Signal Processing Study Guide



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Table of Contents

I. Foreword
II Mathematical Analysis
III. DSP Theory
IV. DSP Sub-fields
V. DSP Techniques
VI. Sampling
VII. Statistical Signal Processing
VIII. Image processing
IX. Control engineering
X. DSP Architecture
I. Foreword

About Signal Processing
About Digital signal processing
II. Mathematical Analysis

Fourier analysis
Fourier series
Fourier transform
Fast Fourier transform
Gibbs phenomenon
Impulse response
Laplace transform
Two-sided Laplace transform
Transfer function
Dirac comb
Dirac delta function
Sinc function
Goertzel algorithm
Abel transform
Spline interpolation
Stochastic differential equation
III. DSP Theory

Nyquist-Shannon sampling theorem
Whittaker-Shannon interpolation formula
Estimation theory
Detection theory
LTI system theory
IV. DSP Sub-fields

Audio signal processing
Digital image processing
Speech processing
Statistical signal processing
Image processing
Control engineering
V. DSP Techniques

Discrete Fourier transform (DFT)
Bilinear transform
Aadvanced Z-transform
Discrete cosine transform
Modified discrete cosine transform
VI. Sampling

Digital frequency
Signal reconstruction
Reconstruction filter
Negative frequency
VII. Statistical Signal Processing

Bayes theorem
Bayes estimator
Recursive Bayesian estimation
Wiener filter
Kalman filter
Fast Kalman filter
Adaptive filter
Finite impulse response
Statistical classification
Hidden Markov models
VIII. Image processing

Principal components analysis
Independent component analysis
Self-organizing maps
Neural networks
Computer vision
Affine shape adaptation
Blob detection
Lanczos resampling
Tomographic reconstruction
IX. Control engineering

Adaptive control
Building Automation
Control reconfiguration
H infinity
Intelligent control
Laplace transform
Model predictive control
Non-linear control
Optimal control
Process control
Quantitative feedback theory
Robotic unicycle
Robust control
State space
X. DSP Architecture

Digital signal processor
von Neumann architectur

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