The third event was not detected by the automatic algorithm because of the unusually broad width of the CME in position angle.

  6. Microcontroller-based real-time QRS detection.

    PubMed

    Sun, Y; Suppappola, S; Wrublewski, T A

    1992-01-01

    The authors describe the design of a system for real-time detection of QRS complexes in the electrocardiogram based on a single-chip microcontroller (Motorola 68HC811). A systematic analysis of the instrumentation requirements for QRS detection and of the various design techniques is also given. Detection algorithms using different nonlinear transforms for the enhancement of QRS complexes are evaluated by using the ECG database of the American Heart Association. The results show that the nonlinear transform involving multiplication of three adjacent, sign-consistent differences in the time domain gives a good performance and a quick response. When implemented with an appropriate sampling rate, this algorithm is also capable of rejecting pacemaker spikes. The eight-bit single-chip microcontroller provides sufficient throughput and shows a satisfactory performance. Implementation of multiple detection algorithms in the same system improves flexibility and reliability. The low chip count in the design also favors maintainability and cost-effectiveness.

  7. Monocular Obstacle Detection for Real-World Environments

    NASA Astrophysics Data System (ADS)

    Einhorn, Erik; Schroeter, Christof; Gross, Horst-Michael

    In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kaiman filters (EKF). Our method processes a sequence of images taken by a single camera mounted in front of a mobile robot. Using various techniques we are able to produce a precise reconstruction that is almost free from outliers and therefore can be used for reliable obstacle detection and avoidance. In real-world field tests we show that the presented approach is able to detect obstacles that can not be seen by other sensors, such as laser range finders. Furthermore, we show that visual obstacle detection combined with a laser range finder can increase the detection rate of obstacles considerably, allowing the autonomous use of mobile robots in complex public and home environments.

  8. Real-time PCR detection of ruminant DNA.

    PubMed

    Mendoza-Romero, Luis; Verkaar, Edward L C; Savelkoul, Paul H; Catsburg, Arnold; Aarts, Henk J M; Buntjer, Jaap B; Lenstra, Johannes A

    2004-03-01

    To control the spread of bovine spongiform encephalopathy, several DNA methods have been described for the detection of the species origin of meat and bone meal. Most of these methods are based on the amplification of a mitochondrial DNA segment. We have developed a semiquantitative method based on real-time PCR for detection of ruminant DNA, targeting an 88-bp segment of the ruminant short interspersed nuclear element Bov-A2. This method is specific for ruminants and is able to detect as little as 10 fg of bovine DNA. Autoclaving decreased the amount of detectable DNA, but positive signals were observed in feeding stuff containing 10% bovine material if this had not been rendered in accordance with the regulations, i.e., heated at 134 degrees C for 3 instead of 20 min.

  9. Real-time Monitoring Network to Characterize Anthropogenic and Natural Events Affecting the Hudson River, NY

    NASA Astrophysics Data System (ADS)

    Islam, M. S.; Bonner, J. S.; Fuller, C.; Kirkey, W.; Ojo, T.

    2011-12-01

    The Hudson River watershed spans 34,700 km2 predominantly in New York State, including agricultural, wilderness, and urban areas. The Hudson River supports many activities including shipping, supplies water for municipal, commercial, and agricultural uses, and is an important recreational resource. As the population increases within this watershed, so does the anthropogenic impact on this natural system. To address the impacts of anthropogenic and natural activities on this ecosystem, the River and Estuary Observatory Network (REON) is being developed through a joint venture between the Beacon Institute, Clarkson University, General Electric Inc. and IBM Inc. to monitor New York's Hudson and Mohawk Rivers in real-time. REON uses four sensor platform types with multiple nodes within the network to capture environmentally relevant episodic events. Sensor platform types include: 1) fixed robotic vertical profiler (FRVP); 2) mobile robotic undulating platform (MRUP); 3) fixed acoustic Doppler current profiler (FADCP) and 4) Autonomous Underwater Vehicle (AUV). The FRVP periodically generates a vertical profile with respect to water temperature, salinity, dissolved oxygen, particle concentration and size distribution, and fluorescence. The MRUP utilizes an undulating tow-body tethered behind a research vessel to measure the same set of water parameters as the FRVP, but does so 'synchronically' over a highly-resolved spatial regime. The fixed ADCP provides continuous water current profiles. The AUV maps four-dimensional (time, latitude, longitude, depth) variation of water quality, water currents and bathymetry along a pre-determined transect route. REON data can be used to identify episodic events, both anthropogenic and natural, that impact the Hudson River. For example, a strong heat signature associated with cooling water discharge from the Indian Point nuclear power plant was detected with the MRUP. The FRVP monitoring platform at Beacon, NY, located in the

  10. Real-time people and vehicle detection from UAV imagery

    NASA Astrophysics Data System (ADS)

    Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

    2011-01-01

    A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

  11. Real-time pose invariant logo and pattern detection

    NASA Astrophysics Data System (ADS)

    Sidla, Oliver; Kottmann, Michal; Benesova, Wanda

    2011-01-01

    The detection of pose invariant planar patterns has many practical applications in computer vision and surveillance systems. The recognition of company logos is used in market studies to examine the visibility and frequency of logos in advertisement. Danger signs on vehicles could be detected to trigger warning systems in tunnels, or brand detection on transport vehicles can be used to count company-specific traffic. We present the results of a study on planar pattern detection which is based on keypoint detection and matching of distortion invariant 2d feature descriptors. Specifically we look at the keypoint detectors of type: i) Lowe's DoG approximation from the SURF algorithm, ii) the Harris Corner Detector, iii) the FAST Corner Detector and iv) Lepetit's keypoint detector. Our study then compares the feature descriptors SURF and compact signatures based on Random Ferns: we use 3 sets of sample images to detect and match 3 logos of different structure to find out which combinations of keypoint detector/feature descriptors work well. A real-world test tries to detect vehicles with a distinctive logo in an outdoor environment under realistic lighting and weather conditions: a camera was mounted on a suitable location for observing the entrance to a parking area so that incoming vehicles could be monitored. In this 2 hour long recording we can successfully detect a specific company logo without false positives.

  12. Case-based damage assessment of storm events in near real-time

    NASA Astrophysics Data System (ADS)

    Möhrle, Stella; Mühr, Bernhard

    2015-04-01

    Damage assessment in times of crisis is complex due to a highly dynamic environment and uncertainty in respect of available information. In order to assess the extent of a disaster in near real-time, historic events and their consequences may facilitate first estimations. Events of the past, which are in the same category or which have similar frame conditions like imminent or just occurring storms, might give preliminary information about possible damages. The challenge here is to identify useful historic events based on little information regarding the current event. This work investigates the potential of drawing conclusions about a current event based on similar historic disasters, exemplarily for storm events in Germany. Predicted wind speed and area affected can be used for roughly classifying a storm event. For this purpose, a grid of equidistant points can be used to split up the area of Germany. In combination with predicted wind speed at these points and the predicted number of points affected, respectively, a storm can be categorized in a fast manner. In contrast to investigate only data taken by the observation network, the grid approach is more objective, since stations are not equally distributed. Based on model data, the determined storm class provides one key factor for identifying similar historic events. Further aspects, such as region or specific event characteristics, complete knowledge about the potential storm scale and result in a similarity function, which automatically identifies useful events from the past. This work presents a case-based approach to estimate damages in the event of an extreme storm event in Germany. The focus in on the similarity function, which is based on model storm classes, particularly wind speed and area affected. In order to determine possible damages more precisely, event specific characteristics and region will be included. In the frame of determining similar storm events, neighboring storm classes will be

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

    SciTech Connect

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

    2014-10-01

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

  14. Detecting modification of biomedical events using a deep parsing approach.

    PubMed

    Mackinlay, Andrew; Martinez, David; Baldwin, Timothy

    2012-04-30

    This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.

  15. Detecting modification of biomedical events using a deep parsing approach

    PubMed Central

    2012-01-01

    Background This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. Method To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. Results Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. Conclusions Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification. PMID:22595089

  16. Towards real-time regional earthquake simulation I: real-time moment tensor monitoring (RMT) for regional events in Taiwan

    NASA Astrophysics Data System (ADS)

    Lee, Shiann-Jong; Liang, Wen-Tzong; Cheng, Hui-Wen; Tu, Feng-Shan; Ma, Kuo-Fong; Tsuruoka, Hiroshi; Kawakatsu, Hitoshi; Huang, Bor-Shouh; Liu, Chun-Chi

    2014-01-01

    We have developed a real-time moment tensor monitoring system (RMT) which takes advantage of a grid-based moment tensor inversion technique and real-time broad-band seismic recordings to automatically monitor earthquake activities in the vicinity of Taiwan. The centroid moment tensor (CMT) inversion technique and a grid search scheme are applied to obtain the information of earthquake source parameters, including the event origin time, hypocentral location, moment magnitude and focal mechanism. All of these source parameters can be determined simultaneously within 117 s after the occurrence of an earthquake. The monitoring area involves the entire Taiwan Island and the offshore region, which covers the area of 119.3°E to 123.0°E and 21.0°N to 26.0°N, with a depth from 6 to 136 km. A 3-D grid system is implemented in the monitoring area with a uniform horizontal interval of 0.1° and a vertical interval of 10 km. The inversion procedure is based on a 1-D Green's function database calculated by the frequency-wavenumber (fk) method. We compare our results with the Central Weather Bureau (CWB) catalogue data for earthquakes occurred between 2010 and 2012. The average differences between event origin time and hypocentral location are less than 2 s and 10 km, respectively. The focal mechanisms determined by RMT are also comparable with the Broadband Array in Taiwan for Seismology (BATS) CMT solutions. These results indicate that the RMT system is realizable and efficient to monitor local seismic activities. In addition, the time needed to obtain all the point source parameters is reduced substantially compared to routine earthquake reports. By connecting RMT with a real-time online earthquake simulation (ROS) system, all the source parameters will be forwarded to the ROS to make the real-time earthquake simulation feasible. The RMT has operated offline (2010-2011) and online (since January 2012 to present) at the Institute of Earth Sciences (IES), Academia Sinica

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-03-19

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

  19. Detection of transient events in the presence of background noise.

    PubMed

    Grange, Wilfried; Haas, Philippe; Wild, Andreas; Lieb, Michael Andreas; Calame, Michel; Hegner, Martin; Hecht, Bert

    2008-06-12

    We describe a method to detect and count transient burstlike signals in the presence of a significant stationary noise. To discriminate a transient signal from the background noise, an optimum threshold is determined using an iterative algorithm that yields the probability distribution of the background noise. Knowledge of the probability distribution of the noise then allows the determination of the number of transient events with a quantifiable error (wrong-positives). We apply the method, which does not rely on the choice of free parameters, to the detection and counting of transient single-molecule fluorescence events in the presence of a strong background noise. The method will be of importance in various ultra sensing applications.

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

    SciTech Connect

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

    2007-02-09

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

  1. Microseismic Events Detection on Xishancun Landslide, Sichuan Province, China

    NASA Astrophysics Data System (ADS)

    Sheng, M.; Chu, R.; Wei, Z.

    2016-12-01

    On landslide, the slope movement and the fracturing of the rock mass often lead to microearthquakes, which are recorded as weak signals on seismographs. The distribution characteristics of temporal and spatial regional unstability as well as the impact of external factors on the unstable regions can be understand and analyzed by monitoring those microseismic events. Microseismic method can provide some information inside the landslide, which can be used as supplementary of geodetic methods for monitoring the movement of landslide surface. Compared to drilling on landslide, microseismic method is more economical and safe. Xishancun Landslide is located about 60km northwest of Wenchuan earthquake centroid, it keep deforming after the earthquake, which greatly increases the probability of disasters. In the autumn of 2015, 30 seismometers were deployed on the landslide for 3 months with intervals of 200 500 meters. First, we used regional earthquakes for time correction of seismometers to eliminate the influence of inaccuracy GPS clocks and the subsurface structure of stations. Due to low velocity of the loose medium, the travel time difference of microseismic events on the landslide up to 5s. According to travel time and waveform characteristics, we found many microseismic events and converted them into envelopes as templates, then we used a sliding-window cross-correlation technique based on waveform envelope to detect the other microseismic events. Consequently, 100 microseismic events were detected with the waveforms recorded on all seismometers. Based on the location, we found most of them located on the front of the landslide while the others located on the back end. The bottom and top of the landslide accumulated considerable energy and deformed largely, radiated waves could be recorded by all stations. What's more, the bottom with more events seemed very active. In addition, there were many smaller events happened in middle part of the landslide where released

  2. Lessons Learned from Real-Time, Event-Based Internet Science Communications

    NASA Technical Reports Server (NTRS)

    Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

  3. Lessons Learned from Real-Time, Event-Based Internet Science Communications

    NASA Technical Reports Server (NTRS)

    Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

  4. Comprehensive GMO detection using real-time PCR array: single-laboratory validation.

    PubMed

    Mano, Junichi; Harada, Mioko; Takabatake, Reona; Furui, Satoshi; Kitta, Kazumi; Nakamura, Kosuke; Akiyama, Hiroshi; Teshima, Reiko; Noritake, Hiromichi; Hatano, Shuko; Futo, Satoshi; Minegishi, Yasutaka; Iizuka, Tayoshi

    2012-01-01

    We have developed a real-time PCR array method to comprehensively detect genetically modified (GM) organisms. In the method, genomic DNA extracted from an agricultural product is analyzed using various qualitative real-time PCR assays on a 96-well PCR plate, targeting for individual GM events, recombinant DNA (r-DNA) segments, taxon-specific DNAs, and donor organisms of the respective r-DNAs. In this article, we report the single-laboratory validation of both DNA extraction methods and component PCR assays constituting the real-time PCR array. We selected some DNA extraction methods for specified plant matrixes, i.e., maize flour, soybean flour, and ground canola seeds, then evaluated the DNA quantity, DNA fragmentation, and PCR inhibition of the resultant DNA extracts. For the component PCR assays, we evaluated the specificity and LOD. All DNA extraction methods and component PCR assays satisfied the criteria set on the basis of previous reports.

  5. Miniaturized holographic imaging system for real-time cellular detection

    NASA Astrophysics Data System (ADS)

    Song, Jun; Im, Hyungsoon; Liong, Monty; Fexon, Lioubov; Pivovarov, Misha; Weissleder, Ralph; Lee, Hakho

    2013-03-01

    We herein present a miniaturized holographic imaging system for high throughput cellular detection. The system consists of an imager chip with a microfluidic channel built on top. Clinical samples (e.g., blood) are introduced into the fluidic channel, and holographic images of cells are recorded by the imager chip. We then perform computational reconstruction of original cell images, retrieving both the intensity and phase information. For fast image reconstruction, we have implemented parallel computing software and utilized multicore GPU (graphics processing unit) chips. The resulting imaging system enabled high throughput cellular detection; up to 1000 cells/ μL could be imaged over a wide detection area (20 mm2), and cellular images could be reconstructed in real time (20 frames/sec). Furthermore, assays can be performed without extra dilution and washing steps, which significantly simplifies the diagnosis process. This cost-effective, real-time holographic imaging system can be used for target cell detection in point-of-care applications.

  6. Statistical language analysis for automatic exfiltration event detection.

    SciTech Connect

    Robinson, David Gerald

    2010-04-01

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

  7. A new approach to measure single-event related brain activity using real-time fMRI: feasibility of sensory, motor, and higher cognitive tasks.

    PubMed

    Posse, S; Binkofski, F; Schneider, F; Gembris, D; Frings, W; Habel, U; Salloum, J B; Mathiak, K; Wiese, S; Kiselev, V; Graf, T; Elghahwagi, B; Grosse-Ruyken, M L; Eickermann, T

    2001-01-01

    Real-time fMRI is a rapidly emerging methodology that enables monitoring changes in brain activity during an ongoing experiment. In this article we demonstrate the feasibility of performing single-event sensory, motor, and higher cognitive tasks in real-time on a clinical whole-body scanner. This approach requires sensitivity optimized fMRI methods: Using statistical parametric mapping we quantified the spatial extent of BOLD contrast signal changes as a function of voxel size and demonstrate that sacrificing spatial resolution and readout bandwidth improves the detection of signal changes in real time. Further increases in BOLD contrast sensitivity were obtained by using real-time multi-echo EPI. Real-time image analysis was performed using our previously described Functional Imaging in REal time (FIRE) software package, which features real-time motion compensation, sliding window correlation analysis, and automatic reference vector optimization. This new fMRI methodology was validated using single-block design paradigms of standard visual, motor, and auditory tasks. Further, we demonstrate the sensitivity of this method for online detection of higher cognitive functions during a language task using single-block design paradigms. Finally, we used single-event fMRI to characterize the variability of the hemodynamic impulse response in primary and supplementary motor cortex in consecutive trials using single movements. Real-time fMRI can improve reliability of clinical and research studies and offers new opportunities for studying higher cognitive functions.

  8. Real-Time Analytics for the Healthcare Industry: Arrhythmia Detection.

    PubMed

    Agneeswaran, Vijay Srinivas; Mukherjee, Joydeb; Gupta, Ashutosh; Tonpay, Pranay; Tiwari, Jayati; Agarwal, Nitin

    2013-09-01

    It is time for the healthcare industry to move from the era of "analyzing our health history" to the age of "managing the future of our health." In this article, we illustrate the importance of real-time analytics across the healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data scientists use R to write the analytics and are not clear on how to make the data work in real-time and on high-velocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and accuracy trade-off in real-time analytics.

  9. Jump point detection for real estate investment success

    NASA Astrophysics Data System (ADS)

    Hui, Eddie C. M.; Yu, Carisa K. W.; Ip, Wai-Cheung

    2010-03-01

    In the literature, studies on real estate market were mainly concentrating on the relation between property price and some key factors. The trend of the real estate market is a major concern. It is believed that changes in trend are signified by some jump points in the property price series. Identifying such jump points reveals important findings that enable policy-makers to look forward. However, not all jump points are observable from the plot of the series. This paper looks into the trend and introduces a new approach to the framework for real estate investment success. The main purpose of this paper is to detect jump points in the time series of some housing price indices and stock price index in Hong Kong by applying the wavelet analysis. The detected jump points reflect to some significant political issues and economic collapse. Moreover, the relations among properties of different classes and between stocks and properties are examined. It can be shown from the empirical result that a lead-lag effect happened between the prices of large-size property and those of small/medium-size property. However, there is no apparent relation or consistent lead in terms of change point measure between property price and stock price. This may be due to the fact that globalization effect has more impact on the stock price than the property price.

  10. Real-time elastography for the detection of prostate cancer.

    PubMed

    Salomon, Georg; Schiffmann, Jonas

    2014-03-01

    The lack of reliable imaging tools in detecting prostate cancer makes a random biopsy still the standard of care to detect prostate cancer. To reduce the number of cores during a biopsy and therefore the risk of biopsy-related complications, an imaging tool which provides reliable guided biopsies is required. Transrectal real-time elastography has shown to have the ability to visualize prostate cancer foci to some extent. In addition to the conventional B-mode image of transrectal ultrasound, it adds information about the stiffness of the prostate tissue. This review highlights the most important studies on elastography to follow the improvements in techniques and to outline the ability to detect prostate cancer and guide biopsies.

  11. Spatial event cluster detection using an approximate normal distribution.

    PubMed

    Torabi, Mahmoud; Rosychuk, Rhonda J

    2008-12-12

    In geographic surveillance of disease, areas with large numbers of disease cases are to be identified so that investigations of the causes of high disease rates can be pursued. Areas with high rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. Typically cluster detection tests are applied to incident or prevalent cases of disease, but surveillance of disease-related events, where an individual may have multiple events, may also be of interest. Previously, a compound Poisson approach that detects clusters of events by testing individual areas that may be combined with their neighbours has been proposed. However, the relevant probabilities from the compound Poisson distribution are obtained from a recursion relation that can be cumbersome if the number of events are large or analyses by strata are performed. We propose a simpler approach that uses an approximate normal distribution. This method is very easy to implement and is applicable to situations where the population sizes are large and the population distribution by important strata may differ by area. We demonstrate the approach on pediatric self-inflicted injury presentations to emergency departments and compare the results for probabilities based on the recursion and the normal approach. We also implement a Monte Carlo simulation to study the performance of the proposed approach. In a self-inflicted injury data example, the normal approach identifies twelve out of thirteen of the same clusters as the compound Poisson approach, noting that the compound Poisson method detects twelve significant clusters in total. Through simulation studies, the normal approach well approximates the compound Poisson approach for a variety of different population sizes and case and event thresholds. A drawback of the compound Poisson approach is that the relevant probabilities must be determined through a

  12. A time-frequency approach for event detection in non-intrusive load monitoring

    NASA Astrophysics Data System (ADS)

    Jin, Yuanwei; Tebekaemi, Eniye; Berges, Mario; Soibelman, Lucio

    2011-06-01

    Non-intrusive load monitoring is an emerging signal processing and analysis technology that aims to identify individual appliance in residential or commercial buildings or to diagnose shipboard electro-mechanical systems through continuous monitoring of the change of On and Off status of various loads. In this paper, we develop a joint time-frequency approach for appliance event detection based on the time varying power signals obtained from the measured aggregated current and voltage waveforms. The short-time Fourier transform is performed to obtain the spectral components of the non-stationary aggregated power signals of appliances. The proposed event detector utilizes a goodness-of-fit Chi-squared test for detecting load activities using the calculated average power followed by a change point detector for estimating the change point of the transient signals using the first harmonic component of the power signals. Unlike the conventional detectors such as the generalized likelihood ratio test, the proposed event detector allows a closed form calculation of the decision threshold and provides a guideline for choosing the size of the detection data window, thus eliminating the need for extensive training for determining the detection threshold while providing robust detection performance against dynamic load activities. Using the real-world power data collected in two residential building testbeds, we demonstrate the superior performance of the proposed algorithm compared to the conventional generalized likelihood ratio detector.

  13. Real-Time Walk Light Detection with a Mobile Phone.

    PubMed

    Ivanchenko, Volodymyr; Coughlan, James; Shen, Huiying

    2010-07-01

    Crossing an urban traffic intersection is one of the most dangerous activities of a blind or visually impaired person's travel. Building on past work by the authors on the issue of proper alignment with the crosswalk, this paper addresses the complementary issue of knowing when it is time to cross. We describe a prototype portable system that alerts the user in real time once the Walk light is illuminated. The system runs as a software application on an off-the-shelf Nokia N95 mobile phone, using computer vision algorithms to analyze video acquired by the built-in camera to determine in real time if a Walk light is currently visible. Once a Walk light is detected, an audio tone is sounded to alert the user. Experiments with a blind volunteer subject at urban traffic intersections demonstrate proof of concept of the system, which successfully alerted the subject when the Walk light appeared.

  14. Discovery, classification, and scientific exploration of transient events from the Catalina Real-time Transient Survey

    NASA Astrophysics Data System (ADS)

    Mahabal, A. A.; Djorgovski, S. G.; Drake, A. J.; Donalek, C.; Graham, M. J.; Williams, R. D.; Chen, Y.; Moghaddam, B.; Turmon, M.; Beshore, E.; Larson, S.

    2011-09-01

    Exploration of the time domain -- variable and transient objects and phenomena -- is rapidly becoming a vibrant research frontier, touching on essentially every field of astronomy and astrophysics, from the Solar system to cosmology. Time domain astronomy is being enabled by the advent of the new generation of synoptic sky surveys that cover large areas on the sky repeatedly, and generating massive data streams. Their scientific exploration poses many challenges, driven mainly by the need for a real-time discovery, classification, and follow-up of the interesting events. Here we describe the Catalina Real-Time Transient Survey (CRTS), that discovers and publishes transient events at optical wavelengths in real time, thus benefiting the entire community. We describe some of the scientific results to date, and then focus on the challenges of the automated classification and prioritization of transient events. CRTS represents a scientific and a technological testbed and precursor for the larger surveys in the future, including the Large Synoptic Survey Telescope (LSST) and the Square Kilometer Array (SKA).

  15. Detection of red tide events in the Ariake Sound, Japan

    NASA Astrophysics Data System (ADS)

    Ishizaka, Joji

    2003-05-01

    High resolution SeaWiFS data was used to detect a red tide event occurred in the Ariake Sound, Japan, in winter of 2000 to 2001. The area is small embayment surrounding by tidal flat, and it is known as one of the most productive areas in coast of Japan. The red tide event damaged to seaweed (Nori) culture, and the relation to the reclamation at the Isahaya Bay in the Sound has been discussed. SeaWiFS chlorophyll data showed the red tide started early December 2000, from the Isahaya Bay, although direct relationship to the reclamation was not clear. The red tide persisted to the end of February. Monthly average of SeaWiFS data from May 1998 to December 2001 indicated that the chlorophyll increased twice a year, early summer and fall after the rain. The red tide event was part of the fall bloom which started later and continued longer than other years. Ocean color is useful to detect the red tide; however, it is required to improve the algorithms to accurately estimate chlorophyll in high turbid water and to discriminate toxic flagellates.

  16. A handheld real time thermal cycler for bacterial pathogen detection.

    PubMed

    Higgins, James A; Nasarabadi, Shanavaz; Karns, Jeffrey S; Shelton, Daniel R; Cooper, Mary; Gbakima, Aiah; Koopman, Ronald P

    2003-08-15

    The handheld advanced nucleic acid analyzer (HANAA) is a portable real time thermal cycler unit that weighs under 1 kg and uses silicon and platinum-based thermalcycler units to conduct rapid heating and cooling of plastic reaction tubes. Two light emitting diodes (LED) provide greater than 1 mW of electrical power at wavelengths of 490 nm (blue) and 525 nm (green), allowing detection of the dyes FAM and JOE/TAMRA. Results are displayed in real time as bar graphs, and up to three, 4-sample assays can be run on the charge of the 12 V portable battery pack. The HANAA was evaluated for detection of defined Escherichia coli strains, and wild-type colonies isolated from stream water, using PCR for the lac Z and Tir genes. PCR reactions using SYBR Green dye allowed detection of E. coli ATCC 11775 and E. coli O157:H7 cells in under 30 min of assay time; however, background fluorescence associated with dye binding to nonspecific PCR products was present. DNA extracted from three isolates of Bacillus anthracis Ames, linked to a bioterrorism incident in Washington DC in October 2001, were also successfully tested on the HANAA using primers for the vrrA and capA genes. Positive results were observed at 32 and 22 min of assay time, respectively. A TaqMan probe specific to the aroQ gene of Erwinia herbicola was tested on the HANAA and when 500 cells were used as template, positive results were observed after only 7 min of assay time. Background fluorescence associated with the use of the probe was negligible. The HANAA is unique in offering real time PCR in a handheld format suitable for field use; a commercial version of the instrument, offering six reaction chambers, is available as of Fall 2002.

  17. Micro seismic event detection based on neural networks in the Groningen area, The Netherlands

    NASA Astrophysics Data System (ADS)

    Paap, Bob; van Maanen, Peter-Paul; Carpentier, Stefan; Meekes, Sjef

    2017-04-01

    Over the past decades, the Groningen gas field has been increasingly faced by induced earthquakes resulting from gas production. The seismic monitoring network at Groningen has been densified in order to acquire more accurate information regarding the onset and origin of seismic events, resulting in increasing amounts of seismic data. Although traditional automated event detection techniques generally are successful in detecting events from continuous data, its detection success is challenged in cases of lower signal-to-noise ratios and often limited availability of seismologists. Besides the recent expansion of the Groningen seismic network, additional new seismic networks have been deployed at several geothermal and CO2 storage fields. The data stream coming from these networks has sparked specific interest in neural networks for automated classification and interpretation. Here we explore the feasibility of neural networks in classifying the occurrence of seismic events. For this purpose a three-layered feedforward neural network was trained using public data related to a seismic event in the Groningen gas field obtained from the Royal Netherlands Meteorological Institute (KNMI) data portal. The first arrival times that were determined by KNMI for a subset of the station data were used to determine the arrival times for the other station data. Different derivatives, using different frequency sub-band and STA/LTA settings, were used as input. Based on these data, the network's parameters were then optimized to predict arrival times accurately. Although this study is still ongoing, we anticipate our approach can significantly increase the performance as compared to detection methods usually applied to the Groningen gas field. This will clear the way for future real-time micro seismic event classification.

  18. High Probabilities of Planet Detection during Microlensing Events.

    NASA Astrophysics Data System (ADS)

    Peale, S. J.

    2000-10-01

    The averaged probability of detecting a planetary companion of a lensing star during a gravitational microlensing event toward the Galactic center when the planet-lens mass ratio is 0.001 is shown to have a maximum exceeding 20% for a distribution of source-lens impact parameters that is determined by the efficiency of event detection, and a maximum exceeding 10% for a uniform distribution of impact parameters. The probability varies as the square root of the planet-lens mass ratio. A planet is assumed detectable if the perturbation of the light curve exceeds 2/(S/N) for a significant number of data points, where S/N is the signal-to noise ratio for the photometry of the source. The probability peaks at a planetary semimajor axis a that is close to the mean Einstein ring radius of the lenses of about 2 AU along the line of sight, and remains significant for 0.6<= a<= 10 AU. The low value of the mean Einstein ring radius results from the dominance of M stars in the mass function of the lenses. The probability is averaged over the distribution of the projected position of the planet onto the lens plane, over the lens mass function, over the distribution of impact parameters, over the distribution of lens along the line of sight to the source star, over the I band luminosity function of the sources adjusted for the source distance, and over the source distribution along the line of sight. If two or more parameters of the lensing event are known, such as the I magnitude of the source and the impact parameter, the averages over these parameters can be omitted and the probability of detection determined for a particular event. The calculated probabilities behave as expected with variations in the line of sight, the mass function of the lenses, the extinction and distance to and magnitude of the source, and with a more demanding detection criterion. The relatively high values of the probabilities are robust to plausible variations in the assumptions. The high

  19. Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

    NASA Astrophysics Data System (ADS)

    Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

    2016-12-01

    Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

  20. Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

    NASA Astrophysics Data System (ADS)

    Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

    2017-07-01

    Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

  1. Automated adverse event detection collaborative: electronic adverse event identification, classification, and corrective actions across academic pediatric institutions.

    PubMed

    Stockwell, David C; Kirkendall, Eric; Muething, Stephen E; Kloppenborg, Elizabeth; Vinodrao, Hima; Jacobs, Brian R

    2013-12-01

    Historically, the gold standard for detecting medical errors has been the voluntary incident reporting system. Voluntary reporting rates significantly underestimate the number of actual adverse events in any given organization. The electronic health record (EHR) contains clinical and administrative data that may indicate the occurrence of an adverse event and can be used to detect adverse events that may otherwise remain unrecognized. Automated adverse event detection has been shown to be efficient and cost effective in the hospital setting. The Automated Adverse Event Detection Collaborative (AAEDC) is a group of academic pediatric organizations working to identify optimal electronic methods of adverse event detection. The Collaborative seeks to aggregate and analyze data around adverse events as well as identify and share specific intervention strategies to reduce the rate of such events, ultimately to deliver higher quality and safer care. The objective of this study is to describe the process of automated adverse event detection, report early results from the Collaborative, identify commonalities and notable differences between 2 organizations, and suggest future directions for the Collaborative. In this retrospective observational study, the implementation and use of an automated adverse event detection system was compared between 2 academic children's hospital participants in the AAEDC, Children's National Medical Center, and Cincinnati Children's Hospital Medical Center. Both organizations use the EHR to identify potential adverse events as designated by specific electronic data triggers. After gathering the electronic data, a clinical investigator at each hospital manually examined the patient record to determine whether an adverse event had occurred, whether the event was preventable, and the level of harm involved. The Automated Adverse Event Detection Collaborative data from the 2 organizations between July 2006 and October 2010 were analyzed. Adverse

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

    NASA Astrophysics Data System (ADS)

    Robinson, Joseph P.; Fu, Yun

    2016-05-01

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

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

    PubMed

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

    2015-08-01

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

  4. Real-time beyond the horizon vessel detection

    NASA Astrophysics Data System (ADS)

    Roarty, Hugh J.; Smith, Michael; Glenn, Scott M.; Barrick, Donald E.

    2013-05-01

    The marine transportation system (MTS) is a vital component of the United States Economy. Waterborne cargo accounts for more than $742 billion of the nation's economy and creates employment for 13 million citizens. A disruption in this system would have far reaching consequences to the security of the country. The US National High Frequency radar network, which comprises 130 radar stations around the country, became operational in May 2009. It provides hourly measurements of surface currents to the US Coast Guard for search and rescue (SAR). This system has the capability of being a dual use system providing information for environmental monitoring as well as vessel position information for maritime security. Real time vessel detection has been implemented at two of the radar stations outside New York Harbor. Several experiments were conducted to see the amount vessel traffic that the radar could capture. The radars were able to detect a majority of the vessels that are reporting via the Automatic Identification System (AIS) as well as 30 percent of mid to large size vessels that are not reporting via AIS. The radars were able to detect vessels out to 60 km from the coast. The addition of a vessel detection capability to the National HF radar network will provide valuable information to maritime security sector. This dual use capability will fill a gap in the current surveillance of US coastal waters. It will also provide longer-range situational awareness necessary to detect and track smaller size vessels in the large vessel clutter.

  5. Real time electrocardiogram QRS detection using combined adaptive threshold

    PubMed Central

    Christov, Ivaylo I

    2004-01-01

    Background QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs Methods A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats. Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number L of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals. Results The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity Se = 99.69 % and specificity Sp = 99.65 % for Algorithm 1 and Se = 99.74 % and Sp = 99.65 % for Algorithm 2. Conclusion The statistical indices are higher than, or comparable to those, cited in the scientific literature. PMID:15333132

  6. Direct Real-Time Detection of Vapors from Explosive Compounds

    SciTech Connect

    Ewing, Robert G.; Clowers, Brian H.; Atkinson, David A.

    2013-10-03

    The real-time detection of vapors from low volatility explosives including PETN, tetryl, RDX and nitroglycerine along with various compositions containing these substances is demonstrated. This was accomplished with an atmospheric flow tube (AFT) using a non-radioactive ionization source and coupled to a mass spectrometer. Direct vapor detection was demonstrated in less than 5 seconds at ambient temperature without sample pre-concentration. The several seconds of residence time of analytes in the AFT provides a significant opportunity for reactant ions to interact with analyte vapors to achieve ionization. This extended reaction time, combined with the selective ionization using the nitrate reactant ions (NO3- and NO3-•HNO3), enables highly sensitive explosives detection. Observed signals from diluted explosive vapors indicate detection limits below 10 ppqv using selected ion monitoring (SIM) of the explosive-nitrate adduct at m/z 349, 378, 284 and 289 for tetryl, PETN, RDX and NG respectively. Also provided is a demonstration of the vapor detection from 10 different energetic formulations, including double base propellants, plastic explosives and commercial blasting explosives using SIM for the NG, PETN and RDX product ions.

  7. Feature selection of seismic waveforms for long period event detection at Cotopaxi Volcano

    NASA Astrophysics Data System (ADS)

    Lara-Cueva, R. A.; Benítez, D. S.; Carrera, E. V.; Ruiz, M.; Rojo-Álvarez, J. L.

    2016-04-01

    Volcano Early Warning Systems (VEWS) have become a research topic in order to preserve human lives and material losses. In this setting, event detection criteria based on classification using machine learning techniques have proven useful, and a number of systems have been proposed in the literature. However, to the best of our knowledge, no comprehensive and principled study has been conducted to compare the influence of the many different sets of possible features that have been used as input spaces in previous works. We present an automatic recognition system of volcano seismicity, by considering feature extraction, event classification, and subsequent event detection, in order to reduce the processing time as a first step towards a high reliability automatic detection system in real-time. We compiled and extracted a comprehensive set of temporal, moving average, spectral, and scale-domain features, for separating long period seismic events from background noise. We benchmarked two usual kinds of feature selection techniques, namely, filter (mutual information and statistical dependence) and embedded (cross-validation and pruning), each of them by using suitable and appropriate classification algorithms such as k Nearest Neighbors (k-NN) and Decision Trees (DT). We applied this approach to the seismicity presented at Cotopaxi Volcano in Ecuador during 2009 and 2010. The best results were obtained by using a 15 s segmentation window, feature matrix in the frequency domain, and DT classifier, yielding 99% of detection accuracy and sensitivity. Selected features and their interpretation were consistent among different input spaces, in simple terms of amplitude and spectral content. Our study provides the framework for an event detection system with high accuracy and reduced computational requirements.

  8. Complex Event Detection in Pedestrian Groups from Uavs

    NASA Astrophysics Data System (ADS)

    Burkert, F.; Butenuth, M.

    2012-07-01

    We present a new hierarchical event detection approach for highly complex scenarios in pedestrian groups on the basis of airborne image sequences from UAVs. Related work on event detection for pedestrians is capable of learning and analyzing recurring motion paths to detect abnormal paths and of analyzing the type of motion interaction between pairs of pedestrians. However, these approaches can only describe basic motion and fail at the analysis of pedestrian groups with complex behavior. We overcome the limitations of the related work by using a dynamic pedestrian graph of a scene which contains basic pairwise pedestrian motion interaction labels in the first layer. In the second layer, pedestrian groups are analyzed based on the dynamic pedestrian graph in order to get higher-level information about group behavior. This is done by a heuristic assignment of predefined scenarios out of a model library to the data. The assignment is based on the motion interaction labels, on dynamic group motion parameters and on a set of subgraph features. Experimental results are shown based on a new UAV dataset which contains group motion of different complexity levels.

  9. Endmember detection in marine environment with oil spill event

    NASA Astrophysics Data System (ADS)

    Andreou, Charoula; Karathanassi, Vassilia

    2011-11-01

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

  10. Real-time extreme weather event attribution with forecast seasonal SSTs

    NASA Astrophysics Data System (ADS)

    Haustein, K.; Otto, F. E. L.; Uhe, P.; Schaller, N.; Allen, M. R.; Hermanson, L.; Christidis, N.; McLean, P.; Cullen, H.

    2016-06-01

    Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event.

  11. Aircraft Fault Detection Using Real-Time Frequency Response Estimation

    NASA Technical Reports Server (NTRS)

    Grauer, Jared A.

    2016-01-01

    A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

  12. Close to real-time robust pedestrian detection and tracking

    NASA Astrophysics Data System (ADS)

    Lipetski, Y.; Loibner, G.; Sidla, O.

    2015-03-01

    Fully automated video based pedestrian detection and tracking is a challenging task with many practical and important applications. We present our work aimed to allow robust and simultaneously close to real-time tracking of pedestrians. The presented approach is stable to occlusions, lighting conditions and is generalized to be applied on arbitrary video data. The core tracking approach is built upon tracking-by-detections principle. We describe our cascaded HOG detector with successive CNN verification in detail. For the tracking and re-identification task, we did an extensive analysis of appearance based features as well as their combinations. The tracker was tested on many hours of video data for different scenarios; the results are presented and discussed.

  13. Real time electrical detection of coherent spin oscillations in silicon

    NASA Astrophysics Data System (ADS)

    Huebl, Hans; Hoehne, Felix; Huck, Christian; Brandt, Martin S.

    2014-03-01

    In this presentation we demonstrate that the bandwidth of pulsed electrically detected magnetic resonance (EDMR) can be increased to at least 80 MHz using a radio frequency-reflectometry scheme based on a tank circuit and homodyne detection. Using this technique, we measure Rabi oscillations of phosphorus donors and Si/SiO2 interface states in real time during a resonant microwave pulse. We find that the observed signal is in quantitative agreement with simulations based on rate equations modeling the recombination dynamics of the spin system under study. The increased bandwidth demonstrated opens the way to study faster spin-dependent transport processes and could therefore significantly broaden the range of spin systems studied by EDMR.

  14. Detection measures in real-life criminal guilty knowledge tests.

    PubMed

    Elaad, E; Ginton, A; Jungman, N

    1992-10-01

    The present study provides a first attempt to compare the validity of the respiration line length (RLL) and skin resistance response (SRR) amplitude in real-life criminal guilty knowledge tests (GKTs). GKT records of 40 innocent and 40 guilty Ss, for whom actual truth was established by confession, were assessed for their accuracy. When a predefined decision rule was used and inconclusive decisions were excluded, 97.4% of the innocent Ss and 53.3% of the guilty Ss were correctly classified with the SRR measure. For the RLL measure, the respective results were 97.2% and 53.1%. The combination of both measures improved detection of guilty Ss to 75.8% and decreased detection of innocent Ss to 94.1%. The combined measure seems to be a more useful means of identifying guilty suspects than each physiological measure alone. The results elaborate and extend those obtained in a previous field study conducted by Elaad (1990).

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  16. Measuring target detection performance in paradigms with high event rates.

    PubMed

    Bendixen, Alexandra; Andersen, Søren K

    2013-05-01

    Combining behavioral and neurophysiological measurements inevitably implies mutual constraints, such as when the neurophysiological measurement requires fast-paced stimulus presentation and hence the attribution of a behavioral response to a particular preceding stimulus becomes ambiguous. We develop and test a method for validly assessing behavioral detection performance in spite of this ambiguity. We examine four approaches taken in the literature to treat such situations. We analytically derive a new variant of computing the classical parameters of signal detection theory, hit and false alarm rates, adapted to fast-paced paradigms. Each of the previous approaches shows specific shortcomings (susceptibility towards response window choice, biased estimates of behavioral detection performance). Superior performance of our new approach is demonstrated for both simulated and empirical behavioral data. Further evidence is provided by reliable correspondence between behavioral performance and the N2b component as an electrophysiological indicator of target detection. The appropriateness of our approach is substantiated by both theoretical and empirical arguments. We demonstrate an easy-to-implement solution for measuring target detection performance independent of the rate of event presentation. Thus overcoming the measurement bias of previous approaches, our method will help to clarify the behavioral relevance of different measures of cortical activation. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  17. Data mining to generate adverse drug events detection rules.

    PubMed

    Chazard, Emmanuel; Ficheur, Grégoire; Bernonville, Stéphanie; Luyckx, Michel; Beuscart, Régis

    2011-11-01

    Adverse drug events (ADEs) are a public health issue. Their detection usually relies on voluntary reporting or medical chart reviews. The objective of this paper is to automatically detect cases of ADEs by data mining. 115,447 complete past hospital stays are extracted from six French, Danish, and Bulgarian hospitals using a common data model including diagnoses, drug administrations, laboratory results, and free-text records. Different kinds of outcomes are traced, and supervised rule induction methods (decision trees and association rules) are used to discover ADE detection rules, with respect to time constraints. The rules are then filtered, validated, and reorganized by a committee of experts. The rules are described in a rule repository, and several statistics are automatically computed in every medical department, such as the confidence, relative risk, and median delay of outcome appearance. 236 validated ADE-detection rules are discovered; they enable to detect 27 different kinds of outcomes. The rules use a various number of conditions related to laboratory results, diseases, drug administration, and demographics. Some rules involve innovative conditions, such as drug discontinuations.

  18. Microfluidic Arrayed Lab-On-A-Chip for Electrochemical Capacitive Detection of DNA Hybridization Events.

    PubMed

    Ben-Yoav, Hadar; Dykstra, Peter H; Bentley, William E; Ghodssi, Reza

    2017-01-01

    A microfluidic electrochemical lab-on-a-chip (LOC) device for DNA hybridization detection has been developed. The device comprises a 3 × 3 array of microelectrodes integrated with a dual layer microfluidic valved manipulation system that provides controlled and automated capabilities for high throughput analysis of microliter volume samples. The surface of the microelectrodes is functionalized with single-stranded DNA (ssDNA) probes which enable specific detection of complementary ssDNA targets. These targets are detected by a capacitive technique which measures dielectric variation at the microelectrode-electrolyte interface due to DNA hybridization events. A quantitative analysis of the hybridization events is carried out based on a sensing modeling that includes detailed analysis of energy storage and dissipation components. By calculating these components during hybridization events the device is able to demonstrate specific and dose response sensing characteristics. The developed microfluidic LOC for DNA hybridization detection offers a technology for real-time and label-free assessment of genetic markers outside of laboratory settings, such as at the point-of-care or in-field environmental monitoring.

  19. Use of sonification in the detection of anomalous events

    NASA Astrophysics Data System (ADS)

    Ballora, Mark; Cole, Robert J.; Kruesi, Heidi; Greene, Herbert; Monahan, Ganesh; Hall, David L.

    2012-06-01

    In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners. The soundtrack generation is flexible, allowing an individual listener to create a unique audio mix from the available information sources. Properly constructed, the display exploits the auditory system's sensitivities to periodicities, to dynamic changes, and to patterns. This type of display could be valuable in environments that demand high levels of situational awareness based on multiple sources of incoming information.

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

    SciTech Connect

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

    1997-12-01

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

  1. Change and Anomaly Detection in Real-Time GPS Data

    NASA Astrophysics Data System (ADS)

    Granat, R.; Pierce, M.; Gao, X.; Bock, Y.

    2008-12-01

    The California Real-Time Network (CRTN) is currently generating real-time GPS position data at a rate of 1-2Hz at over 80 locations. The CRTN data presents the possibility of studying dynamical solid earth processes in a way that complements existing seismic networks. To realize this possibility we have developed a prototype system for detecting changes and anomalies in the real-time data. Through this system, we can can correlate changes in multiple stations in order to detect signals with geographical extent. Our approach involves developing a statistical model for each GPS station in the network, and then using those models to segment the time series into a number of discrete states described by the model. We use a hidden Markov model (HMM) to describe the behavior of each station; fitting the model to the data requires neither labeled training examples nor a priori information about the system. As such, HMMs are well suited to this problem domain, in which the data remains largely uncharacterized. There are two main components to our approach. The first is the model fitting algorithm, regularized deterministic annealing expectation- maximization (RDAEM), which provides robust, high-quality results. The second is a web service infrastructure that connects the data to the statistical modeling analysis and allows us to easily present the results of that analysis through a web portal interface. This web service approach facilitates the automatic updating of station models to keep pace with dynamical changes in the data. Our web portal interface is critical to the process of interpreting the data. A Google Maps interface allows users to visually interpret state changes not only on individual stations but across the entire network. Users can drill down from the map interface to inspect detailed results for individual stations, download the time series data, and inspect fitted models. Alternatively, users can use the web portal look at the evolution of changes on the

  2. Two Neurocognitive Mechanisms of Semantic Integration during the Comprehension of Visual Real-world Events

    PubMed Central

    Sitnikova, Tatiana; Holcomb, Phillip J.; Kiyonaga, Kristi A.; Kuperberg, Gina R.

    2009-01-01

    How do comprehenders build up overall meaning representations of visual real-world events? This question was examined by recording event-related potentials (ERPs) while participants viewed short, silent movie clips depicting everyday events. In two experiments, it was demonstrated that presentation of the contextually inappropriate information in the movie endings evoked an anterior negativity. This effect was similar to the N400 component whose amplitude has been previously reported to inversely correlate with the strength of semantic relationship between the context and the eliciting stimulus in word and static picture paradigms. However, a second, somewhat later, ERP component—a posterior late positivity—was evoked specifically when target objects presented in the movie endings violated goal-related requirements of the action constrained by the scenario context (e.g., an electric iron that does not have a sharp-enough edge was used in place of a knife in a cutting bread scenario context). These findings suggest that comprehension of the visual real world might be mediated by two neurophysiologically distinct semantic integration mechanisms. The first mechanism, reflected by the anterior N400-like negativity, maps the incoming information onto the connections of various strengths between concepts in semantic memory. The second mechanism, reflected by the posterior late positivity, evaluates the incoming information against the discrete requirements of real-world actions. We suggest that there may be a tradeoff between these mechanisms in their utility for integrating across people, objects, and actions during event comprehension, in which the first mechanism is better suited for familiar situations, and the second mechanism is better suited for novel situations. PMID:18416681

  3. Real-Time Depth-Based Hand Detection and Tracking

    PubMed Central

    Weon, Sun-Hee; Choi, Hyung-Il

    2014-01-01

    This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations. PMID:24737965

  4. Bio-inspired WSN architecture: event detection and loacalization in a fault tolerant WSN

    NASA Astrophysics Data System (ADS)

    Alayev, Yosef; Damarla, Thyagaraju

    2009-05-01

    One can think of human body as a sensory network. In particular, skin has several neurons that provide the sense of touch with different sensitivities, and neurons for communicating the sensory signals to the brain. Even though skin might occasionally experience some lacerations, it performs remarkably well (fault tolerant) with the failure of some sensors. One of the challenges in collaborative wireless sensor networks (WSN) is fault tolerant detection and localization of targets. In this paper we present a biologically inspired architecture model for WSN. Diagnosis of sensors in WSN model presented here is derived from the concept of the immune system. We present an architecture for WSN for detection and localization of multiple targets inspired by human nervous system. We show that the advantages of such bio-inspired networks are reduced data for communication, self-diagnosis to detect faulty sensors in real-time and the ability to localize events. We present the results of our algorithms on simulation data.

  5. Detectability of GW150914-like events by gravitational microlensing

    NASA Astrophysics Data System (ADS)

    Eilbott, Daniel; Riley, Alexander; Cohn, Jonathan; Kesden, Michael H.; King, Lindsay J.

    2017-01-01

    The recent discovery of gravitational waves from stellar-mass binary black holes (BBHs) provided direct evidence of the existence of these systems. These BBHs would have gravitational microlensing signatures that are, due to their large masses and small separations, distinct from single-lens signals. We apply Bayesian statistics to examine the distinguishability of BBH microlensing events from single-lens events under ideal observing conditions, using modern photometric and astrometric capabilities. Given one year of ideal observations, a source star at the Galactic center, a GW150914-like BBH lens (total mass 65 M⊙, mass ratio 0.8) at half that distance, and an impact parameter of 0.4 Einstein radii, we find that BBH separations down to 0.00634 Einstein radii are detectable, which is < 0.00716 Einstein radii, the limit at which the BBH would merge within the age of the universe. We encourage analyses of LSST data to search for similar modulation in all long-duration events, providing a new channel for the discovery of short-period BBHs in our Galaxy.

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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

  7. Understanding pharmacist decision making for adverse drug event (ADE) detection.

    PubMed

    Phansalkar, Shobha; Hoffman, Jennifer M; Hurdle, John F; Patel, Vimla L

    2009-04-01

    Manual chart review is an effective but expensive method for adverse drug event (ADE) detection. Building an expert system capable of mimicking the human expert's decision pathway, to deduce the occurrence of an ADE, can improve efficiency and lower cost. As a first step to build such an expert system, this study explores pharmacist's decision-making processes for ADE detection. Think-aloud procedures were used to elicit verbalizations as pharmacists read through ADE case scenarios. Two types of information were extracted, firstly pharmacists' decision-making strategies regarding ADEs and secondly information regarding pharmacists' unmet information needs for ADE detection. Verbal protocols were recorded and analysed qualitatively to extract ADE information signals. Inter-reviewer agreement for classification of ADE information signals was calculated using Cohen's kappa. We extracted a total of 110 information signals, of which 73% consisted of information that was interpreted by the pharmacists from the case scenario and only about half (53%, n = 32) of the information signals were considered relevant for the detection of the ADEs. Excellent reliability was demonstrated between the reviewers for classifying signals. Fifty information signals regarding unmet information needs were extracted and grouped into themes based on the type of missing information. Pharmacists used a forward reasoning approach to make implicit deductions and validate hypotheses about possible ADEs. Verbal protocols also indicated that pharmacists' unmet information needs occurred frequently. Developing alerting systems that meet pharmacists' needs adequately will enhance their ability to reduce preventable ADEs, thus improving patient safety.

  8. Increased SERS detection efficiency for characterizing rare events in flow.

    PubMed

    Jacobs, Kevin T; Schultz, Zachary D

    2015-08-18

    Improved surface-enhanced Raman scattering (SERS) measurements of a flowing aqueous sample are accomplished by combining line focus optics with sheath-flow SERS detection. The straightforward introduction of a cylindrical lens into the optical path of the Raman excitation laser increases the efficiency of SERS detection and the reproducibility of SERS signals at low concentrations. The width of the line focus is matched to the width of the sample capillary from which the analyte elutes under hydrodynamic focusing conditions, allowing for increased collection across the SERS substrate while maintaining the power density below the damage threshold at any specific point. We show that a 4× increase in power spread across the line increases the signal-to-noise ratio by a factor of 2 for a variety of analytes, such as rhodamine 6G, amino acids, and lipid vesicles, without any detectable photodamage. COMSOL simulations and Raman maps elucidate the hydrodynamic focusing properties of the flow cell, providing a clearer picture of the confinement effects at the surface where the sample exits the capillary. The lipid vesicle results suggest that the combination of hydrodynamic focusing and increased optical collection enables the reproducible detection of rare events, in this case individual lipid vesicles.

  9. A Real-Time Automated Point Process Method for Detection and Correction of Erroneous and Ectopic Heartbeats

    PubMed Central

    Citi, Luca; Brown, Emery N; Barbieri, Riccardo

    2012-01-01

    The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affect estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate on-line monitoring of HRV and cardiovascular control. We have developed a novel point process based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the Physionet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an on-line fashion, may provide a solid base for building a more reliable real-time HRV monitoring device. PMID:22875239

  10. Validation of real-time PCR assays for bioforensic detection of model plant pathogens.

    PubMed

    James, Mindy; Blagden, Trenna; Moncrief, Ian; Burans, James P; Schneider, Katherine; Fletcher, Jacqueline

    2014-03-01

    The U.S. agricultural sector is vulnerable to intentionally introduced microbial threats because of its wide and open distribution and economic importance. To investigate such events, forensically valid assays for plant pathogen detection are needed. In this work, real-time PCR assays were developed for three model plant pathogens: Pseudomonas syringae pathovar tomato, Xylella fastidiosa, and Wheat streak mosaic virus. Validation included determination of the linearity and range, limit of detection, sensitivity, specificity, and exclusivity of each assay. Additionally, positive control plasmids, distinguishable from native signature by restriction enzyme digestion, were developed to support forensic application of the assays. Each assay displayed linear amplification of target nucleic acid, detected 100 fg or less of target nucleic acid, and was specific to its target pathogen. Results obtained with these model pathogens provide the framework for development and validation of similar assays for other plant pathogens of high consequence.

  11. Development and characterization of a microheater array device for real-time DNA mutation detection

    NASA Astrophysics Data System (ADS)

    Williams, Layne; Okandan, Murat; Chagovetz, Alex; Blair, Steve

    2008-02-01

    DNA analysis, specifically single nucleotide polymorphism (SNP) detection, is becoming increasingly important in rapid diagnostics and disease detection. Temperature is often controlled to help speed reaction rates and perform melting of hybridized oligonucleotides. The difference in melting temperatures, Tm, between wild-type and SNP sequences, respectively, to a given probe oligonucleotide, is indicative of the specificity of the reaction. We have characterized Tm's in solution and on a solid substrate of three sequences from known mutations associated with Cystic Fibrosis. Taking advantage of Tm differences, a microheater array device was designed to enable individual temperature control of up to 18 specific hybridization events. The device was fabricated at Sandia National Laboratories using surface micromachining techniques. The microheaters have been characterized using an IR camera at Sandia and show individual temperature control with minimal thermal cross talk. Development of the device as a real-time DNA detection platform, including surface chemistry and associated microfluidics, is described.

  12. Development and characterization of a microheater array device for real-time DNA mutation detection

    NASA Astrophysics Data System (ADS)

    Williams, Layne; Okandan, Murat; Chagovetz, Alex; Blair, Steve

    2008-04-01

    DNA analysis, specifically single nucleotide polymorphism (SNP) detection, is becoming increasingly important in rapid diagnostics and disease detection. Temperature is often controlled to help speed reaction rates and perform melting of hybridized oligonucleotides. The difference in melting temperatures, Tm, between wild-type and SNP sequences, respectively, to a given probe oligonucleotide, is indicative of the specificity of the reaction. We have characterized Tm's in solution and on a solid substrate of three sequences from known mutations associated with Cystic Fibrosis. Taking advantage of Tm differences, a microheater array device was designed to enable individual temperature control of up to 18 specific hybridization events. The device was fabricated at Sandia National Laboratories using surface micromachining techniques. The microheaters have been characterized using an IR camera at Sandia and show individual temperature control with minimal thermal cross talk. Development of the device as a real-time DNA detection platform, including surface chemistry and associated microfluidics, is described.

  13. Real-time detection and characterization of rockslides in the European Alps

    NASA Astrophysics Data System (ADS)

    Manconi, A.; Picozzi, M.; Coviello, V.; De Santis, F.; Elia, L.

    2016-12-01

    Several recent studies have demonstrated that broadband seismic networks can detect ground vibrations caused by different mass wasting phenomena, including rockslides. Despite a growing number of studies exploring the use of broadband seismic recordings to trace the evolution of rockslide events in time and space, however, strategies for automatic detection and characterization within an operational and real-time framework are still challenging. Here we present a real-time algorithm to detect, locate, and estimate the volume of rockslides by analyzing seismic data acquired from broadband regional seismic networks [Manconi et al., 2016]. Our procedure leverages algorithms originally developed for operational Earthquake Early Warning (EEW), i.e., the PRobabilistic and Evolutionary early warning SysTem (PRESTo, Satriano et al., [2011]). Waveforms related to rockslides are separated from other seismic sources, such as natural and/or induced earthquakes, by exploiting the ratio between local magnitudes (ML) and duration magnitudes (MD). Indeed, our analysis shows that signals associated with rockslides have ML/MD < 0.8, while for earthquakes ML/MD ≅ 1. We have also derived an empirical relationship between MD and rockslide volumes, which allows obtaining a preliminary characterization of rockslide volumetric magnitudes within seconds after their occurrence. We have tested the hypothesis on several rockslide events occurred in the European Alps, and we show the potential evolution of this methodology for early warning and/or rapid response in an operational framework. Manconi, A., M. Picozzi, V. Coviello, F. De Santis, and L. Elia (2016), Real-time detection, location, and characterization of rockslides using broadband regional seismic networks, Geophys. Res. Lett., 43, 6960-6967, doi:10.1002/2016GL069572. Satriano, C., L. Elia, C. Martino, M. Lancieri, A. Zollo, and G. Iannaccone (2011), PRESTo, the earthquake early warning system for Southern Italy: Concepts

  14. A software framework for real-time multi-modal detection of microsleeps.

    PubMed

    Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

    2017-06-01

    A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

  15. Automated Detection Method of Slow Slip Events in Southwest Japan

    NASA Astrophysics Data System (ADS)

    Kimura, T.; Hirose, H.; Obara, K.; Kimura, H.

    2010-12-01

    In the Nankai subduction zone, southwest Japan, various types of slow earthquakes have been detected using dense seismic and geodetic observation networks such as Hi-net operated by the National Research Institute for Earth Science and Disaster Prevention. Short-term slow slip events (SSEs) which last for several days are detected as crustal deformation by using borehole tiltmeters and strainmeters, and usually accompanied by seismic slow earthquakes such as nonvolcanic deep low-frequency tremor. These coupled phenomena are called episodic tremor and slip (ETS). In previous studies on ETS events in southwest Japan, short-term SSEs have been identified manually consulting with the seismic tremor data. However, in order to clarify the relationship between geodetic SSEs and seismic tremor objectively, an SSE detection method independent of the tremor data is necessary. In this study, we develop a new automated method that identifies signals caused by SSEs and estimates the source model using ground tilt data. Our method is composed of two phases, estimation of the SSE model and identification of SSE. In the model estimation phase, we assume that an SSE must occur in the analyzed time period, and observed ground tilt contains a response to an SSE, background linear trend, random-walk noise, and white noise. An SSE is modeled as a uniform slip on a rectangular fault with a time-invariant slip-rate. We estimate an optimum source model of the possible SSE using the Kalman filter for linear parameters such as total slip and grid-search method for nonlinear parameters such as fault location, origin time and duration. In the identification phase, another model is estimated from the same tilt data with an assumption that no SSE occurs. The tilt changes modeled as an SSE in the estimation phase is evaluated by comparison between the models with and without SSE on the basis of AIC. Then a robustness test is carried and the model is identified as an SSE. We applied the automated

  16. Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project.

    PubMed

    Patadia, Vaishali K; Coloma, Preciosa; Schuemie, Martijn J; Herings, Ron; Gini, Rosa; Mazzaglia, Giampiero; Picelli, Gino; Fornari, Carla; Pedersen, Lars; van der Lei, Johan; Sturkenboom, Miriam; Trifirò, Gianluca

    2015-01-01

    A prospective pharmacovigilance signal detection study, comparing the real-world healthcare data (EU-ADR) and two spontaneous reporting system (SRS) databases, US FDA's Adverse Event Reporting System and WHO's Vigibase is reported. The study compared drug safety signals found in the EU-ADR and SRS databases. The potential for signal detection in the EU-ADR system was found to be dependent on frequency of the event and utilization of drugs in the general population. The EU-ADR system may have a greater potential for detecting signals for events occurring at higher frequency in general population and those that are commonly not considered as potentially a drug-induced event. Factors influencing various differences between the datasets are discussed along with potential limitations and applications to pharmacovigilance practice.

  17. Real-time detection of viable microorganisms by intracellular phototautomerism

    PubMed Central

    2010-01-01

    Background To date, the detection of live microorganisms present in the environment or involved in infections is carried out by enumeration of colony forming units on agar plates, which is time consuming, laborious and limited to readily cultivable microorganisms. Although cultivation-independent methods are available, they involve multiple incubation steps and do mostly not discriminate between dead or live microorganisms. We present a novel generic method that is able to specifically monitor living microorganisms in a real-time manner. Results The developed method includes exposure of cells to a weak acid probe at low pH. The neutral probe rapidly permeates the membrane and enters the cytosol. In dead cells no signal is obtained, as the cytosolic pH reflects that of the acidic extracellular environment. In live cells with a neutral internal pH, the probe dissociates into a fluorescent phototautomeric anion. After reaching peak fluorescence, the population of live cells decays. This decay can be followed real-time as cell death coincides with intracellular acidification and return of the probe to its uncharged non-fluorescent state. The rise and decay of the fluorescence signal depends on the probe structure and appears discriminative for bacteria, fungi, and spores. We identified 13 unique probes, which can be applied in the real-time viability method described here. Under the experimental conditions used in a microplate reader, the reported method shows a detection limit of 106 bacteria ml-1, while the frequently used LIVE/DEAD BacLight™ Syto9 and propidium iodide stains show detection down to 106 and 107 bacteria ml-1, respectively. Conclusions We present a novel fluorescence-based method for viability assessment, which is applicable to all bacteria and eukaryotic cell types tested so far. The RTV method will have a significant impact in many areas of applied microbiology including research on biocidal activity, improvement of preservation strategies and

  18. Real-Time PCR Method for Detection of Zygomycetes ▿

    PubMed Central

    Hata, D. Jane; Buckwalter, Seanne P.; Pritt, Bobbi S.; Roberts, Glenn D.; Wengenack, Nancy L.

    2008-01-01

    Zygomycete infections can be devastating in immunocompromised hosts. Difficulties in the histopathologic differentiation of this class from other filamentous fungi (e.g., Aspergillus spp., Fusarium spp.) may lead to delays in diagnosis and initiation of appropriate treatment, thereby significantly affecting patient outcome. A real-time PCR assay was developed to detect species of the zygomycete genera Absidia, Apophysomyces, Cunninghamella, Mucor, Rhizopus, and Saksenaea in culture and tissue samples. Primers and fluorescence resonance energy transfer hybridization probes were designed to detect a 167-bp conserved region of the multicopy zygomycete cytochrome b gene. A plasmid containing target sequence from Mucor racemosus was constructed as a positive control. The analytical sensitivity of the assay is 10 targets/μl, and a specificity panel consisting of other filamentous fungi, yeasts (Candida spp.), and bacteria demonstrated no cross-reactivity in the assay. The clinical sensitivity and specificity of the assay from culture isolates were 100% (39/39) and 92% (59/64), respectively. Sensitivity and specificity determined using a limited number of fresh tissue specimens were both 100% (2/2). The sensitivity seen with formalin-fixed, paraffin-embedded tissues was 56% (35/62), and the specificity was 100% (19/19). The speed, sensitivity, and specificity of the PCR assay indicate that it is useful for the rapid and accurate detection of zygomycetes. PMID:18480229

  19. Reaching out for patients: public relations and events with real results.

    PubMed

    Kuechel, Marie Czenko

    2010-02-01

    In today's market, the aesthetic physician needs to connect with patients using methods that are personal, educational, and that will glean the interest of prospective patients whose attention and dollars are sought by countless facial plastic surgery competitors near and far. Public relations, or reaching your prospective patient without a direct solicitation (advertising) for services, are traditional means that include media relations and charitable and social events. With the added component of social media, today the opportunities to reach out for new patients and garner real results are more varied and more affordable than ever before.

  20. Robust real-time change detection in high jitter.

    SciTech Connect

    Simonson, Katherine Mary; Ma, Tian J.

    2009-08-01

    A new method is introduced for real-time detection of transient change in scenes observed by staring sensors that are subject to platform jitter, pixel defects, variable focus, and other real-world challenges. The approach uses flexible statistical models for the scene background and its variability, which are continually updated to track gradual drift in the sensor's performance and the scene under observation. Two separate models represent temporal and spatial variations in pixel intensity. For the temporal model, each new frame is projected into a low-dimensional subspace designed to capture the behavior of the frame data over a recent observation window. Per-pixel temporal standard deviation estimates are based on projection residuals. The second approach employs a simple representation of jitter to generate pixelwise moment estimates from a single frame. These estimates rely on spatial characteristics of the scene, and are used gauge each pixel's susceptibility to jitter. The temporal model handles pixels that are naturally variable due to sensor noise or moving scene elements, along with jitter displacements comparable to those observed in the recent past. The spatial model captures jitter-induced changes that may not have been seen previously. Change is declared in pixels whose current values are inconsistent with both models.

  1. Real-time detection of neurite outgrowth using microfluidic device

    NASA Astrophysics Data System (ADS)

    Kim, Samhwan; Jang, Jongmoon; Choi, Hongsoo; Moon, Cheil

    2013-05-01

    We developed a simple method for real-time detection of the neurite outgrowth using microfluidic device. Our microfluidic device contains three compartmentalized channels which are for cell seeding, hydrogel and growth factors. Collagen gel is filled in the middle channel and pheochromocytoma (PC12) cells are seeded in the left channel. To induce differentiation of PC12 cells, 50 ng/ml to1000 ng/ml of nerve growth factor (NGF) is introduced into the right channel. After three days of NGF treatment, PC12 cells begin to extend neurites and formed neurite network from sixth day. Quantification of neurite outgrowth is analyzed by measuring the total area of neurites. On sixth day, the area is doubled compared to the area on third day and increases by 20 times on ninth day.

  2. Real time pre-detection dynamic range compression

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor)

    1992-01-01

    A real time, pre-detection optical dynamic range compression system uses a photorefractive crystal, such as BaTiO3 or LiNbO3, in which light induced scattering from crystal inhomogeneities of the optical input occurs as a nonlinear function of the input intensity. The greater the intensity, the faster random interference gratings are created to scatter the incident light. The unscattered portion of the optical signal is therefore reduced in dynamic range over time. The amount or range of dynamic range compression may be controlled by adjusting the time of application of the unscattered crystal output to the photodetector with regard to the time of application of the optical input to the crystal.

  3. Protective materials with real-time puncture detection capability

    SciTech Connect

    Hermes, R.E.; Stampfer, J.F.; Valdez-Boyle, L.S.; Ramsey, D.R.

    1996-08-01

    The protection of workers from chemical, biological, or radiological hazards requires the use of protective materials that can maintain their integrity during use. An accidental puncture in the protective material can result in a significant exposure to the worker. A five ply material has been developed that incorporates two layers of an electrically conductive polymer sandwiched between three layers of a nonconductive polymer. A normally open circuit that is connected between the conductive layers will be closed by puncturing the material with either a conductive or nonconductive object. This can be used to activate an audible alarm or visual beacon to warn the worker of a breach in the integrity of the material. The worker is not connected to the circuit, and the puncture can be detected in real-time, even when caused by a nonconductor.

  4. Infrared luminescence for real time ionizing radiation detection

    NASA Astrophysics Data System (ADS)

    Veronese, Ivan; Mattia, Cristina De; Fasoli, Mauro; Chiodini, Norberto; Mones, Eleonora; Cantone, Marie Claire; Vedda, Anna

    2014-08-01

    Radio-luminescence (RL) optical fiber sensors enable a remote, punctual, and real time detection of ionizing radiation. However, the employment of such systems for monitoring extended radiation fields with energies above the Cerenkov threshold is still challenging, since a spurious luminescence, namely, the "stem effect," is also generated in the passive fiber portion exposed to radiation. Here, we present experimental measurements on Yb-doped silica optical fibers irradiated with photon fields of different energies and sizes. The results demonstrate that the RL of Yb3+, displaying a sharp emission line at about 975 nm, is free from any spectral superposition with the spurious luminescence. This aspect, in addition with the suitable linearity, reproducibility, and sensitivity properties of the Yb-doped fibers, paves the way to their use in applications where an efficient stem effect removal is required.

  5. Infrared luminescence for real time ionizing radiation detection

    SciTech Connect

    Veronese, Ivan Mattia, Cristina De; Cantone, Marie Claire; Fasoli, Mauro; Chiodini, Norberto; Vedda, Anna; Mones, Eleonora

    2014-08-11

    Radio-luminescence (RL) optical fiber sensors enable a remote, punctual, and real time detection of ionizing radiation. However, the employment of such systems for monitoring extended radiation fields with energies above the Cerenkov threshold is still challenging, since a spurious luminescence, namely, the “stem effect,” is also generated in the passive fiber portion exposed to radiation. Here, we present experimental measurements on Yb-doped silica optical fibers irradiated with photon fields of different energies and sizes. The results demonstrate that the RL of Yb{sup 3+}, displaying a sharp emission line at about 975 nm, is free from any spectral superposition with the spurious luminescence. This aspect, in addition with the suitable linearity, reproducibility, and sensitivity properties of the Yb-doped fibers, paves the way to their use in applications where an efficient stem effect removal is required.

  6. Real-time threat detection using magnetometer arrays

    NASA Astrophysics Data System (ADS)

    Prouty, Mark D.; Tchernychev, Mikhail

    2016-05-01

    In this paper we present a discussion of using an array of atomic magnetometers to locate the presence of ferrous materials, such as concealed weapons, in real time. Ferrous materials create magnetic field anomalies. In order to determine the location of such objects, readings from many positions must be analyzed. This field inversion is typically done in post processing, once readings over a survey area or region of interest have been gathered. With the recent development of small and low power sensors, the dozen or so sensors required to provide information for magnetic field inversion may be deployed. We have built such an array and present here the results of using a realtime inversion algorithm. The inversion algorithm accurately determines target properties at a rate of 10 times per second as objects move past the array. Accuracies are as good as those obtained with target inversion methods used in analyzing data for unexploded ordnance detection. While those methods are typically applied in post processing, we show here those methods work even better when applied in real-time. We further present some analyses of the predicted performance of arrays in various geometries to address issues in security, such as crowd or perimeter monitoring. Target inversion methods may be accurately simulated, allowing for the development and testing of algorithms in an efficient manner. Additional processing may be done using the time history of the inversion results to remove false alarms and enhance detection. The key step is to start with an inversion method, utilizing the mathematical properties of magnetic fields and the known geometry of the measurements.

  7. Improvements in atrial fibrillation detection for real-time monitoring.

    PubMed

    Babaeizadeh, Saeed; Gregg, Richard E; Helfenbein, Eric D; Lindauer, James M; Zhou, Sophia H

    2009-01-01

    Electrocardiographic (ECG) monitoring plays an important role in the management of patients with atrial fibrillation (AF). Automated real-time AF detection algorithm is an integral part of ECG monitoring during AF therapy. Before and after antiarrhythmic drug therapy and surgical procedures require ECG monitoring to ensure the success of AF therapy. This article reports our experience in developing a real-time AF monitoring algorithm and techniques to eliminate false-positive AF alarms. We start by designing an algorithm based on R-R intervals. This algorithm uses a Markov modeling approach to calculate an R-R Markov score. This score reflects the relative likelihood of observing a sequence of R-R intervals in AF episodes versus making the same observation outside AF episodes. Enhancement of the AF algorithm is achieved by adding atrial activity analysis. P-R interval variability and a P wave morphology similarity measure are used in addition to R-R Markov score in classification. A hysteresis counter is applied to eliminate short AF segments to reduce false AF alarms for better suitability in a monitoring environment. A large ambulatory Holter database (n = 633) was used for algorithm development and the publicly available MIT-BIH AF database (n = 23) was used for algorithm validation. This validation database allowed us to compare our algorithm performance with previously published algorithms. Although R-R irregularity is the main characteristic and strongest discriminator of AF rhythm, by adding atrial activity analysis and techniques to eliminate very short AF episodes, we have achieved 92% sensitivity and 97% positive predictive value in detecting AF episodes, and 93% sensitivity and 98% positive predictive value in quantifying AF segment duration.

  8. Empirical study of a unidirectional dense crowd during a real mass event

    NASA Astrophysics Data System (ADS)

    Zhang, X. L.; Weng, W. G.; Yuan, H. Y.; Chen, J. G.

    2013-06-01

    Many tragic crowd disasters have happened across the world in recent years, such as the Phnom Penh stampede in Cambodia, crowd disaster in Mina/Makkah, and the Love Parade disaster in Germany, showing that management of mass events is a tough task for organizers. The study of unidirectional flow, one of the most common forms of motion in mass activities, is essential for safe organization of such events. In this paper, the properties of unidirectional flow in a crowded street during a real mass event in China are quantitatively investigated with sophisticated active infrared counters and an image processing method. A complete dataset of flow rates during the whole celebration is recorded, and a time series analysis gives new insight into such activities. The spatial analysis shows that the velocity and density of the crowd are inhomogeneous due to the boundary effect, whereas the flux is uniform. The estimated capacity of the street indicates that the maximum flow rate under normal condition should be between 1.73 and 1.98 /m/s, which is in good agreement with several field studies available in the existing literature. In consideration of the significant deviation among different studies, fundamental diagrams of dense crowds are also re-verified, and the results here are consistent with those from other field studies of unidirectional flow, but different from the bidirectional and experimental results. It is suggested that the data from multidirectional flow and experiments cannot be directly applied to unidirectional dense flow in a real mass event. The results also imply that the density of a similar unidirectional marching crowd should be controlled to be under 5 /m2, which can produce optimal efficiency and have more possibility to ensure safety. The field study data given here provide a good example of a database for crowd studies.

  9. Real-time elastography for detecting prostate cancer: preliminary experience.

    PubMed

    Pallwein, Leo; Mitterberger, Michael; Struve, Peter; Pinggera, Germar; Horninger, Wolfgang; Bartsch, Georg; Aigner, Friedrich; Lorenz, Andreas; Pedross, Florian; Frauscher, Ferdinand

    2007-07-01

    To assess the use of real-time elastography (RTE) for detecting prostate cancer in patients scheduled for radical prostatectomy (RP), as most solid tumours differ in their consistency from the deriving tissue, and RTE might offer a new tool for cancer detection. We examined 15 patients (mean age 56 years, sd 6.2, range 46-71) with RTE, using an ultrasonography (US) system with a 7.5-MHz transrectal probe as a transducer. RTE is capable of visualizing displacements between pairs of US images of tissues when placed under axial compression. The stiffness of the lesion was displayed from blue (soft) to black (hard). Hard lesions with a diameter of > or = 5 mm were considered as malignant. All patients had the diagnosis of prostate cancer confirmed by biopsy and had a mean (range) prostate specific antigen (PSA) level of 4.6 (1.4-16.1) ng/mL; all were scheduled for RP. US was performed by two investigators and interpreted by consensus. Cancer location and size was determined in the RTE mode only. One pathologist classified tumour location, grade and stage. The RTE findings were compared with the pathological findings. There were no major complications during RP in any patient; all had a pT2 tumour on histopathological examination, the Gleason score was 5-9 and the mean (range) tumour size 1.1 (0.6-2.5) cm. Thirty-five foci of prostate cancer were present at the pathological evaluation; multiple foci were found in 11 of the 15 glands. RTE detected 28 of 35 cancer foci (sensitivity 80%). The per-patient analysis showed that RTE detected at least one cancer area in each of the 15 patients. Only four sites with false-positive findings on RTE and no histopathological correlation were detected; these findings were obtained in the first five patients (period of learning). RTE can be used to visualize differences in tissue elasticity. Our results show that RTE allows the detection of prostate cancer and estimation of tumour location and size. RTE of the prostate is a new

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    Environmental scientists use advanced sensor technology such as meteorological towers, wireless sensor networks and robotic trams equipped with sensors to perform data collection at remote research sites. Because the amount of environmental sensor data acquired in real time by such instruments is increasing, both the ability to evaluate the accuracy of the data at near-real time and check that the instrumentation is operating correctly are critical in order to not lose valuable time and information. The goal of the research is to define a software engineering-based solution that provides the foundation to define reusable templates for formally specifying data properties and automatically generate programming code that can monitor data streams to identify anomalies at near real-time. The research effort has resulted in a data property categorization that is based on a literature survey of 15 projects that collected environmental data from sensors and a case study conducted in the Arctic. More than 500 published data properties were manually extracted and analyzed from the surveyed projects. The data property categorization revealed recurrent data patterns. Using these patterns and the Specification and Pattern System (SPS) from the software-engineering community as a model, we developed the Data Specification and Pattern System (D-SPS) to capture data properties. D-SPS is the foundation for the Data Property Specification (DaProS) prototype tool that assists scientists in specification of sensor data properties. A series of experiments have been conducted in collaboration with experts working with Eddy covariance (EC) data from the Jornada Basin Experimental Range (JER) and with hyper-spectral data collected using robotic tram systems from the Arctic. The goal of the experiments were to determine if the approach for specifying data properties is effective for specifying data properties and identifying anomalies in sensor data. A complementary Sensor Data

  12. Detecting Rare Events in the Time-Domain

    SciTech Connect

    Rest, A; Garg, A

    2008-10-31

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

  13. Detecting and characterising ramp events in wind power time series

    NASA Astrophysics Data System (ADS)

    Gallego, Cristóbal; Cuerva, Álvaro; Costa, Alexandre

    2014-12-01

    In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain.

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

    PubMed

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

    2012-04-01

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

  15. Near real-time detection and characterization of landslides using broadband seismic networks

    NASA Astrophysics Data System (ADS)

    Manconi, Andrea; Coviello, Velio; De Santis, Francesca; Picozzi, Matteo

    2015-04-01

    Broadband seismic networks at regional, national, and global scale are usually deployed for a specific purpose, i.e. earthquake monitoring. However, it has been recently demonstrated that these networks are also capable to efficiently detect failure and transport processes related to landslide phenomena. Indeed, stations located several tens of kilometers away from the source areas can record the ground vibrations produced by large mass movements. In this work, we propose an integrated approach for the near real-time detection, location, and characterization of landslides, by considering data acquired from the Italian broadband seismic networks and available in the European Integrated Data Archive (EIDA). We use an automatic picking of first arrivals to identify significant seismic events recorded by the monitoring network. Secondly, waveforms relevant to landslide phenomena are selected by analyzing the spectral characteristics of seismic signals, which significantly differ from those related to earthquake events. Afterwards, in order to locate the landslide, we use a modified version of the real-time evolutionary location algorithm proposed for earthquakes, which relies on geometrical characteristics of the seismic network and on the relationships between triggered stations and not-triggered stations. Indeed, a first landslide location is roughly estimated as soon as the first station is triggered. The progressive increase over time in the number of triggered stations allows improving the accuracy on the most likely landslide location. Finally, we analyze the seismic energy released to infer an approximate value of the landslide volume in near real time. Here we present few examples relevant to recent well-known landslides where our method was successfully applied. Our results show how it is possible to extract precious information for landslide hazard assessment from seismic monitoring data, which in the field of earthquake warning would be discarded. Moreover

  16. A real time pipeline to link meteorological information and TGFs detected by AGILE

    NASA Astrophysics Data System (ADS)

    Ursi, Alessandro; Tavani, Marco; Dietrich, Stefano; Marisaldi, Martino; Casella, Daniele; Sanò, Paolo; Petracca, Marco; Argan, Andrea

    2015-04-01

    Terrestrial Gamma-ray Flashes (TGFs) are brief (detect TGFs, because of its very wide energy range (up to 100 MeV and beyond), its optimized triggering system and its equatorial orbit. We describe a new alert service that has been developed for the AGILE satellite, whose aim is to provide "real time" meteorological information about the detected TGFs. We take advantage of the Meteosat Second Generation (MSG) satellites data to promptly identify the possible individual thunderstorm or mesoscale convective system associated to the detected TGF event and to follow its evolution in space and time. Data from other meteorological satellites, for example the GPM mission, as well as ground measurements from lightning detection network, can be integrated in the pipeline. This allows us a prompt characterization of the ground meteorological conditions at TGF time which will provide instrument independent trigger validation, fill in a database for subsequent statistical analysis, and eventually, on a longer term perspective, serve as a real time alert system open to the community.

  17. Implementation and performance results of neural network for power quality event detection

    NASA Astrophysics Data System (ADS)

    Huang, Weijian; Tian, Wenzhi

    2008-10-01

    A novel method to detect power quality event in distributed power system combing wavelet network with the improved back-propagation algorithm is presented. The paper tries to explain to design complex supported orthogonal wavelets by compactly supported orthogonal real wavelets, and then explore the extraction of disturbance signal to obtain the feature information, and finally propose several novel wavelet combined information to analyze the disturbance signal, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into wavelet network for power quality disturbance pattern recognition. The power quality disturbance recognition model is established and the improved back-propagation algorithm is used to fulfill the network parameter initialization. By means of choosing enough samples to train the recognition model, the type of disturbance can be obtained when signal representing fault is inputted to the trained network. The results of simulation analysis show that the complex wavelet transform combined with wavelet network are more sensitive to signal singularity, and found to be significant improvement over current methods in real-time detection.

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

    PubMed Central

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

    2010-01-01

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

  19. Seismic Event Identification Using Scanning Detection: A Comparison of Denoising and Classification Methods

    NASA Astrophysics Data System (ADS)

    Rowe, C. A.; MacCarthy, J. K.; Giudicepietro, F.

    2005-12-01

    Automatic detection and classification methods are increasingly important in observatory operations, as the volume and rate of incoming data exceed the capacity of human analysis staff to process the data in near-real-time. We explore the success of scanning detection for similar event identification in a variety of seismic waveform catalogs. Several waveform pre-processing methods are applied to previously recorded events which are scanned through triggered and continuous waveform catalogs to determine the success and false alarm rate for detections of repeating signals. Pre-processing approaches include adaptive, cross-coherency filtering, adaptive, auto-associative neural network filtering, discrete wavelet package decomposition and linear predictive coding as well as suites of standard bandpass filters. Classification / detection methods for the various pre-processed signals are applied to investigate the robustness of the individual and combined approaches. The classifiers as applied to the processed waveforms include dendrogram-based clustering and neural network classifiers. We will present findings for the various combinations of methods as applied to tectonic earthquakes, mine blasts and volcanic seismicity.

  20. Evaluation of real-time PCR detection methods for detecting rice products contaminated by rice genetically modified with a CpTI-KDEL-T-nos transgenic construct.

    PubMed

    Nakamura, Kosuke; Akiyama, Hiroshi; Kawano, Noriaki; Kobayashi, Tomoko; Yoshimatsu, Kayo; Mano, Junichi; Kitta, Kazumi; Ohmori, Kiyomi; Noguchi, Akio; Kondo, Kazunari; Teshima, Reiko

    2013-12-01

    Genetically modified (GM) rice (Oryza sativa) lines, such as insecticidal Kefeng and Kemingdao, have been developed and found unauthorised in processed rice products in many countries. Therefore, qualitative detection methods for the GM rice are required for the GM food regulation. A transgenic construct for expressing cowpea (Vigna unguiculata) trypsin inhibitor (CpTI) was detected in some imported processed rice products contaminated with Kemingdao. The 3' terminal sequence of the identified transgenic construct for expression of CpTI included an endoplasmic reticulum retention signal coding sequence (KDEL) and nopaline synthase terminator (T-nos). The sequence was identical to that in a report on Kefeng. A novel construct-specific real-time polymerase chain reaction (PCR) detection method for detecting the junction region sequence between the CpTI-KDEL and T-nos was developed. The imported processed rice products were evaluated for the contamination of the GM rice using the developed construct-specific real-time PCR methods, and detection frequency was compared with five event-specific detection methods. The construct-specific detection methods detected the GM rice at higher frequency than the event-specific detection methods. Therefore, we propose that the construct-specific detection method is a beneficial tool for screening the contamination of GM rice lines, such as Kefeng, in processed rice products for the GM food regulation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. A Real-Time Satellite-Based Icing Detection System

    NASA Technical Reports Server (NTRS)

    Minnis, Patrick; Smith, William L., Jr.; Nguyen, Louis; Khaiyer, Mandana M.; Spangenberg, Douglas A.; Heck, Patrick W.; Palikonda, Rabindra; Bernstein, Ben C.; McDonough, Frank

    2004-01-01

    Aircraft icing is one of the most dangerous weather conditions for general aviation. Currently, model forecasts and pilot reports (PIREPS) constitute much of the database available to pilots for assessing the icing conditions in a particular area. Such data are often uncertain or sparsely available. Improvements in the temporal and areal coverage of icing diagnoses and prognoses would mark a substantial enhancement of aircraft safety in regions susceptible to heavy supercooled liquid water clouds. The use of 3.9 microns data from meteorological satellite imagers for diagnosing icing conditions has long been recognized (e.g., Ellrod and Nelson, 1996) but to date, no explicit physically based methods have been implemented. Recent advances in cloud detection and cloud property retrievals using operational satellite imagery open the door for real-time objective applications of those satellite datasets for a variety of weather phenomena. Because aircraft icing is related to cloud macro- and microphysical properties (e.g., Cober et al. 1995), it is logical that the cloud properties from satellite data would be useful for diagnosing icing conditions. This paper describes the a prototype realtime system for detecting aircraft icing from space.

  2. A real-time robot arm collision detection system

    NASA Technical Reports Server (NTRS)

    Shaffer, Clifford A.; Herb, Gregory M.

    1990-01-01

    A data structure and update algorithm are presented for a prototype real time collision detection safety system for a multi-robot environment. The data structure is a variant of the octree, which serves as a spatial index. An octree recursively decomposes 3-D space into eight equal cubic octants until each octant meets some decomposition criteria. The octree stores cylspheres (cylinders with spheres on each end) and rectangular solids as primitives (other primitives can easily be added as required). These primitives make up the two seven degrees-of-freedom robot arms and environment modeled by the system. Octree nodes containing more than a predetermined number N of primitives are decomposed. This rule keeps the octree small, as the entire environment for the application can be modeled using a few dozen primitives. As robot arms move, the octree is updated to reflect their changed positions. During most update cycles, any given primitive does not change which octree nodes it is in. Thus, modification to the octree is rarely required. Incidents in which one robot arm comes too close to another arm or an object are reported. Cycle time for interpreting current joint angles, updating the octree, and detecting/reporting imminent collisions averages 30 milliseconds on an Intel 80386 processor running at 20 MHz.

  3. Early and Real-Time Detection of Seasonal Influenza Onset

    PubMed Central

    Marques-Pita, Manuel

    2017-01-01

    Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We present a method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases. PMID:28158192

  4. Early and Real-Time Detection of Seasonal Influenza Onset.

    PubMed

    Won, Miguel; Marques-Pita, Manuel; Louro, Carlota; Gonçalves-Sá, Joana

    2017-02-01

    Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We present a method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.

  5. Towards Real-Time Detection of Freezing of Gait Using Wavelet Transform on Wireless Accelerometer Data.

    PubMed

    Rezvanian, Saba; Lockhart, Thurmon E

    2016-04-02

    Injuries associated with fall incidences continue to pose a significant burden to persons with Parkinson's disease (PD) both in terms of human suffering and economic loss. Freezing of gait (FOG), which is one of the symptoms of PD, is a common cause of falls in this population. Although a significant amount of work has been performed to characterize/detect FOG using both qualitative and quantitative methods, there remains paucity of data regarding real-time detection of FOG, such as the requirements for minimum sensor nodes, sensor placement locations, and appropriate sampling period and update time. Here, the continuous wavelet transform (CWT) is employed to define an index for correctly identifying FOG. Since the CWT method uses both time and frequency components of a waveform in comparison to other methods utilizing only the frequency component, we hypothesized that using this method could lead to a significant improvement in the accuracy of FOG detection. We tested the proposed index on the data of 10 PD patients who experience FOG. Two hundred and thirty seven (237) FOG events were identified by the physiotherapists. The results show that the index could discriminate FOG in the anterior-posterior axis better than other two axes, and is robust to the update time variability. These results suggest that real time detection of FOG may be realized by using CWT of a single shank sensor with window size of 2 s and update time of 1 s (82.1% and 77.1% for the sensitivity and specificity, respectively). Although implicated, future studies should examine the utility of this method in real-time detection of FOG.

  6. Towards Real-Time Detection of Freezing of Gait Using Wavelet Transform on Wireless Accelerometer Data

    PubMed Central

    Rezvanian, Saba; Lockhart, Thurmon E.

    2016-01-01

    Injuries associated with fall incidences continue to pose a significant burden to persons with Parkinson’s disease (PD) both in terms of human suffering and economic loss. Freezing of gait (FOG), which is one of the symptoms of PD, is a common cause of falls in this population. Although a significant amount of work has been performed to characterize/detect FOG using both qualitative and quantitative methods, there remains paucity of data regarding real-time detection of FOG, such as the requirements for minimum sensor nodes, sensor placement locations, and appropriate sampling period and update time. Here, the continuous wavelet transform (CWT) is employed to define an index for correctly identifying FOG. Since the CWT method uses both time and frequency components of a waveform in comparison to other methods utilizing only the frequency component, we hypothesized that using this method could lead to a significant improvement in the accuracy of FOG detection. We tested the proposed index on the data of 10 PD patients who experience FOG. Two hundred and thirty seven (237) FOG events were identified by the physiotherapists. The results show that the index could discriminate FOG in the anterior–posterior axis better than other two axes, and is robust to the update time variability. These results suggest that real time detection of FOG may be realized by using CWT of a single shank sensor with window size of 2 s and update time of 1 s (82.1% and 77.1% for the sensitivity and specificity, respectively). Although implicated, future studies should examine the utility of this method in real-time detection of FOG. PMID:27049389

  7. A Real-Time Web Services Hub to Improve Situation Awareness during Flash Flood Events

    NASA Astrophysics Data System (ADS)

    Salas, F. R.; Liu, F.; Maidment, D. R.; Hodges, B. R.

    2011-12-01

    The central Texas corridor is one of the most flash flood-prone regions in the United States. Over the years, flash floods have resulted in hundreds of flood fatalities and billions of dollars in property damage. In order to mitigate risk to residents and infrastructure during flood events, both citizens and emergency responders need to exhibit proactive behavior instead of reactive. Real-time and forecasted flood information is fairly limited and hard to come by at varying spatial scales. The University of Texas at Austin has collaborated with IBM Research-Austin and ESRI to build a distributed real-time flood information system through a framework that leverages large scale data management and distribution, Open Geospatial Consortium standardized web services, and smart map applications. Within this paradigm, observed precipitation data encoded in WaterML is ingested into HEC-HMS and then delivered to a high performance hydraulic routing software package developed by IBM that utilizes the latest advancements in VLSI design, numerical linear algebra and numerical integration techniques on contemporary multicore architecture to solve fully dynamic Saint Venant equations at both small and large scales. In this paper we present a real-time flood inundation map application that in conjunction with a web services Hub, seamlessly integrates hydrologic information available through both public and private data services, model services and mapping services. As a case study for this project, we demonstrate how this system has been implemented in the City of Austin, Texas.

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

    NASA Astrophysics Data System (ADS)

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

    2014-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Kanou, Naoyuki; Sakuma, Kenji; Nakashima, Kenji

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

  10. Real-time detection of anomalous geoelectric changes

    NASA Astrophysics Data System (ADS)

    Mori, T.; Ozima, M.; Takayama, H.

    1993-04-01

    Aiming at an accurate and quick detection of anomalous changes of the electric field so that we can detect tectonic signals, we developed observation methods for use both on land and on the ocean floor. With these observation methods, that is, observation on land using electrodes and the underground cables of the Nippon Telegraph and Telephone Company, and on the ocean floor using the power-feeding arrangement of the ocean-bottom seismograph of the Japan Meteorological Agency, we can obtain more reliable data than those from ordinary observation methods. Using these data, we further developed two techniques of analysis in real-time which are based on a statistical model, eliminating components induced by the variations of the geomagnetic field and/or the movement of the seawater. For both cases, the geomagnetic field at the Kakioka Magnetic Observatory and/or the seawater level data observed on the ocean floor were used as associated data. The predominant induced component in the variation of the geoelectric field is almost completely separated in the cases of the geoelectric field in the Mito region and on the ocean floor. Owing to the characteristic periodicity of the monopolar variation of noise in the Numazu region, in spite of the large amplitude of the noise (mainly due to the leak-current of the d.c. electric trains) in the original data from the Numazu region, the noises were considerably separated, with these methods, into the induced component part or tidal component part. With these methods, consequently, the detectability of anomalous changes was improved about ten times and about five times in the cases of the geoelectric field in the Mito region and on the ocean floor, and in the case of the geoelectric field in the Numazu region, respectively.

  11. BioSense: implementation of a National Early Event Detection and Situational Awareness System.

    PubMed

    Bradley, Colleen A; Rolka, H; Walker, D; Loonsk, J

    2005-08-26

    BioSense is a CDC initiative to support enhanced early detection, quantification, and localization of possible biologic terrorism attacks and other events of public health concern on a national level. The goals of the BioSense initiative are to advance early detection by providing the standards, infrastructure, and data acquisition for near real-time reporting, analytic evaluation and implementation, and early event detection support for state and local public health officials. BioSense collects and analyzes Department of Defense and Department of Veterans Affairs ambulatory clinical diagnoses and procedures and Laboratory Corporation of America laboratory-test orders. The application summarizes and presents analytical results and data visualizations by source, day, and syndrome for each ZIP code, state, and metropolitan area through maps, graphs, and tables. An initial proof of a concept evaluation project was conducted before the system was made available to state and local users in April 2004. User recruitment involved identifying and training BioSense administrators and users from state and local health departments. User support has been an essential component of the implementation and enhancement process. CDC initiated the BioIntelligence Center (BIC) in June 2004 to conduct internal monitoring of BioSense national data daily. BIC staff have supported state and local system monitoring, conducted data anomaly inquiries, and communicated with state and local public health officials. Substantial investments will be made in providing regional, state, and local data for early event detection and situational awareness, test beds for data and algorithm evaluation, detection algorithm development, and data management technologies, while maintaining the focus on state and local public health needs.

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

    DOEpatents

    Odell, Daniel M. C.

    1994-01-01

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

  13. Real-Time Detection of Dust Devils from Pressure Readings

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri

    2009-01-01

    A method for real-time detection of dust devils at a given location is based on identifying the abrupt, temporary decreases in atmospheric pressure that are characteristic of dust devils as they travel through that location. The method was conceived for use in a study of dust devils on the Martian surface, where bandwidth limitations encourage the transmission of only those blocks of data that are most likely to contain information about features of interest, such as dust devils. The method, which is a form of intelligent data compression, could readily be adapted to use for the same purpose in scientific investigation of dust devils on Earth. In this method, the readings of an atmospheric- pressure sensor are repeatedly digitized, recorded, and processed by an algorithm that looks for extreme deviations from a continually updated model of the current pressure environment. The question in formulating the algorithm is how to model current normal observations and what minimum magnitude deviation can be considered sufficiently anomalous as to indicate the presence of a dust devil. There is no single, simple answer to this question: any answer necessarily entails a compromise between false detections and misses. For the original Mars application, the answer was sought through analysis of sliding time windows of digitized pressure readings. Windows of 5-, 10-, and 15-minute durations were considered. The windows were advanced in increments of 30 seconds. Increments of other sizes can also be used, but computational cost increases as the increment decreases and analysis is performed more frequently. Pressure models were defined using a polynomial fit to the data within the windows. For example, the figure depicts pressure readings from a 10-minute window wherein the model was defined by a third-degree polynomial fit to the readings and dust devils were identified as negative deviations larger than both 3 standard deviations (from the mean) and 0.05 mbar in magnitude. An

  14. Ambulatory REACT: real-time seizure detection with a DSP microprocessor.

    PubMed

    McEvoy, Robert P; Faul, Stephen; Marnane, William P

    2010-01-01

    REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis. Furthermore, the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages. This hardware profile is used to select a reduced, platform-aware feature set, in order to evaluate the seizure classification accuracy of a lower-complexity, lower-power REACT system.

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

    PubMed

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

    2014-04-01

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

  16. Balloon-Borne Infrasound Detection of Energetic Bolide Events

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  17. APNEA list mode data acquisition and real-time event processing

    SciTech Connect

    Hogle, R.A.; Miller, P.; Bramblett, R.L.

    1997-11-01

    The LMSC Active Passive Neutron Examinations and Assay (APNEA) Data Logger is a VME-based data acquisition system using commercial-off-the-shelf hardware with the application-specific software. It receives TTL inputs from eighty-eight {sup 3}He detector tubes and eight timing signals. Two data sets are generated concurrently for each acquisition session: (1) List Mode recording of all detector and timing signals, timestamped to 3 microsecond resolution; (2) Event Accumulations generated in real-time by counting events into short (tens of microseconds) and long (seconds) time bins following repetitive triggers. List Mode data sets can be post-processed to: (1) determine the optimum time bins for TRU assay of waste drums, (2) analyze a given data set in several ways to match different assay requirements and conditions and (3) confirm assay results by examining details of the raw data. Data Logger events are processed and timestamped by an array of 15 TMS320C40 DSPs and delivered to an embedded controller (PowerPC604) for interim disk storage. Three acquisition modes, corresponding to different trigger sources are provided. A standard network interface to a remote host system (Windows NT or SunOS) provides for system control, status, and transfer of previously acquired data. 6 figs.

  18. A real time QRS detection using delay-coordinate mapping for the microcontroller implementation.

    PubMed

    Lee, Jeong-Whan; Kim, Kyeong-Seop; Lee, Bongsoo; Lee, Byungchae; Lee, Myoung-Ho

    2002-01-01

    In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients' ECG and R-R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes.

  19. Solar Flare Detection With SWIFT and Real-time GONG H-alpha Images

    NASA Astrophysics Data System (ADS)

    Henney, Carl John; MacKenzie, D.; Hill, F.; Mills, B.; Pietrzak, J.

    2011-05-01

    The Air Force Weather Agency (AFWA) has begun the process of upgrading the Solar Observing Optical Network (SOON) with an Improved-SOON (ISOON). During the interim period, AFWA is supporting the addition and operation of a solar H-alpha (Hydrogen-alpha, 656.3 nm) full-disk image network utilizing the light feed from the National Solar Observatory's existing GONG (Global Oscillation Network Group) instruments. The H-alpha instruments at the GONG sites have been in operation collectively since the beginning of 2011, providing one to three H-alpha images per minute. Cross-site comparison and calibration of flare detection has begun using an image analysis tool called SWIFT (SWFL/ISOON Flare-cast Tool). SWIFT is a unique and versatile software package, designed originally for ISOON data, that has been attuned to ingest and display GONG H-alpha images in real-time. The SWIFT software allows a user to detect and analyze optical flares from solar active regions. The SWIFT software is in the process of being beta-tested at AFWA in collaboration with the Space Weather Center of Excellence's SWFL (Space Weather Forecasting Laboratory) to better forecast space weather events. Solar flares are of great interest to the Air Force Research Laboratory's Space Vehicles Directorate because they can trigger energetic particle events or coronal mass ejection events that impact the Earth's magnetosphere creating geomagnetic storms. Such events can result in satellite charging damage, increased satellite drag, power grid disruption, navigation system anomalies, and communication fadeouts. An overview of SWIFT, along with preliminary flare detection comparisons between GONG sites and the SOON flare reports, will be presented.

  20. DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals

    PubMed Central

    Lawhern, Vernon; Hairston, W. David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration. PMID:23638169

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

    NASA Astrophysics Data System (ADS)

    Arbab-Zavar, Banafshe; Sabeur, Zoheir

    2013-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  3. An evaluation of generalized likelihood Ratio Outlier Detection to identification of seismic events in Western China

    SciTech Connect

    Taylor, S.R.; Hartse, H.E.

    1996-09-24

    The Generalized Likelihood Ratio Outlier Detection Technique for seismic event identification is evaluated using synthetic test data and frequency-dependent P{sub g}/L{sub g} measurements from western China. For most seismic stations that are to be part of the proposed International Monitoring System for the Comprehensive Test Ban Treaty, there will be few or no nuclear explosions in the magnitude range of interest (e.g. M{sub b} < 4) on which to base an event-identification system using traditional classification techniques. Outlier detection is a reasonable alternative approach to the seismic discrimination problem when no calibration explosions are available. Distance-corrected P{sub g}/L{sub g} data in seven different frequency bands ranging from 0.5 to 8 Hz from the Chinese Digital Seismic Station WMQ are used to evaluate the technique. The data are collected from 157 known earthquakes, 215 unknown events (presumed earthquakes and possibly some industrial explosions), and 18 known nuclear explosions (1 from the Chinese Lop Nor test site and 17 from the East Kazakh test site). A feature selection technique is used to find the best combination of discriminants to use for outlier detection. Good discrimination performance is found by combining a low-frequency (0.5 to 1 Hz) P{sub g}/L{sub g} ratio with high-frequency ratios (e.g. 2 to 4 and 4 to 8 Hz). Although the low-frequency ratio does not discriminate between earthquakes and nuclear explosions well by itself, it can be effectively combined with the high-frequency discriminants. Based on the tests with real and synthetic data, the outlier detection technique appears to be an effective approach to seismic monitoring in uncalibrated regions.

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

    NASA Astrophysics Data System (ADS)

    Hammer, Conny; Ohrnberger, Matthias

    2010-05-01

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

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

    SciTech Connect

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

    2014-12-10

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

  6. AQUA: a very fast automatic reduction pipeline for near real-time GRBs early afterglow detection

    NASA Astrophysics Data System (ADS)

    Di Paola, Andrea; Antonelli, Lucio A.; Testa, Vincenzo; Patria, Giorgio

    2002-12-01

    AQUA (Automated QUick Analysis) is the fast reduction pipeline of the Near Infra-Red (NIR) images obtained by the REM telescope. REM (Rapid Eye Mount) is a robotic NIR/Optical 60cm telescope for fast detection of early afterglow of Gamma Ray Bursts (GRB). NIR observations of GRBs early afterglow are of crucial importance for GRBs science, revealing even optical obscured or high redshift events. On the technical side, they pose a series of problems: luminous background, bright sources (as the counterparts should be observed few seconds after the satellite trigger) and fast detection force high rate images acquisition. Even if the observational strategy will change during the same event observation depending on the counterpart characteristics, we will start with 1 second exposures at the fastest possible rate. The main guideline in the AQUA pipeline development is to allow such a data rate along all the night with nearly real-time results delivery. AQUA will start from the raw images and will deliver an alert with coordinates, photometry and colors to larger telescopes to allow prompt spectroscopic and polarimetric observations. Very fast processing for the raw 512×512 32bit images and variable sources detection with both sources catalogs and images comparison have been implemented to obtain a processing speed of at least 1 image/sec. AQUA is based on ANSI-C code optimized to run on a dual Athlon Linux PC with careful MMX and SSE instructions utilization.

  7. Modeling Real-Time Coordination of Distributed Expertise and Event Response in NASA Mission Control Center Operations

    NASA Astrophysics Data System (ADS)

    Onken, Jeffrey

    This dissertation introduces a multidisciplinary framework for the enabling of future research and analysis of alternatives for control centers for real-time operations of safety-critical systems. The multidisciplinary framework integrates functional and computational models that describe the dynamics in fundamental concepts of previously disparate engineering and psychology research disciplines, such as group performance and processes, supervisory control, situation awareness, events and delays, and expertise. The application in this dissertation is the real-time operations within the NASA Mission Control Center in Houston, TX. This dissertation operationalizes the framework into a model and simulation, which simulates the functional and computational models in the framework according to user-configured scenarios for a NASA human-spaceflight mission. The model and simulation generates data according to the effectiveness of the mission-control team in supporting the completion of mission objectives and detecting, isolating, and recovering from anomalies. Accompanying the multidisciplinary framework is a proof of concept, which demonstrates the feasibility of such a framework. The proof of concept demonstrates that variability occurs where expected based on the models. The proof of concept also demonstrates that the data generated from the model and simulation is useful for analyzing and comparing MCC configuration alternatives because an investigator can give a diverse set of scenarios to the simulation and the output compared in detail to inform decisions about the effect of MCC configurations on mission operations performance.

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

    NASA Astrophysics Data System (ADS)

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

    2003-03-01

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

  9. Real-time measurements of spontaneous breathers and rogue wave events in optical fibre modulation instability.

    PubMed

    Närhi, Mikko; Wetzel, Benjamin; Billet, Cyril; Toenger, Shanti; Sylvestre, Thibaut; Merolla, Jean-Marc; Morandotti, Roberto; Dias, Frederic; Genty, Goëry; Dudley, John M

    2016-12-19

    Modulation instability is a fundamental process of nonlinear science, leading to the unstable breakup of a constant amplitude solution of a physical system. There has been particular interest in studying modulation instability in the cubic nonlinear Schrödinger equation, a generic model for a host of nonlinear systems including superfluids, fibre optics, plasmas and Bose-Einstein condensates. Modulation instability is also a significant area of study in the context of understanding the emergence of high amplitude events that satisfy rogue wave statistical criteria. Here, exploiting advances in ultrafast optical metrology, we perform real-time measurements in an optical fibre system of the unstable breakup of a continuous wave field, simultaneously characterizing emergent modulation instability breather pulses and their associated statistics. Our results allow quantitative comparison between experiment, modelling and theory, and are expected to open new perspectives on studies of instability dynamics in physics.

  10. Prediction of solar energetic particle event histories using real-time particle and solar wind measurements

    NASA Technical Reports Server (NTRS)

    Roelof, E. C.; Gold, R. E.

    1978-01-01

    The comparatively well-ordered magnetic structure in the solar corona during the decline of Solar Cycle 20 revealed a characteristic dependence of solar energetic particle injection upon heliographic longitude. When analyzed using solar wind mapping of the large scale interplanetary magnetic field line connection from the corona to the Earth, particle fluxes display an approximately exponential dependence on heliographic longitude. Since variations in the solar wind velocity (and hence the coronal connection longitude) can severely distort the simple coronal injection profile, the use of real-time solar wind velocity measurements can be of great aid in predicting the decay of solar particle events. Although such exponential injection profiles are commonplace during 1973-1975, they have also been identified earlier in Solar Cycle 20, and hence this structure may be present during the rise and maximum of the cycle, but somewhat obscured by greater temporal variations in particle injection.

  11. Real-time measurements of spontaneous breathers and rogue wave events in optical fibre modulation instability

    NASA Astrophysics Data System (ADS)

    Närhi, Mikko; Wetzel, Benjamin; Billet, Cyril; Toenger, Shanti; Sylvestre, Thibaut; Merolla, Jean-Marc; Morandotti, Roberto; Dias, Frederic; Genty, Goëry; Dudley, John M.

    2016-12-01

    Modulation instability is a fundamental process of nonlinear science, leading to the unstable breakup of a constant amplitude solution of a physical system. There has been particular interest in studying modulation instability in the cubic nonlinear Schrödinger equation, a generic model for a host of nonlinear systems including superfluids, fibre optics, plasmas and Bose-Einstein condensates. Modulation instability is also a significant area of study in the context of understanding the emergence of high amplitude events that satisfy rogue wave statistical criteria. Here, exploiting advances in ultrafast optical metrology, we perform real-time measurements in an optical fibre system of the unstable breakup of a continuous wave field, simultaneously characterizing emergent modulation instability breather pulses and their associated statistics. Our results allow quantitative comparison between experiment, modelling and theory, and are expected to open new perspectives on studies of instability dynamics in physics.

  12. Real-time measurements of spontaneous breathers and rogue wave events in optical fibre modulation instability

    PubMed Central

    Närhi, Mikko; Wetzel, Benjamin; Billet, Cyril; Toenger, Shanti; Sylvestre, Thibaut; Merolla, Jean-Marc; Morandotti, Roberto; Dias, Frederic; Genty, Goëry; Dudley, John M.

    2016-01-01

    Modulation instability is a fundamental process of nonlinear science, leading to the unstable breakup of a constant amplitude solution of a physical system. There has been particular interest in studying modulation instability in the cubic nonlinear Schrödinger equation, a generic model for a host of nonlinear systems including superfluids, fibre optics, plasmas and Bose–Einstein condensates. Modulation instability is also a significant area of study in the context of understanding the emergence of high amplitude events that satisfy rogue wave statistical criteria. Here, exploiting advances in ultrafast optical metrology, we perform real-time measurements in an optical fibre system of the unstable breakup of a continuous wave field, simultaneously characterizing emergent modulation instability breather pulses and their associated statistics. Our results allow quantitative comparison between experiment, modelling and theory, and are expected to open new perspectives on studies of instability dynamics in physics. PMID:27991513

  13. Adverse drug events and medication errors: detection and classification methods.

    PubMed

    Morimoto, T; Gandhi, T K; Seger, A C; Hsieh, T C; Bates, D W

    2004-08-01

    Investigating the incidence, type, and preventability of adverse drug events (ADEs) and medication errors is crucial to improving the quality of health care delivery. ADEs, potential ADEs, and medication errors can be collected by extraction from practice data, solicitation of incidents from health professionals, and patient surveys. Practice data include charts, laboratory, prescription data, and administrative databases, and can be reviewed manually or screened by computer systems to identify signals. Research nurses, pharmacists, or research assistants review these signals, and those that are likely to represent an ADE or medication error are presented to reviewers who independently categorize them into ADEs, potential ADEs, medication errors, or exclusions. These incidents are also classified according to preventability, ameliorability, disability, severity, stage, and responsible person. These classifications, as well as the initial selection of incidents, have been evaluated for agreement between reviewers and the level of agreement found ranged from satisfactory to excellent (kappa = 0.32-0.98). The method of ADE and medication error detection and classification described is feasible and has good reliability. It can be used in various clinical settings to measure and improve medication safety.

  14. Visual traffic surveillance framework: classification to event detection

    NASA Astrophysics Data System (ADS)

    Ambardekar, Amol; Nicolescu, Mircea; Bebis, George; Nicolescu, Monica

    2013-10-01

    Visual traffic surveillance using computer vision techniques can be noninvasive, automated, and cost effective. Traffic surveillance systems with the ability to detect, count, and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. However, vehicle classification poses a difficult problem as vehicles have high intraclass variation and relatively low interclass variation. Five different object recognition techniques are investigated: principal component analysis (PCA)+difference from vehicle space, PCA+difference in vehicle space, PCA+support vector machine, linear discriminant analysis, and constellation-based modeling applied to the problem of vehicle classification. Three of the techniques that performed well were incorporated into a unified traffic surveillance system for online classification of vehicles, which uses tracking results to improve the classification accuracy. To evaluate the accuracy of the system, 31 min of traffic video containing multilane traffic intersection was processed. It was possible to achieve classification accuracy as high as 90.49% while classifying correctly tracked vehicles into four classes: cars, SUVs/vans, pickup trucks, and buses/semis. While processing a video, our system also recorded important traffic parameters such as the appearance, speed, trajectory of a vehicle, etc. This information was later used in a search assistant tool to find interesting traffic events.

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

    PubMed

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

    2015-03-09

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

  16. Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

    NASA Astrophysics Data System (ADS)

    Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

    2015-04-01

    Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations

  17. Getting to know your neighbors: unsupervised learning of topography from real-world, event-based input.

    PubMed

    Boerlin, Martin; Delbruck, Tobi; Eng, Kynan

    2009-01-01

    Biological neural systems must grow their own connections and maintain topological relations between elements that are related to the sensory input surface. Artificial systems have traditionally prewired such maps, but the sensor arrangement is not always known and can be expensive to specify before run time. Here we present a method for learning and updating topographic maps in systems comprising modular, event-based elements. Using an unsupervised neural spike-timing-based learning rule combined with Hebbian learning, our algorithm uses the spatiotemporal coherence of the external world to train its network. It improves on existing algorithms by not assuming a known topography of the target map and includes a novel method for automatically detecting edge elements. We show how, for stimuli that are small relative to the sensor resolution, the temporal learning window parameters can be determined without using any user-specified constants. For stimuli that are larger relative to the sensor resolution, we provide a parameter extraction method that generally outperforms the small-stimulus method but requires one user-specified constant. The algorithm was tested on real data from a 64 x 64-pixel section of an event-based temporal contrast silicon retina and a 360-tile tactile luminous floor. It learned 95.8% of the correct neighborhood relations for the silicon retina within about 400 seconds of real-world input from a driving scene and 98.1% correct for the sensory floor after about 160 minutes of human pedestrian traffic. Residual errors occurred in regions receiving little or ambiguous input, and the learned topological representations were able to update automatically in response to simulated damage. Our algorithm has applications in the design of modular autonomous systems in which the interfaces between components are learned during operation rather than at design time.

  18. Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News.

    PubMed

    Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert

    2016-01-01

    On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.

  19. Signal detection to identify serious adverse events (neuropsychiatric events) in travelers taking mefloquine for chemoprophylaxis of malaria

    PubMed Central

    Naing, Cho; Aung, Kyan; Ahmed, Syed Imran; Mak, Joon Wah

    2012-01-01

    Background For all medications, there is a trade-off between benefits and potential for harm. It is important for patient safety to detect drug-event combinations and analyze by appropriate statistical methods. Mefloquine is used as chemoprophylaxis for travelers going to regions with known chloroquine-resistant Plasmodium falciparum malaria. As such, there is a concern about serious adverse events associated with mefloquine chemoprophylaxis. The objective of the present study was to assess whether any signal would be detected for the serious adverse events of mefloquine, based on data in clinicoepidemiological studies. Materials and methods We extracted data on adverse events related to mefloquine chemoprophylaxis from the two published datasets. Disproportionality reporting of adverse events such as neuropsychiatric events and other adverse events was presented in the 2 × 2 contingency table. Reporting odds ratio and corresponding 95% confidence interval [CI] data-mining algorithm was applied for the signal detection. The safety signals are considered significant when the ROR estimates and the lower limits of the corresponding 95% CI are ≥2. Results Two datasets addressing adverse events of mefloquine chemoprophylaxis (one from a published article and one from a Cochrane systematic review) were included for analyses. Reporting odds ratio 1.58, 95% CI: 1.49–1.68 based on published data in the selected article, and 1.195, 95% CI: 0.94–1.44 based on data in the selected Cochrane review. Overall, in both datasets, the reporting odds ratio values of lower 95% CI were less than 2. Conclusion Based on available data, findings suggested that signals for serious adverse events pertinent to neuropsychiatric event were not detected for mefloquine. Further studies are needed to substantiate this. PMID:22936859

  20. Optimal detection of burst events in gravitational wave interferometric observatories

    NASA Astrophysics Data System (ADS)

    Viceré, Andrea

    2002-09-01

    We consider the problem of detecting a burst signal of unknown shape in the data from gravitational wave interferometric detectors. We introduce a statistic which generalizes the excess power statistic proposed first by Flanagan and Hughes, and then extended by Anderson et al. to the multiple detector case. The statistic that we propose is shown to be optimal for an arbitrary noise spectral characteristic, under the two hypotheses that the noise is Gaussian, albeit colored, and that the prior for the signal is uniform. The statistic derivation is based on the assumption that a signal affects only N|| samples in the data stream, but that no other information is a priori available, and that the value of the signal at each sample can be arbitrary. This is the main difference from previous works, where different assumptions were made, such as a signal distribution uniform with respect to the metric induced by the (inverse) noise correlation matrix. The two choices are equivalent if the noise is white, and in that limit the two statistics do indeed coincide. In the general case, we believe that the statistic we propose may be more appropriate, because it does not reflect the characteristics of the noise affecting the detector on the supposed distribution of the gravitational wave signal. Moreover, we show that the proposed statistic can be easily implemented in its exact form, combining standard time-series analysis tools which can be efficiently implemented. We generalize this version of an excess power statistic to the multiple detector case, considering first a noise uncorrelated among the different instruments, and then including the effect of correlated noise. We discuss exact and approximate forms of the statistic; the choice depends on the characteristics of the noise and on the assumed length of the burst event. As an example, we show the sensitivity of the network of interferometers to a δ-function burst.

  1. Large Time Projection Chambers for Rare Event Detection

    SciTech Connect

    Heffner, M

    2009-11-03

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed Central

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

    2013-01-01

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

  4. Development of a Detection Algorithm for Use with Reflectance-Based, Real-Time Chemical Sensing

    PubMed Central

    Malanoski, Anthony P.; Johnson, Brandy J.; Erickson, Jeffrey S.; Stenger, David A.

    2016-01-01

    Here, we describe our efforts focused on development of an algorithm for identification of detection events in a real-time sensing application relying on reporting of color values using commercially available color sensing chips. The effort focuses on the identification of event occurrence, rather than target identification, and utilizes approaches suitable to onboard device incorporation to facilitate portable and autonomous use. The described algorithm first excludes electronic noise generated by the sensor system and determines response thresholds. This automatic adjustment provides the potential for use with device variations as well as accommodating differing indicator behaviors. Multiple signal channels (RGB) as well as multiple indicator array elements are combined for reporting of an event with a minimum of false responses. While the method reported was developed for use with paper-supported porphyrin and metalloporphyrin indicators, it should be equally applicable to other colorimetric indicators. Depending on device configurations, receiver operating characteristic (ROC) sensitivities of 1 could be obtained with specificities of 0.87 (threshold 160 ppb, ethanol). PMID:27854335

  5. Transient Evoked Potential in a Critical Event Detection Task.

    DTIC Science & Technology

    1984-02-01

    Vigilance and Discrimination: A Reassessment," Science, 164:326-328, 1969. 38. Fabiani , Monica and others. "Individual Differences in the von Restorff...implies that events which elicit a P300 are more likely to be remembered than events which do not 2-23 .... . . ... invoke a P300 (15:507-510). Fabiani

  6. Real-time detection, location, and characterization of rockslides using broadband regional seismic networks

    NASA Astrophysics Data System (ADS)

    Manconi, Andrea; Picozzi, Matteo; Coviello, Velio; De Santis, Francesca; Elia, Luca

    2016-07-01

    We propose a new real-time approach to detect, locate, and estimate the volume of rockslides by analyzing waveforms acquired from broadband regional seismic networks. The identification of signals generated by rockslides from other sources, such as natural and/or induced earthquakes, is accomplished by exploiting the ratio between local magnitudes (ML) and duration magnitudes (MD). We found that signals associated with rockslides have ML/MD < 0.8, while for earthquakes ML/MD ≅ 1. In addition, we derived an empirical relationship between MD and rockslide volumes, obtaining a preliminary characterization of rockslide volume within seconds after their occurrence. The key points of this study are presented by testing the hypothesis on a recent rockslide event that occurred in northern Italy. We discuss also the potential evolution of the methodology for early warning and/or rapid response purposes.

  7. Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News

    PubMed Central

    Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert

    2016-01-01

    On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event’s reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event’s lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience. PMID:27992437

  8. Detection and identification of genetically modified EE-1 brinjal (Solanum melongena) by single, multiplex and SYBR® real-time PCR.

    PubMed

    Ballari, Rajashekhar V; Martin, Asha; Gowda, Lalitha R

    2013-01-01

    Brinjal is an important vegetable crop. Major crop loss of brinjal is due to insect attack. Insect-resistant EE-1 brinjal has been developed and is awaiting approval for commercial release. Consumer health concerns and implementation of international labelling legislation demand reliable analytical detection methods for genetically modified (GM) varieties. End-point and real-time polymerase chain reaction (PCR) methods were used to detect EE-1 brinjal. In end-point PCR, primer pairs specific to 35S CaMV promoter, NOS terminator and nptII gene common to other GM crops were used. Based on the revealed 3' transgene integration sequence, primers specific for the event EE-1 brinjal were designed. These primers were used for end-point single, multiplex and SYBR-based real-time PCR. End-point single PCR showed that the designed primers were highly specific to event EE-1 with a sensitivity of 20 pg of genomic DNA, corresponding to 20 copies of haploid EE-1 brinjal genomic DNA. The limits of detection and quantification for SYBR-based real-time PCR assay were 10 and 100 copies respectively. The prior development of detection methods for this important vegetable crop will facilitate compliance with any forthcoming labelling regulations. Copyright © 2012 Society of Chemical Industry.

  9. wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

    NASA Astrophysics Data System (ADS)

    Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

    2016-05-01

    wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

  10. Real-time quantitative PCR detection of genetically modified Maximizer maize and Roundup Ready soybean in some representative foods.

    PubMed

    Vaïtilingom, M; Pijnenburg, H; Gendre, F; Brignon, P

    1999-12-01

    A fast and quantitative method was developed to detect transgenic "Maximizer" maize "event 176" (Novartis) and "Roundup Ready" soybean (Monsanto) in food by real-time quantitative PCR. The use of the ABI Prism 7700 sequence detection system allowed the determination of the amplified product accumulation through a fluorogenic probe (TaqMan). Fluorescent dyes were chosen in such a way as to coamplify total and transgenic DNA in the same tube. Using real-time quantitative PCR, 2 pg of transgenic or total DNA per gram of starting sample was detected in 3 h after DNA extraction and the relative amounts of "Maximizer" maize and "Roundup Ready" soybean in some representative food products were quantified.

  11. Integrated sorting, concentration and real time PCR based detection system for sensitive detection of microorganisms

    PubMed Central

    Nayak, Monalisha; Singh, Deepak; Singh, Himanshu; Kant, Rishi; Gupta, Ankur; Pandey, Shashank Shekhar; Mandal, Swarnasri; Ramanathan, Gurunath; Bhattacharya, Shantanu

    2013-01-01

    The extremely low limit of detection (LOD) posed by global food and water safety standards necessitates the need to perform a rapid process of integrated detection with high specificity, sensitivity and repeatability. The work reported in this article shows a microchip platform which carries out an ensemble of protocols which are otherwise carried in a molecular biology laboratory to achieve the global safety standards. The various steps in the microchip include pre-concentration of specific microorganisms from samples and a highly specific real time molecular identification utilizing a q-PCR process. The microchip process utilizes a high sensitivity antibody based recognition and an electric field mediated capture enabling an overall low LOD. The whole process of counting, sorting and molecular identification is performed in less than 4 hours for highly dilute samples. PMID:24253282

  12. Detection Method of Three Events to Window and Key Using Light Sensor for Crime Prevention

    NASA Astrophysics Data System (ADS)

    Yamawaki, Akira; Katakami, Takayuki; Kitazono, Yuhki; Serikawa, Seiichi

    The three events to the window and the key occurring when a thief attempts to intrude into the house are detected by the different sensors conventionally. This paper proposes a method detecting the three events by using the simple light-sensor consisting of an infrared LED and a photodiode. In the experiments, the light sensor shows the different tendencies that can detect each event. This fact indicates that our proposal can realize a sensor module more efficiently instead of using different sensors.

  13. Improving Infrasound Signal Detection and Event Location in the Western US Using Atmospheric Modeling

    NASA Astrophysics Data System (ADS)

    Dannemann, F. K.; Park, J.; Marcillo, O. E.; Blom, P. S.; Stump, B. W.; Hayward, C.

    2016-12-01

    Data from five infrasound arrays in the western US jointly operated by University of Utah Seismograph Station and Southern Methodist University are used to test a database-centric processing pipeline, InfraPy, for automated event detection, association and location. Infrasonic array data from a one-year time period (January 1 2012 to December 31 2012) are used. This study focuses on the identification and location of 53 ground-truth verified events produced from near surface military explosions at the Utah Test and Training Range (UTTR). Signals are detected using an adaptive F-detector, which accounts for correlated and uncorrelated time-varying noise in order to reduce false detections due to the presence of coherent noise. Variations in detection azimuth and correlation are found to be consistent with seasonal changes in atmospheric winds. The Bayesian infrasonic source location (BISL) method is used to produce source location and time credibility contours based on posterior probability density functions. Updates to the previous BISL methodology include the application of celerity range and azimuth deviation distributions in order to accurately account for the spatial and temporal variability of infrasound propagation through the atmosphere. These priors are estimated by ray tracing through Ground-to-Space (G2S) atmospheric models as a function of season and time of day using historic atmospheric characterizations from 2007 to 2013. Out of the 53 events, 31 are successfully located using the InfraPy pipeline. Confidence contour areas for maximum a posteriori event locations produce error estimates which are reduced a maximum of 98% and an average of 25% from location estimates utilizing a simple time independent uniform atmosphere. We compare real-time ray tracing results with the statistical atmospheric priors used in this study to examine large time differences between known origin times and estimated origin times that might be due to the misidentification of

  14. Migration Based Event Detection and Automatic P- and S-Phase Picking in Hengill, Southwest Iceland

    NASA Astrophysics Data System (ADS)

    Wagner, F.; Tryggvason, A.; Gudmundsson, O.; Roberts, R.; Bodvarsson, R.; Fehler, M.

    2015-12-01

    Automatic detection of seismic events is a complicated process. Common procedures depend on the detection of seismic phases (e.g. P and S) in single trace analyses and their correct association with locatable point sources. The event detection threshold is thus directly related to the single trace detection threshold. Highly sensitive phase detectors detect low signal-to-noise ratio (S/N) phases but also produce a low percentage of locatable events. Short inter-event times of only a few seconds, which is not uncommon during seismic or volcanic crises, is a complication to any event association algorithm. We present an event detection algorithm based on seismic migration of trace attributes into an a-priori three-dimensional (3D) velocity model. We evaluate its capacity as automatic detector compared to conventional methods. Detecting events using seismic migration removes the need for phase association. The event detector runs on a stack of time shifted traces, which increases S/N and thus allows for a low detection threshold. Detected events come with an origin time and a location estimate, enabling a focused trace analysis, including P- and S-phase recognition, to discard false detections and build a basis for accurate automatic phase picking. We apply the migration based detection algorithm to data from a semi-permanent seismic network at Hengill, an active volcanic region with several geothermal production sites in southwest Iceland. The network includes 26 stations with inter-station distances down to 5 km. Results show a high success rate compared to the manually picked catalogue (up to 90% detected). New detections, that were missed by the standard detection routine, show a generally good ratio of true to false alarms, i.e. most of the new events are locatable.

  15. REAL-TIME PCR METHOD TO DETECT ENTEROCOCCUS FAECALIS IN WATER

    EPA Science Inventory

    A 16S rDNA real-time PCR method was developed to detect Enterococcus faecalis in water samples. The dynamic range for cell detection spanned five logs and the detection limit was determined to be 6 cfu/reaction. The assay was capable of detecting E. faecalis cells added to biof...

  16. Toward real-time particle tracking using an event-based dynamic vision sensor

    NASA Astrophysics Data System (ADS)

    Drazen, David; Lichtsteiner, Patrick; Häfliger, Philipp; Delbrück, Tobi; Jensen, Atle

    2011-11-01

    Optically based measurements in high Reynolds number fluid flows often require high-speed imaging techniques. These cameras typically record data internally and thus are limited by the amount of onboard memory available. A novel camera technology for use in particle tracking velocimetry is presented in this paper. This technology consists of a dynamic vision sensor in which pixels operate in parallel, transmitting asynchronous events only when relative changes in intensity of approximately 10% are encountered with a temporal resolution of 1 μs. This results in a recording system whose data storage and bandwidth requirements are about 100 times smaller than a typical high-speed image sensor. Post-processing times of data collected from this sensor also increase to about 10 times faster than real time. We present a proof-of-concept study comparing this novel sensor with a high-speed CMOS camera capable of recording up to 2,000 fps at 1,024 × 1,024 pixels. Comparisons are made in the ability of each system to track dense (ρ >1 g/cm3) particles in a solid-liquid two-phase pipe flow. Reynolds numbers based on the bulk velocity and pipe diameter up to 100,000 are investigated.

  17. Does psychophysiological predictive anticipatory activity predict real or future probable events?

    PubMed

    Tressoldi, Patrizio E; Martinelli, Massimiliano; Semenzato, Luca; Gonella, Alessandro

    2015-01-01

    The possibility of predicting random future events before any sensory clues by using human physiology as a dependent variable has been supported by the meta-analysis of Moss-bridge et al. (2012)(1) and recent findings by Tressoldi et al. (2011 and 2013)(2,3) and Mossbridge et al. (2014)(4) defined this phenomenon predictive anticipatory activity (PAA). From a theoretical point of view, one interesting question is whether PAA is related to the effective, real future presentation of these stimuli or whether it is related only to the probability of their presentation. This hypothesis was tested with four experiments, two using heart rate and two using pupil dilation as dependent variables. In all four experiments, both a neutral stimulus and a potentially threatening stimulus were predicted 7-10% above chance, independently from whether the predicted threatening stimulus was presented or not. These findings are discussed with reference to the "grandfather paradox," and some candidate explanations for this phenomena are presented. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

    PubMed

    Oliker, Nurit; Ostfeld, Avi

    2015-09-01

    Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes.

  19. Mark 3 real-time fringe detection system

    NASA Technical Reports Server (NTRS)

    Levine, J. I.; Whitney, A. R.

    1980-01-01

    A RAM memory built into the Mark 3 decoder module allows the capture of 1 Megabit of data. Data may be collected either in real time or from a pre-recorded tape. Once collected, the data may be retrieved using a standard EIA serial data link. The data may be transmitted to a remote computer for cross correlation processing with similar data from other stations to verify fringes in real time. The data may also be analyzed by a local computer to verify phase calibration, bandpass, format, etc., during a Mark 3 observing session.

  20. Real-time PCR assay for detection of a new simulant for poxvirus biothreat agents.

    PubMed

    Garnier, Laurence; Gaudin, Jean-Christophe; Bensadoun, Paul; Rebillat, Isabelle; Morel, Yannick

    2009-03-01

    Research and financial efforts spent on biodefense technologies highlight the current concern for biothreat event preparedness. Nonhazardous but relevant "simulant" microorganisms are typically used to simplify technological developments, testing, and staff training. The bacteriophage MS2, a small RNA virus, is classically used as the reference simulant for biothreat viruses within the biodefense community. However, variola virus, considered a major threat, displays very different features (size, envelope, and double-stranded DNA genome). The size parameter is critical for aerosol sampling, detection, and protection/filtration technologies. Therefore, a panel of relevant simulants should be used to cover the diversity of biothreat agents. Thus, we investigated a new virus model, the Cydia pomonella granulovirus (baculovirus), which is currently used as a biopesticide. It displays a size similar to that of poxviruses, is enveloped, and contains double-stranded DNA. To provide a molecular tool to detect and quantify this model virus, we developed an assay based on real-time PCR, with a limit of detection ranging from roughly 10 to a few tens of target copies per microl according to the sample matrix. The specificity of the assay against a large panel of potential cross-reactive microorganisms was checked, and the suitability of the assay for environmental samples, especially aerosol studies, was determined. In conclusion, we suggest that our PCR assay allows Cydia pomonella granulovirus to be used as a simulant for poxviruses. This assay may also be useful for environmental or crop treatment studies.

  1. The design and hardware implementation of a low-power real-time seizure detection algorithm.

    PubMed

    Raghunathan, Shriram; Gupta, Sumeet K; Ward, Matthew P; Worth, Robert M; Roy, Kaushik; Irazoqui, Pedro P

    2009-10-01

    Epilepsy affects more than 1% of the world's population. Responsive neurostimulation is emerging as an alternative therapy for the 30% of the epileptic patient population that does not benefit from pharmacological treatment. Efficient seizure detection algorithms will enable closed-loop epilepsy prostheses by stimulating the epileptogenic focus within an early onset window. Critically, this is expected to reduce neuronal desensitization over time and lead to longer-term device efficacy. This work presents a novel event-based seizure detection algorithm along with a low-power digital circuit implementation. Hippocampal depth-electrode recordings from six kainate-treated rats are used to validate the algorithm and hardware performance in this preliminary study. The design process illustrates crucial trade-offs in translating mathematical models into hardware implementations and validates statistical optimizations made with empirical data analyses on results obtained using a real-time functioning hardware prototype. Using quantitatively predicted thresholds from the depth-electrode recordings, the auto-updating algorithm performs with an average sensitivity and selectivity of 95.3 +/- 0.02% and 88.9 +/- 0.01% (mean +/- SE(alpha = 0.05)), respectively, on untrained data with a detection delay of 8.5 s [5.97, 11.04] from electrographic onset. The hardware implementation is shown feasible using CMOS circuits consuming under 350 nW of power from a 250 mV supply voltage from simulations on the MIT 180 nm SOI process.

  2. Real time imaging of live cell ATP leaking or release events by chemiluminescence microscopy

    SciTech Connect

    Zhang, Yun

    2008-12-18

    The purpose of this research was to expand the chemiluminescence microscopy applications in live bacterial/mammalian cell imaging and to improve the detection sensitivity for ATP leaking or release events. We first demonstrated that chemiluminescence (CL) imaging can be used to interrogate single bacterial cells. While using a luminometer allows detecting ATP from cell lysate extracted from at least 10 bacterial cells, all previous cell CL detection never reached this sensitivity of single bacteria level. We approached this goal with a different strategy from before: instead of breaking bacterial cell membrane and trying to capture the transiently diluted ATP with the firefly luciferase CL assay, we introduced the firefly luciferase enzyme into bacteria using the modern genetic techniques and placed the CL reaction substrate D-luciferin outside the cells. By damaging the cell membrane with various antibacterial drugs including antibiotics such as Penicillins and bacteriophages, the D-luciferin molecules diffused inside the cell and initiated the reaction that produces CL light. As firefly luciferases are large protein molecules which are retained within the cells before the total rupture and intracellular ATP concentration is high at the millmolar level, the CL reaction of firefly luciferase, ATP and D-luciferin can be kept for a relatively long time within the cells acting as a reaction container to generate enough photons for detection by the extremely sensitive intensified charge coupled device (ICCD) camera. The result was inspiring as various single bacterium lysis and leakage events were monitored with 10-s temporal resolution movies. We also found a new way of enhancing diffusion D-luciferin into cells by dehydrating the bacteria. Then we started with this novel single bacterial CL imaging technique, and applied it for quantifying gene expression levels from individual bacterial cells. Previous published result in single cell gene expression quantification

  3. Monitoring the data quality of the real-time event reconstruction in the ALICE High Level Trigger

    NASA Astrophysics Data System (ADS)

    Austrheim Erdal, Hege; Richther, Matthias; Szostak, Artur; Toia, Alberica

    2012-12-01

    ALICE is a dedicated heavy ion experiment at the CERN LHC. The ALICE High Level Trigger was designed to select events with desirable physics properties. Data from several of the major subdetectors in ALICE are processed by the HLT for real-time event reconstruction, for instance the Inner Tracking System, the Time Projection Chamber, the electromagnetc calorimeters, the Transition Radiation Detector and the muon spectrometer. The HLT reconstructs events in real-time and thus provides input for trigger algorithms. It is necessary to monitor the quality of the reconstruction where one focuses on track and event properties. Also, HLT implemented data compression for the TPC during the heavy ion data taking in 2011 to reduce the data rate from the ALICE detector. The key for the data compression is to store clusters (spacepoints) calculated by HLT rather than storing raw data. It is thus very important to monitor the cluster finder performance as a way to monitor the data compression. The data monitoring is divided into two stages. The first stage is performed during data taking. A part of the HLT production chain is dedicated to performing online monitoring and facilities are available in the HLT production cluster to have real-time access to the reconstructed events in the ALICE control room. This includes track and event properties, and in addition, this facility gives a way to display a small fraction of the reconstructed events in an online display. The second part of the monitoring is performed after the data has been transferred to permanent storage. After a post-process of the real-time reconstructed data, one can look in more detail at the cluster finder performance, the quality of the reconstruction of tracks, vertices and vertex position. The monitoring solution is presented in detail, with special attention to the heavy ion data taking of 2011.

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

    NASA Technical Reports Server (NTRS)

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

    1978-01-01

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

  5. Interlaboratory study of DNA extraction from multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for individual kernel detection system of genetically modified maize.

    PubMed

    Akiyama, Hiroshi; Sakata, Kozue; Makiyma, Daiki; Nakamura, Kosuke; Teshima, Reiko; Nakashima, Akie; Ogawa, Asako; Yamagishi, Toru; Futo, Satoshi; Oguchi, Taichi; Mano, Junichi; Kitta, Kazumi

    2011-01-01

    In many countries, the labeling of grains, feed, and foodstuff is mandatory if the genetically modified (GM) organism content exceeds a certain level of approved GM varieties. We previously developed an individual kernel detection system consisting of grinding individual kernels, DNA extraction from the individually ground kernels, GM detection using multiplex real-time PCR, and GM event detection using multiplex qualitative PCR to analyze the precise commingling level and varieties of GM maize in real sample grains. We performed the interlaboratory study of the DNA extraction with multiple ground samples, multiplex real-time PCR detection, and multiplex qualitative PCR detection to evaluate its applicability, practicality, and ruggedness for the individual kernel detection system of GM maize. DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR were evaluated by five laboratories in Japan, and all results from these laboratories were consistent with the expected results in terms of the commingling level and event analysis. Thus, the DNA extraction with multiple ground samples, multiplex real-time PCR, and multiplex qualitative PCR for the individual kernel detection system is applicable and practicable in a laboratory to regulate the commingling level of GM maize grain for GM samples, including stacked GM maize.

  6. Detection of severe weather events using new remote sensing methods

    NASA Astrophysics Data System (ADS)

    Choy, S.; Wang, C.; Zhang, K.; Kuleshov, Y.

    2012-04-01

    The potential of using ground- and space-based Global Positioning System (GPS) observations for studying severe weather events is presented using the March 2010 Melbourne storm as a case study. This event generated record rainfall and flash flooding across the state of Victoria, Australia. The Victorian GPSnet is the only state-wide and densest ground-based GPS infrastructure in Australia. This provides a unique opportunity to study the spatial and temporal variations in precipitable water vapour (PWV) as the storm passed over the network. The results show strong spatial and temporal correlation between variations of the ground-based GPS-PWV estimates and the thunderstorm passage. This finding demonstrates that the ground-based GPS techniques can supplement conventional meteorological observations in studying, monitoring, and potentially predicting severe weather events. The advantage of using ground-based GPS technique is that it is capable of providing continuous observation of the storm passage with high temporal resolution; while the spatial resolution of the distribution of water vapour is dependent on the geographical location and density of the GPS stations. The results from the space-based GPS radio occultation (RO) technique, on the other hand, are not as robust. Although GPS RO can capture the dynamics of the atmosphere with high vertical resolution, its limited geographical coverage in a local region and its temporal resolution over a short period of time raise an important question about its potential for monitoring severe weather events, particularly local thunderstorms which have a relatively short life-span. GPS RO technique will be more suitable for long-term climatology studies over a large area. It is anticipated that the findings from this study will encourage further research into using GPS meteorology technique for monitoring and forecasting severe weather events in the Australian region.

  7. Detecting adverse events for patient safety research: a review of current methodologies.

    PubMed

    Murff, Harvey J; Patel, Vimla L; Hripcsak, George; Bates, David W

    2003-01-01

    Promoting patient safety is a national priority. To evaluate interventions for reducing medical errors and adverse event, effective methods for detecting such events are required. This paper reviews the current methodologies for detection of adverse events and discusses their relative advantages and limitations. It also presents a cognitive framework for error monitoring and detection. While manual chart review has been considered the "gold-standard" for identifying adverse events in many patient safety studies, this methodology is expensive and imperfect. Investigators have developed or are currently evaluating, several electronic methods that can detect adverse events using coded data, free-text clinical narratives, or a combination of techniques. Advances in these systems will greatly facilitate our ability to monitor adverse events and promote patient safety research. But these systems will perform optimally only if we improve our understanding of the fundamental nature of errors and the ways in which the human mind can naturally, but erroneously, contribute to the problems that we observe.

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

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

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

  9. Enabling Near Real-Time Remote Search for Fast Transient Events with Lossy Data Compression

    NASA Astrophysics Data System (ADS)

    Vohl, Dany; Pritchard, Tyler; Andreoni, Igor; Cooke, Jeffrey; Meade, Bernard

    2017-09-01

    We present a systematic evaluation of JPEG2000 (ISO/IEC 15444) as a transport data format to enable rapid remote searches for fast transient events as part of the Deeper Wider Faster programme. Deeper Wider Faster programme uses 20 telescopes from radio to gamma rays to perform simultaneous and rapid-response follow-up searches for fast transient events on millisecond-to-hours timescales. Deeper Wider Faster programme search demands have a set of constraints that is becoming common amongst large collaborations. Here, we focus on the rapid optical data component of Deeper Wider Faster programme led by the Dark Energy Camera at Cerro Tololo Inter-American Observatory. Each Dark Energy Camera image has 70 total coupled-charged devices saved as a 1.2 gigabyte FITS file. Near real-time data processing and fast transient candidate identifications-in minutes for rapid follow-up triggers on other telescopes-requires computational power exceeding what is currently available on-site at Cerro Tololo Inter-American Observatory. In this context, data files need to be transmitted rapidly to a foreign location for supercomputing post-processing, source finding, visualisation and analysis. This step in the search process poses a major bottleneck, and reducing the data size helps accommodate faster data transmission. To maximise our gain in transfer time and still achieve our science goals, we opt for lossy data compression-keeping in mind that raw data is archived and can be evaluated at a later time. We evaluate how lossy JPEG2000 compression affects the process of finding transients, and find only a negligible effect for compression ratios up to 25:1. We also find a linear relation between compression ratio and the mean estimated data transmission speed-up factor. Adding highly customised compression and decompression steps to the science pipeline considerably reduces the transmission time-validating its introduction to the Deeper Wider Faster programme science pipeline and

  10. Detection of upper airway status and respiratory events by a current generation positive airway pressure device.

    PubMed

    Li, Qing Yun; Berry, Richard B; Goetting, Mark G; Staley, Bethany; Soto-Calderon, Haideliza; Tsai, Sheila C; Jasko, Jeffrey G; Pack, Allan I; Kuna, Samuel T

    2015-04-01

    To compare a positive airway pressure (PAP) device's detection of respiratory events and airway status during device-detected apneas with events scored on simultaneous polysomnography (PSG). Prospective PSGs of patients with sleep apnea using a new-generation PAP device. Four clinical and academic sleep centers. Forty-five patients with obstructive sleep apnea (OSA) and complex sleep apnea (Comp SA) performed a PSG on PAP levels adjusted to induce respiratory events. None. PAP device data identifying the type of respiratory event and whether the airway during a device-detected apnea was open or obstructed were compared to time-synced, manually scored respiratory events on simultaneous PSG recording. Intraclass correlation coefficients between device-detected and PSG scored events were 0.854 for apnea-hypopnea index (AHI), 0.783 for apnea index, 0.252 for hypopnea index, and 0.098 for respiratory event-related arousals index. At a device AHI (AHIFlow) of 10 events/h, area under the receiver operating characteristic curve was 0.98, with sensitivity 0.92 and specificity 0.84. AHIFlow tended to overestimate AHI on PSG at values less than 10 events/h. The device detected that the airway was obstructed in 87.4% of manually scored obstructive apneas. Of the device-detected apneas with clear airway, a minority (15.8%) were manually scored as obstructive apneas. A device-detected apnea-hypopnea index (AHIFlow) < 10 events/h on a positive airway pressure device is strong evidence of good treatment efficacy. Device-detected airway status agrees closely with the presumed airway status during polysomnography scored events, but should not be equated with a specific type of respiratory event. © 2015 Associated Professional Sleep Societies, LLC.

  11. Virtual proprioception with real-time step detection and processing.

    PubMed

    Lemoyne, Robert; Coroian, Cristian; Mastroianni, Timothy; Wu, Winston; Grundfest, Warren; Kaiser, William

    2008-01-01

    Virtual proprioception is a novel device for providing near autonomous biofeedback of hemiparetic gait disparity in real time. With virtual proprioception a user may modify gait dynamics to develop a more suitable gait in tandem with real time feedback. Accelerometers are fundamental to the operation of the device, and a thorough consideration of the accelerometry technology space for locomotion quantification is included. The role of traumatic brain injury and respective decrements to gait quality and proprioceptive feedback are addressed. Virtual proprioception conceptual test and evaluation yielded positive results. The active 'on' status of the virtual proprioception biofeedback for alternative gait strategy was bounded by a 90% confidence level with a 10% margin of error.

  12. Real-time observations of stressful events in the operating room

    PubMed Central

    Sami, AlNassar; Waseem, Hajjar; Nourah, AlSubaie; Areej, AlHummaid; Afnan, AlMarshedi; Ghadeer, Al-Shaikh; Abdulaziz, AlSaif; Arthur, Isnani

    2012-01-01

    Aim: To identify and quantify factors causing stress in the operating room (OR) and evaluate the relationship between these factors and surgeons’ stress level. Methods: This is a prospective observational study from 32 elective surgical procedures conducted in the OR of King Khalid University Hospital, Riyadh, Saudi Arabia. Before each operation, each surgeon was asked of stressors. Two interns observed 16 surgeries each, separately. The interns watched and took notes during the entire surgical procedure. During each operation, the observer recorded anxiety-inducing activities and events that occurred in real time by means of a checklist of 8 potential stressors: technical, patient problems, teamwork problems, time and management issues, distractions and interruptions, equipment problems, personal problems, and teaching. After each operation, surgeons were asked to answer the validated State-Trait Anxiety Inventory questionnaire and self-report on their stress level from the 8 sources using a scale of 1–8 (1: stress free, 8: extremely stressful). The observer also recorded perceived stress levels experienced by the surgeons during the operation. Results: One hundred ten stressors were identified. Technical problems most frequently caused stress (16.4%) and personal issues the least often (6.4%). Frequently encountered stressors (teaching and distractions/interruptions) caused less stress to the surgeons. Technical factors, teamwork, and equipment problems occurred frequently and were also a major contributor to OR stress. All patients were discharged in good health and within 1 week of surgery. Conclusion: Certain stressful factors do occur among surgeons in the OR and can increase the potential for errors. Further research is required to determine the impact of stress on performance and the outcome of surgery. PMID:22754439

  13. Real-time observations of stressful events in the operating room.

    PubMed

    Sami, Alnassar; Waseem, Hajjar; Nourah, Alsubaie; Areej, Alhummaid; Afnan, Almarshedi; Ghadeer, Al-Shaikh; Abdulaziz, Alsaif; Arthur, Isnani

    2012-04-01

    To identify and quantify factors causing stress in the operating room (OR) and evaluate the relationship between these factors and surgeons' stress level. This is a prospective observational study from 32 elective surgical procedures conducted in the OR of King Khalid University Hospital, Riyadh, Saudi Arabia. Before each operation, each surgeon was asked of stressors. Two interns observed 16 surgeries each, separately. The interns watched and took notes during the entire surgical procedure. During each operation, the observer recorded anxiety-inducing activities and events that occurred in real time by means of a checklist of 8 potential stressors: technical, patient problems, teamwork problems, time and management issues, distractions and interruptions, equipment problems, personal problems, and teaching. After each operation, surgeons were asked to answer the validated State-Trait Anxiety Inventory questionnaire and self-report on their stress level from the 8 sources using a scale of 1-8 (1: stress free, 8: extremely stressful). The observer also recorded perceived stress levels experienced by the surgeons during the operation. One hundred ten stressors were identified. Technical problems most frequently caused stress (16.4%) and personal issues the least often (6.4%). Frequently encountered stressors (teaching and distractions/interruptions) caused less stress to the surgeons. Technical factors, teamwork, and equipment problems occurred frequently and were also a major contributor to OR stress. All patients were discharged in good health and within 1 week of surgery. Certain stressful factors do occur among surgeons in the OR and can increase the potential for errors. Further research is required to determine the impact of stress on performance and the outcome of surgery.

  14. A Real-Time System for Abusive Network Traffic Detection

    DTIC Science & Technology

    2011-03-01

    modular architecture, SpamAssassin can be extended to include other filtering techniques, such as real-time blackhole lists (RBLs), whitelists...lookups in blackhole lists (RBL), collaborative filtering with Ryzor [30], Pyzor [53], and DCC [33]), because our virtual environment was insulated from...realtime blackhole list (RBL). Available: http://www.mail-abuse.com/pdf/WP_MAPS_RBL_060104.pdf. [13] J. Postel. (1981, September). Internet

  15. Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets

    PubMed Central

    Aziz, Omar; Klenk, Jochen; Schwickert, Lars; Chiari, Lorenzo; Becker, Clemens; Park, Edward J.; Mori, Greg; Robinovitch, Stephen N.

    2017-01-01

    Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets. PMID:28678808

  16. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

    PubMed

    Bourke, Alan K; Klenk, Jochen; Schwickert, Lars; Aminian, Kamiar; Ihlen, Espen A F; Mellone, Sabato; Helbostad, Jorunn L; Chiari, Lorenzo; Becker, Clemens

    2016-08-01

    Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

  17. Fall incidents unraveled: a series of 26 video-based real-life fall events in three frail older persons

    PubMed Central

    2013-01-01

    Background For prevention and detection of falls, it is essential to unravel the way in which older people fall. This study aims to provide a description of video-based real-life fall events and to examine real-life falls using the classification system by Noury and colleagues, which divides a fall into four phases (the prefall, critical, postfall and recovery phase). Methods Observational study of three older persons at high risk for falls, residing in assisted living or residential care facilities: a camera system was installed in each participant’s room covering all areas, using a centralized PC platform in combination with standard Internet Protocol (IP) cameras. After a fall, two independent researchers analyzed recorded images using the camera position with the clearest viewpoint. Results A total of 30 falls occurred of which 26 were recorded on camera over 17 months. Most falls happened in the morning or evening (62%), when no other persons were present (88%). Participants mainly fell backward (initial fall direction and landing configuration) on the pelvis or torso and none could get up unaided. In cases where a call alarm was used (54%), an average of 70 seconds (SD=64; range 15–224) was needed to call for help. Staff responded to the call after an average of eight minutes (SD=8.4; range 2–33). Mean time on the ground was 28 minutes (SD=25.4; range 2–59) without using a call alarm compared to 11 minutes (SD=9.2; range 3–38) when using a call alarm (p=0.445). The real life falls were comparable with the prefall and recovery phase of Noury’s classification system. The critical phase, however, showed a prolonged duration in all falls. We suggest distinguishing two separate phases: a prolonged loss of balance phase and the actual descending phase after failure to recover balance, resulting in the impact of the body on the ground. In contrast to the theoretical description, the postfall phase was not typically characterized by inactivity; this

  18. A Dynamically Configurable Log-based Distributed Security Event Detection Methodology using Simple Event Correlator

    DTIC Science & Technology

    2010-06-01

    from SANS Whitepaper - "... Detecting Attacks on Web Applications from Log Files" #look for image tags type=Single continue=TakeNext ptype=RegExp...shellcmd /home/user/sec -2.5.3/ common/syslogclient "... Synthetic : " "$2|$1|xss detected in image tag: $3" #send the raw log type=Single ptype=RegExp...Expressions taken from SANS Whitepaper - "... Detecting Attacks on Web Applications from Log Files" #look for image tags type=Single continue=TakeNext

  19. Ex-vivo detection of neural events using THz BioMEMS.

    PubMed

    Abbas, Abdennour; Dargent, Thomas; Croix, Dominique; Salzet, Michel; Bocquet, Bertrand

    2009-09-01

    Electromagnetic frequencies up to a few terahertz (THz) can yield real-time and noninvasive measurements on biological matter. Unfortunately, strong absorption in aqueous solutions and low spatial resolution return difficult free-space investigations. A new approach based on integrated THz circuits was used. The authors designed and fabricated a BioMEMS (Biological MicroElectro-Mechanical System) compatible with microfluidic circulation and electromagnetic propagation. It is dedicated to the ex vivo detection of nitric oxide synthase (NOS) activity, which is involved in neurodegenerative phenomena. The biological model was a leech's central nervous system. After its injury, the production of NO was observed and measured in the far-THz spectral domain. The nerve cord was put inside a BioMEMS realized in polydimethylsiloxane (PDMS) sealed on a glass wafer. Glass is a good material for supporting high-frequency integrated waveguides such as coplanar waveguides (CPWs). Measurements were performed with vectorial network analyser (VNA). The transmission parameter in the frequency range of 0.14-0.22 THz was measured through CPWs located just below the microchannel containing the injured leech nerve cord. The lesion caused a decreased transmission coefficient due to the NOS activity. L-NAME was injected inside the microchannel and it was verified that it inhibits this activity. It was demonstrated that THz spectroscopy can detect a biochemical event, such as NOS activity around an injured leech's nerve cord, in real time. Future studies will be dedicated to quantitative measurements of the reaction products using the sophisticated management of several drugs allowed with microfluidic microsystems.

  20. One Novel Multiple-Target Plasmid Reference Molecule Targeting Eight Genetically Modified Canola Events for Genetically Modified Canola Detection.

    PubMed

    Li, Zhuqing; Li, Xiang; Wang, Canhua; Song, Guiwen; Pi, Liqun; Zheng, Lan; Zhang, Dabing; Yang, Litao

    2017-09-27

    Multiple-target plasmid DNA reference materials have been generated and utilized as good substitutes of matrix-based reference materials in the analysis of genetically modified organisms (GMOs). Herein, we report the construction of one multiple-target plasmid reference molecule, pCAN, which harbors eight GM canola event-specific sequences (RF1, RF2, MS1, MS8, Topas 19/2, Oxy235, RT73, and T45) and a partial sequence of the canola endogenous reference gene PEP. The applicability of this plasmid reference material in qualitative and quantitative PCR assays of the eight GM canola events was evaluated, including the analysis of specificity, limit of detection (LOD), limit of quantification (LOQ), and performance of pCAN in the analysis of various canola samples, etc. The LODs are 15 copies for RF2, MS1, and RT73 assays using pCAN as the calibrator and 10 genome copies for the other events. The LOQ in each event-specific real-time PCR assay is 20 copies. In quantitative real-time PCR analysis, the PCR efficiencies of all event-specific and PEP assays are between 91% and 97%, and the squared regression coefficients (R(2)) are all higher than 0.99. The quantification bias values varied from 0.47% to 20.68% with relative standard deviation (RSD) from 1.06% to 24.61% in the quantification of simulated samples. Furthermore, 10 practical canola samples sampled from imported shipments in the port of Shanghai, China, were analyzed employing pCAN as the calibrator, and the results were comparable with those assays using commercial certified materials as the calibrator. Concluding from these results, we believe that this newly developed pCAN plasmid is one good candidate for being a plasmid DNA reference material in the detection and quantification of the eight GM canola events in routine analysis.

  1. Supervised machine learning on a network scale: application to seismic event classification and detection

    NASA Astrophysics Data System (ADS)

    Reynen, Andrew; Audet, Pascal

    2017-09-01

    A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

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

    PubMed

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

    2014-08-01

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

  3. Rapid detection of Salmonella in bovine lymph nodes using a commercial real-time PCR system

    USDA-ARS?s Scientific Manuscript database

    Rapid Salmonella detection is needed to help prevent the distribution of contaminated food products. Using traditional culture methods, Salmonella detection can take up to 3-5 days. Using an improved protocol and a commercial real-time PCR system, we have shortened the detection time to under 24 h...

  4. Automated Sensor Tuning for Seismic Event Detection at a Carbon Capture, Utilization, and Storage Site, Farnsworth Unit, Ochiltree County, Texas

    NASA Astrophysics Data System (ADS)

    Ziegler, A.; Balch, R. S.; Knox, H. A.; Van Wijk, J. W.; Draelos, T.; Peterson, M. G.

    2016-12-01

    We present results (e.g. seismic detections and STA/LTA detection parameters) from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Specifically, we evaluate data from a passive vertical monitoring array consisting of 16 levels of 3-component 15Hz geophones installed in the field and continuously recording since January 2014. This detection database is directly compared to ancillary data (i.e. wellbore pressure) to determine if there is any relationship between seismic observables and CO2 injection and pressure maintenance in the field. Of particular interest is detection of relatively low-amplitude signals constituting long-period long-duration (LPLD) events that may be associated with slow shear-slip analogous to low frequency tectonic tremor. While this category of seismic event provides great insight into dynamic behavior of the pressurized subsurface, it is inherently difficult to detect. To automatically detect seismic events using effective data processing parameters, an automated sensor tuning (AST) algorithm developed by Sandia National Laboratories is being utilized. AST exploits ideas from neuro-dynamic programming (reinforcement learning) to automatically self-tune and determine optimal detection parameter settings. AST adapts in near real-time to changing conditions and automatically self-tune a signal detector to identify (detect) only signals from events of interest, leading to a reduction in the number of missed legitimate event detections and the number of false event detections. Funding for this project is provided by the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) through the Southwest Regional Partnership on Carbon Sequestration (SWP) under Award No. DE-FC26-05NT42591. Additional support has been provided by site operator Chaparral Energy, L.L.C. and Schlumberger Carbon Services. Sandia National

  5. Toward automatic detection and prevention of adverse drug events.

    PubMed

    Leroy, Nicolas; Chazard, Emmanuel; Beuscart, Régis; Beuscart-Zephir, Marie Catherine

    2009-01-01

    Adverse Drug Events (ADE) due to medication errors and human factors are a major public health issue. They endanger patient safety and cause considerable extra healthcare costs. The European project PSIP (Patient Safety through Intelligent Procedures in medication) aims to identify and prevent ADE. Data mining of the structured hospital data bases will give a list of observed ADE with frequencies and probabilities, thereby giving a better understanding of potential risks. The main objective of the project is to develop innovative knowledge based on the mining results and to deliver to professionals and patients, in the form of alerts and decision support functions, a contextualized knowledge fitting the local risk parameters.

  6. Power System Extreme Event Detection: The VulnerabilityFrontier

    SciTech Connect

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

    2007-10-17

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

  7. Data-mining-based detection of adverse drug events.

    PubMed

    Chazard, Emmanuel; Preda, Cristian; Merlin, Béatrice; Ficheur, Grégoire; Beuscart, Régis

    2009-01-01

    Every year adverse drug events (ADEs) are known to be responsible for 98,000 deaths in the USA. Classical methods rely on report statements, expert knowledge, and staff operated record review. One of our objectives, in the PSIP project framework, is to use data mining (e.g., decision trees) to electronically identify situations leading to risk of ADEs. 10,500 hospitalization records from Denmark and France were used. 500 rules were automatically obtained, which are currently being validated by experts. A decision support system to prevent ADEs is then to be developed. The article examines a decision tree and the rules in the field of vitamin K antagonists.

  8. Near-Real-Time Detection and Monitoring of Intense Pyroconvection from Geostationary Satellites

    NASA Astrophysics Data System (ADS)

    Peterson, D. A.; Fromm, M. D.; Hyer, E. J.; Surratt, M. L.; Solbrig, J. E.; Campbell, J. R.

    2016-12-01

    Intense fire-triggered thunderstorms, known as pyrocumulonimbus (or pyroCb), can alter fire behavior, influence smoke plume trajectories, and hinder fire suppression efforts. PyroCb are also known for injecting a significant quantity of aerosol mass into the upper-troposphere and lower-stratosphere (UTLS). Near-real-time (NRT) detection and monitoring of pyroCb is highly desirable for a variety of forecasting and research applications. The Naval Research Laboratory (NRL) recently developed the first automated NRT pyroCb detection algorithm for geostationary satellite sensors. The algorithm uses multispectral infrared observations to isolate deep convective clouds with the distinct microphysical signal of pyroCb. Application of this algorithm to 88 intense wildfires observed during the 2013 fire season in western North America resulted in detection of individual intense events, pyroCb embedded within traditional convection, and multiple, short-lived pulses of activity. Comparisons with a community inventory indicate that this algorithm captures the majority of pyroCb. The primary limitation of the current system is that pyroCb anvils can be small relative to satellite pixel size, especially in in regions with large viewing angles. The algorithm is also sensitive to some false positives from traditional convection that either ingests smoke or exhibits extreme updraft velocities. This algorithm has been automated using the GeoIPS processing system developed at NRL, which produces a variety of imagery products and statistical output for rapid analysis of potential pyroCb events. NRT application of this algorithm has been extended to the majority of regions worldwide known to have a high frequency of pyroCb occurrence. This involves a constellation comprised of GOES-East, GOES-West, and Himawari-8. Imagery is posted immediately to an NRL-maintained web page. Alerts are generated by the system and disseminated via email. This detection system also has potential to serve

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

    NASA Astrophysics Data System (ADS)

    Maity, Debotyam; Salehi, Iraj

    2016-01-01

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

  10. Real-Time Nucleic Acid Sequence-Based Amplification Assay for Detection of Hepatitis A Virus

    PubMed Central

    Abd El Galil, Khaled H.; El Sokkary, M. A.; Kheira, S. M.; Salazar, Andre M.; Yates, Marylynn V.; Chen, Wilfred; Mulchandani, Ashok

    2005-01-01

    A nucleic acid sequence-based amplification (NASBA) assay in combination with a molecular beacon was developed for the real-time detection and quantification of hepatitis A virus (HAV). A 202-bp, highly conserved 5′ noncoding region of HAV was targeted. The sensitivity of the real-time NASBA assay was tested with 10-fold dilutions of viral RNA, and a detection limit of 1 PFU was obtained. The specificity of the assay was demonstrated by testing with other environmental pathogens and indicator microorganisms, with only HAV positively identified. When combined with immunomagnetic separation, the NASBA assay successfully detected as few as 10 PFU from seeded lake water samples. Due to its isothermal nature, its speed, and its similar sensitivity compared to the real-time RT-PCR assay, this newly reported real-time NASBA method will have broad applications for the rapid detection of HAV in contaminated food or water. PMID:16269748

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

    NASA Astrophysics Data System (ADS)

    Alexander, Caroline; Fayock, Brian; Winebarger, Amy

    2016-05-01

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

  12. Significance of "Not Detected but Amplified" Results by Real-Time PCR Method for HPV DNA Detection.

    PubMed

    Kim, Taek Soo; Lim, Mi Suk; Hong, Yun Ji; Hwang, Sang Mee; Park, Kyoung Un; Song, Junghan; Kim, Eui-Chong

    2016-01-01

    Human papillomavirus (HPV) infection is an important etiologic factor in cervical carcinogenesis. Various HPV DNA detection methods have been evaluated for clinicopathological level. For the specimens with normal cytological finding, discrepancies among the detection methods were frequently found and adequate interpretation can be difficult. 6,322 clinical specimens were submitted and evaluated for real-time PCR and Hybrid Capture 2 (HC2). 573 positive or "Not Detected but Amplified" (NDBA) specimens by real-time PCR were additionally tested using genetic analyzer. For the reliability of real-time PCR, 325 retests were performed. Optimal cut-off cycle threshold (CT ) value was evaluated also. 78.7% of submitted specimens showed normal or nonspecific cytological finding. The distributions of HPV types by real-time PCR were not different between positive and NDBA cases. For positive cases by fragment analysis, concordance rates with real-time PCR and HC2 were 94.2% and 84.2%. In NDBA cases, fragment analysis and real-time PCR showed identical results in 77.0% and HC2 revealed 27.6% of concordance with fragment analysis. Optimal cut-off CT value was different for HPV types. NDBA results in real-time PCR should be regarded as equivocal, not negative. The adjustment of cut-off CT value for HPV types will be helpful for the appropriate result interpretation.

  13. Significance of “Not Detected but Amplified” Results by Real-Time PCR Method for HPV DNA Detection

    PubMed Central

    Kim, Taek Soo; Lim, Mi Suk; Hwang, Sang Mee; Song, Junghan; Kim, Eui-Chong

    2016-01-01

    Human papillomavirus (HPV) infection is an important etiologic factor in cervical carcinogenesis. Various HPV DNA detection methods have been evaluated for clinicopathological level. For the specimens with normal cytological finding, discrepancies among the detection methods were frequently found and adequate interpretation can be difficult. 6,322 clinical specimens were submitted and evaluated for real-time PCR and Hybrid Capture 2 (HC2). 573 positive or “Not Detected but Amplified” (NDBA) specimens by real-time PCR were additionally tested using genetic analyzer. For the reliability of real-time PCR, 325 retests were performed. Optimal cut-off cycle threshold (CT) value was evaluated also. 78.7% of submitted specimens showed normal or nonspecific cytological finding. The distributions of HPV types by real-time PCR were not different between positive and NDBA cases. For positive cases by fragment analysis, concordance rates with real-time PCR and HC2 were 94.2% and 84.2%. In NDBA cases, fragment analysis and real-time PCR showed identical results in 77.0% and HC2 revealed 27.6% of concordance with fragment analysis. Optimal cut-off CT value was different for HPV types. NDBA results in real-time PCR should be regarded as equivocal, not negative. The adjustment of cut-off CT value for HPV types will be helpful for the appropriate result interpretation. PMID:28097135

  14. Real Time Detection of Sodium in Size-Segregated Marine Aerosols

    DTIC Science & Technology

    2002-09-30

    Real Time Measurement of Sea- Salt Aerosol during the SEAS Campaign: Comparison of Emission based Sodium Detection with an Aerosol Volatility Technique. Submitted to the Journal of Atmospheric and Oceanic Technology. ...Real Time Detection of Sodium in Size-Segregated Marine Aerosols Anthony J. Hynes Rosenstiel School of Marine and Atmospheric Science 4600...this capability for sodium and a prototype has been deployed as part of an ONR-sponsored field campaign (SEAS). The ultimate goal of the project is to

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

    SciTech Connect

    McKenna, Sean Andrew; Gutierrez, Karen A.

    2011-10-01

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

  16. Detecting impacts of extreme events with ecological in situ monitoring networks

    NASA Astrophysics Data System (ADS)

    Mahecha, Miguel D.; Gans, Fabian; Sippel, Sebastian; Donges, Jonathan F.; Kaminski, Thomas; Metzger, Stefan; Migliavacca, Mirco; Papale, Dario; Rammig, Anja; Zscheischler, Jakob

    2017-09-01

    Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log-log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio

  17. Simple Movement and Detection in Discrete Event Simulation

    DTIC Science & Technology

    2005-12-01

    with a description of uniform linear motion in the following section. We will then con- sider the simplest kind of sensing, the “ cookie -cutter.” A... cookie -cutter sensor sees everything that is within its range R, and must be notified at the precise time a target enters it range. In a time-step...simulation, cookie -cutter detection is very easy. Simply compute the distance between the sensor and the target at each time step. If the target is

  18. Detection of intermittent events in atmospheric time series

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  19. Multi-Detection Events, Probability Density Functions, and Reduced Location Area

    SciTech Connect

    Eslinger, Paul W.; Schrom, Brian T.

    2016-03-01

    Abstract Several efforts have been made in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) community to assess the benefits of combining detections of radionuclides to improve the location estimates available from atmospheric transport modeling (ATM) backtrack calculations. We present a Bayesian estimation approach rather than a simple dilution field of regard approach to allow xenon detections and non-detections to be combined mathematically. This system represents one possible probabilistic approach to radionuclide event formation. Application of this method to a recent interesting radionuclide event shows a substantial reduction in the location uncertainty of that event.

  20. Real-time PCR assay to detect smallpox virus.

    PubMed

    Sofi Ibrahim, M; Kulesh, David A; Saleh, Sharron S; Damon, Inger K; Esposito, Joseph J; Schmaljohn, Alan L; Jahrling, Peter B

    2003-08-01

    We developed a highly sensitive and specific assay for the rapid detection of smallpox virus DNA on both the Smart Cycler and LightCycler platforms. The assay is based on TaqMan chemistry with the orthopoxvirus hemagglutinin gene used as the target sequence. With genomic DNA purified from variola virus Bangladesh 1975, the limit of detection was estimated to be approximately 25 copies on both machines. The assay was evaluated in a blinded study with 322 coded samples that included genomic DNA from 48 different isolates of variola virus; 25 different strains and isolates of camelpox, cowpox, ectromelia, gerbilpox, herpes, monkeypox, myxoma, rabbitpox, raccoonpox, skunkpox, vaccinia, and varicella-zoster viruses; and two rickettsial species at concentrations mostly ranging from 100 fg/ microl to 1 ng/ microl. Contained within those 322 samples were variola virus DNA, obtained from purified viral preparations, at concentrations of 1 fg/ microl to 1 ng/ microl. On the Smart Cycler platform, 2 samples with false-positive results were detected among the 116 samples not containing variola virus tested; i.e., the overall specificity of the assay was 98.3%. On the LightCycler platform, five samples with false-positive results were detected (overall specificity, 95.7%). Of the 206 samples that contained variola virus DNA ranging in concentrations from 100 fg/ microl to 1 ng/ microl, 8 samples were considered negative on the Smart Cycler platform and 1 sample was considered negative on the LightCycler platform. Thus, the clinical sensitivities were 96.1% for the Smart Cycler instrument and 99.5% for the LightCycler instrument. The vast majority of these samples were derived from virus-infected cell cultures and variola virus-infected tissues; thus, the DNA material contained both viral DNA and cellular DNA. Of the 43 samples that contained purified variola virus DNA ranging in concentration from 1 fg/ microl to 1 ng/ microl, the assay correctly detected the virus in all 43

  1. Real-Time PCR Assay To Detect Smallpox Virus

    PubMed Central

    Sofi Ibrahim, M.; Kulesh, David A.; Saleh, Sharron S.; Damon, Inger K.; Esposito, Joseph J.; Schmaljohn, Alan L.; Jahrling, Peter B.

    2003-01-01

    We developed a highly sensitive and specific assay for the rapid detection of smallpox virus DNA on both the Smart Cycler and LightCycler platforms. The assay is based on TaqMan chemistry with the orthopoxvirus hemagglutinin gene used as the target sequence. With genomic DNA purified from variola virus Bangladesh 1975, the limit of detection was estimated to be approximately 25 copies on both machines. The assay was evaluated in a blinded study with 322 coded samples that included genomic DNA from 48 different isolates of variola virus; 25 different strains and isolates of camelpox, cowpox, ectromelia, gerbilpox, herpes, monkeypox, myxoma, rabbitpox, raccoonpox, skunkpox, vaccinia, and varicella-zoster viruses; and two rickettsial species at concentrations mostly ranging from 100 fg/μl to 1 ng/μl. Contained within those 322 samples were variola virus DNA, obtained from purified viral preparations, at concentrations of 1 fg/μl to 1 ng/μl. On the Smart Cycler platform, 2 samples with false-positive results were detected among the 116 samples not containing variola virus tested; i.e., the overall specificity of the assay was 98.3%. On the LightCycler platform, five samples with false-positive results were detected (overall specificity, 95.7%). Of the 206 samples that contained variola virus DNA ranging in concentrations from 100 fg/μl to 1 ng/μl, 8 samples were considered negative on the Smart Cycler platform and 1 sample was considered negative on the LightCycler platform. Thus, the clinical sensitivities were 96.1% for the Smart Cycler instrument and 99.5% for the LightCycler instrument. The vast majority of these samples were derived from virus-infected cell cultures and variola virus-infected tissues; thus, the DNA material contained both viral DNA and cellular DNA. Of the 43 samples that contained purified variola virus DNA ranging in concentration from 1 fg/μl to 1 ng/μl, the assay correctly detected the virus in all 43 samples on both the Smart Cycler

  2. THE DETECTION OF A SN IIn IN OPTICAL FOLLOW-UP OBSERVATIONS OF ICECUBE NEUTRINO EVENTS

    SciTech Connect

    Aartsen, M. G.; Abraham, K.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Altmann, D.; Anderson, T.; Archinger, M.; Arguelles, C.; Arlen, T. C.; Auffenberg, J.; Bai, X.; Barwick, S. W.; Baum, V.; Bay, R.; Beatty, J. J.; Becker Tjus, J.; Becker, K.-H.; Collaboration: IceCube Collaboration; for the PTF Collaboration; for the Swift Collaboration; for the Pan-STARRS1 Science Consortium; and others

    2015-09-20

    The IceCube neutrino observatory pursues a follow-up program selecting interesting neutrino events in real-time and issuing alerts for electromagnetic follow-up observations. In 2012 March, the most significant neutrino alert during the first three years of operation was issued by IceCube. In the follow-up observations performed by the Palomar Transient Factory (PTF), a Type IIn supernova (SN IIn) PTF12csy was found 0.°2 away from the neutrino alert direction, with an error radius of 0.°54. It has a redshift of z = 0.0684, corresponding to a luminosity distance of about 300 Mpc and the Pan-STARRS1 survey shows that its explosion time was at least 158 days (in host galaxy rest frame) before the neutrino alert, so that a causal connection is unlikely. The a posteriori significance of the chance detection of both the neutrinos and the SN at any epoch is 2.2σ within IceCube's 2011/12 data acquisition season. Also, a complementary neutrino analysis reveals no long-term signal over the course of one year. Therefore, we consider the SN detection coincidental and the neutrinos uncorrelated to the SN. However, the SN is unusual and interesting by itself: it is luminous and energetic, bearing strong resemblance to the SN IIn 2010jl, and shows signs of interaction of the SN ejecta with a dense circumstellar medium. High-energy neutrino emission is expected in models of diffusive shock acceleration, but at a low, non-detectable level for this specific SN. In this paper, we describe the SN PTF12csy and present both the neutrino and electromagnetic data, as well as their analysis.

  3. The power to detect recent fragmentation events using genetic differentiation methods.

    PubMed

    Lloyd, Michael W; Campbell, Lesley; Neel, Maile C

    2013-01-01

    Habitat loss and fragmentation are imminent threats to biological diversity worldwide and thus are fundamental issues in conservation biology. Increased isolation alone has been implicated as a driver of negative impacts in populations associated with fragmented landscapes. Genetic monitoring and the use of measures of genetic divergence have been proposed as means to detect changes in landscape connectivity. Our goal was to evaluate the sensitivity of Wright's F st, Hedrick' G'st , Sherwin's MI, and Jost's D to recent fragmentation events across a range of population sizes and sampling regimes. We constructed an individual-based model, which used a factorial design to compare effects of varying population size, presence or absence of overlapping generations, and presence or absence of population sub-structuring. Increases in population size, overlapping generations, and population sub-structuring each reduced F st, G'st , MI, and D. The signal of fragmentation was detected within two generations for all metrics. However, the magnitude of the change in each was small in all cases, and when N e was >100 individuals it was extremely small. Multi-generational sampling and population estimates are required to differentiate the signal of background divergence from changes in Fst , G'st , MI, and D associated with fragmentation. Finally, the window during which rapid change in Fst , G'st , MI, and D between generations occurs can be small, and if missed would lead to inconclusive results. For these reasons, use of F st, G'st , MI, or D for detecting and monitoring changes in connectivity is likely to prove difficult in real-world scenarios. We advocate use of genetic monitoring only in conjunction with estimates of actual movement among patches such that one could compare current movement with the genetic signature of past movement to determine there has been a change.

  4. The Power to Detect Recent Fragmentation Events Using Genetic Differentiation Methods

    PubMed Central

    Lloyd, Michael W.; Campbell, Lesley; Neel, Maile C.

    2013-01-01

    Habitat loss and fragmentation are imminent threats to biological diversity worldwide and thus are fundamental issues in conservation biology. Increased isolation alone has been implicated as a driver of negative impacts in populations associated with fragmented landscapes. Genetic monitoring and the use of measures of genetic divergence have been proposed as means to detect changes in landscape connectivity. Our goal was to evaluate the sensitivity of Wright’s Fst, Hedrick’ G’st, Sherwin’s MI, and Jost’s D to recent fragmentation events across a range of population sizes and sampling regimes. We constructed an individual-based model, which used a factorial design to compare effects of varying population size, presence or absence of overlapping generations, and presence or absence of population sub-structuring. Increases in population size, overlapping generations, and population sub-structuring each reduced Fst, G’st, MI, and D. The signal of fragmentation was detected within two generations for all metrics. However, the magnitude of the change in each was small in all cases, and when Ne was >100 individuals it was extremely small. Multi-generational sampling and population estimates are required to differentiate the signal of background divergence from changes in Fst, G’st, MI, and D associated with fragmentation. Finally, the window during which rapid change in Fst, G’st, MI, and D between generations occurs can be small, and if missed would lead to inconclusive results. For these reasons, use of Fst, G’st, MI, or D for detecting and monitoring changes in connectivity is likely to prove difficult in real-world scenarios. We advocate use of genetic monitoring only in conjunction with estimates of actual movement among patches such that one could compare current movement with the genetic signature of past movement to determine there has been a change. PMID:23704965

  5. Multi-instance dictionary learning for detecting abnormal events in surveillance videos.

    PubMed

    Huo, Jing; Gao, Yang; Yang, Wanqi; Yin, Hujun

    2014-05-01

    In this paper, a novel method termed Multi-Instance Dictionary Learning (MIDL) is presented for detecting abnormal events in crowded video scenes. With respect to multi-instance learning, each event (video clip) in videos is modeled as a bag containing several sub-events (local observations); while each sub-event is regarded as an instance. The MIDL jointly learns a dictionary for sparse representations of sub-events (instances) and multi-instance classifiers for classifying events into normal or abnormal. We further adopt three different multi-instance models, yielding the Max-Pooling-based MIDL (MP-MIDL), Instance-based MIDL (Inst-MIDL) and Bag-based MIDL (Bag-MIDL), for detecting both global and local abnormalities. The MP-MIDL classifies observed events by using bag features extracted via max-pooling over sparse representations. The Inst-MIDL and Bag-MIDL classify observed events by the predicted values of corresponding instances. The proposed MIDL is evaluated and compared with the state-of-the-art methods for abnormal event detection on the UMN (for global abnormalities) and the UCSD (for local abnormalities) datasets and results show that the proposed MP-MIDL and Bag-MIDL achieve either comparable or improved detection performances. The proposed MIDL method is also compared with other multi-instance learning methods on the task and superior results are obtained by the MP-MIDL scheme.

  6. Detecting and Locating Seismic Events Without Phase Picks or Velocity Models

    NASA Astrophysics Data System (ADS)

    Arrowsmith, S.; Young, C. J.; Ballard, S.; Slinkard, M.

    2015-12-01

    The standard paradigm for seismic event monitoring is to scan waveforms from a network of stations and identify the arrival time of various seismic phases. A signal association algorithm then groups the picks to form events, which are subsequently located by minimizing residuals between measured travel times and travel times predicted by an Earth model. Many of these steps are prone to significant errors which can lead to erroneous arrival associations and event locations. Here, we revisit a concept for event detection that does not require phase picks or travel time curves and fuses detection, association and location into a single algorithm. Our pickless event detector exploits existing catalog and waveform data to build an empirical stack of the full regional seismic wavefield, which is subsequently used to detect and locate events at a network level using correlation techniques. Because the technique uses more of the information content of the original waveforms, the concept is particularly powerful for detecting weak events that would be missed by conventional methods. We apply our detector to seismic data from the University of Utah Seismograph Stations network and compare our results with the earthquake catalog published by the University of Utah. We demonstrate that the pickless detector can detect and locate significant numbers of events previously missed by standard data processing techniques.

  7. Real-time Microseismic Processing for Induced Seismicity Hazard Detection

    SciTech Connect

    Matzel, Eric M.

    2016-10-31

    Induced seismicity is inherently associated with underground fluid injections. If fluids are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures can trigger dynamic earthquake slip, which could both damage surface structures and create new migration pathways. The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

  8. Near real-time fluorescence detection of beryllium

    SciTech Connect

    McCleskey, T. M.; Ehler, D. S.; Minogue, E. M.; Collis, G. E.; Keizer, T. S.; Burrell, A. K.; Sauer, N. N.; John, K. D.

    2004-01-01

    We report on a fluorescent test for beryllium designed for analyzing swipes. The detection is rapid, quantitative and deployable in the field with $5,000 portable fluorimeter. Swipes are placed in a vial and a dilution solution is added. The vials are then rotated for 30 minutes and then syringe filtered. An aliquot of 100 pL is added to a detector solution and fluorescence measured with a portable ocean optics unit. We can readily detect down to 0.02 {micro}g on a filter paper. Interference studies have been carried out with various metals including Al, Fe, Pb, U, Ca, W, Ni, Co and Cu. The technique has proven to be successful under various conditions including a variety of surfaces both in the lab and in field. It is a user-friendly, cost effective method.

  9. Real-Time PCR: Revolutionizing Detection and Expression Analysis of Genes

    PubMed Central

    Deepak, SA; Kottapalli, KR; Rakwal, R; Oros, G; Rangappa, KS; Iwahashi, H; Masuo, Y; Agrawal, GK

    2007-01-01

    Invention of polymerase chain reaction (PCR) technology by Kary Mullis in 1984 gave birth to real-time PCR. Real-time PCR — detection and expression analysis of gene(s) in real-time — has revolutionized the 21st century biological science due to its tremendous application in quantitative genotyping, genetic variation of inter and intra organisms, early diagnosis of disease, forensic, to name a few. We comprehensively review various aspects of real-time PCR, including technological refinement and application in all scientific fields ranging from medical to environmental issues, and to plant. PMID:18645596

  10. Real-Time PCR: Revolutionizing Detection and Expression Analysis of Genes.

    PubMed

    Deepak, Sa; Kottapalli, Kr; Rakwal, R; Oros, G; Rangappa, Ks; Iwahashi, H; Masuo, Y; Agrawal, Gk

    2007-06-01

    Invention of polymerase chain reaction (PCR) technology by Kary Mullis in 1984 gave birth to real-time PCR. Real-time PCR - detection and expression analysis of gene(s) in real-time - has revolutionized the 21(st) century biological science due to its tremendous application in quantitative genotyping, genetic variation of inter and intra organisms, early diagnosis of disease, forensic, to name a few. We comprehensively review various aspects of real-time PCR, including technological refinement and application in all scientific fields ranging from medical to environmental issues, and to plant.

  11. Flexible Computing Architecture for Real Time Skin Detection

    DTIC Science & Technology

    2010-03-01

    Figure 6. Spectra of Light and Dark Skin Compared with Spectra of Other Materials .............. 2-5 Figure 7(a.) Joint Distribution of (NDSI/ NDVI ...Vegetation Index ( NDVI ). In Eqn. 2, and are the estimated reflectances of the 660 and 750 nm wavelengths, respectively. As can be seen from Eqn...values, while the rules based detectors use a rectangular bound on either (NDSI, NDVI ) or (NDSI, NDGRI) pairs. The last detection algorithm is the

  12. Real Time Intelligent Target Detection and Analysis with Machine Vision

    NASA Technical Reports Server (NTRS)

    Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

    2000-01-01

    We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

  13. [Research progress of real-time quantitative PCR method for group A rotavirus detection].

    PubMed

    Guo, Yan-Qing; Li, Dan-Di; Duan, Zhao-Jun

    2013-11-01

    Group A rotavirus is one of the most significant etiological agents which causes acute gastroenteritis among infants and young children worldwide. So far, several method which includes electron microscopy (EM), enzyme immunoassay (EIA), reverse transcription-polymerase chain reaction (RT-PCR)and Real-time Quantitative PCR has been established for the detection of rotavirus. Compared with other methods, Real-time quantitative PCR have advantages in specificity, sensitivity, genotyping and quantitative accuracy. This article shows a overview of the application of real-time quantitative PCR technique to detecte group A rotavirus.

  14. Real-time human pose detection and tracking for tele-rehabilitation in virtual reality.

    PubMed

    Obdržálek, Stěpán; Kurillo, Gregorij; Han, Jay; Abresch, Ted; Bajcsy, Ruzena

    2012-01-01

    We present a real-time algorithm for human pose detection and tracking from vision-based 3D data and its application to tele-rehabilitation in virtual environments. We employ stereo camera(s) to capture 3D avatars of geographically dislocated patient and therapist in real-time, while sending the data remotely and displaying it in a virtual scene. A pose detection and tracking algorithm extracts kinematic parameters from each participant and determines pose similarity. The pose similarity score is used to quantify patient's performance and provide real-time feedback for remote rehabilitation.

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

    PubMed Central

    Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

    2014-01-01

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

  16. Effects of life-event stress and hardiness on peripheral vision in a real-life stress situation.

    PubMed

    Rogers, Tracie J; Alderman, Brandon L; Landers, Daniel M

    2003-01-01

    Previous research has only examined perceptual deficits that are hypothesized in a model of stress and injury under laboratory-induced stress conditions. The generalizability of findings from such induced-stress conditions is limited beyond the laboratory. The current research examined the influence of life-event stress and hardiness on peripheral narrowing in a real-life stress situation. Athletes completed life-stress and hardiness questionnaires, along with measures of state anxiety and peripheral vision. The stress condition was obtained by assessing the athletes within 2 hours of a competition. The real-life stress condition had a larger effect on state anxiety and peripheral narrowing than the laboratory-induced situations used in previous research, with effect sizes twice and three times as large as those reported in the literature. All athletes experienced significant reductions in peripheral vision prior to competition, regardless of life-event stress or hardiness levels.

  17. Detection and quantification of Aeromonas salmonicida in fish tissue by real-time PCR.

    PubMed

    Bartkova, S; Kokotovic, B; Skall, H F; Lorenzen, N; Dalsgaard, I

    2017-02-01

    Furunculosis, a septicaemic infection caused by the bacterium Aeromonas salmonicida subsp. salmonicida, currently causes problems in Danish seawater rainbow trout production. Detection has mainly been achieved by bacterial culture, but more rapid and sensitive methods are needed. A previously developed real-time PCR assay targeting the plasmid encoded aopP gene of A. salmonicida was, in parallel with culturing, used for the examination of five organs of 40 fish from Danish freshwater and seawater farms. Real-time PCR showed overall a higher frequency of positives than culturing (65% of positive fish by real-time PCR compared to 30% by a culture approach). Also, no real-time PCR-negative samples were found positive by culturing. A. salmonicida was detected by real-time PCR, though not by culturing, in freshwater fish showing no signs of furunculosis, indicating possible presence of carrier fish. In seawater fish examined after an outbreak and antibiotics treatment, real-time PCR showed the presence of the bacterium in all examined organs (1-482 genomic units mg(-1) ). With a limit of detection of 40 target copies (1-2 genomic units) per reaction, a high reproducibility and an excellent efficiency, the present real-time PCR assay provides a sensitive tool for the detection of A. salmonicida. © 2016 John Wiley & Sons Ltd.

  18. Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS).

    PubMed

    Xu, Rong; Wang, Quanqiu

    2014-02-01

    Targeted drugs dramatically improve the treatment outcomes in cancer patients; however, these innovative drugs are often associated with unexpectedly high cardiovascular toxicity. Currently, cardiovascular safety represents both a challenging issue for drug developers, regulators, researchers, and clinicians and a concern for patients. While FDA drug labels have captured many of these events, spontaneous reporting systems are a main source for post-marketing drug safety surveillance in 'real-world' (outside of clinical trials) cancer patients. In this study, we present approaches to extracting, prioritizing, filtering, and confirming cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS). The dataset includes records of 4,285,097 patients from FAERS. We first extracted drug-cardiovascular event (drug-CV) pairs from FAERS through named entity recognition and mapping processes. We then compared six ranking algorithms in prioritizing true positive signals among extracted pairs using known drug-CV pairs derived from FDA drug labels. We also developed three filtering algorithms to further improve precision. Finally, we manually validated extracted drug-CV pairs using 21 million published MEDLINE records. We extracted a total of 11,173 drug-CV pairs from FAERS. We showed that ranking by frequency is significantly more effective than by the five standard signal detection methods (246% improvement in precision for top-ranked pairs). The filtering algorithm we developed further improved overall precision by 91.3%. By manual curation using literature evidence, we show that about 51.9% of the 617 drug-CV pairs that appeared in both FAERS and MEDLINE sentences are true positives. In addition, 80.6% of these positive pairs have not been captured by FDA drug labeling. The unique drug-CV association dataset that we created based on FAERS could facilitate our understanding and prediction of cardiotoxic events associated with

  19. Find your manners: how do infants detect the invariant manner of motion in dynamic events?

    PubMed

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

    2012-01-01

    To learn motion verbs, infants must be sensitive to the specific event features lexicalized in their language. One event feature important for the acquisition of English motion verbs is the manner of motion. This article examines when and how infants detect manners of motion across variations in the figure's path. Experiment 1 shows that 13- to 15-month-olds (N = 30) can detect an invariant manner of motion when the figure's path changes. Experiment 2 reveals that reducing the complexity of the events, by dampening the figure's path, helps 10- to 12-month-olds (N = 19) detect the invariant manner. These findings suggest that: (a) infants notice event features lexicalized in English motion verbs, and (b) attention to manner can be promoted by reducing event complexity. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

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

    NASA Astrophysics Data System (ADS)

    Ganea, Ion Eugen

    2015-09-01

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

  1. MCD for detection of event-based landslides

    NASA Astrophysics Data System (ADS)

    Mondini, A. C.; Chang, K.; Guzzetti, F.

    2011-12-01

    Landslides play an important role in the landscape evolution of mountainous terrain. They also present a socioeconomic problem in terms of risk for people and properties. Landslide inventory maps are not available for many areas affected by slope instabilities, resulting in a lack of primary information for the comprehension of the phenomenon, evaluation of relative landslide statistics, and civil protection operations on large scales. Traditional methods for the preparation of landslide inventory maps are based on the geomorphological interpretation of stereoscopic aerial photography and field surveys. These methods are expensive and time consuming. The exploitation of new remote sensing data, in particular very high resolution (VHR) satellite images, and new dedicated methods present an alternative to the traditional methods and are at the forefront of modern landslide research. Recent studies have showed the possibility to produce accurate landslide maps, reducing the time and resources required for their compilation and systematic update. This paper presents the Multiple Change Detection (MCD) technique, a new method that has shown promising results in landslide mapping. Through supervised or unsupervised classifiers, MCD combines different algorithms of change detection metrics, such as change in Normalized Differential Vegetation Index, spectral angle, principal component analysis, and independent component analysis, and applies them to a multi-temporal set of VHR satellite images to distinguish new landslides from stable areas. MCD has been applied with success in different geographical areas and with different satellite images, suggesting it is a reliable and robust technique. The technique can distinguish old from new landslides and capture runout features. Results of these case studies will be presented in the conference. Also to be presented are new developments of MCD involving the introduction of a priori information on landslide susceptibility within

  2. Detection of Upper Airway Status and Respiratory Events by a Current Generation Positive Airway Pressure Device

    PubMed Central

    Li, Qing Yun; Berry, Richard B.; Goetting, Mark G.; Staley, Bethany; Soto-Calderon, Haideliza; Tsai, Sheila C.; Jasko, Jeffrey G.; Pack, Allan I.; Kuna, Samuel T.

    2015-01-01

    Study Objectives: To compare a positive airway pressure (PAP) device's detection of respiratory events and airway status during device-detected apneas with events scored on simultaneous polysomnography (PSG). Design: Prospective PSGs of patients with sleep apnea using a new-generation PAP device. Settings: Four clinical and academic sleep centers. Patients: Forty-five patients with obstructive sleep apnea (OSA) and complex sleep apnea (Comp SA) performed a PSG on PAP levels adjusted to induce respiratory events. Interventions: None. Measurements and Results: PAP device data identifying the type of respiratory event and whether the airway during a device-detected apnea was open or obstructed were compared to time-synced, manually scored respiratory events on simultaneous PSG recording. Intraclass correlation coefficients between device-detected and PSG scored events were 0.854 for apnea-hypopnea index (AHI), 0.783 for apnea index, 0.252 for hypopnea index, and 0.098 for respiratory event-related arousals index. At a device AHI (AHIFlow) of 10 events/h, area under the receiver operating characteristic curve was 0.98, with sensitivity 0.92 and specificity 0.84. AHIFlow tended to overestimate AHI on PSG at values less than 10 events/h. The device detected that the airway was obstructed in 87.4% of manually scored obstructive apneas. Of the device-detected apneas with clear airway, a minority (15.8%) were manually scored as obstructive apneas. Conclusions: A device-detected apnea-hypopnea index (AHIFlow) < 10 events/h on a positive airway pressure device is strong evidence of good treatment efficacy. Device-detected airway status agrees closely with the presumed airway status during polysomnography scored events, but should not be equated with a specific type of respiratory event. Citation: Li QY, Berry RB, Goetting MG, Staley B, Soto-Calderon H, Tsai SC, Jasko JG, Pack AI, Kuna ST. Detection of upper airway status and respiratory events by a current generation positive

  3. Novel real-time PCR detection assay for Brucella suis

    PubMed Central

    Hänsel, C.; Mertens, K.; Elschner, M. C.; Melzer, F.

    2015-01-01

    Introduction Brucella suis is the causative agent of brucellosis in suidae and is differentiated into five biovars (bv). Biovars 1 and 3 possess zoonotic potential and can infect humans, whereas biovar 2 represents the main source of brucellosis in feral and domestic pigs in Europe. Both aspects, the zoonotic threat and the economic loss, emphasize the necessity to monitor feral and domestic pig populations. Available serological or PCR based methods lack sensitivity and specificity. Results Here a bioinformatics approach was used to identify a B. suis specific 17 bp repeat on chromosome II (BS1330_II0657 locus). This repeat is common for B. suis bv 1 to 4 and was used to develop a TaqMan probe assay. The average PCR efficiency was determined as 95% and the limit of detection as 12,5 fg/µl of DNA, equally to 3.7 bacterial genomes. This assay has the highest sensitivity of all previously described B. suis specific PCR assays, making it possible to detect 3-4 bacterial genomes per 1 µl of sample. The assay was tested 100% specific for B. suis and negative for other Brucella spp. and closely related non-Brucella species. Conclusions This novel qPCR assay could become a rapid, inexpensive and reliable screening method for large sample pools of B. suis 1 to 4. This method will be applicable for field samples after validation. PMID:26392898

  4. Real time QRS complex detection using DFA and regular grammar.

    PubMed

    Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed Hedi

    2017-02-28

    The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

  5. Real-time defect detection on highly reflective curved surfaces

    NASA Astrophysics Data System (ADS)

    Rosati, G.; Boschetti, G.; Biondi, A.; Rossi, A.

    2009-03-01

    This paper presents an automated defect detection system for coated plastic components for the automotive industry. This research activity came up as an evolution of a previous study which employed a non-flat mirror to illuminate and inspect high reflective curved surfaces. According to this method, the rays emitted from a light source are conveyed on the surface under investigation by means of a suitably curved mirror. After the reflection on the surface, the light rays are collected by a CCD camera, in which the coating defects appear as shadows of various shapes and dimensions. In this paper we present an evolution of the above-mentioned method, introducing a simplified mirror set-up in order to reduce the costs and the complexity of the defect detection system. In fact, a set of plane mirrors is employed instead of the curved one. Moreover, the inspection of multiple bend radius parts is investigated. A prototype of the machine vision system has been developed in order to test this simplified method. This device is made up of a light projector, a set of plane mirrors for light rays reflection, a conveyor belt for handling components, a CCD camera and a desktop PC which performs image acquisition and processing. Like in the previous system, the defects are identified as shadows inside a high brightness image. At the end of the paper, first experimental results are presented.

  6. An adaptive fault-tolerant event detection scheme for wireless sensor networks.

    PubMed

    Yim, Sung-Jib; Choi, Yoon-Hwa

    2010-01-01

    In this paper, we present an adaptive fault-tolerant event detection scheme for wireless sensor networks. Each sensor node detects an event locally in a distributed manner by using the sensor readings of its neighboring nodes. Confidence levels of sensor nodes are used to dynamically adjust the threshold for decision making, resulting in consistent performance even with increasing number of faulty nodes. In addition, the scheme employs a moving average filter to tolerate most transient faults in sensor readings, reducing the effective fault probability. Only three bits of data are exchanged to reduce the communication overhead in detecting events. Simulation results show that event detection accuracy and false alarm rate are kept very high and low, respectively, even in the case where 50% of the sensor nodes are faulty.

  7. Global Health Security: Building Capacities for Early Event Detection, Epidemiologic Workforce, and Laboratory Response.

    PubMed

    Balajee, S Arunmozhi; Arthur, Ray; Mounts, Anthony W

    The Global Health Security Agenda (GHSA) was launched in February 2014 to bring countries with limited capacity into compliance with the International Health Regulations (IHR) (2005). Recent international public health events, such as the appearance of Middle Eastern respiratory syndrome coronavirus and the reappearance of Ebola in West Africa, have highlighted the importance of early detection of disease events and the interconnectedness of countries. Surveillance systems that allow early detection and recognition of signal events, a public health infrastructure that allows rapid notification and information sharing within countries and across borders, a trained epidemiologic workforce, and a laboratory network that can respond appropriately and rapidly are emerging as critical components of an early warning and response system. This article focuses on 3 aspects of the GHSA that will lead to improved capacities for the detection and response to outbreaks as required by the IHR: (1) early detection and reporting of events, (2) laboratory capacity, and (3) a trained epidemiologic workforce.

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

    PubMed Central

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

    2014-01-01

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

  9. Real-time PCR for the detection of precise transgene copy number in durum wheat.

    PubMed

    Gadaleta, Agata; Giancaspro, Angelica; Cardone, Maria Francesca; Blanco, Antonio

    2011-12-01

    Recent results obtained in various crops indicate that real-time PCR could be a powerful tool for the detection and characterization of transgene locus structures. The determination of transgenic locus number through real-time PCR overcomes the problems linked to phenotypic segregation analysis (i.e. lack of detectable expression even when the transgenes are present) and can analyse hundreds of samples in a day, making it an efficient method for estimating gene copy number. Despite these advantages, many authors speak of "estimating" copy number by real-time PCR, and this is because the detection of a precise number of transgene depends on how well real-time PCR performs.This study was conducted to determine transgene copy number in transgenic wheat lines and to investigate potential variability in sensitivity and resolution of real-time chemistry by TaqMan probes. We have applied real-time PCR to a set of four transgenic durum wheat lines previously obtained. A total of 24 experiments (three experiments for two genes in each transgenic line) were conducted and standard curves were obtained from serial dilutions of the plasmids containing the genes of interest. The correlation coefficients ranged from 0.95 to 0.97. By using TaqMan quantitative real-time PCR we were able to detect 1 to 41 copies of transgenes per haploid genome in the DNA of homozygous T4 transformants. Although a slight variability was observed among PCR experiments, in our study we found real-time PCR to be a fast, sensitive and reliable method for the detection of transgene copy number in durum wheat, and a useful adjunct to Southern blot and FISH analyses to detect the presence of transgenic DNA in plant material.

  10. Event-related complexity analysis and its application in the detection of facial attractiveness.

    PubMed

    Deng, Zhidong; Zhang, Zimu

    2014-11-01

    In this study, an event-related complexity (ERC) analysis method is proposed and used to explore the neural correlates of facial attractiveness detection in the context of a cognitive experiment. The ERC method gives a quantitative index for measuring the diverse brain activation properties that represent the neural correlates of event-related responses. This analysis reveals distinct effects of facial attractiveness processing and also provides further information that could not have been achieved from event-related potential alone.

  11. Fractal analysis of GPS time series for early detection of disastrous seismic events

    NASA Astrophysics Data System (ADS)

    Filatov, Denis M.; Lyubushin, Alexey A.

    2017-03-01

    A new method of fractal analysis of time series for estimating the chaoticity of behaviour of open stochastic dynamical systems is developed. The method is a modification of the conventional detrended fluctuation analysis (DFA) technique. We start from analysing both methods from the physical point of view and demonstrate the difference between them which results in a higher accuracy of the new method compared to the conventional DFA. Then, applying the developed method to estimate the measure of chaoticity of a real dynamical system - the Earth's crust, we reveal that the latter exhibits two distinct mechanisms of transition to a critical state: while the first mechanism has already been known due to numerous studies of other dynamical systems, the second one is new and has not previously been described. Using GPS time series, we demonstrate efficiency of the developed method in identification of critical states of the Earth's crust. Finally we employ the method to solve a practically important task: we show how the developed measure of chaoticity can be used for early detection of disastrous seismic events and provide a detailed discussion of the numerical results, which are shown to be consistent with outcomes of other researches on the topic.

  12. Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets

    NASA Astrophysics Data System (ADS)

    Bazzica, A.; van Gemert, J. C.; Liem, C. C. S.; Hanjalic, A.

    2017-05-01

    Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \\eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.

  13. Seismicity Detection off Kushiro-Tokachi Using JAMSTEC Real-time Cabled Seafloor Earthquake Observation System

    NASA Astrophysics Data System (ADS)

    Otsuka, R.; Hirata, K.; Sugioka, H.; Fujie, G.; Morita, S.; Mikada, H.

    2001-12-01

    The off-Kushiro and Tokachi area, the land ward slope area of Kuril Trench, is characterized by its active seismicity, which is directly related to the subduction process of the Pacific plate. From the land observations, however, it is difficult to obtain the detailed seaward seismicity which provides us with important information on plate subduction beneath the Okhotsk (North American) plate. In July 1999, Japan Marine Science and Technology Center (JAMSTEC) established a real time seafloor cabled observation system off Kushiro and Tokachi. This system is composed of three OBS_¢A_ªr, two Tsumani gauges, a set of deep sea floor environmental sensors, and two branch MUX_¢A_ªrs. The OBS_¢A_ªrs are installed to form a tripartite network for the hypocenter determination of minor seismic events right around the cabled system. We can also estimate hypocenters of earthquakes with much accuracy by combining data from the OBS_¢A_ªrs with land data than those from land data only. Since the installation of the system, we relocated about 1500 events from February to June 2001 using the OBS data and land network data. These events were chosen under a condition that they are triggered at least at one OBS location. For obtaining the accuracy enhancement in hypocenter determination by combining the OBS data, locations of these seismic events were determined only with the land data, too. It turned out that estimate error in hypocenters after the combination was reduced to range around a few km. We also found out that a seismically active area is located along the Kushiro-submarine valley that has been suspected as a structural discontinuity. We would like to demonstrate, (1) the great improvement in the detection of earthquakes around off Kushiro-Tokachi seismogenic zone after the deployment of the cabled sea floor observatories and (2) associate understandings on the seismicity led from the relocated earthquake hypocenters. These results implies the importance of cabled

  14. Real-time vehicle detection and tracking in video based on faster R-CNN

    NASA Astrophysics Data System (ADS)

    Zhang, Yongjie; Wang, Jian; Yang, Xin

    2017-08-01

    Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

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

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  16. Screening DNA chip and event-specific multiplex PCR detection methods for biotech crops.

    PubMed

    Lee, Seong-Hun

    2014-11-01

    There are about 80 biotech crop events that have been approved by safety assessment in Korea. They have been controlled by genetically modified organism (GMO) and living modified organism (LMO) labeling systems. The DNA-based detection method has been used as an efficient scientific management tool. Recently, the multiplex polymerase chain reaction (PCR) and DNA chip have been developed as simultaneous detection methods for several biotech crops' events. The event-specific multiplex PCR method was developed to detect five biotech maize events: MIR604, Event 3272, LY 038, MON 88017 and DAS-59122-7. The specificity was confirmed and the sensitivity was 0.5%. The screening DNA chip was developed from four endogenous genes of soybean, maize, cotton and canola respectively along with two regulatory elements and seven genes: P35S, tNOS, pat, bar, epsps1, epsps2, pmi, cry1Ac and cry3B. The specificity was confirmed and the sensitivity was 0.5% for four crops' 12 events: one soybean, six maize, three cotton and two canola events. The multiplex PCR and DNA chip can be available for screening, gene-specific and event-specific analysis of biotech crops as efficient detection methods by saving on workload and time. © 2014 Society of Chemical Industry. © 2014 Society of Chemical Industry.

  17. Systematic detection of seismic events at Mount St. Helens with an ultra-dense array

    NASA Astrophysics Data System (ADS)

    Meng, X.; Hartog, J. R.; Schmandt, B.; Hotovec-Ellis, A. J.; Hansen, S. M.; Vidale, J. E.; Vanderplas, J.

    2016-12-01

    During the summer of 2014, an ultra-dense array of 900 geophones was deployed around the crater of Mount St. Helens and continuously operated for 15 days. This dataset provides us an unprecedented opportunity to systematically detect seismic events around an active volcano and study their underlying mechanisms. We use a waveform-based matched filter technique to detect seismic events from this dataset. Due to the large volume of continuous data ( 1 TB), we performed the detection on the GPU cluster Stampede (https://www.tacc.utexas.edu/systems/stampede). We build a suite of template events from three catalogs: 1) the standard Pacific Northwest Seismic Network (PNSN) catalog (45 events); 2) the catalog from Hansen&Schmandt (2015) obtained with a reverse-time imaging method (212 events); and 3) the catalog identified with a matched filter technique using the PNSN permanent stations (190 events). By searching for template matches in the ultra-dense array, we find 2237 events. We then calibrate precise relative magnitudes for template and detected events, using a principal component fit to measure waveform amplitude ratios. The magnitude of completeness and b-value of the detected catalog is -0.5 and 1.1, respectively. Our detected catalog shows several intensive swarms, which are likely driven by fluid pressure transients in conduits or slip transients on faults underneath the volcano. We are currently relocating the detected catalog with HypoDD and measuring the seismic velocity changes at Mount St. Helens using the coda wave interferometry of detected repeating earthquakes. The accurate temporal-spatial migration pattern of seismicity and seismic property changes should shed light on the physical processes beneath Mount St. Helens.

  18. Event Detection Challenges, Methods, and Applications in Natural and Artificial Systems

    DTIC Science & Technology

    2009-03-01

    directly addressed in this exposition; such methods include particle filtering, genetic algorithms , neural networks , and intelligent agents. A simple...Dependence – Criticality of Application – Numerous and Diverse Data Sources – Network Topology – Event Detection Algorithms • Typical Event Detection... algorithms • Neural networks • Intelligent agents 23© 2009 Lockheed Martin MS2 Composite Methods • Those methods that combine techniques within a category or

  19. Real-time RT-PCR assay for detection and differentiation of Citrus tristeza virus isolates

    USDA-ARS?s Scientific Manuscript database

    For universal detection of Citrus tristeza virus (CTV) strains by real time RT-PCR, a protocol was developed based on a set of primers and a Cy5-labeled TaqMan probe. This test included primers and a TET-labeled TaqMan probe selected on the mitochondrial nad5 gene for the simultaneous detection of ...

  20. Molecular-Beacon Multiplex Real-Time PCR Assay for Detection of Vibrio cholerae

    PubMed Central

    Gubala, Aneta J.; Proll, David F.

    2006-01-01

    A multiplex real-time PCR assay was developed using molecular beacons for the detection of Vibrio cholerae by targeting four important virulence and regulatory genes. The specificity and sensitivity of this assay, when tested with pure culture and spiked environmental water samples, were high, surpassing those of currently published PCR assays for the detection of this organism. PMID:16957277

  1. Detection of Toxoplasma gondii oocysts in water sample concentrates by real-time PCR

    USDA-ARS?s Scientific Manuscript database

    PCR techniques in combination with conventional parasite concentration procedures have potential for sensitive and specific detection of Toxoplasma gondii oocysts in water. Three real-time PCR assays based on the B1 gene and a 529-bp repetitive element were compared for detection of T. gondii tachyz...

  2. Improving linear accelerator service response with a real- time electronic event reporting system.

    PubMed

    Hoisak, Jeremy D P; Pawlicki, Todd; Kim, Gwe-Ya; Fletcher, Richard; Moore, Kevin L

    2014-09-08

    To track linear accelerator performance issues, an online event recording system was developed in-house for use by therapists and physicists to log the details of technical problems arising on our institution's four linear accelerators. In use since October 2010, the system was designed so that all clinical physicists would receive email notification when an event was logged. Starting in October 2012, we initiated a pilot project in collaboration with our linear accelerator vendor to explore a new model of service and support, in which event notifications were also sent electronically directly to dedicated engineers at the vendor's technical help desk, who then initiated a response to technical issues. Previously, technical issues were reported by telephone to the vendor's call center, which then disseminated information and coordinated a response with the Technical Support help desk and local service engineers. The purpose of this work was to investigate the improvements to clinical operations resulting from this new service model. The new and old service models were quantitatively compared by reviewing event logs and the oncology information system database in the nine months prior to and after initiation of the project. Here, we focus on events that resulted in an inoperative linear accelerator ("down" machine). Machine downtime, vendor response time, treatment cancellations, and event resolution were evaluated and compared over two equivalent time periods. In 389 clinical days, there were 119 machine-down events: 59 events before and 60 after introduction of the new model. In the new model, median time to service response decreased from 45 to 8 min, service engineer dispatch time decreased 44%, downtime per event decreased from 45 to 20 min, and treatment cancellations decreased 68%. The decreased vendor response time and reduced number of on-site visits by a service engineer resulted in decreased downtime and decreased patient treatment cancellations.

  3. Improving linear accelerator service response with a real-time electronic event reporting system.

    PubMed

    Hoisak, Jeremy D P; Pawlicki, Todd; Kim, Gwe-Ya; Fletcher, Richard; Moore, Kevin L

    2014-09-01

    To track linear accelerator performance issues, an online event recording system was developed in-house for use by therapists and physicists to log the details of technical problems arising on our institution's four linear accelerators. In use since October 2010, the system was designed so that all clinical physicists would receive email notification when an event was logged. Starting in October 2012, we initiated a pilot project in collaboration with our linear accelerator vendor to explore a new model of service and support, in which event notifications were also sent electronically directly to dedicated engineers at the vendor's technical help desk, who then initiated a response to technical issues. Previously, technical issues were reported by telephone to the vendor's call center, which then disseminated information and coordinated a response with the Technical Support help desk and local service engineers. The purpose of this work was to investigate the improvements to clinical operations resulting from this new service model. The new and old service models were quantitatively compared by reviewing event logs and the oncology information system database in the nine months prior to and after initiation of the project. Here, we focus on events that resulted in an inoperative linear accelerator ("down" machine). Machine downtime, vendor response time, treatment cancellations, and event resolution were evaluated and compared over two equivalent time periods. In 389 clinical days, there were 119 machine-down events: 59 events before and 60 after introduction of the new model. In the new model, median time to service response decreased from 45 to 8 min, service engineer dispatch time decreased 44%, downtime per event decreased from 45 to 20 min, and treatment cancellations decreased 68%. The decreased vendor response time and reduced number of on-site visits by a service engineer resulted in decreased downtime and decreased patient treatment cancellations. PACS

  4. Evaluation of various real-time reverse transcription quantitative PCR assays for norovirus detection.

    PubMed

    Yoo, Ju Eun; Lee, Cheonghoon; Park, SungJun; Ko, GwangPyo

    2017-02-01

    Human noroviruses are widespread and contagious viruses causing nonbacterial gastroenteritis. Real-time reverse transcription quantitative PCR (real-time RT-qPCR) is currently the gold standard for sensitive and accurate detection for these pathogens and serves as a critical tool in outbreak prevention and control. Different surveillance teams, however, may use different assays and variability in specimen conditions may lead to disagreement in results. Furthermore, the norovirus genome is highly variable and continuously evolving. These issues necessitate the re-examination of the real-time RT-qPCR's robustness in the context of accurate detection as well as the investigation of practical strategies to enhance assay performance. Four widely referenced real-time RT-qPCR assays (Assay A-D) were simultaneously performed to evaluate characteristics such as PCR efficiency, detection limit, as well as sensitivity and specificity with RT-PCR, and to assess the most accurate method for detecting norovirus genogroups I and II. Overall, Assay D was evaluated to be the most precise and accurate assay in this study. A Zen internal quencher, which decreases nonspecific fluorescence during the PCR reaction, was added to Assay D's probe which further improved assay performance. This study compared several detection assays for noroviruses and an improvement strategy based on such comparisons provided useful characterizations of a highly optimized real-time RT-qPCR assay for norovirus detection.

  5. Probabilistic approaches to fault detection in networked discrete event systems.

    PubMed

    Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

    2005-09-01

    In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.

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

    NASA Astrophysics Data System (ADS)

    Vaezi, Yoones; Van der Baan, Mirko

    2015-12-01

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

  7. Detecting cardiac events - state-of-the-art.

    PubMed

    Collinson, Paul

    2015-11-01

    Cardiac biomarker measurement currently addresses two key questions in patient management: the differential diagnosis of chest pain and the differential diagnosis of the patient with breathlessness. There are currently three major themes in the strategies for the differential diagnosis of chest pain. The first is to undertake troponin measurement in patients selected to be at lower risk, hence to have a low prior probability of disease. The second is the introduction of high-sensitivity cardiac troponin (hs cTn) assays into routine clinical use with measurement at 0 and 3 h from admission. Two other approaches that utilize the diagnostic characteristics of these assays have also been suggested. The first is to use the limit of detection or limit of blank of the assay as the diagnostic discriminant. The second approach is to use the low imprecision of the assay within the reference interval and combine a discriminant value with an absolute rate of change (delta value). The third is the use of additional biomarkers to allow early discharge from the emergency department. The concept is to measure high-sensitivity cardiac troponin plus the extra marker on admission. The role of measurement of B-type natriuretic peptide or its N-terminal prohormone, N-terminal pro-B-type natriuretic peptide, has been accepted and incorporated into guidelines for chronic heart failure for some time. More recently, guidelines for acute heart failure can also recommend a single measurement of B-type natriuretic peptide or N-terminal pro-B-type natriuretic peptide in people presenting with new suspected acute heart failure.

  8. CISN ShakeAlert: Faster Warning Information Through Multiple Threshold Event Detection in the Virtual Seismologist (VS) Early Warning Algorithm

    NASA Astrophysics Data System (ADS)

    Cua, G. B.; Fischer, M.; Caprio, M.; Heaton, T. H.; Cisn Earthquake Early Warning Project Team

    2010-12-01

    The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system that could potentially be implemented in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network since July 2008, and at the Northern California Seismic Network since February 2009. We discuss recent enhancements to the VS EEW algorithm that are being integrated into CISN ShakeAlert. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to initiate an event declaration, with the goal of reducing false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and the requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) into an on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Real-time and offline analysis on Swiss and California waveform datasets indicate that the

  9. Video-based real-time on-street parking occupancy detection system

    NASA Astrophysics Data System (ADS)

    Bulan, Orhan; Loce, Robert P.; Wu, Wencheng; Wang, YaoRong; Bernal, Edgar A.; Fan, Zhigang

    2013-10-01

    Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5 frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

  10. Algorithm for real-time detection of signal patterns using phase synchrony: an application to an electrode array

    NASA Astrophysics Data System (ADS)

    Sadeghi, Saman; MacKay, William A.; van Dam, R. Michael; Thompson, Michael

    2011-02-01

    Real-time analysis of multi-channel spatio-temporal sensor data presents a considerable technical challenge for a number of applications. For example, in brain-computer interfaces, signal patterns originating on a time-dependent basis from an array of electrodes on the scalp (i.e. electroencephalography) must be analyzed in real time to recognize mental states and translate these to commands which control operations in a machine. In this paper we describe a new technique for recognition of spatio-temporal patterns based on performing online discrimination of time-resolved events through the use of correlation of phase dynamics between various channels in a multi-channel system. The algorithm extracts unique sensor signature patterns associated with each event during a training period and ranks importance of sensor pairs in order to distinguish between time-resolved stimuli to which the system may be exposed during real-time operation. We apply the algorithm to electroencephalographic signals obtained from subjects tested in the neurophysiology laboratories at the University of Toronto. The extension of this algorithm for rapid detection of patterns in other sensing applications, including chemical identification via chemical or bio-chemical sensor arrays, is also discussed.

  11. The First Ground-Level Enhancement of Solar Cycle 24 on 17 May 2012 and Its Real-Time Detection

    NASA Astrophysics Data System (ADS)

    Papaioannou, A.; Souvatzoglou, G.; Paschalis, P.; Gerontidou, M.; Mavromichalaki, H.

    2014-01-01

    Ground-level enhancements (GLEs) are defined as sudden increases in the recorded intensity of cosmic-ray particles, usually by neutron monitors (NMs). In this work we present a time-shifting analysis (TSA) for the first arriving particles that were detected at Earth by NMs. We also present an automated real-time GLE alert that has been developed and is operating via the Neutron Monitor Database (NMDB), which successfully identified the 17 May 2012 event, designated as GLE71. We discuss the time evolution of the real-time GLE alert that was issued for GLE71 and present the event onset-time for NMs that contributed to this GLE alert based on their archived data. A comparison with their real-time time-stamp was made to illustrate the necessity for high-resolution data ( e.g. 1-min time resolution) made available at every minute. The first results on the propagation of relativistic protons that have been recorded by NMs, as inferred by the TSA, imply that they are most probably accelerated by the coronal-mass-ejection-driven shock. Furthermore, the successful usage of NM data and the corresponding achievement of issuing a timely GLE alert are discussed.

  12. Event-specific qualitative and quantitative detection of five genetically modified rice events using a single standard reference molecule.

    PubMed

    Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Shin, Min-Ki; Moon, Gui-Im; Hong, Jin-Hwan; Kim, Hae-Yeong

    2017-07-01

    One novel standard reference plasmid, namely pUC-RICE5, was constructed as a positive control and calibrator for event-specific qualitative and quantitative detection of genetically modified (GM) rice (Bt63, Kemingdao1, Kefeng6, Kefeng8, and LLRice62). pUC-RICE5 contained fragments of a rice-specific endogenous reference gene (sucrose phosphate synthase) as well as the five GM rice events. An existing qualitative PCR assay approach was modified using pUC-RICE5 to create a quantitative method with limits of detection correlating to approximately 1-10 copies of rice haploid genomes. In this quantitative PCR assay, the square regression coefficients ranged from 0.993 to 1.000. The standard deviation and relative standard deviation values for repeatability ranged from 0.02 to 0.22 and 0.10% to 0.67%, respectively. The Ministry of Food and Drug Safety (Korea) validated the method and the results suggest it could be used routinely to identify five GM rice events.

  13. Focused array radar for real-time imaging and detection

    NASA Astrophysics Data System (ADS)

    Rappaport, Carey M.; Reidy, Denis M.

    1996-06-01

    The Focused Array Radar (FAR) is a unique time-domain radar system which uses adjustable time delayed signals in a wide multi-element array which focuses transmitted and received signals to detect targets in lossy soil. By making use of specially-designed folded rhombus antenna elements--which are both ultra-wideband and more omnidirectional in the forward direction than a comparable dipole--the FAR optimizes the trade-off between target resolution and penetration depth. These projectory antenna elements, patented by GEO-CENTERS, INC., faithfully radiate sub- nanosecond pulses with frequency response varying from about 700 MHz to 1.3 GHz, so targets in wet soils within 60 cm of the surface and as small as 8 cm can be resolved. The array signals are focused by establishing the time delay from each element to each sample point in the soil medium, taking into consideration the differing propagation speed in air and various soils, as well as ray path refraction at the air/ground interface. These delays are applied in roughly ten picosecond intervals to the transmitted signal and used to time gate the received signal. By using time delays for a focused wideband pulse, the phases of each frequency component of the radar signal are in effect properly specified for constructive interference. Also, as a result of the time-gating, the large ground surface reflection signal is avoided.

  14. Exploiting semantics for sensor re-calibration in event detection systems

    NASA Astrophysics Data System (ADS)

    Vaisenberg, Ronen; Ji, Shengyue; Hore, Bijit; Mehrotra, Sharad; Venkatasubramanian, Nalini

    2008-01-01

    Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example--Coffee Pot level detection based on video data--to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly evolved system state and to resume monitoring with a higher rate of accuracy.

  15. Novel Use of Matched Filtering for Synaptic Event Detection and Extraction

    PubMed Central

    Shi, Yulin; Nenadic, Zoran; Xu, Xiangmin

    2010-01-01

    Efficient and dependable methods for detection and measurement of synaptic events are important for studies of synaptic physiology and neuronal circuit connectivity. As the published methods with detection algorithms based upon amplitude thresholding and fixed or scaled template comparisons are of limited utility for detection of signals with variable amplitudes and superimposed events that have complex waveforms, previous techniques are not applicable for detection of evoked synaptic events in photostimulation and other similar experimental situations. Here we report on a novel technique that combines the design of a bank of approximate matched filters with the detection and estimation theory to automatically detect and extract photostimluation-evoked excitatory postsynaptic currents (EPSCs) from individually recorded neurons in cortical circuit mapping experiments. The sensitivity and specificity of the method were evaluated on both simulated and experimental data, with its performance comparable to that of visual event detection performed by human operators. This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons. In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses. Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals. PMID:21124805

  16. The real-time complex cruise scene motion detection system based on DSP

    NASA Astrophysics Data System (ADS)

    Wu, Zhi-guo; Wang, Ming-jia

    2014-11-01

    Dynamic target recognition is an important issue in the field of image processing research. It is widely used in photoelectric detection, target tracking, video surveillance areas. Complex cruise scene of target detection, compared to the static background, since the target and background objects together and both are in motion, greatly increases the complexity of moving target detection and recognition. Based on the practical engineering applications, combining an embedded systems and real-time image detection technology, this paper proposes a real-time movement detection method on an embedded system based on the FPGA + DSP system architecture on an embedded system. The DSP digital image processing system takes high speed digital signal processor DSP TMS320C6416T as the main computing components. And we take large capacity FPGA as coprocessor. It is designed and developed a high-performance image processing card. The FPGA is responsible for the data receiving and dispatching, DSP is responsible for data processing. The FPGA collects image data and controls SDRAM according to the digital image sequence. The SDRAM realizes multiport image buffer. DSP reads real-time image through SDRAM and performs scene motion detection algorithm. Then we implement the data reception and data processing parallelization. This system designs and realizes complex cruise scene motion detection for engineering application. The image edge information has the anti-light change and the strong anti-interference ability. First of all, the adjacent frame and current frame image are processed by convolution operation, extract the edge images. Then we compute correlation strength and the value of movement offset. We can complete scene motion parameters estimation by the result, in order to achieve real-time accurate motion detection. We use images in resolution of 768 * 576 and 25Hz frame rate to do the real-time cruise experiment. The results show that the proposed system achieves real

  17. Real-Time Label-Free Embolus Detection Using In Vivo Photoacoustic Flow Cytometry

    PubMed Central

    Juratli, Mazen A.; Menyaev, Yulian A.; Sarimollaoglu, Mustafa; Siegel, Eric R.; Nedosekin, Dmitry A.; Suen, James Y.; Melerzanov, Alexander V.; Juratli, Tareq A.; Galanzha, Ekaterina I.; Zharov, Vladimir P.

    2016-01-01

    Thromboembolic events are one of the world’s leading causes of death among patients. Embolus or clot formations have several etiologies including paraneoplastic, post-surgery, cauterization, transplantation, or extracorporeal circuits. Despite its medical significance, little progress has been made in early embolus detection, screening and control. The aim of our study is to test the utility of the in vivo photoacoustic (PA) flow cytometry (PAFC) technique for non-invasive embolus detection in real-time. Using in vivo PAFC, emboli were non-invasively monitored in the bloodstream of two different mouse models. The tumor-free mouse model consisted of two groups, one in which the limbs were clamped to produce vessel stasis (7 procedures), and one where the mice underwent surgery (7 procedures). The melanoma-bearing mouse model also consisted of two groups, one in which the implanted tumor underwent compression (8 procedures), and one where a surgical excision of the implanted tumor was performed (8 procedures). We demonstrated that the PAFC can detect a single embolus, and has the ability to distinguish between erythrocyte–rich (red) and leukocyte/platelet-rich (white) emboli in small vessels. We show that, in tumor-bearing mice, the level of circulating emboli was increased compared to tumor-free mice (p = 0.0013). The number of circulating emboli temporarily increased in the tumor-free control mice during vessel stasis (p = 0.033) and after surgical excisions (signed-rank p = 0.031). Similar observations were noted during tumor compression (p = 0.013) and after tumor excisions (p = 0.012). For the first time, it was possible to detect unlabeled emboli in vivo non-invasively, and to confirm the presence of pigmented tumor cells within circulating emboli. The insight on embolus dynamics during cancer progression and medical procedures highlight the clinical potential of PAFC for early detection of cancer and surgery-induced emboli to prevent the fatal

  18. Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

    NASA Astrophysics Data System (ADS)

    Ziegler, Abra E.

    The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was

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

    PubMed

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

    2002-10-01

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

  20. Ultrasensitive microchip based on smart microgel for real-time online detection of trace threat analytes

    PubMed Central

    Lin, Shuo; Wang, Wei; Ju, Xiao-Jie; Xie, Rui; Liu, Zhuang; Yu, Hai-Rong; Zhang, Chuan; Chu, Liang-Yin

    2016-01-01

    Real-time online detection of trace threat analytes is critical for global sustainability, whereas the key challenge is how to efficiently convert and amplify analyte signals into simple readouts. Here we report an ultrasensitive microfluidic platform incorporated with smart microgel for real-time online detection of trace threat analytes. The microgel can swell responding to specific stimulus in flowing solution, resulting in efficient conversion of the stimulus signal into significantly amplified signal of flow-rate change; thus highly sensitive, fast, and selective detection can be achieved. We demonstrate this by incorporating ion-recognizable microgel for detecting trace Pb2+, and connecting our platform with pipelines of tap water and wastewater for real-time online Pb2+ detection to achieve timely pollution warning and terminating. This work provides a generalizable platform for incorporating myriad stimuli-responsive microgels to achieve ever-better performance for real-time online detection of various trace threat molecules, and may expand the scope of applications of detection techniques. PMID:26858435

  1. Ultrasensitive microchip based on smart microgel for real-time online detection of trace threat analytes.

    PubMed

    Lin, Shuo; Wang, Wei; Ju, Xiao-Jie; Xie, Rui; Liu, Zhuang; Yu, Hai-Rong; Zhang, Chuan; Chu, Liang-Yin

    2016-02-23

    Real-time online detection of trace threat analytes is critical for global sustainability, whereas the key challenge is how to efficiently convert and amplify analyte signals into simple readouts. Here we report an ultrasensitive microfluidic platform incorporated with smart microgel for real-time online detection of trace threat analytes. The microgel can swell responding to specific stimulus in flowing solution, resulting in efficient conversion of the stimulus signal into significantly amplified signal of flow-rate change; thus highly sensitive, fast, and selective detection can be achieved. We demonstrate this by incorporating ion-recognizable microgel for detecting trace Pb(2+), and connecting our platform with pipelines of tap water and wastewater for real-time online Pb(2+) detection to achieve timely pollution warning and terminating. This work provides a generalizable platform for incorporating myriad stimuli-responsive microgels to achieve ever-better performance for real-time online detection of various trace threat molecules, and may expand the scope of applications of detection techniques.

  2. Insider Threat Control: Using Plagiarism Detection Algorithms to Prevent Data Exfiltration in Near Real Time

    DTIC Science & Technology

    2013-10-01

    Insider Threat Control: Using Plagiarism Detection Algorithms to Prevent Data Exfiltration in Near Real Time Todd Lewellen George J. Silowash...algorithms used in plagiarism detection software—to search the index for bodies of text similar to the text found in the outgoing web request. If the...2. REPORT DATE October 2013 3. REPORT TYPE AND DATES COVERED Final 4. TITLE AND SUBTITLE Insider Threat Control: Using Plagiarism Detection

  3. Observing quantum gases in real time: Single-atom detection on a chip

    SciTech Connect

    Guenther, A.; Bender, H.; Stibor, A.; Fortagh, J.; Zimmermann, C.

    2009-07-15

    Single magnetically trapped {sup 87}Rb atoms on an atom chip are detected with 67{+-}12 % efficiency by three photon ionization and subsequent ion detection in a channel electron multiplier. State selective detection of single atoms and high-resolution optical spectroscopy on trapped atom clouds are demonstrated. The temperature and particle density of a trapped atomic gas is monitored in situ and in real time with negligible atom loss due to ionization below 5%.

  4. Real-time detection of transients in OGLE-IV with application of machine learning

    NASA Astrophysics Data System (ADS)

    Klencki, Jakub; Wyrzykowski, Łukasz

    2016-06-01

    The current bottleneck of transient detection in most surveys is the problem of rejecting numerous artifacts from detected candidates. We present a triple-stage hierarchical machine learning system for automated artifact filtering in difference imaging, based on self-organizing maps. The classifier, when tested on the OGLE-IV Transient Detection System, accepts 97% of real transients while removing up to 97.5% of artifacts.

  5. Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

    NASA Astrophysics Data System (ADS)

    Thompson, D. R.; Leifer, I.; Bovensmann, H.; Eastwood, M.; Fladeland, M.; Frankenberg, C.; Gerilowski, K.; Green, R. O.; Kratwurst, S.; Krings, T.; Luna, B.; Thorpe, A. K.

    2015-06-01

    Localized anthropogenic sources of atmospheric CH4 are highly uncertain and temporally variable. Airborne remote measurement is an effective method to detect and quantify these emissions. In a campaign context, the science yield can be dramatically increased by real-time retrievals that allow operators to coordinate multiple measurements of the most active areas. This can improve science outcomes for both single- and multiple-platform missions. We describe a case study of the NASA/ESA CO2 and Methane Experiment (COMEX) campaign in California during June and August/September 2014. COMEX was a multi-platform campaign to measure CH4 plumes released from anthropogenic sources including oil and gas infrastructure. We discuss principles for real-time spectral signature detection and measurement, and report performance on the NASA Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully detected CH4 plumes in real-time at Gb s-1 data rates, characterizing fugitive releases in concert with other in situ and remote instruments. The teams used these real-time CH4 detections to coordinate measurements across multiple platforms, including airborne in situ, airborne non-imaging remote sensing, and ground-based in situ instruments. To our knowledge this is the first reported use of real-time trace gas signature detection in an airborne science campaign, and presages many future applications.

  6. Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

    PubMed

    Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

    2016-12-08

    This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

  7. Qualitative Characteristics of Memories for Real, Imagined, and Media-Based Events

    ERIC Educational Resources Information Center

    Gordon, Ruthanna; Gerrig, Richard J.; Franklin, Nancy

    2009-01-01

    People's memories must be able to represent experiences with multiple types of origins--including the real world and our own imaginations, but also printed texts (prose-based media), movies, and television (screen-based media). This study was intended to identify cues that distinguish prose- and screen-based media memories from each other, as well…

  8. Qualitative Characteristics of Memories for Real, Imagined, and Media-Based Events

    ERIC Educational Resources Information Center

    Gordon, Ruthanna; Gerrig, Richard J.; Franklin, Nancy

    2009-01-01

    People's memories must be able to represent experiences with multiple types of origins--including the real world and our own imaginations, but also printed texts (prose-based media), movies, and television (screen-based media). This study was intended to identify cues that distinguish prose- and screen-based media memories from each other, as well…

  9. Energy efficient data representation and aggregation with event region detection in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Torsha

    Unlike conventional networks, wireless sensor networks (WSNs) are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this dissertation, we exploit the spatio-temporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighboring sensors and itself. We propose a Tree based polynomial REGression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, a polynomial function (P) is computed by the regression function, TREG. The coefficients of P are then passed to achieve the following goals: (i) The sink can get attribute values in the regions devoid of sensor nodes, and (ii) Readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area. Since physical attributes exhibit a gradual change over time, we propose an iterative scheme, UPDATE_COEFF, which obviates the need to perform the regression function repeatedly and uses approximations based on previous readings. Extensive simulations are performed on real world data to demonstrate the effectiveness of our proposed aggregation algorithm, TREG. Results reveal that for a network density of 0.0025 nodes/m2, a complete binary tree of depth 4 could provide the absolute error to be less than 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of the tree depth. In addition, our proposed updating scheme makes the aggregation process faster while maintaining the desired error bounds. We also propose a Polynomial-based scheme that addresses the problem of Event Region

  10. Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors.

    PubMed

    Deng, Fucheng; Zhu, Xiaorui; He, Chao

    2017-09-13

    Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.

  11. A cost effective real-time PCR for the detection of adenovirus from viral swabs

    PubMed Central

    2013-01-01

    Compared to traditional testing strategies, nucleic acid amplification tests such as real-time PCR offer many advantages for the detection of human adenoviruses. However, commercial assays are expensive and cost prohibitive for many clinical laboratories. To overcome fiscal challenges, a cost effective strategy was developed using a combination of homogenization and heat treatment with an “in-house” real-time PCR. In 196 swabs submitted for adenovirus detection, this crude extraction method showed performance characteristics equivalent to viral DNA obtained from a commercial nucleic acid extraction. In addition, the in-house real-time PCR outperformed traditional testing strategies using virus culture, with sensitivities of 100% and 69.2%, respectively. Overall, the combination of homogenization and heat treatment with a sensitive in-house real-time PCR provides accurate results at a cost comparable to viral culture. PMID:23758993

  12. Event specific qualitative and quantitative polymerase chain reaction detection of genetically modified MON863 maize based on the 5'-transgene integration sequence.

    PubMed

    Yang, Litao; Xu, Songci; Pan, Aihu; Yin, Changsong; Zhang, Kewei; Wang, Zhenying; Zhou, Zhigang; Zhang, Dabing

    2005-11-30

    Because of the genetically modified organisms (GMOs) labeling policies issued in many countries and areas, polymerase chain reaction (PCR) methods were developed for the execution of GMO labeling policies, such as screening, gene specific, construct specific, and event specific PCR detection methods, which have become a mainstay of GMOs detection. The event specific PCR detection method is the primary trend in GMOs detection because of its high specificity based on the flanking sequence of the exogenous integrant. This genetically modified maize, MON863, contains a Cry3Bb1 coding sequence that produces a protein with enhanced insecticidal activity against the coleopteran pest, corn rootworm. In this study, the 5'-integration junction sequence between the host plant DNA and the integrated gene construct of the genetically modified maize MON863 was revealed by means of thermal asymmetric interlaced-PCR, and the specific PCR primers and TaqMan probe were designed based upon the revealed 5'-integration junction sequence; the conventional qualitative PCR and quantitative TaqMan real-time PCR detection methods employing these primers and probes were successfully developed. In conventional qualitative PCR assay, the limit of detection (LOD) was 0.1% for MON863 in 100 ng of maize genomic DNA for one reaction. In the quantitative TaqMan real-time PCR assay, the LOD and the limit of quantification were eight and 80 haploid genome copies, respectively. In addition, three mixed maize samples with known MON863 contents were detected using the established real-time PCR systems, and the ideal results indicated that the established event specific real-time PCR detection systems were reliable, sensitive, and accurate.

  13. Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

    NASA Astrophysics Data System (ADS)

    Alese Semmens, Kathryn; Ramage, Joan; Bartsch, Annett; Liston, Glen E.

    2013-03-01

    High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.

  14. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    PubMed

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    Mobility characteristics associated with activity of daily living such as sitting down, lying down, rising up, and walking are considered to be important in maintaining functional independence and healthy life style especially for the growing elderly population. Characteristics of postural transitions such as sit-to-stand are widely used by clinicians as a physical indicator of health, and walking is used as an important mobility assessment tool. Many tools have been developed to assist in the assessment of functional levels and to detect a person’s activities during daily life. These include questionnaires, observation, diaries, kinetic and kinematic systems, and validated functional tests. These measures are costly and time consuming, rely on subjective patient recall and may not accurately reflect functional ability in the patient’s home. In order to provide a low-cost, objective assessment of functional ability, inertial measurement unit (IMU) using MEMS technology has been employed to ascertain ADLs. These measures facilitate long-term monitoring of activity of daily living using wearable sensors. IMU system are desirable in monitoring human postures since they respond to both frequency and the intensity of movements and measure both dc (gravitational acceleration vector) and ac (acceleration due to body movement) components at a low cost. This has enabled the development of a small, lightweight, portable system that can be worn by a free-living subject without motion impediment – TEMPO (Technology Enabled Medical Precision Observation). Using this IMU system, we acquired indirect measures of biomechanical variables that can be used as an assessment of individual mobility characteristics with accuracy and recognition rates that are comparable to the modern motion capture systems. In this study, five subjects performed various ADLs and mobility measures such as posture transitions and gait characteristics were obtained. We developed postural event detection

  15. Synchronizing to real events: Subjective audiovisual alignment scales with perceived auditory depth and speed of sound

    PubMed Central

    Alais, David; Carlile, Simon

    2005-01-01

    Because of the slow speed of sound relative to light, acoustic and visual signals from a distant event often will be received asynchronously. Here, using acoustic signals with a robust cue to sound source distance, we show that judgments of perceived temporal alignment with a visual marker depend on the depth simulated in the acoustic signal. For distant sounds, a large delay of sound relative to vision is required for the signals to be perceived as temporally aligned. For nearer sources, the time lag corresponding to audiovisual alignment is smaller and scales at rate approximating the speed of sound. Thus, when robust cues to auditory distance are present, the brain can synchronize disparate audiovisual signals to external events despite considerable differences in time of arrival at the perceiver. This ability is functionally important as it allows auditory and visual signals to be synchronized to the external event that caused them. PMID:15668388

  16. Synchronizing to real events: subjective audiovisual alignment scales with perceived auditory depth and speed of sound.

    PubMed

    Alais, David; Carlile, Simon

    2005-02-08

    Because of the slow speed of sound relative to light, acoustic and visual signals from a distant event often will be received asynchronously. Here, using acoustic signals with a robust cue to sound source distance, we show that judgments of perceived temporal alignment with a visual marker depend on the depth simulated in the acoustic signal. For distant sounds, a large delay of sound relative to vision is required for the signals to be perceived as temporally aligned. For nearer sources, the time lag corresponding to audiovisual alignment is smaller and scales at rate approximating the speed of sound. Thus, when robust cues to auditory distance are present, the brain can synchronize disparate audiovisual signals to external events despite considerable differences in time of arrival at the perceiver. This ability is functionally important as it allows auditory and visual signals to be synchronized to the external event that caused them.

  17. Using machine learning to detect events in eye-tracking data.

    PubMed

    Zemblys, Raimondas; Niehorster, Diederick C; Komogortsev, Oleg; Holmqvist, Kenneth

    2017-02-23

    Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to be adjusted based on eye movement data quality. Here we show that a fully automated classification of raw gaze samples as belonging to fixations, saccades, or other oculomotor events can be achieved using a machine-learning approach. Any already manually or algorithmically detected events can be used to train a classifier to produce similar classification of other data without the need for a user to set parameters. In this study, we explore the application of random forest machine-learning technique for the detection of fixations, saccades, and post-saccadic oscillations (PSOs). In an effort to show practical utility of the proposed method to the applications that employ eye movement classification algorithms, we provide an example where the method is employed in an eye movement-driven biometric application. We conclude that machine-learning techniques lead to superior detection compared to current state-of-the-art event detection algorithms and can reach the performance of manual coding.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  19. Comparison of different real-time PCR chemistries and their suitability for detection and quantification of genetically modified organisms.

    PubMed

    Buh Gasparic, Meti; Cankar, Katarina; Zel, Jana; Gruden, Kristina

    2008-03-06

    The real-time polymerase chain reaction is currently the method of choice for quantifying nucleic acids in different DNA based quantification applications. It is widely used also for detecting and quantifying genetically modified components in food and feed, predominantly employing TaqMan and SYBR Green real-time PCR chemistries. In our st