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Fourier Analysis and Its Applications
A carefully prepared account of the basic ideas in Fourier analysis and its applications to the study of partial differential equations. The author succeeds to make his exposition accessible to readers with a limited background, for example, those not acquainted with the Lebesgue integral. Readers should be familiar with calculus, linear algebra, and complex numbers. At the same time, the author has managed to include discussions of more advanced topics such as the Gibbs phenomenon, distributions, Sturm-Liouville theory, Cesaro summability and multi-dimensional Fourier analysis, topics which one usually does not find in books at this level. A variety of worked examples and exercises will help the readers to apply their newly acquired knowledge.
Beyond Public Engagement
University collections have unquestionably played a central role in the production of knowledge. They are valuable resources for studying the construction of traditions and identities, proving particularly interesting for understanding how universities have shaped societies. Furthermore, they have also been mobilised as cultural mediators to legitimise academic institutions and bring the results of their activities into the public sphere. As such, academic collections undoubtedly enable reflection on the complex relationships between heritage, knowledge, scholars, and the public. Given their importance, the development of successful strategies in terms of public engagement has recently become a major concern for those working with these academic collections. However, the complexity of university heritage encompasses a diversity of issues that are connected with more than just the public sphere. This volume discusses some of the problems, challenges, and opportunities of academic heritage, beyond the mere concern for engaging with the public.
An Intermediate Course in Probability
The purpose of this book is to provide the reader with a solid background and understanding of the basic results and methods in probability the ory before entering into more advanced courses (in probability and/or statistics). The presentation is fairly thorough and detailed with many solved examples. Several examples are solved with different methods in order to illustrate their different levels of sophistication, their pros, and their cons. The motivation for this style of exposition is that experi ence has proved that the hard part in courses of this kind usually in the application of the results and methods; to know how, when, and where to apply what; and then, technically, to solve a gi...
Pedestrian Detection Algorithms using Shearlets
In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on pedestrian detection benchmarks, the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is stu...
Tools for Infinite Dimensional Analysis
Over the past six decades, several extremely important fields in mathematics have been developed. Among these are Itô calculus, Gaussian measures on Banach spaces, Malliavan calculus, and white noise distribution theory. These subjects have many applications, ranging from finance and economics to physics and biology. Unfortunately, the background information required to conduct research in these subjects presents a tremendous roadblock. The background material primarily stems from an abstract subject known as infinite dimensional topological vector spaces. While this information forms the backdrop for these subjects, the books and papers written about topological vector spaces were never tr...
