Before discussing the manual, one must understand the beast it tames. Moon and Stirling’s work is unique because it refuses to separate mathematics from code. Each chapter introduces a theoretical concept—say, the Singular Value Decomposition (SVD)—and immediately asks the student to implement it to solve a real signal processing problem, such as denoising a heartbeat signal or compressing an image.
Many problems in this book are designed to be solved via simulation. You can verify your manual work by coding the algorithm in: Use the Signal Processing Toolbox. Python (NumPy/SciPy): Before discussing the manual, one must understand the
: The Fourier transform of a rectangular pulse is given by: Many problems in this book are designed to
Chapter 3: Representation and Approximation in Vector Spaces – How signals are represented in mathematical spaces. Chapter 4: Linear Operators and Matrix Inverses – Mathematical operations on signal vectors. Chapter 5: Some Important Matrix Factorizations Chapter 4: Linear Operators and Matrix Inverses –
Suppose $\mathbfA$ is orthogonal. Then, by definition, $\mathbfA^T\mathbfA = \mathbfI$.
If you look at a solution, don't just copy it. Close the manual and try to reproduce the entire derivation from memory.