Relevant bibliographies by topics / Spline Function

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spline Function'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Spline Function"

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YOSHIMOTO, Fujiichi. "Spline Function." Journal of Japan Society for Fuzzy Theory and Systems 6, no. 5 (1994): 895–98. http://dx.doi.org/10.3156/jfuzzy.6.5_895.

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Toraichi, Kazuo, and Takahiko Horiuchi. "Spline Function." IEEJ Transactions on Electronics, Information and Systems 114, no. 2 (1994): 209–16. http://dx.doi.org/10.1541/ieejeiss1987.114.2_209.

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K.H., Faraidun, Karwan H. F. Jwamer, and Sabah Ali Mohammed. "An Algorithm for Computing Spline Function." Journal of Zankoy Sulaimani - Part A 18, no. 3 (April 21, 2016): 251–58. http://dx.doi.org/10.17656/jzs.10554.

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MacCarthy, B. L., and N. D. Burns. "An Evaluation of Spline Functions for use in Cam Design." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 199, no. 3 (July 1985): 239–48. http://dx.doi.org/10.1243/pime_proc_1985_199_118_02.

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This paper shows how spline functions can be employed for kinematic motion specification in cam design. The polynomial spline is introduced as a special case of a continuous piecewise function. Cubic and quintic splines are derived and their properties are discussed in the cam design context. It is shown how standard cam laws can be approximated extremely accurately with a small number of points and appropriate boundary conditions. The modified sinusoidal acceleration cam law is used as an example. The application of quintic splines to non-standard and special motions is discussed. The algebraic and B-spline representations of spline functions are compared. The former is considered preferable in this context and a list of useful algorithms is given. The real power of the spline function, in particular the algebraic quintic spline, is its simplicity, ease of computation and adaptability to non-standard design problems. The use of parametrized, deficient and exponential splines is proposed for specific applications.
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Budakçı, Gülter, and Halil Oruç. "Further Properties of Quantum Spline Spaces." Mathematics 8, no. 10 (October 14, 2020): 1770. http://dx.doi.org/10.3390/math8101770.

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We construct q-B-splines using a new form of truncated power functions. We give basic properties to show that q-B-splines form a basis for quantum spline spaces. On the other hand, we derive algorithmic formulas for 1/q-integration and 1/q-differentiation for q-spline functions. Moreover, we show a way to find the polynomial pieces on each interval of a q-spline function.
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Toraichi, Kazuo, and Masaru Kamada. "Spline Function II." IEEJ Transactions on Electronics, Information and Systems 114, no. 7-8 (1994): 773–82. http://dx.doi.org/10.1541/ieejeiss1987.114.7-8_773.

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K. S., Rostam, and Karwan H.J. "Lacunary Interpolation by Spline function (0,3) Case." Journal of Zankoy Sulaimani - Part A 6, no. 1 (September 16, 2003): 43–49. http://dx.doi.org/10.17656/jzs.10111.

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Strelkovskaya, Irina, Irina Solovskaya, and Juliya Strelkovska. "Application of real and complex splines in infocommunication problems." Problemi telekomunìkacìj, no. 1(28) (December 22, 2021): 3–19. http://dx.doi.org/10.30837/pt.2021.1.01.

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The work offers the solution to problems of analysis and synthesis of infocommunication systems with the help of real and complex spline functions. The use of the spline approximation method for solving problems of recovery of random signals and self-similar traffic, management of network objects and network as a whole, and procedures of infocommunication objects and networks functioning is offered. To solve the problems of forecasting, in particular, forecasting the characteristics of network traffic and maintaining the QoS characteristics in its service and formation of requirements for network buffer devices, developed spline extrapolation based on different types of real spline functions, namely: linear, quadratic, quadratic B-splines, cubic, cubic B-splines, cubic Hermite splines. As a criterion for choosing the type of spline function, the prediction error is selected, the accuracy of which can be increased by using a particular kind of spline, depending on the object being predicted. The use of complex flat spline functions is considered to solve the class of user positioning problems in the radio access network. In general, the use of real and complex spline functions allows obtaining the results of improving the Quality of Service in the infocommunication network and ensuring the scalability of the obtained solutions.
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Toraichi, Kazuo, Ryoichi Mori, and Masaru Kamada. "Design of window function using spline functions." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 72, no. 11 (1989): 10–19. http://dx.doi.org/10.1002/ecjc.4430721102.

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Sivak, Roman. "DEFINITION OF KINEMATICS OF DEFORMATION BASED ON SPLINE-APPROXIMATIONS." Vibrations in engineering and technology, no. 2(97) (August 27, 2020): 101–7. http://dx.doi.org/10.37128/2306-8744-2020-2-11.

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It is suggested to use smoothing cubic spline functions to approximate the current functions. The structure of the selected functional provides the minimum curvature of the spline and the smallest deviation of the spline from the function smoothed at the nodes. The necessary correlation between these requirements is provided by the choice of weights. The algorithm for approximating the current function is implemented by moving from a grid created by current lines and auxiliary lines to a rectangular grid. To reduce the effect of random errors of experimental information on the current line, a smoothing algorithm with a given accuracy was used. The current function is interpolated from the physical grid to the calculated one. The flow velocities were approximated to calculate the strain rates. To estimate the accuracy of the calculations, it is proposed to use the deviation from the incompatibility condition. The main disadvantage is that their behavior around a point determines their behavior as a whole. In this regard, other approaches to the approximation free of this deficiency have been developed in recent years. One such approach that has proven itself well in both theoretical and practical applications is the use of so-called splines. Splines are called functions that are glued together from different pieces of polynomials by a fixed system. The simplest example is broken. Splines naturally occur in many mechanical problems. For example, the spline shape has an elastic beam with point loads. Among the splines, polynomial splines glued together from pieces of polynomials play the most important role. The development of the theory of such splines and their popularization were facilitated by I. Schoenberg's work. Polynomial splines are beginning to be used in many application problems related to function approximation.
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Dissertations / Theses on the topic "Spline Function"

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Wang, Lu. "Cure Rate Model with Spline Estimated Components." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28359.

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In some survival analysis of medical studies, there are often long term survivors who can be considered as permanently cured. The goals in these studies are to estimate the cure probability of the whole population and the hazard rate of the noncured subpopulation. The existing methods for cure rate models have been limited to parametric and semiparametric models. More specifically, the hazard function part is estimated by parametric or semiparametric model where the effect of covariate takes a parametric form. And the cure rate part is often estimated by a parametric logistic regression model. We introduce a non-parametric model employing smoothing splines. It provides non-parametric smooth estimates for both hazard function and cure rate. By introducing a latent cure status variable, we implement the method using a smooth EM algorithm. Louisâ formula for covariance estimation in an EM algorithm is generalized to yield point-wise confidence intervals for both functions. A simple model selection procedure based on the Kullback-Leibler geometry is derived for the proposed cure rate model. Numerical studies demonstrate excellent performance of the proposed method in estimation, inference and model selection. The application of the method is illustrated by the analysis of a melanoma study.
Ph. D.
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Kulkarni, Rekha Panditrao. "Fonctions spline cardinales tronquées." Grenoble 1, 1985. http://tel.archives-ouvertes.fr/tel-00318472.

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On propose des conditions de bout pour les fonctions spines polynomiales d'interpolation de degré p (p≥2) associées aux abscisses équidistantes qui économisent le calcul et entraînent un ordre de convergence optimal. Cette fonction spline peut être interprétée comme une fonction spline cardinale tronquée avec une correction convenable. La technique utilisée pour les fonctions splines polynomiales est applicable dans le cas des fonctions splines sous tension. On donne aussi quelques résultats pour les fonctions splines cubiques de lissage
On propose des conditions de bout pour les fonctions spines polynomiales d'interpolation de degre p (p >ou= 2) associees aux abscisses equidistantes qui economisent le calcul et entrainent un ordre de convergence optimal. Cette fonction spline peut etre interpretee comme une fonction spline cardinale tronquee avec une correction convenable. La technique utilisee pour les fonctions splines polynomiales est applicable dans le cas des fonctions splines sous tension. On donne aussi quelques resultats pour les fonctions splines cubiques de lissage
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Burton, Christina Marie. "Quadratic Spline Approximation of the Newsvendor Problem Optimal Cost Function." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3087.

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We consider a single-product dynamic inventory problem where the demand distributions in each period are known and independent but with density. We assume the lead time and the fixed cost for ordering are zero and that there are no capacity constraints. There is a holding cost and a backorder cost for unfulfilled demand, which is backlogged until it is filled by another order. The problem may be nonstationary, and in fact our approximation of the optimal cost function using splines is most advantageous when demand falls suddenly. In this case the myopic policy, which is most often used in practice to calculate optimal inventory level, would be very costly. Our algorithm uses quadratic splines to approximate the optimal cost function for this dynamic inventory problem and calculates the optimal inventory level and optimal cost.
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Kim, Heeyoung. "Statistical methods for function estimation and classification." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44806.

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This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for fitting functions with varying roughness. In the first part of the first chapter, we study an asymptotically optimal procedure to choose the value of a discretized version of the variable smoothing parameter in adaptive smoothing splines. With the choice given by the multivariate version of the generalized cross validation, the resulting adaptive smoothing spline estimator is shown to be consistent and asymptotically optimal under some general conditions. In the second part, we derive the asymptotically optimal local penalty function, which is subsequently used for the derivation of the locally optimal smoothing spline estimator. In the second chapter, we propose a Lipschitz regularity based statistical model, and apply it to coordinate measuring machine (CMM) data to estimate the form error of a manufactured product and to determine the optimal sampling positions of CMM measurements. Our proposed wavelet-based model takes advantage of the fact that the Lipschitz regularity holds for the CMM data. The third chapter focuses on the classification of functional data which are known to be well separable within a particular interval. We propose an interval based classifier. We first estimate a baseline of each class via convex optimization, and then identify an optimal interval that maximizes the difference among the baselines. Our interval based classifier is constructed based on the identified optimal interval. The derived classifier can be implemented via a low-order-of-complexity algorithm.
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Wu, Yuan. "The partially monotone tensor spline estimation of joint distribution function with bivariate current status data." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/762.

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The analysis of joint distribution function with bivariate event time data is a challenging problem both theoretically and numerically. This thesis develops a tensor splinebased nonparametric maximum likelihood estimation method to estimate the joint distribution function with bivariate current status data. The tensor I-splines are developed to replace the traditional tensor B-splines in approximating joint distribution function in order to simplify the restricted maximum likelihood estimation problem in computing. The generalized gradient projection algorithm is used to compute the restricted optimization problem. We show that the proposed tensor spline-based nonparametric estimator is consistent and that the rate of convergence is obtained. Simulation studies with moderate sample sizes show that the finite-sample performance of the proposed estimator is generally satisfactory.
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Wilson, Brigham Bond. "Infinitesimal Perturbation Analysis for the Capacitated Finite-Horizon Multi-Period Multiproduct Newsvendor Problem." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/2988.

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An optimal ordering scheme for the capacitated, finite-horizon, multi-period, multiproduct newsvendor problem was proposed by cite {shao06} using a hedging point policy. This solution requires the calculation of a central curve that divides the different ordering regions and a vector that defines the target inventory levels. The central curve is a nonlinear curve that determines the optimal order quantities as a function of the initial inventory levels. In this paper we propose a method for calculating this curve and vector using spline functions, infinitesimal perturbation analysis (IPA), and convex optimization. Using IPA the derivatives of the cost with respect to the variables that determine the spline function are efficiently calculated. A convex optimization algorithm is used to optimize the spline function, resulting in a optimal policy. We present the mathematical derivations and simulation results validating this solution.
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Gomes, José Clelto Barros. "Estimação não-parametrica para função de covariancia de processos gaussianos espaciais." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306507.

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Orientador: Ronaldo Dias
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
Made available in DSpace on 2018-08-13T14:28:48Z (GMT). No. of bitstreams: 1 Gomes_JoseCleltoBarros_M.pdf: 1798618 bytes, checksum: db671b29b83f0321e8dbc03c5af42cde (MD5) Previous issue date: 2009
Resumo: O desafio na modelagem de processos espaciais está na descrição da estrutura de covariância do fenômeno sob estudo. Um estimador não-paramétrico da função de covariância foi construído de forma a usar combinações lineares de funções B-splines. Estas bases são usadas com muita frequência na literatura graças ao seu suporte compacto e a computação tão rápida quanto a habilidade de criar aproximações suaves e apropriadas. Verificouse que a função de covariância estimada era definida positiva por meio do teorema de Bochner. Para a estimação da função de covariância foi implementado um algoritmo que fornece um procedimento completamente automático baseado no número de funções bases. Então foram realizados estudos numéricos que evidenciaram que assintoticamente o procedimento é consistente, enquanto que para pequenas amostras deve-se considerar as restrições das funções de covariância. As funções de covariâncias usadas na estimação foram as de exponencial potência, gaussiana, cúbica, esférica, quadrática racional, ondular e família de Matérn. Foram estimadas ainda covariâncias encaixadas. Simulações foram realizadas também a fim de verificar o comportamento da distribuição da afinidade. As estimativas apresentaram-se satisfatórias
Abstract: The challenge in modeling of spatials processes is in description of the framework of covariance of the phenomenon about study. The estimation of covariance functions was done using a nonparametric linear combinations of basis functions B-splines. These bases are used frequently in literature thanks to its compact support and fast computing as the ability to create smooth and appropriate approaches There was positive definiteness of the estimator proposed by the Bochner's theorem. For the estimation of the covariance functions was implemented an algorithm that provides a fully automated procedure based on the number of basis functions. Then numerical studies were performed that showed that the procedure is consistent assynthotically. While for small samples should consider the restrictions of the covariance functions, so the process of optimization was non-linear optimization with restrictions. The following covariance functions were used in estimating: powered exponential, Gaussian, cubic, spherical, rational quadratic and Matérn family. Nested covariance funtions still were estimated. Simulations were also performed to verify the behavior of affinity and affinity partial, which measures how good is the true function of the estimated function. Estimates showed satisfactory
Mestrado
Mestre em Estatística
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Ludwig, Guilherme Vieira Nunes. "Estimação não paramétrica da função de covariância para dados funcionais agregados." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306198.

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Orientadores: Nancy Lopes Garcia, Ronaldo Dias
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica
Made available in DSpace on 2018-08-18T04:43:13Z (GMT). No. of bitstreams: 1 Ludwig_GuilhermeVieiraNunes_M.pdf: 4540322 bytes, checksum: c767b4a6c7cd883a70e9ebbc33fe04ec (MD5) Previous issue date: 2011
Resumo: O objetivo desta dissertação é desenvolver estimadores não paramétricos para a função de covariância de dados funcionais agregados, que consistem em combinações lineares de dados funcionais que não podem ser observados separadamente. Estes métodos devem ser capazes de produzir estimativas que separem a covariância típica de cada uma das subpopulações que geram os dados, e que sejam funções não negativas definidas. Sob estas restrições, foi definida uma classe de funções de covariância não estacionarias, à qual resultados da teoria de estimação de covariância de processos estacionários podem ser estendidos. Os métodos desenvolvidos foram ilustrados com a aplicação em dois problemas reais: a estimação do perfil de consumidores de energia elétrica, em função do tempo, e a estimação da transmitância de substâncias puras em espectroscopia de infravermelho, através da inspeção de misturas, em função do espectro da luz
Abstract: The goal of this dissertation is to develop nonparametric estimators for the covariance function of aggregated functional data, which consists into linear combinations of functional data that cannot be sampled separately. Such methods must be able to produce estimates that not only separate the typical covariance of the subpopulations composing the data, but also be nonnegative definite functions. Under these restrictions, a class of nonstationary covariance functions was proposed, to which stationary processes' covariance function estimation results can be readily extended. The developed methods were illustrated with an application to two real problems: the estimation of electric energy consumers' profiles, as a function of the time of the day, and the estimation of the transmittance of pure substances in infrared spectroscopy, while inspecting mixtures of them, as a function of light spectrum
Mestrado
Estatistica Não Parametrica
Mestre em Estatística
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Mo, Eirik. "Nonlinear stochastic dynamics and chaos by numerical path integration." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1786.

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The numerical path integration method for solving stochastic differential equations is extended to solve systems up to six spatial dimensions, angular variables, and highly nonlinear systems - including systems that results in discontinuities in the response probability density function of the system. Novel methods to stabilize the numerical method and increase computation speed are presented and discussed. This includes the use of the fast Fourier transform (FFT) and some new spline interpolation methods. Some sufficient criteria for the path integration theory to be applicable is also presented. The development of complex numerical code is made possible through automatic code generation by scripting. The resulting code is applied to chaotic dynamical systems by adding a Gaussian noise term to the deterministic equation. Various methods and approximations to compute the largest Lyapunov exponent of these systems are presented and illustrated, and the results are compared. Finally, it is shown that the location and size of the additive noise term affects the results, and it is shown that additive noise for specific systems could make a non-chaotic system chaotic, and a chaotic system non-chaotic.

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Chen, Tianlei. "Cure Rate Models with Nonparametric Form of Covariate Effects." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52894.

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This thesis focuses on development of spline-based hazard estimation models for cure rate data. Such data can be found in survival studies with long term survivors. Consequently, the population consists of the susceptible and non-susceptible sub-populations with the latter termed as "cured". The modeling of both the cure probability and the hazard function of the susceptible sub-population is of practical interest. Here we propose two smoothing-splines based models falling respectively into the popular classes of two component mixture cure rate models and promotion time cure rate models. Under the framework of two component mixture cure rate model, Wang, Du and Liang (2012) have developed a nonparametric model where the covariate effects on both the cure probability and the hazard component are estimated by smoothing splines. Our first development falls under the same framework but estimates the hazard component based on the accelerated failure time model, instead of the proportional hazards model in Wang, Du and Liang (2012). Our new model has better interpretation in practice. The promotion time cure rate model, motivated from a simplified biological interpretation of cancer metastasis, was first proposed only a few decades ago. Nonetheless, it has quickly become a competitor to the mixture models. Our second development aims to provide a nonparametric alternative to the existing parametric or semiparametric promotion time models.
Ph. D.
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Books on the topic "Spline Function"

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1939-, Dubuc Serge, and Deslauriers Gilles 1941-, eds. Spline functions and the theory of wavelets. Providence, R.I: American Mathematical Society, 1999.

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Nürnberger, G. Approximation by spline functions. Berlin: Springer-Verlag, 1989.

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Bojanov, B. D. Spline functions and multivariate interpolations. Dordrecht: Kluwer Academic Publishers, 1993.

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Nürnberger, Günther. Approximation by Spline Functions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-61342-5.

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Schumaker, Larry L. Spline functions: Basic theory. Malabar, Fla: Krieger, 1993.

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Spline functions: Basic theory. 3rd ed. New York: Cambridge University Press, 2007.

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Spline functions: Computational methods. Philadelphia: Society for Industrial and Applied Mathematics, 2015.

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Two dimensional spline interpolation algorithms. Wellesley, Mass: A.K. Peters, 1995.

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Bojanov, B. D., H. A. Hakopian, and A. A. Sahakian. Spline Functions and Multivariate Interpolations. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8169-1.

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Hilbertian kernels and spline functions. Amsterdam: North-Holland, 1992.

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Book chapters on the topic "Spline Function"

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Anthony, Ralston, and Rabinowitz Philip. "Function SPLINE function SMOOTH." In Time Series Package (TSPACK), 51–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/3-540-15202-4_16.

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Penner, Alvin. "ODF Using a Beta1-Spline." In Fitting Splines to a Parametric Function, 67–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12551-6_10.

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Penner, Alvin. "ODF Using a Beta2-Spline." In Fitting Splines to a Parametric Function, 57–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12551-6_9.

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Light, W. A. "Some Aspects of Radial Basis Function Approximation." In Approximation Theory, Spline Functions and Applications, 163–90. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2634-2_8.

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Penner, Alvin. "ODF Using a 5-Point B-Spline." In Fitting Splines to a Parametric Function, 37–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12551-6_6.

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Penner, Alvin. "ODF Using a 6-Point B-Spline." In Fitting Splines to a Parametric Function, 43–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12551-6_7.

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Gaab, M. R., and H. E. Heissler. "Intracranial Elastance: Spline Interpolation vs Exponential Function." In Intracranial Pressure VII, 267–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-73987-3_73.

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Petrushev, Pencho P. "Direct and converse theorems for spline and rational approximation and besov spaces." In Function Spaces and Applications, 363–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0078887.

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Yuan, Hanning, Wenzhong Shi, and Jiabing Sun. "Mining Standard Land Price with Tension Spline Function." In Advanced Data Mining and Applications, 792–803. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_94.

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Bogdanovich, Alexander E. "Spline Function Aided Analysis of Inhomogeneous Materials and Structures." In Local Mechanics Concepts for Composite Material Systems, 355–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84792-9_18.

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Conference papers on the topic "Spline Function"

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Noboru Noguchi, John F. Reid, Qin Zhang, and Jeffrey D. Will. "Turning Function for Robot Tractor Based on Spline Function." In 2001 Sacramento, CA July 29-August 1,2001. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2001. http://dx.doi.org/10.13031/2013.7298.

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Bedabrata Chand, Arya Kumar. "Natural Bicubic Spline Coalescence Fractal Interpolation Function." In Annual International Conference on Computational Mathematics, Computational Geometry & Statistics. Global Science and Technology Forum (GSTF), 2012. http://dx.doi.org/10.5176/2251-1911_cmcgs53.

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Dechevsky, Lubomir T., and Peter Zanaty. "Beta-function B-spline smoothing on triangulations." In IS&T/SPIE Electronic Imaging, edited by Atilla M. Baskurt and Robert Sitnik. SPIE, 2013. http://dx.doi.org/10.1117/12.2007681.

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Hani, Ahmad Fadzil M., Arwan A. Khoiruddin, Nicolas Walter, Ibrahima Faye, and Thong C. Mun. "3D reconstruction using spline inverse function analysis." In 2012 4th International Conference on Intelligent & Advanced Systems (ICIAS). IEEE, 2012. http://dx.doi.org/10.1109/icias.2012.6306211.

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Velimirovic, Lazar, Zoran Peric, and Miomir Stankovic. "Approximation of the optimal compressor function combining different spline functions in segments." In TELSIKS 2013 - 2013 11th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services. IEEE, 2013. http://dx.doi.org/10.1109/telsks.2013.6704436.

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Song, Xinghua, Bert Jüttler, and Adrien Poteaux. "Hierarchical Spline Approximation of the Signed Distance Function." In 2010 Shape Modeling International (SMI). IEEE, 2010. http://dx.doi.org/10.1109/smi.2010.18.

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Jiang, R. "Determination of degradation change point using spline function." In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2012. http://dx.doi.org/10.1109/icqr2mse.2012.6246371.

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Kitahara, Daichi, and Isao Yamada. "Probability density function estimation by positive quartic C2-spline functions." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178633.

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Xu, Tao, Peng Zou, Tianshuang Xu, and Chenmeng Jiye. "Study on Weight Function of Meshless Method Based on B-spline Wavelet Function." In 2010 Third International Joint Conference on Computational Science and Optimization. IEEE, 2010. http://dx.doi.org/10.1109/cso.2010.136.

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Hussain, Malik Zawwar, Misbah Irshad, Muhammad Sarfraz, and Nousheen Zafar. "Interpolation of Discrete Time Signals Using Cubic Spline Function." In 2015 19th International Conference on Information Visualisation (iV). IEEE, 2015. http://dx.doi.org/10.1109/iv.2015.82.

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Reports on the topic "Spline Function"

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Fearon, M. Finding the cubic smoothing spline function by scale invariants. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128121.

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De Boor, Carl. Spline Functions and Surfaces. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada230651.

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Stein, P. C., and W. L. White. Smoothing Surface Data by Spline Functions. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada155829.

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Villez, Kris. Computation and illustration of the generalized order restricted criterion (GORIC) for shape constrained spline functions. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1659607.

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Saunders, Bonita, and Qiming Wang. From b-spline mesh generation to effective visualizations for the NIST digital library of mathematical functions. Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7402.

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Nagayama, Shinobu, Tsuotomu Sasao, and Jon T. Butler. Floating-Point Numeric Function Generators Based on Piecewise-Split EVMDDs. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada547647.

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Greer, Earl D. Joint Staff Organization: Is there a Planning and Programming Function Split. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada208040.

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Srivastava, Shiv. Structure and Function of the Splice Variants of TMPRSS2-ERG, a Prevalent Genomic Alteration in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada517260.

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Srivastava, Shiv. Structure and Function of the Splice Variants of TMPRSS2-ERG, a Prevalent Genomic Alteration in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada566991.

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Srivastava, Shiv. Structure and Function of the Splice Variants of TMPRSS2-ERG, a Prevalent Genomic Alteration in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada552720.

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