
Explore digital audio signal processing, from continuous acoustic to discrete digital signals. Learn sampling theory, filtering, and time-frequency domain transformations through practical methods and hands-on exercises.

Explore an automatic circuit solving library for simulating analog audio circuits. Learn to implement and analyze component-level circuits for real-time plugins and mobile apps.

Explore topological learning for integrating multimodal brain networks, focusing on a novel framework using persistent homology and Wasserstein distance-based loss for efficient computations in neuroimaging studies.

Learn to solve systems of linear ODEs using eigenvalues and eigenvectors. Covers problem setup, solution methods, and intuitive interpretation, with code examples in Python and MATLAB.

Decomposing chaos into linear systems with intermittent forcing using HAVOK analysis. Explores eigen-time-delay coordinates for regression models, offering new insights into complex dynamical systems.

Explore Laplace transforms, ODEs, and transfer functions in engineering mathematics, focusing on spring-mass-damper systems and frequency response analysis.

Explore FFT-based image compression techniques, including natural image analysis, compression algorithms, and practical examples for efficient data representation.

Explore Fourier Series: representation of functions, L2 integral norm, and MATLAB examples. Gain practical insights into engineering mathematics with real-world applications.

Explore vectorized integration and the Lorenz equation, focusing on chaos, turbulence, and uncertainty in engineering mathematics, with practical MATLAB implementations.

Explore Runge-Kutta integration methods for ODEs and delve into the fascinating Lorenz equation, uncovering its chaotic behavior and applications in engineering mathematics.

Explore matrix systems of first-order equations, diagonalization techniques, and higher-order linear ODEs. Learn to solve complex differential equations using eigenvectors and eigenvalues.

Explore engineering mathematics through weather modeling, covering fluid dynamics, disease networks, and systems of equations using MATLAB.

Explore chaos and uncertainty propagation in dynamical systems through numerical integration. Learn efficient vectorization techniques in Python and MATLAB for faster computations.

Learn to implement a 4th-order Runge-Kutta integrator in Python and Matlab, and apply it to simulate the chaotic Lorenz 1963 system. Gain practical coding skills for numerical integration.

Explore Forward and Backward Euler integration methods for ODEs using Python and Matlab. Compare stability, error, and implementation on a spring-mass-damper system with analytic solutions.

Explore linearization of nonlinear systems around fixed points, demonstrated with a simple pendulum example using both manual calculations and MATLAB implementation.

Explore numerical solutions to Laplace's Equation using MATLAB and get introduced to Fourier Series in this engineering mathematics lecture, with practical code examples and comprehensive notes provided.

Explore numerical methods for solving ordinary differential equations, focusing on Forward and Backward Euler techniques in engineering mathematics applications.

Explore numerical methods for integration and solving ordinary differential equations, enhancing your computational skills in engineering mathematics.

Explore numerical differentiation techniques using finite difference methods, with practical applications in engineering mathematics and accompanying Matlab code examples.

Explore degenerate equation systems and non-normal energy growth in engineering mathematics, focusing on key concepts and practical applications in dynamic systems analysis.