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Journal articles on the topic 'Nonlinear Data processing'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Beigi, Salman. "Sandwiched Rényi divergence satisfies data processing inequality." Journal of Mathematical Physics 54, no. 12 (December 2013): 122202. http://dx.doi.org/10.1063/1.4838855.

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12

Gao Gao Liu, Lin Rang Zhang, and Xin Liu. "General Bistatic SAR Data Processing Based on Extended Nonlinear Chirp Scaling." IEEE Geoscience and Remote Sensing Letters 10, no. 5 (September 2013): 976–80. http://dx.doi.org/10.1109/lgrs.2012.2227296.

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13

RICO-MARTÍNEZ, R., K. KRISCHER, I. G. KEVREKIDIS, M. C. KUBE, and J. L. HUDSON. "DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF Cu ELECTRODISSOLUTION DATA." Chemical Engineering Communications 118, no. 1 (November 1992): 25–48. http://dx.doi.org/10.1080/00986449208936084.

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14

Blume, W., and R. Eigenmann. "Nonlinear and symbolic data dependence testing." IEEE Transactions on Parallel and Distributed Systems 9, no. 12 (1998): 1180–94. http://dx.doi.org/10.1109/71.737695.

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15

Ortíz-Trejo, Federico, Martín Trejo-Valdez, Juan Pablo Campos-López, Joel Humberto Castro-Chacón, and Carlos Torres-Torres. "Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures." Applied Sciences 10, no. 16 (August 17, 2020): 5688. http://dx.doi.org/10.3390/app10165688.

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Simultaneous activation of multiphotonic absorption and nonlinear optical refraction were employed for developing a multipath load balancing technique assisted by light and zinc oxide nanostructures. Data storage as the real and imaginary part of the third-order nonlinear optical behavior of different regions in bidimensional systems was proposed. By recording the third-order nonlinear optical response exhibited by the nanomaterials, a straightforward XOR encryption system controlled by laser pulses was developed. Identification of nonlinear optical signals in propagation through the samples was monitored by a standard optical Kerr gate configuration. Vectorial two-wave mixing experiments were carried out for measuring the third-order optical nonlinearities of the samples explored by nanosecond pulses at 532 nm wavelength. A spray pyrolysis processing route was employed for the preparation of the samples in thin film form. Third-order nonlinear optical characteristics were analyzed taking into account the modification in the ZnO-based nanocomposites by single-pulsed irradiation close to the ablation threshold effect. The combination of nonlinear optics and nanostructured films can be considered for developing ultrafast smart objects with immediate applications for signal processing functions driven by multiphotonic phenomena.

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16

Florescu, Dorian, and Daniel Coca. "Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data." Neural Computation 30, no. 3 (March 2018): 670–707. http://dx.doi.org/10.1162/neco_a_01051.

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Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

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17

SCHREIBER, THOMAS, and MARCUS RICHTER. "FAST NONLINEAR PROJECTIVE FILTERING IN A DATA STREAM." International Journal of Bifurcation and Chaos 09, no. 10 (October 1999): 2039–45. http://dx.doi.org/10.1142/s0218127499001474.

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We introduce a modified algorithm to perform nonlinear filtering of a time series by locally linear phase space projections. Unlike previous implementations, the algorithm can be used not only for a posteriori processing but also includes the possibility to perform real-time filtering in a data stream. Apart from making the filter causal by restricting neighbor searches to the past, the data base that represents the phase space structure generated by the data is updated dynamically. This also allows filtering of nonstationary signals and dynamic parameter adjustment. We discuss exemplary applications, including the real-time extraction of the fetal electrocardiogram from abdominal recordings.

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18

Tseng, C. H. "Identification of cubically nonlinear systems using undersampled data." IEE Proceedings - Vision, Image, and Signal Processing 144, no. 5 (1997): 267. http://dx.doi.org/10.1049/ip-vis:19971416.

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19

Sethares, W. A. "Adaptive algorithms with nonlinear data and error functions." IEEE Transactions on Signal Processing 40, no. 9 (1992): 2199–206. http://dx.doi.org/10.1109/78.157220.

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20

Oxenløwe, L. K., M. Galili, H. C. Hansen Mulvad, H. Hu, J. L. Areal, E. Palushani, H. Ji, A. T. Clausen, and P. Jeppesen. "Nonlinear Optical Signal Processing for Tbit/s Ethernet Applications." International Journal of Optics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/573843.

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Wereviewrecent experimental demonstrations of Tbaud optical signal processing. In particular, we describe a successful 1.28 Tbit/s serial data generation based on single polarization 1.28 Tbaud symbol rate pulses with binary data modulation (OOK) and subsequent all-optical demultiplexing. We also describe the first error-free 5.1 Tbit/s data generation and demodulation based on a single laser, where a 1.28 Tbaud symbol rate is used together with quaternary phase modulation (DQPSK) and polarization multiplexing. The 5.1 Tbit/s data signal is all-optically demultiplexed and demodulated by direct detection in a delay-interferometer-balanced detector-based receiver, yielding a BER less than 10−9. We also present subsystems making serial optical Tbit/s systems compatible with standard Ethernet data for data centre applications and present Tbit/s results using, for instance silicon nanowires.

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21

Gong, Han Kun, Xiao Shan Zhao, and Guan Hua Zhao. "Data Processing in Abound Solutions of (2 + 1)-dimensional Boussinesq Equation." Advanced Materials Research 1056 (October 2014): 215–20. http://dx.doi.org/10.4028/www.scientific.net/amr.1056.215.

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In this paper, the repeated exp-function method is applied to construct exact traveling wave solutions of the (2+1)-dimensional Boussinesq equation. With aid of symbolic computation, many generalized solitary solutions, periodic solutions and other exact solutions are successfully obtained. Thus, it is proved that the method is straightforward and effective to solve the nonlinear evolutions equations.

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22

Licciardi, Giorgio, Ruggero Giuseppe Avezzano, Fabio Del Frate, Giovanni Schiavon, and Jocelyn Chanussot. "A novel approach to polarimetric SAR data processing based on Nonlinear PCA." Pattern Recognition 47, no. 5 (May 2014): 1953–67. http://dx.doi.org/10.1016/j.patcog.2013.11.009.

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23

Karabutov, N. N. "Structure field construction for nonlinear static systems based on measurement data processing." Measurement Techniques 52, no. 12 (December 2009): 1281–88. http://dx.doi.org/10.1007/s11018-010-9434-9.

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24

Davies, Simon J., Christopher Bauer, P. J. Hore, and Ray Freeman. "Resolution enhancement by nonlinear data processing. “HOGWASH” and the maximum entropy method." Journal of Magnetic Resonance (1969) 76, no. 3 (February 1988): 476–93. http://dx.doi.org/10.1016/0022-2364(88)90351-4.

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25

Turitsyn, Sergei K., Jaroslaw E. Prilepsky, Son Thai Le, Sander Wahls, Leonid L. Frumin, Morteza Kamalian, and Stanislav A. Derevyanko. "Nonlinear Fourier transform for optical data processing and transmission: advances and perspectives." Optica 4, no. 3 (February 28, 2017): 307. http://dx.doi.org/10.1364/optica.4.000307.

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26

Liu, Chan Yuan. "Research of Method about Geomagnetic Sensor Data Processing by Optimal Idea." Advanced Materials Research 320 (August 2011): 647–50. http://dx.doi.org/10.4028/www.scientific.net/amr.320.647.

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A method of optimal idea, in the paper, is used to process geomagnetic sensor data. The curve fitting by use of the method is more convenient than least square method (LSM). It adapts especially to process nonlinear curve fitting. Circular curve equation is fitted depending on a set of geomagnetic sensor data. It proves that the way is convenient and feasible

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27

Yuan, De Bao, Xi Min Cui, Guo Wang, Jing Jing Jin, and Wan Yang Xu. "Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method." Advanced Materials Research 446-449 (January 2012): 926–36. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.926.

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Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.

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28

Li, Ye, Zhen Lu, and Yong Jie Pang. "Strong Tracking Filter Based on Extended Kalman Filter for Data Processing of Underwater Vehicle." Advanced Materials Research 219-220 (March 2011): 569–73. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.569.

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A strong tracking filter based on suboptimal fading extended Kalman filter was proposed to ensure the perception for the motion state of underwater vehicles accurate in the paper. For the uncertainty of nonlinear system model, the strong tracking filter theory was introduced, orthogonality principle was put forward. Then suboptimal fading factor was pulled in, and extended Kalman filter for nonlinear system was established. The strong tracking filter was applied to data processing of underwater vehicle, and results indicate that it can effectively improve the accuracy and robustness of underwater navigation information.

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29

Zhemchuzhnikov, Dmitrii, Ilia Igashov, and Sergei Grudinin. "6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4707–15. http://dx.doi.org/10.1609/aaai.v36i4.20396.

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In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data. 6DCNN also includes SE(3)-equivariant message-passing and nonlinear activation operations constructed in the Fourier space. Working in the Fourier space allows significantly reducing the computational complexity of our operations. We demonstrate the properties of the 6D convolution and its efficiency in the recognition of spatial patterns. We also assess the 6DCNN model on several datasets from the recent CASP protein structure prediction challenges. Here, 6DCNN improves over the baseline architecture and also outperforms the state of the art.

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30

Gao, Ning, Xi Min Cui, and Cai Yun Gao. "An Effective Hybrid Approach for Processing Deformation Monitoring Data." Advanced Materials Research 446-449 (January 2012): 3247–51. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.3247.

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This paper describes the procedure of a hybrid approach based on grey model and artificial neural network (GM&ANN) to analysis and forecast of deformation data. The GM&ANN is formulated into three steps:(1)according to the monotonously increasing characteristics and the nonlinear characteristics of deformation time series, total deformation can be divided into tendency part and stochastic part.(2) use GM(1,1)to fit the trend of the data and obtain the residual series, on this basis by using artificial neural network to fit the stochastic part (residual series) .Then the forecasting value of deformation is obtained by adding the calculated predictive displacement value of each sub-stack. (3) validate the model. The results of experiments show that this hybrid has higher performances not only on model fitting but also on forecasting and therefore can be applied to deformation data processing.

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31

DiMattina, Christopher, and Kechen Zhang. "Active Data Collection for Efficient Estimation and Comparison of Nonlinear Neural Models." Neural Computation 23, no. 9 (September 2011): 2242–88. http://dx.doi.org/10.1162/neco_a_00167.

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The stimulus-response relationship of many sensory neurons is nonlinear, but fully quantifying this relationship by a complex nonlinear model may require too much data to be experimentally tractable. Here we present a theoretical study of a general two-stage computational method that may help to significantly reduce the number of stimuli needed to obtain an accurate mathematical description of nonlinear neural responses. Our method of active data collection first adaptively generates stimuli that are optimal for estimating the parameters of competing nonlinear models and then uses these estimates to generate stimuli online that are optimal for discriminating these models. We applied our method to simple hierarchical circuit models, including nonlinear networks built on the spatiotemporal or spectral-temporal receptive fields, and confirmed that collecting data using our two-stage adaptive algorithm was far more effective for estimating and comparing competing nonlinear sensory processing models than standard nonadaptive methods using random stimuli.

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32

Xiong, Yi, Buge Liang, Hanwen Yu, Jianlai Chen, Yanghao Jin, and Mengdao Xing. "Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 5750–59. http://dx.doi.org/10.1109/jstars.2021.3084619.

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33

Liu, Huan, Zheng Liu, Shuo Liu, Yihao Liu, Junchi Bin, Fang Shi, and Haobin Dong. "A Nonlinear Regression Application via Machine Learning Techniques for Geomagnetic Data Reconstruction Processing." IEEE Transactions on Geoscience and Remote Sensing 57, no. 1 (January 2019): 128–40. http://dx.doi.org/10.1109/tgrs.2018.2852632.

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34

Csurcsia, Péter Zoltán, Muhammad Faheem Siddiqui, Mark Charles Runacres, and Tim De Troyer. "Unsteady Aerodynamic Lift Force on a Pitching Wing: Experimental Measurement and Data Processing." Vibration 6, no. 1 (January 4, 2023): 29–44. http://dx.doi.org/10.3390/vibration6010003.

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This work discusses the experimental challenges and processing of unsteady experiments for a pitching wing in the low-speed wind tunnel of the Vrije Universiteit Brussel. The setup used for unsteady experiments consisted of two independent devices: (a) a position control device to steer the pitch angle of the wing, and (b) a pressure measurement device to measure the aerodynamic loads. The position control setup can pitch the wing for a range of frequencies, amplitude, and offset levels. In this work, a NACA-0018 wing profile was used with an aspect ratio of 1.8. The position control and the pressure measurement setups operate independently of each other, necessitating advanced signal processing techniques to synchronize the pitch angle and the lift force. Furthermore, there is a (not well-documented) issue with the (sampling) clock frequency of the pressure measurement setup, which was resolved using a fully automated spectral analysis technique. The wing was pitched using a simple harmonic sine excitation signal at eight different offset levels (between 6° and 21°) for a fixed amplitude variation (std) of 6°. At each offset level, the wing was pitched at five different frequencies between 0.1 Hz and 2 Hz (that correspond to reduced frequencies k ranging from 0.006 to 0.125). All the experiments were conducted at a fixed chord-based Reynolds number of 2.85 × 105. The choice of operating parameters invokes the linear and nonlinear behavior of the wing. The linear unsteady measurements agreed with the analytical results. The unsteady pressure measurements at higher offset levels revealed the nonlinear aerodynamic phenomenon of dynamic stall. This confirms that a nonlinear and dynamic model is required to capture the salient characteristics of the lift force on a pitching wing.

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35

Wilde, Mark M. "Optimized quantumf-divergences and data processing." Journal of Physics A: Mathematical and Theoretical 51, no. 37 (August 10, 2018): 374002. http://dx.doi.org/10.1088/1751-8121/aad5a1.

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36

Pedersen, L. B., and T. M. Rasmussen. "The gradient tensor of potential field anomalies: Some implications on data collection and data processing of maps." GEOPHYSICS 55, no. 12 (December 1990): 1558–66. http://dx.doi.org/10.1190/1.1442807.

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The full gradient tensor is presently not measured routinely onboard airplanes or on land. This paper describes some improvements that can be made in strategies of data collection and in processing of potential field maps if such tensor measurements were available. We suggest that, in addition to producing for example standard total field anomaly maps, the invariants of the tensor be mapped. Strikes of magnetic or gravimetric structures may be determined from minimizing the power in the first row and column of the tensor. Invariants can be looked upon as nonlinear filters enhancing sources with big volumes. Their lateral resolution is superior to that of the field proper and, for a given resolution, the flight altitude and separation between flight lines can be increased compared with the standard mode of operation. In airborne surveys the distance between flight lines is normally much larger than the height above the ground. This may introduce severe aliasing effects in the direction perpendicular to the flight lines. By increasing the flight altitude, aliasing effects are reduced at the expense of lateral resolution which, however, may be improved by mapping the tensor invariants in addition to the magnetic field. The estimated gradient tensor from total field magnetic data over the Siljan impact region shows that the magnetic properties of the area are very nonuniform even from a height of 430 m above the topography. The nonlinear filters discriminate major anomalies into separate units.

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37

Lopes, Elizeu Pereira, Eliezer Pereira Lopes, Jorge Muniz Barreto, and Luiz Muniz Barreto. "Robust nonlinear inversion of wave-tilt data." Inverse Problems 14, no. 4 (August 1, 1998): 955–77. http://dx.doi.org/10.1088/0266-5611/14/4/013.

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38

Victor, Jonathan D., and Mary M. Conte. "Cortical interactions in texture processing: Scale and dynamics." Visual Neuroscience 2, no. 3 (March 1989): 297–313. http://dx.doi.org/10.1017/s0952523800001218.

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AbstractWe investigate the neural computations underlying pattern processing with stimuli based on textures balanced for spatial frequency content (and second-order correlations) but not for higher-order correlations (Julesz et al. 1978). Interchange between two such isodipole textures produces a robust human visual evoked potential (VEP). The difference in population activity driven by two isodipole textures is quantified by the antisymmetric component of the VEP. Statistical properties of the textures eliminate contributions from linear mechanisms to the antisymmetric VEP.The dependence of the antisymmetric VEP on check size and fourth-order correlation statistics is used to test nonlinear models for the underlying neural computations. Linear summation, followed by a simple nonlinearity (such as rectification, saturation, or threshold), is inconsistent with the data. More elaborate models, in which a second nonlinear stage combines the output of local nonlinear mechanisms, are consistent with the data, provided that an appropriate spatial scale is chosen for the second stage of processing. For checks 4 min or smaller, the deduced interaction length is 10–15 min. For checks larger than 4 min, the interaction length is proportional to check size.

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39

Zhang, Qiang, Xiaoying Liang, and Xiaopeng Wei. "Scattered Data Processing Approach Based on Optical Facial Motion Capture." Applied Bionics and Biomechanics 10, no. 2-3 (2013): 75–87. http://dx.doi.org/10.1155/2013/463235.

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In recent years, animation reconstruction of facial expressions has become a popular research field in computer science and motion capture-based facial expression reconstruction is now emerging in this field. Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. To reduce the noise, we initially apply preprocessing to eliminate impulsive noise, before our proposed three-order quasi-uniform B-spline-based fitting method is used to reduce the remaining noise. Our experiments showed that the principles that underlie this method are simple and straightforward, and it delivered acceptable precision during reconstruction.

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40

Zhang, Xingxing, Chao Xu, Wanli Xue, Jing Hu, Yongchuan He, and Mengxin Gao. "Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing." Sensors 18, no. 11 (November 11, 2018): 3886. http://dx.doi.org/10.3390/s18113886.

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Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.

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41

Pavlenko, Vitaliy, Tetiana Shamanina, and Vladislav Chori. "Nonlinear Dynamics Identification of the Oculo-Motor System based on Eye Tracking Data." International Journal of Circuits, Systems and Signal Processing 15 (July 5, 2021): 569–77. http://dx.doi.org/10.46300/9106.2021.15.63.

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Instrumental computing and software tools have been developed for constructing a nonlinear dynamic model of the human oculo-motor system (OMS) based on the data of input–output experiments using test visual stimuli and innovative technology eye tracking. For identification the Volterra model is used in the form of multidimensional transient functions of the 1st, 2nd and 3rd orders, taking into account the inertial and nonlinear properties of the OMS. Software tools for processing eye tracking data developed in the Matlab environment are tested on real data from an experimental study of OMS.

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42

Xiangguo, Lin, and Liang Yong. "New algorithm model for processing generalized dynamic nonlinear data derived from deformation monitoring network." Geo-spatial Information Science 8, no. 2 (January 2005): 133–37. http://dx.doi.org/10.1007/bf02826853.

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43

Gao, Cai Yun, Xi Min Cui, and Xue Qian Hong. "Study on the Applications of Neural Networks for Processing Deformation Monitoring Data." Applied Mechanics and Materials 501-504 (January 2014): 2149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.2149.

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Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

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Zeng, Li, and Jun Wei Wang. "Data Processing in Bifurcation Analysis of Maps with Time-Delays in the Frequency Domain." Applied Mechanics and Materials 685 (October 2014): 634–37. http://dx.doi.org/10.4028/www.scientific.net/amm.685.634.

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A unified frequency-domain approach to analyze the NS (Neimark-Sacker) bifurcations and the period-doubling bifurcations of nonlinear maps with time-delays in the linear feed-forward term is presented. The technique relies on the HBA (harmonic balance approximation, a very important method in data processing ) and feedback systems theory. The expressions of the bifurcation solution and the stability are derived.

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Izumi, Tetsuya, and Youji Iiguni. "Data compression of nonlinear time series using a hybrid linear/nonlinear predictor." Signal Processing 86, no. 9 (September 2006): 2439–46. http://dx.doi.org/10.1016/j.sigpro.2005.11.013.

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Azaña, J., L. K. Oxenløwe, E. Palushani, R. Slavík, M. Galili, H. C. H. Mulvad, H. Hu, Y. Park, A. T. Clausen, and P. Jeppesen. "In-Fiber Subpicosecond Pulse Shaping for Nonlinear Optical Telecommunication Data Processing at 640 Gbit/s." International Journal of Optics 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/895281.

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We review recent work on all-fiber (long-period fiber grating) devices for optical pulse shaping, particularly flat-top pulse generation, down to the subpicosecond range and their application for nonlinear switching (demultiplexing) of optical time-division multiplexed (OTDM) data signals in fiber-optic telecommunication links operating up to 640 Gbit/s. Experiments are presented demonstrating error-free 640-to-10 Gbit/s demultiplexing of the 64 tributary channels using the generated flat-top pulses for temporal gating in a Kerr-effect-based nonlinear optical loop mirror. The use of flat-top pulses has critical benefits in the demultiplexing process, including a significantly increased timing-jitter tolerance (up to ~500 fs, i.e., 30% of the bit period) and the associated improvement in the bit-error-rate performance (e.g., with a sensitivity increase of up to ~13 dB as compared with the use of Gaussian-like gating pulses). Long-period fiber grating pulse shapers with reduced polarization dependence are fabricated and successfully used for polarization-independent 640-to-10 Gbit/s demultiplexing experiments.

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Bluhm, Andreas, and Ángela Capel. "A strengthened data processing inequality for the Belavkin–Staszewski relative entropy." Reviews in Mathematical Physics 32, no. 02 (August 22, 2019): 2050005. http://dx.doi.org/10.1142/s0129055x20500051.

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In this work, we provide a strengthening of the data processing inequality for the relative entropy introduced by Belavkin and Staszewski (BS-entropy). This extends previous results by Carlen and Vershynina for the relative entropy and other standard [Formula: see text]-divergences. To this end, we provide two new equivalent conditions for the equality case of the data processing inequality for the BS-entropy. Subsequently, we extend our result to a larger class of maximal [Formula: see text]-divergences. Here, we first focus on quantum channels which are conditional expectations onto subalgebras and use the Stinespring dilation to lift our results to arbitrary quantum channels.

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Leditzky, Felix, Cambyse Rouzé, and Nilanjana Datta. "Data processing for the sandwiched Rényi divergence: a condition for equality." Letters in Mathematical Physics 107, no. 1 (November 15, 2016): 61–80. http://dx.doi.org/10.1007/s11005-016-0896-9.

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Zhang, Zhenhai, Kaiming Li, and Xiaoping Hu. "Mapping nonlinear brain dynamics by phase space embedding with fMRI data." Biomedical Signal Processing and Control 82 (April 2023): 104521. http://dx.doi.org/10.1016/j.bspc.2022.104521.

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Moore, Keegan J. "Characteristic nonlinear system identification: A data-driven approach for local nonlinear attachments." Mechanical Systems and Signal Processing 131 (September 2019): 335–47. http://dx.doi.org/10.1016/j.ymssp.2019.05.066.

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