Curve spline generalizzate di interpolazione locale

Questa tesi presenta un metodo generale per la costruzione di curve spline generalizzate di interpolazione locale. Costruiremo quest'ultime miscelando polinomi interpolanti generalizzati a blending function generalizzate. Verrano inoltre verificate sperimentalmente alcune delle proprietà di queste curve.

Unity3d e spline: sviluppo di un videogame basato sulle curve spline

Il contesto della tesi è quello dello sviluppo di applicazioni grafiche interattive. Si parlerà di come si implementano determinate tecniche per la progettazione di un videogioco basato su un motore grafico 3D. La tesi tratterà sia della teoria delle curve, cercando di spiegare come è possibile descrivere dei percorsi nel calcolatore, giustificando per quale motivo sono stati scelti determinati algoritmi, e sia di quali strumenti sono stati utilizzati per la creazione del videogame, soffermandosi sul funzionamento dell’engine (Unity3D) e fornendo informazioni sull’implementazione del codice. Non saranno escluse dalla tesi informazioni riguardanti lo sviluppo dell’idea e del lato artistico di un videogame.

Mammografia digitale: confronto tra qualità immagine in relazione alle curve di acquisizione dosimetriche

Il presente lavoro è stato svolto presso il Servizio di Fisica Sanitaria dell’Azienda USL della Romagna, Presidio Ospedaliero di Ravenna e consiste nella validazione del dato dosimetrico visualizzato su due apparecchiature per mammografia digitale e nel confronto tra qualità immagine di diverse curve di acquisizione in funzione della dose e della post-elaborazione. Presupposto per l’acquisizione delle immagini è stata la validazione del dato dosimetrico visualizzato sui mammografi, tramite misura diretta della dose in ingresso con strumentazione idonea, secondo protocolli standard e linee guida europee. A seguire, sono state effettuate prove di acquisizione delle immagini radiografiche su due diversi fantocci, contenenti inserti a diverso contrasto e risoluzione, ottenute in corrispondenza di tre curve dosimetriche e in funzione dei due livelli di post-elaborazione delle immagini grezze. Una volta verificati i vari passaggi si è proceduto con l’analisi qualitativa e quantitativa sulle immagini prodotte: la prima valutazione è stata eseguita su monitor di refertazione mammografica, mentre la seconda è stata effettuata calcolando il contrasto in relazione alla dose ghiandolare media. In particolare è stato studiato l’andamento del contrasto cambiando le modalità del software Premium View e lo spessore interposto tra la sorgente di raggi X ed il fantoccio, in modo da simulare mammelle con grandezze differenti.

Search For Critical Slip Surface In Slope Stability Analysis By Spline-Based Ga Method

The stability of a soil slope is usually analyzed by limit equilibrium methods, in which the identification of the critical slip surface is of principal importance. In this study the spline curve in conjunction with a genetic algorithm is used to search the critical slip surface, and Spencer's method is employed to calculate the factor of safety. Three examples are presented to illustrate the reliability and efficiency of the method. Slip surfaces defined by a series of straight lines are compared with those defined by spline curves, and the results indicate that use of spline curves renders better results for a given number of slip surface nodal points comparing with the approximation using straight line segments.

Relaxation time and conductivity of comb-like gel polymer electrolytes

Using the copolymer of acrylonitrile (AN), methyl methacrylate (MMA), and poly(ethylene glycol) methyl ether methacrylate as a backbone and poly(ethylene glycol) methyl ether (PEGME) with 1100 molecular weight as side chains, comb-like gel polymers and their Li salt complexes were synthesized. The dynamic mechanical properties and conductivities were investigated. Results showed that the gel copolymer electrolytes possess two glass transitions: alpha-transition and beta-transition. Based on the time-temperature equivalence principle, a master curve was constructed by selecting T. as reference temperature. By reference to T-0 = 50 degrees C, the relation between log c, and c was found to be linear. The master curves are displaced progressively to higher frequencies as the content of plasticizer is increased. The relation between log tau(p) and the content of plasticizer is also linear.

Study of the conductivity and side chain mobility of a comb-like polymer electrolyte using the dynamic mechanical method

Using poly(styrene-co-maleic anhydride) as a backbone and poly(ethylene glycol) methyl ether (PEGME) with different molecular weights as side chains, three comb-like polymers and their Li salt complexes were synthesized. The dynamic mechanical properties and conductivities were investigated. Results showed that the polymer electrolytes possess two glass transitions: alpha -transition and beta -transition, and the temperature dependence of the ionic conductivity shows WLF (Williams-Landel-Ferry) behavior. Based on the time-temperature equivalence principle, a master curve was constructed by selecting T-beta as reference temperature. The values of the WLF parameters (C-1 and C-2) were obtained and were found to be almost independent of the length of the PEGME side chain and the content of Li salt. By reference to T-0 = 50 degreesC. the relation between log tau (c) and c was found to be linear. The master curves are displaced progressively to higher frequencies as the molecular weight of the side chain is increased. The relation between log tau (n) and the molecular weight of the side chain is also linear. (C) 2001 Elsevier Science B.V. All rights reserved.

A New Principal Curve Algorithm for Nonlinear Principal Component Analysis

This paper emerged from work supported by EPSRC grant GR/S84354/01 and proposes a method of determining principal curves, using spline functions, in principal component analysis (PCA) for the representation of non-linear behaviour in process monitoring. Although principal curves are well established, they are difficult to implement in practice if a large number of variables are analysed. The significant contribution of this paper is that the proposed method has minimal complexity, assuming simple spline geometry, thus enabling efficient computation. The paper provides a foundation for further work where multiple curves may be required to represent underlying non-linear information in complex data.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.