Relevant bibliographies by topics / Nonlinear Data processing

Academic literature on the topic 'Nonlinear Data processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Nonlinear Data processing"

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Efremov, G. I., T. Yu Zhuravleva, and B. S. Sazhin. "Data processing by nonlinear regression analysis." Theoretical Foundations of Chemical Engineering 34, no. 2 (March 2000): 194–96. http://dx.doi.org/10.1007/bf02757840.

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Tu, Chengyuan, Yanjun Zeng, and Xinchun Yang. "Nonlinear processing and analysis of ECG data." Technology and Health Care 12, no. 1 (April 13, 2004): 1–9. http://dx.doi.org/10.3233/thc-2004-12101.

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de Andrade Lima, L. R. P. "Nonlinear data reconciliation in gold processing plants." Minerals Engineering 19, no. 9 (July 2006): 938–51. http://dx.doi.org/10.1016/j.mineng.2005.10.018.

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Kim, Myungjun, Jae-Eun Lee, Chuljun Lee, Yubin Song, Geonhui Han, Jongseon Seo, Dong-Wook Kim, Young-Ho Seo, Hyunsang Hwang, and Daeseok Lee. "Multinary Data Processing Based on Nonlinear Synaptic Devices." Journal of Electronic Materials 50, no. 6 (March 25, 2021): 3471–77. http://dx.doi.org/10.1007/s11664-021-08841-8.

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Chen, Chih-Sung, and Yih Jeng. "Nonlinear data processing method for the signal enhancement of GPR data." Journal of Applied Geophysics 75, no. 1 (September 2011): 113–23. http://dx.doi.org/10.1016/j.jappgeo.2011.06.017.

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Li, Gui Ling. "Nonlinear Measurement Data Processing Based on Improved Damping Least Squares." Applied Mechanics and Materials 105-107 (September 2011): 2034–38. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.2034.

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Datum are the key of “Digital Earth”.In measurement, dealing with nonlinear models of observation datum, we may take their approximate values at observation values by Taylor series expansion, say, taking first-order item as a linear function of classical adjustment. But requirements of observation data, processing and accuracy assessment are higher and higher with today's fast-growing of high-tech mapping and surveying. So study on nonlinear least squares adjustment has been paid more and more attention. Damping least squares, as a modified algorithm of Gauss-Newton’s algorithm, is necessary to add a damping factor to improve the nature of a coefficient matrix. But it is difficult to choose a suitable damping factor, and needs to solve a group of linear equations repeatedly. In this paper, an improved damping least square was utilized for the non-linear processing of measurement datum in order to reduce a lot of computational workload.
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Panja, Palash, Pranay Asai, Raul Velasco, and Milind Deo. "Pre-processing Protocol for Nonlinear Regression of Uneven Spaced-Data." Journal of Modeling and Optimization 12, no. 1 (June 15, 2020): 23–37. http://dx.doi.org/10.32732/jmo.2020.12.1.23.

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Regression of experimental or simulated data has important implications in sensitivity studies, uncertainty analysis, and prediction accuracy. The fitness of a model is highly dependent on the number of data points and the locations of the chosen points on the curve. The objective of the research is to find the best scheme for a nonlinear regression model using a fraction of total data points without losing any features or trends in the data. Six different schemes are developed by setting criteria such as equal spacing along axes, equal distance between two consecutive points, constraint in the angle of curvature, etc. A workflow is provided to summarize the entire protocol of data preprocessing, training and testing nonlinear regression models with various schemes using a simulated temperature profile from an enhanced geothermal system. It is shown that only 5% of data points are sufficient to represent the entire curve using a regression model with a proper scheme.
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Yan, Yihua, Chengming Tan, Long Xu, Huirong Ji, Qijun Fu, and Guoxiang Song. "Nonlinear calibration and data processing of the solar radio burst." Science in China Series A: Mathematics 45, S1 (October 2002): 89–96. http://dx.doi.org/10.1007/bf02889689.

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Herrmann, Felix J., Deli Wang, Gilles Hennenfent, and Peyman P. Moghaddam. "Curvelet-based seismic data processing: A multiscale and nonlinear approach." GEOPHYSICS 73, no. 1 (January 2008): A1—A5. http://dx.doi.org/10.1190/1.2799517.

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Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little “plane waves,” complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet's ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology.
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Wong, F. W., and T. S. Yeo. "New applications of nonlinear chirp scaling in SAR data processing." IEEE Transactions on Geoscience and Remote Sensing 39, no. 5 (May 2001): 946–53. http://dx.doi.org/10.1109/36.921412.

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Dissertations / Theses on the topic "Nonlinear Data processing"

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Park, Joonam. "A visualization system for nonlinear frame analysis." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/19172.

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Herrmann, Felix J., Deli Wang, Gilles Hennenfent, and Peyman P. Moghaddam. "Seismic data processing with curvelets: a multiscale and nonlinear approach." Society of Exploration Geophysicists, 2007. http://hdl.handle.net/2429/557.

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In this abstract, we present a nonlinear curvelet-based sparsity promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet’s wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data.
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Herrmann, Felix J. "Seismic data processing with curvelets: a multiscale and nonlinear approach." Society of Exploration Geophysicists, 2007. http://hdl.handle.net/2429/600.

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In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet's wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data.
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Jin, Ruxiang. "Nonlinear etalons and nonlinear waveguides as decision-making elements in photonic switching." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184807.

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This dissertation describes our recent results in the study of various types of photonic switches. Special attention is given to the devices with Fabry-Perot etalon or planar waveguide structures based on dispersive optical nonlinearities. Basic optical logic functions, such as digital pattern recognition, symbolic substitution, and all-optical compare-and-exchange operation are demonstrated using ZnS and ZnSe nonlinear interference filters. Differential gain, cascading, and optical latching circuits are demonstrated using GaAs/AlGaAs multiple-quantum-well nonlinear etalons that are compatible with diode-laser sources, and the relationship between differential gain and device response time is established through a thorough investigation of the switching dynamics. Preliminary results also indicate that optical fibers can be used as interconnects between optical logic gates. Picosecond all-optical switching with good (> 3:1) contrast is demonstrated for the first time in single-mode strip-loaded GaAs/AlGaAs nonlinear directional couplers (NLDC's). The anisotropy of quantum-well structure to light polarization is used to achieve polarization-dependent two-beam switching, and the optical Stark effect is used to demonstrate all-optical modulation in an NLDC with subpicosecond recovery time.
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Svensson, Barbro Christina. "Nonlinear distributed couplers in zinc-sulfide waveguides." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184500.

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Nonlinear phenomena originating from the distributed coupling process were observed when distributed couplers, such as prisms and gratings, were used to couple light into nonlinear ZnS thin film waveguides. The efficiency of the nonlinear distributed coupling process was found to depend on two independent parameters, the angle of the incident beam and the power of the incident beam. Depending on the detuning of the incident angle, from the optimum incident angle at low powers, either optical limiting, power-dependent switching, or power-dependent bistability of the coupling efficiency, and thereby of the in-coupled power, was observed. At zero detuning, a twenty-fold decrease of the coupling efficiency with increasing powers was measured. At a nonzero detuning of the incident angle, power-dependent switching at milliwatt powers was observed. At larger angular detunings, corresponding to the angular width. FWHM, of the coupling peak at low powers, power-dependent bistability was observed, and the width of the bistability loop was found to increase with increasing detunings. All-optical beam scanning via a nonlinear grating coupler was also demonstrated, utilizing a control-signal beam configuration, where the signal beam scanned through an angle of 0.5° when the power of the control beam was varied. The observed nonlinearity in ZnS was positive and of thermal origin. The power-induced change in the refractive index was found to be 0.01 and a relaxation time of 10 μsec was measured. Problems with the long-term stability of the nonlinear distributed coupling process were traced to the occurrence of desorption and adsorption of water vapor in the ZnS films.
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Lakshmanan, Nithya M. "Estimation and control of nonlinear batch processes using multiple linear models." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/11835.

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Ogidan, Olugbenga Kayode. "Design of nonlinear networked control for wastewater distributed systems." Thesis, Cape Peninsula University of Technology, 2014. http://hdl.handle.net/20.500.11838/1201.

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Thesis submitted in fulfilment of the requirements for the degree Doctor of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014
This thesis focuses on the design, development and real-time simulation of a robust nonlinear networked control for the dissolved oxygen concentration as part of the wastewater distributed systems. This concept differs from previous methods of wastewater control in the sense that the controller and the wastewater treatment plants are separated by a wide geographical distance and exchange data through a communication medium. The communication network introduced between the controller and the DO process creates imperfections during its operation, as time delays which are an object of investigation in the thesis. Due to the communication network imperfections, new control strategies that take cognisance of the network imperfections in the process of the controller design are needed to provide adequate robustness for the DO process control system. This thesis first investigates the effects of constant and random network induced time delays and the effects of controller parameters on the DO process behaviour with a view to using the obtained information to design an appropriate controller for the networked closed loop system. On the basis of the above information, a Smith predictor delay compensation controller is developed in the thesis to eliminate the deadtime, provide robustness and improve the performance of the DO process. Two approaches are adopted in the design of the Smith predictor compensation scheme. The first is the transfer function approach that allows a linearized model of the DO process to be described in the frequency domain. The second one is the nonlinear linearising approach in the time domain. Simulation results reveal that the developed Smith predictor controllers out-performed the nonlinear linearising controller designed for the DO process without time delays by compensating for the network imperfections and maintaining the DO concentration within a desired acceptable level. The transfer function approach of designing the Smith predictor is found to perform better under small time delays but the performance deteriorates under large time delays and disturbances. It is also found to respond faster than the nonlinear approach. The nonlinear feedback linearisig approach is slower in response time but out-performs the transfer function approach in providing robustness and performance for the DO process under large time delays and disturbances. The developed Smith predictor compensation schemes were later simulated in a real-time platform using LabVIEW. The Smith predictor controllers developed in this thesis can be applied to other process control plants apart from the wastewater plants, where distributed control is required. It can also be applied in the nuclear reactor plants where remote control is required in hazardous conditions. The developed LabVIEW real-time simulation environment would be a valuable tool for researchers and students in the field of control system engineering. Lastly, this thesis would form the basis for further research in the field of distributed wastewater control.
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Hernańdez, Correa Evelio. "Control of nonlinear systems using input-output information." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/11176.

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Herrmann, Felix J. "Seismic imaging and processing with curvelets." European Association of Geoscientists & Engineers, 2007. http://hdl.handle.net/2429/582.

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Cereijo, Martinez Maria. "A new parallel technique for the solution of sparse nonlinear equations." FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/2097.

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Solving nonlinear systems of equations is a central problem in numerical analysis, with enormous significance for science and engineering. A special case, sparse systems of equations, occurs frequently in various applications. Sparsity occurs in the analysis of many types of complex systems because of the local nature of the dependence or connectivity among system components. One such system which may be modeled by a nonlinear sparse set of equations is the power system load flow analysis. This is a mathematical study performed by electrical utilities to monitor the electrical power system. The data from system components are used to create a set of nonlinear equations. These equations are then solved to find the voltage profile of the power network. With these data, control and security of the power system are achieved. Solving problems of this type is very time consuming when the system is large. This dissertation proposes a highly parallel computer architecture for solving large sets of nonlinear sparse equations. The goal of this architecture is to reduce the processing time required to solve this type of problem. In particular, the load flow problem is analyzed and implemented on this architecture. For the FPL network, the speed is increased by a factor of about 2000.
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Books on the topic "Nonlinear Data processing"

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Emiris, Ioannis Z. Nonlinear computational geometry. Edited by Theobald Thorsten 1971- and Institute of Mathematics and Its Applications. New York, N.Y: Springer, 2010.

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S, Marcellucci, and Chester A. N, eds. Nonlinear optics and optical computing. New York: Plenum Press, 1990.

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Martellucci, S. Nonlinear Optics and Optical Computing. Boston, MA: Springer US, 1990.

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Rusling, James F. Nonlinear computer modeling of chemical and biochemical data. San Diego: Academic Press, 1996.

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1937-, Natsuyama H. H., ed. Numerical derivatives and nonlinear analysis. New York, N.Y: Plenum Press, 1986.

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Philip, Rabinowitz, and British Computer Society. Numerical Analysis Specialist Group., eds. Numerical methods for nonlinear algebraic equations. New York: Gordon and Breach Science Publishers, 1988.

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S, Mel'nik Valery, Kasyanov Pavlo O, and SpringerLink (Online service), eds. Evolution Inclusions and Variation Inequalities for Earth Data Processing II: Differential-Operator Inclusions and Evolution Variation Inequalities for Earth Data Processing. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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P, Mazurik V., and Evtushenko I͡U︡ G, eds. Nelineĭnoe programmirovanie v sisteme DISO/PK. Moskva: Vychislitelʹnyĭ t͡s︡entr AN SSSR, 1988.

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Topics in nonlinear dynamics with computer algebra. Langhorne: Gordon and Breach Science, 1994.

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Nonlinear editing basics: Electronic film and video editing. Boston: Focal Press, 1998.

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Book chapters on the topic "Nonlinear Data processing"

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Schreiber, Thomas. "Processing of Physiological Data." In Nonlinear Analysis of Physiological Data, 7–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71949-3_2.

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Moura, José M. F., and Carlos A. C. Belo. "Threshold Extension by Nonlinear Techniques." In Underwater Acoustic Data Processing, 433–52. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_48.

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Daras, Nicholas J. "Selective Priorities in Processing of Big Data." In Applications of Nonlinear Analysis, 141–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89815-5_6.

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Foxall, Rob, Igor Krcmar, Gavin Cawley, Stephen Dorling, and Danilo P. Mandic. "On Nonlinear Processing of Air Pollution Data." In Artificial Neural Nets and Genetic Algorithms, 477–80. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_119.

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Larkin, E. V., V. S. Nguyen, and A. N. Privalov. "Simulation of Digital Control Systems by Nonlinear Objects." In Artificial Intelligence in Data and Big Data Processing, 711–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_56.

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Gao, Weinan, and Zhong-Ping Jiang. "Data-Driven Nonlinear Adaptive Optimal Control of Connected Vehicles." In Neural Information Processing, 122–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70136-3_13.

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Chollet, Gérard, Kevin McTait, and Dijana Petrovska-Delacrétaz. "Data Driven Approaches to Speech and Language Processing." In Nonlinear Speech Modeling and Applications, 164–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11520153_8.

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Su, Jing, and Saturnino Luz. "Predicting Cognitive Load Levels from Speech Data." In Recent Advances in Nonlinear Speech Processing, 255–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28109-4_26.

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Wakitani, Shin, Takuya Nawachi, and Toru Yamamoto. "Design of a Data-Oriented PID Controller for Nonlinear Systems." In Neural Information Processing, 169–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_21.

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Chung, Younjin, and Masahiro Takatsuka. "The Self-Organizing Map Tree (SOMT) for Nonlinear Data Causality Prediction." In Neural Information Processing, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_16.

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Conference papers on the topic "Nonlinear Data processing"

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Frison, Ted W., C. K. Peng, Ary Goldberger, and Richard A. Katz. "Chaotic data analysis of heart R-R interval EKG data." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51003.

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Hammel, Stephen, and P. W. Bo Hammer. "System identification in experimental data." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51022.

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Janson, Natalie B., and Vadim S. Anishchenko. "Modeling the dynamical systems on experimental data." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51006.

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Larsen, M. L., J. Ridgway, C. H. Waldman, M. Gabbay, B. Battista, R. Buntzen, and C. D. Rees. "Nonlinear signal processing of ocean electromagnetic data." In Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492). IEEE, 2003. http://dx.doi.org/10.1109/oceans.2003.178208.

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Gang, Grace J., Xueqi Guo, and Joseph W. Stayman. "Performance analysis for nonlinear tomographic data processing." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534983.

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Runnalls, Andrew R. "The IGMARP Data Fusion Algorithm." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378814.

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Maskell, Simon, Ben Alun-Jones, and Malcolm Macleod. "A Single Instruction Multiple Data Particle Filter." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378818.

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Ulbikas, J., A. Čenys, D. Žemaitytė, and G. Varoneckas. "Correlations in heart beat data as quantitative characterization of heart pathology." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51004.

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Kremliovsky, Michael, and James Kadtke. "Signal classification using global dynamical models, Part II: SONAR data analysis." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51025.

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Jacobo, Adrian, and Pere Colet. "Optical image and data processing with cavity type-II Second Harmonic Generation." In Nonlinear Guided Waves and Their Applications. Washington, D.C.: OSA, 2005. http://dx.doi.org/10.1364/nlgw.2005.thb22.

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Reports on the topic "Nonlinear Data processing"

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Owechko, Yuri, and Bernard Soffer. Real-Time Implementation of Nonlinear Optical Data Processing Functions. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada233521.

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