Toolbox of signal analysis techniques with MATLAB problems and solutions, bridging the gap between signal processing and data science

Topics in Applied Signal Processing with MATLAB offers readers powerful tools for analyzing data, forecasting trends, and identifying features useful in machine learning via datasets with MATLAB examples, plus theoretical and MATLAB problems. The book's chapters include signal analysis, Fourier spectral and nonlinear signal analysis, neural networks, and other techniques used in machine learning. The book also provides a chapter dedicated to Linear Time-Invariant Filtering, and reviews emerging trends and challenges.

The Author also includes information on:

• Analog filter design, impulse invariance and bilinear transformation IIR filter design, and filter implementation
• Power spectral density, periodogram, spectral windows, averaged periodograms, and the Welch method
• Parametric spectral analysis and modeling, covering autoregressive (AR), autoregressive with exogenous inputs (ARX), moving average (MA), and autoregressive moving average (ARMA) models
• The discrete wavelet transform, covering vector spaces, norms, and projections, Mallat's algorithm, and perfect reconstruction filter banks
• Time-frequency methods, covering the uncertainty principle, quadratic time frequency representations, and the continuous wavelet transform

Topics in Applied Signal Processing with MATLAB is a powerful toolbox for graduate students and professionals working to an advanced level in the signal processing space, with required pre-requisites including knowledge of signal processing, statistics, and calculus.