Relevant bibliographies by topics / Near real-time estimation

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Near real-time estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Near real-time estimation"

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Kim, Hyeoneui, Marcelline R. Harris, Guergana K. Savova, Stuart M. Speedie, and Christopher G. Chute. "Toward Near Real-Time Acuity Estimation." Nursing Research 56, no. 4 (July 2007): 288–94. http://dx.doi.org/10.1097/01.nnr.0000280617.21189.c3.

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Gallo, A., G. Costa, and P. Suhadolc. "Near real-time automatic moment magnitude estimation." Bulletin of Earthquake Engineering 12, no. 1 (January 24, 2014): 185–202. http://dx.doi.org/10.1007/s10518-013-9565-x.

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Porter, Keith, Judith Mitrani-Reiser, and James L. Beck. "Near-real-time loss estimation for instrumented buildings." Structural Design of Tall and Special Buildings 15, no. 1 (March 2006): 3–20. http://dx.doi.org/10.1002/tal.340.

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Ohta, Yusaku, Takuya Inoue, Shunichi Koshimura, Satoshi Kawamoto, and Ryota Hino. "Role of Real-Time GNSS in Near-Field Tsunami Forecasting." Journal of Disaster Research 13, no. 3 (June 1, 2018): 453–59. http://dx.doi.org/10.20965/jdr.2018.p0453.

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This short paper reviews the role of real-time global navigation satellite system (GNSS) in near-field tsunami forecasting. Recent efforts highlight that coseismic fault model estimation based on real-time GNSS has contributed substantially to our understanding of large magnitude earthquakes and their fault expansions. We briefly introduce the history of use of real-time GNSS processing in the rapid estimation of the coseismic finite fault model. Additionally, we discuss our recent trials on the estimation of quasi real-time tsunami inundation based on real-time GNSS data. Obtained results clearly suggest the effectiveness of real-time GNSS for tsunami inundation estimation as the GNSS can capture fault expansion and its slip amount in a relatively accurate manner within a short time period. We also discuss the future prospects of using real-time GNSS data for tsunami warning including effective combination of different methods for more reliable forecasting.
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Mitrescu, Cristian, Steven Miller, Jeffrey Hawkins, Tristan L’Ecuyer, Joseph Turk, Philip Partain, and Graeme Stephens. "Near-Real-Time Applications of CloudSat Data." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1982–94. http://dx.doi.org/10.1175/2007jamc1794.1.

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Abstract Within 2 months of its launch in April 2006 as part of the Earth Observing System A-Train satellite constellation, the National Aeronautics and Space Administration Earth System Science Pathfinder (ESSP) CloudSat mission began making significant contributions toward broadening the understanding of detailed cloud vertical structures around the earth. Realizing the potential benefit of CloudSat to both the research objectives and operational requirements of the U.S. Navy, the Naval Research Laboratory coordinated early on with the CloudSat Data Processing Center to receive and process first-look 94-GHz Cloud Profiling Radar datasets in near–real time (4–8 h latency), thereby making the observations more relevant to the operational community. Applications leveraging these unique data, described herein, include 1) analysis/validation of cloud structure and properties derived from conventional passive radiometers, 2) tropical cyclone vertical structure analysis, 3) support of research field programs, 4) validation of numerical weather prediction model cloud fields, and 5) quantitative precipitation estimation in light rainfall regimes.
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Douša, J. "Towards an operational near real-time precipitable water vapor estimation." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 3 (January 2001): 189–94. http://dx.doi.org/10.1016/s1464-1895(01)00045-x.

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Abate, Andrea F., Paola Barra, Carmen Bisogni, Michele Nappi, and Stefano Ricciardi. "Near Real-Time Three Axis Head Pose Estimation Without Training." IEEE Access 7 (2019): 64256–65. http://dx.doi.org/10.1109/access.2019.2917451.

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Abdalla, Saleh, Peter A. E. M. Janssen, and Jean-Raymond Bidlot. "Altimeter Near Real Time Wind and Wave Products: Random Error Estimation." Marine Geodesy 34, no. 3-4 (July 1, 2011): 393–406. http://dx.doi.org/10.1080/01490419.2011.585113.

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Joo, Kyungdon, Tae-Hyun Oh, Junsik Kim, and In So Kweon. "Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 3 (March 1, 2019): 682–96. http://dx.doi.org/10.1109/tpami.2018.2799944.

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Sezen, U., F. Arikan, O. Arikan, O. Ugurlu, and A. Sadeghimorad. "Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC." Space Weather 11, no. 5 (May 2013): 297–305. http://dx.doi.org/10.1002/swe.20054.

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Dissertations / Theses on the topic "Near real-time estimation"

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Hadley, Jennifer Lyn. "Near real-time runoff estimation using spatially distributed radar rainfall data." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/346.

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The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.
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Gupta, Manish. "Complexity Reduction for Near Real-Time High Dimensional Filtering and Estimation Applied to Biological Signals." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493389.

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Real-time processing of physiological signals collected from wearable sensors that can be done with low computational power is a requirement for continuous health monitoring. Such processing involves identifying underlying physiological state x from a measured biomedical signal y, that are related stochastically: y = f(x; e) (here e is a random variable). Often the state space of x is large, and the dimensionality of y is low: if y has dimension N and S is the state space of x then |S| >> N, since the purpose is to infer a complex physiological state from minimal measurements. This makes real-time inference a challenging task. We present algorithms that address this problem by using lower dimensional approximations of the state. Our algorithms are based on two techniques often used for state dimensionality reduction: (a) decomposition where variables can be grouped into smaller sets, and (b) factorization where variables can be factored into smaller sets. The algorithms are computationally inexpensive, and permit online application. We demonstrate their use in dimensionality reduction by successfully solving two real complex problems in medicine and public safety. Motivated originally by the problem of predicting cognitive fatigue state from EEG (Chapter 1), we developed the Correlated Sparse Signal Recovery (CSSR) algorithm and successfully applied it to the problem of elimination of blink artifacts in EEG from awake subjects (Chapter 2). Finding the decomposition x = x1+ x2 into a low dimensional representation of the artifact signal x1 is a non-trivial problem and currently there are no online real-time methods accurately solve the problem for small N (dimensionality of y). By using a skew-Gaussian dictionary and a novel method to represent group statistical structure, CSSR is able to identify and remove blink artifacts even from few (e.g. 4-6) channels of EEG recordings in near real-time. The method uses a Bayesian framework. It results in more effective decomposition, as measured by spectral and entropy properties of the decomposed signals, compared to some state-of-the-art artifact subtraction and structured sparse recovery methods. CSSR is novel in structured sparsity: unlike existing group sparse methods (such as block sparse recovery) it does not rely on the assumption of a common sparsity profile. It is also a novel EEG denoising method: unlike state-of-the art artifact removal technique such as independent components analysis, it does not require manual intervention, long recordings or high density (e.g. 32 or more channels) recordings. Potentially this method of denoising is of tremendous utility to the medical community since EEG artifact removal is usually done manually, which is a lengthy tedious process requiring trained technicians and often making entire epochs of data unuseable. Identification of the artifact in itself can be used to determine some physiological state relevant from the artifact properties (for example, blink duration and frequency can be used as a marker of fatigue). A potential application of CSSR is to determine if structurally decomposed cortical EEG (i.e. non-spectral ) representation can instead be used for fatigue prediction. A new E-M based active learning algorithm for ensemble classification is presented in Chapter 3 and applied to the problem of detection of artifactual epochs based upon several criteria including the sparse features obtained from CSSR. The algorithm offers higher accuracy than existing ensemble methods for unsupervised learning such as similarity- and graph-based ensemble clustering, as well as higher accuracy and lower computational complexity than several active learning methods such as Query-by-Committee and Importance-Weighted Active Learning when tested on data comprising of noisy Gaussian mixtures. In one case we were to successfully identify artifacts with approximately 98% accuracy based upon 31-dimensional data from 700,000 epochs in a matter of seconds on a personal laptop using less than 10% active labels. This is to be compared to a maximum of 94% from other methods. As far as we know, the area of active learning for ensemble-based classification has not been previously applied to biomedical signal classification including artifact detection; it can also be applied to other medical areas, including classification of polysomnographic signals into sleep stages. Algorithms based upon state-space factorization in the case where there is unidirectional dependence amongst the dynamics groups of variables ( the "Cascade Markov Model") are presented in Chapters 4. An algorithm for estimation of factored state where dynamics follow a Markov model from observations is developed using E-M (i.e. a version of Baum-Welch algorithm on factored state spaces) and applied to real-time human gait and fall detection. The application of factored HMMs to gait and fall detection is novel; falls in the elderly are a major safety issue. Results from the algorithm show higher fall detection accuracy (95%) than that achieved with PCA based estimation (70%). In this chapter, a new algorithm for optimal control on factored Markov decision processes is derived. The algorithm, in the form of decoupled matrix differential equations, both is (i) computationally efficient requiring solution of a one-point instead of two-point boundary value problem and (ii) obviates the "curse of dimensionality" inherent in HJB equations thereby facilitating real-time solution. The algorithm may have application to medicine, such as finding optimal schedules of light exposure for correction of circadian misalignment and optimal schedules for drug intervention in patients. The thesis demonstrates development of new methods for complexity reduction in high dimensional systems and that their application solves some problems in medicine and public safety more efficiently than state-of-the-art methods.
Engineering and Applied Sciences - Applied Math
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Kandasamy, Sivasathivel. "Leaf Area Index (LAI) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations." Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00967319.

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Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.
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Zhao, Kaiguang. "Estimating forest structural characteristics with airborne lidar scanning and a near-real time profiling laser systems." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2964.

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Tsai, Yi-Jeng, and 蔡亦證. "Near Real-Time Estimation of Tropospheric Delay Effect Based on GPS Tracking Network in Taiwan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/97401861629192682384.

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碩士
國立宜蘭大學
土木工程學系碩士班
94
The global positioning system was developed and has extensive applied in a great deal of fields up to now. The relevant application on meteorology is called GPS meteorology. Its main purpose is to utilize the earth atmosphere delay effect of the GPS satellite signal, to get useful atmosphere information. A lot of instances have verified that it is helpful to long-term climate monitoring and short-term weather forecast to use GPS tracking network to monitor the earth atmosphere. Because of development of social economy, causes the use of the land has overbalanced and combined with the violent change of global climate. Therefore, the meteorological calamity takes place again and again in Taiwan in recent years, and the frequency and scale have the tendency to increase. So we estimation the tropospheric delay effect in near real-time, using the GPS tracking network of Taiwan. Hope to obtain the good results and put forward the useful suggestion for relevant research. Difference between the results of final and near real-time process mode was within centimeter grades. This verified near real-time process mode would provide enough accuracy, to estimate the zenith tropospheric total delay, in this research. Then join the ground meteorological observation (with good quality) and use Saastamoinen dry delay mode to get accurate zenith tropospheric wet delay. Finally, we substitutes a priori tropospheric parameter with near real-time tropospheric parameter results to applies in a rapid static task and find some hint. The near real-time tropospheric parameter results helps estimating coordinate more steadily, and can reduce the standard deviation effectively. This has shown that near real-time tropospheric parameter was helpful for the relevant research work on meteorological and climatic, also can improve GPS positioning result.
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Yang, Cheng-Yi, and 楊承益. "Estimating near real time precipitable water from GPS observations." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/swume9.

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碩士
國立中央大學
太空科學研究所
96
Water vapor in the atmosphere is an influential factor of the hydrosphere cycle, which exchanges heat through phase change and is essential to precipitation. Because of its significance in altering weather, the estimation of water vapor amount and distribution in near real time is crucial to determine the precision of the weather forecasting and the understanding of regional/local climate. There are two key points for estimating PW in near real time precisely: using ultra-rapid ephemeris provided by International GNSS Service (IGS), the other is the combination of current observations and previous observations of a certain period. In this study, the GPS data process had been done by Bernese GPS Software 5.0 which is a software developed by University of Bern, Switzerland. The GPS data used in this study are from Ministry of Interior (MOI) and IGS, and MOI sites are capable of surface meteorological measurements. The radiosonde data from Central Weather Bereau were used to develop Taiwan-specified conversion factors. The precision of the result is 1.6 mm in general weather condition and 2.0 mm in turbulent weather condition. The general latency of near real time PW estimates is 5 minutes.
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Book chapters on the topic "Near real-time estimation"

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Arnold, Daniel, Simon Lutz, Rolf Dach, Adrian Jäggi, and Jens Steinborn. "Near Real-Time Coordinate Estimation from Double-Difference GNSS Data." In International Association of Geodesy Symposia, 691–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/1345_2015_173.

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Rózsa, Sz, A. Kenyeres, T. Weidinger, and A. Z. Gyöngyösi. "Near Real Time Estimation of Integrated Water Vapour from GNSS Observations in Hungary." In International Association of Geodesy Symposia, 31–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37222-3_5.

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Mendi, C. Deniz, and Eystein S. Husebye. "Near Real Time Estimation of Seismic Event Magnitude and Moment via P and L g phases." In Earthquakes Induced by Underground Nuclear Explosions, 281–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57764-2_22.

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Bock, Yehuda, Jie Zhang, Peng Fang, Joachim Genrich, Keith Stark, and Shimon Wdowinski. "One Year of Daily Satellite Orbit and Polar Motion Estimation for Near Real Time Crustal Deformation Monitoring." In Developments in Astrometry and Their Impact on Astrophysics and Geodynamics, 279–84. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1711-1_50.

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Morley, Michael, Maiamuna S. Majumder, Tony Gallanis, and Joseph Wilson. "Using Non-traditional Data Sources for Near Real-Time Estimation of Transmission Dynamics in the Hepatitis-E Outbreak in Namibia, 2017–2018." In Leveraging Data Science for Global Health, 443–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_28.

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Conference papers on the topic "Near real-time estimation"

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Chen, Yiming, Dongfang Zheng, Paul A. Miller, and Jay A. Farrell. "Underwater vehicle near real time state estimation." In 2013 IEEE International Conference on Control Applications (CCA). IEEE, 2013. http://dx.doi.org/10.1109/cca.2013.6662806.

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Spaulding, Timothy, Cory Naddy, Garrett Knowlan, Jennifer Hines, Zachary Schaffer, Danny Riley, and Timothy Jorris. "Near Real-time Parameter Estimation in the C-12C." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-6275.

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Smith, A. C., C. P. Fall, and A. T. Sornborger. "Near-real-time connectivity estimation for multivariate neural data." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091169.

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Sawant, Suryakant, Jayantrao Mohite, Mariappan Sakkan, and Srinivasu Pappula. "Near Real Time Crop Loss Estimation using Remote Sensing Observations." In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019. http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820217.

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Vilnrotter, Victor, and Kar-Ming Cheung. "Near-Optimum Real-Time Range Estimation Algorithms for Proximity Links." In 2021 IEEE Aerospace Conference. IEEE, 2021. http://dx.doi.org/10.1109/aero50100.2021.9438379.

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Ivanov, Igor, Dmitriy Dubinin, and Andrey Zhukov. "Overhead Line Parameter Estimation Through Synchrophasor Data In Near Real-Time." In 2019 2nd International Youth Scientific and Technical Conference on Relay Protection and Automation (RPA). IEEE, 2019. http://dx.doi.org/10.1109/rpa47751.2019.8958451.

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Chaudhuri, Nilanjan Ray, and Balarko Chaudhuri. "Damping and relative mode-shape estimation in near real-time through phasor approach." In 2011 IEEE Power & Energy Society General Meeting. IEEE, 2011. http://dx.doi.org/10.1109/pes.2011.6039363.

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Patel, Vijay, Girish Deodhare, and Shyam Chetty. "Near Real Time Stability Margin Estimation from Piloted 3-2-1-1 Inputs." In AIAA's Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical Forum. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5820.

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Verger, A., F. Baret, and M. Weiss. "GEOV2/VGT: near real time estimation of global biophysical variables from VEGETATION-P data." In MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2013. http://dx.doi.org/10.1109/multi-temp.2013.6866023.

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Kashani, Alireza G., Andrew Graettinger, and Thang Dao. "3D Data Collection and Automated Damage Assessment for Near Real-time Tornado Loss Estimation." In Construction Research Congress 2014. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413517.124.

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