Gallery — Math

«Multiport conversions between S, Z, Y, h, ABCD, and T parameters.» Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMIC 2018), Brive-la-gaillarde, France, July 2018. Article: http://www.microwave.fr/publications/151.pdf Poster: http://www.microwave.fr/publications/151p.pdf

Cheat sheet for Linear Algebra and Differential Equations

Template for the final assignment report for the course MV013 Statistics for Computer Science taught at the Faculy of Informatics, Masaryk University, Brno.

For math 53 homework

This is a template for students in MATH 3000 at FSU to use for the final draft of their final exam.

The Weekly Work Report template for students in ZhengLab, Dept. Electronic Engineering, Ocean University of China.

En el presente post, haremos una breve introducción de la herramienta MATLAB. Presentaremos funciones, operaciones y gráficos con más de dos datos, para asi luego ordenar y hacer cálculos con matrices.

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.

This is a template for students in MATH 3000 at FSU to use for the final draft of their midterm exam.