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Th\'eorie cin\'etique des gaz parfaits.
Th\'eorie cin\'etique des gaz parfaits.
Chapitre 2, Peip2, Polytech'Nice Sophia, sans les sch\'emas et applications sch\'ematis\'ees
Rémy Collie
Convolution Image-based Watermarking for 2D Greyscale Image
Convolution Image-based Watermarking for 2D Greyscale Image
Watermarking technique for the image is an efficient method for protecting copyright image, and also a huge topic in cryptography. In this paper, two spread spectrum watermarking scheme, the Convolution Image-based Model (CIM) and the Exponential Convolution Image-based Model (ECIM) are going to be formulated and discussed. The watermarking experiment result will be shown and discussed, focusing on the attack scheme, protectability, and the information encryption of the watermark. We will show that the convolution image-based model for invisible watermark is weak of protectability, but it is able to hide the information (the size of watermark must be less than the original image) and store inside the image.
Homer
Real Analysis I (Workshop 2)
Real Analysis I (Workshop 2)
Real Analysis Workshop 2 1.3.10
Philip Mak
Resolucion pregunta 2 del parcial de osman
Resolucion pregunta 2 del parcial de osman
trabajo realizado para la cátedra de señales y sistemas
Josue Maldonado
An application of the Ncut algorithm, with an open-source implementation (in the R environment).
An application of the Ncut algorithm, with an open-source implementation (in the R environment).
Although the analysis of data is a task that has gained the interest of the statistical community in recent years and whose familiarity with the statistical computing environment, they encourage the current statistical community (to students and teachers of the area) to complete statistical analysis reproducible by means of the tool R. However for years there has been a gap between the calculation of matrices on a large scale and the term "big data", in this work the Normalized Cut algorithm for images is applied. Despite the expected, the R environment to do image analysis is poorly, in comparison with other computing platforms such as the Python language or with specialized software such as OpenCV. Being well known the absence of such function, in this work we share an implementation of the Normalized Cut algorithm in the R environment with extensions to programs and processes performed in C ++, to provide the user with a friendly interface in R to segment images. The article concludes by evaluating the current implementation and looking for ways to generalize the implementation for a large scale context and reuse the developed code. Key words: Normaliced Cut, image segmentation, Lanczos algorithm, eigenvalues and eigenvectors, graphs, similarity matrix, R (the statistical computing environment), open source, large scale and big data.
José Antonio garcia
CT Problem Set
CT Problem Set
CT problem set for undergraduate Medical Physics course.
David Rigie
A Statistical Analysis of the Simpson College Football Offense
A Statistical Analysis of the Simpson College Football Offense
This project explores the predictability of play-calling of the Simpson College football offense during the years of 2012-2014. The models used to assess the predictability include simple analysis as well as linear and logistic regression.
Erik Hall
Week 11 Tutorial Solutions
Week 11 Tutorial Solutions
174 tutorial
Palmira Pereira
Mimetic postprocessing for LFRic
Mimetic postprocessing for LFRic
We describe what mimetic interpolation is and why it is critical for some pre- and post-processing tasks. A simple test case shows how using bilinear interpolation for a flux calculation introduces numerical errors that depend on the grid, the number of segments and the number of quadrature points. In contrast, mimetic interpolation will return the exact result regardless of the grid resolution and the number of segments.
Alex Pletzer and Wolfgang Hayek and Jorge Bornemann

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