Orientador: Jorge Tulio Mujica Ascui / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-25T12:22:20Z (GMT). No. of bitstreams: 1 Araujo_MariaAngelica_M.pdf: 648625 bytes, checksum: 6d3a2ed1bef26a3213e505351e408696 (MD5) Previous issue date: 2014 / Resumo: O objetivo desta dissertação é apresentar e demonstrar alguns teoremas da Análise matemática, são eles, O Teorema de Aproximação de Weierstrass, o Teorema de Kakutani-Stone, os Teoremas de Stone-Weierstrass e o Teorema de Nachbin. Para demonstrá-los relembraremos algumas definições e resultados básicos da teoria de Análise e Topologia e abordaremos as demais ferramentas necessárias para suas respectivas demonstrações / Abstract: The aim of this dissertation is to present and prove some theorems of mathematical analysis, that are, the Weierstrass Approximation Theorem, the Kakutani-Stone Theorem, the Stone-Weierstrass Theorems and the Nachbin Theorem. To prove them we recall some basic definitions and results of analysis and topology and we discuss other tools that are necessary for their respective proofs / Mestrado / Matematica / Mestra em Matemática

No description available.

In this paper, we use the following scheme to construct a continuous, nowhere-differentiable function 𝑓 which is the uniform limit of a sequence of sawtooth functions 𝑓ₙ : [0, 1] → [0, 1] with increasingly sharp teeth. Let 𝑋 = [0, 1] x [0, 1] and 𝐹(𝑋) be the Hausdorff metric space determined by 𝑋. We define contraction maps 𝑤₁ , 𝑤₂ , 𝑤₃ on 𝑋. These maps define a contraction map 𝑤 on 𝐹(𝑋) via 𝑤(𝐴) = 𝑤₁(𝐴) ⋃ 𝑤₂(𝐴) ⋃ 𝑤₃(𝐴). The iteration under 𝑤 of the diagonal in 𝑋 defines a sequence of graphs of continuous functions 𝑓ₙ. Since 𝑤 is a contraction map in the compact metric space 𝐹(𝑋), 𝑤 has a unique fixed point. Hence, these iterations converge to the fixed point-which turns out to be the graph of our continuous, nowhere-differentiable function 𝑓. Chapter 2 contains the background we will need to engage our task. Chapter 3 includes two results from the Baire Category Theorem. The first is the well known fact that the set of continuous, nowhere-differentiable functions on [0,1] is a residual set in 𝐶[0,1]. The second fact is that the set of continuous functions on [0,1] which have a dense set of proper local extrema is residual in 𝐶[0,1]. In the fourth and last chapter we actually construct our function and prove it is continuous, nowhere-differentiable and has a dense set of proper local extrema. Lastly we iterate the set {(0,0), (1,1)} under 𝑤 and plot its points. Any terms not defined in Chapters 2 through 4 may be found in [2,4]. The same applies to the basic properties of metric spaces which have not been explicitly stated. Throughout, we will let 𝒩 and 𝕽 denote the natural numbers and the real numbers, respectively.

abstract: Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this thesis, physics-based approaches are incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. First, sparse regularization and constraints are implemented by adding differentiable penalty terms to a cost function to avoid unrealistic predictions. Secondly, a physics-based dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented. Then, this dispersion model is utilized as a generative model within an analysis-by-synthesis spectral unmixing algorithm. Further, a technique for inverse rendering using a convolutional neural network to predict parameters of the generative model is introduced to enhance performance and speed when training data are available. Results achieve state-of-the-art on both infrared and visible-to-near-infrared (VNIR) datasets as compared to baselines, and show promise for the synergy between physics-based models and deep learning in hyperspectral unmixing in the future. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Orientador: Selene Maria Coelho Loibel / Banca: Maiko Fernandes Buzzi / Banca: Carmen Maria Andreazza / Resumo: Estimar os estados de um sistema é um problema que a cada dia assume maior importância devido ao grande interesse por conhecer com exatidão os resultados dados pelos sistemas dinâmicos em qualquer tempo. Principalmente nos casos onde o sistema é estocástico, o problema da estimação apresenta uma maior complexidade. É nesse contexto que os estudos que Kalman realizou no século XX, sobre a estimação de sistemas dinâmicos estocásticos, ganharam maior relevância. O ltro de Kalman foi o principal resultado desses estudos, pela e cácia demonstrada dentro desse campo de estudo. Este trabalho tem como eixo principal o ltro de Kalman e sua aplicação tendo importância como o melhor estimador para os estados de sistemas dinâmicos lineares estocásticos em tempo discreto / Abstract: Estimating the states of a system is a problem of great importance due to interest in knowing exactly the results given by dynamic systems at any time. Moreover, if the system is stochastic, what causes the estimation problem to have complexity. In this context, Kalman studies in the previous century on the estimation of stochastic dynamical systems, whose result is the lter, which, due to its e ciency, is the most used in this eld. In this work the main focus is the Kalman lter and its application having in view its importance as the best estimator for the states of linear dynamic stochastic systems of discrete time / Mestre

Every continuous convex function defined on a separable Banach space is Gateaux differentiable on a dense G^ subset of the space E [Mazur]. Suppose we are given a sequence (xn) that Is dense in E. Can we always find a Gateaux differentiable point x such that x = z^=^anxn.for some sequence (an) with infinitely many non-zero terms so that Ση∞=1||anxn|| < co ? According to this paper, such points are called of "simple type," and shown to be dense in E. Mazur's theorem follows directly from the result and Rybakov's theorem (A countably additive vector measure F: E -* X on a cr-field is absolutely continuous with respect to |x*F] for some x* e Xs) can be shown without deep measure theoretic Involvement.

In this paper, we prove, for a certain class of open billiard dynamical systems, the existence of a family of smooth probability measures on the leaves of the dynamical system's unstable manifold. These measures describe the conditional asymptotic behavior of forward trajectories of the system. Furthermore, properties of these families are proven which are germane to the PYC programme for these systems. Strong sufficient conditions for the uniqueness of such families are given which depend upon geometric properties of the system's phase space. In particular, these results hold for a fairly nonrestrictive class of triangular configurations of scatterers.

Imaging systems have long been designed in separated steps: the experience-driven optical design followed by sophisticated image processing. Such a general-propose approach achieves success in the past but left the question open for specific tasks and the best compromise between optics and post-processing, as well as minimizing costs. Driven from this, a series of works are proposed to bring the imaging system design into end-to-end fashion step by step, from joint optics design, point spread function (PSF) optimization, phase map optimization to a general end-to-end complex lens camera. To demonstrate the joint optics application with image recovery, we applied it to flat lens imaging with a large field of view (LFOV). In applying a super-resolution single-photon avalanche diode (SPAD) camera, the PSF encoded by diffractive op tical element (DOE) is optimized together with the post-processing, which brings the optics design into the end-to-end stage. Expanding to color imaging, optimizing PSF to achieve DOE fails to find the best compromise between different wavelengths. Snapshot HDR imaging is achieved by optimizing a phase map directly. All works are demonstrated with prototypes and experiments in the real world. To further compete for the blueprint of end-to-end camera design and break the limits of a simple wave optics model and a single lens surface. Finally, we propose a general end-to-end complex lens design framework enabled by a differentiable ray tracing image formation model. All works are demonstrated with prototypes and experiments in the real world. Our frameworks offer competitive alternatives for the design of modern imaging systems and several challenging imaging applications.

Wavefront sensing is a fundamental problem in applied optics. Wavefront sensors that work in a deterministic manner are of particular interest. Initialized with a unified theory for classical wavefront sensors, this dissertation discusses relevant properties of wavefront sensor designs. Based on which, a new wavefront sensor, termed Coded Wavefront Sensor, is proposed to leverage the advantages of the analysis, especially the lateral wavefront resolution. A prototype was built to demonstrate this new wavefront sensor. Given that, two specific applications are demonstrated: megapixel adaptive optics and simultaneous intensity and phase imaging. Combined with a spatial light modulator, a hardware deconvolution approach is demonstrated for computational cameras via a high resolution adaptive optics system. By simply switching the normal image sensor with the proposed one, as well as slight change of illumination, a bright field microscope can be configured to a simultaneous intensity and phase microscope. These show the broad application range of the proposed computational wavefront sensing approach. Lastly, this dissertation proposes the idea of differentiable optics for wavefront engineering and lens metrology. By making use of automatic differentiation, a physically-correct differentiable ray tracing engine is built, with its potentials being illustrated via several challenging applications in optical design and metrology.

Computer simulation of a control system is a valuable tool in design or performance evaluation. This is especially true when non-linear elements cannot be ignored and must be included within the model. A general purpose block diagram oriented simulation program will be developed which can utilize continuous, discrete and non-linear building blocks. The software tool will be demonstrated by means of an example.