The 2014 Mw 6.0 Napa Earthquake, California: Observations from Real-time GPS-enhanced Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Johanson, I. A.; Grapenthin, R.; Allen, R. M.

2014-12-01

Recently, progress has been made to demonstrate feasibility and benefits of including real-time GPS (rtGPS) in earthquake early warning and rapid response systems. While most concepts have yet to be integrated into operational environments, the Berkeley Seismological Laboratory is currently running an rtGPS based finite fault inversion scheme in true real-time, which is triggered by the seismic-based ShakeAlert system and then sends updated earthquake alerts to a test receiver. The Geodetic Alarm System (G-larmS) was online and responded to the 2014 Mw6.0 South Napa earthquake in California. We review G-larmS' performance during this event and for 13 aftershocks, and we present rtGPS observations and real-time modeling results for the main shock. The first distributed slip model and a magnitude estimate of Mw5.5 were available 24 s after the event origin time, which could be reduced to 14 s after a bug fix (~8 s S-wave travel time, ~6 s data latency). The system continued to re-estimate the magnitude once every second: it increased to Mw5.9 3 s after the first alert and stabilized at Mw5.8 after 15 s. G-larmS' solutions for the subsequent small magnitude aftershocks demonstrate that Mw~6.0 is the current limit for alert updates to contribute back to the seismic-based early warning system.

Real-time estimation of ionospheric delay using GPS measurements

NASA Astrophysics Data System (ADS)

Lin, Lao-Sheng

1997-12-01

When radio waves such as the GPS signals propagate through the ionosphere, they experience an extra time delay. The ionospheric delay can be eliminated (to the first order) through a linear combination of L1 and L2 observations from dual-frequency GPS receivers. Taking advantage of this dispersive principle, one or more dual- frequency GPS receivers can be used to determine a model of the ionospheric delay across a region of interest and, if implemented in real-time, can support single-frequency GPS positioning and navigation applications. The research objectives of this thesis were: (1) to develop algorithms to obtain accurate absolute Total Electron Content (TEC) estimates from dual-frequency GPS observables, and (2) to develop an algorithm to improve the accuracy of real-time ionosphere modelling. In order to fulfil these objectives, four algorithms have been proposed in this thesis. A 'multi-day multipath template technique' is proposed to mitigate the pseudo-range multipath effects at static GPS reference stations. This technique is based on the assumption that the multipath disturbance at a static station will be constant if the physical environment remains unchanged from day to day. The multipath template, either single-day or multi-day, can be generated from the previous days' GPS data. A 'real-time failure detection and repair algorithm' is proposed to detect and repair the GPS carrier phase 'failures', such as the occurrence of cycle slips. The proposed algorithm uses two procedures: (1) application of a statistical test on the state difference estimated from robust and conventional Kalman filters in order to detect and identify the carrier phase failure, and (2) application of a Kalman filter algorithm to repair the 'identified carrier phase failure'. A 'L1/L2 differential delay estimation algorithm' is proposed to estimate GPS satellite transmitter and receiver L1/L2 differential delays. This algorithm, based on the single-site modelling technique, is

Real-time estimation of BDS/GPS high-rate satellite clock offsets using sequential least squares

NASA Astrophysics Data System (ADS)

Fu, Wenju; Yang, Yuanxi; Zhang, Qin; Huang, Guanwen

2018-07-01

The real-time precise satellite clock product is one of key prerequisites for real-time Precise Point Positioning (PPP). The accuracy of the 24-hour predicted satellite clock product with 15 min sampling interval and an update of 6 h provided by the International GNSS Service (IGS) is only 3 ns, which could not meet the needs of all real-time PPP applications. The real-time estimation of high-rate satellite clock offsets is an efficient method for improving the accuracy. In this paper, the sequential least squares method to estimate real-time satellite clock offsets with high sample rate is proposed to improve the computational speed by applying an optimized sparse matrix operation to compute the normal equation and using special measures to take full advantage of modern computer power. The method is first applied to BeiDou Navigation Satellite System (BDS) and provides real-time estimation with a 1 s sample rate. The results show that the amount of time taken to process a single epoch is about 0.12 s using 28 stations. The Standard Deviation (STD) and Root Mean Square (RMS) of the real-time estimated BDS satellite clock offsets are 0.17 ns and 0.44 ns respectively when compared to German Research Center for Geosciences (GFZ) final clock products. The positioning performance of the real-time estimated satellite clock offsets is evaluated. The RMSs of the real-time BDS kinematic PPP in east, north, and vertical components are 7.6 cm, 6.4 cm and 19.6 cm respectively. The method is also applied to Global Positioning System (GPS) with a 10 s sample rate and the computational time of most epochs is less than 1.5 s with 75 stations. The STD and RMS of the real-time estimated GPS satellite clocks are 0.11 ns and 0.27 ns, respectively. The accuracies of 5.6 cm, 2.6 cm and 7.9 cm in east, north, and vertical components are achieved for the real-time GPS kinematic PPP.

Statistical tools for transgene copy number estimation based on real-time PCR.

PubMed

Yuan, Joshua S; Burris, Jason; Stewart, Nathan R; Mentewab, Ayalew; Stewart, C Neal

2007-11-01

As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. These statistical methods allow the real-time PCR-based transgene copy number estimation

Strategies for Near Real Time Estimates of Precipitable Water Vapor from GPS Ground Receivers

NASA Technical Reports Server (NTRS)

Y., Bar-Sever; Runge, T.; Kroger, P.

1995-01-01

GPS-based estimates of precipitable water vapor (PWV) may be useful in numerical weather models to improve short-term weather predictions. To be effective in numerical weather prediction models, GPS PWV estimates must be produced with sufficient accuracy in near real time. Several estimation strategies for the near real time processing of GPS data are investigated.

A TRMM-Based System for Real-Time Quasi-Global Merged Precipitation Estimates

NASA Technical Reports Server (NTRS)

Starr, David OC. (Technical Monitor); Huffman, G. J.; Adler, R. F.; Stocker, E. F.; Bolvin, D. T.; Nelkin, E. J.

2002-01-01

A new processing system has been developed to combine IR and microwave data into 0.25 degree x 0.25 degree gridded precipitation estimates in near-real time over the latitude band plus or minus 50 degrees. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) precipitation estimates are used to calibrate Special Sensor Microwave/Imager (SSM/I) estimates, and Advanced Microwave Sounding Unit (AMSU) and Advanced Microwave Scanning Radiometer (AMSR) estimates, when available. The merged microwave estimates are then used to create a calibrated IR estimate in a Probability-Matched-Threshold approach for each individual hour. The microwave and IR estimates are combined for each 3-hour interval. Early results will be shown, including typical tropical and extratropical storm evolution and examples of the diurnal cycle. Major issues will be discussed, including the choice of IR algorithm, the approach for merging the IR and microwave estimates, extension to higher latitudes, retrospective processing back to 1999, and extension to the GPCP One-Degree Daily product (for which the authors are responsible). The work described here provides one approach to using data from the future NASA Global Precipitation Measurement program, which is designed to provide Jill global coverage by low-orbit passive microwave satellites every three hours beginning around 2008.

Real-time orbit estimation for ATS-6 from redundant attitude sensors

NASA Technical Reports Server (NTRS)

Englar, T. S., Jr.

1975-01-01

A program installed in the ATSOCC on-line computer operates with attitude sensor data to produce a smoothed real-time orbit estimate. This estimate is obtained from a Kalman filter which enables the estimate to be maintained in the absence of T/M data. The results are described of analytical and numerical investigations into the sensitivity of Control Center output to the position errors resulting from the real-time estimation. The results of the numerical investigation, which used several segments of ATS-6 data gathered during the Sensor Data Acquisition run on August 19, 1974, show that the implemented system can achieve absolute position determination with an error of about 100 km, implying pointing errors of less than 0.2 deg in latitude and longitude. This compares very favorably with ATS-6 specifications of approximately 0.5 deg in latitude-longitude.

The potential role of real-time geodetic observations in tsunami early warning

NASA Astrophysics Data System (ADS)

Tinti, Stefano; Armigliato, Alberto

2016-04-01

Tsunami warning systems (TWS) have the final goal to launch a reliable alert of an incoming dangerous tsunami to coastal population early enough to allow people to flee from the shore and coastal areas according to some evacuation plans. In the last decade, especially after the catastrophic 2004 Boxing Day tsunami in the Indian Ocean, much attention has been given to filling gaps in the existing TWSs (only covering the Pacific Ocean at that time) and to establishing new TWSs in ocean regions that were uncovered. Typically, TWSs operating today work only on earthquake-induced tsunamis. TWSs estimate quickly earthquake location and size by real-time processing seismic signals; on the basis of some pre-defined "static" procedures (either based on decision matrices or on pre-archived tsunami simulations), assess the tsunami alert level on a large regional scale and issue specific bulletins to a pre-selected recipients audience. Not unfrequently these procedures result in generic alert messages with little value. What usually operative TWSs do not do, is to compute earthquake focal mechanism, to calculate the co-seismic sea-floor displacement, to assess the initial tsunami conditions, to input these data into tsunami simulation models and to compute tsunami propagation up to the threatened coastal districts. This series of steps is considered nowadays too time consuming to provide the required timely alert. An equivalent series of steps could start from the same premises (earthquake focal parameters) and reach the same result (tsunami height at target coastal areas) by replacing the intermediate steps of real-time tsunami simulations with proper selection from a large archive of pre-computed tsunami scenarios. The advantage of real-time simulations and of archived scenarios selection is that estimates are tailored to the specific occurring tsunami and alert can be more detailed (less generic) and appropriate for local needs. Both these procedures are still at an

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

A real time neural net estimator of fatigue life

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1990-01-01

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

A real time neural net estimator of fatigue life

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1990-01-01

A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

Novel Algorithms Enabling Rapid, Real-Time Earthquake Monitoring and Tsunami Early Warning Worldwide

NASA Astrophysics Data System (ADS)

Lomax, A.; Michelini, A.

2012-12-01

We have introduced recently new methods to determine rapidly the tsunami potential and magnitude of large earthquakes (e.g., Lomax and Michelini, 2009ab, 2011, 2012). To validate these methods we have implemented them along with other new algorithms within the Early-est earthquake monitor at INGV-Rome (http://early-est.rm.ingv.it, http://early-est.alomax.net). Early-est is a lightweight software package for real-time earthquake monitoring (including phase picking, phase association and event detection, location, magnitude determination, first-motion mechanism determination, ...), and for tsunami early warning based on discriminants for earthquake tsunami potential. In a simulation using archived broadband seismograms for the devastating M9, 2011 Tohoku earthquake and tsunami, Early-est determines: the epicenter within 3 min after the event origin time, discriminants showing very high tsunami potential within 5-7 min, and magnitude Mwpd(RT) 9.0-9.2 and a correct shallow-thrusting mechanism within 8 min. Real-time monitoring with Early-est givess similar results for most large earthquakes using currently available, real-time seismogram data. Here we summarize some of the key algorithms within Early-est that enable rapid, real-time earthquake monitoring and tsunami early warning worldwide: >>> FilterPicker - a general purpose, broad-band, phase detector and picker (http://alomax.net/FilterPicker); >>> Robust, simultaneous association and location using a probabilistic, global-search; >>> Period-duration discriminants TdT0 and TdT50Ex for tsunami potential available within 5 min; >>> Mwpd(RT) magnitude for very large earthquakes available within 10 min; >>> Waveform P polarities determined on broad-band displacement traces, focal mechanisms obtained with the HASH program (Hardebeck and Shearer, 2002); >>> SeisGramWeb - a portable-device ready seismogram viewer using web-services in a browser (http://alomax.net/webtools/sgweb/info.html). References (see also: http

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Combining Real-Time Seismic and GPS Data for Earthquake Early Warning (Invited)

NASA Astrophysics Data System (ADS)

Boese, M.; Heaton, T. H.; Hudnut, K. W.

2013-12-01

Scientists at Caltech, UC Berkeley, the Univ. of SoCal, the Univ. of Washington, the US Geological Survey, and ETH Zurich have developed an earthquake early warning (EEW) demonstration system for California and the Pacific Northwest. To quickly determine the earthquake magnitude and location, 'ShakeAlert' currently processes and interprets real-time data-streams from ~400 seismic broadband and strong-motion stations within the California Integrated Seismic Network (CISN). Based on these parameters, the 'UserDisplay' software predicts and displays the arrival and intensity of shaking at a given user site. Real-time ShakeAlert feeds are currently shared with around 160 individuals, companies, and emergency response organizations to educate potential users about EEW and to identify needs and applications of EEW in a future operational warning system. Recently, scientists at the contributing institutions have started to develop algorithms for ShakeAlert that make use of high-rate real-time GPS data to improve the magnitude estimates for large earthquakes (M>6.5) and to determine slip distributions. Knowing the fault slip in (near) real-time is crucial for users relying on or operating distributed systems, such as for power, water or transportation, especially if these networks run close to or across large faults. As shown in an earlier study, slip information is also useful to predict (in a probabilistic sense) how far a fault rupture will propagate, thus enabling more robust probabilistic ground-motion predictions at distant locations. Finally, fault slip information is needed for tsunami warning, such as in the Cascadia subduction-zone. To handle extended fault-ruptures of large earthquakes in real-time, Caltech and USGS Pasadena are currently developing and testing a two-step procedure that combines seismic and geodetic data; in the first step, high-frequency strong-motion amplitudes are used to rapidly classify near-and far-source stations. Then, the location and

Real-Time Earthquake Analysis for Disaster Mitigation (READI) Network

NASA Astrophysics Data System (ADS)

Bock, Y.

2014-12-01

Real-time GNSS networks are making a significant impact on our ability to forecast, assess, and mitigate the effects of geological hazards. I describe the activities of the Real-time Earthquake Analysis for Disaster Mitigation (READI) working group. The group leverages 600+ real-time GPS stations in western North America operated by UNAVCO (PBO network), Central Washington University (PANGA), US Geological Survey & Scripps Institution of Oceanography (SCIGN project), UC Berkeley & US Geological Survey (BARD network), and the Pacific Geosciences Centre (WCDA project). Our goal is to demonstrate an earthquake and tsunami early warning system for western North America. Rapid response is particularly important for those coastal communities that are in the near-source region of large earthquakes and may have only minutes of warning time, and who today are not adequately covered by existing seismic and basin-wide ocean-buoy monitoring systems. The READI working group is performing comparisons of independent real time analyses of 1 Hz GPS data for station displacements and is participating in government-sponsored earthquake and tsunami exercises in the Western U.S. I describe a prototype seismogeodetic system using a cluster of southern California stations that includes GNSS tracking and collocation with MEMS accelerometers for real-time estimation of seismic velocity and displacement waveforms, which has advantages for improved earthquake early warning and tsunami forecasts compared to seismic-only or GPS-only methods. The READI working group's ultimate goal is to participate in an Indo-Pacific Tsunami early warning system that utilizes GNSS real-time displacements and ionospheric measurements along with seismic, near-shore buoys and ocean-bottom pressure sensors, where available, to rapidly estimate magnitude and finite fault slip models for large earthquakes, and then forecast tsunami source, energy scale, geographic extent, inundation and runup. This will require

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Deng, Bin; Wang, Jiang; Li, Huiyan; Liu, Chen; Fietkiewicz, Chris; Loparo, Kenneth A.

2017-01-01

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Near real-time estimation of the seismic source parameters in a compressed domain

NASA Astrophysics Data System (ADS)

Rodriguez, Ismael A. Vera

Seismic events can be characterized by its origin time, location and moment tensor. Fast estimations of these source parameters are important in areas of geophysics like earthquake seismology, and the monitoring of seismic activity produced by volcanoes, mining operations and hydraulic injections in geothermal and oil and gas reservoirs. Most available monitoring systems estimate the source parameters in a sequential procedure: first determining origin time and location (e.g., epicentre, hypocentre or centroid of the stress glut density), and then using this information to initialize the evaluation of the moment tensor. A more efficient estimation of the source parameters requires a concurrent evaluation of the three variables. The main objective of the present thesis is to address the simultaneous estimation of origin time, location and moment tensor of seismic events. The proposed method displays the benefits of being: 1) automatic, 2) continuous and, depending on the scale of application, 3) of providing results in real-time or near real-time. The inversion algorithm is based on theoretical results from sparse representation theory and compressive sensing. The feasibility of implementation is determined through the analysis of synthetic and real data examples. The numerical experiments focus on the microseismic monitoring of hydraulic fractures in oil and gas wells, however, an example using real earthquake data is also presented for validation. The thesis is complemented with a resolvability analysis of the moment tensor. The analysis targets common monitoring geometries employed in hydraulic fracturing in oil wells. Additionally, it is presented an application of sparse representation theory for the denoising of one-component and three-component microseismicity records, and an algorithm for improved automatic time-picking using non-linear inversion constraints.

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

Research on intelligent scenic security early warning platform based on high resolution image: real scene linkage and real-time LBS

NASA Astrophysics Data System (ADS)

Li, Baishou; Huang, Yu; Lan, Guangquan; Li, Tingting; Lu, Ting; Yao, Mingxing; Luo, Yuandan; Li, Boxiang; Qian, Yongyou; Gao, Yujiu

2015-12-01

This paper design and implement security monitor system within a scenic spot for tourists, the scenic spot staff can be automatic real time for visitors to perception and monitoring, and visitors can also know about themselves location in the scenic, real-time and obtain the 3D imaging conditions of scenic area. Through early warning can realize "parent-child relation", preventing the old man and child lost and wandering. Research results to the further development of virtual reality to provide effective security early warning platform of the theoretical basis and practical reference.

A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots

NASA Astrophysics Data System (ADS)

Li, Yuankai; Ding, Liang; Zheng, Zhizhong; Yang, Qizhi; Zhao, Xingang; Liu, Guangjun

2018-05-01

For motion control of wheeled planetary rovers traversing on deformable terrain, real-time terrain parameter estimation is critical in modeling the wheel-terrain interaction and compensating the effect of wheel slipping. A multi-mode real-time estimation method is proposed in this paper to achieve accurate terrain parameter estimation. The proposed method is composed of an inner layer for real-time filtering and an outer layer for online update. In the inner layer, sinkage exponent and internal frictional angle, which have higher sensitivity than that of the other terrain parameters to wheel-terrain interaction forces, are estimated in real time by using an adaptive robust extended Kalman filter (AREKF), whereas the other parameters are fixed with nominal values. The inner layer result can help synthesize the current wheel-terrain contact forces with adequate precision, but has limited prediction capability for time-variable wheel slipping. To improve estimation accuracy of the result from the inner layer, an outer layer based on recursive Gauss-Newton (RGN) algorithm is introduced to refine the result of real-time filtering according to the innovation contained in the history data. With the two-layer structure, the proposed method can work in three fundamental estimation modes: EKF, REKF and RGN, making the method applicable for flat, rough and non-uniform terrains. Simulations have demonstrated the effectiveness of the proposed method under three terrain types, showing the advantages of introducing the two-layer structure.

A multi-camera system for real-time pose estimation

NASA Astrophysics Data System (ADS)

Savakis, Andreas; Erhard, Matthew; Schimmel, James; Hnatow, Justin

2007-04-01

This paper presents a multi-camera system that performs face detection and pose estimation in real-time and may be used for intelligent computing within a visual sensor network for surveillance or human-computer interaction. The system consists of a Scene View Camera (SVC), which operates at a fixed zoom level, and an Object View Camera (OVC), which continuously adjusts its zoom level to match objects of interest. The SVC is set to survey the whole filed of view. Once a region has been identified by the SVC as a potential object of interest, e.g. a face, the OVC zooms in to locate specific features. In this system, face candidate regions are selected based on skin color and face detection is accomplished using a Support Vector Machine classifier. The locations of the eyes and mouth are detected inside the face region using neural network feature detectors. Pose estimation is performed based on a geometrical model, where the head is modeled as a spherical object that rotates upon the vertical axis. The triangle formed by the mouth and eyes defines a vertical plane that intersects the head sphere. By projecting the eyes-mouth triangle onto a two dimensional viewing plane, equations were obtained that describe the change in its angles as the yaw pose angle increases. These equations are then combined and used for efficient pose estimation. The system achieves real-time performance for live video input. Testing results assessing system performance are presented for both still images and video.

Real-time recognition of feedback error-related potentials during a time-estimation task.

PubMed

Lopez-Larraz, Eduardo; Iturrate, Iñaki; Montesano, Luis; Minguez, Javier

2010-01-01

Feedback error-related potentials are a promising brain process in the field of rehabilitation since they are related to human learning. Due to the fact that many therapeutic strategies rely on the presentation of feedback stimuli, potentials generated by these stimuli could be used to ameliorate the patient's progress. In this paper we propose a method that can identify, in real-time, feedback evoked potentials in a time-estimation task. We have tested our system with five participants in two different days with a separation of three weeks between them, achieving a mean single-trial detection performance of 71.62% for real-time recognition, and 78.08% in offline classification. Additionally, an analysis of the stability of the signal between the two days is performed, suggesting that the feedback responses are stable enough to be used without the needing of training again the user.

A New Model for Real-Time Regional Vertical Total Electron Content and Differential Code Bias Estimation Using IGS Real-Time Service (IGS-RTS) Products

NASA Astrophysics Data System (ADS)

Abdelazeem, Mohamed; Çelik, Rahmi N.; El-Rabbany, Ahmed

2016-04-01

The international global navigation satellite system (GNSS) real-time service (IGS-RTS) products have been used extensively for real-time precise point positioning and ionosphere modeling applications. In this study, we develop a regional model for real-time vertical total electron content (RT-VTEC) and differential code bias (RT-DCB) estimation over Europe using the IGS-RTS satellite orbit and clock products. The developed model has a spatial and temporal resolution of 1°×1° and 15 minutes, respectively. GPS observations from a regional network consisting of 60 IGS and EUREF reference stations are processed in the zero-difference mode using the Bernese-5.2 software package in order to extract the geometry-free linear combination of the smoothed code observations. The spherical harmonic expansion function is used to model the VTEC, the receiver and the satellite DCBs. To validate the proposed model, the RT-VTEC values are computed and compared with the final IGS-global ionospheric map (IGS-GIM) counterparts in three successive days under high solar activity including one of an extreme geomagnetic activity. The real-time satellite DCBs are also estimated and compared with the IGS-GIM counterparts. Moreover, the real-time receiver DCB for six IGS stations are obtained and compared with the IGS-GIM counterparts. The examined stations are located in different latitudes with different receiver types. The findings reveal that the estimated RT-VTEC values show agreement with the IGS-GIM counterparts with root mean-square-errors (RMSEs) values less than 2 TEC units. In addition, RMSEs of both the satellites and receivers DCBs are less than 0.85 ns and 0.65 ns, respectively in comparison with the IGS-GIM.

Can real time location system technology (RTLS) provide useful estimates of time use by nursing personnel?

PubMed

Jones, Terry L; Schlegel, Cara

2014-02-01

Accurate, precise, unbiased, reliable, and cost-effective estimates of nursing time use are needed to insure safe staffing levels. Direct observation of nurses is costly, and conventional surrogate measures have limitations. To test the potential of electronic capture of time and motion through real time location systems (RTLS), a pilot study was conducted to assess efficacy (method agreement) of RTLS time use; inter-rater reliability of RTLS time-use estimates; and associated costs. Method agreement was high (mean absolute difference = 28 seconds); inter-rater reliability was high (ICC = 0.81-0.95; mean absolute difference = 2 seconds); and costs for obtaining RTLS time-use estimates on a single nursing unit exceeded $25,000. Continued experimentation with RTLS to obtain time-use estimates for nursing staff is warranted. © 2013 Wiley Periodicals, Inc.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

NASA Astrophysics Data System (ADS)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

Analytical model for real time, noninvasive estimation of blood glucose level.

PubMed

Adhyapak, Anoop; Sidley, Matthew; Venkataraman, Jayanti

2014-01-01

The paper presents an analytical model to estimate blood glucose level from measurements made non-invasively and in real time by an antenna strapped to a patient's wrist. Some promising success has been shown by the RIT ETA Lab research group that an antenna's resonant frequency can track, in real time, changes in glucose concentration. Based on an in-vitro study of blood samples of diabetic patients, the paper presents a modified Cole-Cole model that incorporates a factor to represent the change in glucose level. A calibration technique using the input impedance technique is discussed and the results show a good estimation as compared to the glucose meter readings. An alternate calibration methodology has been developed that is based on the shift in the antenna resonant frequency using an equivalent circuit model containing a shunt capacitor to represent the shift in resonant frequency with changing glucose levels. Work under progress is the optimization of the technique with a larger sample of patients.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

PubMed Central

Han, Sungmin; Chu, Jun-Uk; Kim, Hyungmin; Park, Jong Woong; Youn, Inchan

2017-01-01

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems. PMID:28276474

Real-time determination of the worst tsunami scenario based on Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Furuya, Takashi; Koshimura, Shunichi; Hino, Ryota; Ohta, Yusaku; Inoue, Takuya

2016-04-01

In recent years, real-time tsunami inundation forecasting has been developed with the advances of dense seismic monitoring, GPS Earth observation, offshore tsunami observation networks, and high-performance computing infrastructure (Koshimura et al., 2014). Several uncertainties are involved in tsunami inundation modeling and it is believed that tsunami generation model is one of the great uncertain sources. Uncertain tsunami source model has risk to underestimate tsunami height, extent of inundation zone, and damage. Tsunami source inversion using observed seismic, geodetic and tsunami data is the most effective to avoid underestimation of tsunami, but needs to expect more time to acquire the observed data and this limitation makes difficult to terminate real-time tsunami inundation forecasting within sufficient time. Not waiting for the precise tsunami observation information, but from disaster management point of view, we aim to determine the worst tsunami source scenario, for the use of real-time tsunami inundation forecasting and mapping, using the seismic information of Earthquake Early Warning (EEW) that can be obtained immediately after the event triggered. After an earthquake occurs, JMA's EEW estimates magnitude and hypocenter. With the constraints of earthquake magnitude, hypocenter and scaling law, we determine possible multi tsunami source scenarios and start searching the worst one by the superposition of pre-computed tsunami Green's functions, i.e. time series of tsunami height at offshore points corresponding to 2-dimensional Gaussian unit source, e.g. Tsushima et al., 2014. Scenario analysis of our method consists of following 2 steps. (1) Searching the worst scenario range by calculating 90 scenarios with various strike and fault-position. From maximum tsunami height of 90 scenarios, we determine a narrower strike range which causes high tsunami height in the area of concern. (2) Calculating 900 scenarios that have different strike, dip, length

Real-time state estimation in a flight simulator using fNIRS.

PubMed

Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

2015-01-01

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.

Real-Time State Estimation in a Flight Simulator Using fNIRS

PubMed Central

Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

2015-01-01

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development. PMID:25816347

Real Time GPS- Satellite Clock Estimation Development of a RTIGS Web Service

NASA Astrophysics Data System (ADS)

Opitz, M.; Weber, R.; Caissy, M.

2006-12-01

Since 3 years the IGS (International GNSS Service) Real-Time Working Group disseminates via Internet raw observation data of a subset of stations of the IGS network. This observation data can be used to establish a real-time integrity monitoring of the IGS predicted orbits (Ultra Rapid (IGU-) Orbits) and clocks, according to the recommendations of the IGS Workshop 2004 in Bern. The Institute for "Geodesy and Geophysics" of the TU-Vienna develops in cooperation with the IGS Real-Time Working Group the software "RTR- Control", which currently provides a real-time integrity monitoring of predicted IGU Clock Corrections to GPS Time. Our poster presents the results of a prototype version which is in operation since August this year. Besides RTR-Control allows for the comparison of pseudoranges measured at any permanent station in the global network with theoretical pseudoranges calculated on basis of the IGU- orbits. Thus, the programme can diagnose incorrectly predicted satellite orbits and clocks as well as detect multi-path distorted pseudoranges in real- time. RTR- Control calculates every 15 seconds Satellite Clock Corrections with respect to the most recent IGU- clocks (updated in a 6 hours interval). The clock estimations are referenced to a stable station clock (H-maser) with a small offset to GPS- time. This real-time Satellite Clocks are corrected for individual outliers and modelling errors. The most recent GPS- Satellite Clock Corrections (updated every 60 seconds) are published in Real Time via the Internet. The user group interested in a rigorous integrity monitoring comprises on the one hand the components of IGS itself to qualify the issued orbital data and on the other hand all users of the IGS Ultra Rapid Products (e.g. for PPP in Real Time).

Real-time adjusting of rainfall estimates from commercial microwave links

NASA Astrophysics Data System (ADS)

Fencl, Martin; Dohnal, Michal; Bareš, Vojtěch

2017-04-01

Urban stormwater predictions require reliable rainfall information with space-time resolution higher than commonly provided by standard rainfall monitoring networks of national weather services. Rainfall data from commercial microwave links (CMLs) could fill this gap. CMLs are line-of-sight radio connections widely used by cellular operators which operate at millimeter bands, where radio waves are attenuated by raindrops. Attenuation data of each single CML in the cellular network can be remotely accessed in (near) real-time with virtually arbitrary sampling frequency and convert to rainfall intensity. Unfortunately, rainfall estimates from CMLs can be substantially biased. Fencl et al., (2017), therefore, proposed adjusting method which enables to correct for this bias. They used rain gauge (RG) data from existing rainfall monitoring networks, which would have otherwise insufficient spatial and temporal resolution for urban rainfall monitoring when used alone without CMLs. In this investigation, we further develop the method to improve its performance in a real-time setting. First, a shortcoming of the original algorithm which delivers unreliable results at the beginning of a rainfall event is overcome by introducing model parameter prior distributions estimated from previous parameter realizations. Second, weights reflecting variance between RGs are introduced into cost function, which is minimized when optimizing model parameters. Finally, RG data used for adjusting are preprocessed by moving average filter. The performance of improved adjusting method is evaluated on four short CMLs (path length < 2 km) located in the small urban catchment (2.3 km2) in Prague-Letnany (CZ). The adjusted CMLs are compared to reference rainfall calculated from six RGs in the catchment. The suggested improvements of the method lead on average to 10% higher Nash-Sutcliffe efficiency coefficient (median value 0.85) for CML adjustment to hourly RG data. Reliability of CML rainfall

An error-based micro-sensor capture system for real-time motion estimation

NASA Astrophysics Data System (ADS)

Yang, Lin; Ye, Shiwei; Wang, Zhibo; Huang, Zhipei; Wu, Jiankang; Kong, Yongmei; Zhang, Li

2017-10-01

A wearable micro-sensor motion capture system with 16 IMUs and an error-compensatory complementary filter algorithm for real-time motion estimation has been developed to acquire accurate 3D orientation and displacement in real life activities. In the proposed filter algorithm, the gyroscope bias error, orientation error and magnetic disturbance error are estimated and compensated, significantly reducing the orientation estimation error due to sensor noise and drift. Displacement estimation, especially for activities such as jumping, has been the challenge in micro-sensor motion capture. An adaptive gait phase detection algorithm has been developed to accommodate accurate displacement estimation in different types of activities. The performance of this system is benchmarked with respect to the results of VICON optical capture system. The experimental results have demonstrated effectiveness of the system in daily activities tracking, with estimation error 0.16 ± 0.06 m for normal walking and 0.13 ± 0.11 m for jumping motions. Research supported by the National Natural Science Foundation of China (Nos. 61431017, 81272166).

Near real-time estimation of burned area using VIIRS 375 m active fire product

NASA Astrophysics Data System (ADS)

Oliva, P.; Schroeder, W.

2016-12-01

Every year, more than 300 million hectares of land burn globally, causing significant ecological and economic consequences, and associated climatological effects as a result of fire emissions. In recent decades, burned area estimates generated from satellite data have provided systematic global information for ecological analysis of fire impacts, climate and carbon cycle models, and fire regimes studies, among many others. However, there is still need of near real-time burned area estimations in order to assess the impacts of fire and estimate smoke and emissions. The enhanced characteristics of the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m channels on board the Suomi National Polar-orbiting Partnesship (S-NPP) make possible the use of near real-time active fire detection data for burned area estimation. In this study, consecutive VIIRS 375 m active fire detections were aggregated to produce the VIIRS 375 m burned area (BA) estimation over ten ecologically diverse study areas. The accuracy of the BA estimations was assessed by comparison with Landsat-8 supervised burned area classification. The performance of the VIIRS 375 m BA estimates was dependent on the ecosystem characteristics and fire behavior. Higher accuracy was observed in forested areas characterized by large long-duration fires, while grasslands, savannas and agricultural areas showed the highest omission and commission errors. Complementing those analyses, we performed the burned area estimation of the largest fires in Oregon and Washington states during 2015 and the Fort McMurray fire in Canada 2016. The results showed good agreement with NIROPs airborne fire perimeters proving that the VIIRS 375 m BA estimations can be used for near real-time assessments of fire effects.

Exploring the utility of real-time hydrologic data for landslide early warning

NASA Astrophysics Data System (ADS)

Mirus, B. B.; Smith, J. B.; Becker, R.; Baum, R. L.; Koss, E.

2017-12-01

Early warning systems can provide critical information for operations managers, emergency planners, and the public to help reduce fatalities, injuries, and economic losses due to landsliding. For shallow, rainfall-triggered landslides early warning systems typically use empirical rainfall thresholds, whereas the actual triggering mechanism involves the non-linear hydrological processes of infiltration, evapotranspiration, and hillslope drainage that are more difficult to quantify. Because hydrologic monitoring has demonstrated that shallow landslides are often preceded by a rise in soil moisture and pore-water pressures, some researchers have developed early warning criteria that attempt to account for these antecedent wetness conditions through relatively simplistic storage metrics or soil-water balance modeling. Here we explore the potential for directly incorporating antecedent wetness into landslide early warning criteria using recent landslide inventories and in-situ hydrologic monitoring near Seattle, WA, and Portland, OR. We use continuous, near-real-time telemetered soil moisture and pore-water pressure data measured within a few landslide-prone hillslopes in combination with measured and forecasted rainfall totals to inform easy-to-interpret landslide initiation thresholds. Objective evaluation using somewhat limited landslide inventories suggests that our new thresholds based on subsurface hydrologic monitoring and rainfall data compare favorably to the capabilities of existing rainfall-only thresholds for the Seattle area, whereas there are no established rainfall thresholds for the Portland area. This preliminary investigation provides a proof-of-concept for the utility of developing landslide early warning criteria in two different geologic settings using real-time subsurface hydrologic measurements from in-situ instrumentation.

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.

An anti-disturbing real time pose estimation method and system

NASA Astrophysics Data System (ADS)

Zhou, Jian; Zhang, Xiao-hu

2011-08-01

Pose estimation relating two-dimensional (2D) images to three-dimensional (3D) rigid object need some known features to track. In practice, there are many algorithms which perform this task in high accuracy, but all of these algorithms suffer from features lost. This paper investigated the pose estimation when numbers of known features or even all of them were invisible. Firstly, known features were tracked to calculate pose in the current and the next image. Secondly, some unknown but good features to track were automatically detected in the current and the next image. Thirdly, those unknown features which were on the rigid and could match each other in the two images were retained. Because of the motion characteristic of the rigid object, the 3D information of those unknown features on the rigid could be solved by the rigid object's pose at the two moment and their 2D information in the two images except only two case: the first one was that both camera and object have no relative motion and camera parameter such as focus length, principle point, and etc. have no change at the two moment; the second one was that there was no shared scene or no matched feature in the two image. Finally, because those unknown features at the first time were known now, pose estimation could go on in the followed images in spite of the missing of known features in the beginning by repeating the process mentioned above. The robustness of pose estimation by different features detection algorithms such as Kanade-Lucas-Tomasi (KLT) feature, Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) were compared and the compact of the different relative motion between camera and the rigid object were discussed in this paper. Graphic Processing Unit (GPU) parallel computing was also used to extract and to match hundreds of features for real time pose estimation which was hard to work on Central Processing Unit (CPU). Compared with other pose estimation methods, this new

CISN ShakeAlert: Accounting for site amplification effects and quantifying time and spatial dependence of uncertainty estimates in the Virtual Seismologist earthquake early warning algorithm

NASA Astrophysics Data System (ADS)

Caprio, M.; Cua, G. B.; Wiemer, S.; Fischer, M.; Heaton, T. H.; CISN EEW Team

2011-12-01

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 being tested in real-time 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 (SCSN) since July 2008, and at the Northern California Seismic Network (NCSN) since February 2009. With the aim of improving the convergence of real-time VS magnitude estimates to network magnitudes, we evaluate various empirical and Vs30-based approaches to accounting for site amplification. Empirical station corrections for SCSN stations are derived from M>3.0 events from 2005 through 2009. We evaluate the performance of the various approaches using an independent 2010 dataset. In addition, we analyze real-time VS performance from 2008 to the present to quantify the time and spatial dependence of VS uncertainty estimates. We also summarize real-time VS performance for significant 2011 events in California. Improved magnitude and uncertainty estimates potentially increase the utility of EEW information for end-users, particularly those intending to automate damage-mitigating actions based on real-time information.

Real-Time Tropospheric Delay Estimation using IGS Products

NASA Astrophysics Data System (ADS)

Stürze, Andrea; Liu, Sha; Söhne, Wolfgang

2014-05-01

The Federal Agency for Cartography and Geodesy (BKG) routinely provides zenith tropospheric delay (ZTD) parameter for the assimilation in numerical weather models since more than 10 years. Up to now the results flowing into the EUREF Permanent Network (EPN) or E-GVAP (EUMETNET EIG GNSS water vapour programme) analysis are based on batch processing of GPS+GLONASS observations in differential network mode. For the recently started COST Action ES1206 about "Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate" (GNSS4SWEC), however, rapid updates in the analysis of the atmospheric state for nowcasting applications require changing the processing strategy towards real-time. In the RTCM SC104 (Radio Technical Commission for Maritime Services, Special Committee 104) a format combining the advantages of Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) is under development. The so-called State Space Representation approach is defining corrections, which will be transferred in real-time to the user e.g. via NTRIP (Network Transport of RTCM via Internet Protocol). Meanwhile messages for precise orbits, satellite clocks and code biases compatible to the basic PPP mode using IGS products are defined. Consequently, the IGS Real-Time Service (RTS) was launched in 2013 in order to extend the well-known precise orbit and clock products by a real-time component. Further messages e.g. with respect to ionosphere or phase biases are foreseen. Depending on the level of refinement, so different accuracies up to the RTK level shall be reachable. In co-operation of BKG and the Technical University of Darmstadt the real-time software GEMon (GREF EUREF Monitoring) is under development. GEMon is able to process GPS and GLONASS observation and RTS product data streams in PPP mode. Furthermore, several state-of-the-art troposphere models, for example based on numerical weather prediction data, are implemented. Hence, it

Development of a new real-time GNSS data analysis system in GEONET for rapid Mw estimates in Japan

NASA Astrophysics Data System (ADS)

Kawamoto, S.; Miyagawa, K.; Yahagi, T.; Yamaguchi, K.; Tsuji, H.; Nishimura, T.; Ohta, Y.; Hino, R.; Miura, S.

2013-12-01

The 2011 off the Pacific Coast of Tohoku Earthquake (Mw 9.0) occurred on March 11, 2011. The earthquake and following tsunami caused serious damages to the broad coastal area of east Japan. Japan Meteorological Agency (JMA) operates the Tsunami Warning system, which is designed to forecast the tsunami height and its arrival time around 3 minutes after a large event. However, the first estimated magnitude of Mj, which was used for Tsunami Warning issuance, was far below the real one at the Tohoku event because of a saturation problem. In principle, as well as most other magnitude scales, Mj is saturated at certain values around 8.0. On the other hand, Mw represents the earthquake energy itself and it can be directly calculated by permanent displacements derived from geodetic measurements without the saturation problem. GNSS Earth Observation Network System (GEONET) is one of the densest real-time GNSS networks in the world operated by Geospatial Information Authority of Japan (GSI). The GEONET data and recent rapid advancement of GNSS analysis techniques motivate us to develop a new system for tackling the tsunami disasters. In order to provide the more reliable magnitude for Tsunami Warning, GSI and Tohoku University have jointly developed a new real-time analysis system in GEONET for quasi real-time Mw estimation. Its targets are large earthquakes, especially ones of Mw > 8.0, which would be saturated by the Tsunami Warning system. The real-time analysis system in GEONET mainly consists of three parts: (1) real-time GNSS positioning, (2) automated extraction of displacement fields due to the large earthquake, and (3) automated estimation of Mw by an approximated single rectangular fault. The positions of each station are calculated by using RTKLIB 2.4.1 (Takasu, 2011) with the baseline mode and the predicted part of the IGS Ultra Rapid precise orbit. For the event detection, we adopt the 'RAPiD' algorithm (Ohta et al., 2012) or Earthquake Early Warning issued by

Estimation and enhancement of real-time software reliability through mutation analysis

NASA Technical Reports Server (NTRS)

Geist, Robert; Offutt, A. J.; Harris, Frederick C., Jr.

1992-01-01

A simulation-based technique for obtaining numerical estimates of the reliability of N-version, real-time software is presented. An extended stochastic Petri net is employed to represent the synchronization structure of N versions of the software, where dependencies among versions are modeled through correlated sampling of module execution times. Test results utilizing specifications for NASA's planetary lander control software indicate that mutation-based testing could hold greater potential for enhancing reliability than the desirable but perhaps unachievable goal of independence among N versions.

Real-Time Algebraic Derivative Estimations Using a Novel Low-Cost Architecture Based on Reconfigurable Logic

PubMed Central

Morales, Rafael; Rincón, Fernando; Gazzano, Julio Dondo; López, Juan Carlos

2014-01-01

Time derivative estimation of signals plays a very important role in several fields, such as signal processing and control engineering, just to name a few of them. For that purpose, a non-asymptotic algebraic procedure for the approximate estimation of the system states is used in this work. The method is based on results from differential algebra and furnishes some general formulae for the time derivatives of a measurable signal in which two algebraic derivative estimators run simultaneously, but in an overlapping fashion. The algebraic derivative algorithm presented in this paper is computed online and in real-time, offering high robustness properties with regard to corrupting noises, versatility and ease of implementation. Besides, in this work, we introduce a novel architecture to accelerate this algebraic derivative estimator using reconfigurable logic. The core of the algorithm is implemented in an FPGA, improving the speed of the system and achieving real-time performance. Finally, this work proposes a low-cost platform for the integration of hardware in the loop in MATLAB. PMID:24859033

Real-Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

NASA Astrophysics Data System (ADS)

Kodera, Yuki

2018-01-01

Large earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late-onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku-oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

Real-time PCR for the early detection and quantification of Coxiella burnetii as an alternative to the murine bioassay.

PubMed

Howe, Gerald B; Loveless, Bonnie M; Norwood, David; Craw, Philip; Waag, David; England, Marilyn; Lowe, John R; Courtney, Bernard C; Pitt, M Louise; Kulesh, David A

2009-01-01

Real-time PCR was used to analyze archived blood from non-human primates (NHP) and fluid samples originating from a well-controlled Q fever vaccine efficacy trial. The PCR targets were the IS1111 element and the com1 gene of Coxiella burnetii. Data from that previous study were used to evaluate real-time PCR as an alternative to the use of sero-conversion by mouse bioassay for both quantification and early detection of C. burnetii bacteria. Real-time PCR and the mouse bioassay exhibited no statistical difference in quantifying the number of microorganisms delivered in the aerosol challenge dose. The presence of C. burnetii in peripheral blood of non-human primates was detected by real-time PCR as early after exposure as the mouse bioassay with results available within hours instead of weeks. This study demonstrates that real-time PCR has the ability to replace the mouse bioassay to measure dosage and monitor infection of C. burnetii in a non-human primate model.

Real-time earthquake source imaging: An offline test for the 2011 Tohoku earthquake

NASA Astrophysics Data System (ADS)

Zhang, Yong; Wang, Rongjiang; Zschau, Jochen; Parolai, Stefano; Dahm, Torsten

2014-05-01

In recent decades, great efforts have been expended in real-time seismology aiming at earthquake and tsunami early warning. One of the most important issues is the real-time assessment of earthquake rupture processes using near-field seismogeodetic networks. Currently, earthquake early warning systems are mostly based on the rapid estimate of P-wave magnitude, which contains generally large uncertainties and the known saturation problem. In the case of the 2011 Mw9.0 Tohoku earthquake, JMA (Japan Meteorological Agency) released the first warning of the event with M7.2 after 25 s. The following updates of the magnitude even decreased to M6.3-6.6. Finally, the magnitude estimate stabilized at M8.1 after about two minutes. This led consequently to the underestimated tsunami heights. By using the newly developed Iterative Deconvolution and Stacking (IDS) method for automatic source imaging, we demonstrate an offline test for the real-time analysis of the strong-motion and GPS seismograms of the 2011 Tohoku earthquake. The results show that we had been theoretically able to image the complex rupture process of the 2011 Tohoku earthquake automatically soon after or even during the rupture process. In general, what had happened on the fault could be robustly imaged with a time delay of about 30 s by using either the strong-motion (KiK-net) or the GPS (GEONET) real-time data. This implies that the new real-time source imaging technique is helpful to reduce false and missing warnings, and therefore should play an important role in future tsunami early warning and earthquake rapid response systems.

Real-Time Personalized Monitoring to Estimate Occupational Heat Stress in Ambient Assisted Working.

PubMed

Pancardo, Pablo; Acosta, Francisco D; Hernández-Nolasco, José Adán; Wister, Miguel A; López-de-Ipiña, Diego

2015-07-13

Ambient Assisted Working (AAW) is a discipline aiming to provide comfort and safety in the workplace through customization and technology. Workers' comfort may be compromised in many labor situations, including those depending on environmental conditions, like extremely hot weather conduces to heat stress. Occupational heat stress (OHS) happens when a worker is in an uninterrupted physical activity and in a hot environment. OHS can produce strain on the body, which leads to discomfort and eventually to heat illness and even death. Related ISO standards contain methods to estimate OHS and to ensure the safety and health of workers, but they are subjective, impersonal, performed a posteriori and even invasive. This paper focuses on the design and development of real-time personalized monitoring for a more effective and objective estimation of OHS, taking into account the individual user profile, fusing data from environmental and unobtrusive body sensors. Formulas employed in this work were taken from different domains and joined in the method that we propose. It is based on calculations that enable continuous surveillance of physical activity performance in a comfortable and healthy manner. In this proposal, we found that OHS can be estimated by satisfying the following criteria: objective, personalized, in situ, in real time, just in time and in an unobtrusive way. This enables timely notice for workers to make decisions based on objective information to control OHS.

Real-Time Personalized Monitoring to Estimate Occupational Heat Stress in Ambient Assisted Working

PubMed Central

Pancardo, Pablo; Acosta, Francisco D.; Hernández-Nolasco, José Adán; Wister, Miguel A.; López-de-Ipiña, Diego

2015-01-01

Ambient Assisted Working (AAW) is a discipline aiming to provide comfort and safety in the workplace through customization and technology. Workers' comfort may be compromised in many labor situations, including those depending on environmental conditions, like extremely hot weather conduces to heat stress. Occupational heat stress (OHS) happens when a worker is in an uninterrupted physical activity and in a hot environment. OHS can produce strain on the body, which leads to discomfort and eventually to heat illness and even death. Related ISO standards contain methods to estimate OHS and to ensure the safety and health of workers, but they are subjective, impersonal, performed a posteriori and even invasive. This paper focuses on the design and development of real-time personalized monitoring for a more effective and objective estimation of OHS, taking into account the individual user profile, fusing data from environmental and unobtrusive body sensors. Formulas employed in this work were taken from different domains and joined in the method that we propose. It is based on calculations that enable continuous surveillance of physical activity performance in a comfortable and healthy manner. In this proposal, we found that OHS can be estimated by satisfying the following criteria: objective, personalized, in situ, in real time, just in time and in an unobtrusive way. This enables timely notice for workers to make decisions based on objective information to control OHS. PMID:26184218

Real-Time PCR Quantification Using A Variable Reaction Efficiency Model

PubMed Central

Platts, Adrian E.; Johnson, Graham D.; Linnemann, Amelia K.; Krawetz, Stephen A.

2008-01-01

Quantitative real-time PCR remains a cornerstone technique in gene expression analysis and sequence characterization. Despite the importance of the approach to experimental biology the confident assignment of reaction efficiency to the early cycles of real-time PCR reactions remains problematic. Considerable noise may be generated where few cycles in the amplification are available to estimate peak efficiency. An alternate approach that uses data from beyond the log-linear amplification phase is explored with the aim of reducing noise and adding confidence to efficiency estimates. PCR reaction efficiency is regressed to estimate the per-cycle profile of an asymptotically departed peak efficiency, even when this is not closely approximated in the measurable cycles. The process can be repeated over replicates to develop a robust estimate of peak reaction efficiency. This leads to an estimate of the maximum reaction efficiency that may be considered primer-design specific. Using a series of biological scenarios we demonstrate that this approach can provide an accurate estimate of initial template concentration. PMID:18570886

Real-time moving horizon estimation for a vibrating active cantilever

NASA Astrophysics Data System (ADS)

Abdollahpouri, Mohammad; Takács, Gergely; Rohaľ-Ilkiv, Boris

2017-03-01

Vibrating structures may be subject to changes throughout their operating lifetime due to a range of environmental and technical factors. These variations can be considered as parameter changes in the dynamic model of the structure, while their online estimates can be utilized in adaptive control strategies, or in structural health monitoring. This paper implements the moving horizon estimation (MHE) algorithm on a low-cost embedded computing device that is jointly observing the dynamic states and parameter variations of an active cantilever beam in real time. The practical behavior of this algorithm has been investigated in various experimental scenarios. It has been found, that for the given field of application, moving horizon estimation converges faster than the extended Kalman filter; moreover, it handles atypical measurement noise, sensor errors or other extreme changes, reliably. Despite its improved performance, the experiments demonstrate that the disadvantage of solving the nonlinear optimization problem in MHE is that it naturally leads to an increase in computational effort.

Real-time earthquake monitoring: Early warning and rapid response

NASA Technical Reports Server (NTRS)

1991-01-01

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

Software algorithm and hardware design for real-time implementation of new spectral estimator

PubMed Central

2014-01-01

Background Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time. PMID:24886214

Using Indirect Turbulence Measurements for Real-Time Parameter Estimation in Turbulent Air

NASA Technical Reports Server (NTRS)

Martos, Borja; Morelli, Eugene A.

2012-01-01

The use of indirect turbulence measurements for real-time estimation of parameters in a linear longitudinal dynamics model in atmospheric turbulence was studied. It is shown that measuring the atmospheric turbulence makes it possible to treat the turbulence as a measured explanatory variable in the parameter estimation problem. Commercial off-the-shelf sensors were researched and evaluated, then compared to air data booms. Sources of colored noise in the explanatory variables resulting from typical turbulence measurement techniques were identified and studied. A major source of colored noise in the explanatory variables was identified as frequency dependent upwash and time delay. The resulting upwash and time delay corrections were analyzed and compared to previous time shift dynamic modeling research. Simulation data as well as flight test data in atmospheric turbulence were used to verify the time delay behavior. Recommendations are given for follow on flight research and instrumentation.

Real-Time Frequency Response Estimation Using Joined-Wing SensorCraft Aeroelastic Wind-Tunnel Data

NASA Technical Reports Server (NTRS)

Grauer, Jared A; Heeg, Jennifer; Morelli, Eugene A

2012-01-01

A new method is presented for estimating frequency responses and their uncertainties from wind-tunnel data in real time. The method uses orthogonal phase-optimized multi- sine excitation inputs and a recursive Fourier transform with a least-squares estimator. The method was first demonstrated with an F-16 nonlinear flight simulation and results showed that accurate short period frequency responses were obtained within 10 seconds. The method was then applied to wind-tunnel data from a previous aeroelastic test of the Joined- Wing SensorCraft. Frequency responses describing bending strains from simultaneous control surface excitations were estimated in a time-efficient manner.

Estimating the Mechanical Behavior of the Knee Joint during Crouch Gait: Implications for Real-Time Motor Control of Robotic Knee Orthoses

PubMed Central

Damiano, Diane L.; Bulea, Thomas C.

2016-01-01

Individuals with cerebral palsy frequently exhibit crouch gait, a pathological walking pattern characterized by excessive knee flexion. Knowledge of the knee joint moment during crouch gait is necessary for the design and control of assistive devices used for treatment. Our goal was to 1) develop statistical models to estimate knee joint moment extrema and dynamic stiffness during crouch gait, and 2) use the models to estimate the instantaneous joint moment during weight-acceptance. We retrospectively computed knee moments from 10 children with crouch gait and used stepwise linear regression to develop statistical models describing the knee moment features. The models explained at least 90% of the response value variability: peak moment in early (99%) and late (90%) stance, and dynamic stiffness of weight-acceptance flexion (94%) and extension (98%). We estimated knee extensor moment profiles from the predicted dynamic stiffness and instantaneous knee angle. This approach captured the timing and shape of the computed moment (root-mean-squared error: 2.64 Nm); including the predicted early-stance peak moment as a correction factor improved model performance (root-mean-squared error: 1.37 Nm). Our strategy provides a practical, accurate method to estimate the knee moment during crouch gait, and could be used for real-time, adaptive control of robotic orthoses. PMID:27101612

Real-Time Integration of Positioning and Accelerometer Data for Early Earthquake Warning on Canada's West Coast

NASA Astrophysics Data System (ADS)

Biffard, B.; Rosenberger, A.; Pirenne, B.; Valenzuela, M.; MacArthur, M.

2017-12-01

Ocean Networks Canada (ONC) operates ocean and coastal observatories on all three of Canada's coasts, and more particularly across the Cascadia subduction zone. The data are acquired, parsed, calibrated and archived by ONC's data management system (Oceans 2.0), with real-time event detection, reaction and access capabilities. As such, ONC is in a unique position to develop early warning systems for earthquakes, near- and far-field tsunamis and other events. ONC is leading the development of a system to alert southwestern British Columbia of an impending Cascadia subduction zone earthquake on behalf of the provincial government and with the support of the Canadian Federal Government. Similarly to other early earthquake warning systems, an array of accelerometers is used to detect the initial earthquake p-waves. This can provide 5-60 seconds of warning to subscribers who can then take action, such as stopping trains and surgeries, closing valves, taking cover, etc. To maximize the detection capability and the time available to react to a notification, instruments are placed both underwater and on land on Vancouver Island. A novel feature of ONC's system is, for land-based sites, the combination of real-time satellite positioning (GNSS) and accelerometer data in the calculations to improve earthquake intensity estimates. This results in higher accuracy, dynamic range and responsiveness than either type of sensor is capable of alone. P-wave detections and displacement data are sent from remote stations to a data centre that must calculate epicentre locations and magnitude. The latter are then delivered to subscribers with client software that, given their position, will calculate arrival time and intensity. All of this must occur with very high standards for latency, reliability and accuracy.

Implementing a combined polar-geostationary algorithm for smoke emissions estimation in near real time

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Schmidt, C. C.; Hoffman, J.; Giglio, L.; Peterson, D. A.

2013-12-01

Polar and geostationary satellites are used operationally for fire detection and smoke source estimation by many near-real-time operational users, including operational forecast centers around the globe. The input satellite radiance data are processed by data providers to produce Level-2 and Level -3 fire detection products, but processing these data into spatially and temporally consistent estimates of fire activity requires a substantial amount of additional processing. The most significant processing steps are correction for variable coverage of the satellite observations, and correction for conditions that affect the detection efficiency of the satellite sensors. We describe a system developed by the Naval Research Laboratory (NRL) that uses the full raster information from the entire constellation to diagnose detection opportunities, calculate corrections for factors such as angular dependence of detection efficiency, and generate global estimates of fire activity at spatial and temporal scales suitable for atmospheric modeling. By incorporating these improved fire observations, smoke emissions products, such as NRL's FLAMBE, are able to produce improved estimates of global emissions. This talk provides an overview of the system, demonstrates the achievable improvement over older methods, and describes challenges for near-real-time implementation.

A Real-Time Method for Estimating Viscous Forebody Drag Coefficients

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Hurtado, Marco; Rivera, Jose; Naughton, Jonathan W.

2000-01-01

This paper develops a real-time method based on the law of the wake for estimating forebody skin-friction coefficients. The incompressible law-of-the-wake equations are numerically integrated across the boundary layer depth to develop an engineering model that relates longitudinally averaged skin-friction coefficients to local boundary layer thickness. Solutions applicable to smooth surfaces with pressure gradients and rough surfaces with negligible pressure gradients are presented. Model accuracy is evaluated by comparing model predictions with previously measured flight data. This integral law procedure is beneficial in that skin-friction coefficients can be indirectly evaluated in real-time using a single boundary layer height measurement. In this concept a reference pitot probe is inserted into the flow, well above the anticipated maximum thickness of the local boundary layer. Another probe is servomechanism-driven and floats within the boundary layer. A controller regulates the position of the floating probe. The measured servomechanism position of this second probe provides an indirect measurement of both local and longitudinally averaged skin friction. Simulation results showing the performance of the control law for a noisy boundary layer are then presented.

Real-time earthquake data feasible

NASA Astrophysics Data System (ADS)

Bush, Susan

Scientists agree that early warning devices and monitoring of both Hurricane Hugo and the Mt. Pinatubo volcanic eruption saved thousands of lives. What would it take to develop this sort of early warning and monitoring system for earthquake activity?Not all that much, claims a panel assigned to study the feasibility, costs, and technology needed to establish a real-time earthquake monitoring (RTEM) system. The panel, drafted by the National Academy of Science's Committee on Seismology, has presented its findings in Real-Time Earthquake Monitoring. The recently released report states that “present technology is entirely capable of recording and processing data so as to provide real-time information, enabling people to mitigate somewhat the earthquake disaster.” RTEM systems would consist of two parts—an early warning system that would give a few seconds warning before severe shaking, and immediate postquake information within minutes of the quake that would give actual measurements of the magnitude. At this time, however, this type of warning system has not been addressed at the national level for the United States and is not included in the National Earthquake Hazard Reduction Program, according to the report.

Evaluation of Real-Time and Off-Line Performance of the Virtual Seismologist Earthquake Early Warning Algorithm in Switzerland

NASA Astrophysics Data System (ADS)

Behr, Yannik; Clinton, John; Cua, Georgia; Cauzzi, Carlo; Heimers, Stefan; Kästli, Philipp; Becker, Jan; Heaton, Thomas

2013-04-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) originally formulated by Cua and Heaton (2007). Implementation of VS into real-time EEW codes has been an on-going effort of the Swiss Seismological Service at ETH Zürich since 2006, with support from ETH Zürich, various European projects, and the United States Geological Survey (USGS). VS is one of three EEW algorithms that form the basis of the California Integrated Seismic Network (CISN) ShakeAlert system, a USGS-funded prototype end-to-end EEW system that could potentially be implemented in California. In Europe, VS is currently operating as a real-time test system in Switzerland. As part of the on-going EU project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction), VS installations in southern Italy, western Greece, Istanbul, Romania, and Iceland are planned or underway. In Switzerland, VS has been running in real-time on stations monitored by the Swiss Seismological Service (including stations from Austria, France, Germany, and Italy) since 2010. While originally based on the Earthworm system it has recently been ported to the SeisComp3 system. Besides taking advantage of SeisComp3's picking and phase association capabilities it greatly simplifies the potential installation of VS at networks in particular those already running SeisComp3. We present the architecture of the new SeisComp3 based version and compare its results from off-line tests with the real-time performance of VS in Switzerland over the past two years. We further show that the empirical relationships used by VS to estimate magnitudes and ground motion, originally derived from southern California data, perform well in Switzerland.

Research Directions in Real-Time Systems.

DTIC Science & Technology

1996-09-01

This report summarizes a survey of published research in real time systems . Material is presented that provides an overview of the topic, focusing on...communications protocols and scheduling techniques. It is noted that real - time systems deserve special attention separate from other areas because of...formal tools for design and analysis of real - time systems . The early work on applications as well as notable theoretical advances are summarized

Real-time hydraulic interval state estimation for water transport networks: a case study

NASA Astrophysics Data System (ADS)

Vrachimis, Stelios G.; Eliades, Demetrios G.; Polycarpou, Marios M.

2018-03-01

Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to determine the existence of unaccounted-for water in water distribution networks. As a case study, the method is applied to a modified transport network in Cyprus, using actual data in real time.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris.

PubMed

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-07-22

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the

Near-real-time and scenario earthquake loss estimates for Mexico

NASA Astrophysics Data System (ADS)

Wyss, M.; Zuñiga, R.

2017-12-01

The large earthquakes of 8 September 2017, M8.1, and 19 September 2017, M7.1 have focused attention on the dangers of Mexican seismicity. The near-real-time alerts by QLARM estimated 10 to 300 fatalities and 0 to 200 fatalities, respectively. At the time of this submission the reported death tolls are 96 and 226, respectively. These alerts were issued within 96 and 57 minutes of the occurrence times. For the M8.1 earthquake the losses due to a line model could be calculated. The line with length L=110 km extended from the initial epicenter to the NE, where the USGS had reported aftershocks. On September 19, no aftershocks were available in near-real-time, so a point source had to be used for the quick calculation of likely casualties. In both cases, the casualties were at least an order of magnitude smaller than what they could have been because on 8 September the source was relatively far offshore and on 19 September the hypocenter was relatively deep. The largest historic earthquake in Mexico occurred on 28 March 1787 and likely had a rupture length of 450 km and M8.6. Based on this event, and after verifying our tool for Mexico, we estimated the order of magnitude of a disaster, given the current population, in a maximum credible earthquake along the Pacific coast. In the countryside along the coast we expect approximately 27,000 fatalities and 480,000 injured. In the special case of Mexico City the casualties in a worst possible earthquake along the Pacific plate boundary would likely be counted as five digit numbers. The large agglomerate of the capital with its lake bed soil attracts most attention. Nevertheless, one should pay attention to the fact that the poor, rural segment of society, living in buildings of weak resistance to shaking, are likely to sustain a mortality rate about 20% larger than the population in cities on average soil.

Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients.

PubMed

Li, Zhan; Guiraud, David; Andreu, David; Benoussaad, Mourad; Fattal, Charles; Hayashibe, Mitsuhiro

2016-06-22

Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

NASA Astrophysics Data System (ADS)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal

Designing a Dynamic Data Driven Application System for Estimating Real-Time Load of DOC in a River

NASA Astrophysics Data System (ADS)

Ouyang, Y.; None

2011-12-01

Understanding the dynamics of naturally occurring dissolved organic carbon (DOC) in a river is central to estimating surface water quality, aquatic carbon cycling, and climate change. Currently, determination of DOC in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 hours. In other words, no effort has been devoted to monitoring real-time variations of DOC in a river due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health, carbon footprints, and effects of urbanization, industry, and agriculture on water resource supply, timely DOC information may be critical. We have developed here a new paradigm, a dynamic data driven application system (DDDAS), for estimating the real-time load of DOC into a river. This DDDAS consisted of the following four components: (1) a Visual Basic (VB) program for downloading US Geological Survey real-time chlorophyll and discharge data; (2) a STELLA model for evaluating real-time DOC load based on the relationship between chlorophyll a, DOC, and river discharge; (3) a batch file for linking the VB program and STELLA model; and (4) a Microsoft Windows Scheduled Tasks wizard for executing the model and displaying output on a computer screen at selected times. Results show that the real-time load of DOC into the St. Johns River basin near Satsuma, Putnam County, Florida, USA varied over a range from -13,143 to 29,248 kg/h at the selected site in Florida, USA. The negative loads occurred because of the back flow in the estuarine reach of the river. The cumulative load of DOC in the river for the selected site at the end of the simulation (178 hours) was about 1.2 tons. Our results support the utility of the DDDAS developed in this study for estimating the real-time variations of DOC in river ecosystems.

Real-Time Aerodynamic Parameter Estimation without Air Flow Angle Measurements

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2010-01-01

A technique for estimating aerodynamic parameters in real time from flight data without air flow angle measurements is described and demonstrated. The method is applied to simulated F-16 data, and to flight data from a subscale jet transport aircraft. Modeling results obtained with the new approach using flight data without air flow angle measurements were compared to modeling results computed conventionally using flight data that included air flow angle measurements. Comparisons demonstrated that the new technique can provide accurate aerodynamic modeling results without air flow angle measurements, which are often difficult and expensive to obtain. Implications for efficient flight testing and flight safety are discussed.

Head movement compensation in real-time magnetoencephalographic recordings.

PubMed

Little, Graham; Boe, Shaun; Bardouille, Timothy

2014-01-01

Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.•Data acquisition•Head position estimation•Source localization•Real-time source estimation This work explains the technical details and validates each of these steps.

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

Design and Implementation of Real-Time Vehicular Camera for Driver Assistance and Traffic Congestion Estimation

PubMed Central

Son, Sanghyun; Baek, Yunju

2015-01-01

As society has developed, the number of vehicles has increased and road conditions have become complicated, increasing the risk of crashes. Therefore, a service that provides safe vehicle control and various types of information to the driver is urgently needed. In this study, we designed and implemented a real-time traffic information system and a smart camera device for smart driver assistance systems. We selected a commercial device for the smart driver assistance systems, and applied a computer vision algorithm to perform image recognition. For application to the dynamic region of interest, dynamic frame skip methods were implemented to perform parallel processing in order to enable real-time operation. In addition, we designed and implemented a model to estimate congestion by analyzing traffic information. The performance of the proposed method was evaluated using images of a real road environment. We found that the processing time improved by 15.4 times when all the proposed methods were applied in the application. Further, we found experimentally that there was little or no change in the recognition accuracy when the proposed method was applied. Using the traffic congestion estimation model, we also found that the average error rate of the proposed model was 5.3%. PMID:26295230

Design and Implementation of Real-Time Vehicular Camera for Driver Assistance and Traffic Congestion Estimation.

PubMed

Son, Sanghyun; Baek, Yunju

2015-08-18

As society has developed, the number of vehicles has increased and road conditions have become complicated, increasing the risk of crashes. Therefore, a service that provides safe vehicle control and various types of information to the driver is urgently needed. In this study, we designed and implemented a real-time traffic information system and a smart camera device for smart driver assistance systems. We selected a commercial device for the smart driver assistance systems, and applied a computer vision algorithm to perform image recognition. For application to the dynamic region of interest, dynamic frame skip methods were implemented to perform parallel processing in order to enable real-time operation. In addition, we designed and implemented a model to estimate congestion by analyzing traffic information. The performance of the proposed method was evaluated using images of a real road environment. We found that the processing time improved by 15.4 times when all the proposed methods were applied in the application. Further, we found experimentally that there was little or no change in the recognition accuracy when the proposed method was applied. Using the traffic congestion estimation model, we also found that the average error rate of the proposed model was 5.3%.

Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data

NASA Astrophysics Data System (ADS)

George, Daniel; Huerta, E. A.

2018-03-01

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.

A novel technique for real-time estimation of edge pedestal density gradients via reflectometer time delay data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, L., E-mail: zeng@fusion.gat.com; Doyle, E. J.; Rhodes, T. L.

2016-11-15

A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layermore » density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.« less

Real-Time Parameter Estimation Method Applied to a MIMO Process and its Comparison with an Offline Identification Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaplanoglu, Erkan; Safak, Koray K.; Varol, H. Selcuk

2009-01-12

An experiment based method is proposed for parameter estimation of a class of linear multivariable systems. The method was applied to a pressure-level control process. Experimental time domain input/output data was utilized in a gray-box modeling approach. Prior knowledge of the form of the system transfer function matrix elements is assumed to be known. Continuous-time system transfer function matrix parameters were estimated in real-time by the least-squares method. Simulation results of experimentally determined system transfer function matrix compare very well with the experimental results. For comparison and as an alternative to the proposed real-time estimation method, we also implemented anmore » offline identification method using artificial neural networks and obtained fairly good results. The proposed methods can be implemented conveniently on a desktop PC equipped with a data acquisition board for parameter estimation of moderately complex linear multivariable systems.« less

Evaluation of the real-time earthquake information system in Japan

NASA Astrophysics Data System (ADS)

Nakamura, Hiromitsu; Horiuchi, Shigeki; Wu, Changjiang; Yamamoto, Shunroku; Rydelek, Paul A.

2009-01-01

The real-time earthquake information system (REIS) of the Japanese seismic network is developed for automatically determining earthquake parameters within a few seconds after the P-waves arrive at the closest stations using both the P-wave arrival times and the timing data that P-waves have not yet arrived at other stations. REIS results play a fundamental role in the real-time information for earthquake early warning in Japan. We show the rapidity and accuracy of REIS from the analysis of 4,050 earthquakes in three years since 2005; 44 percent of the first reports are issued within 5 seconds after the first P-wave arrival and 80 percent of the events have a difference in epicenter distance less than 20 km relative to manually determined locations. We compared the formal catalog to the estimated magnitude from the real-time analysis and found that 94 percent of the events had a magnitude difference of +/-1.0 unit.

Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

NASA Astrophysics Data System (ADS)

Kulisek, J. A.; Schweppe, J. E.; Stave, S. C.; Bernacki, B. E.; Jordan, D. V.; Stewart, T. N.; Seifert, C. E.; Kernan, W. J.

2015-06-01

Helicopter-mounted gamma-ray detectors can provide law enforcement officials the means to quickly and accurately detect, identify, and locate radiological threats over a wide geographical area. The ability to accurately distinguish radiological threat-generated gamma-ray signatures from background gamma radiation in real time is essential in order to realize this potential. This problem is non-trivial, especially in urban environments for which the background may change very rapidly during flight. This exacerbates the challenge of estimating background due to the poor counting statistics inherent in real-time airborne gamma-ray spectroscopy measurements. To address this challenge, we have developed a new technique for real-time estimation of background gamma radiation from aerial measurements without the need for human analyst intervention. The method can be calibrated using radiation transport simulations along with data from previous flights over areas for which the isotopic composition need not be known. Over the examined measured and simulated data sets, the method generated accurate background estimates even in the presence of a strong, 60Co source. The potential to track large and abrupt changes in background spectral shape and magnitude was demonstrated. The method can be implemented fairly easily in most modern computing languages and environments.

Real-Time PPP Based on the Coupling Estimation of Clock Bias and Orbit Error with Broadcast Ephemeris

PubMed Central

Pan, Shuguo; Chen, Weirong; Jin, Xiaodong; Shi, Xiaofei; He, Fan

2015-01-01

Satellite orbit error and clock bias are the keys to precise point positioning (PPP). The traditional PPP algorithm requires precise satellite products based on worldwide permanent reference stations. Such an algorithm requires considerable work and hardly achieves real-time performance. However, real-time positioning service will be the dominant mode in the future. IGS is providing such an operational service (RTS) and there are also commercial systems like Trimble RTX in operation. On the basis of the regional Continuous Operational Reference System (CORS), a real-time PPP algorithm is proposed to apply the coupling estimation of clock bias and orbit error. The projection of orbit error onto the satellite-receiver range has the same effects on positioning accuracy with clock bias. Therefore, in satellite clock estimation, part of the orbit error can be absorbed by the clock bias and the effects of residual orbit error on positioning accuracy can be weakened by the evenly distributed satellite geometry. In consideration of the simple structure of pseudorange equations and the high precision of carrier-phase equations, the clock bias estimation method coupled with orbit error is also improved. Rovers obtain PPP results by receiving broadcast ephemeris and real-time satellite clock bias coupled with orbit error. By applying the proposed algorithm, the precise orbit products provided by GNSS analysis centers are rendered no longer necessary. On the basis of previous theoretical analysis, a real-time PPP system was developed. Some experiments were then designed to verify this algorithm. Experimental results show that the newly proposed approach performs better than the traditional PPP based on International GNSS Service (IGS) real-time products. The positioning accuracies of the rovers inside and outside the network are improved by 38.8% and 36.1%, respectively. The PPP convergence speeds are improved by up to 61.4% and 65.9%. The new approach can change the

A Real-Time Systems Symposium Preprint.

DTIC Science & Technology

1983-09-01

Real - Time Systems Symposium Preprint Interim Tech...estimate of the occurence of the error. Unclassii ledSECUqITY CLASSIF’ICA T" NO MI*IA If’ inDI /’rrd erter for~~ble. ’Corrputnqg A REAL - TIME SYSTEMS SYMPOSIUM...ABSTRACT This technical report contains a preprint of a paper accepted for presentation at the REAL - TIME SYSTEMS SYMPOSIUM, Arlington,

Real-time flutter identification

NASA Technical Reports Server (NTRS)

Roy, R.; Walker, R.

1985-01-01

The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability.

Real-time and rapid GNSS solutions from the M8.2 September 2017 Tehuantepec Earthquake and implications for Earthquake and Tsunami Early Warning Systems

NASA Astrophysics Data System (ADS)

Mencin, D.; Hodgkinson, K. M.; Mattioli, G. S.

2017-12-01

In support of hazard research and Earthquake Early Warning (EEW) Systems UNAVCO operates approximately 800 RT-GNSS stations throughout western North America and Alaska (EarthScope Plate Boundary Observatory), Mexico (TLALOCNet), and the pan-Caribbean region (COCONet). Our system produces and distributes raw data (BINEX and RTCM3) and real-time Precise Point Positions via the Trimble PIVOT Platform (RTX). The 2017-09-08 earthquake M8.2 located 98 km SSW of Tres Picos, Mexico is the first great earthquake to occur within the UNAVCO RT-GNSS footprint, which allows for a rigorous analysis of our dynamic and static processing methods. The need for rapid geodetic solutions ranges from seconds (EEW systems) to several minutes (Tsunami Warning and NEIC moment tensor and finite fault models). Here, we compare and quantify the relative processing strategies for producing static offsets, moment tensors and geodetically determined finite fault models using data recorded during this event. We also compare the geodetic solutions with the USGS NEIC seismically derived moment tensors and finite fault models, including displacement waveforms generated from these models. We define kinematic post-processed solutions from GIPSY-OASISII (v6.4) with final orbits and clocks as a "best" case reference to evaluate the performance of our different processing strategies. We find that static displacements of a few centimeters or less are difficult to resolve in the real-time GNSS position estimates. The standard daily 24-hour solutions provide the highest-quality data-set to determine coseismic offsets, but these solutions are delayed by at least 48 hours after the event. Dynamic displacements, estimated in real-time, however, show reasonable agreement with final, post-processed position estimates, and while individual position estimates have large errors, the real-time solutions offer an excellent operational option for EEW systems, including the use of estimated peak-ground displacements or

Real-Time Airborne Gamma-Ray Background Estimation Using NASVD with MLE and Radiation Transport for Calibration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kulisek, Jonathan A.; Schweppe, John E.; Stave, Sean C.

2015-06-01

Helicopter-mounted gamma-ray detectors can provide law enforcement officials the means to quickly and accurately detect, identify, and locate radiological threats over a wide geographical area. The ability to accurately distinguish radiological threat-generated gamma-ray signatures from background gamma radiation in real time is essential in order to realize this potential. This problem is non-trivial, especially in urban environments for which the background may change very rapidly during flight. This exacerbates the challenge of estimating background due to the poor counting statistics inherent in real-time airborne gamma-ray spectroscopy measurements. To address this, we have developed a new technique for real-time estimation ofmore » background gamma radiation from aerial measurements. This method is built upon on the noise-adjusted singular value decomposition (NASVD) technique that was previously developed for estimating the potassium (K), uranium (U), and thorium (T) concentrations in soil post-flight. The method can be calibrated using K, U, and T spectra determined from radiation transport simulations along with basis functions, which may be determined empirically by applying maximum likelihood estimation (MLE) to previously measured airborne gamma-ray spectra. The method was applied to both measured and simulated airborne gamma-ray spectra, with and without man-made radiological source injections. Compared to schemes based on simple averaging, this technique was less sensitive to background contamination from the injected man-made sources and may be particularly useful when the gamma-ray background frequently changes during the course of the flight.« less

Estimating degradation in real time and accelerated stability tests with random lot-to-lot variation: a simulation study.

PubMed

Magari, Robert T

2002-03-01

The effect of different lot-to-lot variability levels on the prediction of stability are studied based on two statistical models for estimating degradation in real time and accelerated stability tests. Lot-to-lot variability is considered as random in both models, and is attributed to two sources-variability at time zero, and variability of degradation rate. Real-time stability tests are modeled as a function of time while accelerated stability tests as a function of time and temperatures. Several data sets were simulated, and a maximum likelihood approach was used for estimation. The 95% confidence intervals for the degradation rate depend on the amount of lot-to-lot variability. When lot-to-lot degradation rate variability is relatively large (CV > or = 8%) the estimated confidence intervals do not represent the trend for individual lots. In such cases it is recommended to analyze each lot individually. Copyright 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 91: 893-899, 2002

Concept, Implementation and Testing of PRESTo: Real-time experimentation in Southern Italy and worldwide applications

NASA Astrophysics Data System (ADS)

Zollo, Aldo; Emolo, Antonio; Festa, Gaetano; Picozzi, Matteo; Elia, Luca; Martino, Claudio; Colombelli, Simona; Brondi, Piero; Caruso, Alessandro

2016-04-01

The past two decades have witnessed a huge progress in the development, implementation and testing of Earthquakes Early Warning Systems (EEWS) worldwide, as the result of a joint effort of the seismological and earthquake engineering communities to set up robust and efficient methodologies for the real-time seismic risk mitigation. This work presents an overview of the worldwide applications of the system PRESTo (PRobabilistic and Evolutionary early warning SysTem), which is the highly configurable and easily portable platform for Earthquake Early Warning developed by the RISSCLab group of the University of Naples Federico II. In particular, we first present the results of the real-time experimentation of PRESTo in Suthern Italy on the data streams of the Irpinia Seismic Network (ISNet), in Southern Italy. ISNet is a dense high-dynamic range, earthquake observing system, which operates in true real-time mode, thanks to a mixed data transmission system based on proprietary digital terrestrial links, standard ADSL and UMTS technologies. Using the seedlink protocol data are transferred to the network center unit, running the software platform PRESTo which is devoted to process the real-time data streaming, estimate source parameters and issue the alert. The software platform PRESTo uses a P-wave, network-based approach which has evolved and improved during the time since its first release. In its original version consisted in a series of modules, aimed at the event detection/picking, probabilistic real-time earthquake location and magnitude estimation, prediction of peak ground motion at distant sites through ground motion prediction equations for the area. In the recent years, PRESTo has been also implemented at the accelerometric and broad-band seismic networks in South Korea, Romania, North-East Italy, and Turkey and off-line tested in Iberian Peninsula, Israel, and Japan. Moreover, the feasibility of a PRESTo-based, EEWS at national scale in Italy, has been tested

Accurate estimation of camera shot noise in the real-time

NASA Astrophysics Data System (ADS)

Cheremkhin, Pavel A.; Evtikhiev, Nikolay N.; Krasnov, Vitaly V.; Rodin, Vladislav G.; Starikov, Rostislav S.

2017-10-01

Nowadays digital cameras are essential parts of various technological processes and daily tasks. They are widely used in optics and photonics, astronomy, biology and other various fields of science and technology such as control systems and video-surveillance monitoring. One of the main information limitations of photo- and videocameras are noises of photosensor pixels. Camera's photosensor noise can be divided into random and pattern components. Temporal noise includes random noise component while spatial noise includes pattern noise component. Temporal noise can be divided into signal-dependent shot noise and signal-nondependent dark temporal noise. For measurement of camera noise characteristics, the most widely used methods are standards (for example, EMVA Standard 1288). It allows precise shot and dark temporal noise measurement but difficult in implementation and time-consuming. Earlier we proposed method for measurement of temporal noise of photo- and videocameras. It is based on the automatic segmentation of nonuniform targets (ASNT). Only two frames are sufficient for noise measurement with the modified method. In this paper, we registered frames and estimated shot and dark temporal noises of cameras consistently in the real-time. The modified ASNT method is used. Estimation was performed for the cameras: consumer photocamera Canon EOS 400D (CMOS, 10.1 MP, 12 bit ADC), scientific camera MegaPlus II ES11000 (CCD, 10.7 MP, 12 bit ADC), industrial camera PixeLink PL-B781F (CMOS, 6.6 MP, 10 bit ADC) and video-surveillance camera Watec LCL-902C (CCD, 0.47 MP, external 8 bit ADC). Experimental dependencies of temporal noise on signal value are in good agreement with fitted curves based on a Poisson distribution excluding areas near saturation. Time of registering and processing of frames used for temporal noise estimation was measured. Using standard computer, frames were registered and processed during a fraction of second to several seconds only. Also the

Resolving Peak Ground Displacements in Real-Time GNSS PPP Solutions

NASA Astrophysics Data System (ADS)

Hodgkinson, K. M.; Mencin, D.; Mattioli, G. S.; Sievers, C.; Fox, O.

2017-12-01

The goal of early earthquake warning (EEW) systems is to provide warning of impending ground shaking to the public, infrastructure managers, and emergency responders. Shaking intensity can be estimated using Ground Motion Prediction Equations (GMPEs), but only if site characteristics, hypocentral distance and event magnitude are known. In recent years work has been done analyzing the first few seconds of the seismic P wave to derive event location and magnitude. While initial rupture locations seem to be sufficiently constrained, it has been shown that P-wave magnitude estimates tend to saturate at M>7. Regions where major and great earthquakes occur may therefore be vulnerable to an underestimation of shaking intensity if only P waves magnitudes are used. Crowell et al., (2013) first demonstrated that Peak Ground Displacement (PGD) from long-period surface waves recorded by GNSS receivers could provide a source-scaling relation that does not saturate with event magnitude. GNSS PGD derived magnitudes could improve the accuracy of EEW GMPE calculations. If such a source-scaling method were to be implemented in EEW algorithms it is critical that the noise levels in real-time GNSS processed time-series are low enough to resolve long-period surface waves. UNAVCO currently operates 770 real-time GNSS sites, most of which are located along the North American-Pacific Plate Boundary. In this study, we present an analysis of noise levels observed in the GNSS Precise Point Positioning (PPP) solutions generated and distributed in real-time by UNAVCO for periods from seconds to hours. The analysis is performed using the 770 sites in the real-time network and data collected through July 2017. We compare noise levels determined from various monument types and receiver-antenna configurations. This analysis gives a robust estimation of noise levels in PPP solutions because the solutions analyzed are those that were generated in real-time and thus contain all the problems observed

On the real-time estimation of the wheel-rail contact force by means of a new nonlinear estimator design model

NASA Astrophysics Data System (ADS)

Strano, Salvatore; Terzo, Mario

2018-05-01

The dynamics of the railway vehicles is strongly influenced by the interaction between the wheel and the rail. This kind of contact is affected by several conditioning factors such as vehicle speed, wear, adhesion level and, moreover, it is nonlinear. As a consequence, the modelling and the observation of this kind of phenomenon are complex tasks but, at the same time, they constitute a fundamental step for the estimation of the adhesion level or for the vehicle condition monitoring. This paper presents a novel technique for the real time estimation of the wheel-rail contact forces based on an estimator design model that takes into account the nonlinearities of the interaction by means of a fitting model functional to reproduce the contact mechanics in a wide range of slip and to be easily integrated in a complete model based estimator for railway vehicle.

Early warning by near-real time disturbance monitoring (Invited)

NASA Astrophysics Data System (ADS)

Verbesselt, J.; Zeileis, A.; Herold, M.

2013-12-01

Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. Satellite image time series of vegetation greenness provide a global record of terrestrial vegetation productivity over the past decades. Here, we assess and demonstrate the method by applying it to (1) real-world satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought-related vegetation disturbances (2) landsat image time series to detect forest disturbances. First, results illustrate that disturbances are successfully detected in near real-time while being robust to seasonality and noise. Second, major drought-related disturbance corresponding with most drought-stressed regions in Somalia are detected from mid-2010 onwards. Third, the method can be applied to landsat image time series having a lower temporal data density. Furthermore the method can analyze in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling. While the data and methods used are appropriate for proof-of-concept development of global scale disturbance monitoring, specific applications (e.g., drought or deforestation monitoring) mandates integration within an operational monitoring framework. Furthermore, the real-time monitoring method is implemented in open-source environment and is freely available in the BFAST package for R software. Information

The investigation and implementation of real-time face pose and direction estimation on mobile computing devices

NASA Astrophysics Data System (ADS)

Fu, Deqian; Gao, Lisheng; Jhang, Seong Tae

2012-04-01

The mobile computing device has many limitations, such as relative small user interface and slow computing speed. Usually, augmented reality requires face pose estimation can be used as a HCI and entertainment tool. As far as the realtime implementation of head pose estimation on relatively resource limited mobile platforms is concerned, it is required to face different constraints while leaving enough face pose estimation accuracy. The proposed face pose estimation method met this objective. Experimental results running on a testing Android mobile device delivered satisfactory performing results in the real-time and accurately.

Integrated SeismoGeodetic Systsem with High-Resolution, Real-Time GNSS and Accelerometer Observation For Earthquake Early Warning Application.

NASA Astrophysics Data System (ADS)

Passmore, P. R.; Jackson, M.; Zimakov, L. G.; Raczka, J.; Davidson, P.

2014-12-01

The key requirements for Earthquake Early Warning and other Rapid Event Notification Systems are: Quick delivery of digital data from a field station to the acquisition and processing center; Data integrity for real-time earthquake notification in order to provide warning prior to significant ground shaking in the given target area. These two requirements are met in the recently developed Trimble SG160-09 SeismoGeodetic System, which integrates both GNSS and acceleration measurements using the Kalman filter algorithm to create a new high-rate (200 sps), real-time displacement with sufficient accuracy and very low latency for rapid delivery of the acquired data to a processing center. The data acquisition algorithm in the SG160-09 System provides output of both acceleration and displacement digital data with 0.2 sec delay. This is a significant reduction in the time interval required for real-time transmission compared to data delivery algorithms available in digitizers currently used in other Earthquake Early Warning networks. Both acceleration and displacement data are recorded and transmitted to the processing site in a specially developed Multiplexed Recording Format (MRF) that minimizes the bandwidth required for real-time data transmission. In addition, a built in algorithm calculates the τc and Pd once the event is declared. The SG160-09 System keeps track of what data has not been acknowledged and re-transmits the data giving priority to current data. Modified REF TEK Protocol Daemon (RTPD) receives the digital data and acknowledges data received without error. It forwards this "good" data to processing clients of various real-time data processing software including Earthworm and SeisComP3. The processing clients cache packets when a data gap occurs due to a dropped packet or network outage. The cache packet time is settable, but should not exceed 0.5 sec in the Earthquake Early Warning network configuration. The rapid data transmission algorithm was tested

Real-time upper-body human pose estimation from depth data using Kalman filter for simulator

NASA Astrophysics Data System (ADS)

Lee, D.; Chi, S.; Park, C.; Yoon, H.; Kim, J.; Park, C. H.

2014-08-01

Recently, many studies show that an indoor horse riding exercise has a positive effect on promoting health and diet. However, if a rider has an incorrect posture, it will be the cause of back pain. In spite of this problem, there is only few research on analyzing rider's posture. Therefore, the purpose of this study is to estimate a rider pose from a depth image using the Asus's Xtion sensor in real time. In the experiments, we show the performance of our pose estimation algorithm in order to comparing the results between our joint estimation algorithm and ground truth data.

[Evaluation on stability of internal controls in human cardiac muscle by real-time RT-PCR during early postmortem interval].

PubMed

Zhang, Ping; Ma, Kai-Jun; Zhang, Heng; Wang, Hui-Jun; Shen, Yi-Wen; Chen, Long

2012-04-01

To explore the stability of internal controls in human cardiac muscle by real-time RT-PCR during early postmortem interval (PMI) in order to find the most stable marker. Ten individuals with similar environmental conditions (the average store temperature: 25 degrees C) and different PMI ranging from 4.3 to 22.3 h were selected. Total RNA was extracted from each sample and six commonly internal controls were used including beta-actin, GAPDH, B2M, U6, 18S rRNA and HSA-miR-1, and the expression was detected in cardiac muscle by real-time RT-PCR. The expression stability of internal controls was evaluated using genormPLUS software during early PMI. The internal control with the most stability was selected. The relationship between the most stable marker and its expression level affected by some other parameters such as age, gender and cause of death was also analyzed. The U6 showed the most stable expression during early PMI in cardiac muscle, and its expression level was not affected by those parameters including age, gender and cause of death (P > 0.05). U6 may be a valuable internal control for the study of relationship between PMI determination and degradation of nucleic acid in human cardiac muscle by real-time RT-PCR.

Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 1: Real-time flight experiment support

NASA Technical Reports Server (NTRS)

Rediess, Herman A.; Ramnath, Rudrapatna V.; Vrable, Daniel L.; Hirvo, David H.; Mcmillen, Lowell D.; Osofsky, Irving B.

1991-01-01

The results are presented of a study to identify potential real time remote computational applications to support monitoring HRV flight test experiments along with definitions of preliminary requirements. A major expansion of the support capability available at Ames-Dryden was considered. The focus is on the use of extensive computation and data bases together with real time flight data to generate and present high level information to those monitoring the flight. Six examples were considered: (1) boundary layer transition location; (2) shock wave position estimation; (3) performance estimation; (4) surface temperature estimation; (5) critical structural stress estimation; and (6) stability estimation.

Time estimates in a long-term time-free environment. [human performance

NASA Technical Reports Server (NTRS)

Lavie, P.; Webb, W. B.

1975-01-01

Subjects in a time-free environment for 14 days estimated the hour and day several times a day. Half of the subjects were under a heavy exercise regime. During the waking hours, the no-exercise group showed no difference between estimated and real time, whereas the exercise group showed significantly shorter estimated than real time. Neither group showed a difference after the sleeping periods. However, the mean accumulated error for the two groups was 48.73 hours and was strongly related to the displacements of sleep/waking behavior. It is concluded that behavioral cues are the primary determinants of time estimates in time-free environments.

Real-time data for estimating a forward-looking interest rate rule of the ECB.

PubMed

Bletzinger, Tilman; Wieland, Volker

2017-12-01

The purpose of the data presented in this article is to use it in ex post estimations of interest rate decisions by the European Central Bank (ECB), as it is done by Bletzinger and Wieland (2017) [1]. The data is of quarterly frequency from 1999 Q1 until 2013 Q2 and consists of the ECB's policy rate, inflation rate, real output growth and potential output growth in the euro area. To account for forward-looking decision making in the interest rate rule, the data consists of expectations about future inflation and output dynamics. While potential output is constructed based on data from the European Commission's annual macro-economic database, inflation and real output growth are taken from two different sources both provided by the ECB: the Survey of Professional Forecasters and projections made by ECB staff. Careful attention was given to the publication date of the collected data to ensure a real-time dataset only consisting of information which was available to the decision makers at the time of the decision.

Real-time estimation of wildfire perimeters from curated crowdsourcing

NASA Astrophysics Data System (ADS)

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-04-01

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available “curated” crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires.

Real-time estimation of wildfire perimeters from curated crowdsourcing

PubMed Central

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-01-01

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available “curated” crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires. PMID:27063569

Real-time estimation of wildfire perimeters from curated crowdsourcing.

PubMed

Zhong, Xu; Duckham, Matt; Chong, Derek; Tolhurst, Kevin

2016-04-11

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available "curated" crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires.

USGS approach to real-time estimation of earthquake-triggered ground failure - Results of 2015 workshop

USGS Publications Warehouse

Allstadt, Kate E.; Thompson, Eric M.; Wald, David J.; Hamburger, Michael W.; Godt, Jonathan W.; Knudsen, Keith L.; Jibson, Randall W.; Jessee, M. Anna; Zhu, Jing; Hearne, Michael; Baise, Laurie G.; Tanyas, Hakan; Marano, Kristin D.

2016-03-30

The U.S. Geological Survey (USGS) Earthquake Hazards and Landslide Hazards Programs are developing plans to add quantitative hazard assessments of earthquake-triggered landsliding and liquefaction to existing real-time earthquake products (ShakeMap, ShakeCast, PAGER) using open and readily available methodologies and products. To date, prototype global statistical models have been developed and are being refined, improved, and tested. These models are a good foundation, but much work remains to achieve robust and defensible models that meet the needs of end users. In order to establish an implementation plan and identify research priorities, the USGS convened a workshop in Golden, Colorado, in October 2015. This document summarizes current (as of early 2016) capabilities, research and operational priorities, and plans for further studies that were established at this workshop. Specific priorities established during the meeting include (1) developing a suite of alternative models; (2) making use of higher resolution and higher quality data where possible; (3) incorporating newer global and regional datasets and inventories; (4) reducing barriers to accessing inventory datasets; (5) developing methods for using inconsistent or incomplete datasets in aggregate; (6) developing standardized model testing and evaluation methods; (7) improving ShakeMap shaking estimates, particularly as relevant to ground failure, such as including topographic amplification and accounting for spatial variability; and (8) developing vulnerability functions for loss estimates.

Combining multiple earthquake models in real time for earthquake early warning

USGS Publications Warehouse

Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

NASA Astrophysics Data System (ADS)

Roman, J.

2015-12-01

The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning

USGS Publications Warehouse

Mirus, Benjamin B.; Becker, Rachel E.; Baum, Rex L.; Smith, Joel B.

2018-01-01

Early warning for rainfall-induced shallow landsliding can help reduce fatalities and economic losses. Although these commonly occurring landslides are typically triggered by subsurface hydrological processes, most early warning criteria rely exclusively on empirical rainfall thresholds and other indirect proxies for subsurface wetness. We explore the utility of explicitly accounting for antecedent wetness by integrating real-time subsurface hydrologic measurements into landslide early warning criteria. Our efforts build on previous progress with rainfall thresholds, monitoring, and numerical modeling along the landslide-prone railway corridor between Everett and Seattle, Washington, USA. We propose a modification to a previously established recent versus antecedent (RA) cumulative rainfall thresholds by replacing the antecedent 15-day rainfall component with an average saturation observed over the same timeframe. We calculate this antecedent saturation with real-time telemetered measurements from five volumetric water content probes installed in the shallow subsurface within a steep vegetated hillslope. Our hybrid rainfall versus saturation (RS) threshold still relies on the same recent 3-day rainfall component as the existing RA thresholds, to facilitate ready integration with quantitative precipitation forecasts. During the 2015–2017 monitoring period, this RS hybrid approach has an increase of true positives and a decrease of false positives and false negatives relative to the previous RA rainfall-only thresholds. We also demonstrate that alternative hybrid threshold formats could be even more accurate, which suggests that further development and testing during future landslide seasons is needed. The positive results confirm that accounting for antecedent wetness conditions with direct subsurface hydrologic measurements can improve thresholds for alert systems and early warning of rainfall-induced shallow landsliding.

Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors

NASA Astrophysics Data System (ADS)

Kropivnitskaya, Yelena; Tiampo, Kristy F.; Qin, Jinhui; Bauer, Michael A.

2017-06-01

Earthquake intensity is one of the key components of the decision-making process for disaster response and emergency services. Accurate and rapid intensity calculations can help to reduce total loss and the number of casualties after an earthquake. Modern intensity assessment procedures handle a variety of information sources, which can be divided into two main categories. The first type of data is that derived from physical sensors, such as seismographs and accelerometers, while the second type consists of data obtained from social sensors, such as witness observations of the consequences of the earthquake itself. Estimation approaches using additional data sources or that combine sources from both data types tend to increase intensity uncertainty due to human factors and inadequate procedures for temporal and spatial estimation, resulting in precision errors in both time and space. Here we present a processing approach for the real-time analysis of streams of data from both source types. The physical sensor data is acquired from the U.S. Geological Survey (USGS) seismic network in California and the social sensor data is based on Twitter user observations. First, empirical relationships between tweet rate and observed Modified Mercalli Intensity (MMI) are developed using data from the M6.0 South Napa, CAF earthquake that occurred on August 24, 2014. Second, the streams of both data types are analyzed together in simulated real-time to produce one intensity map. The second implementation is based on IBM InfoSphere Streams, a cloud platform for real-time analytics of big data. To handle large processing workloads for data from various sources, it is deployed and run on a cloud-based cluster of virtual machines. We compare the quality and evolution of intensity maps from different data sources over 10-min time intervals immediately following the earthquake. Results from the joint analysis shows that it provides more complete coverage, with better accuracy and higher

Statistical Properties of Real-Time Amplitude Estimate of Harmonics Affected by Frequency Instability

NASA Astrophysics Data System (ADS)

Bellan, Diego; Pignari, Sergio A.

2016-07-01

This work deals with the statistical characterization of real-time digital measurement of the amplitude of harmonics affected by frequency instability. In fact, in modern power systems both the presence of harmonics and frequency instability are well-known and widespread phenomena mainly due to nonlinear loads and distributed generation, respectively. As a result, real-time monitoring of voltage/current frequency spectra is of paramount importance as far as power quality issues are addressed. Within this framework, a key point is that in many cases real-time continuous monitoring prevents the application of sophisticated algorithms to extract all the information from the digitized waveforms because of the required computational burden. In those cases only simple evaluations such as peak search of discrete Fourier transform are implemented. It is well known, however, that a slight change in waveform frequency results in lack of sampling synchronism and uncertainty in amplitude estimate. Of course the impact of this phenomenon increases with the order of the harmonic to be measured. In this paper an approximate analytical approach is proposed in order to describe the statistical properties of the measured magnitude of harmonics affected by frequency instability. By providing a simplified description of the frequency behavior of the windows used against spectral leakage, analytical expressions for mean value, variance, cumulative distribution function, and probability density function of the measured harmonics magnitude are derived in closed form as functions of waveform frequency treated as a random variable.

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces.

PubMed

Pizzolato, Claudio; Reggiani, Monica; Saxby, David J; Ceseracciu, Elena; Modenese, Luca; Lloyd, David G

2017-09-01

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this paper, we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed five healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only three subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this paper focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

Real-time two-dimensional temperature imaging using ultrasound.

PubMed

Liu, Dalong; Ebbini, Emad S

2009-01-01

We present a system for real-time 2D imaging of temperature change in tissue media using pulse-echo ultrasound. The frontend of the system is a SonixRP ultrasound scanner with a research interface giving us the capability of controlling the beam sequence and accessing radio frequency (RF) data in real-time. The beamformed RF data is streamlined to the backend of the system, where the data is processed using a two-dimensional temperature estimation algorithm running in the graphics processing unit (GPU). The estimated temperature is displayed in real-time providing feedback that can be used for real-time control of the heating source. Currently we have verified our system with elastography tissue mimicking phantom and in vitro porcine heart tissue, excellent repeatability and sensitivity were demonstrated.

Real Time Radiation Monitoring Using Nanotechnology

NASA Technical Reports Server (NTRS)

Li, Jing (Inventor); Hanratty, James J. (Inventor); Wilkins, Richard T. (Inventor); Lu, Yijiang (Inventor)

2016-01-01

System and method for monitoring receipt and estimating flux value, in real time, of incident radiation, using two or more nanostructures (NSs) and associated terminals to provide closed electrical paths and to measure one or more electrical property change values .DELTA.EPV, associated with irradiated NSs, during a sequence of irradiation time intervals. Effects of irradiation, without healing and with healing, of the NSs, are separately modeled for first order and second order healing. Change values.DELTA.EPV are related to flux, to cumulative dose received by NSs, and to radiation and healing effectivity parameters and/or.mu., associated with the NS material and to the flux. Flux and/or dose are estimated in real time, based on EPV change values, using measured .DELTA.EPV values. Threshold dose for specified changes of biological origin (usually undesired) can be estimated. Effects of time-dependent radiation flux are analyzed in pre-healing and healing regimes.

Biochemical methane potential (BMP) tests: Reducing test time by early parameter estimation.

PubMed

Da Silva, C; Astals, S; Peces, M; Campos, J L; Guerrero, L

2018-01-01

Biochemical methane potential (BMP) test is a key analytical technique to assess the implementation and optimisation of anaerobic biotechnologies. However, this technique is characterised by long testing times (from 20 to >100days), which is not suitable for waste utilities, consulting companies or plants operators whose decision-making processes cannot be held for such a long time. This study develops a statistically robust mathematical strategy using sensitivity functions for early prediction of BMP first-order model parameters, i.e. methane yield (B 0 ) and kinetic constant rate (k). The minimum testing time for early parameter estimation showed a potential correlation with the k value, where (i) slowly biodegradable substrates (k≤0.1d -1 ) have a minimum testing times of ≥15days, (ii) moderately biodegradable substrates (0.1times between 8 and 15 days, and (iii) rapidly biodegradable substrates (k≥0.2d -1 ) have testing times lower than 7days. Copyright © 2017 Elsevier Ltd. All rights reserved.

Toward a Real-Time Measurement-Based System for Estimation of Helicopter Engine Degradation Due to Compressor Erosion

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Simo, Donald L.

2007-01-01

This paper presents a preliminary demonstration of an automated health assessment tool, capable of real-time on-board operation using existing engine control hardware. The tool allows operators to discern how rapidly individual turboshaft engines are degrading. As the compressor erodes, performance is lost, and with it the ability to generate power. Thus, such a tool would provide an instant assessment of the engine s fitness to perform a mission, and would help to pinpoint any abnormal wear or performance anomalies before they became serious, thereby decreasing uncertainty and enabling improved maintenance scheduling. The research described in the paper utilized test stand data from a T700-GE-401 turboshaft engine that underwent sand-ingestion testing to scale a model-based compressor efficiency degradation estimation algorithm. This algorithm was then applied to real-time Health Usage and Monitoring System (HUMS) data from a T700-GE-701C to track compressor efficiency on-line. The approach uses an optimal estimator called a Kalman filter. The filter is designed to estimate the compressor efficiency using only data from the engine s sensors as input.

Real-Time State Estimation and Long-Term Model Adaptation: A Two-Sided Approach toward Personalized Diagnosis of Glucose and Insulin Levels

PubMed Central

Eberle, Claudia; Ament, Christoph

2012-01-01

Background With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patient’s subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification. Methods To obtain a dynamical model, Bergman’s nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter. Results (1) Observability of different state subsets is evaluated, e.g., from CGSs, {G, I} or {G, X} can be observed and the set {G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain kG2 additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application. Conclusions A real-time estimation of states (such as plasma insulin I) and parameters (such as kG2) is possible, which allows an improved real-time state prediction and a personalized model. PMID:23063042

A real-time signal combining system for Ka-band feed arrays using maximum-likelihood weight estimates

NASA Technical Reports Server (NTRS)

Vilnrotter, V. A.; Rodemich, E. R.

1990-01-01

A real-time digital signal combining system for use with Ka-band feed arrays is proposed. The combining system attempts to compensate for signal-to-noise ratio (SNR) loss resulting from antenna deformations induced by gravitational and atmospheric effects. The combining weights are obtained directly from the observed samples by using a sliding-window implementation of a vector maximum-likelihood parameter estimator. It is shown that with averaging times of about 0.1 second, combining loss for a seven-element array can be limited to about 0.1 dB in a realistic operational environment. This result suggests that the real-time combining system proposed here is capable of recovering virtually all of the signal power captured by the feed array, even in the presence of severe wind gusts and similar disturbances.

Learning time-dependent noise to reduce logical errors: real time error rate estimation in quantum error correction

NASA Astrophysics Data System (ADS)

Huo, Ming-Xia; Li, Ying

2017-12-01

Quantum error correction is important to quantum information processing, which allows us to reliably process information encoded in quantum error correction codes. Efficient quantum error correction benefits from the knowledge of error rates. We propose a protocol for monitoring error rates in real time without interrupting the quantum error correction. Any adaptation of the quantum error correction code or its implementation circuit is not required. The protocol can be directly applied to the most advanced quantum error correction techniques, e.g. surface code. A Gaussian processes algorithm is used to estimate and predict error rates based on error correction data in the past. We find that using these estimated error rates, the probability of error correction failures can be significantly reduced by a factor increasing with the code distance.

Real-time long term measurement using integrated framework for ubiquitous smart monitoring

NASA Astrophysics Data System (ADS)

Heo, Gwanghee; Lee, Giu; Lee, Woosang; Jeon, Joonryong; Kim, Pil-Joong

2007-04-01

Ubiquitous monitoring combining internet technologies and wireless communication is one of the most promising technologies of infrastructure health monitoring against the natural of man-made hazards. In this paper, an integrated framework of the ubiquitous monitoring is developed for real-time long term measurement in internet environment. This framework develops a wireless sensor system based on Bluetooth technology and sends measured acceleration data to the host computer through TCP/IP protocol. And it is also designed to respond to the request of web user on real time basis. In order to verify this system, real time monitoring tests are carried out on a prototype self-anchored suspension bridge. Also, wireless measurement system is analyzed to estimate its sensing capacity and evaluate its performance for monitoring purpose. Based on the evaluation, this paper proposes the effective strategies for integrated framework in order to detect structural deficiencies and to design an early warning system.

On protecting the planet against cosmic attack: Ultrafast real-time estimate of the asteroid's radial velocity

NASA Astrophysics Data System (ADS)

Zakharchenko, V. D.; Kovalenko, I. G.

2014-05-01

A new method for the line-of-sight velocity estimation of a high-speed near-Earth object (asteroid, meteorite) is suggested. The method is based on the use of fractional, one-half order derivative of a Doppler signal. The algorithm suggested is much simpler and more economical than the classical one, and it appears preferable for use in orbital weapon systems of threat response. Application of fractional differentiation to quick evaluation of mean frequency location of the reflected Doppler signal is justified. The method allows an assessment of the mean frequency in the time domain without spectral analysis. An algorithm structure for the real-time estimation is presented. The velocity resolution estimates are made for typical asteroids in the X-band. It is shown that the wait time can be shortened by orders of magnitude compared with similar value in the case of a standard spectral processing.

Real-time Space-time Integration in GIScience and Geography

PubMed Central

Richardson, Douglas B.

2013-01-01

Space-time integration has long been the topic of study and speculation in geography. However, in recent years an entirely new form of space-time integration has become possible in GIS and GIScience: real-time space-time integration and interaction. While real-time spatiotemporal data is now being generated almost ubiquitously, and its applications in research and commerce are widespread and rapidly accelerating, the ability to continuously create and interact with fused space-time data in geography and GIScience is a recent phenomenon, made possible by the invention and development of real-time interactive (RTI) GPS/GIS technology and functionality in the late 1980s and early 1990s. This innovation has since functioned as a core change agent in geography, cartography, GIScience and many related fields, profoundly realigning traditional relationships and structures, expanding research horizons, and transforming the ways geographic data is now collected, mapped, modeled, and used, both in geography and in science and society more broadly. Real-time space-time interactive functionality remains today the underlying process generating the current explosion of fused spatiotemporal data, new geographic research initiatives, and myriad geospatial applications in governments, businesses, and society. This essay addresses briefly the development of these real-time space-time functions and capabilities; their impact on geography, cartography, and GIScience; and some implications for how discovery and change can occur in geography and GIScience, and how we might foster continued innovation in these fields. PMID:24587490

Real-time Space-time Integration in GIScience and Geography.

PubMed

Richardson, Douglas B

2013-01-01

Space-time integration has long been the topic of study and speculation in geography. However, in recent years an entirely new form of space-time integration has become possible in GIS and GIScience: real-time space-time integration and interaction. While real-time spatiotemporal data is now being generated almost ubiquitously, and its applications in research and commerce are widespread and rapidly accelerating, the ability to continuously create and interact with fused space-time data in geography and GIScience is a recent phenomenon, made possible by the invention and development of real-time interactive (RTI) GPS/GIS technology and functionality in the late 1980s and early 1990s. This innovation has since functioned as a core change agent in geography, cartography, GIScience and many related fields, profoundly realigning traditional relationships and structures, expanding research horizons, and transforming the ways geographic data is now collected, mapped, modeled, and used, both in geography and in science and society more broadly. Real-time space-time interactive functionality remains today the underlying process generating the current explosion of fused spatiotemporal data, new geographic research initiatives, and myriad geospatial applications in governments, businesses, and society. This essay addresses briefly the development of these real-time space-time functions and capabilities; their impact on geography, cartography, and GIScience; and some implications for how discovery and change can occur in geography and GIScience, and how we might foster continued innovation in these fields.

Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

USGS Publications Warehouse

Daamen, Ruby C.; Edwin A. Roehl, Jr.; Conrads, Paul

2010-01-01

A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography.

PubMed

Hládek, Ľuboš; Porr, Bernd; Brimijoin, W Owen

2018-01-01

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for light-weight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user's eye gaze.

Real-time flutter analysis

NASA Technical Reports Server (NTRS)

Walker, R.; Gupta, N.

1984-01-01

The important algorithm issues necessary to achieve a real time flutter monitoring system; namely, the guidelines for choosing appropriate model forms, reduction of the parameter convergence transient, handling multiple modes, the effect of over parameterization, and estimate accuracy predictions, both online and for experiment design are addressed. An approach for efficiently computing continuous-time flutter parameter Cramer-Rao estimate error bounds were developed. This enables a convincing comparison of theoretical and simulation results, as well as offline studies in preparation for a flight test. Theoretical predictions, simulation and flight test results from the NASA Drones for Aerodynamic and Structural Test (DAST) Program are compared.

Development of real-time PCR technique for the estimation of population density of Pythium intermedium in forest soils.

PubMed

Li, Mingzhu; Senda, Masako; Komatsu, Tsutomu; Suga, Haruhisa; Kageyama, Koji

2010-10-20

Pythium intermedium is known to play an important role in the carbon cycling of cool-temperate forest soils. In this study, a fast, precise and effective real-time PCR technique for estimating the population densities of P. intermedium from soils was developed using species-specific primers. Specificity was confirmed both with conventional PCR and real-time PCR. The detection limit (sensitivity) was determined and amplification standard curves were generated using SYBR Green II fluorescent dye. A rapid and accurate assay for quantification of P. intermedium in Takayama forest soils of Japan was developed using a combination of a new DNA extraction method and PCR primers were developed for real-time PCR. And the distribution of P. intermedium in forest soil was investigated with both soil plating method and the developed real-time PCR technique. This new technique will be a useful tool and can be applied to practical use for studying the role of Pythium species in forest and agricultural ecosystems. Copyright © 2009 Elsevier GmbH. All rights reserved.

Earthquake Loss Estimates in Near Real-Time

NASA Astrophysics Data System (ADS)

Wyss, Max; Wang, Rongjiang; Zschau, Jochen; Xia, Ye

2006-10-01

The usefulness to rescue teams of nearreal-time loss estimates after major earthquakes is advancing rapidly. The difference in the quality of data available in highly developed compared with developing countries dictates that different approaches be used to maximize mitigation efforts. In developed countries, extensive information from tax and insurance records, together with accurate census figures, furnish detailed data on the fragility of buildings and on the number of people at risk. For example, these data are exploited by the method to estimate losses used in the Hazards U.S. Multi-Hazard (HAZUSMH)software program (http://www.fema.gov/plan/prevent/hazus/). However, in developing countries, the population at risk is estimated from inferior data sources and the fragility of the building stock often is derived empirically, using past disastrous earthquakes for calibration [Wyss, 2004].

Real-Time Systems

DTIC Science & Technology

1992-02-01

Postgraduate School Autonomous Under Vehicle (AUV) are then examined. Autonomous underwater vehicle (AUV), hard real-time system, real - time operating system , real-time programming language, real-time system, soft real-time system.

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.

Geomagnetic Observatory Data for Real-Time Applications

NASA Astrophysics Data System (ADS)

Love, J. J.; Finn, C. A.; Rigler, E. J.; Kelbert, A.; Bedrosian, P.

2015-12-01

The global network of magnetic observatories represents a unique collective asset for the scientific community. Historically, magnetic observatories have supported global magnetic-field mapping projects and fundamental research of the Earth's interior and surrounding space environment. More recently, real-time data streams from magnetic observatories have become an important contributor to multi-sensor, operational monitoring of evolving space weather conditions, especially during magnetic storms. In this context, the U.S. Geological Survey (1) provides real-time observatory data to allied space weather monitoring projects, including those of NOAA, the U.S. Air Force, NASA, several international agencies, and private industry, (2) collaborates with Schlumberger to provide real-time geomagnetic data needed for directional drilling for oil and gas in Alaska, (3) develops products for real-time evaluation of hazards for the electric-power grid industry that are associated with the storm-time induction of geoelectric fields in the Earth's conducting lithosphere. In order to implement strategic priorities established by the USGS Natural Hazards Mission Area and the National Science and Technology Council, and with a focus on developing new real-time products, the USGS is (1) leveraging data management protocols already developed by the USGS Earthquake Program, (2) developing algorithms for mapping geomagnetic activity, a collaboration with NASA and NOAA, (3) supporting magnetotelluric surveys and developing Earth conductivity models, a collaboration with Oregon State University and the NSF's EarthScope Program, (4) studying the use of geomagnetic activity maps and Earth conductivity models for real-time estimation of geoelectric fields, (5) initiating geoelectric monitoring at several observatories, (6) validating real-time estimation algorithms against historical geomagnetic and geoelectric data. The success of these long-term projects is subject to funding constraints

Development of sustainable precision farming systems for swine: estimating real-time individual amino acid requirements in growing-finishing pigs.

PubMed

Hauschild, L; Lovatto, P A; Pomar, J; Pomar, C

2012-07-01

The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation

Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography

PubMed Central

2018-01-01

The manuscript proposes and evaluates a real-time algorithm for estimating eye gaze angle based solely on single-channel electrooculography (EOG), which can be obtained directly from the ear canal using conductive ear moulds. In contrast to conventional high-pass filtering, we used an algorithm that calculates absolute eye gaze angle via statistical analysis of detected saccades. The estimated eye positions of the new algorithm were still noisy. However, the performance in terms of Pearson product-moment correlation coefficients was significantly better than the conventional approach in some instances. The results suggest that in-ear EOG signals captured with conductive ear moulds could serve as a basis for light-weight and portable horizontal eye gaze angle estimation suitable for a broad range of applications. For instance, for hearing aids to steer the directivity of microphones in the direction of the user’s eye gaze. PMID:29304120

Real time freeway incident detection.

DOT National Transportation Integrated Search

2014-04-01

The US Department of Transportation (US-DOT) estimates that over half of all congestion : events are caused by highway incidents rather than by rush-hour traffic in big cities. Real-time : incident detection on freeways is an important part of any mo...

Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG.

PubMed

Mesin, Luca

2015-02-01

Developing a real time method to estimate generation, extinction and propagation of muscle fibre action potentials from bi-dimensional and high density surface electromyogram (EMG). A multi-frame generalization of an optical flow technique including a source term is considered. A model describing generation, extinction and propagation of action potentials is fit to epochs of surface EMG. The algorithm is tested on simulations of high density surface EMG (inter-electrode distance equal to 5mm) from finite length fibres generated using a multi-layer volume conductor model. The flow and source term estimated from interference EMG reflect the anatomy of the muscle, i.e. the direction of the fibres (2° of average estimation error) and the positions of innervation zone and tendons under the electrode grid (mean errors of about 1 and 2mm, respectively). The global conduction velocity of the action potentials from motor units under the detection system is also obtained from the estimated flow. The processing time is about 1 ms per channel for an epoch of EMG of duration 150 ms. A new real time image processing algorithm is proposed to investigate muscle anatomy and activity. Potential applications are proposed in prosthesis control, automatic detection of optimal channels for EMG index extraction and biofeedback. Copyright © 2014 Elsevier Ltd. All rights reserved.

Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA 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. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges 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 generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

Real-time video quality monitoring

NASA Astrophysics Data System (ADS)

Liu, Tao; Narvekar, Niranjan; Wang, Beibei; Ding, Ran; Zou, Dekun; Cash, Glenn; Bhagavathy, Sitaram; Bloom, Jeffrey

2011-12-01

The ITU-T Recommendation G.1070 is a standardized opinion model for video telephony applications that uses video bitrate, frame rate, and packet-loss rate to measure the video quality. However, this model was original designed as an offline quality planning tool. It cannot be directly used for quality monitoring since the above three input parameters are not readily available within a network or at the decoder. And there is a great room for the performance improvement of this quality metric. In this article, we present a real-time video quality monitoring solution based on this Recommendation. We first propose a scheme to efficiently estimate the three parameters from video bitstreams, so that it can be used as a real-time video quality monitoring tool. Furthermore, an enhanced algorithm based on the G.1070 model that provides more accurate quality prediction is proposed. Finally, to use this metric in real-world applications, we present an example emerging application of real-time quality measurement to the management of transmitted videos, especially those delivered to mobile devices.

Near Real-time Operational Use of eMODIS Expedited NDVI for Monitoring Applications and Famine Early Warning

NASA Astrophysics Data System (ADS)

Rowland, J.; Budde, M. E.

2010-12-01

The Famine Early Warning Systems Network (FEWS NET) has requirements for near real-time monitoring of vegetation conditions for food security applications. Accurate and timely assessments of crop conditions are an important element of food security decision making. FEWS NET scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center are utilizing a new Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset for operational monitoring of crop and pasture conditions in parts of the world where food availability is highly dependent on subsistence agriculture and animal husbandry. The expedited MODIS, or eMODIS, production system processes NDVI data using MODIS surface reflectance provided by the Land Atmosphere Near-real-time Capability for EOS (LANCE). Benefits of this production system include customized compositing schedules, near real-time data availability, and minimized re-sampling. FEWS NET has implemented a 10-day compositing scheme every five days to accommodate the need for timely information on vegetation conditions. The data are currently being processed at 250-meter spatial resolution for Central America, Hispaniola, and Africa. Data are further enhanced by the application of a temporal smoothing filter which helps remove contamination due to clouds and other atmospheric effects. The results of this near real-time monitoring capability have been the timely provision of NDVI and NDVI anomaly maps for each of the FEWS NET monitoring regions and the availability of a consistently processed dataset to aid crop assessment missions and to facilitate customized analyses of crop production, drought, and agro-pastoral conditions.

Early diagnosis of dengue in travelers: comparison of a novel real-time RT-PCR, NS1 antigen detection and serology.

PubMed

Huhtamo, Eili; Hasu, Essi; Uzcátegui, Nathalie Y; Erra, Elina; Nikkari, Simo; Kantele, Anu; Vapalahti, Olli; Piiparinen, Heli

2010-01-01

The increased traveling to dengue endemic regions and the numerous epidemics have led to a rise in imported dengue. The laboratory diagnosis of acute dengue requires several types of tests and often paired samples are needed for obtaining reliable results. Although several diagnostic methods are available, proper comparative data on their performance are lacking. To compare the performance of novel methods including a novel pan-DENV real-time RT-PCR and a commercially available NS1 capture-EIA in regard to IgM detection for optimizing the early diagnosis of DENV in travelers. A panel of 99 selected early phase serum samples of dengue patients was studied by real-time RT-PCR, NS1 antigen ELISA, IgM-EIA, IgG-IFA and cell culture virus isolation. The novel real-time RT-PCR was shown specific and sensitive for detection of DENV-1-4 RNA and suitable for diagnostic use. The diagnostic rate using combination of RNA and IgM detection was 99% and using NS1 and IgM detection 95.9%. The results of RNA and NS1 antigen detection disagreed in 15.5% of samples that had only RNA or NS1 antigen detected. The diagnostic rates of early samples are higher when either RNA or NS1 antigen detection is combined with IgM detection. Besides the differences in the RNA and NS1 detection assays, the observed discrepancy of results could suggest individual variation or differences in timing of these markers in patient serum. Copyright (c) 2009 Elsevier B.V. All rights reserved.

Apprentices & Trainees: Early Trend Estimates. December Quarter, 2014

ERIC Educational Resources Information Center

National Centre for Vocational Education Research (NCVER), 2015

2015-01-01

This publication presents early estimates of apprentice and trainee commencements for the December quarter 2014. Indicative information about this quarter is presented here; the most recent figures are estimated, taking into account reporting lags that occur at the time of data collection. The early trend estimates are derived from the National…

Apprentices & Trainees: Early Trend Estimates. March Quarter, 2012

ERIC Educational Resources Information Center

National Centre for Vocational Education Research (NCVER), 2012

2012-01-01

This publication presents early estimates of apprentice and trainee commencements for the March quarter 2012. Indicative information about this quarter is presented here; the most recent figures are estimated, taking into account reporting lags that occur at the time of data collection. The early trend estimates are derived from the National…

A Real-Time Method to Estimate Speed of Object Based on Object Detection and Optical Flow Calculation

NASA Astrophysics Data System (ADS)

Liu, Kaizhan; Ye, Yunming; Li, Xutao; Li, Yan

2018-04-01

In recent years Convolutional Neural Network (CNN) has been widely used in computer vision field and makes great progress in lots of contents like object detection and classification. Even so, combining Convolutional Neural Network, which means making multiple CNN frameworks working synchronously and sharing their output information, could figure out useful message that each of them cannot provide singly. Here we introduce a method to real-time estimate speed of object by combining two CNN: YOLOv2 and FlowNet. In every frame, YOLOv2 provides object size; object location and object type while FlowNet providing the optical flow of whole image. On one hand, object size and object location help to select out the object part of optical flow image thus calculating out the average optical flow of every object. On the other hand, object type and object size help to figure out the relationship between optical flow and true speed by means of optics theory and priori knowledge. Therefore, with these two key information, speed of object can be estimated. This method manages to estimate multiple objects at real-time speed by only using a normal camera even in moving status, whose error is acceptable in most application fields like manless driving or robot vision.

Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China

NASA Astrophysics Data System (ADS)

Chen, Sheng; Hu, Junjun; Zhang, Asi; Min, Chao; Huang, Chaoying; Liang, Zhenqing

2018-02-01

This study assesses the performance of near real-time Global Satellite Mapping of Precipitation (GSMaP_NRT) estimates over northern China, including Beijing and its adjacent regions, during three heavy precipitation events from 21 July 2012 to 2 August 2012. Two additional near real-time satellite-based products, the Climate Prediction Center morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), were used for parallel comparison with GSMaP_NRT. Gridded gauge observations were used as reference for a performance evaluation with respect to spatiotemporal variability, probability distribution of precipitation rate and volume, and contingency scores. Overall, GSMaP_NRT generally captures the spatiotemporal variability of precipitation and shows promising potential in near real-time mapping applications. GSMaP_NRT misplaced storm centers in all three storms. GSMaP_NRT demonstrated higher skill scores in the first high-impact storm event on 21 July 2015. GSMaP_NRT passive microwave only precipitation can generally capture the pattern of heavy precipitation distributions over flat areas but failed to capture the intensive rain belt over complicated mountainous terrain. The results of this study can be useful to both algorithm developers and the scientific end users, providing a better understanding of strengths and weaknesses to hydrologists using satellite precipitation products.

Development of a real-time PCR assay for Penicillium expansum quantification and patulin estimation in apples.

PubMed

Tannous, Joanna; Atoui, Ali; El Khoury, André; Kantar, Sally; Chdid, Nader; Oswald, Isabelle P; Puel, Olivier; Lteif, Roger

2015-09-01

Due to the occurrence and spread of the fungal contaminants in food and the difficulties to remove their resulting mycotoxins, rapid and accurate methods are needed for early detection of these mycotoxigenic fungi. The polymerase chain reaction and the real time PCR have been widely used for this purpose. Apples are suitable substrates for fungal colonization mostly caused by Penicillium expansum, which produces the mycotoxin patulin during fruit infection. This study describes the development of a real-time PCR assay incorporating an internal amplification control (IAC) to specifically detect and quantify P. expansum. A specific primer pair was designed from the patF gene, involved in patulin biosynthesis. The selected primer set showed a high specificity for P. expansum and was successfully employed in a standardized real-time PCR for the direct quantification of this fungus in apples. Using the developed system, twenty eight apples were analyzed for their DNA content. Apples were also analyzed for patulin content by HPLC. Interestingly, a positive correlation (R(2) = 0.701) was found between P. expansum DNA content and patulin concentration. This work offers an alternative to conventional methods of patulin quantification and mycological detection of P. expansum and could be very useful for the screening of patulin in fruits through the application of industrial quality control. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Slow Developmental Time Course of Real-Time Spoken Word Recognition

ERIC Educational Resources Information Center

Rigler, Hannah; Farris-Trimble, Ashley; Greiner, Lea; Walker, Jessica; Tomblin, J. Bruce; McMurray, Bob

2015-01-01

This study investigated the developmental time course of spoken word recognition in older children using eye tracking to assess how the real-time processing dynamics of word recognition change over development. We found that 9-year-olds were slower to activate the target words and showed more early competition from competitor words than…

Near real-time estimation of ionosphere vertical total electron content from GNSS satellites using B-splines in a Kalman filter

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Schmidt, Michael; Seitz, Florian; Durmaz, Murat

2017-02-01

Although the number of terrestrial global navigation satellite system (GNSS) receivers supported by the International GNSS Service (IGS) is rapidly growing, the worldwide rather inhomogeneously distributed observation sites do not allow the generation of high-resolution global ionosphere products. Conversely, with the regionally enormous increase in highly precise GNSS data, the demands on (near) real-time ionosphere products, necessary in many applications such as navigation, are growing very fast. Consequently, many analysis centers accepted the responsibility of generating such products. In this regard, the primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution. The global VTEC representation developed in this work is based on a series expansion in terms of compactly supported B-spline functions, which allow for an appropriate handling of the heterogeneous data distribution, including data gaps. The corresponding series coefficients and additional parameters such as differential code biases of the GNSS satellites and receivers constitute the set of unknown parameters. The Kalman filter (KF), as a popular recursive estimator, allows processing of the data immediately after acquisition and paves the way of sequential (near) real-time estimation of the unknown parameters. To exploit the advantages of the chosen data representation and the estimation procedure, the B-spline model is incorporated into the KF under the consideration of necessary constraints. Based on a preprocessing strategy, the developed approach utilizes hourly batches of GPS and GLONASS observations provided by the IGS data centers with a latency of 1 h in its current realization. Two methods for validation of the results are performed, namely the self consistency analysis and a comparison

A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Reichle, Rolf H.; Mahanama, Sarith P. P.

2017-01-01

NASAs Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2-3 days and a latency of 24 hours. Here, to enhance the utility of the SMAP data, we present an approach for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States (CONUS) is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

Real-Time CORBA

DTIC Science & Technology

2000-10-01

control systems and prototyped the approach by porting the ILU ORB from Xerox to the Lynx real - time operating system . They then provided a distributed...compliant real - time operating system , a real-time ORB, and an ODMG-compliant real-time ODBMS [12]. The MITRE system is an infrastructure for...the server’s local operating system can handle. For instance, on a node controlled by the VXWorks real - time operating system with 256 local

Real-Time In-Situ Measurements for Earthquake Early Warning and Space-Borne Deformation Measurement Mission Support

NASA Astrophysics Data System (ADS)

Kedar, S.; Bock, Y.; Webb, F.; Clayton, R. W.; Owen, S. E.; Moore, A. W.; Yu, E.; Dong, D.; Fang, P.; Jamason, P.; Squibb, M. B.; Crowell, B. W.

2010-12-01

In situ geodetic networks for observing crustal motion have proliferated over the last two decades and are now recognized as indispensable tools in geophysical research, along side more traditional seismic networks. The 2007 National Research Council’s Decadal Survey recognizes that space-borne and in situ observations, such as Interferometric Synthetic Aperture Radar (InSAR) and ground-based continuous GPS (CGPS) are complementary in forecasting, in assessing, and in mitigating natural hazards. However, the information content and timeliness of in situ geodetic observations have not been fully exploited, particularly at higher frequencies than traditional daily CGPS position time series. Nor have scientists taken full advantage of the complementary natures of geodetic and seismic data, as well as those of space-based and in situ observations. To address these deficits we are developing real-time CGPS data products for earthquake early warning and for space-borne deformation measurement mission support. Our primary mission objective is in situ verification and validation for DESDynI, but our work is also applicable to other international missions (Sentinel 1a/1b, SAOCOM, ALOS 2). Our project is developing new capabilities to continuously observe and mitigate earthquake-related hazards (direct seismic damage, tsunamis, landslides, volcanoes) in near real-time with high spatial-temporal resolution, to improve the planning and accuracy of space-borne observations. We also are using GPS estimates of tropospheric zenith delay combined with water vapor data from weather models to generate tropospheric calibration maps for mitigating the largest source of error, atmospheric artifacts, in InSAR interferograms. These functions will be fully integrated into a Geophysical Resource Web Services and interactive GPS Explorer data portal environment being developed as part of an ongoing MEaSUREs project and NASA’s contribution to the EarthScope project. GPS Explorer

Real-time optical flow estimation on a GPU for a skied-steered mobile robot

NASA Astrophysics Data System (ADS)

Kniaz, V. V.

2016-04-01

Accurate egomotion estimation is required for mobile robot navigation. Often the egomotion is estimated using optical flow algorithms. For an accurate estimation of optical flow most of modern algorithms require high memory resources and processor speed. However simple single-board computers that control the motion of the robot usually do not provide such resources. On the other hand, most of modern single-board computers are equipped with an embedded GPU that could be used in parallel with a CPU to improve the performance of the optical flow estimation algorithm. This paper presents a new Z-flow algorithm for efficient computation of an optical flow using an embedded GPU. The algorithm is based on the phase correlation optical flow estimation and provide a real-time performance on a low cost embedded GPU. The layered optical flow model is used. Layer segmentation is performed using graph-cut algorithm with a time derivative based energy function. Such approach makes the algorithm both fast and robust in low light and low texture conditions. The algorithm implementation for a Raspberry Pi Model B computer is discussed. For evaluation of the algorithm the computer was mounted on a Hercules mobile skied-steered robot equipped with a monocular camera. The evaluation was performed using a hardware-in-the-loop simulation and experiments with Hercules mobile robot. Also the algorithm was evaluated using KITTY Optical Flow 2015 dataset. The resulting endpoint error of the optical flow calculated with the developed algorithm was low enough for navigation of the robot along the desired trajectory.

Real-Time Dynamic Modeling - Data Information Requirements and Flight Test Results

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Smith, Mark S.

2008-01-01

Practical aspects of identifying dynamic models for aircraft in real time were studied. Topics include formulation of an equation-error method in the frequency domain to estimate non-dimensional stability and control derivatives in real time, data information content for accurate modeling results, and data information management techniques such as data forgetting, incorporating prior information, and optimized excitation. Real-time dynamic modeling was applied to simulation data and flight test data from a modified F-15B fighter aircraft, and to operational flight data from a subscale jet transport aircraft. Estimated parameter standard errors and comparisons with results from a batch output-error method in the time domain were used to demonstrate the accuracy of the identified real-time models.

Real-Time Dynamic Modeling - Data Information Requirements and Flight Test Results

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Smith, Mark S.

2010-01-01

Practical aspects of identifying dynamic models for aircraft in real time were studied. Topics include formulation of an equation-error method in the frequency domain to estimate non-dimensional stability and control derivatives in real time, data information content for accurate modeling results, and data information management techniques such as data forgetting, incorporating prior information, and optimized excitation. Real-time dynamic modeling was applied to simulation data and flight test data from a modified F-15B fighter aircraft, and to operational flight data from a subscale jet transport aircraft. Estimated parameter standard errors, prediction cases, and comparisons with results from a batch output-error method in the time domain were used to demonstrate the accuracy of the identified real-time models.

AMON: Transition to real-time operations

NASA Astrophysics Data System (ADS)

Cowen, D. F.; Keivani, A.; Tešić, G.

2016-04-01

The Astrophysical Multimessenger Observatory Network (AMON) will link the world's leading high-energy neutrino, cosmic-ray, gamma-ray and gravitational wave observatories by performing real-time coincidence searches for multimessenger sources from observatories' subthreshold data streams. The resulting coincidences will be distributed to interested parties in the form of electronic alerts for real-time follow-up observation. We will present the science case, design elements, current and projected partner observatories, status of the AMON project, and an initial AMON-enabled analysis. The prototype of the AMON server has been online since August 2014 and processing archival data. Currently, we are deploying new high-uptime servers and will be ready to start issuing alerts as early as winter 2015/16.

Blind estimation of reverberation time

NASA Astrophysics Data System (ADS)

Ratnam, Rama; Jones, Douglas L.; Wheeler, Bruce C.; O'Brien, William D.; Lansing, Charissa R.; Feng, Albert S.

2003-11-01

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. Many state-of-the-art audio signal processing algorithms, for example in hearing-aids and telephony, are expected to have the ability to characterize the listening environment, and turn on an appropriate processing strategy accordingly. Thus, a method for characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, a method for estimating RT without prior knowledge of sound sources or room geometry is presented. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time-constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Estimating formation properties from early-time oscillatory water levels in a pumped well

USGS Publications Warehouse

Shapiro, A.M.; Oki, D.S.

2000-01-01

Hydrologists often attempt to estimate formation properties from aquifer tests for which only the hydraulic responses in a pumped well are available. Borehole storage, turbulent head losses, and borehole skin, however, can mask the hydraulic behavior of the formation inferred from the water level in the pumped well. Also, in highly permeable formations or in formations at significant depth below land surface, where there is a long column of water in the well casing, oscillatory water levels may arise during the onset of pumping to further mask formation responses in the pumped well. Usually borehole phenomena are confined to the early stages of pumping or recovery, and late-time hydraulic data can be used to estimate formation properties. In many instances, however, early-time hydraulic data provide valuable information about the formation, especially if there are interferences in the late-time data. A mathematical model and its Laplace transform solution that account for inertial influences and turbulent head losses during pumping is developed for the coupled response between the pumped borehole and the formation. The formation is assumed to be homogeneous, isotropic, of infinite areal extent, and uniform thickness, with leakage from an overlying aquifer, and the screened or open interval of the pumped well is assumed to fully penetrate the pumped aquifer. Other mathematical models of aquifer flow can also be coupled with the equations describing turbulent head losses and the inertial effects on the water column in the pumped well. The mathematical model developed in this paper is sufficiently general to consider both underdamped conditions for which oscillations arise, and overdamped conditions for which there are no oscillations. Through numerical inversion of the Laplace transform solution, type curves from the mathematical model are developed to estimate formation properties through comparison with the measured hydraulic response in the pumped well. The

Integrating landslide and liquefaction hazard and loss estimates with existing USGS real-time earthquake information products

USGS Publications Warehouse

Allstadt, Kate E.; Thompson, Eric M.; Hearne, Mike; Nowicki Jessee, M. Anna; Zhu, J.; Wald, David J.; Tanyas, Hakan

2017-01-01

The U.S. Geological Survey (USGS) has made significant progress toward the rapid estimation of shaking and shakingrelated losses through their Did You Feel It? (DYFI), ShakeMap, ShakeCast, and PAGER products. However, quantitative estimates of the extent and severity of secondary hazards (e.g., landsliding, liquefaction) are not currently included in scenarios and real-time post-earthquake products despite their significant contributions to hazard and losses for many events worldwide. We are currently running parallel global statistical models for landslides and liquefaction developed with our collaborators in testing mode, but much work remains in order to operationalize these systems. We are expanding our efforts in this area by not only improving the existing statistical models, but also by (1) exploring more sophisticated, physics-based models where feasible; (2) incorporating uncertainties; and (3) identifying and undertaking research and product development to provide useful landslide and liquefaction estimates and their uncertainties. Although our existing models use standard predictor variables that are accessible globally or regionally, including peak ground motions, topographic slope, and distance to water bodies, we continue to explore readily available proxies for rock and soil strength as well as other susceptibility terms. This work is based on the foundation of an expanding, openly available, case-history database we are compiling along with historical ShakeMaps for each event. The expected outcome of our efforts is a robust set of real-time secondary hazards products that meet the needs of a wide variety of earthquake information users. We describe the available datasets and models, developments currently underway, and anticipated products. 

Real Time Revisited

NASA Astrophysics Data System (ADS)

Allen, Phillip G.

1985-12-01

The call for abolishing photo reconnaissance in favor of real time is once more being heard. Ten years ago the same cries were being heard with the introduction of the Charge Coupled Device (CCD). The real time system problems that existed then and stopped real time proliferation have not been solved. The lack of an organized program by either DoD or industry has hampered any efforts to solve the problems, and as such, very little has happened in real time in the last ten years. Real time is not a replacement for photo, just as photo is not a replacement for infra-red or radar. Operational real time sensors can be designed only after their role has been defined and improvements made to the weak links in the system. Plodding ahead on a real time reconnaissance suite without benefit of evaluation of utility will allow this same paper to be used ten years from now.

Developing Real-Time Emissions Estimates for Enhanced Air Quality Forecasting

EPA Science Inventory

Exploring the relationship between ambient temperature, energy demand, and electric generating unit point source emissions and potential techniques for incorporating real-time information on the modulating effects of these variables using the Mid-Atlantic/Northeast Visibility Uni...

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy.

PubMed

Aqil, Muhammad; Jeong, Myung Yung

2018-04-24

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

Apprentices and Trainees: Early Trend Estimates. December Quarter 2010

ERIC Educational Resources Information Center

National Centre for Vocational Education Research (NCVER), 2011

2011-01-01

This publication presents early estimates of apprentice and trainee commencements for the December quarter 2010. Indicative information about this quarter is presented here; the most recent figures are estimated, taking into account reporting lags that occur at the time of data collection. The early trend estimates are derived from the National…

Real-time visualization of early metastasis events in Danio rerio

NASA Astrophysics Data System (ADS)

Tanner, Kandice

Metastasis, the process by which cancer cells travel from a primary tumor to establish lesions in distant organs, is the cause of most cancer-related deaths. One critical process during metastasis is the transit of cells from a primary tumor and through the vasculature or lymphatic systems to a distant site prior to metastatic colonization. However, visualization of cellular behavior in the vasculature is difficult in most model systems, where final cell destination is not known beforehand. Here, we used bone- and brain-tropic subclones of MDA-MB-231 breast adenocarcinoma cells (231BO and 231BR, respectively) injected into the circulation of embryonic zebrafish as a model xenograft system of metastasis. The zebrafish vasculature contains vessels on the scale of human capillaries. Real-time intravital imaging revealed metastatic spread to be an inefficient process, with less than 20% of cells passing through a given organ remaining there following 14 h of imaging. Additionally, there was no significant difference in the organ-specific residence time or migration speed of single 231BO and 231BR cells in the organ vasculature. Instead, cell capture was dependent on vessel topography and the function of integrin β1. Interestingly, a fraction of cells extravasated from the vasculature and survived in a perivascular position in the head and caudal venous plexus for up to two weeks. In conclusion, use of the zebrafish vasculature as a model capillary bed has revealed critical steps in early metastasis that are difficult to capture in other systems.

A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

PubMed Central

Guerrero-Castellanos, José Fermi; Madrigal-Sastre, Heberto; Durand, Sylvain; Torres, Lizeth; Muñoz-Hernández, German Ardul

2013-01-01

This paper deals with the attitude estimation of a rigid body equipped with angular velocity sensors and reference vector sensors. A quaternion-based nonlinear observer is proposed in order to fuse all information sources and to obtain an accurate estimation of the attitude. It is shown that the observer error dynamics can be separated into two passive subsystems connected in “feedback”. Then, this property is used to show that the error dynamics is input-to-state stable when the measurement disturbance is seen as an input and the error as the state. These results allow one to affirm that the observer is “robustly stable”. The proposed observer is evaluated in real-time with the design and implementation of an Attitude and Heading Reference System (AHRS) based on low-cost MEMS (Micro-Electro-Mechanical Systems) Inertial Measure Unit (IMU) and magnetic sensors and a 16-bit microcontroller. The resulting estimates are compared with a high precision motion system to demonstrate its performance. PMID:24201316

Regression models to estimate real-time concentrations of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-07

USGS Publications Warehouse

Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.

2009-01-01

In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the

Estimating Infection Attack Rates and Severity in Real Time during an Influenza Pandemic: Analysis of Serial Cross-Sectional Serologic Surveillance Data

PubMed Central

Wu, Joseph T.; Ho, Andrew; Ma, Edward S. K.; Lee, Cheuk Kwong; Chu, Daniel K. W.; Ho, Po-Lai; Hung, Ivan F. N.; Ho, Lai Ming; Lin, Che Kit; Tsang, Thomas; Lo, Su-Vui; Lau, Yu-Lung; Leung, Gabriel M.

2011-01-01

Background In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity. Methods and Findings We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1–2 wk before, and 3 wk after epidemic peak for individuals aged 5–14 y, 15–29 y, and 30–59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5–14 y, 15–19 y, and 20–29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30–59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%–10%. Conclusions Serial cross-sectional serologic data together with clinical surveillance data can

Real-time estimation of TP load in a Mississippi Delta Stream using a dynamic data driven application system

Treesearch

Ying Ouyang; Theodor D. Leininger; Jeff Hatten

2013-01-01

Elevated phosphorus (P) in surface waters can cause eutrophication of aquatic ecosystems and can impair water for drinking, industry, agriculture, and recreation. Currently, no effort has been devoted to estimating real-time variation and load of total P (TP) in surface waters due to the lack of suitable and/or cost-effective wireless sensors. However, when considering...

Real Time Precise Point Positioning: Preliminary Results for the Brazilian Region

NASA Astrophysics Data System (ADS)

Marques, Haroldo; Monico, João.; Hirokazu Shimabukuro, Milton; Aquino, Marcio

2010-05-01

GNSS positioning can be carried out in relative or absolute approach. In the last years, more attention has been driven to the real time precise point positioning (PPP). To achieve centimeter accuracy with this method in real time it is necessary to have available the satellites precise coordinates as well as satellites clocks corrections. The coordinates can be used from the predicted IGU ephemeris, but the satellites clocks must be estimated in a real time. It can be made from a GNSS network as can be seen from EUREF Permanent Network. The infra-structure to realize the PPP in real time is being available in Brazil through the Brazilian Continuous Monitoring Network (RBMC) together with the Sao Paulo State GNSS network which are transmitting GNSS data using NTRIP (Networked Transport of RTCM via Internet Protocol) caster. Based on this information it was proposed a PhD thesis in the Univ. Estadual Paulista (UNESP) aiming to investigate and develop the methodology to estimate the satellites clocks and realize PPP in real time. Then, software is being developed to process GNSS data in the real time PPP mode. A preliminary version of the software was called PPP_RT and is able to process GNSS code and phase data using precise ephemeris and satellites clocks. The PPP processing can be accomplished considering the absolute satellite antenna Phase Center Variation (PCV), Ocean Tide Loading (OTL), Earth Body Tide, among others. The first order ionospheric effects can be eliminated or minimized by ion-free combination or parameterized in the receiver-satellite direction using a stochastic process, e.g. random walk or white noise. In the case of ionosphere estimation, a pseudo-observable is introduced in the mathematical model for each satellite and the initial value can be computed from Klobuchar model or from Global Ionospheric Map (GIM). The adjustment is realized in the recursive mode and the DIA (Detection Identification and Adaptation) is used for quality control. In

Progress in using real-time GPS for seismic monitoring of the Cascadia megathrust

NASA Astrophysics Data System (ADS)

Szeliga, W. M.; Melbourne, T. I.; Santillan, V. M.; Scrivner, C.; Webb, F.

2014-12-01

We report on progress in our development of a comprehensive real-time GPS-based seismic monitoring system for the Cascadia subduction zone. This system is based on 1 Hz point position estimates computed in the ITRF08 reference frame. Convergence from phase and range observables to point position estimates is accelerated using a Kalman filter based, on-line stream editor. Positions are estimated using a short-arc approach and algorithms from JPL's GIPSY-OASIS software with satellite clock and orbit products from the International GNSS Service (IGS). The resulting positions show typical RMS scatter of 2.5 cm in the horizontal and 5 cm in the vertical with latencies below 2 seconds. To facilitate the use of these point position streams for applications such as seismic monitoring, we broadcast real-time positions and covariances using custom-built streaming software. This software is capable of buffering 24-hour streams for hundreds of stations and providing them through a REST-ful web interface. To demonstrate the power of this approach, we have developed a Java-based front-end that provides a real-time visual display of time-series, vector displacement, and contoured peak ground displacement. We have also implemented continuous estimation of finite fault slip along the Cascadia megathrust using an NIF approach. The resulting continuous slip distributions are combined with pre-computed tsunami Green's functions to generate real-time tsunami run-up estimates for the entire Cascadia coastal margin. This Java-based front-end is available for download through the PANGA website. We currently analyze 80 PBO and PANGA stations along the Cascadia margin and are gearing up to process all 400+ real-time stations operating in the Pacific Northwest, many of which are currently telemetered in real-time to CWU. These will serve as milestones towards our over-arching goal of extending our processing to include all of the available real-time streams from the Pacific rim. In addition

Deepwater Horizon - Estimating surface oil volume distribution in real time

NASA Astrophysics Data System (ADS)

Lehr, B.; Simecek-Beatty, D.; Leifer, I.

2011-12-01

Spill responders to the Deepwater Horizon (DWH) oil spill required both the relative spatial distribution and total oil volume of the surface oil. The former was needed on a daily basis to plan and direct local surface recovery and treatment operations. The latter was needed less frequently to provide information for strategic response planning. Unfortunately, the standard spill observation methods were inadequate for an oil spill this size, and new, experimental, methods, were not ready to meet the operational demands of near real-time results. Traditional surface oil estimation tools for large spills include satellite-based sensors to define the spatial extent (but not thickness) of the oil, complemented with trained observers in small aircraft, sometimes supplemented by active or passive remote sensing equipment, to determine surface percent coverage of the 'thick' part of the slick, where the vast majority of the surface oil exists. These tools were also applied to DWH in the early days of the spill but the shear size of the spill prevented synoptic information of the surface slick through the use small aircraft. Also, satellite images of the spill, while large in number, varied considerably in image quality, requiring skilled interpretation of them to identify oil and eliminate false positives. Qualified staff to perform this task were soon in short supply. However, large spills are often events that overcome organizational inertia to the use of new technology. Two prime examples in DWH were the application of hyper-spectral scans from a high-altitude aircraft and more traditional fixed-wing aircraft using multi-spectral scans processed by use of a neural network to determine, respectively, absolute or relative oil thickness. But, with new technology, come new challenges. The hyper-spectral instrument required special viewing conditions that were not present on a daily basis and analysis infrastructure to process the data that was not available at the command

Real-time estimation of prostate tumor rotation and translation with a kV imaging system based on an iterative closest point algorithm.

PubMed

Tehrani, Joubin Nasehi; O'Brien, Ricky T; Poulsen, Per Rugaard; Keall, Paul

2013-12-07

Previous studies have shown that during cancer radiotherapy a small translation or rotation of the tumor can lead to errors in dose delivery. Current best practice in radiotherapy accounts for tumor translations, but is unable to address rotation due to a lack of a reliable real-time estimate. We have developed a method based on the iterative closest point (ICP) algorithm that can compute rotation from kilovoltage x-ray images acquired during radiation treatment delivery. A total of 11 748 kilovoltage (kV) images acquired from ten patients (one fraction for each patient) were used to evaluate our tumor rotation algorithm. For each kV image, the three dimensional coordinates of three fiducial markers inside the prostate were calculated. The three dimensional coordinates were used as input to the ICP algorithm to calculate the real-time tumor rotation and translation around three axes. The results show that the root mean square error was improved for real-time calculation of tumor displacement from a mean of 0.97 mm with the stand alone translation to a mean of 0.16 mm by adding real-time rotation and translation displacement with the ICP algorithm. The standard deviation (SD) of rotation for the ten patients was 2.3°, 0.89° and 0.72° for rotation around the right-left (RL), anterior-posterior (AP) and superior-inferior (SI) directions respectively. The correlation between all six degrees of freedom showed that the highest correlation belonged to the AP and SI translation with a correlation of 0.67. The second highest correlation in our study was between the rotation around RL and rotation around AP, with a correlation of -0.33. Our real-time algorithm for calculation of rotation also confirms previous studies that have shown the maximum SD belongs to AP translation and rotation around RL. ICP is a reliable and fast algorithm for estimating real-time tumor rotation which could create a pathway to investigational clinical treatment studies requiring real-time

Real-time estimation of prostate tumor rotation and translation with a kV imaging system based on an iterative closest point algorithm

NASA Astrophysics Data System (ADS)

Nasehi Tehrani, Joubin; O'Brien, Ricky T.; Rugaard Poulsen, Per; Keall, Paul

2013-12-01

Previous studies have shown that during cancer radiotherapy a small translation or rotation of the tumor can lead to errors in dose delivery. Current best practice in radiotherapy accounts for tumor translations, but is unable to address rotation due to a lack of a reliable real-time estimate. We have developed a method based on the iterative closest point (ICP) algorithm that can compute rotation from kilovoltage x-ray images acquired during radiation treatment delivery. A total of 11 748 kilovoltage (kV) images acquired from ten patients (one fraction for each patient) were used to evaluate our tumor rotation algorithm. For each kV image, the three dimensional coordinates of three fiducial markers inside the prostate were calculated. The three dimensional coordinates were used as input to the ICP algorithm to calculate the real-time tumor rotation and translation around three axes. The results show that the root mean square error was improved for real-time calculation of tumor displacement from a mean of 0.97 mm with the stand alone translation to a mean of 0.16 mm by adding real-time rotation and translation displacement with the ICP algorithm. The standard deviation (SD) of rotation for the ten patients was 2.3°, 0.89° and 0.72° for rotation around the right-left (RL), anterior-posterior (AP) and superior-inferior (SI) directions respectively. The correlation between all six degrees of freedom showed that the highest correlation belonged to the AP and SI translation with a correlation of 0.67. The second highest correlation in our study was between the rotation around RL and rotation around AP, with a correlation of -0.33. Our real-time algorithm for calculation of rotation also confirms previous studies that have shown the maximum SD belongs to AP translation and rotation around RL. ICP is a reliable and fast algorithm for estimating real-time tumor rotation which could create a pathway to investigational clinical treatment studies requiring real-time

Benchmarking real-time RGBD odometry for light-duty UAVs

NASA Astrophysics Data System (ADS)

Willis, Andrew R.; Sahawneh, Laith R.; Brink, Kevin M.

2016-06-01

This article describes the theoretical and implementation challenges associated with generating 3D odometry estimates (delta-pose) from RGBD sensor data in real-time to facilitate navigation in cluttered indoor environments. The underlying odometry algorithm applies to general 6DoF motion; however, the computational platforms, trajectories, and scene content are motivated by their intended use on indoor, light-duty UAVs. Discussion outlines the overall software pipeline for sensor processing and details how algorithm choices for the underlying feature detection and correspondence computation impact the real-time performance and accuracy of the estimated odometry and associated covariance. This article also explores the consistency of odometry covariance estimates and the correlation between successive odometry estimates. The analysis is intended to provide users information needed to better leverage RGBD odometry within the constraints of their systems.

"Fast" Is Not "Real-Time": Designing Effective Real-Time AI Systems

NASA Astrophysics Data System (ADS)

O'Reilly, Cindy A.; Cromarty, Andrew S.

1985-04-01

Realistic practical problem domains (such as robotics, process control, and certain kinds of signal processing) stand to benefit greatly from the application of artificial intelligence techniques. These problem domains are of special interest because they are typified by complex dynamic environments in which the ability to select and initiate a proper response to environmental events in real time is a strict prerequisite to effective environmental interaction. Artificial intelligence systems developed to date have been sheltered from this real-time requirement, however, largely by virtue of their use of simplified problem domains or problem representations. The plethora of colloquial and (in general) mutually inconsistent interpretations of the term "real-time" employed by workers in each of these domains further exacerbates the difficul-ties in effectively applying state-of-the-art problem solving tech-niques to time-critical problems. Indeed, the intellectual waters are by now sufficiently muddied that the pursuit of a rigorous treatment of intelligent real-time performance mandates the redevelopment of proper problem perspective on what "real-time" means, starting from first principles. We present a simple but nonetheless formal definition of real-time performance. We then undertake an analysis of both conventional techniques and AI technology with respect to their ability to meet substantive real-time performance criteria. This analysis provides a basis for specification of problem-independent design requirements for systems that would claim real-time performance. Finally, we discuss the application of these design principles to a pragmatic problem in real-time signal understanding.

An evolutive real-time source inversion based on a linear inverse formulation

NASA Astrophysics Data System (ADS)

Sanchez Reyes, H. S.; Tago, J.; Cruz-Atienza, V. M.; Metivier, L.; Contreras Zazueta, M. A.; Virieux, J.

2016-12-01

Finite source inversion is a steppingstone to unveil earthquake rupture. It is used on ground motion predictions and its results shed light on seismic cycle for better tectonic understanding. It is not yet used for quasi-real-time analysis. Nowadays, significant progress has been made on approaches regarding earthquake imaging, thanks to new data acquisition and methodological advances. However, most of these techniques are posterior procedures once seismograms are available. Incorporating source parameters estimation into early warning systems would require to update the source build-up while recording data. In order to go toward this dynamic estimation, we developed a kinematic source inversion formulated in the time-domain, for which seismograms are linearly related to the slip distribution on the fault through convolutions with Green's functions previously estimated and stored (Perton et al., 2016). These convolutions are performed in the time-domain as we progressively increase the time window of records at each station specifically. Selected unknowns are the spatio-temporal slip-rate distribution to keep the linearity of the forward problem with respect to unknowns, as promoted by Fan and Shearer (2014). Through the spatial extension of the expected rupture zone, we progressively build-up the slip-rate when adding new data by assuming rupture causality. This formulation is based on the adjoint-state method for efficiency (Plessix, 2006). The inverse problem is non-unique and, in most cases, underdetermined. While standard regularization terms are used for stabilizing the inversion, we avoid strategies based on parameter reduction leading to an unwanted non-linear relationship between parameters and seismograms for our progressive build-up. Rise time, rupture velocity and other quantities can be extracted later on as attributs from the slip-rate inversion we perform. Satisfactory results are obtained on a synthetic example (FIgure 1) proposed by the Source

Rapid Earthquake Magnitude Estimation for Early Warning Applications

NASA Astrophysics Data System (ADS)

Goldberg, Dara; Bock, Yehuda; Melgar, Diego

2017-04-01

Earthquake magnitude is a concise metric that provides invaluable information about the destructive potential of a seismic event. Rapid estimation of magnitude for earthquake and tsunami early warning purposes requires reliance on near-field instrumentation. For large magnitude events, ground motions can exceed the dynamic range of near-field broadband seismic instrumentation (clipping). Strong motion accelerometers are designed with low gains to better capture strong shaking. Estimating earthquake magnitude rapidly from near-source strong-motion data requires integration of acceleration waveforms to displacement. However, integration amplifies small errors, creating unphysical drift that must be eliminated with a high pass filter. The loss of the long period information due to filtering is an impediment to magnitude estimation in real-time; the relation between ground motion measured with strong-motion instrumentation and magnitude saturates, leading to underestimation of earthquake magnitude. Using station displacements from Global Navigation Satellite System (GNSS) observations, we can supplement the high frequency information recorded by traditional seismic systems with long-period observations to better inform rapid response. Unlike seismic-only instrumentation, ground motions measured with GNSS scale with magnitude without saturation [Crowell et al., 2013; Melgar et al., 2015]. We refine the current magnitude scaling relations using peak ground displacement (PGD) by adding a large GNSS dataset of earthquakes in Japan. Because it does not suffer from saturation, GNSS alone has significant advantages over seismic-only instrumentation for rapid magnitude estimation of large events. The earthquake's magnitude can be estimated within 2-3 minutes of earthquake onset time [Melgar et al., 2013]. We demonstrate that seismogeodesy, the optimal combination of GNSS and seismic data at collocated stations, provides the added benefit of improving the sensitivity of

Vaccination against pandemic influenza A/H1N1v in England: a real-time economic evaluation.

PubMed

Baguelin, Marc; Hoek, Albert Jan Van; Jit, Mark; Flasche, Stefan; White, Peter J; Edmunds, W John

2010-03-11

Decisions on how to mitigate an evolving pandemic are technically challenging. We present a real-time assessment of the effectiveness and cost-effectiveness of alternative influenza A/H1N1v vaccination strategies. A transmission dynamic model was fitted to the estimated number of cases in real-time, and used to generate plausible autumn scenarios under different vaccination options. The proportion of these cases by age and risk group leading to primary care consultations, National Pandemic Flu Service consultations, emergency attendances, hospitalisations, intensive care and death was then estimated using existing data from the pandemic. The real-time model suggests that the epidemic will peak in early November, with the peak height being similar in magnitude to the summer wave. Vaccination of the high-risk groups is estimated to prevent about 45 deaths (80% credibility interval 26-67), and save around 2900 QALYs (80% credibility interval 1600-4500). Such a programme is very likely to be cost-effective if the cost of vaccine purchase itself is treated as a sunk cost. Extending vaccination to low-risk individuals is expected to result in more modest gains in deaths and QALYs averted. Extending vaccination to school-age children would be the most cost-effective extension. The early availability of vaccines is crucial in determining the impact of such extensions. There have been a considerable number of cases of H1N1v in England, and so the benefits of vaccination to mitigate the ongoing autumn wave are limited. However, certain groups appear to be at significantly higher risk of complications and deaths, and so it appears both effective and cost-effective to vaccinate them. The United Kingdom was the first country to have a major epidemic in Europe. In countries where the epidemic is not so far advanced vaccination of children may be cost-effective. Similar, detailed, real-time modelling and economic studies could help to clarify the situation.

Real-Time Tropospheric Product Establishment and Accuracy Assessment in China

NASA Astrophysics Data System (ADS)

Chen, M.; Guo, J.; Wu, J.; Song, W.; Zhang, D.

2018-04-01

Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north (N), east (E) and up (U) direction increase by 31.8 %,15.6 % and 38.3 %, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement.

Towards real-time medical diagnostics using hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Bjorgan, Asgeir; Randeberg, Lise L.

2015-07-01

Hyperspectral imaging provides non-contact, high resolution spectral images which has a substantial diagnostic potential. This can be used for e.g. diagnosis and early detection of arthritis in finger joints. Processing speed is currently a limitation for clinical use of the technique. A real-time system for analysis and visualization using GPU processing and threaded CPU processing is presented. Images showing blood oxygenation, blood volume fraction and vessel enhanced images are among the data calculated in real-time. This study shows the potential of real-time processing in this context. A combination of the processing modules will be used in detection of arthritic finger joints from hyperspectral reflectance and transmittance data.

Real-time stylistic prediction for whole-body human motions.

PubMed

Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

2012-01-01

The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

Estimation of total bacteria by real-time PCR in patients with periodontal disease.

PubMed

Brajović, Gavrilo; Popović, Branka; Puletić, Miljan; Kostić, Marija; Milasin, Jelena

2016-01-01

Periodontal diseases are associated with the presence of elevated levels of bacteria within the gingival crevice. The aim of this study was to evaluate a total amount of bacteria in subgingival plaque samples in patients with a periodontal disease. A quantitative evaluation of total bacteria amount using quantitative real-time polymerase chain reaction (qRT-PCR) was performed on 20 samples of patients with ulceronecrotic periodontitis and on 10 samples of healthy subjects. The estimation of total bacterial amount was based on gene copy number for 16S rRNA that was determined by comparing to Ct values/gene copy number of the standard curve. A statistically significant difference between average gene copy number of total bacteria in periodontal patients (2.55 x 10⁷) and healthy control (2.37 x 10⁶) was found (p = 0.01). Also, a trend of higher numbers of the gene copy in deeper periodontal lesions (> 7 mm) was confirmed by a positive value of coefficient of correlation (r = 0.073). The quantitative estimation of total bacteria based on gene copy number could be an important additional tool in diagnosing periodontitis.

UNAVCO Geodetic HIgh-Rate and Real-Time Products and Services: A next generation geodetic network

NASA Astrophysics Data System (ADS)

Mattioli, G. S.; Mencin, D.; Meertens, C. M.; Feaux, K.; Looney, S.

2012-12-01

Recent advances in GPS technology and data processing are providing position estimates with centimeter-level precision at high-rate (1 Hz) and low latency (<1 s). These data will have the potential to improve our understanding in diverse areas of geophysics including properties of seismic, volcanic, magmatic and tsunami deformation sources, and moreover profoundly transforming rapid event characterization, early warning, as well as hazard mitigation and response. Other scientific and operational applications for high-rate GPS also include glacier and ice sheet motions, tropospheric modeling, and better constraints on the dynamics of space weather. UNAVCO, through community input and the recent Plate Boundary Observatory (PBO) NSF-ARRA Cascadia initiative, has nearly completed the process of upgrading a total of 373 PBO GPS sites to real-time high-rate capability and these streams are now being archived in our data center. In addition, UNAVCO hosted an NSF funded workshop in Boulder, CO on March 26-28, which brought together 70 participants representing a spectrum of research fields with a goal to develop a community plan for the use of real-time GPS data products within the UNAVCO and EarthScope communities. These data products are expected to improve and expand the use of real-time GPS data over the next decade. Additionally, in collaboration with NOAA, 74 of these stations will provide meteorological data in real-time, primarily to support watershed and flood analyses for regional early-warning systems related to NOAA's work with California Department of Water Resources. As part of this upgrade UNAVCO is also exploring making the 75 PBO borehole strainmeter sites, whose data are now collected with a latency of 24 hours, available in SEED format in real-time in the near future, providing an opportunity to combine high-rate surface positioning and strain data together.

[Chickenpox case estimation in acyclovir pharmacy survey and early bioterrorism detection].

PubMed

Sugawara, Tamie; Ohkusa, Yasushi; Kawanohara, Hirokazu; Taniguchi, Kiyosu; Okabe, Nobuhiko

2011-11-01

Early potential health hazards and bioterrorism threats require early detection. Smallpox cases caused by terrorist could, for example, be treated by prescribing acyclovir to those having fever and vesicle exanthema diagnosed as chicken pox. We have constructed real-time pharmacy surveillance scenarios using information technology (IT) to monitor acyclovir prescription. We collected the number of acyclovir prescriptions from 5138 pharmacies using the Application Server Provider System (ASP) to estimate the number of cases. We then compared the number of those given acyclovir under 15 years old from pharmacy surveillance and sentinel surveillance for chickenpox under the Infection Disease Control Law. The estimated number of under 15 years old prescribed acyclovir in pharmacy surveillance resembled sentinel surveillance results and showed a similar seasonal chickenpox pattern. The correlation coefficient was 0.8575. The estimated numbers of adults, older than 15 but under 65 years old, and elderly, older than 65, prescribed acyclovir showed no clear seasonal pattern. Pharmacy surveillance for acyclovir identified the baseline and can be used to detect unusual chickenpox outbreak. Bioterrorism attack could potentially be detected using smallpox virus when acyclovir prescription for adults suddenly increases without outbreaks in children or the elderly. This acyclovir prescription monitoring such as an application is, to our knowledge, the first of its kind anywhre.

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

PubMed Central

Baygin, Mehmet; Karakose, Mehmet

2013-01-01

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods. PMID:23935433

Procedure for estimating stability and control parameters from flight test data by using maximum likelihood methods employing a real-time digital system

NASA Technical Reports Server (NTRS)

Grove, R. D.; Bowles, R. L.; Mayhew, S. C.

1972-01-01

A maximum likelihood parameter estimation procedure and program were developed for the extraction of the stability and control derivatives of aircraft from flight test data. Nonlinear six-degree-of-freedom equations describing aircraft dynamics were used to derive sensitivity equations for quasilinearization. The maximum likelihood function with quasilinearization was used to derive the parameter change equations, the covariance matrices for the parameters and measurement noise, and the performance index function. The maximum likelihood estimator was mechanized into an iterative estimation procedure utilizing a real time digital computer and graphic display system. This program was developed for 8 measured state variables and 40 parameters. Test cases were conducted with simulated data for validation of the estimation procedure and program. The program was applied to a V/STOL tilt wing aircraft, a military fighter airplane, and a light single engine airplane. The particular nonlinear equations of motion, derivation of the sensitivity equations, addition of accelerations into the algorithm, operational features of the real time digital system, and test cases are described.

Application and API for Real-time Visualization of Ground-motions and Tsunami

NASA Astrophysics Data System (ADS)

Aoi, S.; Kunugi, T.; Suzuki, W.; Kubo, T.; Nakamura, H.; Azuma, H.; Fujiwara, H.

2015-12-01

Due to the recent progress of seismograph and communication environment, real-time and continuous ground-motion observation becomes technically and economically feasible. K-NET and KiK-net, which are nationwide strong motion networks operated by NIED, cover all Japan by about 1750 stations in total. More than half of the stations transmit the ground-motion indexes and/or waveform data in every second. Traditionally, strong-motion data were recorded by event-triggering based instruments with non-continues telephone line which is connected only after an earthquake. Though the data from such networks mainly contribute to preparations for future earthquakes, huge amount of real-time data from dense network are expected to directly contribute to the mitigation of ongoing earthquake disasters through, e.g., automatic shutdown plants and helping decision-making for initial response. By generating the distribution map of these indexes and uploading them to the website, we implemented the real-time ground motion monitoring system, Kyoshin (strong-motion in Japanese) monitor. This web service (www.kyoshin.bosai.go.jp) started in 2008 and anyone can grasp the current ground motions of Japan. Though this service provides only ground-motion map in GIF format, to take full advantage of real-time strong-motion data to mitigate the ongoing disasters, digital data are important. We have developed a WebAPI to provide real-time data and related information such as ground motions (5 km-mesh) and arrival times estimated from EEW (earthquake early warning). All response data from this WebAPI are in JSON format and are easy to parse. We also developed Kyoshin monitor application for smartphone, 'Kmoni view' using the API. In this application, ground motions estimated from EEW are overlapped on the map with the observed one-second-interval indexes. The application can playback previous earthquakes for demonstration or disaster drill. In mobile environment, data traffic and battery are

Software Tools for Formal Specification and Verification of Distributed Real-Time Systems.

DTIC Science & Technology

1997-09-30

set of software tools for specification and verification of distributed real time systems using formal methods. The task of this SBIR Phase II effort...to be used by designers of real - time systems for early detection of errors. The mathematical complexity of formal specification and verification has

Introducing Undergraduate Students to Real-Time PCR

ERIC Educational Resources Information Center

Hancock, Dale; Funnell, Alister; Jack, Briony; Johnston, Jill

2010-01-01

An experiment is conducted, which in four 3 h laboratory sessions, introduces third year undergraduate Biochemistry students to the technique of real-time PCR in a biological context. The model used is a murine erythroleukemia cell line (MEL cells). These continuously cycling, immature red blood cells, arrested at an early stage in erythropoiesis,…

Online estimation of room reverberation time

NASA Astrophysics Data System (ADS)

Ratnam, Rama; Jones, Douglas L.; Wheeler, Bruce C.; Feng, Albert S.

2003-04-01

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. State-of-the-art signal processing algorithms for hearing aids are expected to have the ability to evaluate the characteristics of the listening environment and turn on an appropriate processing strategy accordingly. Thus, a method for the characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method or regression, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, we describe a method for estimating RT without prior knowledge of sound sources or room geometry. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Flight Investigation of Prescribed Simultaneous Independent Surface Excitations for Real-Time Parameter Identification

NASA Technical Reports Server (NTRS)

Moes, Timothy R.; Smith, Mark S.; Morelli, Eugene A.

2003-01-01

Near real-time stability and control derivative extraction is required to support flight demonstration of Intelligent Flight Control System (IFCS) concepts being developed by NASA, academia, and industry. Traditionally, flight maneuvers would be designed and flown to obtain stability and control derivative estimates using a postflight analysis technique. The goal of the IFCS concept is to be able to modify the control laws in real time for an aircraft that has been damaged in flight. In some IFCS implementations, real-time parameter identification (PID) of the stability and control derivatives of the damaged aircraft is necessary for successfully reconfiguring the control system. This report investigates the usefulness of Prescribed Simultaneous Independent Surface Excitations (PreSISE) to provide data for rapidly obtaining estimates of the stability and control derivatives. Flight test data were analyzed using both equation-error and output-error PID techniques. The equation-error PID technique is known as Fourier Transform Regression (FTR) and is a frequency-domain real-time implementation. Selected results were compared with a time-domain output-error technique. The real-time equation-error technique combined with the PreSISE maneuvers provided excellent derivative estimation in the longitudinal axis. However, the PreSISE maneuvers as presently defined were not adequate for accurate estimation of the lateral-directional derivatives.

Advantages of estimating rate corrections during dynamic propagation of spacecraft rates: Applications to real-time attitude determination of SAMPEX

NASA Technical Reports Server (NTRS)

Challa, M. S.; Natanson, G. A.; Baker, D. F.; Deutschmann, J. K.

1994-01-01

This paper describes real-time attitude determination results for the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX), a gyroless spacecraft, using a Kalman filter/Euler equation approach denoted the real-time sequential filter (RTSF). The RTSF is an extended Kalman filter whose state vector includes the attitude quaternion and corrections to the rates, which are modeled as Markov processes with small time constants. The rate corrections impart a significant robustness to the RTSF against errors in modeling the environmental and control torques, as well as errors in the initial attitude and rates, while maintaining a small state vector. SAMPLEX flight data from various mission phases are used to demonstrate the robustness of the RTSF against a priori attitude and rate errors of up to 90 deg and 0.5 deg/sec, respectively, as well as a sensitivity of 0.0003 deg/sec in estimating rate corrections in torque computations. In contrast, it is shown that the RTSF attitude estimates without the rate corrections can degrade rapidly. RTSF advantages over single-frame attitude determination algorithms are also demonstrated through (1) substantial improvements in attitude solutions during sun-magnetic field coalignment and (2) magnetic-field-only attitude and rate estimation during the spacecraft's sun-acquisition mode. A robust magnetometer-only attitude-and-rate determination method is also developed to provide for the contingency when both sun data as well as a priori knowledge of the spacecraft state are unavailable. This method includes a deterministic algorithm used to initialize the RTSF with coarse estimates of the spacecraft attitude and rates. The combined algorithm has been found effective, yielding accuracies of 1.5 deg in attitude and 0.01 deg/sec in the rates and convergence times as little as 400 sec.

Real-time flood extent maps based on social media

NASA Astrophysics Data System (ADS)

Eilander, Dirk; van Loenen, Arnejan; Roskam, Ruud; Wagemaker, Jurjen

2015-04-01

During a flood event it is often difficult to get accurate information about the flood extent and the people affected. This information is very important for disaster risk reduction management and crisis relief organizations. In the post flood phase, information about the flood extent is needed for damage estimation and calibrating hydrodynamic models. Currently, flood extent maps are derived from a few sources such as satellite images, areal images and post-flooding flood marks. However, getting accurate real-time or maximum flood extent maps remains difficult. With the rise of social media, we now have a new source of information with large numbers of observations. In the city of Jakarta, Indonesia, the intensity of unique flood related tweets during a flood event, peaked at 8 tweets per second during floods in early 2014. A fair amount of these tweets also contains observations of water depth and location. Our hypothesis is that based on the large numbers of tweets it is possible to generate real-time flood extent maps. In this study we use tweets from the city of Jakarta, Indonesia, to generate these flood extent maps. The data-mining procedure looks for tweets with a mention of 'banjir', the Bahasa Indonesia word for flood. It then removes modified and retweeted messages in order to keep unique tweets only. Since tweets are not always sent directly from the location of observation, the geotag in the tweets is unreliable. We therefore extract location information using mentions of names of neighborhoods and points of interest. Finally, where encountered, a mention of a length measure is extracted as water depth. These tweets containing a location reference and a water level are considered to be flood observations. The strength of this method is that it can easily be extended to other regions and languages. Based on the intensity of tweets in Jakarta during a flood event we can provide a rough estimate of the flood extent. To provide more accurate flood extend

New multiplex real-time PCR approach to detect gene mutations for spinal muscular atrophy.

PubMed

Liu, Zhidai; Zhang, Penghui; He, Xiaoyan; Liu, Shan; Tang, Shi; Zhang, Rong; Wang, Xinbin; Tan, Junjie; Peng, Bin; Jiang, Li; Hong, Siqi; Zou, Lin

2016-08-17

Spinal muscular atrophy (SMA) is the most common autosomal recessive disease in children, and the diagnosis is complicated and difficult, especially at early stage. Early diagnosis of SMA is able to improve the outcome of SMA patients. In our study, Real-time PCR was developed to measure the gene mutation or deletion of key genes for SMA and to further analyse genotype-phenotype correlation. The multiple real-time PCR for detecting the mutations of survival of motor neuron (SMN), apoptosis inhibitory protein (NAIP) and general transcription factor IIH, polypeptide 2 gene (GTF2H2) was established and confirmed by DNA sequencing and multiplex ligation-dependent probe amplification (MLPA). The diagnosis and prognosis of 141 hospitalized children, 100 normal children and further 2000 cases of dry blood spot (DBS) samples were analysed by this multiple real-time PCR. The multiple real-time PCR was established and the accuracy of it to detect the mutations of SMN, NAIP and GTF2H2 was at least 98.8 % comparing with DNA sequencing and MLPA. Among 141 limb movement disorders children, 75 cases were SMA. 71 cases of SMA (94.67 %) were with SMN c.840 mutation, 9 cases (12 %) with NAIP deletion and 3 cases (4 %) with GTF2H2 deletion. The multiple real-time PCR was able to diagnose and predict the prognosis of SMA patients. Simultaneously, the real-time PCR was applied to detect trace DNA from DBS and able to make an early diagnosis of SMA. The clinical and molecular characteristics of SMA in Southwest of China were presented. Our work provides a novel way for detecting SMA in children by using real-time PCR and the potential usage in newborn screening for early diagnosis of SMA.

A real-time biomimetic acoustic localizing system using time-shared architecture

NASA Astrophysics Data System (ADS)

Nourzad Karl, Marianne; Karl, Christian; Hubbard, Allyn

2008-04-01

In this paper a real-time sound source localizing system is proposed, which is based on previously developed mammalian auditory models. Traditionally, following the models, which use interaural time delay (ITD) estimates, the amount of parallel computations needed by a system to achieve real-time sound source localization is a limiting factor and a design challenge for hardware implementations. Therefore a new approach using a time-shared architecture implementation is introduced. The proposed architecture is a purely sample-base-driven digital system, and it follows closely the continuous-time approach described in the models. Rather than having dedicated hardware on a per frequency channel basis, a specialized core channel, shared for all frequency bands is used. Having an optimized execution time, which is much less than the system's sample rate, the proposed time-shared solution allows the same number of virtual channels to be processed as the dedicated channels in the traditional approach. Hence, the time-shared approach achieves a highly economical and flexible implementation using minimal silicon area. These aspects are particularly important in efficient hardware implementation of a real time biomimetic sound source localization system.

Further development of the attitude difference method for estimating deflections of the vertical in real time

NASA Astrophysics Data System (ADS)

Zhu, Jing; Zhou, Zebo; Li, Yong; Rizos, Chris; Wang, Xingshu

2016-07-01

An improvement of the attitude difference method (ADM) to estimate deflections of the vertical (DOV) in real time is described in this paper. The ADM without offline processing estimates the DOV with a limited accuracy due to the response delay. The proposed model selection-based self-adaptive delay feedback (SDF) method takes the results of the ADM as the a priori information, then uses fitting and extrapolation to estimate the DOV at the current epoch. The active region selection factor F th is used to take full advantage of the Earth model EGM2008 and the SDF with different DOV exhibitions. The factors which affect the DOV estimation accuracy are analyzed and modeled. An external observation which is specified by the velocity difference between the global navigation satellite system (GNSS) and the inertial navigation system (INS) with DOV compensated is used to select the optimal model. The response delay induced by the weak observability of an integrated INS/GNSS to the violent DOV disturbances in the ADM is compensated. The DOV estimation accuracy of the SDF method is improved by approximately 40% and 50% respectively compared to that of the EGM2008 and the ADM. With an increase in GNSS accuracy, the DOV estimation accuracy could improve further.

A real-time computational model for estimating kinematics of ankle ligaments.

PubMed

Zhang, Mingming; Davies, T Claire; Zhang, Yanxin; Xie, Sheng Quan

2016-01-01

An accurate assessment of ankle ligament kinematics is crucial in understanding the injury mechanisms and can help to improve the treatment of an injured ankle, especially when used in conjunction with robot-assisted therapy. A number of computational models have been developed and validated for assessing the kinematics of ankle ligaments. However, few of them can do real-time assessment to allow for an input into robotic rehabilitation programs. An ankle computational model was proposed and validated to quantify the kinematics of ankle ligaments as the foot moves in real-time. This model consists of three bone segments with three rotational degrees of freedom (DOFs) and 12 ankle ligaments. This model uses inputs for three position variables that can be measured from sensors in many ankle robotic devices that detect postures within the foot-ankle environment and outputs the kinematics of ankle ligaments. Validation of this model in terms of ligament length and strain was conducted by comparing it with published data on cadaver anatomy and magnetic resonance imaging. The model based on ligament lengths and strains is in concurrence with those from the published studies but is sensitive to ligament attachment positions. This ankle computational model has the potential to be used in robot-assisted therapy for real-time assessment of ligament kinematics. The results provide information regarding the quantification of kinematics associated with ankle ligaments related to the disability level and can be used for optimizing the robotic training trajectory.

Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

NASA Astrophysics Data System (ADS)

Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

2013-12-01

Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so

Methods for Expanding Rotary Wing Aircraft Health and Usage Monitoring Systems to the Rotating Frame through Real-time Rotor Blade Kinematics Estimation

NASA Astrophysics Data System (ADS)

Allred, Charles Jefferson

Since the advent of Health and Usage Monitoring Systems (HUMS) in the early 1990's, there has been a steady decrease in the number of component failure related helicopter accidents. Additionally, measurable cost benefits due to improved maintenance practices based on HUMS data has led to a desire to expand HUMS from its traditional area of helicopter drive train monitoring. One of the areas of greatest interest for this expansion of HUMS is monitoring of the helicopter rotor head loads. Studies of rotor head load and blade motions have primarily focused on wind tunnel testing with technology which would not be applicable for production helicopter HUMS deployment, or measuring bending along the blade, rather than where it is attached to the rotor head and the location through which all the helicopter loads pass. This dissertation details research into finding methods for real time methods of estimating rotor blade motion which could be applied across helicopter fleets as an expansion of current HUMS technology. First, there is a brief exploration of supporting technologies which will be crucial in enabling the expansion of HUMS from the fuselage of helicopters to the rotor head: wireless data transmission and energy harvesting. A brief overview of the commercially available low power wireless technology selected for this research is presented. The development of a relatively high-powered energy harvester specific to the motion of helicopter rotor blades is presented and two different prototypes of the device are shown. Following the overview of supporting technologies, two novel methods of monitoring rotor blade motion in real time are developed. The first method employs linear displacement sensors embedded in the elastomer layers of a high-capacity laminate bearing of the type commonly used in fully articulated rotors throughout the helicopter industry. The configuration of these displacement sensors allows modeling of the sensing system as a robotic parallel

Real-Time Detection of Tsunami Ionospheric Disturbances with a Stand-Alone GNSS Receiver: An Integration of GPS and Galileo Systems

NASA Astrophysics Data System (ADS)

Savastano, Giorgio; Komjathy, Attila; Verkhoglyadova, Olga; Wei, Yong; Mazzoni, Augusto; Crespi, Mattia

2017-04-01

Tsunamis can produce gravity waves that propagate up to the ionosphere generating disturbed electron densities in the E and F regions. These ionospheric disturbances are studied in detail using ionospheric total electron content (TEC) measurements collected by continuously operating ground-based receivers from the Global Navigation Satellite Systems (GNSS). Here, we present results using a new approach, named VARION (Variometric Approach for Real-Time Ionosphere Observation), and for the first time, we estimate slant TEC (sTEC) variations in a real-time scenario from GPS and Galileo constellations. Specifically, we study the 2016 New Zealand tsunami event using GNSS receivers with multi-constellation tracking capabilities located in the Pacific region. We compare sTEC estimates obtained using GPS and Galileo constellations. The efficiency of the real-time sTEC estimation using the VARION algorithm has been demonstrated for the 2012 Haida Gwaii tsunami event. TEC variations induced by the tsunami event are computed using 56 GPS receivers in Hawai'i. We observe TEC perturbations with amplitudes up to 0.25 TEC units and traveling ionospheric disturbances moving away from the epicenter at a speed of about 316 m/s. We present comparisons with the real-time tsunami model MOST (Method of Splitting Tsunami) provided by the NOAA Center for Tsunami Research. We observe variations in TEC that correlate well in time and space with the propagating tsunami waves. We conclude that the integration of different satellite constellations is a crucial step forward to increasing the reliability of real-time tsunami detection systems using ground-based GNSS receivers as an augmentation to existing tsunami early warning systems.

Methodology for Time-Domain Estimation of Storm-Time Electric Fields Using the 3D Earth Impedance

NASA Astrophysics Data System (ADS)

Kelbert, A.; Balch, C. C.; Pulkkinen, A. A.; Egbert, G. D.; Love, J. J.; Rigler, E. J.; Fujii, I.

2016-12-01

Magnetic storms can induce geoelectric fields in the Earth's electrically conducting interior, interfering with the operations of electric-power grid industry. The ability to estimate these electric fields at Earth's surface in close to real-time and to provide local short-term predictions would improve the ability of the industry to protect their operations. At any given time, the electric field at the Earth's surface is a function of the time-variant magnetic activity (driven by the solar wind), and the local electrical conductivity structure of the Earth's crust and mantle. For this reason, implementation of an operational electric field estimation service requires an interdisciplinary, collaborative effort between space science, real-time space weather operations, and solid Earth geophysics. We highlight in this talk an ongoing collaboration between USGS, NOAA, NASA, Oregon State University, and the Japan Meteorological Agency, to develop algorithms that can be used for scenario analyses and which might be implemented in a real-time, operational setting. We discuss the development of a time domain algorithm that employs discrete time domain representation of the impedance tensor for a realistic 3D Earth, known as the discrete time impulse response (DTIR), convolved with the local magnetic field time series, to estimate the local electric field disturbances. The algorithm is validated against measured storm-time electric field data collected in the United States and Japan. We also discuss our plans for operational real-time electric field estimation using 3D Earth impedances.

Robust Real-Time Wide-Area Differential GPS Navigation

NASA Technical Reports Server (NTRS)

Yunck, Thomas P. (Inventor); Bertiger, William I. (Inventor); Lichten, Stephen M. (Inventor); Mannucci, Anthony J. (Inventor); Muellerschoen, Ronald J. (Inventor); Wu, Sien-Chong (Inventor)

1998-01-01

The present invention provides a method and a device for providing superior differential GPS positioning data. The system includes a group of GPS receiving ground stations covering a wide area of the Earth's surface. Unlike other differential GPS systems wherein the known position of each ground station is used to geometrically compute an ephemeris for each GPS satellite. the present system utilizes real-time computation of satellite orbits based on GPS data received from fixed ground stations through a Kalman-type filter/smoother whose output adjusts a real-time orbital model. ne orbital model produces and outputs orbital corrections allowing satellite ephemerides to be known with considerable greater accuracy than from die GPS system broadcasts. The modeled orbits are propagated ahead in time and differenced with actual pseudorange data to compute clock offsets at rapid intervals to compensate for SA clock dither. The orbital and dock calculations are based on dual frequency GPS data which allow computation of estimated signal delay at each ionospheric point. These delay data are used in real-time to construct and update an ionospheric shell map of total electron content which is output as part of the orbital correction data. thereby allowing single frequency users to estimate ionospheric delay with an accuracy approaching that of dual frequency users.

Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

NASA Astrophysics Data System (ADS)

O, Sungmin; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Juergen; Tan, Jackson; Petersen, Walter A.

2017-12-01

The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N-60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1° × 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April-October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.

PubMed

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan; Fisman, David N

2017-01-01

Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015-2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. The 2015-2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R 0 approximately 1.4 for all fits). Lower R 0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.

Real-time estimation and biofeedback of single-neuron firing rates using local field potentials

PubMed Central

Hall, Thomas M.; Nazarpour, Kianoush; Jackson, Andrew

2014-01-01

The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain–machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials. PMID:25394574

Early mortality in multiple myeloma: the time-dependent impact of comorbidity: A population-based study in 621 real-life patients.

PubMed

Ríos-Tamayo, Rafael; Sáinz, Juan; Martínez-López, Joaquín; Puerta, José Manuel; Chang, Daysi-Yoe-Ling; Rodríguez, Teresa; Garrido, Pilar; de Veas, José Luís García; Romero, Antonio; Moratalla, Lucía; López-Fernández, Elisa; González, Pedro Antonio; Sánchez, María José; Jiménez-Moleón, José Juan; Jurado, Manuel; Lahuerta, Juan José

2016-07-01

Multiple myeloma is a heterogeneous disease with variable survival; this variability cannot be fully explained by the current systems of risk stratification. Early mortality remains a serious obstacle to further improve the trend toward increased survival demonstrated in recent years. However, the definition of early mortality is not standardized yet. Importantly, no study has focused on the impact of comorbidity on early mortality in multiple myeloma to date. Therefore, we analyzed the role of baseline comorbidity in a large population-based cohort of 621 real-life myeloma patients over a 31-year period. To evaluate early mortality, a sequential multivariate regression model at 2, 6, and 12 months from diagnosis was performed. It was demonstrated that comorbidity had an independent impact on early mortality, which is differential and time-dependent. Besides renal failure, respiratory disease at 2 months, liver disease at 6 months, and hepatitis virus C infection at 12 months, were, respectively, associated with early mortality, adjusting for other well-established prognostic factors. On the other hand, the long-term monitoring in our study points out a modest downward trend in early mortality over time. This is the first single institution population-based study aiming to assess the impact of comorbidity on early mortality in multiple myeloma. It is suggested that early mortality should be analyzed at three key time points (2, 6, and 12 months), in order to allow comparisons between studies. Comorbidity plays a critical role in the outcome of myeloma patients in terms of early mortality. Am. J. Hematol. 91:700-704, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Moes, Timothy R.; Morelli, Eugene A.

2003-01-01

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

Real-Time Aircraft Engine-Life Monitoring

NASA Technical Reports Server (NTRS)

Klein, Richard

2014-01-01

This project developed an inservice life-monitoring system capable of predicting the remaining component and system life of aircraft engines. The embedded system provides real-time, inflight monitoring of the engine's thrust, exhaust gas temperature, efficiency, and the speed and time of operation. Based upon this data, the life-estimation algorithm calculates the remaining life of the engine components and uses this data to predict the remaining life of the engine. The calculations are based on the statistical life distribution of the engine components and their relationship to load, speed, temperature, and time.

Language Use in Real-time Interactions during Early Elementary Science Lessons: The Bidirectional Dynamics of the Language Complexity of Teachers and Students

ERIC Educational Resources Information Center

Menninga, Astrid; van Dijk, Marijn; Steenbeek, Henderien; van Geert, Paul

2017-01-01

This study used a dynamic approach to explore bidirectional sequential relations between the real-time language use of teachers and students in naturalistic early elementary science lessons. It also compared experienced teachers (n = 22) with novice teachers (n = 8) with respect to such relations. Verbal interactions were transcribed and coded at…

Real-time tracking using stereo and motion: Visual perception for space robotics

NASA Technical Reports Server (NTRS)

Nishihara, H. Keith; Thomas, Hans; Huber, Eric; Reid, C. Ann

1994-01-01

The state-of-the-art in computing technology is rapidly attaining the performance necessary to implement many early vision algorithms at real-time rates. This new capability is helping to accelerate progress in vision research by improving our ability to evaluate the performance of algorithms in dynamic environments. In particular, we are becoming much more aware of the relative stability of various visual measurements in the presence of camera motion and system noise. This new processing speed is also allowing us to raise our sights toward accomplishing much higher-level processing tasks, such as figure-ground separation and active object tracking, in real-time. This paper describes a methodology for using early visual measurements to accomplish higher-level tasks; it then presents an overview of the high-speed accelerators developed at Teleos to support early visual measurements. The final section describes the successful deployment of a real-time vision system to provide visual perception for the Extravehicular Activity Helper/Retriever robotic system in tests aboard NASA's KC135 reduced gravity aircraft.

Real-Time GNSS Positioning with JPL's new GIPSYx Software

NASA Astrophysics Data System (ADS)

Bar-Sever, Y. E.

2016-12-01

The JPL Global Differential GPS (GDGPS) System is now producing real-time orbit and clock solutions for GPS, GLONASS, BeiDou, and Galileo. The operations are based on JPL's next generation geodetic analysis and data processing software, GIPSYx (also known at RTGx). We will examine the impact of the nascent GNSS constellations on real-time kinematic positioning for earthquake monitoring, and assess the marginal benefits from each constellation. We will discus the options for signal selection, inter-signal bias modeling, and estimation strategies in the context of real-time point positioning. We will provide a brief overview of the key features and attributes of GIPSYx. Finally we will describe the current natural hazard monitoring services from the GDGPS System.

Real-time correction of tsunami site effect by frequency-dependent tsunami-amplification factor

NASA Astrophysics Data System (ADS)

Tsushima, H.

2017-12-01

For tsunami early warning, I developed frequency-dependent tsunami-amplification factor and used it to design a recursive digital filter that can be applicable for real-time correction of tsunami site response. In this study, I assumed that a tsunami waveform at an observing point could be modeled by convolution of source, path and site effects in time domain. Under this assumption, spectral ratio between offshore and the nearby coast can be regarded as site response (i.e. frequency-dependent amplification factor). If the amplification factor can be prepared before tsunamigenic earthquakes, its temporal convolution to offshore tsunami waveform provides tsunami prediction at coast in real time. In this study, tsunami waveforms calculated by tsunami numerical simulations were used to develop frequency-dependent tsunami-amplification factor. Firstly, I performed numerical tsunami simulations based on nonlinear shallow-water theory from many tsuanmigenic earthquake scenarios by varying the seismic magnitudes and locations. The resultant tsunami waveforms at offshore and the nearby coastal observing points were then used in spectral-ratio analysis. An average of the resulted spectral ratios from the tsunamigenic-earthquake scenarios is regarded as frequency-dependent amplification factor. Finally, the estimated amplification factor is used in design of a recursive digital filter that can be applicable in time domain. The above procedure is applied to Miyako bay at the Pacific coast of northeastern Japan. The averaged tsunami-height spectral ratio (i.e. amplification factor) between the location at the center of the bay and the outside show a peak at wave-period of 20 min. A recursive digital filter based on the estimated amplification factor shows good performance in real-time correction of tsunami-height amplification due to the site effect. This study is supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grant 15K16309.

Transcriptional profiling of the early stages of germination in Candida albicans by real-time RT-PCR.

PubMed

Toyoda, Mika; Cho, Tamaki; Kaminishi, Hidenori; Sudoh, Masayuki; Chibana, Hiroji

2004-12-01

By using real-time RT-PCR, we profiled the expression of CGR1, CaMSI3, EFG1, NRG1, and TUP1 in Candida albicans strains JCM9061 and CAI4 under several conditions, including induction of morphological transition, heat shock, and treatment with calcium inhibitors. Expression of CaMSI3 changed under these growth conditions except during heat shock. CGR1 expression increased during the early stages of hyphal growth in JCM9061, while expression was strain-dependent during heat shock. Both EFG1 and NRG1 were similarly expressed under hypha-inducing conditions and heat shock. Expression of TUP1 was slightly different from the expression of EFG1 or NRG1.

Fusion of real-time simulation, sensing, and geo-informatics in assessing tsunami impact

NASA Astrophysics Data System (ADS)

Koshimura, S.; Inoue, T.; Hino, R.; Ohta, Y.; Kobayashi, H.; Musa, A.; Murashima, Y.; Gokon, H.

2015-12-01

Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we establish a method of real-time tsunami inundation forecasting, damage estimation and mapping to enhance disaster response. Right after a major (near field) earthquake is triggered, we perform a real-time tsunami inundation forecasting with use of high-performance computing platform (Koshimura et al., 2014). Using Tohoku University's vector supercomputer, we accomplished "10-10-10 challenge", to complete tsunami source determination in 10 minutes, tsunami inundation modeling in 10 minutes with 10 m grid resolution. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data and mobile phone data, and the numbers of potential death and damaged structures by applying tsunami fragility curve. After the potential tsunami-affected areas are estimated, the analysis gets focused and moves on to the "detection" phase using remote sensing. Recent advances of remote sensing technologies expand capabilities of detecting spatial extent of tsunami affected area and structural damage. Especially, a semi-automated method to estimate building damage in tsunami affected areas is developed using pre- and post-event high-resolution SAR (Synthetic Aperture Radar) data. The method is verified through the case studies in the 2011 Tohoku and other potential tsunami scenarios, and the prototype system development is now underway in Kochi prefecture, one of at-risk coastal city against Nankai trough earthquake. In the trial operation, we verify the capability of the method as a new tsunami early warning and response system for stakeholders and responders.

Real Time Earthquake Information System in Japan

NASA Astrophysics Data System (ADS)

Doi, K.; Kato, T.

2003-12-01

An early earthquake notification system in Japan had been developed by the Japan Meteorological Agency (JMA) as a governmental organization responsible for issuing earthquake information and tsunami forecasts. The system was primarily developed for prompt provision of a tsunami forecast to the public with locating an earthquake and estimating its magnitude as quickly as possible. Years after, a system for a prompt provision of seismic intensity information as indices of degrees of disasters caused by strong ground motion was also developed so that concerned governmental organizations can decide whether it was necessary for them to launch emergency response or not. At present, JMA issues the following kinds of information successively when a large earthquake occurs. 1) Prompt report of occurrence of a large earthquake and major seismic intensities caused by the earthquake in about two minutes after the earthquake occurrence. 2) Tsunami forecast in around three minutes. 3) Information on expected arrival times and maximum heights of tsunami waves in around five minutes. 4) Information on a hypocenter and a magnitude of the earthquake, the seismic intensity at each observation station, the times of high tides in addition to the expected tsunami arrival times in 5-7 minutes. To issue information above, JMA has established; - An advanced nationwide seismic network with about 180 stations for seismic wave observation and about 3,400 stations for instrumental seismic intensity observation including about 2,800 seismic intensity stations maintained by local governments, - Data telemetry networks via landlines and partly via a satellite communication link, - Real-time data processing techniques, for example, the automatic calculation of earthquake location and magnitude, the database driven method for quantitative tsunami estimation, and - Dissemination networks, via computer-to-computer communications and facsimile through dedicated telephone lines. JMA operationally

Real time microcontroller implementation of an adaptive myoelectric filter.

PubMed

Bagwell, P J; Chappell, P H

1995-03-01

This paper describes a real time digital adaptive filter for processing myoelectric signals. The filter time constant is automatically selected by the adaptation algorithm, giving a significant improvement over linear filters for estimating the muscle force and controlling a prosthetic device. Interference from mains sources often produces problems for myoelectric processing, and so 50 Hz and all harmonic frequencies are reduced by an averaging filter and differential process. This makes practical electrode placement and contact less critical and time consuming. An economic real time implementation is essential for a prosthetic controller, and this is achieved using an Intel 80C196KC microcontroller.

Real-Time Continuous Response Spectra Exceedance Calculation

NASA Astrophysics Data System (ADS)

Vernon, Frank; Harvey, Danny; Lindquist, Kent; Franke, Mathias

2017-04-01

A novel approach is presented for near real-time earthquake alarms for critical structures at distributed locations using real-time estimation of response spectra obtained from near free-field motions. Influential studies dating back to the 1980s identified spectral response acceleration as a key ground motion characteristic that correlates well with observed damage in structures. Thus, monitoring and reporting on exceedance of spectra-based thresholds are useful tools for assessing the potential for damage to facilities or multi-structure campuses based on input ground motions only. With as little as one strong-motion station per site, this scalable approach can provide rapid alarms on the damage status of remote towns, critical infrastructure (e.g., hospitals, schools) and points of interests (e.g., bridges) for a very large number of locations enabling better rapid decision making during critical and difficult immediate post-earthquake response actions. Real-time calculation of PSA exceedance and alarm dissemination are enabled with Bighorn, a module included in the Antelope software package that combines real-time spectral monitoring and alarm capabilities with a robust built-in web display server. Examples of response spectra from several M 5 events recorded by the ANZA seismic network in southern California will be presented.

Dependable Real-Time Systems

DTIC Science & Technology

1991-09-30

0196 or 413 545-0720 PI E-mail Address: krithi@nirvan.cs.umass.edu, stankovic(ocs.umass.edu Grant or Contract Title: Dependable Real - Time Systems Grant...Dependable Real - Time Systems " Grant or Contract Number: N00014-85-k-0398 L " Reporting Period: 1 Oct 87 - 30 Sep 91 , 2. Summary of Accomplishments ’ 2.1 Our...in developing a sound approach to scheduling tasks in complex real - time systems , (2) developed a real-time operating system kernel, a preliminary

Real-Time Precise Point Positioning (RTPPP) with raw observations and its application in real-time regional ionospheric VTEC modeling

NASA Astrophysics Data System (ADS)

Liu, Teng; Zhang, Baocheng; Yuan, Yunbin; Li, Min

2018-01-01

Precise Point Positioning (PPP) is an absolute positioning technology mainly used in post data processing. With the continuously increasing demand for real-time high-precision applications in positioning, timing, retrieval of atmospheric parameters, etc., Real-Time PPP (RTPPP) and its applications have drawn more and more research attention in recent years. This study focuses on the models, algorithms and ionospheric applications of RTPPP on the basis of raw observations, in which high-precision slant ionospheric delays are estimated among others in real time. For this purpose, a robust processing strategy for multi-station RTPPP with raw observations has been proposed and realized, in which real-time data streams and State-Space-Representative (SSR) satellite orbit and clock corrections are used. With the RTPPP-derived slant ionospheric delays from a regional network, a real-time regional ionospheric Vertical Total Electron Content (VTEC) modeling method is proposed based on Adjusted Spherical Harmonic Functions and a Moving-Window Filter. SSR satellite orbit and clock corrections from different IGS analysis centers are evaluated. Ten globally distributed real-time stations are used to evaluate the positioning performances of the proposed RTPPP algorithms in both static and kinematic modes. RMS values of positioning errors in static/kinematic mode are 5.2/15.5, 4.7/17.4 and 12.8/46.6 mm, for north, east and up components, respectively. Real-time slant ionospheric delays from RTPPP are compared with those from the traditional Carrier-to-Code Leveling (CCL) method, in terms of function model, formal precision and between-receiver differences of short baseline. Results show that slant ionospheric delays from RTPPP are more precise and have a much better convergence performance than those from the CCL method in real-time processing. 30 real-time stations from the Asia-Pacific Reference Frame network are used to model the ionospheric VTECs over Australia in real time

Tumor-derived exosomes in ovarian cancer – liquid biopsies for early detection and real-time monitoring of cancer progression

PubMed Central

Sharma, Shayna; Zuñiga, Felipe; Rice, Gregory E.; Perrin, Lewis C.; Hooper, John D.; Salomon, Carlos

2017-01-01

Ovarian cancer usually has a poor prognosis because it predominantly presents as high stage disease. New approaches are required to develop more effective early detection strategies and real-time treatment response monitoring. Nano-sized extracellular vesicles (EVs, including exosomes) may provide an approach to enrich tumor biomarker detection and address this clinical need. Exosomes are membranous extracellular vesicles of approximately 100 nm in diameter that have potential to be used as biomarkers and therapeutic delivery tools for ovarian cancer. Exosomal content (proteins and miRNA) is often parent cell specific thus providing an insight or “fingerprint” of the intracellular environment. Furthermore, exosomes can aid cell-cell communication and have the ability to modify target cells by transferring their content. Additionally, via the capacity to evade the immune system and remain stable over long periods in circulation, exosomes have potential as natural drug agents. This review examines the potential role of exosomes in diagnosis, drug delivery and real-time monitoring in ovarian cancer. PMID:29262670

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

PubMed

Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

2018-01-04

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

A real-time ionospheric model based on GNSS Precise Point Positioning

NASA Astrophysics Data System (ADS)

Tu, Rui; Zhang, Hongping; Ge, Maorong; Huang, Guanwen

2013-09-01

This paper proposes a method of real-time monitoring and modeling the ionospheric Total Electron Content (TEC) by Precise Point Positioning (PPP). Firstly, the ionospheric TEC and receiver’s Differential Code Biases (DCB) are estimated with the undifferenced raw observation in real-time, then the ionospheric TEC model is established based on the Single Layer Model (SLM) assumption and the recovered ionospheric TEC. In this study, phase observations with high precision are directly used instead of phase smoothed code observations. In addition, the DCB estimation is separated from the establishment of the ionospheric model which will limit the impacts of the SLM assumption impacts. The ionospheric model is established at every epoch for real time application. The method is validated with three different GNSS networks on a local, regional, and global basis. The results show that the method is feasible and effective, the real-time ionosphere and DCB results are very consistent with the IGS final products, with a bias of 1-2 TECU and 0.4 ns respectively.

Real-Time Rotational Activity Detection in Atrial Fibrillation

PubMed Central

Ríos-Muñoz, Gonzalo R.; Arenal, Ángel; Artés-Rodríguez, Antonio

2018-01-01

Rotational activations, or spiral waves, are one of the proposed mechanisms for atrial fibrillation (AF) maintenance. We present a system for assessing the presence of rotational activity from intracardiac electrograms (EGMs). Our system is able to operate in real-time with multi-electrode catheters of different topologies in contact with the atrial wall, and it is based on new local activation time (LAT) estimation and rotational activity detection methods. The EGM LAT estimation method is based on the identification of the highest sustained negative slope of unipolar signals. The method is implemented as a linear filter whose output is interpolated on a regular grid to match any catheter topology. Its operation is illustrated on selected signals and compared to the classical Hilbert-Transform-based phase analysis. After the estimation of the LAT on the regular grid, the detection of rotational activity in the atrium is done by a novel method based on the optical flow of the wavefront dynamics, and a rotation pattern match. The methods have been validated using in silico and real AF signals. PMID:29593566

Induced earthquake during the 2016 Kumamoto earthquake (Mw7.0): Importance of real-time shake monitoring for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Hoshiba, M.; Ogiso, M.

2016-12-01

Sequence of the 2016 Kumamoto earthquakes (Mw6.2 on April 14, Mw7.0 on April 16, and many aftershocks) caused a devastating damage at Kumamoto and Oita prefectures, Japan. During the Mw7.0 event, just after the direct S waves passing the central Oita, another M6 class event occurred there more than 80 km apart from the Mw7.0 event. The M6 event is interpreted as an induced earthquake; but it brought stronger shaking at the central Oita than that from the Mw7.0 event. We will discuss the induced earthquake from viewpoint of Earthquake Early Warning. In terms of ground shaking such as PGA and PGV, the Mw7.0 event is much smaller than those of the M6 induced earthquake at the central Oita (for example, 1/8 smaller at OIT009 station for PGA), and then it is easy to discriminate two events. However, PGD of the Mw7.0 is larger than that of the induced earthquake, and its appearance is just before the occurrence of the induced earthquake. It is quite difficult to recognize the induced earthquake from displacement waveforms only, because the displacement is strongly contaminated by that of the preceding Mw7.0 event. In many methods of EEW (including current JMA EEW system), magnitude is used for prediction of ground shaking through Ground Motion Prediction Equation (GMPE) and the magnitude is often estimated from displacement. However, displacement magnitude does not necessarily mean the best one for prediction of ground shaking, such as PGA and PGV. In case of the induced earthquake during the Kumamoto earthquake, displacement magnitude could not be estimated because of the strong contamination. Actually JMA EEW system could not recognize the induced earthquake. One of the important lessons we learned from eight years' operation of EEW is an issue of the multiple simultaneous earthquakes, such as aftershocks of the 2011 Mw9.0 Tohoku earthquake. Based on this lesson, we have proposed enhancement of real-time monitor of ground shaking itself instead of rapid estimation of

Implementation of a landslide early warning system based on near-real-time monitoring, multisensor mapping and geophysical measurements

NASA Astrophysics Data System (ADS)

Teza, Giordano; Galgaro, Antonio; Francese, Roberto; Ninfo, Andrea; Mariani, Rocco

2017-04-01

An early warning system has been implemented to monitor the Perarolo di Cadore landslide (North-Eastern Italian Alps), which is a slump whose induced risk is fairly high because a slope collapse could form a temporary dam on the underlying torrent and, therefore, could directly threaten the close village. A robotic total station (RTS) measures, with 6h returning time, the positions of 23 retro-reflectors placed on the landslide upper and middle sectors. The landslide's kinematical behavior derived from these near-real-time (NRT) surface displacements is interpreted on the basis of available geomorphological and geological information, geometrical data provided by some laser scanning and photogrammetric surveys, and a landslide model obtained by means of 3D Electrical Resistivity Tomography (3D ERT) measurements. In this way, an analysis of the time series provided by RTS and a pluviometer, which cover several years, allows the definition of some pre-alert and alert kinematical and rainfall thresholds. These thresholds, as well as the corresponding operational recommendations, are currently used for early warning purposes by Authorities involved in risk management for the Perarolo landslide. It should be noted the fact that, as new RTS and pluviometric data are available, the thresholds can be updated and, therefore, a fine tuning of the early warning system can be carried out in order to improve its performance. Although the proposed approach has been implemented in a particular case, it can be used to develop an early warning system based on NRT data in each site where a landslide threatens infrastructures and/or villages that cannot be relocated.

Real-time LMR control parameter generation using advanced adaptive synthesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, R.W.; Mott, J.E.

1990-01-01

The reactor delta T'', the difference between the average core inlet and outlet temperatures, for the liquid-sodium-cooled Experimental Breeder Reactor 2 is empirically synthesized in real time from, a multitude of examples of past reactor operation. The real-time empirical synthesis is based on reactor operation. The real-time empirical synthesis is based on system state analysis (SSA) technology embodied in software on the EBR 2 data acquisition computer. Before the real-time system is put into operation, a selection of reactor plant measurements is made which is predictable over long periods encompassing plant shutdowns, core reconfigurations, core load changes, and plant startups.more » A serial data link to a personal computer containing SSA software allows the rapid verification of the predictability of these plant measurements via graphical means. After the selection is made, the real-time synthesis provides a fault-tolerant estimate of the reactor delta T accurate to {plus}/{minus}1{percent}. 5 refs., 7 figs.« less

Real-Time Gaze Tracking for Public Displays

NASA Astrophysics Data System (ADS)

Sippl, Andreas; Holzmann, Clemens; Zachhuber, Doris; Ferscha, Alois

In this paper, we explore the real-time tracking of human gazes in front of large public displays. The aim of our work is to estimate at which area of a display one ore more people are looking at a time, independently from the distance and angle to the display as well as the height of the tracked people. Gaze tracking is relevant for a variety of purposes, including the automatic recognition of the user's focus of attention, or the control of interactive applications with gaze gestures. The scope of the present paper is on the former, and we show how gaze tracking can be used for implicit interaction in the pervasive advertising domain. We have developed a prototype for this purpose, which (i) uses an overhead mounted camera to distinguish four gaze areas on a large display, (ii) works for a wide range of positions in front of the display, and (iii) provides an estimation of the currently gazed quarters in real time. A detailed description of the prototype as well as the results of a user study with 12 participants, which show the recognition accuracy for different positions in front of the display, are presented.

Real-time Retrieving Atmospheric Parameters from Multi-GNSS Constellations

NASA Astrophysics Data System (ADS)

Li, X.; Zus, F.; Lu, C.; Dick, G.; Ge, M.; Wickert, J.; Schuh, H.

2016-12-01

The multi-constellation GNSS (e.g. GPS, GLONASS, Galileo, and BeiDou) bring great opportunities and challenges for real-time retrieval of atmospheric parameters for supporting numerical weather prediction (NWP) nowcasting or severe weather event monitoring. In this study, the observations from different GNSS are combined together for atmospheric parameter retrieving based on the real-time precise point positioning technique. The atmospheric parameters retrieved from multi-GNSS observations, including zenith total delay (ZTD), integrated water vapor (IWV), horizontal gradient (especially high-resolution gradient estimates) and slant total delay (STD), are carefully analyzed and evaluated by using the VLBI, radiosonde, water vapor radiometer and numerical weather model to independently validate the performance of individual GNSS and also demonstrate the benefits of multi-constellation GNSS for real-time atmospheric monitoring. Numerous results show that the multi-GNSS processing can provide real-time atmospheric products with higher accuracy, stronger reliability and better distribution, which would be beneficial for atmospheric sounding systems, especially for nowcasting of extreme weather.

Confidence interval estimation of the difference between two sensitivities to the early disease stage.

PubMed

Dong, Tuochuan; Kang, Le; Hutson, Alan; Xiong, Chengjie; Tian, Lili

2014-03-01

Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer's disease (AD) is analyzed using the proposed approaches. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model

PubMed Central

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan

2017-01-01

Abstract Background Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. Methods We used the previously described “incidence decay with exponential adjustment” (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015–2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. Results The 2015–2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R0 approximately 1.4 for all fits). Lower R0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. Conclusions A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance. PMID:29497629

Real-Time Tracking of Knee Adduction Moment in Patients with Knee Osteoarthritis

PubMed Central

Kang, Sang Hoon; Lee, Song Joo; Zhang, Li-Qun

2014-01-01

Background The external knee adduction moment (EKAM) is closely associated with the presence, progression, and severity of knee osteoarthritis (OA). However, there is a lack of convenient and practical method to estimate and track in real-time the EKAM of patients with knee OA for clinical evaluation and gait training, especially outside of gait laboratories. New Method A real-time EKAM estimation method was developed and applied to track and investigate the EKAM and other knee moments during stepping on an elliptical trainer in both healthy subjects and a patient with knee OA. Results Substantial changes were observed in the EKAM and other knee moments during stepping in the patient with knee OA. Comparison with Existing Method(s) This is the first study to develop and test feasibility of real-time tracking method of the EKAM on patients with knee OA using 3-D inverse dynamics. Conclusions The study provides us an accurate and practical method to evaluate in real-time the critical EKAM associated with knee OA, which is expected to help us to diagnose and evaluate patients with knee OA and provide the patients with real-time EKAM feedback rehabilitation training. PMID:24361759

Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time.

PubMed

McIver, David J; Brownstein, John S

2014-04-01

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012-2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.

Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time

PubMed Central

McIver, David J.; Brownstein, John S.

2014-01-01

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools. PMID:24743682

The Brave New World of Real-time GPS for Hazards Mitigation

NASA Astrophysics Data System (ADS)

Melbourne, T. I.; Szeliga, W. M.; Santillan, V. M.; Scrivner, C. W.

2015-12-01

Over 600 continuously-operating, real-time telemetered GPS receivers operate throughout California, Oregon, Washington and Alaska. These receivers straddle active crustal faults, volcanoes and landslides, the magnitude-9 Cascadia and northeastern Alaskan subduction zones and their attendant tsunamigenic regions along the Pacific coast. Around the circum-Pacific, there are hundreds more and the number is growing steadily as real-time networks proliferate. Despite offering the potential for sub-cm positioning accuracy in real-time useful for a broad array of hazards mitigation, these GPS stations are only now being incorporated into routine seismic, tsunami, volcanic, land-slide, space-weather, or meterologic monitoring. We will discuss NASA's READI (Real-time Earthquake Analysis for DIsasters) initiative. This effort is focussed on developing all aspects of real-time GPS for hazards mitigation, from establishing international data-sharing agreements to improving basic positioning algorithms. READI's long-term goal is to expand real-time GPS monitoring throughout the circum-Pacific as overseas data become freely available, so that it may be adopted by NOAA, USGS and other operational agencies responsible for natural hazards monitoring. Currently ~100 stations are being jointly processed by CWU and Scripps Inst. of Oceanography for algorithm comparison and downstream merging purposes. The resultant solution streams include point-position estimates in a global reference frame every second with centimeter accuracy, ionospheric total electron content and tropospheric zenith water content. These solutions are freely available to third-party agencies over several streaming protocols to enable their incorporation and use in hazards monitoring. This number will ramp up to ~400 stations over the next year. We will also discuss technical efforts underway to develop a variety of downstream applications of the real-time position streams, including the ability to broadcast

Evaluation of radar rainfall estimates and nowcasts to prevent flash flood in real time by using a road submersion warning tool

NASA Astrophysics Data System (ADS)

Versini, Pierre-Antoine; Sempere-Torres, Daniel

2010-05-01

Important damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions. These are major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods and different kinds of rainfall observations are available in real time: radar rainfall estimates and nowcasts from METEO FRANCE and the CALAMAR system from SPC (state authority in charge of flood forecasting). An application devoted to the road network, has also been recently developed for this region. It combines distributed hydro-meteorological very short range forecasts and vulnerability analysis to provide warnings of road submersions. The first results demonstrate that it is technically possible to provide distributed short-term forecasts for a large number of sites. The study also demonstrates that a reliable estimation of the spatial distribution of rainfall is essential. For this reason, the road submersion warning system can be used to evaluate the quality of rainfall estimates and nowcasts. The warning system has been tested on the specific storm of the 29-30 September 2007. During this event, more than 300mm dropped on the South part of the Gard and many roads were submerged. Each of the mentioned rainfall datasets (i.e. estimates and nowcasts) was available in real time. They have been used to forecast the exact location of

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

USGS Publications Warehouse

Thornton, P. K.; Bowen, W. T.; Ravelo, A.C.; Wilkens, P. W.; Farmer, G.; Brock, J.; Brink, J. E.

1997-01-01

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Real-time motion compensated patient positioning and non-rigid deformation estimation using 4-D shape priors.

PubMed

Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim

2012-01-01

Over the last years, range imaging (RI) techniques have been proposed for patient positioning and respiration analysis in motion compensation. Yet, current RI based approaches for patient positioning employ rigid-body transformations, thus neglecting free-form deformations induced by respiratory motion. Furthermore, RI based respiration analysis relies on non-rigid registration techniques with run-times of several seconds. In this paper we propose a real-time framework based on RI to perform respiratory motion compensated positioning and non-rigid surface deformation estimation in a joint manner. The core of our method are pre-procedurally obtained 4-D shape priors that drive the intra-procedural alignment of the patient to the reference state, simultaneously yielding a rigid-body table transformation and a free-form deformation accounting for respiratory motion. We show that our method outperforms conventional alignment strategies by a factor of 3.0 and 2.3 in the rotation and translation accuracy, respectively. Using a GPU based implementation, we achieve run-times of 40 ms.

Real-time Estimation of Fault Rupture Extent for Recent Large Earthquakes

NASA Astrophysics Data System (ADS)

Yamada, M.; Mori, J. J.

2009-12-01

Current earthquake early warning systems assume point source models for the rupture. However, for large earthquakes, the fault rupture length can be of the order of tens to hundreds of kilometers, and the prediction of ground motion at a site requires the approximated knowledge of the rupture geometry. Early warning information based on a point source model may underestimate the ground motion at a site, if a station is close to the fault but distant from the epicenter. We developed an empirical function to classify seismic records into near-source (NS) or far-source (FS) records based on the past strong motion records (Yamada et al., 2007). Here, we defined the near-source region as an area with a fault rupture distance less than 10km. If we have ground motion records at a station, the probability that the station is located in the near-source region is; P = 1/(1+exp(-f)) f = 6.046log10(Za) + 7.885log10(Hv) - 27.091 where Za and Hv denote the peak values of the vertical acceleration and horizontal velocity, respectively. Each observation provides the probability that the station is located in near-source region, so the resolution of the proposed method depends on the station density. The information of the fault rupture location is a group of points where the stations are located. However, for practical purposes, the 2-dimensional configuration of the fault is required to compute the ground motion at a site. In this study, we extend the methodology of NS/FS classification to characterize 2-dimensional fault geometries and apply them to strong motion data observed in recent large earthquakes. We apply a cosine-shaped smoothing function to the probability distribution of near-source stations, and convert the point fault location to 2-dimensional fault information. The estimated rupture geometry for the 2007 Niigata-ken Chuetsu-oki earthquake 10 seconds after the origin time is shown in Figure 1. Furthermore, we illustrate our method with strong motion data of the

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Estimating snow depth in real time using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Witek, Matylda; Spallek, Waldemar; Szymanowski, Mariusz

2016-04-01

In frame of the project no. LIDER/012/223/L-5/13/NCBR/2014, financed by the National Centre for Research and Development of Poland, we elaborated a fully automated approach for estimating snow depth in real time in the field. The procedure uses oblique aerial photographs taken by the unmanned aerial vehicle (UAV). The geotagged images of snow-covered terrain are processed by the Structure-from-Motion (SfM) method which is used to produce a non-georeferenced dense point cloud. The workflow includes the enhanced RunSFM procedure (keypoint detection using the scale-invariant feature transform known as SIFT, image matching, bundling using the Bundler, executing the multi-view stereo PMVS and CMVS2 software) which is preceded by multicore image resizing. The dense point cloud is subsequently automatically georeferenced using the GRASS software, and the ground control points are borrowed from positions of image centres acquired from the UAV-mounted GPS receiver. Finally, the digital surface model (DSM) is produced which - to improve the accuracy of georeferencing - is shifted using a vector obtained through precise geodetic GPS observation of a single ground control point (GCP) placed on the Laboratory for Unmanned Observations of Earth (mobile lab established at the University of Wroclaw, Poland). The DSM includes snow cover and its difference with the corresponding snow-free DSM or digital terrain model (DTM), following the concept of the digital elevation model of differences (DOD), produces a map of snow depth. Since the final result depends on the snow-free model, two experiments are carried out. Firstly, we show the performance of the entire procedure when the snow-free model reveals a very high resolution (3 cm/px) and is produced using the UAV-taken photographs and the precise GCPs measured by the geodetic GPS receiver. Secondly, we perform a similar exercise but the 1-metre resolution light detection and ranging (LIDAR) DSM or DTM serves as the snow-free model

Comprehensive seismic monitoring of the Cascadia megathrust with real-time GPS

NASA Astrophysics Data System (ADS)

Melbourne, T. I.; Szeliga, W. M.; Santillan, V. M.; Scrivner, C. W.; Webb, F.

2013-12-01

We have developed a comprehensive real-time GPS-based seismic monitoring system for the Cascadia subduction zone based on 1- and 5-second point position estimates computed within the ITRF08 reference frame. A Kalman filter stream editor that uses a geometry-free combination of phase and range observables to speed convergence while also producing independent estimation of carrier phase biases and ionosphere delay pre-cleans raw satellite measurements. These are then analyzed with GIPSY-OASIS using satellite clock and orbit corrections streamed continuously from the International GNSS Service (IGS) and the German Aerospace Center (DLR). The resulting RMS position scatter is less than 3 cm, and typical latencies are under 2 seconds. Currently 31 coastal Washington, Oregon, and northern California stations from the combined PANGA and PBO networks are analyzed. We are now ramping up to include all of the remaining 400+ stations currently operating throughout the Cascadia subduction zone, all of which are high-rate and telemetered in real-time to CWU. These receivers span the M9 megathrust, M7 crustal faults beneath population centers, several active Cascades volcanoes, and a host of other hazard sources. To use the point position streams for seismic monitoring, we have developed an inter-process client communication package that captures, buffers and re-broadcasts real-time positions and covariances to a variety of seismic estimation routines running on distributed hardware. An aggregator ingests, re-streams and can rebroadcast up to 24 hours of point-positions and resultant seismic estimates derived from the point positions to application clients distributed across web. A suite of seismic monitoring applications has also been written, which includes position time series analysis, instantaneous displacement vectors, and peak ground displacement contouring and mapping. We have also implemented a continuous estimation of finite-fault slip along the Cascadia megathrust

A real-time monitoring system for the facial nerve.

PubMed

Prell, Julian; Rachinger, Jens; Scheller, Christian; Alfieri, Alex; Strauss, Christian; Rampp, Stefan

2010-06-01

Damage to the facial nerve during surgery in the cerebellopontine angle is indicated by A-trains, a specific electromyogram pattern. These A-trains can be quantified by the parameter "traintime," which is reliably correlated with postoperative functional outcome. The system presented was designed to monitor traintime in real-time. A dedicated hardware and software platform for automated continuous analysis of the intraoperative facial nerve electromyogram was specifically designed. The automatic detection of A-trains is performed by a software algorithm for real-time analysis of nonstationary biosignals. The system was evaluated in a series of 30 patients operated on for vestibular schwannoma. A-trains can be detected and measured automatically by the described method for real-time analysis. Traintime is monitored continuously via a graphic display and is shown as an absolute numeric value during the operation. It is an expression of overall, cumulated length of A-trains in a given channel; a high correlation between traintime as measured by real-time analysis and functional outcome immediately after the operation (Spearman correlation coefficient [rho] = 0.664, P < .001) and in long-term outcome (rho = 0.631, P < .001) was observed. Automated real-time analysis of the intraoperative facial nerve electromyogram is the first technique capable of reliable continuous real-time monitoring. It can critically contribute to the estimation of functional outcome during the course of the operative procedure.

The Informed Railroad Traveler (smartphone application) real-time street parking availability estimation.

DOT National Transportation Integrated Search

2016-10-03

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. This project presents a software solution called PhonePark for detecting the availability of on-street p...

Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

NASA Technical Reports Server (NTRS)

Yong, Bin; Hong, Yang; Ren, Li-Liang; Gourley, Jonathan; Huffman, George J.; Chen, Xi; Wang, Wen; Khan, Sadiq I.

2013-01-01

The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.

Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

Real-Time Reconnaissance-A Systems Look At Advanced Technology

NASA Astrophysics Data System (ADS)

Lapp, Henry

1981-12-01

An important role for reconnaissance is the location and identification of targets in real time. Current technology has been compartmented into sensors, automatic target recognizers, data links, ground exploitation and finally dissemination. In the days of bring home film recce, this segmentation of functions was appropriate. With the current emphasis on real time decision making from outputs of high resolution sensors this thinking has to be re-analyzed. A total systems approach to data management must be employed using the constraints imposed by technology as well as the atmosphere, survivable flight profiles, and the human workload. This paper will analyze the target acquisition through exploitation tasks and discuss the current advanced development technology that are applicable. A philosophy of processing data to get information as early as possible in the data handling chain is examined in the context of ground exploitation and dissemination needs. Examples of how the various real time sensors (screeners and processors), jam resistant data links and near real time ground data handling systems fit into this scenario are discussed. Specific DoD programs will be used to illustrate the credibility of this integrated approach.

Real-time GPS integration for prototype earthquake early warning and near-field imaging of the earthquake rupture process

NASA Astrophysics Data System (ADS)

Hudnut, K. W.; Given, D.; King, N. E.; Lisowski, M.; Langbein, J. O.; Murray-Moraleda, J. R.; Gomberg, J. S.

2011-12-01

Over the past several years, USGS has developed the infrastructure for integrating real-time GPS with seismic data in order to improve our ability to respond to earthquakes and volcanic activity. As part of this effort, we have tested real-time GPS processing software components , and identified the most robust and scalable options. Simultaneously, additional near-field monitoring stations have been built using a new station design that combines dual-frequency GPS with high quality strong-motion sensors and dataloggers. Several existing stations have been upgraded in this way, using USGS Multi-Hazards Demonstration Project and American Recovery and Reinvestment Act funds in southern California. In particular, existing seismic stations have been augmented by the addition of GPS and vice versa. The focus of new instrumentation as well as datalogger and telemetry upgrades to date has been along the southern San Andreas fault in hopes of 1) capturing a large and potentially damaging rupture in progress and augmenting inputs to earthquake early warning systems, and 2) recovering high quality recordings on scale of large dynamic displacement waveforms, static displacements and immediate and long-term post-seismic transient deformation. Obtaining definitive records of large ground motions close to a large San Andreas or Cascadia rupture (or volcanic activity) would be a fundamentally important contribution to understanding near-source large ground motions and the physics of earthquakes, including the rupture process and friction associated with crack propagation and healing. Soon, telemetry upgrades will be completed in Cascadia and throughout the Plate Boundary Observatory as well. By collaborating with other groups on open-source automation system development, we will be ready to process the newly available real-time GPS data streams and to fold these data in with existing strong-motion and other seismic data. Data from these same stations will also serve the very

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Open-circuit respirometry: real-time, laboratory-based systems.

PubMed

Ward, Susan A

2018-05-04

This review explores the conceptual and technological factors integral to the development of laboratory-based, automated real-time open-circuit mixing-chamber and breath-by-breath (B × B) gas-exchange systems, together with considerations of assumptions and limitations. Advances in sensor technology, signal analysis, and digital computation led to the emergence of these technologies in the mid-20th century, at a time when investigators were beginning to recognise the interpretational advantages of nonsteady-state physiological-system interrogation in understanding the aetiology of exercise (in)tolerance in health, sport, and disease. Key milestones include the 'Auchincloss' description of an off-line system to estimate alveolar O 2 uptake B × B during exercise. This was followed by the first descriptions of real-time automated O 2 uptake and CO 2 output B × B measurement by Beaver and colleagues and by Linnarsson and Lindborg, and mixing-chamber measurement by Wilmore and colleagues. Challenges to both approaches soon emerged: e.g., the influence of mixing-chamber washout kinetics on mixed-expired gas concentration determination, and B × B alignment of gas-concentration signals with respired flow. The challenging algorithmic and technical refinements required for gas-exchange estimation at the alveolar level have also been extensively explored. In conclusion, while the technology (both hardware and software) underpinning real-time automated gas-exchange measurement has progressively advanced, there are still concerns regarding accuracy especially under the challenging conditions of changing metabolic rate.

Performance Analysis of a Citywide Real-time Landslide Early Warning System in Korea

NASA Astrophysics Data System (ADS)

Park, Joon-Young; Lee, Seung-Rae; Kang, Sinhang; Lee, Deuk-hwan; Nedumpallile Vasu, Nikhil

2017-04-01

Rainfall-induced landslide has been one of the major disasters in Korea since the beginning of 21st century when the global climate change started to give rise to the growth of the magnitude and frequency of extreme precipitation events. In order to mitigate the increasing damage to properties and loss of lives and to provide an effective tool for public officials to manage the landslide disasters, a real-time landslide early warning system with an advanced concept has been developed by taking into account for Busan, the second largest metropolitan city in Korea, as an operational test-bed. The system provides with warning information based on a five-level alert scheme (Normal, Attention, Watch, Alert, and Emergency) using the forecasted/observed rainfall data or the data obtained from ground monitoring (volumetric water content and matric suction). The alert levels are determined by applying seven different thresholds in a step-wise manner following a decision tree. In the pursuit of improved reliability of an early warning level assigned to a specific area, the system makes assessments repetitively using the thresholds of different theoretical backgrounds including statistical(empirical), physically-based, and mathematical analyses as well as direct measurement-based approaches. By mapping the distribution of the five early warning levels determined independently for each of tens of millions grids covering the entire mountainous area of Busan, the regional-scale system can also provide with the early warning information for a specific local area. The fact that the highest warning level is determined by using a concept of a numerically-modelled potential debris-flow risk is another distinctive feature of the system. This study tested the system performance by applying it for four previous rainy seasons in order to validate the operational applicability. During the rainy seasons of 2009, 2011, and 2014, the number of landslides recorded throughout Busan's territory

UNAVCO GPS High-Rate and Real-Time Products and Services: Building a next generation geodetic network.

NASA Astrophysics Data System (ADS)

Mencin, David; Meertens, Charles; Mattioli, Glen; Feaux, Karl; Looney, Sara; Sievers, Charles; Austin, Ken

2013-04-01

Recent advances in GPS technology and data processing are providing position estimates with centimeter-level precision at high-rate (1-5 Hz) and low latency (<1 s). Broad community interest in these data is growing rapidly because these data will have the potential to improve our understanding in diverse areas of geophysics including properties of seismic, volcanic, magmatic and tsunami deformation sources, and moreover profoundly transforming rapid event characterization, early warning, as well as hazard mitigation and response. Other scientific and operational applications for high-rate GPS also include glacier and ice sheet motions, tropospheric modeling, and better constraints on the dynamics of space weather. UNAVCO, through community input and the recent Plate Boundary Observatory (PBO) NSF-ARRA Cascadia initiative, has nearly completed the process of upgrading a total of 373 PBO GPS sites to real-time high-rate capability and these streams are now being archived in the UNAVCO data center. Further, through the UNAVCO core proposal (GAGE), currently under review at NSF, UNAVCO has proposed upgrading a significant portion of the ~1100 GPS stations that PBO currently operates to real-time high-rate capability to address community science and operational needs. In addition, in collaboration with NOAA, 74 of these stations will provide meteorological data in real-time, primarily to support watershed and flood analyses for regional early-warning systems related to NOAA's work with California Department of Water Resources. In preparation for this increased emphasis on high-rate GPS data, UNAVCO hosted an NSF funded workshop in Boulder, CO on March 26-28, 2012, which brought together 70 participants representing a spectrum of research fields with a goal to develop a community plan for the use of real-time GPS data products within the UNAVCO and EarthScope communities. These data products are expected to improve and expand the use of real-time, high-rate GPS data over

Comparison of real-time SYBR green dengue assay with real-time taqman RT-PCR dengue assay and the conventional nested PCR for diagnosis of primary and secondary dengue infection

PubMed Central

Paudel, Damodar; Jarman, Richard; Limkittikul, Kriengsak; Klungthong, Chonticha; Chamnanchanunt, Supat; Nisalak, Ananda; Gibbons, Robert; Chokejindachai, Watcharee

2011-01-01

Background: Dengue fever and dengue hemorrhagic fever are caused by dengue virus. Dengue infection remains a burning problem of many countries. To diagnose acute dengue in the early phase we improve the low cost, rapid SYBR green real time assay and compared the sensitivity and specificity with real time Taqman® assay and conventional nested PCR assay. Aims: To develop low cost, rapid and reliable real time SYBR green diagnostic dengue assay and compare with Taqman real-time assay and conventional nested PCR (modified Lanciotti). Materials and Methods: Eight cultured virus strains were diluted in tenth dilution down to undetectable level by the PCR to optimize the primer, temperature (annealing, and extension and to detect the limit of detection of the assay. Hundred and ninety three ELISA and PCR proved dengue clinical samples were tested with real time SYBR® Green assay, real time Taqman® assay to compare the sensitivity and specificity. Results: Sensitivity and specificity of real time SYBR® green dengue assay (84% and 66%, respectively) was almost comparable to those (81% and 74%) of Taqman real time PCR dengue assay. Real time SYBR® green RT-PCR was equally sensitive in primary and secondary infection while real time Taqman was less sensitive in the secondary infection. Sensitivity of real time Taqman on DENV3 (87%) was equal to SYBR green real time PCR dengue assay. Conclusion: We developed low cost rapid diagnostic SYBR green dengue assay. Further study is needed to make duplex primer assay for the serotyping of dengue virus. PMID:22363089

Real-time 3D change detection of IEDs

NASA Astrophysics Data System (ADS)

Wathen, Mitch; Link, Norah; Iles, Peter; Jinkerson, John; Mrstik, Paul; Kusevic, Kresimir; Kovats, David

2012-06-01

Road-side bombs are a real and continuing threat to soldiers in theater. CAE USA recently developed a prototype Volume based Intelligence Surveillance Reconnaissance (VISR) sensor platform for IED detection. This vehicle-mounted, prototype sensor system uses a high data rate LiDAR (1.33 million range measurements per second) to generate a 3D mapping of roadways. The mapped data is used as a reference to generate real-time change detection on future trips on the same roadways. The prototype VISR system is briefly described. The focus of this paper is the methodology used to process the 3D LiDAR data, in real-time, to detect small changes on and near the roadway ahead of a vehicle traveling at moderate speeds with sufficient warning to stop the vehicle at a safe distance from the threat. The system relies on accurate navigation equipment to geo-reference the reference run and the change-detection run. Since it was recognized early in the project that detection of small changes could not be achieved with accurate navigation solutions alone, a scene alignment algorithm was developed to register the reference run with the change detection run prior to applying the change detection algorithm. Good success was achieved in simultaneous real time processing of scene alignment plus change detection.

Flood and Landslide Applications of Near Real-time Satellite Rainfall Products

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Negri, Andrew; Huffman, George J.

2007-01-01

Floods and associated landslides are one of the most widespread natural hazards on Earth, responsible for tens of thousands of deaths and billions of dollars in property damage every year. During 1993-2002, over 1000 of the more than 2,900 natural disasters reported were due to floods. These floods and associated landslides claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion. The impact of these disasters is often felt most acutely in less developed regions. In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to lack of surface observing networks. Satellite observations can be of essential value in improving our understanding of the occurrence of hazardous events and possibly in lessening their impact on local economies and in reducing injuries, if they can be used to create reliable warning systems in cost-effective ways. This article addressed these opportunities and challenges by describing a combination of satellite-based real-time precipitation estimation with land surface characteristics as input, with empirical and numerical models to map potential of landslides and floods. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg. By careful integration of remote sensing and in-situ observations, and assimilation of these observations into hydrological and landslide/debris flow models with surface topographic information, prediction of useful

Real Time Radiation Exposure And Health Risks

NASA Technical Reports Server (NTRS)

Hu, Shaowen; Barzilla, Janet E.; Semones, Edward J.

2015-01-01

Radiation from solar particle events (SPEs) poses a serious threat to future manned missions outside of low Earth orbit (LEO). Accurate characterization of the radiation environment in the inner heliosphere and timely monitoring the health risks to crew are essential steps to ensure the safety of future Mars missions. In this project we plan to develop an approach that can use the particle data from multiple satellites and perform near real-time simulations of radiation exposure and health risks for various exposure scenarios. Time-course profiles of dose rates will be calculated with HZETRN and PDOSE from the energy spectrum and compositions of the particles archived from satellites, and will be validated from recent radiation exposure measurements in space. Real-time estimation of radiation risks will be investigated using ARRBOD. This cross discipline integrated approach can improve risk mitigation by providing critical information for risk assessment and medical guidance to crew during SPEs.

Real-time estimation of differential piston at the LBT

NASA Astrophysics Data System (ADS)

Böhm, Michael; Pott, Jörg-Uwe; Sawodny, Oliver; Herbst, Tom; Kürster, Martin

2014-07-01

In this paper, we present and compare different strategies to minimize the effects of telescope vibrations to the differential piston (OPD) for LINC/NIRVANA at the LBT using an accelerometer feedforward compensation approach. We summarize why this technology is of importance for LINC/NIRVANA, but also for future telescopes and instruments. We outline the estimation problem in general and its specifics at the LBT. Model based estimation and broadband filtering techniques can be used to solve the estimation task, each having its own advantages and disadvantages, which will be discussed. Simulation results and measurements at the LBT are shown to motivate and support our choice of the estimation algorithm for the instrument LINC/NIRVANA. We explain our laboratory setup aimed at imitating the vibration behaviour at the LBT in general, and the M2 as main contributor in particular, and we demonstrate the controller's ability to suppress vibrations in the frequency range of 8 Hz to 60 Hz. In this range, telescope vibrations are the most dominant disturbance to the optical path. For our measurements, we introduce a disturbance time series which has a frequency spectrum comparable to what can be measured at the LBT on a typical night. We show promising experimental results, indicating the ability to suppress differential piston induced by telescope vibrations by a factor of about 5 (RMS), which is significantly better than any currently commissioned system.

Load balancing for massively-parallel soft-real-time systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hailperin, M.

1988-09-01

Global load balancing, if practical, would allow the effective use of massively-parallel ensemble architectures for large soft-real-problems. The challenge is to replace quick global communications, which is impractical in a massively-parallel system, with statistical techniques. In this vein, the author proposes a novel approach to decentralized load balancing based on statistical time-series analysis. Each site estimates the system-wide average load using information about past loads of individual sites and attempts to equal that average. This estimation process is practical because the soft-real-time systems of interest naturally exhibit loads that are periodic, in a statistical sense akin to seasonality in econometrics.more » It is shown how this load-characterization technique can be the foundation for a load-balancing system in an architecture employing cut-through routing and an efficient multicast protocol.« less

FPGA-based architecture for motion recovering in real-time

NASA Astrophysics Data System (ADS)

Arias-Estrada, Miguel; Maya-Rueda, Selene E.; Torres-Huitzil, Cesar

2002-03-01

A key problem in the computer vision field is the measurement of object motion in a scene. The main goal is to compute an approximation of the 3D motion from the analysis of an image sequence. Once computed, this information can be used as a basis to reach higher level goals in different applications. Motion estimation algorithms pose a significant computational load for the sequential processors limiting its use in practical applications. In this work we propose a hardware architecture for motion estimation in real time based on FPGA technology. The technique used for motion estimation is Optical Flow due to its accuracy, and the density of velocity estimation, however other techniques are being explored. The architecture is composed of parallel modules working in a pipeline scheme to reach high throughput rates near gigaflops. The modules are organized in a regular structure to provide a high degree of flexibility to cover different applications. Some results will be presented and the real-time performance will be discussed and analyzed. The architecture is prototyped in an FPGA board with a Virtex device interfaced to a digital imager.

Subjective Estimation of Task Time and Task Difficulty of Simple Movement Tasks.

PubMed

Chan, Alan H S; Hoffmann, Errol R

2017-01-01

It has been demonstrated in previous work that the same neural structures are used for both imagined and real movements. To provide a strong test of the similarity of imagined and actual movement times, 4 simple movement tasks were used to determine the relationship between estimated task time and actual movement time. The tasks were single-component visually controlled movements, 2-component visually controlled, low index of difficulty (ID) moves and pin-to-hole transfer movements. For each task there was good correspondence between the mean estimated times and actual movement times. In all cases, the same factors determined the actual and estimated movement times: the amplitudes of movement and the IDs of the component movements, however the contribution of each of these variables differed for the imagined and real tasks. Generally, the standard deviations of the estimated times were linearly related to the estimated time values. Overall, the data provide strong evidence for the same neural structures being used for both imagined and actual movements.

Contribution of Near Real Time MODIS-Based Forest Disturbance Detection Products to a National Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Gasser, Gerald; Smoot, James; Kuper, Philip

2011-01-01

U.S. forests occupy approx. 751 million acres (approx. 1/3 of total land). These forests are exposed to multiple biotic and abiotic threats that collectively damage extensive acreages each year. Hazardous forest disturbances can threaten human life and property, bio-diversity and water supplies. Timely regional forest monitoring products are needed to aid forest management and decision making by the US Forest Service and its state and private partners. Daily MODIS data products provide a means to monitor regional forest disturbances on a weekly basis. In response, we began work in 2006 to develop a Near Real Time (NRT) forest monitoring capability, based on MODIS NDVI data, as part of a national forest threat early warning system (EWS)

JPL's GNSS Real-Time Earthquake and Tsunami (GREAT) Alert System

NASA Astrophysics Data System (ADS)

Bar-Sever, Yoaz; Miller, Mark; Vallisneri, Michele; Khachikyan, Robert; Meyer, Robert

2017-04-01

We describe recent developments to the GREAT Alert natural hazard monitoring service from JPL's Global Differential GPS (GDGPS) System. GREAT Alert provides real-time, 1 Hz positioning solutions for hundreds of GNSS tracking sites, from both global and regional networks, aiming to monitor ground motion in the immediate aftermath of earthquakes. We take advantage of the centralized data processing, which is collocated with the GNSS orbit determination operations of the GDGPS System, to combine orbit determination with large-scale point-positioning in a grand estimation scheme, and as a result realize significant improvement to the positioning accuracy compared to conventional stand-alone point positioning techniques. For example, the measured median site (over all sites) real-time horizontal positioning accuracy is 2 cm 1DRMS, and the median real-time vertical accuracy is 4 cm RMS. The GREAT Alert positioning service is integrated with automated global earthquake notices from the United States Geodetic Survey (USGS) to support near-real-time calculations of co-seismic displacements with attendant formal errors based both short-term and long-term error analysis for each individual site. We will show the millimeter-level resolution of co-seismic displacement can be achieved by this system. The co-seismic displacements, in turn, are fed into a JPL geodynamics and ocean models, that estimate the Earthquake magnitude and predict the potential tsunami scale.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

PubMed

Torija, Antonio J; Ruiz, Diego P

2012-10-01

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%. Copyright © 2012 Elsevier B.V. All rights reserved.

Improving the effectiveness of real-time flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach

NASA Astrophysics Data System (ADS)

Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Todini, Ezio

2015-04-01

-Curve Model in Real Time (RCM-RT) (Barbetta and Moramarco, 2014) are used to this end. Both models without considering rainfall information explicitly considers, at each time of forecast, the estimate of lateral contribution along the river reach for which the stage forecast is performed at downstream end. The analysis is performed for several reaches using different lead times according to the channel length. Barbetta, S., Moramarco, T., Brocca, L., Franchini, M. and Melone, F. 2014. Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3),729-743. Barbetta, S. and Moramarco, T. 2014. Real-time flood forecasting by relating local stage and remote discharge. Hydrological Sciences Journal, 59(9 ), 1656-1674. Coccia, G. and Todini, E. 2011. Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274. doi:10.5194/hess-15-3253-2011. Krzysztofowicz, R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750. Todini, E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743_2746. Todini, E. 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. Intl. J. River Basin Management, 6(2): 123-137.

Real-time yield estimation based on deep learning

NASA Astrophysics Data System (ADS)

Rahnemoonfar, Maryam; Sheppard, Clay

2017-05-01

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

Improving Mid-Course Flight Through an Application of Real-Time Optimal Control

DTIC Science & Technology

2017-12-01

COURSE FLIGHT THROUGH AN APPLICATION OF REAL- TIME OPTIMAL CONTROL by Mark R. Roncoroni December 2017 Thesis Advisor: Ronald Proulx Co...collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources...AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE IMPROVING MID-COURSE FLIGHT THROUGH AN APPLICATION OF REAL- TIME OPTIMAL CONTROL 5. FUNDING

Real-Time Estimation of 3-D Needle Shape and Deflection for MRI-Guided Interventions

PubMed Central

Park, Yong-Lae; Elayaperumal, Santhi; Daniel, Bruce; Ryu, Seok Chang; Shin, Mihye; Savall, Joan; Black, Richard J.; Moslehi, Behzad; Cutkosky, Mark R.

2015-01-01

We describe a MRI-compatible biopsy needle instrumented with optical fiber Bragg gratings for measuring bending deflections of the needle as it is inserted into tissues. During procedures, such as diagnostic biopsies and localized treatments, it is useful to track any tool deviation from the planned trajectory to minimize positioning errors and procedural complications. The goal is to display tool deflections in real time, with greater bandwidth and accuracy than when viewing the tool in MR images. A standard 18 ga × 15 cm inner needle is prepared using a fixture, and 350-μm-deep grooves are created along its length. Optical fibers are embedded in the grooves. Two sets of sensors, located at different points along the needle, provide an estimate of the bent profile, as well as temperature compensation. Tests of the needle in a water bath showed that it produced no adverse imaging artifacts when used with the MR scanner. PMID:26405428

Safety analytics for integrating crash frequency and real-time risk modeling for expressways.

PubMed

Wang, Ling; Abdel-Aty, Mohamed; Lee, Jaeyoung

2017-07-01

To find crash contributing factors, there have been numerous crash frequency and real-time safety studies, but such studies have been conducted independently. Until this point, no researcher has simultaneously analyzed crash frequency and real-time crash risk to test whether integrating them could better explain crash occurrence. Therefore, this study aims at integrating crash frequency and real-time safety analyses using expressway data. A Bayesian integrated model and a non-integrated model were built: the integrated model linked the crash frequency and the real-time models by adding the logarithm of the estimated expected crash frequency in the real-time model; the non-integrated model independently estimated the crash frequency and the real-time crash risk. The results showed that the integrated model outperformed the non-integrated model, as it provided much better model results for both the crash frequency and the real-time models. This result indicated that the added component, the logarithm of the expected crash frequency, successfully linked and provided useful information to the two models. This study uncovered few variables that are not typically included in the crash frequency analysis. For example, the average daily standard deviation of speed, which was aggregated based on speed at 1-min intervals, had a positive effect on crash frequency. In conclusion, this study suggested a methodology to improve the crash frequency and real-time models by integrating them, and it might inspire future researchers to understand crash mechanisms better. Copyright © 2017 Elsevier Ltd. All rights reserved.

Feasibility of real-time echocardiographic evaluation during patient transport.

PubMed

Garrett, Paul D; Boyd, Sheri Y N; Bauch, Terry D; Rubal, Bernard J; Bulgrin, James R; Kinkler, E Sterling

2003-03-01

Echocardiography is a key diagnostic tool in evaluating patients with cardiac emergencies and chest trauma. The lack of qualified real-time interpretation limits its use by emergency first responders. Early diagnosis of cardiac emergencies has the potential to facilitate triage and medical intervention to improve outcomes. We investigated the feasibility of remote, real-time interpretation of echocardiograms during patient transport. Echocardiograms using a hand-carried ultrasound device were transmitted from an ambulance in transit to a tertiary care facility using a distributed mobile local area network. Transmitted studies were reviewed by a cardiologist for ability to interpret predefined features. Transmission quality and reliability were assessed. Echocardiographic images were successfully transmitted greater than 88% of transport time. The evaluation of left-ventricular size and function, and presence of pericardial effusion were greater than 90% concordant, but only 66% of all echocardiographic features were concordant. Most transmission losses were brief (real-time transmission of echocardiograms during patient transport of adequate quality for accurate interpretation.

MonoSLAM: real-time single camera SLAM.

PubMed

Davison, Andrew J; Reid, Ian D; Molton, Nicholas D; Stasse, Olivier

2007-06-01

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.

TerraSAR-X precise orbit determination with real-time GPS ephemerides

NASA Astrophysics Data System (ADS)

Wermuth, Martin; Hauschild, Andre; Montenbruck, Oliver; Kahle, Ralph

TerraSAR-X is a German Synthetic Aperture Radar (SAR) satellite, which was launched in June 2007 from Baikonour. Its task is to acquire radar images of the Earth's surface. In order to locate the radar data takes precisely, the satellite is equipped with a high-quality dual-frequency GPS receiver -the Integrated Geodetic and Occultation Receiver (IGOR) provided by the GeoForschungsZentrum Potsdam (GFZ). Using GPS observations from the IGOR instrument in a reduced dynamic precise orbit determination (POD), the German Space Operations Center (DLR/GSOC) is computing rapid and science orbit products on a routine basis. The rapid orbit products arrive with a latency of about one hour after data reception with an accuracy of 10-20 cm. Science orbit products are computed with a latency of five days achieving an accuracy of about 5cm (3D-RMS). For active and future Earth observation missions, the availability of near real-time precise orbit information is becoming more and more important. Other applications of near real-time orbit products include the processing of GNSS radio occulation measurements for atmospheric sounding as well as altimeter measurements of ocean surface heights, which are nowadays employed in global weather and ocean circulation models with short latencies. For example after natural disasters it is necessary to evaluate the damage by satellite images as soon as possible. The latency and quality of POD results is mainly driven by the availability of precise GPS ephemerides. In order to have high-quality GPS ephemerides available at real-time, GSOC has developed the real-time clock estimation system RETICLE. The system receives NTRIP-data streams with GNSS observations from the global tracking network of IGS in real-time. Using the known station position, RETICLE estimates precise GPS satellite clock offsets and drifts based on the most recent available IGU predicted orbits. The clock offset estimates have an accuracy of better than 0.3 ns and are

Interdyad differences in early mother-infant face-to-face communication: real-time dynamics and developmental pathways.

PubMed

Lavelli, Manuela; Fogel, Alan

2013-12-01

A microgenetic research design with a multiple case study method and a combination of quantitative and qualitative analyses was used to investigate interdyad differences in real-time dynamics and developmental change processes in mother-infant face-to-face communication over the first 3 months of life. Weekly observations of 24 mother-infant dyads with analyses performed dyad by dyad showed that most dyads go through 2 qualitatively different developmental phases of early face-to-face communication: After a phase of mutual attentiveness, mutual engagement begins in Weeks 7-8, with infant smiling and cooing bidirectionally linked with maternal mirroring. This gives rise to sequences of positive feedback that, by the 3rd month, dynamically stabilizes into innovative play routines. However, when there is a lack of bidirectional positive feedback between infant and maternal behaviors, and a lack of permeability of the early communicative patterns to incorporate innovations, the development of the mutual engagement phase is compromised. The findings contribute both to theories of relationship change processes and to clinical work with at-risk mother-infant interactions. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Real-time decision support systems: the famine early warning system network

USGS Publications Warehouse

Funk, Christopher C.; Verdin, James P.

2010-01-01

A multi-institutional partnership, the US Agency for International Development’s Famine Early Warning System Network (FEWS NET) provides routine monitoring of climatic, agricultural, market, and socioeconomic conditions in over 20 countries. FEWS NET supports and informs disaster relief decisions that impact millions of people and involve billions of dollars. In this chapter, we focus on some of FEWS NET’s hydrologic monitoring tools, with a specific emphasis on combining “low frequency” and “high frequency” assessment tools. Low frequency assessment tools, tied to water and food balance estimates, enable us to evaluate and map long-term tendencies in food security. High frequency assessments are supported by agrohydrologic models driven by satellite rainfall estimates, such as the Water Requirement Satisfaction Index (WRSI). Focusing on eastern Africa, we suggest that both these high and low frequency approaches are necessary to capture the interaction of slow variations in vulnerability and the relatively rapid onset of climatic shocks.

Rapid magnitude estimation from time-dependent displacement amplitude measured with seismogeodetic instrumentation

NASA Astrophysics Data System (ADS)

Goldberg, D.; Bock, Y.; Melgar, D.

2017-12-01

Earthquake magnitude is a concise metric that illuminates the destructive potential of a seismic event. Rapid determination of earthquake magnitude is currently the main prerequisite for dissemination of a tsunami early warning, thus timely and automated calculation is of high importance. Seismic instrumentation experiences well-documented complications at long periods, making the accurate measurement of ground displacement in the near field unreliable. As a result, the relation between ground motion measured with seismic instrumentation and magnitude saturates, causing underestimation of the size of very large events. In the case of tsunamigenic earthquakes, magnitude underestimation in turn leads to a flawed tsunami inundation assessment, which limits the effectiveness of an early warning, in particular for local tsunamis. Global Navigation Satellite System (GNSS) instrumentation measures the displacement field directly, leading to more accurate magnitude estimates with near-field data. Unlike seismic-only instrumentation, near-field GNSS has been shown to provide an accurate magnitude estimate using the peak ground displacement (PGD) after just 2 minutes [Melgar et al., 2015]. However, GNSS alone is too noisy to detect the first seismic wave arrivals (P-waves), thus it cannot be as timely as a seismic system on its own. Using collocated seismic and geodetic instrumentation, we refine magnitude scaling relations by incorporating a large dataset of earthquakes in Japan. We demonstrate that consideration of the time-dependence of displacement amplitude with respect to P-wave arrival time reduces the time to convergence of the magnitude estimate. We present findings on the growth of events of large magnitude, and demonstrate time-dependent scaling relations that adapt to the amount of recorded data, starting with the P-wave arrival and continuing through PGD. We illustrate real-time, automated implementation of this method, and consider network improvements to

SOFTWARE DESIGN FOR REAL-TIME SYSTEMS.

DTIC Science & Technology

Real-time computer systems and real-time computations are defined for the purposes of this report. The design of software for real - time systems is...discussed, employing the concept that all real - time systems belong to one of two types. The types are classified according to the type of control...program used; namely: Pre-assigned Iterative Cycle and Real-time Queueing. The two types of real - time systems are described in general, with supplemental

Real-time video analysis for retail stores

NASA Astrophysics Data System (ADS)

Hassan, Ehtesham; Maurya, Avinash K.

2015-03-01

With the advancement in video processing technologies, we can capture subtle human responses in a retail store environment which play decisive role in the store management. In this paper, we present a novel surveillance video based analytic system for retail stores targeting localized and global traffic estimate. Development of an intelligent system for human traffic estimation in real-life poses a challenging problem because of the variation and noise involved. In this direction, we begin with a novel human tracking system by an intelligent combination of motion based and image level object detection. We demonstrate the initial evaluation of this approach on available standard dataset yielding promising result. Exact traffic estimate in a retail store require correct separation of customers from service providers. We present a role based human classification framework using Gaussian mixture model for this task. A novel feature descriptor named graded colour histogram is defined for object representation. Using, our role based human classification and tracking system, we have defined a novel computationally efficient framework for two types of analytics generation i.e., region specific people count and dwell-time estimation. This system has been extensively evaluated and tested on four hours of real-life video captured from a retail store.

Near-Real-Time Sismo-acoustic Submarine Station for offshore monitoring

NASA Astrophysics Data System (ADS)

D'Anna, Giuseppe; D'Alessandro, Antonino; Fertitta, Gioacchino; Fraticelli, Nicola; Calore, Daniele

2016-04-01

From the early 1980's, Italian seismicity is monitored by the National Seismic Network (NSN). The network has been considerably enhanced by INGV since 2005 by 24-bit digital stations equipped with broad-band sensors. The NSN is nowadays constituted by about 300 on-land seismic station able to detect and locate also small magnitude earthquake in the whole Italian peninsula. However, the lack of offshore seismic stations does not allow the accurate estimation of hypocentral and focal parameters of small magnitude earthquakes occurring in offshore areas. As in the Mediterranean area there is an intense offshore seismic activity, an extension of the seismic monitoring to the sea would be beneficial. There are two types of stations that could be used to extend the network towards the sea: the first type is connected to the coast though a cable, the second type is isolated (or stand alone) and works autonomously. Both solutions have serious limitations: the first one, for several technical and economic problems, linked to the indispensable transmission/alimentation cable, cannot be installed far from the coast; the second one, allows access to the recorded data, only after they are recovered from the seabed. It is clear that these technical solutions are not suitable for the real time monitoring of the offshore seismicity or for the realization of a tsunami warning system. For this reason, in early 2010, the OBSLab of Gibilmanna begins the design of a submarine station able to overcome the limitations of the two systems above. The station isbuilt under the project EMSO-MedIT. The two stations built have already been tested in dock and ready for installation. One of this station will be installed, in few time, in the southern Tyrrhenian Sea, near the epicentre of the Palermo 2002 main shock. The sea bottom station will be equipped with 2 very broadband 3C seismometers, a broad band hydrophone, a differential and an absolute pressure gauge. The station includes a submarine

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.

A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters.

PubMed

Ferrari, Alberto; Ginis, Pieter; Hardegger, Michael; Casamassima, Filippo; Rocchi, Laura; Chiari, Lorenzo

2016-07-01

Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinson's disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.

FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

PubMed Central

Millan-Almaraz, Jesus Roberto; de Jesus Romero-Troncoso, Rene; Guevara-Gonzalez, Ramon Gerardo; Contreras-Medina, Luis Miguel; Carrillo-Serrano, Roberto Valentin; Osornio-Rios, Roque Alfredo; Duarte-Galvan, Carlos; Rios-Alcaraz, Miguel Angel; Torres-Pacheco, Irineo

2010-01-01

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities. PMID:22163656

An interdimensional correlation framework for real-time estimation of six degree of freedom target motion using a single x-ray imager during radiotherapy

NASA Astrophysics Data System (ADS)

Nguyen, D. T.; Bertholet, J.; Kim, J.-H.; O'Brien, R.; Booth, J. T.; Poulsen, P. R.; Keall, P. J.

2018-01-01

Increasing evidence suggests that intrafraction tumour motion monitoring needs to include both 3D translations and 3D rotations. Presently, methods to estimate the rotation motion require the 3D translation of the target to be known first. However, ideally, translation and rotation should be estimated concurrently. We present the first method to directly estimate six-degree-of-freedom (6DoF) motion from the target’s projection on a single rotating x-ray imager in real-time. This novel method is based on the linear correlations between the superior-inferior translations and the motion in the other five degrees-of-freedom. The accuracy of the method was evaluated in silico with 81 liver tumour motion traces from 19 patients with three implanted markers. The ground-truth motion was estimated using the current gold standard method where each marker’s 3D position was first estimated using a Gaussian probability method, and the 6DoF motion was then estimated from the 3D positions using an iterative method. The 3D position of each marker was projected onto a gantry-mounted imager with an imaging rate of 11 Hz. After an initial 110° gantry rotation (200 images), a correlation model between the superior-inferior translations and the five other DoFs was built using a least square method. The correlation model was then updated after each subsequent frame to estimate 6DoF motion in real-time. The proposed algorithm had an accuracy (±precision) of  -0.03  ±  0.32 mm, -0.01  ±  0.13 mm and 0.03  ±  0.52 mm for translations in the left-right (LR), superior-inferior (SI) and anterior-posterior (AP) directions respectively; and, 0.07  ±  1.18°, 0.07  ±  1.00° and 0.06  ±  1.32° for rotations around the LR, SI and AP axes respectively on the dataset. The first method to directly estimate real-time 6DoF target motion from segmented marker positions on a 2D imager was devised. The algorithm was evaluated using 81

Real-time flood forecasting

USGS Publications Warehouse

Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

2009-01-01

Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

Fault Tolerant Real-Time Systems

DTIC Science & Technology

1993-09-30

The ART (Advanced Real-Time Technology) Project of Carnegie Mellon University is engaged in wide ranging research on hard real - time systems . The...including hardware and software fault tolerance using temporal redundancy and analytic redundancy to permit the construction of real - time systems whose

A High-Speed, Real-Time Visualization and State Estimation Platform for Monitoring and Control of Electric Distribution Systems: Implementation and Field Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundstrom, Blake; Gotseff, Peter; Giraldez, Julieta

Continued deployment of renewable and distributed energy resources is fundamentally changing the way that electric distribution systems are controlled and operated; more sophisticated active system control and greater situational awareness are needed. Real-time measurements and distribution system state estimation (DSSE) techniques enable more sophisticated system control and, when combined with visualization applications, greater situational awareness. This paper presents a novel demonstration of a high-speed, real-time DSSE platform and related control and visualization functionalities, implemented using existing open-source software and distribution system monitoring hardware. Live scrolling strip charts of meter data and intuitive annotated map visualizations of the entire state (obtainedmore » via DSSE) of a real-world distribution circuit are shown. The DSSE implementation is validated to demonstrate provision of accurate voltage data. This platform allows for enhanced control and situational awareness using only a minimum quantity of distribution system measurement units and modest data and software infrastructure.« less

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim

PubMed Central

Modenese, L.; Lloyd, D.G.

2017-01-01

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time. PMID:27723992

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim.

PubMed

Pizzolato, C; Reggiani, M; Modenese, L; Lloyd, D G

2017-03-01

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.

Computing moment to moment BOLD activation for real-time neurofeedback

PubMed Central

Hinds, Oliver; Ghosh, Satrajit; Thompson, Todd W.; Yoo, Julie J.; Whitfield-Gabrieli, Susan; Triantafyllou, Christina; Gabrieli, John D.E.

2013-01-01

Estimating moment to moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment to moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment to moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI timeseries, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method. PMID:20682350

End-User Applications of Real-Time Earthquake Information in Europe

NASA Astrophysics Data System (ADS)

Cua, G. B.; Gasparini, P.; Giardini, D.; Zschau, J.; Filangieri, A. R.; Reakt Wp7 Team

2011-12-01

The primary objective of European FP7 project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction) is to improve the efficiency of real-time earthquake risk mitigation methods and their capability of protecting structures, infrastructures, and populations. REAKT aims to address the issues of real-time earthquake hazard and response from end-to-end, with efforts directed along the full spectrum of methodology development in earthquake forecasting, earthquake early warning, and real-time vulnerability systems, through optimal decision-making, and engagement and cooperation of scientists and end users for the establishment of best practices for use of real-time information. Twelve strategic test cases/end users throughout Europe have been selected. This diverse group of applications/end users includes civil protection authorities, railway systems, hospitals, schools, industrial complexes, nuclear plants, lifeline systems, national seismic networks, and critical structures. The scale of target applications covers a wide range, from two school complexes in Naples, to individual critical structures, such as the Rion Antirion bridge in Patras, and the Fatih Sultan Mehmet bridge in Istanbul, to large complexes, such as the SINES industrial complex in Portugal and the Thessaloniki port area, to distributed lifeline and transportation networks and nuclear plants. Some end-users are interested in in-depth feasibility studies for use of real-time information and development of rapid response plans, while others intend to install real-time instrumentation and develop customized automated control systems. From the onset, REAKT scientists and end-users will work together on concept development and initial implementation efforts using the data products and decision-making methodologies developed with the goal of improving end-user risk mitigation. The aim of this scientific/end-user partnership is to ensure that scientific efforts are applicable to operational

Avian influenza virus detection and quantitation by real-time RT-PCR

USDA-ARS?s Scientific Manuscript database

Real-time RT-PCR (rRT-PCR) has been used for avian influenza virus (AIV) detection since the early 2000’s for routine surveillance, during outbreaks and for research. Some of the advantages of rRT-PCR are: high sensitivity, high specificity, rapid time-to-result, scalability, cost, and its inherentl...

A real-time digital program for estimating aircraft stability and control parameters from flight test data by using the maximum likelihood method

NASA Technical Reports Server (NTRS)

Grove, R. D.; Mayhew, S. C.

1973-01-01

A computer program (Langley program C1123) has been developed for estimating aircraft stability and control parameters from flight test data. These parameters are estimated by the maximum likelihood estimation procedure implemented on a real-time digital simulation system, which uses the Control Data 6600 computer. This system allows the investigator to interact with the program in order to obtain satisfactory results. Part of this system, the control and display capabilities, is described for this program. This report also describes the computer program by presenting the program variables, subroutines, flow charts, listings, and operational features. Program usage is demonstrated with a test case using pseudo or simulated flight data.

Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Welch, Gregory Francis; Zhang, Jinghe

2014-06-10

Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We developed advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we looked at complex system issues including severe nonlinearity of system equations, discontinuitiesmore » caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.« less

Early results of MitraClip system implantation by real-time three-dimensional speckle-tracking left ventricle analysis.

PubMed

Scandura, Salvatore; Dipasqua, Fabio; Gargiulo, Giuseppe; Capodanno, Davide; Caggegi, Anna; Grasso, Carmelo; Mangiafico, Sarah; Pistritto, Anna Maria; Immè, Sebastiano; Chiarandà, Marta; Ministeri, Margherita; Ronsivalle, Giuseppe; Cannata, Stefano; Arcidiacono, Antonio Andrea; Capranzano, Piera; Tamburino, Corrado

2016-11-01

To appraise the early effect of percutaneous mitral valve repair with the MitraClip system on myocardial function using real-time three-dimensional speckle-tracking echocardiography (3D-STE). Consecutive patients with moderate-to-severe or severe mitral regurgitation, undergoing mitral valve repair with the MitraClip system, were prospectively evaluated during the peri-procedural workout and follow-up. Left ventricular deformation was evaluated by a two-dimensional and 3D speckle-tracking analysis. 3D-STE acquisitions were elaborated obtaining real-time 3D global longitudinal strain evaluation, and by appraising both volumetric and hemodynamic parameters (i.e. left ventricular end-diastolic volume, left ventricular end-systolic volume, left ventricular ejection fraction, cardiac output, and stroke volume). In all, 30 patients were included. At 1-month follow-up, 3D-STE analysis revealed no changes in left ventricular end-diastolic volume (162.6 ± 73.7 ml at baseline vs. 159.8 ± 64.5 ml at 1-month follow-up; P = 0.63) and a downward trend in left ventricular end-systolic volume (104.7 ± 52.0 vs. 100.1 ± 50.4 ml, respectively; P = 0.06). Left ventricular ejection fraction did not significantly increase (38.1 ± 11.3% at baseline vs. 39.4 ± 11.0% at 1-month follow-up; P = 0.20). No significant changes were reported in cardiac output (4.3 ± 2.0 l/min at baseline vs. 4.0 ± 1.5 l/min at follow-up; P = 0.377) and in stroke volume (59.5 ± 25.5 ml at baseline vs. 59.9 ± 20.7 ml at follow-up; P = 0.867). On the contrary, left ventricular deformation capability significantly improved, with the real-time 3D global longitudinal strain value changing from -9.8 ± 4.1% at baseline to -11.0 ± 4.4% at follow-up (P = 0.018). Accurately assessing myocardial function by the use of 3D-STE, this study reported irrelevant early changes in left ventricular size, but a positive effect on left

Evaluation of Real-Time Ground-Based GPS Meteorology

NASA Astrophysics Data System (ADS)

Fang, P.; Bock, Y.; Gutman, S.

2003-04-01

We demonstrate and evaluate a system to estimate zenith tropospheric delays in real time (5-10 minute latency) based on the technique of instantaneous GPS positioning as described by Bock et al. [2000] using data from the Orange County Real Time GPS Network. OCRTN is an upgrade of a sub-network of SCIGN sites in southern California to low latency (1-2 sec), high-rate (1 Hz) data streaming. Currently, ten sites are streaming data (Ashtech binary MBEN format) by means of dedicated, point-to-point radio modems to a network hub that translates the asynchronous serial data to TCP/IP and onto a PC workstation residing on a local area network. Software residing on the PC allows multiple clients to access the raw data simultaneously though TCP/IP. One of the clients is a Geodetics RTD server that receives and archives (1) the raw 1 Hz network data, (2) estimates of instantaneous positions and zenith tropospheric delays, and (3) RINEX data to decimated to 30 seconds. The network is composed of ten sites. The distribution of nine of the sites approximates a right triangle with two 60 km legs, and a tenth site on Catalina Island a distance of about 50 km (over water) from the hypotenuse of the triangle. Relative zenith delays are estimated every second with a latency less than a second. Median values are computed at a user-specified interval (e.g., 10 minutes) with outliers greater than 4 times the interquartile range rejected. We describe the results with those generated by our operational system using the GAMIT software, with a latency of 30-60 minutes. Earlier results (from a similar network) comparing 30-minute median RTD values to GAMIT 30-minute estimates indicate that the two solutions differ by about 1 cm. We also describe our approach to determining absolute zenith delays. If an Internet connection is available we will present a real-time demonstration. [Bock, Y., R. Nikolaidis, P. J. de Jonge, and M. Bevis, Instantaneous resolution of crustal motion at medium

VERSE - Virtual Equivalent Real-time Simulation

NASA Technical Reports Server (NTRS)

Zheng, Yang; Martin, Bryan J.; Villaume, Nathaniel

2005-01-01

Distributed real-time simulations provide important timing validation and hardware in the- loop results for the spacecraft flight software development cycle. Occasionally, the need for higher fidelity modeling and more comprehensive debugging capabilities - combined with a limited amount of computational resources - calls for a non real-time simulation environment that mimics the real-time environment. By creating a non real-time environment that accommodates simulations and flight software designed for a multi-CPU real-time system, we can save development time, cut mission costs, and reduce the likelihood of errors. This paper presents such a solution: Virtual Equivalent Real-time Simulation Environment (VERSE). VERSE turns the real-time operating system RTAI (Real-time Application Interface) into an event driven simulator that runs in virtual real time. Designed to keep the original RTAI architecture as intact as possible, and therefore inheriting RTAI's many capabilities, VERSE was implemented with remarkably little change to the RTAI source code. This small footprint together with use of the same API allows users to easily run the same application in both real-time and virtual time environments. VERSE has been used to build a workstation testbed for NASA's Space Interferometry Mission (SIM PlanetQuest) instrument flight software. With its flexible simulation controls and inexpensive setup and replication costs, VERSE will become an invaluable tool in future mission development.

Real-time vehicle noise cancellation techniques for gunshot acoustics

NASA Astrophysics Data System (ADS)

Ramos, Antonio L. L.; Holm, Sverre; Gudvangen, Sigmund; Otterlei, Ragnvald

2012-06-01

Acoustical sniper positioning systems rely on the detection and direction-of-arrival (DOA) estimation of the shockwave and the muzzle blast in order to provide an estimate of a potential snipers location. Field tests have shown that detecting and estimating the DOA of the muzzle blast is a rather difficult task in the presence of background noise sources, e.g., vehicle noise, especially in long range detection and absorbing terrains. In our previous work presented in the 2011 edition of this conference we highlight the importance of improving the SNR of the gunshot signals prior to the detection and recognition stages, aiming at lowering the false alarm and miss-detection rates and, thereby, increasing the reliability of the system. This paper reports on real-time noise cancellation techniques, like Spectral Subtraction and Adaptive Filtering, applied to gunshot signals. Our model assumes the background noise as being short-time stationary and uncorrelated to the impulsive gunshot signals. In practice, relatively long periods without signal occur and can be used to estimate the noise spectrum and its first and second order statistics as required in the spectral subtraction and adaptive filtering techniques, respectively. The results presented in this work are supported with extensive simulations based on real data.

MNE Scan: Software for real-time processing of electrophysiological data.

PubMed

Esch, Lorenz; Sun, Limin; Klüber, Viktor; Lew, Seok; Baumgarten, Daniel; Grant, P Ellen; Okada, Yoshio; Haueisen, Jens; Hämäläinen, Matti S; Dinh, Christoph

2018-06-01

Magnetoencephalography (MEG) and Electroencephalography (EEG) are noninvasive techniques to study the electrophysiological activity of the human brain. Thus, they are well suited for real-time monitoring and analysis of neuronal activity. Real-time MEG/EEG data processing allows adjustment of the stimuli to the subject's responses for optimizing the acquired information especially by providing dynamically changing displays to enable neurofeedback. We introduce MNE Scan, an acquisition and real-time analysis software based on the multipurpose software library MNE-CPP. MNE Scan allows the development and application of acquisition and novel real-time processing methods in both research and clinical studies. The MNE Scan development follows a strict software engineering process to enable approvals required for clinical software. We tested the performance of MNE Scan in several device-independent use cases, including, a clinical epilepsy study, real-time source estimation, and Brain Computer Interface (BCI) application. Compared to existing tools we propose a modular software considering clinical software requirements expected by certification authorities. At the same time the software is extendable and freely accessible. We conclude that MNE Scan is the first step in creating a device-independent open-source software to facilitate the transition from basic neuroscience research to both applied sciences and clinical applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

PubMed Central

Sampaio, Renato Coral; Vargas, José A. R.

2018-01-01

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments. PMID:29570698

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes.

PubMed

Bestard, Guillermo Alvarez; Sampaio, Renato Coral; Vargas, José A R; Alfaro, Sadek C Absi

2018-03-23

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.

Combining instruction prefetching with partial cache locking to improve WCET in real-time systems.

PubMed

Ni, Fan; Long, Xiang; Wan, Han; Gao, Xiaopeng

2013-01-01

Caches play an important role in embedded systems to bridge the performance gap between fast processor and slow memory. And prefetching mechanisms are proposed to further improve the cache performance. While in real-time systems, the application of caches complicates the Worst-Case Execution Time (WCET) analysis due to its unpredictable behavior. Modern embedded processors often equip locking mechanism to improve timing predictability of the instruction cache. However, locking the whole cache may degrade the cache performance and increase the WCET of the real-time application. In this paper, we proposed an instruction-prefetching combined partial cache locking mechanism, which combines an instruction prefetching mechanism (termed as BBIP) with partial cache locking to improve the WCET estimates of real-time applications. BBIP is an instruction prefetching mechanism we have already proposed to improve the worst-case cache performance and in turn the worst-case execution time. The estimations on typical real-time applications show that the partial cache locking mechanism shows remarkable WCET improvement over static analysis and full cache locking.

Combining Instruction Prefetching with Partial Cache Locking to Improve WCET in Real-Time Systems

PubMed Central

Ni, Fan; Long, Xiang; Wan, Han; Gao, Xiaopeng

2013-01-01

Caches play an important role in embedded systems to bridge the performance gap between fast processor and slow memory. And prefetching mechanisms are proposed to further improve the cache performance. While in real-time systems, the application of caches complicates the Worst-Case Execution Time (WCET) analysis due to its unpredictable behavior. Modern embedded processors often equip locking mechanism to improve timing predictability of the instruction cache. However, locking the whole cache may degrade the cache performance and increase the WCET of the real-time application. In this paper, we proposed an instruction-prefetching combined partial cache locking mechanism, which combines an instruction prefetching mechanism (termed as BBIP) with partial cache locking to improve the WCET estimates of real-time applications. BBIP is an instruction prefetching mechanism we have already proposed to improve the worst-case cache performance and in turn the worst-case execution time. The estimations on typical real-time applications show that the partial cache locking mechanism shows remarkable WCET improvement over static analysis and full cache locking. PMID:24386133

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

Combination of High Rate, Real-time GNSS and Accelerometer Observations - Preliminary Results Using a Shake Table and Historic Earthquake Events.

NASA Astrophysics Data System (ADS)

Jackson, Michael; Passmore, Paul; Zimakov, Leonid; Raczka, Jared

2014-05-01

One of the fundamental requirements of an Earthquake Early Warning (EEW) system (and other mission critical applications) is to quickly detect and process the information from the strong motion event, i.e. event detection and location, magnitude estimation, and the peak ground motion estimation at the defined targeted site, thus allowing the civil protection authorities to provide pre-programmed emergency response actions: Slow down or stop rapid transit trains and high-speed trains; shutoff of gas pipelines and chemical facilities; stop elevators at the nearest floor; send alarms to hospitals, schools and other civil institutions. An important question associated with the EEW system is: can we measure displacements in real time with sufficient accuracy? Scientific GNSS networks are moving towards a model of real-time data acquisition, storage integrity, and real-time position and displacement calculations. This new paradigm allows the integration of real-time, high-rate GNSS displacement information with acceleration and velocity data to create very high-rate displacement records. The mating of these two instruments allows the creation of a new, very high-rate (200 Hz) displacement observable that has the full-scale displacement characteristics of GNSS and high-precision dynamic motions of seismic technologies. It is envisioned that these new observables can be used for earthquake early warning studies and other mission critical applications, such as volcano monitoring, building, bridge and dam monitoring systems. REF TEK a Division of Trimble has developed the integrated GNSS/Accelerograph system, model 160-09SG, which consists of REF TEK's fourth generation electronics, a 147-01 high-resolution ANSS Class A accelerometer, and Trimble GNSS receiver and antenna capable of real time, on board Precise Point Positioning (PPP) techniques with satellite clock and orbit corrections delivered to the receiver directly via L-band satellite communications. The test we

SeismoGeodesy: Combination of High Rate, Real-time GNSS and Accelerometer Observations and Rapid Seismic Event Notification for Earth Quake Early Warning and Volcano Monitoring

NASA Astrophysics Data System (ADS)

Jackson, Michael; Zimakov, Leonid; Moessmer, Matthias

2015-04-01

Scientific GNSS networks are moving towards a model of real-time data acquisition, epoch-by-epoch storage integrity, and on-board real-time position and displacement calculations. This new paradigm allows the integration of real-time, high-rate GNSS displacement information with acceleration and velocity data to create very high-rate displacement records. The mating of these two instruments allows the creation of a new, very high-rate (200 Hz) displacement observable that has the full-scale displacement characteristics of GNSS and high-precision dynamic motions of seismic technologies. It is envisioned that these new observables can be used for earthquake early warning studies, volcano monitoring, and critical infrastructure monitoring applications. Our presentation will focus on the characteristics of GNSS, seismic, and strong motion sensors in high dynamic environments, including historic earthquakes replicated on a shake table over a range of displacements and frequencies. We will explore the optimum integration of these sensors from a filtering perspective including simple harmonic impulses over varying frequencies and amplitudes and under the dynamic conditions of various earthquake scenarios. We will also explore the tradeoffs between various GNSS processing schemes including real-time precise point positioning (PPP) and real-time kinematic (RTK) as applied to seismogeodesy. In addition we will discuss implementation of a Rapid Seismic Event Notification System that provides quick delivery of digital data from seismic stations to the acquisition and processing center and a full data integrity model for real-time earthquake notification that provides warning prior to significant ground shaking.

Solar Demon: near real-time Flare, Dimming and EUV wave monitoring

NASA Astrophysics Data System (ADS)

Kraaikamp, Emil; Verbeeck, Cis

Dimmings and EUV waves have been observed routinely in EUV images since 1996. They are closely associated with coronal mass ejections (CMEs), and therefore provide useful information for early space weather alerts. On the one hand, automatic detection and characterization of dimmings and EUV waves can be used to gain better understanding of the underlying physical mechanisms. On the other hand, every dimming and EUV wave provides extra information on the associated front side CME, and can improve estimates of the geo-effectiveness and arrival time of the CME. Solar Demon has been designed to detect and characterize dimmings, EUV waves, as well as solar flares in near real-time on Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) data. The detection modules are running continuously at the Royal Observatory of Belgium on both quick-look data, as well as 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 show several interesting flare, dimming and EUV wave events, and present general statistics of the detections made so far during solar cycle 24.

Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors

USGS Publications Warehouse

Kelbert, Anna; Balch, Christopher; Pulkkinen, Antti; Egbert, Gary D; Love, Jeffrey J.; Rigler, E. Joshua; Fujii, Ikuko

2017-01-01

Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.

Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors

NASA Astrophysics Data System (ADS)

Kelbert, Anna; Balch, Christopher C.; Pulkkinen, Antti; Egbert, Gary D.; Love, Jeffrey J.; Rigler, E. Joshua; Fujii, Ikuko

2017-07-01

Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.

A web-based Tamsui River flood early-warning system with correction of real-time water stage using monitoring data

NASA Astrophysics Data System (ADS)

Liao, H. Y.; Lin, Y. J.; Chang, H. K.; Shang, R. K.; Kuo, H. C.; Lai, J. S.; Tan, Y. C.

2017-12-01

Taiwan encounters heavy rainfalls frequently. There are three to four typhoons striking Taiwan every year. To provide lead time for reducing flood damage, this study attempt to build a flood early-warning system (FEWS) in Tanshui River using time series correction techniques. The predicted rainfall is used as the input for the rainfall-runoff model. Then, the discharges calculated by the rainfall-runoff model is converted to the 1-D river routing model. The 1-D river routing model will output the simulating water stages in 487 cross sections for the future 48-hr. The downstream water stage at the estuary in 1-D river routing model is provided by storm surge simulation. Next, the water stages of 487 cross sections are corrected by time series model such as autoregressive (AR) model using real-time water stage measurements to improve the predicted accuracy. The results of simulated water stages are displayed on a web-based platform. In addition, the models can be performed remotely by any users with web browsers through a user interface. The on-line video surveillance images, real-time monitoring water stages, and rainfalls can also be shown on this platform. If the simulated water stage exceeds the embankments of Tanshui River, the alerting lights of FEWS will be flashing on the screen. This platform runs periodically and automatically to generate the simulation graphic data of flood water stages for flood disaster prevention and decision making.

Development of a real-time transport performance optimization methodology

NASA Technical Reports Server (NTRS)

Gilyard, Glenn

1996-01-01

The practical application of real-time performance optimization is addressed (using a wide-body transport simulation) based on real-time measurements and calculation of incremental drag from forced response maneuvers. Various controller combinations can be envisioned although this study used symmetric outboard aileron and stabilizer. The approach is based on navigation instrumentation and other measurements found on state-of-the-art transports. This information is used to calculate winds and angle of attack. Thrust is estimated from a representative engine model as a function of measured variables. The lift and drag equations are then used to calculate lift and drag coefficients. An expression for drag coefficient, which is a function of parasite drag, induced drag, and aileron drag, is solved from forced excitation response data. Estimates of the parasite drag, curvature of the aileron drag variation, and minimum drag aileron position are produced. Minimum drag is then obtained by repositioning the symmetric aileron. Simulation results are also presented which evaluate the affects of measurement bias and resolution.

Citizen earthquake alert using near real time PGA estimation from a local array combining a variety of accelerometric instruments

NASA Astrophysics Data System (ADS)

Melis, Nikolaos S.; Konstantinou, Konstantinos; Kalogeras, Ioannis; Sokos, Efthimios; Tselentis, G.-Akis

2017-04-01

It is of a great importance to assess rapidly the intensity of a felt event in a highly populated environment. Rapid and reliable information plays a key role to decision making responses, by performing correctly the first steps after a felt ground shaking. Thus, it is important to accurately respond to urgent societal demand using reliable information. A strong motion array is under deployment and trial operation in the area of Patras, Greece. It combines: (a) standard accelerometric stations operated by the National Observatory of Athens, Institute of Geodynamics (NOA), (b) QCN-type USB MEMS acceleration sensors deployed in schools and (c) P-alert MEMS acceleration devices deployed in public sector buildings as well as in private dwellings. The array intends to cover the whole city of Patras and the populated suburbs. All instruments are operating in near real time and they are linked to a combined Earthworm - SeisComP3 server at NOA, Athens. Rapid intensity estimation can be also performed by the P-alert accelerometers locally, but the performance of a near real time intensity estimation system is under operation at NOA. The procedure is based on observing the maximum PGA value at each instrument and empirically estimate the corresponding intensity. The values are also fed to a SeisComP3 based ShakeMap procedure that is served at NOA and uses the scwfparam module of SeisComP3. Earthquake activity has been recorded so far from the western Corinth Gulf, the Ionian Islands and Achaia-Elia area, western Peloponnesus. The first phase involves correlation tests of collocated instruments and assessment of their performance to low intensity as well as to strongly felt events in the Patras city area. Steps of expanding the array are also under consideration, in order to cover the wider area of northwestern Peloponnesus and Ionian islands.

NDBC - NDBC Real-Time Data

Science.gov Websites

Subtropical Storm Alberto. NDBC Real-Time Data NDBC moored buoy, C-MAN, and drifting buoy data are available in real-time through selecting either: NDBC Station locator map: a series of regional maps which show : a tabular list of station identifiers. Real-time data are available for the last 45 days (at least

Multiple Input Design for Real-Time Parameter Estimation in the Frequency Domain

NASA Technical Reports Server (NTRS)

Morelli, Eugene

2003-01-01

A method for designing multiple inputs for real-time dynamic system identification in the frequency domain was developed and demonstrated. The designed inputs are mutually orthogonal in both the time and frequency domains, with reduced peak factors to provide good information content for relatively small amplitude excursions. The inputs are designed for selected frequency ranges, and therefore do not require a priori models. The experiment design approach was applied to identify linear dynamic models for the F-15 ACTIVE aircraft, which has multiple control effectors.

Real-time detection of small and dim moving objects in IR video sequences using a robust background estimator and a noise-adaptive double thresholding

NASA Astrophysics Data System (ADS)

Zingoni, Andrea; Diani, Marco; Corsini, Giovanni

2016-10-01

We developed an algorithm for automatically detecting small and poorly contrasted (dim) moving objects in real-time, within video sequences acquired through a steady infrared camera. The algorithm is suitable for different situations since it is independent of the background characteristics and of changes in illumination. Unlike other solutions, small objects of any size (up to single-pixel), either hotter or colder than the background, can be successfully detected. The algorithm is based on accurately estimating the background at the pixel level and then rejecting it. A novel approach permits background estimation to be robust to changes in the scene illumination and to noise, and not to be biased by the transit of moving objects. Care was taken in avoiding computationally costly procedures, in order to ensure the real-time performance even using low-cost hardware. The algorithm was tested on a dataset of 12 video sequences acquired in different conditions, providing promising results in terms of detection rate and false alarm rate, independently of background and objects characteristics. In addition, the detection map was produced frame by frame in real-time, using cheap commercial hardware. The algorithm is particularly suitable for applications in the fields of video-surveillance and computer vision. Its reliability and speed permit it to be used also in critical situations, like in search and rescue, defence and disaster monitoring.

Improved Short-Term Clock Prediction Method for Real-Time Positioning.

PubMed

Lv, Yifei; Dai, Zhiqiang; Zhao, Qile; Yang, Sheng; Zhou, Jinning; Liu, Jingnan

2017-06-06

because it solves the problems of interruption and delay in data broadcast in real-time clock estimation and can meet the requirements of real-time PPP.

Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.

2008-11-06

This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use,more » a filtering algorithm based on linear approximations of the real observations is proposed.« less

Achieving Real-Time Tracking Mobile Wireless Sensors Using SE-KFA

NASA Astrophysics Data System (ADS)

Kadhim Hoomod, Haider, Dr.; Al-Chalabi, Sadeem Marouf M.

2018-05-01

Nowadays, Real-Time Achievement is very important in different fields, like: Auto transport control, some medical applications, celestial body tracking, controlling agent movements, detections and monitoring, etc. This can be tested by different kinds of detection devices, which named "sensors" as such as: infrared sensors, ultrasonic sensor, radars in general, laser light sensor, and so like. Ultrasonic Sensor is the most fundamental one and it has great impact and challenges comparing with others especially when navigating (as an agent). In this paper, concerning to the ultrasonic sensor, sensor(s) detecting and delimitation by themselves then navigate inside a limited area to estimating Real-Time using Speed Equation with Kalman Filter Algorithm as an intelligent estimation algorithm. Then trying to calculate the error comparing to the factual rate of tracking. This paper used Ultrasonic Sensor HC-SR04 with Arduino-UNO as Microcontroller.

Real-time stereo matching using orthogonal reliability-based dynamic programming.

PubMed

Gong, Minglun; Yang, Yee-Hong

2007-03-01

A novel algorithm is presented in this paper for estimating reliable stereo matches in real time. Based on the dynamic programming-based technique we previously proposed, the new algorithm can generate semi-dense disparity maps using as few as two dynamic programming passes. The iterative best path tracing process used in traditional dynamic programming is replaced by a local minimum searching process, making the algorithm suitable for parallel execution. Most computations are implemented on programmable graphics hardware, which improves the processing speed and makes real-time estimation possible. The experiments on the four new Middlebury stereo datasets show that, on an ATI Radeon X800 card, the presented algorithm can produce reliable matches for 60% approximately 80% of pixels at the rate of 10 approximately 20 frames per second. If needed, the algorithm can be configured for generating full density disparity maps.

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

PubMed

Tizzoni, Michele; Bajardi, Paolo; Poletto, Chiara; Ramasco, José J; Balcan, Duygu; Gonçalves, Bruno; Perra, Nicola; Colizza, Vittoria; Vespignani, Alessandro

2012-12-13

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high

Development of a Laboratory Synchrophasor Network and an Application to Estimate Transmission Line Parameters in Real Time

NASA Astrophysics Data System (ADS)

Almiron Bonnin, Rubens Eduardo

The development of an experimental synchrophasors network and application of synchrophasors for real-time transmission line parameter monitoring are presented in this thesis. In the laboratory setup, a power system is simulated in a RTDS real-time digital simulator, and the simulated voltages and currents are input to hardware phasor measurement units (PMUs) through the analog outputs of the simulator. Time synchronizing signals for the PMU devices are supplied from a common GPS clock. The real time data collected from PMUs are sent to a phasor data concentrator (PDC) through Ethernet using the TCP/IP protocol. A real-time transmission line parameter monitoring application program that uses the synchrophasor data provided by the PDC is implemented and validated. The experimental synchrophasor network developed in this thesis is expected to be used in research on synchrophasor applications as well as in graduate and undergraduate teaching.

An FPGA-Based Real-Time Maximum Likelihood 3D Position Estimation for a Continuous Crystal PET Detector

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Xiao, Yong; Cheng, Xinyi; Li, Deng; Wang, Liwei

2016-02-01

For the continuous crystal-based positron emission tomography (PET) detector built in our lab, a maximum likelihood algorithm adapted for implementation on a field programmable gate array (FPGA) is proposed to estimate the three-dimensional (3D) coordinate of interaction position with the single-end detected scintillation light response. The row-sum and column-sum readout scheme organizes the 64 channels of photomultiplier (PMT) into eight row signals and eight column signals to be readout for X- and Y-coordinates estimation independently. By the reference events irradiated in a known oblique angle, the probability density function (PDF) for each depth-of-interaction (DOI) segment is generated, by which the reference events in perpendicular irradiation are assigned to DOI segments for generating the PDFs for X and Y estimation in each DOI layer. Evaluated by the experimental data, the algorithm achieves an average X resolution of 1.69 mm along the central X-axis, and DOI resolution of 3.70 mm over the whole thickness (0-10 mm) of crystal. The performance improvements from 2D estimation to the 3D algorithm are also presented. Benefiting from abundant resources of FPGA and a hierarchical storage arrangement, the whole algorithm can be implemented into a middle-scale FPGA. By a parallel structure in pipelines, the 3D position estimator on the FPGA can achieve a processing throughput of 15 M events/s, which is sufficient for the requirement of real-time PET imaging.

Real-Time Simulation

NASA Technical Reports Server (NTRS)

1997-01-01

Coryphaeus Software, founded in 1989 by former NASA electronic engineer Steve Lakowske, creates real-time 3D software. Designer's Workbench, the company flagship product, is a modeling and simulation tool for the development of both static and dynamic 3D databases. Other products soon followed. Activation, specifically designed for game developers, allows developers to play and test the 3D games before they commit to a target platform. Game publishers can shorten development time and prove the "playability" of the title, maximizing their chances of introducing a smash hit. Another product, EasyT, lets users create massive, realistic representation of Earth terrains that can be viewed and traversed in real time. Finally, EasyScene software control the actions among interactive objects within a virtual world. Coryphaeus products are used on Silican Graphics workstation and supercomputers to simulate real-world performance in synthetic environments. Customers include aerospace, aviation, architectural and engineering firms, game developers, and the entertainment industry.

Estimating Distance in Real and Virtual Environments: Does Order Make a Difference?

PubMed Central

Ziemer, Christine J.; Plumert, Jodie M.; Cremer, James F.; Kearney, Joseph K.

2010-01-01

This investigation examined how the order in which people experience real and virtual environments influences their distance estimates. Participants made two sets of distance estimates in one of the following conditions: 1) real environment first, virtual environment second; 2) virtual environment first, real environment second; 3) real environment first, real environment second; or 4) virtual environment first, virtual environment second. In Experiment 1, participants imagined how long it would take to walk to targets in real and virtual environments. Participants’ first estimates were significantly more accurate in the real than in the virtual environment. When the second environment was the same as the first environment (real-real and virtual-virtual), participants’ second estimates were also more accurate in the real than in the virtual environment. When the second environment differed from the first environment (real-virtual and virtual-real), however, participants’ second estimates did not differ significantly across the two environments. A second experiment in which participants walked blindfolded to targets in the real environment and imagined how long it would take to walk to targets in the virtual environment replicated these results. These subtle, yet persistent order effects suggest that memory can play an important role in distance perception. PMID:19525540

Characterization of real-time computers

NASA Technical Reports Server (NTRS)

Shin, K. G.; Krishna, C. M.

1984-01-01

A real-time system consists of a computer controller and controlled processes. Despite the synergistic relationship between these two components, they have been traditionally designed and analyzed independently of and separately from each other; namely, computer controllers by computer scientists/engineers and controlled processes by control scientists. As a remedy for this problem, in this report real-time computers are characterized by performance measures based on computer controller response time that are: (1) congruent to the real-time applications, (2) able to offer an objective comparison of rival computer systems, and (3) experimentally measurable/determinable. These measures, unlike others, provide the real-time computer controller with a natural link to controlled processes. In order to demonstrate their utility and power, these measures are first determined for example controlled processes on the basis of control performance functionals. They are then used for two important real-time multiprocessor design applications - the number-power tradeoff and fault-masking and synchronization.

Real-time volcano monitoring using GNSS single-frequency receivers

NASA Astrophysics Data System (ADS)

Lee, Seung-Woo; Yun, Sung-Hyo; Kim, Do Hyeong; Lee, Dukkee; Lee, Young J.; Schutz, Bob E.

2015-12-01

We present a real-time volcano monitoring strategy that uses the Global Navigation Satellite System (GNSS), and we examine the performance of the strategy by processing simulated and real data and comparing the results with published solutions. The cost of implementing the strategy is reduced greatly by using single-frequency GNSS receivers except for one dual-frequency receiver that serves as a base receiver. Positions of the single-frequency receivers are computed relative to the base receiver on an epoch-by-epoch basis using the high-rate double-difference (DD) GNSS technique, while the position of the base station is fixed to the values obtained with a deferred-time precise point positioning technique and updated on a regular basis. Since the performance of the single-frequency high-rate DD technique depends on the conditions of the ionosphere over the monitoring area, the ionospheric total electron content is monitored using the dual-frequency data from the base receiver. The surface deformation obtained with the high-rate DD technique is eventually processed by a real-time inversion filter based on the Mogi point source model. The performance of the real-time volcano monitoring strategy is assessed through a set of tests and case studies, in which the data recorded during the 2007 eruption of Kilauea and the 2005 eruption of Augustine are processed in a simulated real-time mode. The case studies show that the displacement time series obtained with the strategy seem to agree with those obtained with deferred-time, dual-frequency approaches at the level of 10-15 mm. Differences in the estimated volume change of the Mogi source between the real-time inversion filter and previously reported works were in the range of 11 to 13% of the maximum volume changes of the cases examined.

REAL-TIME MODELING OF MOTOR VEHICLE EMISSIONS FOR ESTIMATING HUMAN EXPOSURES NEAR ROADWAYS

EPA Science Inventory

The United States Environmental Protection Agency's (EPA) National Exposure Research Laboratory is developing a real-time model of motor vehicle emissions to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop ...

Model- based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time.

PubMed

Tronstad, Christian; Staal, Odd M; Saelid, Steinar; Martinsen, Orjan G

2015-08-01

Measurement of electrodermal activity (EDA) has recently made a transition from the laboratory into daily life with the emergence of wearable devices. Movement and nongelled electrodes make these devices more susceptible to noise and artifacts. In addition, real-time interpretation of the measurement is needed for user feedback. The Kalman filter approach may conveniently deal with both these issues. This paper presents a biophysical model for EDA implemented in an extended Kalman filter. Employing the filter on data from Physionet along with simulated noise and artifacts demonstrates noise and artifact suppression while implicitly providing estimates of model states and parameters such as the sudomotor nerve activation.

A real-time Global Warming Index.

PubMed

Haustein, K; Allen, M R; Forster, P M; Otto, F E L; Mitchell, D M; Matthews, H D; Frame, D J

2017-11-13

We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change.

Real-time realizations of the Bayesian Infrasonic Source Localization Method

NASA Astrophysics Data System (ADS)

Pinsky, V.; Arrowsmith, S.; Hofstetter, A.; Nippress, A.

2015-12-01

The Bayesian Infrasonic Source Localization method (BISL), introduced by Mordak et al. (2010) and upgraded by Marcillo et al. (2014) is destined for the accurate estimation of the atmospheric event origin at local, regional and global scales by the seismic and infrasonic networks and arrays. The BISL is based on probabilistic models of the source-station infrasonic signal propagation time, picking time and azimuth estimate merged with a prior knowledge about celerity distribution. It requires at each hypothetical source location, integration of the product of the corresponding source-station likelihood functions multiplied by a prior probability density function of celerity over the multivariate parameter space. The present BISL realization is generally time-consuming procedure based on numerical integration. The computational scheme proposed simplifies the target function so that integrals are taken exactly and are represented via standard functions. This makes the procedure much faster and realizable in real-time without practical loss of accuracy. The procedure executed as PYTHON-FORTRAN code demonstrates high performance on a set of the model and real data.

A real-time recursive filter for the attitude determination of the Spacelab instrument pointing subsystem

NASA Technical Reports Server (NTRS)

West, M. E.

1992-01-01

A real-time estimation filter which reduces sensitivity to system variations and reduces the amount of preflight computation is developed for the instrument pointing subsystem (IPS). The IPS is a three-axis stabilized platform developed to point various astronomical observation instruments aboard the shuttle. Currently, the IPS utilizes a linearized Kalman filter (LKF), with premission defined gains, to compensate for system drifts and accumulated attitude errors. Since the a priori gains are generated for an expected system, variations result in a suboptimal estimation process. This report compares the performance of three real-time estimation filters with the current LKF implementation. An extended Kalman filter and a second-order Kalman filter are developed to account for the system nonlinearities, while a linear Kalman filter implementation assumes that the nonlinearities are negligible. The performance of each of the four estimation filters are compared with respect to accuracy, stability, settling time, robustness, and computational requirements. It is shown, that for the current IPS pointing requirements, the linear Kalman filter provides improved robustness over the LKF with less computational requirements than the two real-time nonlinear estimation filters.

Acting to gain information: Real-time reasoning meets real-time perception

NASA Technical Reports Server (NTRS)

Rosenschein, Stan

1994-01-01

Recent advances in intelligent reactive systems suggest new approaches to the problem of deriving task-relevant information from perceptual systems in real time. The author will describe work in progress aimed at coupling intelligent control mechanisms to real-time perception systems, with special emphasis on frame rate visual measurement systems. A model for integrated reasoning and perception will be discussed, and recent progress in applying these ideas to problems of sensor utilization for efficient recognition and tracking will be described.

Developments in real-time monitoring for geologic hazard warnings (Invited)

NASA Astrophysics Data System (ADS)

Leith, W. S.; Mandeville, C. W.; Earle, P. S.

2013-12-01

Real-time data from global, national and local sensor networks enable prompt alerts and warnings of earthquakes, tsunami, volcanic eruptions, geomagnetic storms , broad-scale crustal deformation and landslides. State-of-the-art seismic systems can locate and evaluate earthquake sources in seconds, enabling 'earthquake early warnings' to be broadcast ahead of the damaging surface waves so that protective actions can be taken. Strong motion monitoring systems in buildings now support near-real-time structural damage detection systems, and in quiet times can be used for state-of-health monitoring. High-rate GPS data are being integrated with seismic strong motion data, allowing accurate determination of earthquake displacements in near-real time. GPS data, combined with rainfall, groundwater and geophone data, are now used for near-real-time landslide monitoring and warnings. Real-time sea-floor water pressure data are key for assessing tsunami generation by large earthquakes. For monitoring remote volcanoes that lack local ground-based instrumentation, the USGS uses new technologies such as infrasound arrays and the worldwide lightning detection array to detect eruptions in progress. A new real-time UV-camera system for measuring the two dimensional SO2 flux from volcanic plumes will allow correlations with other volcano monitoring data streams to yield fundamental data on changes in gas flux as an eruption precursor, and how magmas de-gas prior to and during eruptions. Precision magnetic field data support the generation of real-time indices of geomagnetic disturbances (Dst, K and others), and can be used to model electrical currents in the crust and bulk power system. Ground-induced electrical current monitors are used to track those currents so that power grids can be effectively managed during geomagnetic storms. Beyond geophysical sensor data, USGS is using social media to rapidly detect possible earthquakes and to collect firsthand accounts of the impacts of

A real-time cabled observatory on the Cascadia subduction zone

NASA Astrophysics Data System (ADS)

Vidale, J. E.; Delaney, J. R.; Toomey, D. R.; Bodin, P.; Roland, E. C.; Wilcock, W. S. D.; Houston, H.; Schmidt, D. A.; Allen, R. M.

2015-12-01

Subduction zones are replete with mystery and rife with hazard. Along most of the Pacific Northwest margin, the traditional methods of monitoring offshore geophysical activity use onshore sensors or involve conducting infrequent oceanographic expeditions. This results in a limited capacity for detecting and monitoring subduction processes offshore. We propose that the next step in geophysical observations of Cascadia should include real-time data delivered by a seafloor cable with seismic, geodetic, and pressure-sensing instruments. Along the Cascadia subduction zone, we need to monitor deformation, earthquakes, and fluid fluxes on short time scales. High-quality long-term time series are needed to establish baseline observations and evaluate secular changes in the subduction environment. Currently we lack a basic knowledge of the plate convergence rate, direction and its variations along strike and of how convergence is accommodated across the plate boundary. We also would like to seek cycles of microseismicity, how far locking extends up-dip, and the transient processes (i.e., fluid pulsing, tremor, and slow slip) that occur near the trench. For reducing risk to society, real-time monitoring has great benefit for immediate and accurate assessment through earthquake early warning systems. Specifically, the improvement to early warning would be in assessing the location, geometry, and progression of ongoing faulting and obtaining an accurate tsunami warning, as well as simply speeding up the early warning. It would also be valuable to detect strain transients and map the locked portion of the megathrust, and detect changes in locking over the earthquake cycle. Development of the US portion of a real-time cabled seismic and geodetic observatory should build upon the Ocean Observatories Initiative's cabled array, which was recently completed and is currently delivering continuous seismic and pressure data from the seafloor. Its implementation would require

Real-time monitoring of a microbial electrolysis cell using an electrical equivalent circuit model.

PubMed

Hussain, S A; Perrier, M; Tartakovsky, B

2018-04-01

Efforts in developing microbial electrolysis cells (MECs) resulted in several novel approaches for wastewater treatment and bioelectrosynthesis. Practical implementation of these approaches necessitates the development of an adequate system for real-time (on-line) monitoring and diagnostics of MEC performance. This study describes a simple MEC equivalent electrical circuit (EEC) model and a parameter estimation procedure, which enable such real-time monitoring. The proposed approach involves MEC voltage and current measurements during its operation with periodic power supply connection/disconnection (on/off operation) followed by parameter estimation using either numerical or analytical solution of the model. The proposed monitoring approach is demonstrated using a membraneless MEC with flow-through porous electrodes. Laboratory tests showed that changes in the influent carbon source concentration and composition significantly affect MEC total internal resistance and capacitance estimated by the model. Fast response of these EEC model parameters to changes in operating conditions enables the development of a model-based approach for real-time monitoring and fault detection.

Merged Real Time GNSS Solutions for the READI System

NASA Astrophysics Data System (ADS)

Santillan, V. M.; Geng, J.

2014-12-01

Real-time measurements from increasingly dense Global Navigational Satellite Systems (GNSS) networks located throughout the western US offer a substantial, albeit largely untapped, contribution towards the mitigation of seismic and other natural hazards. Analyzed continuously in real-time, currently over 600 instruments blanket the San Andreas and Cascadia fault systems of the North American plate boundary and can provide on-the-fly characterization of transient ground displacements highly complementary to traditional seismic strong-motion monitoring. However, the utility of GNSS systems depends on their resolution, and merged solutions of two or more independent estimation strategies have been shown to offer lower scatter and higher resolution. Towards this end, independent real time GNSS solutions produced by Scripps Inst. of Oceanography and Central Washington University (PANGA) are now being formally combined in pursuit of NASA's Real-Time Earthquake Analysis for Disaster Mitigation (READI) positioning goals. CWU produces precise point positioning (PPP) solutions while SIO produces ambiguity resolved PPP solutions (PPP-AR). The PPP-AR solutions have a ~5 mm RMS scatter in the horizontal and ~10mm in the vertical, however PPP-AR solutions can take tens of minutes to re-converge in case of data gaps. The PPP solutions produced by CWU use pre-cleaned data in which biases are estimated as non-integer ambiguities prior to formal positioning with GIPSY 6.2 using a real time stream editor developed at CWU. These solutions show ~20mm RMS scatter in the horizontal and ~50mm RMS scatter in the vertical but re-converge within 2 min. or less following cycle-slips or data outages. We have implemented the formal combination of the CWU and SCRIPPS ENU displacements using the independent solutions as input measurements to a simple 3-element state Kalman filter plus white noise. We are now merging solutions from 90 stations, including 30 in Cascadia, 39 in the Bay Area, and 21

Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System

PubMed Central

Yang, Che-Chang; Hsu, Yeh-Liang; Shih, Kao-Shang; Lu, Jun-Ming

2011-01-01

This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications. PMID:22164019

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Modeling Performance in a Real-Time Air Quality Estimation System

NASA Technical Reports Server (NTRS)

Li,Hui; Faruque, Fazlay; Williams, Worth; Al-Hamdan, Mohammad; Luvall, Jeffrey; Crosson, William; Rickman, Douglas; Limaye, Ashutosh

2008-01-01

Aerosol optical depth (AOD), derived from satellite measurements using Moderate Resolution Imaging Spectrometer (MODIS), offers indirect estimates of particle matter. Research shows a significant positive correlation between satellite-based measurements of AOD and ground-based measurements of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5). In addition, satellite observations have also shown great promise in improving estimates of PM2.5 air quality surface. Research shows that correlations between AOD and ground PM2.5 are affected by a combination of many factors such as inherent characteristics of satellite observations, terrain, cloud cover, height of the mixing layer, and weather conditions, and thus might vary widely in different regions, different seasons, and even different days in a same location. Analysis of correlating AOD with ground measured PM2.5 on a day-to-day basis suggests the temporal scale, a number of immediate latest days for a given run's day, for their correlations needs to be considered to improve air quality surface estimates, especially when satellite observations are used in a real-time pollution system. The second reason is that correlation coefficients between AOD and ground PM2.5 cannot be predetermined and needs to be calculated for each day's run for a real-time system because the coefficients can vary over space and time. Few studies have been conducted to explore the optimal way to apply AOD data to improve model accuracies of PM2.5 surface estimation in a real-time air quality system. This paper discusses the best temporal scale to calculate the correlation of AOD and ground particle matter data to improve the results of pollution models in real-time system.

A real-time PCR approach to detect predation on anchovy and sardine early life stages

NASA Astrophysics Data System (ADS)

Cuende, Elsa; Mendibil, Iñaki; Bachiller, Eneko; Álvarez, Paula; Cotano, Unai; Rodriguez-Ezpeleta, Naiara

2017-12-01

Recruitment of sardine (Sardina pilchardus Walbaum, 1792) and anchovy (Engraulis encrasicolus Linnaeus, 1758) is thought to be regulated by predation of their eggs and larvae. Predators of sardine and anchovy can be identified by visual taxonomic identification of stomach contents, but this method is time consuming, tedious and may underestimate predation, especially in small predators such as fish larvae. Alternatively, genetic tools may offer a more cost-effective and accurate alternative. Here, we have developed a multiplex real-time polymerase chain reaction (RT-PCR) assay based on TaqMan probes to simultaneously detect sardine and anchovy remains in gut contents of potential predators. The assay combines previously described and newly generated species-specific primers and probes for anchovy and sardine detection respectively, and allows the detection of 0,001 ng of target DNA (which corresponds to about one hundredth of the total DNA present in a single egg). We applied the method to candidate anchovy and sardine egg predators in the Bay of Biscay, Atlantic Mackerel (Scomber scombrus) larvae. Egg predation observed was limited primarily to those stations where sardine and/or anchovy eggs were present. Our developed assay offers a suitable tool to understand the effects of predation on the survival of anchovy and sardine early life stages.

Real-time real-world analysis of seasonal influenza vaccine effectiveness: method development and assessment of a population-based cohort in Stockholm County, Sweden, seasons 2011/12 to 2014/15.

PubMed

Leval, Amy; Hergens, Maria Pia; Persson, Karin; Örtqvist, Åke

2016-10-27

Real-world estimates of seasonal influenza vaccine effectiveness (VE) are important for early detection of vaccine failure. We developed a method for evaluating real-time in-season vaccine effectiveness (IVE) and overall seasonal VE. In a retrospective, register-based, cohort study including all two million individuals in Stockholm County, Sweden, during the influenza seasons from 2011/12 to 2014/15, vaccination status was obtained from Stockholm's vaccine register. Main outcomes were hospitalisation or primary care visits for influenza (International Classification of Disease (ICD)-10 codes J09-J11). VE was assessed using Cox multivariate stratified and non-stratified analyses adjusting for age, sex, socioeconomic status, comorbidities and previous influenza vaccinations. Stratified analyses showed moderate VE in prevention of influenza hospitalisations among chronically ill adults ≥ 65 years in two of four seasons, and lower but still significant VE in one season; 53% (95% confidence interval (CI): 33-67) in 2012/13, 55% (95% CI: 25-73) in 2013/14 and 18% (95% CI: 3-31) in 2014/15. In conclusion, seasonal influenza vaccination was associated with substantial reductions in influenza-specific hospitalisation, particularly in adults ≥ 65 years with underlying chronic conditions. With the use of population-based patient register data on influenza-specific outcomes it will be possible to obtain real-time estimates of seasonal influenza VE. This article is copyright of The Authors, 2016.

Real-time real-world analysis of seasonal influenza vaccine effectiveness: method development and assessment of a population-based cohort in Stockholm County, Sweden, seasons 2011/12 to 2014/15

PubMed Central

Leval, Amy; Hergens, Maria Pia; Persson, Karin; Örtqvist, Åke

2016-01-01

Real-world estimates of seasonal influenza vaccine effectiveness (VE) are important for early detection of vaccine failure. We developed a method for evaluating real-time in-season vaccine effectiveness (IVE) and overall seasonal VE. In a retrospective, register-based, cohort study including all two million individuals in Stockholm County, Sweden, during the influenza seasons from 2011/12 to 2014/15, vaccination status was obtained from Stockholm’s vaccine register. Main outcomes were hospitalisation or primary care visits for influenza (International Classification of Disease (ICD)-10 codes J09-J11). VE was assessed using Cox multivariate stratified and non-stratified analyses adjusting for age, sex, socioeconomic status, comorbidities and previous influenza vaccinations. Stratified analyses showed moderate VE in prevention of influenza hospitalisations among chronically ill adults ≥ 65 years in two of four seasons, and lower but still significant VE in one season; 53% (95% confidence interval (CI): 33–67) in 2012/13, 55% (95% CI: 25–73) in 2013/14 and 18% (95% CI: 3–31) in 2014/15. In conclusion, seasonal influenza vaccination was associated with substantial reductions in influenza-specific hospitalisation, particularly in adults ≥ 65 years with underlying chronic conditions. With the use of population-based patient register data on influenza-specific outcomes it will be possible to obtain real-time estimates of seasonal influenza VE. PMID:27813473

Indirect rotor position sensing in real time for brushless permanent magnet motor drives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ertugrul, N.; Acarnley, P.P.

1998-07-01

This paper describes a modern solution to real-time rotor position estimation of brushless permanent magnet (PM) motor drives. The position estimation scheme, based on flux linkage and line-current estimation, is implemented in real time by using the abc reference frame, and it is tested dynamically. The position estimation model of the test motor, development of hardware, and basic operation of the digital signal processor (DSP) are discussed. The overall position estimation strategy is accomplished with a fast DSP (TMS320C30). The method is a shaft position sensorless method that is applicable to a wide range of excitation types in brushless PMmore » motors without any restriction on the motor model and the current excitation. Both rectangular and sinewave-excited brushless PM motor drives are examined, and the results are given to demonstrate the effectiveness of the method with dynamic loads in closed estimated position loop.« less

Real time numerical shake prediction incorporating attenuation structure: a case for the 2016 Kumamoto Earthquake

NASA Astrophysics Data System (ADS)

Ogiso, M.; Hoshiba, M.; Shito, A.; Matsumoto, S.

2016-12-01

Needless to say, heterogeneous attenuation structure is important for ground motion prediction, including earthquake early warning, that is, real time ground motion prediction. Hoshiba and Ogiso (2015, AGU Fall meeting) showed that the heterogeneous attenuation and scattering structure will lead to earlier and more accurate ground motion prediction in the numerical shake prediction scheme proposed by Hoshiba and Aoki (2015, BSSA). Hoshiba and Ogiso (2015) used assumed heterogeneous structure, and we discuss the effect of them in the case of 2016 Kumamoto Earthquake, using heterogeneous structure estimated by actual observation data. We conducted Multiple Lapse Time Window Analysis (Hoshiba, 1993, JGR) to the seismic stations located on western part of Japan to estimate heterogeneous attenuation and scattering structure. The characteristics are similar to the previous work of Carcole and Sato (2010, GJI), e.g. strong intrinsic and scattering attenuation around the volcanoes located on the central part of Kyushu, and relatively weak heterogeneities in the other area. Real time ground motion prediction simulation for the 2016 Kumamoto Earthquake was conducted using the numerical shake prediction scheme with 474 strong ground motion stations. Comparing the snapshot of predicted and observed wavefield showed a tendency for underprediction around the volcanic area in spite of the heterogeneous structure. These facts indicate the necessity of improving the heterogeneous structure for the numerical shake prediction scheme.In this study, we used the waveforms of Hi-net, K-NET, KiK-net stations operated by the NIED for estimating structure and conducting ground motion prediction simulation. Part of this study was supported by the Earthquake Research Institute, the University of Tokyo cooperative research program and JSPS KAKENHI Grant Number 25282114.

Volumetric ambient occlusion for real-time rendering and games.

PubMed

Szirmay-Kalos, L; Umenhoffer, T; Toth, B; Szecsi, L; Sbert, M

2010-01-01

This new algorithm, based on GPUs, can compute ambient occlusion to inexpensively approximate global-illumination effects in real-time systems and games. The first step in deriving this algorithm is to examine how ambient occlusion relates to the physically founded rendering equation. The correspondence stems from a fuzzy membership function that defines what constitutes nearby occlusions. The next step is to develop a method to calculate ambient occlusion in real time without precomputation. The algorithm is based on a novel interpretation of ambient occlusion that measures the relative volume of the visible part of the surface's tangent sphere. The new formula's integrand has low variation and thus can be estimated accurately with a few samples.

First Zenith Total Delay and Integrated Water Vapour Estimates from the Near Real-Time GNSS Data Processing Systems at the University of Luxembourg

NASA Astrophysics Data System (ADS)

Ahmed, F.; Teferle, F. N.; Bingley, R. M.

2012-04-01

Since September 2011 the University of Luxembourg in collaboration with the University of Nottingham has been setting up two near real-time processing systems for ground-based GNSS data for the provision of zenith total delay (ZTD) and integrated water vapour (IWV) estimates. Both systems are based on Bernese v5.0, use the double-differenced network processing strategy and operate with a 1-hour (NRT1h) and 15-minutes (NRT15m) update cycle. Furthermore, the systems follow the approach of the E-GVAP METO and IES2 systems in that the normal equations for the latest data are combined with those from the previous four updates during the estimation of the ZTDs. NRT1h currently takes the hourly data from over 130 GNSS stations in Europe whereas NRT15m is primarily using the real-time streams of EUREF-IP. Both networks include additional GNSS stations in Luxembourg, Belgium and France. The a priori station coordinates for all of these stem from a moving average computed over the last 20 to 50 days and are based on the precise point positioning processing strategy. In this study we present the first ZTD and IWV estimates obtained from the NRT1h and NRT15m systems in development at the University of Luxembourg. In a preliminary evaluation we compare their performance to the IES2 system at the University of Nottingham and find the IWV estimates to agree at the sub-millimetre level.

Real-time Interplanetary Shock Prediction System

NASA Astrophysics Data System (ADS)

Vandegriff, J. D.; Ho, G. C.; Plauger, J. M.

2002-05-01

We are creating a system to predict the arrival times and maximum intensities of energetic storm particle (ESP) events at the earth using particle fluxes measured by the EPAM instrument aboard NASA's ACE spacecraft. Real-time flux measurements, consisting of 5 minute averages made available 24 hours per day by the NOAA Space Environment Center, are fed into algorithms looking for characteristic changes in flux, velocity dispersion, and anisotropy. These quantities typically show changes up to 3 hours before shock passage, and thus we expect our system to deliver enhanced probabilities for shock arrival with approximately the same lead time. Forecasting information will be made publicly available through http://sd-www.jhuapl.edu/ACE/EPAM/, the Johns Hopkins University Applied Physics Lab web site for the ACE/EPAM instrument. Early results on the training of our algorithms and comparisons with past shock data will be presented.

A Model for Real-Time Data Reputation Via Cyber Telemetry

DTIC Science & Technology

2016-06-01

TIME DATA REPUTATION VIA CYBER TELEMETRY by Beau M. Houser June 2016 Thesis Advisor: Dorothy E. Denning Co-Advisor: Phyllis Schneck...information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and...Master’s Thesis 4. TITLE AND SUBTITLE A MODEL FOR REAL- TIME DATA REPUTATION VIA CYBER TELEMETRY 5. FUNDING NUMBERS 6. AUTHOR(S) Beau M

A real-time architecture for time-aware agents.

PubMed

Prouskas, Konstantinos-Vassileios; Pitt, Jeremy V

2004-06-01

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.

Application of troposphere model from NWP and GNSS data into real-time precise positioning

NASA Astrophysics Data System (ADS)

Wilgan, Karina; Hadas, Tomasz; Kazmierski, Kamil; Rohm, Witold; Bosy, Jaroslaw

2016-04-01

The tropospheric delay empirical models are usually functions of meteorological parameters (temperature, pressure and humidity). The application of standard atmosphere parameters or global models, such as GPT (global pressure/temperature) model or UNB3 (University of New Brunswick, version 3) model, may not be sufficient, especially for positioning in non-standard weather conditions. The possible solution is to use regional troposphere models based on real-time or near-real time measurements. We implement a regional troposphere model into the PPP (Precise Point Positioning) software GNSS-WARP (Wroclaw Algorithms for Real-time Positioning) developed at Wroclaw University of Environmental and Life Sciences. The software is capable of processing static and kinematic multi-GNSS data in real-time and post-processing mode and takes advantage of final IGS (International GNSS Service) products as well as IGS RTS (Real-Time Service) products. A shortcoming of PPP technique is the time required for the solution to converge. One of the reasons is the high correlation among the estimated parameters: troposphere delay, receiver clock offset and receiver height. To efficiently decorrelate these parameters, a significant change in satellite geometry is required. Alternative solution is to introduce the external high-quality regional troposphere delay model to constrain troposphere estimates. The proposed model consists of zenith total delays (ZTD) and mapping functions calculated from meteorological parameters from Numerical Weather Prediction model WRF (Weather Research and Forecasting) and ZTDs from ground-based GNSS stations using the least-squares collocation software COMEDIE (Collocation of Meteorological Data for Interpretation and Estimation of Tropospheric Pathdelays) developed at ETH Zurich.

Real Time Conference 2014 Overview

NASA Astrophysics Data System (ADS)

Nomachi, Masaharu

2015-06-01

This article presents an overview of the 19th Real Time Conference held last May 26-30, 2014, at the Nara Prefectural New Public Hall, Nara, Japan, organized by the Research Center for Nuclear Physics of the Osaka University. The program included many invited talks and oral sessions offering an extensive overview on the following topics: real-time system architectures, intelligent signal processing, fast data transfer links and networks, trigger systems, data acquisition, processing-farms, control, monitoring and test systems, emerging real-time technologies, new standards, real-time safety and security, and some feedback on experiences. In parallel to the oral and poster presentations, industrial exhibits by companies, workshops and short courses also ran through the week.

HEVC real-time decoding

NASA Astrophysics Data System (ADS)

Bross, Benjamin; Alvarez-Mesa, Mauricio; George, Valeri; Chi, Chi Ching; Mayer, Tobias; Juurlink, Ben; Schierl, Thomas

2013-09-01

The new High Efficiency Video Coding Standard (HEVC) was finalized in January 2013. Compared to its predecessor H.264 / MPEG4-AVC, this new international standard is able to reduce the bitrate by 50% for the same subjective video quality. This paper investigates decoder optimizations that are needed to achieve HEVC real-time software decoding on a mobile processor. It is shown that HEVC real-time decoding up to high definition video is feasible using instruction extensions of the processor while decoding 4K ultra high definition video in real-time requires additional parallel processing. For parallel processing, a picture-level parallel approach has been chosen because it is generic and does not require bitstreams with special indication.

Real-time Tsunami Inundation Prediction Using High Performance Computers

NASA Astrophysics Data System (ADS)

Oishi, Y.; Imamura, F.; Sugawara, D.

2014-12-01

Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the

Fusion of Kinect depth data with trifocal disparity estimation for near real-time high quality depth maps generation

NASA Astrophysics Data System (ADS)

Boisson, Guillaume; Kerbiriou, Paul; Drazic, Valter; Bureller, Olivier; Sabater, Neus; Schubert, Arno

2014-03-01

Generating depth maps along with video streams is valuable for Cinema and Television production. Thanks to the improvements of depth acquisition systems, the challenge of fusion between depth sensing and disparity estimation is widely investigated in computer vision. This paper presents a new framework for generating depth maps from a rig made of a professional camera with two satellite cameras and a Kinect device. A new disparity-based calibration method is proposed so that registered Kinect depth samples become perfectly consistent with disparities estimated between rectified views. Also, a new hierarchical fusion approach is proposed for combining on the flow depth sensing and disparity estimation in order to circumvent their respective weaknesses. Depth is determined by minimizing a global energy criterion that takes into account the matching reliability and the consistency with the Kinect input. Thus generated depth maps are relevant both in uniform and textured areas, without holes due to occlusions or structured light shadows. Our GPU implementation reaches 20fps for generating quarter-pel accurate HD720p depth maps along with main view, which is close to real-time performances for video applications. The estimated depth is high quality and suitable for 3D reconstruction or virtual view synthesis.

Validation of travel times to hospital estimated by GIS.

PubMed

Haynes, Robin; Jones, Andrew P; Sauerzapf, Violet; Zhao, Hongxin

2006-09-19

An increasing number of studies use GIS estimates of car travel times to health services, without presenting any evidence that the estimates are representative of real travel times. This investigation compared GIS estimates of travel times with the actual times reported by a sample of 475 cancer patients who had travelled by car to attend clinics at eight hospitals in the North of England. Car travel times were estimated by GIS using the shortest road route between home address and hospital and average speed assumptions. These estimates were compared with reported journey times and straight line distances using graphical, correlation and regression techniques. There was a moderately strong association between reported times and estimated travel times (r = 0.856). Reported travel times were similarly related to straight line distances. Altogether, 50% of travel time estimates were within five minutes of the time reported by respondents, 77% were within ten minutes and 90% were within fifteen minutes. The distribution of over- and under-estimates was symmetrical, but estimated times tended to be longer than reported times with increasing distance from hospital. Almost all respondents rounded their travel time to the nearest five or ten minutes. The reason for many cases of reported journey times exceeding the estimated times was confirmed by respondents' comments as traffic congestion. GIS estimates of car travel times were moderately close approximations to reported times. GIS travel time estimates may be superior to reported travel times for modelling purposes because reported times contain errors and can reflect unusual circumstances. Comparison with reported times did not suggest that estimated times were a more sensitive measure than straight line distance.

Real-time PCR in virology.

PubMed

Mackay, Ian M; Arden, Katherine E; Nitsche, Andreas

2002-03-15

The use of the polymerase chain reaction (PCR) in molecular diagnostics has increased to the point where it is now accepted as the gold standard for detecting nucleic acids from a number of origins and it has become an essential tool in the research laboratory. Real-time PCR has engendered wider acceptance of the PCR due to its improved rapidity, sensitivity, reproducibility and the reduced risk of carry-over contamination. There are currently five main chemistries used for the detection of PCR product during real-time PCR. These are the DNA binding fluorophores, the 5' endonuclease, adjacent linear and hairpin oligoprobes and the self-fluorescing amplicons, which are described in detail. We also discuss factors that have restricted the development of multiplex real-time PCR as well as the role of real-time PCR in quantitating nucleic acids. Both amplification hardware and the fluorogenic detection chemistries have evolved rapidly as the understanding of real-time PCR has developed and this review aims to update the scientist on the current state of the art. We describe the background, advantages and limitations of real-time PCR and we review the literature as it applies to virus detection in the routine and research laboratory in order to focus on one of the many areas in which the application of real-time PCR has provided significant methodological benefits and improved patient outcomes. However, the technology discussed has been applied to other areas of microbiology as well as studies of gene expression and genetic disease.

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

PubMed Central

Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C.; Rinaldo, Andrea

2018-01-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

PubMed

Pasetto, Damiano; Finger, Flavio; Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C; Azman, Andrew S; Luquero, Francisco J; Bertuzzo, Enrico; Rinaldo, Andrea

2018-05-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi

Regression analysis and real-time water-quality monitoring to estimate constituent concentrations, loads, and yields in the Little Arkansas River, south-central Kansas, 1995-99

USGS Publications Warehouse

Christensen, Victoria G.; Jian, Xiaodong; Ziegler, Andrew C.

2000-01-01

Water from the Little Arkansas River is used as source water for artificial recharge to the Equus Beds aquifer, which provides water for the city of Wichita in south-central Kansas. To assess the quality of the source water, continuous in-stream water-quality monitors were installed at two U.S. Geological Survey stream-gaging stations to provide real-time measurement of specific conductance, pH, water temperature, dissolved oxygen, and turbidity in the Little Arkansas River. In addition, periodic water samples were collected manually and analyzed for selected constituents, including alkalinity, dissolved solids, total suspended solids, chloride, sulfate, atrazine, and fecal coliform bacteria. However, these periodic samples do not provide real-time data on which to base aquifer-recharge operational decisions to prevent degradation of the Equus Beds aquifer. Continuous and periodic monitoring enabled identification of seasonal trends in selected physical properties and chemical constituents and estimation of chemical mass transported in the Little Arkansas River. Identification of seasonal trends was especially important because high streamflows have a substantial effect on chemical loads and because concentration data from manually collected samples often were not available. Therefore, real-time water-quality monitoring of surrogates for the estimation of selected chemical constituents in streamflow can increase the accuracy of load and yield estimates and can decrease some manual data-collection activities. Regression equations, which were based on physical properties and analysis of water samples collected from 1995 through 1998 throughout 95 percent of the stream's flow duration, were developed to estimate alkalinity, dissolved solids, total suspended solids, chloride, sulfate, atrazine, and fecal coliform bacteria concentrations. Error was evaluated for the first year of data collection and each subsequent year, and a decrease in error was observed as the

Real-time automatic registration in optical surgical navigation

NASA Astrophysics Data System (ADS)

Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

2016-05-01

An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

MARTe: A Multiplatform Real-Time Framework

NASA Astrophysics Data System (ADS)

Neto, André C.; Sartori, Filippo; Piccolo, Fabio; Vitelli, Riccardo; De Tommasi, Gianmaria; Zabeo, Luca; Barbalace, Antonio; Fernandes, Horacio; Valcarcel, Daniel F.; Batista, Antonio J. N.

2010-04-01

Development of real-time applications is usually associated with nonportable code targeted at specific real-time operating systems. The boundary between hardware drivers, system services, and user code is commonly not well defined, making the development in the target host significantly difficult. The Multithreaded Application Real-Time executor (MARTe) is a framework built over a multiplatform library that allows the execution of the same code in different operating systems. The framework provides the high-level interfaces with hardware, external configuration programs, and user interfaces, assuring at the same time hard real-time performances. End-users of the framework are required to define and implement algorithms inside a well-defined block of software, named Generic Application Module (GAM), that is executed by the real-time scheduler. Each GAM is reconfigurable with a set of predefined configuration meta-parameters and interchanges information using a set of data pipes that are provided as inputs and required as output. Using these connections, different GAMs can be chained either in series or parallel. GAMs can be developed and debugged in a non-real-time system and, only once the robustness of the code and correctness of the algorithm are verified, deployed to the real-time system. The software also supplies a large set of utilities that greatly ease the interaction and debugging of a running system. Among the most useful are a highly efficient real-time logger, HTTP introspection of real-time objects, and HTTP remote configuration. MARTe is currently being used to successfully drive the plasma vertical stabilization controller on the largest magnetic confinement fusion device in the world, with a control loop cycle of 50 ?s and a jitter under 1 ?s. In this particular project, MARTe is used with the Real-Time Application Interface (RTAI)/Linux operating system exploiting the new ?86 multicore processors technology.

Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data.

PubMed

Fourment, Mathieu; Holmes, Edward C

2014-07-24

Early methods for estimating divergence times from gene sequence data relied on the assumption of a molecular clock. More sophisticated methods were created to model rate variation and used auto-correlation of rates, local clocks, or the so called "uncorrelated relaxed clock" where substitution rates are assumed to be drawn from a parametric distribution. In the case of Bayesian inference methods the impact of the prior on branching times is not clearly understood, and if the amount of data is limited the posterior could be strongly influenced by the prior. We develop a maximum likelihood method--Physher--that uses local or discrete clocks to estimate evolutionary rates and divergence times from heterochronous sequence data. Using two empirical data sets we show that our discrete clock estimates are similar to those obtained by other methods, and that Physher outperformed some methods in the estimation of the root age of an influenza virus data set. A simulation analysis suggests that Physher can outperform a Bayesian method when the real topology contains two long branches below the root node, even when evolution is strongly clock-like. These results suggest it is advisable to use a variety of methods to estimate evolutionary rates and divergence times from heterochronous sequence data. Physher and the associated data sets used here are available online at http://code.google.com/p/physher/.

Diffusive real-time dynamics of a particle with Berry curvature

NASA Astrophysics Data System (ADS)

Misaki, Kou; Miyashita, Seiji; Nagaosa, Naoto

2018-02-01

We study theoretically the influence of Berry phase on the real-time dynamics of the single particle focusing on the diffusive dynamics, i.e., the time dependence of the distribution function. Our model can be applied to the real-time dynamics of intraband relaxation and diffusion of optically excited excitons, trions, or particle-hole pair. We found that the dynamics at the early stage is deeply influenced by the Berry curvature in real space (B ), momentum space (Ω ), and also the crossed space between these two (C ). For example, it is found that Ω induces the rotation of the wave packet and causes the time dependence of the mean square displacement of the particle to be linear in time t at the initial stage; it is qualitatively different from the t3 dependence in the absence of the Berry curvature. It is also found that Ω and C modify the characteristic time scale of the thermal equilibration of momentum distribution. Moreover, the dynamics under various combinations of B ,Ω , and C shows singular behaviors such as the critical slowing down or speeding up of the momentum equilibration and the reversals of the direction of rotations. The relevance of our model for time-resolved experiments in transition metal dichalcogenides is also discussed.

Real-time high-velocity resolution color Doppler OCT

NASA Astrophysics Data System (ADS)

Westphal, Volker; Yazdanfar, Siavash; Rollins, Andrew M.; Izatt, Joseph A.

2001-05-01

Color Doppler optical coherence tomography (CDOCT), also called Optical Doppler Tomography) is a noninvasive optical imaging technique, which allows for micron-scale physiological flow mapping simultaneous with morphological OCT imaging. Current systems for real-time endoscopic optical coherence tomography (EOCT) would be enhanced by the capability to visualize sub-surface blood flow for applications in early cancer diagnosis and the management of bleeding ulcers. Unfortunately, previous implementations of CDOCT have either been sufficiently computationally expensive (employing Fourier or Hilbert transform techniques) to rule out real-time imaging of flow, or have been restricted to imaging of excessively high flow velocities when used in real time. We have developed a novel Doppler OCT signal-processing strategy capable of imaging physiological flow rates in real time. This strategy employs cross-correlation processing of sequential A-scans in an EOCT image, as opposed to autocorrelation processing as described previously. To measure Doppler shifts in the kHz range using this technique, it was necessary to stabilize the EOCT interferometer center frequency, eliminate parasitic phase noise, and to construct a digital cross correlation unit able to correlate signals of megahertz bandwidth by a fixed lag of up to a few ms. The performance of the color Doppler OCT system was demonstrated in a flow phantom, demonstrating a minimum detectable flow velocity of ~0.8 mm/s at a data acquisition rate of 8 images/second (with 480 A-scans/image) using a handheld probe. Dynamic flow as well as using it freehanded was shown. Flow was also detectable in a phantom in combination with a clinical usable endoscopic probe.

Real-Time Prognostics of a Rotary Valve Actuator

NASA Technical Reports Server (NTRS)

Daigle, Matthew

2015-01-01

Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

NASA Astrophysics Data System (ADS)

Marchetti, E.; Ripepe, M.; Ulivieri, G.; Kogelnig, A.

2015-11-01

Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool to identify clear signals related to avalanches. We present here a method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria), which provides a significant improvement to overcome this limit. The method is based on array-derived wave parameters, such as back azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification by considering avalanches as a moving source of infrasound. We validate the efficiency of the automatic infrasound detection with continuous observations with Doppler radar and we show how the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that is able to provide the number and the time of occurrence of snow avalanches occurring all around the array, which represent key information for a proper validation of avalanche forecast models and risk management in a given area.

Development of a real-time system for ITER first wall heat load control

NASA Astrophysics Data System (ADS)

Anand, Himank; de Vries, Peter; Gribov, Yuri; Pitts, Richard; Snipes, Joseph; Zabeo, Luca

2017-10-01

The steady state heat flux on the ITER first wall (FW) panels are limited by the heat removal capacity of the water cooling system. In case of off-normal events (e.g. plasma displacement during H-L transitions), the heat loads are predicted to exceed the design limits (2-4.7 MW/m2). Intense heat loads are predicted on the FW, even well before the burning plasma phase. Thus, a real-time (RT) FW heat load control system is mandatory from early plasma operation of the ITER tokamak. A heat load estimator based on the RT equilibrium reconstruction has been developed for the plasma control system (PCS). A scheme, estimating the energy state for prescribed gaps defined as the distance between the last closed flux surface (LCFS)/separatrix and the FW is presented. The RT energy state is determined by the product of a weighted function of gap distance and the power crossing the plasma boundary. In addition, a heat load estimator assuming a simplified FW geometry and parallel heat transport model in the scrape-off layer (SOL), benchmarked against a full 3-D magnetic field line tracer is also presented.

Shelf Life of Food Products: From Open Labeling to Real-Time Measurements.

PubMed

Corradini, Maria G

2018-03-25

The labels currently used on food and beverage products only provide consumers with a rough guide to their expected shelf lives because they assume that a product only experiences a limited range of predefined handling and storage conditions. These static labels do not take into consideration conditions that might shorten a product's shelf life (such as temperature abuse), which can lead to problems associated with food safety and waste. Advances in shelf-life estimation have the potential to improve the safety, reliability, and sustainability of the food supply. Selection of appropriate kinetic models and data-analysis techniques is essential to predict shelf life, to account for variability in environmental conditions, and to allow real-time monitoring. Novel analytical tools to determine safety and quality attributes in situ coupled with modern tracking technologies and appropriate predictive tools have the potential to provide accurate estimations of the remaining shelf life of a food product in real time. This review summarizes the necessary steps to attain a transition from open labeling to real-time shelf-life measurements.

Real-time processing of radar return on a parallel computer

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1992-01-01

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Matthies, Larry

2004-01-01

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include real-time, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identity other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

Scheduling Dependent Real-Time Activities

DTIC Science & Technology

1990-08-01

dependency relationships in a way that is suitable for all real - time systems . This thesis provides an algorithm, called DASA, that is effective for...scheduling the class of real - time systems known as supervisory control systems. Simulation experiments that account for the time required to make scheduling

Real-Time Nonlinear Optical Information Processing.

DTIC Science & Technology

1979-06-01

operations aree presented. One approach realizes the halftone method of nonlinear optical processing in real time by replacing the conventional...photographic recording medium with a real-time image transducer. In the second approach halftoning is eliminated and the real-time device is used directly

Three-dimensional liver motion tracking using real-time two-dimensional MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brix, Lau, E-mail: lau.brix@stab.rm.dk; Ringgaard, Steffen; Sørensen, Thomas Sangild

2014-04-15

Purpose: Combined magnetic resonance imaging (MRI) systems and linear accelerators for radiotherapy (MR-Linacs) are currently under development. MRI is noninvasive and nonionizing and can produce images with high soft tissue contrast. However, new tracking methods are required to obtain fast real-time spatial target localization. This study develops and evaluates a method for tracking three-dimensional (3D) respiratory liver motion in two-dimensional (2D) real-time MRI image series with high temporal and spatial resolution. Methods: The proposed method for 3D tracking in 2D real-time MRI series has three steps: (1) Recording of a 3D MRI scan and selection of a blood vessel (ormore » tumor) structure to be tracked in subsequent 2D MRI series. (2) Generation of a library of 2D image templates oriented parallel to the 2D MRI image series by reslicing and resampling the 3D MRI scan. (3) 3D tracking of the selected structure in each real-time 2D image by finding the template and template position that yield the highest normalized cross correlation coefficient with the image. Since the tracked structure has a known 3D position relative to each template, the selection and 2D localization of a specific template translates into quantification of both the through-plane and in-plane position of the structure. As a proof of principle, 3D tracking of liver blood vessel structures was performed in five healthy volunteers in two 5.4 Hz axial, sagittal, and coronal real-time 2D MRI series of 30 s duration. In each 2D MRI series, the 3D localization was carried out twice, using nonoverlapping template libraries, which resulted in a total of 12 estimated 3D trajectories per volunteer. Validation tests carried out to support the tracking algorithm included quantification of the breathing induced 3D liver motion and liver motion directionality for the volunteers, and comparison of 2D MRI estimated positions of a structure in a watermelon with the actual positions. Results: Axial

Real-time sonography to estimate muscle thickness: comparison with MRI and CT.

PubMed

Dupont, A C; Sauerbrei, E E; Fenton, P V; Shragge, P C; Loeb, G E; Richmond, F J

2001-05-01

We investigated the feasibility of using real-time sonography to measure muscle thickness. Clinically, this technique would be used to measure the thickness of human muscles in which intramuscular microstimulators have been implanted to treat or prevent disuse atrophy. Porcine muscles were implanted with microstimulators and imaged with sonography, MRI, and CT to assess image artifacts created by the microstimulators and to design protocols for image alignment between methods. Sonography and MRI were then used to image the deltoid and supraspinatus muscles of 6 healthy human subjects. Microstimulators could be imaged with all 3 methods, producing only small imaging artifacts. Muscle-thickness measurements agreed well between methods, particularly when external markers were used to precisely align the imaging planes. The correlation coefficients for sonographic and MRI measurements were 0.96 for the supraspinatus and 0.97 for the deltoid muscle. Repeated sonographic measurements had a low coefficient of variation: 2.3% for the supraspinatus and 3.1% for the deltoid muscle. Real-time sonography is a relatively simple and inexpensive method of accurately measuring muscle thickness as long as the operator adheres to a strict imaging protocol and avoids excessive pressure with the transducer. Copyright 2001 John Wiley & Sons, Inc.

Rapid classification of hippocampal replay content for real-time applications

PubMed Central

Liu, Daniel F.; Karlsson, Mattias P.; Frank, Loren M.; Eden, Uri T.

2016-01-01

Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments. PMID:27535369

High-resolution near real-time drought monitoring in South Asia

NASA Astrophysics Data System (ADS)

Aadhar, Saran; Mishra, Vimal

2017-10-01

Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning, and management of water resources at sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. We develop a high-resolution (0.05°) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat and cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature, which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05°. The bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub-basin levels.

High-Resolution Near Real-Time Drought Monitoring in South Asia

NASA Astrophysics Data System (ADS)

Aadhar, S.; Mishra, V.

2017-12-01

Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning and management of water resources at the sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. Here we develop a high resolution (0.05 degree) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat waves, cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature (maximum and minimum), which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05˚. We find that the bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub- basin levels.

Real-time sensor validation and fusion for distributed autonomous sensors

NASA Astrophysics Data System (ADS)

Yuan, Xiaojing; Li, Xiangshang; Buckles, Bill P.

2004-04-01

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Real-time depth camera tracking with geometrically stable weight algorithm

NASA Astrophysics Data System (ADS)

Fu, Xingyin; Zhu, Feng; Qi, Feng; Wang, Mingming

2017-03-01

We present an approach for real-time camera tracking with depth stream. Existing methods are prone to drift in sceneries without sufficient geometric information. First, we propose a new weight method for an iterative closest point algorithm commonly used in real-time dense mapping and tracking systems. By detecting uncertainty in pose and increasing weight of points that constrain unstable transformations, our system achieves accurate and robust trajectory estimation results. Our pipeline can be fully parallelized with GPU and incorporated into the current real-time depth camera tracking system seamlessly. Second, we compare the state-of-the-art weight algorithms and propose a weight degradation algorithm according to the measurement characteristics of a consumer depth camera. Third, we use Nvidia Kepler Shuffle instructions during warp and block reduction to improve the efficiency of our system. Results on the public TUM RGB-D database benchmark demonstrate that our camera tracking system achieves state-of-the-art results both in accuracy and efficiency.

Real-Time and Near Real-Time Data for Space Weather Applications and Services

NASA Astrophysics Data System (ADS)

Singer, H. J.; Balch, C. C.; Biesecker, D. A.; Matsuo, T.; Onsager, T. G.

2015-12-01

Space weather can be defined as conditions in the vicinity of Earth and in the interplanetary environment that are caused primarily by solar processes and influenced by conditions on Earth and its atmosphere. Examples of space weather are the conditions that result from geomagnetic storms, solar particle events, and bursts of intense solar flare radiation. These conditions can have impacts on modern-day technologies such as GPS or electric power grids and on human activities such as astronauts living on the International Space Station or explorers traveling to the moon or Mars. While the ultimate space weather goal is accurate prediction of future space weather conditions, for many applications and services, we rely on real-time and near-real time observations and model results for the specification of current conditions. In this presentation, we will describe the space weather system and the need for real-time and near-real time data that drive the system, characterize conditions in the space environment, and are used by models for assimilation and validation. Currently available data will be assessed and a vision for future needs will be given. The challenges for establishing real-time data requirements, as well as acquiring, processing, and disseminating the data will be described, including national and international collaborations. In addition to describing how the data are used for official government products, we will also give examples of how these data are used by both the public and private sector for new applications that serve the public.

Software Design Methods for Real-Time Systems

DTIC Science & Technology

1989-12-01

This module describes the concepts and methods used in the software design of real time systems . It outlines the characteristics of real time systems , describes...the role of software design in real time system development, surveys and compares some software design methods for real - time systems , and

Real Time Tracking of Magmatic Intrusions by means of Ground Deformation Modeling during Volcanic Crises.

PubMed

Cannavò, Flavio; Camacho, Antonio G; González, Pablo J; Mattia, Mario; Puglisi, Giuseppe; Fernández, José

2015-06-09

Volcano observatories provide near real-time information and, ultimately, forecasts about volcano activity. For this reason, multiple physical and chemical parameters are continuously monitored. Here, we present a new method to efficiently estimate the location and evolution of magmatic sources based on a stream of real-time surface deformation data, such as High-Rate GPS, and a free-geometry magmatic source model. The tool allows tracking inflation and deflation sources in time, providing estimates of where a volcano might erupt, which is important in understanding an on-going crisis. We show a successful simulated application to the pre-eruptive period of May 2008, at Mount Etna (Italy). The proposed methodology is able to track the fast dynamics of the magma migration by inverting the real-time data within seconds. This general method is suitable for integration in any volcano observatory. The method provides first order unsupervised and realistic estimates of the locations of magmatic sources and of potential eruption sites, information that is especially important for civil protection purposes.

Real Time Tracking of Magmatic Intrusions by means of Ground Deformation Modeling during Volcanic Crises

PubMed Central

Cannavò, Flavio; Camacho, Antonio G.; González, Pablo J.; Mattia, Mario; Puglisi, Giuseppe; Fernández, José

2015-01-01

Volcano observatories provide near real-time information and, ultimately, forecasts about volcano activity. For this reason, multiple physical and chemical parameters are continuously monitored. Here, we present a new method to efficiently estimate the location and evolution of magmatic sources based on a stream of real-time surface deformation data, such as High-Rate GPS, and a free-geometry magmatic source model. The tool allows tracking inflation and deflation sources in time, providing estimates of where a volcano might erupt, which is important in understanding an on-going crisis. We show a successful simulated application to the pre-eruptive period of May 2008, at Mount Etna (Italy). The proposed methodology is able to track the fast dynamics of the magma migration by inverting the real-time data within seconds. This general method is suitable for integration in any volcano observatory. The method provides first order unsupervised and realistic estimates of the locations of magmatic sources and of potential eruption sites, information that is especially important for civil protection purposes. PMID:26055494

Research of real-time communication software

NASA Astrophysics Data System (ADS)

Li, Maotang; Guo, Jingbo; Liu, Yuzhong; Li, Jiahong

2003-11-01

Real-time communication has been playing an increasingly important role in our work, life and ocean monitor. With the rapid progress of computer and communication technique as well as the miniaturization of communication system, it is needed to develop the adaptable and reliable real-time communication software in the ocean monitor system. This paper involves the real-time communication software research based on the point-to-point satellite intercommunication system. The object-oriented design method is adopted, which can transmit and receive video data and audio data as well as engineering data by satellite channel. In the real-time communication software, some software modules are developed, which can realize the point-to-point satellite intercommunication in the ocean monitor system. There are three advantages for the real-time communication software. One is that the real-time communication software increases the reliability of the point-to-point satellite intercommunication system working. Second is that some optional parameters are intercalated, which greatly increases the flexibility of the system working. Third is that some hardware is substituted by the real-time communication software, which not only decrease the expense of the system and promotes the miniaturization of communication system, but also aggrandizes the agility of the system.

Computer considerations for real time simulation of a generalized rotor model

NASA Technical Reports Server (NTRS)

Howe, R. M.; Fogarty, L. E.

1977-01-01

Scaled equations were developed to meet requirements for real time computer simulation of the rotor system research aircraft. These equations form the basis for consideration of both digital and hybrid mechanization for real time simulation. For all digital simulation estimates of the required speed in terms of equivalent operations per second are developed based on the complexity of the equations and the required intergration frame rates. For both conventional hybrid simulation and hybrid simulation using time-shared analog elements the amount of required equipment is estimated along with a consideration of the dynamic errors. Conventional hybrid mechanization using analog simulation of those rotor equations which involve rotor-spin frequencies (this consititutes the bulk of the equations) requires too much analog equipment. Hybrid simulation using time-sharing techniques for the analog elements appears possible with a reasonable amount of analog equipment. All-digital simulation with affordable general-purpose computers is not possible because of speed limitations, but specially configured digital computers do have the required speed and consitute the recommended approach.

Smooth time-dependent receiver operating characteristic curve estimators.

PubMed

Martínez-Camblor, Pablo; Pardo-Fernández, Juan Carlos

2018-03-01

The receiver operating characteristic curve is a popular graphical method often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic receiver operating characteristic curve and the incident/dynamic receiver operating characteristic curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. In this article, the authors explore the use of a bivariate kernel density estimator which accounts for censored observations in the sample and produces smooth estimators of the time-dependent receiver operating characteristic curves. The performance of the resulting cumulative/dynamic and incident/dynamic receiver operating characteristic curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. Finally, two real-applications are considered. An R package is also provided as a complement to this article.

Real-time amyloid aggregation monitoring with a photonic crystal-based approach.

PubMed

Santi, Sara; Musi, Valeria; Descrovi, Emiliano; Paeder, Vincent; Di Francesco, Joab; Hvozdara, Lubos; van der Wal, Peter; Lashuel, Hilal A; Pastore, Annalisa; Neier, Reinhard; Herzig, Hans Peter

2013-10-21

We propose the application of a new label-free optical technique based on photonic nanostructures to real-time monitor the amyloid-beta 1-42 (Aβ(1-42)) fibrillization, including the early stages of the aggregation process, which are related to the onset of the Alzheimer's Disease (AD). The aggregation of Aβ peptides into amyloid fibrils has commonly been associated with neuronal death, which culminates in the clinical features of the incurable degenerative AD. Recent studies revealed that cell toxicity is determined by the formation of soluble oligomeric forms of Aβ peptides in the early stages of aggregation. At this phase, classical amyloid detection techniques lack in sensitivity. Upon a chemical passivation of the sensing surface by means of polyethylene glycol, the proposed approach allows an accurate, real-time monitoring of the refractive index variation of the solution, wherein Aβ(1-42) peptides are aggregating. This measurement is directly related to the aggregation state of the peptide throughout oligomerization and subsequent fibrillization. Our findings open new perspectives in the understanding of the dynamics of amyloid formation, and validate this approach as a new and powerful method to screen aggregation at early stages. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identifying financial crises in real time

NASA Astrophysics Data System (ADS)

da Fonseca, Eder Lucio; Ferreira, Fernando F.; Muruganandam, Paulsamy; Cerdeira, Hilda A.

2013-03-01

Following the thermodynamic formulation of a multifractal measure that was shown to enable the detection of large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crises in real time. We calculate the partition function from which we can obtain thermodynamic quantities analogous to the free energy and specific heat. The index is defined as the normalized energy variation and it can be used to study the behavior of stochastic time series, such as financial market daily data. Famous financial market crashes-Black Thursday (1929), Black Monday (1987) and the subprime crisis (2008)-are identified with clear and robust results. The method is also applied to the market fluctuations of 2011. From these results it appears as if the apparent crisis of 2011 is of a different nature to the other three. We also show that the analysis has forecasting capabilities.

Real-time forecasts of dengue epidemics

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Shaman, J. L.

2015-12-01

Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.

Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI)

PubMed Central

George, Ansel; Dorval, Alan D.; Christini, David J.

2017-01-01

The ability to experimentally perturb biological systems has traditionally been limited to static pre-programmed or operator-controlled protocols. In contrast, real-time control allows dynamic probing of biological systems with perturbations that are computed on-the-fly during experimentation. Real-time control applications for biological research are available; however, these systems are costly and often restrict the flexibility and customization of experimental protocols. The Real-Time eXperiment Interface (RTXI) is an open source software platform for achieving hard real-time data acquisition and closed-loop control in biological experiments while retaining the flexibility needed for experimental settings. RTXI has enabled users to implement complex custom closed-loop protocols in single cell, cell network, animal, and human electrophysiology studies. RTXI is also used as a free and open source, customizable electrophysiology platform in open-loop studies requiring online data acquisition, processing, and visualization. RTXI is easy to install, can be used with an extensive range of external experimentation and data acquisition hardware, and includes standard modules for implementing common electrophysiology protocols. PMID:28557998

Active contour configuration model for estimating the posterior ablative margin in image fusion of real-time ultrasound and 3D ultrasound or magnetic resonance images for radiofrequency ablation: an experimental study.

PubMed

Lee, Junkyo; Lee, Min Woo; Choi, Dongil; Cha, Dong Ik; Lee, Sunyoung; Kang, Tae Wook; Yang, Jehoon; Jo, Jaemoon; Bang, Won-Chul; Kim, Jongsik; Shin, Dongkuk

2017-12-21

The purpose of this study was to evaluate the accuracy of an active contour model for estimating the posterior ablative margin in images obtained by the fusion of real-time ultrasonography (US) and 3-dimensional (3D) US or magnetic resonance (MR) images of an experimental tumor model for radiofrequency ablation. Chickpeas (n=12) and bovine rump meat (n=12) were used as an experimental tumor model. Grayscale 3D US and T1-weighted MR images were pre-acquired for use as reference datasets. US and MR/3D US fusion was performed for one group (n=4), and US and 3D US fusion only (n=8) was performed for the other group. Half of the models in each group were completely ablated, while the other half were incompletely ablated. Hyperechoic ablation areas were extracted using an active contour model from real-time US images, and the posterior margin of the ablation zone was estimated from the anterior margin. After the experiments, the ablated pieces of bovine rump meat were cut along the electrode path and the cut planes were photographed. The US images with the estimated posterior margin were compared with the photographs and post-ablation MR images. The extracted contours of the ablation zones from 12 US fusion videos and post-ablation MR images were also matched. In the four models fused under real-time US with MR/3D US, compression from the transducer and the insertion of an electrode resulted in misregistration between the real-time US and MR images, making the estimation of the ablation zones less accurate than was achieved through fusion between real-time US and 3D US. Eight of the 12 post-ablation 3D US images were graded as good when compared with the sectioned specimens, and 10 of the 12 were graded as good in a comparison with nicotinamide adenine dinucleotide staining and histopathologic results. Estimating the posterior ablative margin using an active contour model is a feasible way of predicting the ablation area, and US/3D US fusion was more accurate than US

Real-Time Monitoring and Prediction of the Pilot Vehicle System (PVS) Closed-Loop Stability

NASA Astrophysics Data System (ADS)

Mandal, Tanmay Kumar

Understanding human control behavior is an important step for improving the safety of future aircraft. Considerable resources are invested during the design phase of an aircraft to ensure that the aircraft has desirable handling qualities. However, human pilots exhibit a wide range of control behaviors that are a function of external stimulus, aircraft dynamics, and human psychological properties (such as workload, stress factor, confidence, and sense of urgency factor). This variability is difficult to address comprehensively during the design phase and may lead to undesirable pilot-aircraft interaction, such as pilot-induced oscillations (PIO). This creates the need to keep track of human pilot performance in real-time to monitor the pilot vehicle system (PVS) stability. This work focused on studying human pilot behavior for the longitudinal axis of a remotely controlled research aircraft and using human-in-the-loop (HuIL) simulations to obtain information about the human controlled system (HCS) stability. The work in this dissertation is divided into two main parts: PIO analysis and human control model parameters estimation. To replicate different flight conditions, this study included time delay and elevator rate limiting phenomena, typical of actuator dynamics during the experiments. To study human control behavior, this study employed the McRuer model for single-input single-output manual compensatory tasks. McRuer model is a lead-lag controller with time delay which has been shown to adequately model manual compensatory tasks. This dissertation presents a novel technique to estimate McRuer model parameters in real-time and associated validation using HuIL simulations to correctly predict HCS stability. The McRuer model parameters were estimated in real-time using a Kalman filter approach. The estimated parameters were then used to analyze the stability of the closed-loop HCS and verify them against the experimental data. Therefore, the main contribution of

ControlShell - A real-time software framework

NASA Technical Reports Server (NTRS)

Schneider, Stanley A.; Ullman, Marc A.; Chen, Vincent W.

1991-01-01

ControlShell is designed to enable modular design and impplementation of real-time software. It is an object-oriented tool-set for real-time software system programming. It provides a series of execution and data interchange mechansims that form a framework for building real-time applications. These mechanisms allow a component-based approach to real-time software generation and mangement. By defining a set of interface specifications for intermodule interaction, ControlShell provides a common platform that is the basis for real-time code development and exchange.

Real-time tsunami inundation forecasting and damage mapping towards enhancing tsunami disaster resiliency

NASA Astrophysics Data System (ADS)

Koshimura, S.; Hino, R.; Ohta, Y.; Kobayashi, H.; Musa, A.; Murashima, Y.

2014-12-01

With use of modern computing power and advanced sensor networks, a project is underway to establish a new system of real-time tsunami inundation forecasting, damage estimation and mapping to enhance society's resilience in the aftermath of major tsunami disaster. The system consists of fusion of real-time crustal deformation monitoring/fault model estimation by Ohta et al. (2012), high-performance real-time tsunami propagation/inundation modeling with NEC's vector supercomputer SX-ACE, damage/loss estimation models (Koshimura et al., 2013), and geo-informatics. After a major (near field) earthquake is triggered, the first response of the system is to identify the tsunami source model by applying RAPiD Algorithm (Ohta et al., 2012) to observed RTK-GPS time series at GEONET sites in Japan. As performed in the data obtained during the 2011 Tohoku event, we assume less than 10 minutes as the acquisition time of the source model. Given the tsunami source, the system moves on to running tsunami propagation and inundation model which was optimized on the vector supercomputer SX-ACE to acquire the estimation of time series of tsunami at offshore/coastal tide gauges to determine tsunami travel and arrival time, extent of inundation zone, maximum flow depth distribution. The implemented tsunami numerical model is based on the non-linear shallow-water equations discretized by finite difference method. The merged bathymetry and topography grids are prepared with 10 m resolution to better estimate the tsunami inland penetration. Given the maximum flow depth distribution, the system performs GIS analysis to determine the numbers of exposed population and structures using census data, then estimates the numbers of potential death and damaged structures by applying tsunami fragility curve (Koshimura et al., 2013). Since the tsunami source model is determined, the model is supposed to complete the estimation within 10 minutes. The results are disseminated as mapping products to

Virtual sensor models for real-time applications

NASA Astrophysics Data System (ADS)

Hirsenkorn, Nils; Hanke, Timo; Rauch, Andreas; Dehlink, Bernhard; Rasshofer, Ralph; Biebl, Erwin

2016-09-01

Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

OPAD-EDIFIS Real-Time Processing

NASA Technical Reports Server (NTRS)

Katsinis, Constantine

1997-01-01

The Optical Plume Anomaly Detection (OPAD) detects engine hardware degradation of flight vehicles through identification and quantification of elemental species found in the plume by analyzing the plume emission spectra in a real-time mode. Real-time performance of OPAD relies on extensive software which must report metal amounts in the plume faster than once every 0.5 sec. OPAD software previously written by NASA scientists performed most necessary functions at speeds which were far below what is needed for real-time operation. The research presented in this report improved the execution speed of the software by optimizing the code without changing the algorithms and converting it into a parallelized form which is executed in a shared-memory multiprocessor system. The resulting code was subjected to extensive timing analysis. The report also provides suggestions for further performance improvement by (1) identifying areas of algorithm optimization, (2) recommending commercially available multiprocessor architectures and operating systems to support real-time execution and (3) presenting an initial study of fault-tolerance requirements.