Journal articles: 'Error propagation'

1

Kubáček, Lubomír. "Nonlinear error propagation law." Applications of Mathematics 41, no. 5 (1996): 329–45. http://dx.doi.org/10.21136/am.1996.134330.

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2

Seiler, Fritz A. "Error Propagation for Large Errors." Risk Analysis 7, no. 4 (December 1987): 509–18. http://dx.doi.org/10.1111/j.1539-6924.1987.tb00487.x.

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3

Singh, Arvind, and Priyanka Chaturvedi. "Error Propagation." Resonance 26, no. 6 (June 2021): 853–61. http://dx.doi.org/10.1007/s12045-021-1185-1.

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4

Tellinghuisen, Joel. "Statistical Error Propagation." Journal of Physical Chemistry A 105, no. 15 (April 2001): 3917–21. http://dx.doi.org/10.1021/jp003484u.

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5

Nelson, Lloyd S. "Propagation of Error." Journal of Quality Technology 24, no. 4 (October 1992): 232–35. http://dx.doi.org/10.1080/00224065.1992.11979404.

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6

van Laar, A. "The Quality of Information: Errors and Error Propagation." South African Forestry Journal 132, no. 1 (March 1, 1985): 22–25. http://dx.doi.org/10.1080/00382167.1985.9629545.

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7

Asbjornsen, O. A. "Error in the propagation of error formula." AIChE Journal 32, no. 2 (February 1986): 332–34. http://dx.doi.org/10.1002/aic.690320225.

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8

Meija, Juris. "Random error propagation challenge." Analytical and Bioanalytical Chemistry 395, no. 1 (July 21, 2009): 5–6. http://dx.doi.org/10.1007/s00216-009-2936-0.

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9

Fowler, Austin G., David S. Wang, and Lloyd C. L. Hollenberg. "Surface code quantum error correction incorporating accurate error propagation." Quantum Information and Computation 11, no. 1&2 (January 2011): 8–18. http://dx.doi.org/10.26421/qic11.1-2-2.

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The surface code is a powerful quantum error correcting code that can be defined on a 2-D square lattice of qubits with only nearest neighbor interactions. Syndrome and data qubits form a checkerboard pattern. Information about errors is obtained by repeatedly measuring each syndrome qubit after appropriate interaction with its four nearest neighbor data qubits. Changes in the measurement value indicate the presence of chains of errors in space and time. The standard method of determining operations likely to return the code to its error-free state is to use the minimum weight matching algorithm to connect pairs of measurement changes with chains of corrections such that the minimum total number of corrections is used. Prior work has not taken into account the propagation of errors in space and time by the two-qubit interactions. We show that taking this into account leads to a quadratic improvement of the logical error rate.

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10

Huang, Lian-Jie, and Michael C. Fehler. "Accuracy analysis of the split-step Fourier propagator: Implications for seismic modeling and migration." Bulletin of the Seismological Society of America 88, no. 1 (February 1, 1998): 18–29. http://dx.doi.org/10.1785/bssa0880010018.

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Abstract The split-step Fourier propagator is a one-way wave propagation method that has been widely used to simulate primary forward and backward (reflected) deterministic/random wave propagation due to its fast computational speed and limited computer memory requirement. The method is useful for rapid modeling of seismic-wave propagation in heterogeneous media where forward scattered waveforms can be considered to be dominant or reverberations can be ignored. The method is based on a solution to the one-way wave equation that requires expanding the square root of an operator and splitting of the resulting noncommutative operators to allow calculation by transferring wave fields between the space and wavenumber domains. Previous analysis of the accuracy of the method has focused on the error related to only a portion of the approximations involved in the propagator. To better understand the accuracy of the propagator, we present a complete formal and numerical accuracy analyses. Our formal analysis indicates that the dominant error of the propagator increases as the first order in the marching interval. We show that nonsymmetrically and symmetrically split-step marching solutions have the same first-order error term. Their second- and third-order error terms are similar. Therefore, the differences between the accuracy of different split-step marching solutions are insignificant. This conclusion is confirmed by our numerical tests. The relation among the phase error of the split-step Fourier propagator, relative velocity perturbation, and propagation angle is numerically studied. The results suggest that the propagator is accurate for up to a 60° propagation angle from the main propagation direction for media with small relative velocity perturbations (10%).

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11

Kelly, Alonzo. "Linearized Error Propagation in Odometry." International Journal of Robotics Research 23, no. 2 (February 2004): 179–218. http://dx.doi.org/10.1177/0278364904041326.

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12

Campos, P., J. Fontanari, and P. Stadler. "Error propagation in the hypercycle." Physical Review E 61, no. 3 (March 2000): 2996–3002. http://dx.doi.org/10.1103/physreve.61.2996.

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13

Soltanian-Zadeh, H., J. P. Windham, and J. M. Jenkins. "Error propagation in eigenimage filtering." IEEE Transactions on Medical Imaging 9, no. 4 (1990): 405–20. http://dx.doi.org/10.1109/42.61756.

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14

Li, Aiguo. "Error Propagation Analysis in Software." Journal of Computer Research and Development 44, no. 11 (2007): 1962. http://dx.doi.org/10.1360/crad20071121.

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15

Ramos, A. Asensio, and M. Collados. "Error propagation in polarimetric demodulation." Applied Optics 47, no. 14 (May 1, 2008): 2541. http://dx.doi.org/10.1364/ao.47.002541.

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16

West, Allen C. "Significant Figures and Error Propagation." Journal of Chemical Education 66, no. 3 (March 1989): 272. http://dx.doi.org/10.1021/ed066p272.2.

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17

Röhr, Ancus. "CHAIN LADDER AND ERROR PROPAGATION." ASTIN Bulletin 46, no. 2 (April 19, 2016): 293–330. http://dx.doi.org/10.1017/asb.2016.9.

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AbstractWe show how estimators for the chain ladder prediction error in Mack's (1993) distribution-free stochastic model can be derived using the error propagation formula. Our method allows for the treatment of the general case of the prediction error of the loss development result between two arbitrary future horizons. In the well-known special cases considered previously by Mack (1993) and Merz and Wüthrich (2008), our estimators coincide with theirs. However, the algebraic form in which we cast them is new, considerably more compact and more intuitive to understand. For example, in the classical case treated by Mack (1993), we show that the mean squared prediction error divided by the squared estimated ultimate loss can be written as ∑jû2j, where ûj measures the (relative) uncertainty around the jth development factor and the proportion of the estimated ultimate loss that it affects. The error propagation method also provides a natural split into process error and parameter error. Our proofs identify and exploit symmetries of “chain ladder processes” in a novel way. For the sake of wider practical applicability of the formulae derived, we allow for incomplete historical data and the exclusion of outliers in the triangles.

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18

Turner, Barry A. "Accidents and Nonrandom Error Propagation." Risk Analysis 9, no. 4 (December 1989): 437–44. http://dx.doi.org/10.1111/j.1539-6924.1989.tb01254.x.

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19

Haining, Robert, and Giuseppe Arbia. "Error Propagation Through Map Operations." Technometrics 35, no. 3 (August 1993): 293–305. http://dx.doi.org/10.1080/00401706.1993.10485325.

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20

Hughes, Ifan G., and Thomas P. A. Hase. "Error Propagation: A Functional Approach." Journal of Chemical Education 89, no. 6 (April 5, 2012): 821–22. http://dx.doi.org/10.1021/ed2004627.

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21

Wallner, J., R. Krasauskas, and H. Pottmann. "Error propagation in geometric constructions." Computer-Aided Design 32, no. 11 (September 2000): 631–41. http://dx.doi.org/10.1016/s0010-4485(00)00053-1.

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22

Holliday, Robin. "Chromosome error propagation and cancer." Trends in Genetics 5 (1989): 42–45. http://dx.doi.org/10.1016/0168-9525(89)90020-6.

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23

Holmes, Daniel T., and Kevin A. Buhr. "Error propagation in calculated ratios." Clinical Biochemistry 40, no. 9-10 (June 2007): 728–34. http://dx.doi.org/10.1016/j.clinbiochem.2006.12.014.

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24

Doerr, Benjamin, and Ulf Lorenz. "Error Propagation in Game Trees." Mathematical Methods of Operations Research 64, no. 1 (June 9, 2006): 79–93. http://dx.doi.org/10.1007/s00186-006-0064-6.

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25

Yi, Seungku, Robert M. Haralick, and Linda G. Shapiro. "Error propagation in machine vision." Machine Vision and Applications 7, no. 2 (June 1994): 93–114. http://dx.doi.org/10.1007/bf01215805.

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26

Sztandera, Les M. "Error propagation fuzzy control system." Information Sciences - Applications 3, no. 2 (March 1995): 75–89. http://dx.doi.org/10.1016/1069-0115(94)00045-4.

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27

Molckovsky, A., M. M. Vickers, and P. A. Tang. "Characterization of published errors in high-impact oncology journals." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): 6627. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.6627.

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6627 Background: Knowledge within oncology is disseminated primarily via peer-reviewed journals. The potential for dissemination of erroneous data exists, an issue that has not been explored in oncology. We evaluated errata from the Journal of Clinical Oncology and the Journal of the National Cancer Institute published between 2004–2007. Methods: Two authors independently abstracted data regarding errata and classified them as trivial (eg typographical error) or serious (eg change in outcome). For serious errors, the frequency of citation and error propagation was determined using the Science Citation Index in Web of Science. For publications cited > 150 times, a random sample of 10% were evaluated for error propagation. Canadian oncologists were surveyed regarding attitudes towards published errata. Results: There were 190 published errors, out of a total of 5118 papers, for an error rate of 4 ± 1% (SD) per year. 26/190 errors were identified as serious (14%). The median time from publication of the original article to publication of the erratum was 3.5 mo for trivial errors compared to 8.3 mo for serious errors (p = 0.03). A median of 1 error per article was reported for papers with trivial errors compared to a median of 2 errors per article with serious errors (p < 0.01). The 26 articles with serious errors were cited 256 times before publication of the error and 1056 times afterwards; of these, 96 and 527, respectively, were evaluated for propagation. Error propagation occurred in 14.6% of the citations published before error publication, and in 3.4% of citations published afterwards (p < 0.001). Survey results indicate that 30% of oncologists do not read the erratum section of journals, and that 45% of oncologists have only read the abstract of an article before citing it in a publication. Although 58% of oncologists have noticed errors in cancer publications, only 15% of these errors were reported. Conclusions: Error rates in high impact oncology journals average 4% per year, but this is likely an underestimate since errors noticed by readers are not consistently reported to the journal. The accuracy of articles submitted for publication is of utmost importance; while error propagation decreases after erratum publication, serious errors continue to be propagated in the literature. No significant financial relationships to disclose.

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28

Gao, Tingran, Shahab Asoodeh, Yi Huang, and James Evans. "Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3630–37. http://dx.doi.org/10.1609/aaai.v33i01.33013630.

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Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating ”soft labels” (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation. Borrowing insights from Wasserstein propagation on graphs [Solomon et al. 2014], we re-formulate the label propagation procedure as a message-passing algorithm, which renders itself naturally to a generalization applicable to hypergraphs through Wasserstein barycenters. Furthermore, in a PAC learning framework, we provide generalization error bounds for propagating one-dimensional distributions on graphs and hypergraphs using 2-Wasserstein distance, by establishing the algorithmic stability of the proposed semisupervised learning algorithm. These theoretical results also shed new lights upon deeper understandings of the Wasserstein propagation on graphs.

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29

Ni, Wei. "Minimized Error Propagation Location Method Based on Error Estimation." Computer Journal 59, no. 9 (October 5, 2015): 1282–88. http://dx.doi.org/10.1093/comjnl/bxv081.

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30

Chen, Liang, Mojtaba Ebrahimi, and Mehdi B. Tahoori. "CEP: Correlated Error Propagation for Hierarchical Soft Error Analysis." Journal of Electronic Testing 29, no. 2 (April 2013): 143–58. http://dx.doi.org/10.1007/s10836-013-5365-0.

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31

Narasimhan, R. "Error Propagation Analysis of V-BLAST With Channel-Estimation Errors." IEEE Transactions on Communications 53, no. 1 (January 2005): 27–31. http://dx.doi.org/10.1109/tcomm.2004.840670.

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32

Guo, Feiyan, Fang Zou, Jian Hua Liu, Qingdong Xiao, and Zhongqi Wang. "Assembly error propagation modeling and coordination error chain construction for aircraft." Assembly Automation 39, no. 2 (April 1, 2019): 308–22. http://dx.doi.org/10.1108/aa-07-2018-100.

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Purpose Manufacturing errors, which will propagate along the assembly process, are inevitable and difficult to analyze for complex products, such as aircraft. To realize the goal of precise assembly for an aircraft, with revealing the nonlinear transfer mechanism of assembly error, a set of analytical methods with response to the assembly error propagation process are developed. The purpose of this study is to solve the error problems by modeling and constructing the coordination dimension chain to control the consistency of accumulated assembly errors for different assemblies. Design/methodology/approach First, with the modeling of basic error sources, mutual interaction relationship of matting error and deformation error is analyzed, and influence matrix is formed. Second, by defining coordination datum transformation process, practical establishing error of assembly coordinate system is studied, and the position of assembly features is modified with actual relocation error considering datum changing. Third, considering the progressive assembly process, error propagation for a single assembly station and multi assembly stations is precisely modeled to gain coordination error chain for different assemblies, and the final coordination error is optimized by controlling the direction and value of accumulated error range. Findings Based on the proposed methodology, coordination error chain, which has a direct influence on the property of stealthy and reliability for modern aircrafts, is successfully constructed for the assembly work of the jointing between leading edge flap component and wing component at different assembly stations. Originality/value Precise assembly work at different assembly stations is completed to verify methodology’s feasibility. With analyzing the main comprised error items and some optimized solutions, benefit results for the practical engineering application showing that the maximum value of the practical flush of the profiles between the two components is only 0.681 mm, the minimum value is only 0.021 mm, and the average flush of the entire wing component is 0.358 mm, which are in accordance with theoretical calculation results and can successfully fit the assembly requirement. The potential user can be the engineers for manufacturing the complex products.

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33

Singarimbun, Roy Nuary. "Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm." Data Science: Journal of Computing and Applied Informatics 4, no. 1 (February 5, 2020): 27–46. http://dx.doi.org/10.32734/jocai.v4.i1-1160.

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Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.

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34

Porr, Bernd, and Paul Miller. "Forward propagation closed loop learning." Adaptive Behavior 28, no. 3 (May 31, 2019): 181–94. http://dx.doi.org/10.1177/1059712319851070.

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For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such ‘forward-propagation’ of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.

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35

Lobo, C. C., N. C. Bertola, and E. M. Contreras. "Error propagation in open respirometric assays." Brazilian Journal of Chemical Engineering 31, no. 2 (June 2014): 303–12. http://dx.doi.org/10.1590/0104-6632.20140312s00002659.

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36

Voas, J. "Error propagation analysis for COTS systems." Computing & Control Engineering Journal 8, no. 6 (December 1, 1997): 269–72. http://dx.doi.org/10.1049/cce:19970607.

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37

Rubio-González, Cindy, Haryadi S. Gunawi, Ben Liblit, Remzi H. Arpaci-Dusseau, and Andrea C. Arpaci-Dusseau. "Error propagation analysis for file systems." ACM SIGPLAN Notices 44, no. 6 (May 28, 2009): 270–80. http://dx.doi.org/10.1145/1543135.1542506.

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38

Immink, K. A. S. "Code configuration for avoiding error propagation." Electronics Letters 32, no. 24 (1996): 2191. http://dx.doi.org/10.1049/el:19961488.

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39

Lutes, James, and Jacek Grodecki. "Error Propagation in Ikonos Mapping Blocks." Photogrammetric Engineering & Remote Sensing 70, no. 8 (August 1, 2004): 947–55. http://dx.doi.org/10.14358/pers.70.8.947.

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40

Christman, Zachary J., and John Rogan. "Error Propagation in Raster Data Integration." Photogrammetric Engineering & Remote Sensing 78, no. 6 (June 1, 2012): 617–24. http://dx.doi.org/10.14358/pers.78.6.617.

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41

Yakimiw, Evhen. "Cross‐equatorial error propagation: Research note." Atmosphere-Ocean 26, no. 4 (December 1988): 653–58. http://dx.doi.org/10.1080/07055900.1988.9649321.

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42

Necsulescu, D. S., A. Fahim, and C. Lu. "Stochastic error propagation in robot arms." Advanced Robotics 8, no. 5 (January 1993): 459–76. http://dx.doi.org/10.1163/156855394x00095.

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43

Miranda, Marta, V. Álvarez-Valado, Benito V. Dorrío, and Higinio González-Jorge. "Error propagation in differential phase evaluation." Optics Express 18, no. 3 (January 29, 2010): 3199. http://dx.doi.org/10.1364/oe.18.003199.

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44

Torcini, A., P. Grassberger, and A. Politi. "Error propagation in extended chaotic systems." Journal of Physics A: Mathematical and General 28, no. 16 (August 21, 1995): 4533–41. http://dx.doi.org/10.1088/0305-4470/28/16/011.

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45

Xiang-Guo Tang and Zhi Ding. "Error propagation in blind sequence estimation." IEEE Communications Letters 6, no. 6 (June 2002): 265–67. http://dx.doi.org/10.1109/lcomm.2002.1010876.

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46

Groves, P. D., and S. J. Harding. "Ionosphere Propagation Error Correction for Galileo." Journal of Navigation 56, no. 1 (January 2003): 45–50. http://dx.doi.org/10.1017/s0373463302002084.

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A theoretical analysis of the effects of dual frequency ionosphere propagation correction on GNSS code tracking noise is presented. The effect on tracking noise of using different combinations of the proposed Galileo signals for ionosphere propagation correction is investigated. It is concluded that, for the open or commercial service user, the Galileo signals E2-L1-E1 near to and around GPS L1 should be used to apply ionosphere propagation corrections to the E5 signals. This produces a tracking noise standard deviation about three times larger than that on an E5 signal alone. An ionosphere correction-smoothing algorithm is presented that reduces the tracking noise on the corrected pseudo-range measurements.

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47

Ji, Qiang, and Robert M. Haralick. "Error propagation for the Hough transform." Pattern Recognition Letters 22, no. 6-7 (May 2001): 813–23. http://dx.doi.org/10.1016/s0167-8655(01)00026-5.

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48

Spijker, M. N. "Error propagation in Runge-Kutta methods." Applied Numerical Mathematics 22, no. 1-3 (November 1996): 309–25. http://dx.doi.org/10.1016/s0168-9274(96)00040-2.

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49

Tellinghuisen, Joel. "Using Least Squares for Error Propagation." Journal of Chemical Education 92, no. 5 (February 12, 2015): 864–70. http://dx.doi.org/10.1021/ed500888r.

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50

Meija, Juris. "Solution to random error propagation challenge." Analytical and Bioanalytical Chemistry 396, no. 1 (November 25, 2009): 187–88. http://dx.doi.org/10.1007/s00216-009-3255-1.

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Journal articles on the topic 'Error propagation'

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