Laboratory on Matlab

Considers various basic features of Matla

Assignment on Matlab

Considers sampling, quantisation, filters and lines of best fit

A computational approach for cam size optimization of disc cam-follower mechanisms with translating roller followers

The main objective of this work is to present a computational approach for design optimization of disc cam mechanisms with eccentric translating roller followers. For this purpose, the objective function defined here takes into account the three major parameters that influence the final cam size, namely the base circle radius of the cam, the radius of the roller and the offset of the follower. Furthermore, geometric constraints related to the maximum pressure angle and minimum radius of curvature are included to ensure good working conditions of the system. Finally, an application example is presented and used to discuss the main assumptions and procedure adopted throughout this work.Fundação para a Ciência e a Tecnologia (FCT

Feature Selection Library (MATLAB Toolbox)

Feature Selection Library (FSLib) is a widely applicable MATLAB library for Feature Selection (FS). FS is an essential component of machine learning and data mining which has been studied for many years under many different conditions and in diverse scenarios. These algorithms aim at ranking and selecting a subset of relevant features according to their degrees of relevance, preference, or importance as defined in a specific application. Because feature selection can reduce the amount of features used for training classification models, it alleviates the effect of the curse of dimensionality, speeds up the learning process, improves model's performance, and enhances data understanding. This short report provides an overview of the feature selection algorithms included in the FSLib MATLAB toolbox among filter, embedded, and wrappers methods.Comment: Feature Selection Library (FSLib) 201

High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm

MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time domain data simultaneously, without relying on the FFT. To do this at high-resolution, specialized algorithms are required to solve the underlying large-scale non-linear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high performance computing cluster and is demonstrated to be able to generate high-resolution ($1mm \times 1mm$ in-plane resolution) quantitative parameter maps in simulation, phantom and in-vivo brain experiments. Reconstructed $T_1$ and $T_2$ values for the gel phantoms are in agreement with results from gold standard measurements and for the in-vivo experiments the quantitative values show good agreement with literature values. In all experiments short pulse sequences with robust Cartesian sampling are used for which conventional MR Fingerprinting reconstructions are shown to fail.Comment: Accepted by NMR in Biomedicine on 2019-12-0

Tvarování vstupu pro podtlumené systémy

The main aim of this paper is to provide possibility to test and validate multiple damping and input shaping methods for underdamped systems. Mathematical model for specific development and demonstration was created in Matlab Simscape environment. Additional Matlab Simulink system was modelled to achieve proper data acquisition and comparison in contrast to plain system without any special damping methods.Hlavním cílem této práce je poskytnout možnost testování a validace více metod tlumení a vstupních tvarů u podtlumených systémů. Matematický model pro specifický vývoj a demonstraci byl vytvořen v prostředí Matlab Simscape. Další systém Matlab Simulink byl modelován tak, aby dosáhl správného získávání a srovnání dat v porovnání s běžným systémem bez zvláštních metod tlumení

Matlab code for Lyapunov exponents of fractional order systems

In this paper the Benettin-Wolf algorithm to determine all Lyapunov exponents for a class of fractional-order systems modeled by Caputo's derivative and the corresponding Matlab code are presented. First it is proved that the considered class of fractional-order systems admits the necessary variational system necessary to find the Lyapunov exponents. The underlying numerical method to solve the extended system of fractional order, composed of the initial value problem and the variational system, is the predictor-corrector Adams-Bashforth-Moulton for fractional differential equations. The Matlab program prints and plots the Lyapunov exponents as function of time. Also, the programs to obtain Lyapunov exponents as function of the bifurcation parameter and as function of the fractional order are described. The Matlab program for Lyapunov exponents is developed from an existing Matlab program for Lyapunov exponents of integer order. To decrease the computing time, a fast Matlab program which implements the Adams-Bashforth-Moulton method, is utilized. Four representative examples are considered.Comment: accepte