Robotics Education - MATLAB and Simulink Robotics Arena
Learn how to use MATLAB and Simulink with platforms like VEX Robotics, LEGO and other educational robotics kits. MathWorks experts share knowledge on elementary concepts in robotics for beginners.
Mechatronics with MATLAB and Simulink
Learn to develop mechatronic systems using MATLAB and Simulink. Explore simulation techniques, interdisciplinary design challenges, and rapid deployment to hardware for robotics, vehicles, and more.
Structural Dynamics
The objective of the course is to understand the behavior of structures especially building to various dynamic loads: such as wind, earthquake, machine vibration and ambient vibration.
Basic Electrical Technology
Comprehensive exploration of electrical technology fundamentals, covering circuits, components, analysis methods, transformers, DC machines, three-phase systems, and motors for a solid foundation in electrical engineering.
Matlab Tutorial
Learn MATLAB basics for data analysis, control systems, and signal processing. Covers variables, operations, scripts, functions, and linear algebra in a beginner-friendly tutorial.
Singular Value Decomposition
Comprehensive exploration of Singular Value Decomposition, covering mathematical foundations, applications in data processing, and implementation in MATLAB and Python for various real-world scenarios.
An Introduction to CFD Using MATLAB
Learn how to solve fundamental problems in fluid dynamics using Matlab.
Kalman Filter for Beginners - Recursive Filters & MATLAB Examples
Explore recursive filters, including average, moving average, and low-pass filters, with practical MATLAB examples. Learn the foundations of Kalman filtering for enhanced estimation and data analysis skills.
Optical Engineering
This course in optical engineering covers topics such as geometric optics, refraction, image formation, aberrations, Gaussian beams, and interferometry.
Modeling and Simulation of Dynamic Systems
Learn MATLAB techniques for solving ODEs, analyzing spring-mass-damper systems, and applying Laplace transforms in dynamic system modeling and simulation.
Image Processing in MATLAB
Learn essential image processing techniques in MATLAB, from basic operations to advanced filters and edge detection. Gain practical skills for manipulating, analyzing, and enhancing digital images efficiently.
Deep Learning Using Matlab
Explore deep learning concepts and implement neural networks, including CNNs, using MATLAB for practical applications in image classification and beyond.
Control Bootcamp - Kalman Filter Example in Matlab
Explore Kalman Filter implementation on inverted pendulum using Matlab, covering theory and practical application in dynamical systems and control engineering.
Taylor Series and Power Series Made Easy - Review of Calculus
Comprehensive review of Taylor and Power Series with visual aids and coding examples in Python and Matlab. Covers definitions, expansions for sine and cosine, and practical implementations.
Digital Signal Processing - MATLAB Helper
Explore Digital Signal Processing with MATLAB: from impulse responses to Fourier transforms, filter designs, and spectral analysis. Gain practical skills for real-world DSP applications.
Software - Mathematica, MATLAB, Aspen Plus
Learn to use Mathematica, MATLAB, and Aspen Plus for engineering computing through comprehensive screencasts covering interactive simulations, data analysis, programming, and chemical process modeling.
Numerical Analysis
Explore differential equations, numerical integration, and root-finding methods using software tools. Learn practical techniques for solving complex mathematical problems in engineering and science.
Face Recognition Techniques and Applications - Lecture 14
Explore face recognition techniques, from simple approaches to advanced methods like PCA and Linear Discriminant Analysis. Learn about eigenvectors, image compression, and scatter matrices.
Matlab Tutorial
Learn MATLAB basics for computer vision: variables, vectors, matrices, image operations, filters, and more. Practical tutorial for beginners and intermediate users.
Filtering in Computer Vision - Lecture 2
Learn about image filtering techniques, including noise reduction, derivatives, convolution, and Gaussian filters. Explore practical applications in computer vision using MATLAB.
Numerical Modeling
Learn to solve linear and nonlinear algebraic equations using various numerical methods and MATLAB, including Gaussian elimination, Newton-Raphson, and secant methods for practical engineering applications.