# Difference between revisions of "Signal Processing"

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==Subfields and Concepts== | ==Subfields and Concepts== | ||

''See [http://kourouklides.wikia.com/wiki/Category:Signal_Processing Category:Signal Processing] for some of its subfields.'' | ''See [http://kourouklides.wikia.com/wiki/Category:Signal_Processing Category:Signal Processing] for some of its subfields.'' | ||

+ | * Signal = [[Information Theory|Information]] and (additive/multiplicative) noise | ||

+ | * Noise | ||

+ | ** Additive vs Multiplicative noise | ||

+ | ** White, Pink, Red/Brownian, Grey noise | ||

+ | ** Gaussian vs Non-Gaussian noise | ||

+ | ** Additive White Gaussian noise (AWGN) | ||

+ | * Energy (in Signal Processing) | ||

+ | ** Energy (in Physics) and Characteristic Impedance | ||

+ | ** [[Artificial Neural Network|Energy-Based Model]] (EBM) | ||

* [[Statistical Signal Processing|Statistical Signal Processing / Adaptive Signal Processing]] | * [[Statistical Signal Processing|Statistical Signal Processing / Adaptive Signal Processing]] | ||

** Bayesian Signal Processing | ** Bayesian Signal Processing | ||

* [[Digital Signal Processing]] | * [[Digital Signal Processing]] | ||

* [[Digital Image Processing]] | * [[Digital Image Processing]] | ||

− | * [[Linear Dynamical System|State Space | + | * [[Linear Dynamical System|State Space Analysis]] |

− | * Linear Systems | + | * [[Linear Dynamical System|Linear Systems]] |

** Discrete-time Systems | ** Discrete-time Systems | ||

** Continuous-time Systems | ** Continuous-time Systems | ||

** Linear Time-Invariant (LTI) Systems | ** Linear Time-Invariant (LTI) Systems | ||

** Time-Variant Systems | ** Time-Variant Systems | ||

− | * Sampling | + | * Sampling (in Signal Processing) |

− | ** Sampling Theorem | + | ** Sampling Theorem (by Whittaker–Nyquist–Kotelnikov–Shannon) |

+ | ** Nyquist rate | ||

** Signal Reconstruction | ** Signal Reconstruction | ||

+ | ** Analog-to-Digital Conversion | ||

+ | ** Digital-to-Analog Conversion | ||

** [[Compressed Sensing|Sparse Sampling / Compressed Sensing]] | ** [[Compressed Sensing|Sparse Sampling / Compressed Sensing]] | ||

+ | ** Adaptive Sampling / [[Machine Learning|Active Learning]] | ||

+ | ** Aliasing | ||

* Transformations | * Transformations | ||

** Fourier Transform | ** Fourier Transform | ||

** Laplace Transform | ** Laplace Transform | ||

** Z-Transform | ** Z-Transform | ||

+ | * Harmonic Analysis | ||

+ | ** [[Topology|Topological]] groups | ||

+ | ** Pontryagin duality | ||

+ | * Filter Theory / Filter Analysis / Filter Design | ||

+ | ** Low-pass filter (LPF) Vs High-pass filter (HPF) | ||

+ | ** Linear filter Vs Nonlinear filter | ||

+ | ** Finite Impulse Response (FIR) filter Vs Infinite Impulse Response (IIR) filter | ||

+ | ** Time-invariant filer Vs Time-variant filter | ||

+ | ** Causal filter Vs Non-causal filter | ||

+ | ** Analog filter Vs Digital filter | ||

+ | ** Discrete-time (sampled) filter or Continuous-time filter | ||

+ | ** Gabor filter (in [[Digital Image Processing]]) | ||

* Applications | * Applications | ||

** Electric Circuit Analysis | ** Electric Circuit Analysis | ||

− | |||

− | |||

− | |||

** Communication Systems | ** Communication Systems | ||

** [[Control Theory|Feedback Systems / Control Systems]] | ** [[Control Theory|Feedback Systems / Control Systems]] | ||

+ | ** [[Computational Finance|Financial Engineering / Computational Finance]] | ||

==Online Courses== | ==Online Courses== | ||

Line 68: | Line 92: | ||

* [http://www.mathworks.co.uk/help/signal/index.html Signal Processing Toolbox] - MATLAB | * [http://www.mathworks.co.uk/help/signal/index.html Signal Processing Toolbox] - MATLAB | ||

* [http://python-for-signal-processing.blogspot.co.uk/ Python for Signal Processing] using [http://ipython.org/ IPython] | * [http://python-for-signal-processing.blogspot.co.uk/ Python for Signal Processing] using [http://ipython.org/ IPython] | ||

− | * [https://docs.scipy.org/doc/scipy | + | * [https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html Signal Processing (SciPy.Signal)] - Python |

− | *[http://libit.sourceforge.net/ Information Theory and Signal Processing Library (libit)] - C | + | * [https://www.idiap.ch/software/bob/ Bob] - Python, C++ |

+ | * [https://github.com/epfl-lts2/gspbox GSPBox] - MATLAB | ||

+ | * [https://github.com/epfl-lts2/pygsp PyGSP] - Python | ||

+ | * [http://libit.sourceforge.net/ Information Theory and Signal Processing Library (libit)] - C | ||

==See also== | ==See also== | ||

Line 76: | Line 103: | ||

==Other Resources== | ==Other Resources== | ||

* [https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_signalprocessing Signal Processing] - Google Scholar Metrics (Top Publications) | * [https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_signalprocessing Signal Processing] - Google Scholar Metrics (Top Publications) | ||

+ | * [https://www.nature.com/subjects/signal-processing Signal Processing] - Nature | ||

* [http://linux-sound.org/dsp.html Signal Analysis/Processing Software] - a list of software packages | * [http://linux-sound.org/dsp.html Signal Analysis/Processing Software] - a list of software packages | ||

* [http://www.embeddedrelated.com/showarticle/197.php MATLAB Vs Python for Signal Processing] - a discussion why MATLAB is essential for this field | * [http://www.embeddedrelated.com/showarticle/197.php MATLAB Vs Python for Signal Processing] - a discussion why MATLAB is essential for this field |

## Latest revision as of 20:33, 16 August 2018

This page contains resources about Signal Processing in general.

More specific information is included in each subfield.

## Contents

## Subfields and Concepts[edit]

*See Category:Signal Processing for some of its subfields.*

- Signal = Information and (additive/multiplicative) noise
- Noise
- Additive vs Multiplicative noise
- White, Pink, Red/Brownian, Grey noise
- Gaussian vs Non-Gaussian noise
- Additive White Gaussian noise (AWGN)

- Energy (in Signal Processing)
- Energy (in Physics) and Characteristic Impedance
- Energy-Based Model (EBM)

- Statistical Signal Processing / Adaptive Signal Processing
- Bayesian Signal Processing

- Digital Signal Processing
- Digital Image Processing
- State Space Analysis
- Linear Systems
- Discrete-time Systems
- Continuous-time Systems
- Linear Time-Invariant (LTI) Systems
- Time-Variant Systems

- Sampling (in Signal Processing)
- Sampling Theorem (by Whittaker–Nyquist–Kotelnikov–Shannon)
- Nyquist rate
- Signal Reconstruction
- Analog-to-Digital Conversion
- Digital-to-Analog Conversion
- Sparse Sampling / Compressed Sensing
- Adaptive Sampling / Active Learning
- Aliasing

- Transformations
- Fourier Transform
- Laplace Transform
- Z-Transform

- Harmonic Analysis
- Topological groups
- Pontryagin duality

- Filter Theory / Filter Analysis / Filter Design
- Low-pass filter (LPF) Vs High-pass filter (HPF)
- Linear filter Vs Nonlinear filter
- Finite Impulse Response (FIR) filter Vs Infinite Impulse Response (IIR) filter
- Time-invariant filer Vs Time-variant filter
- Causal filter Vs Non-causal filter
- Analog filter Vs Digital filter
- Discrete-time (sampled) filter or Continuous-time filter
- Gabor filter (in Digital Image Processing)

- Applications
- Electric Circuit Analysis
- Communication Systems
- Feedback Systems / Control Systems
- Financial Engineering / Computational Finance

## Online Courses[edit]

### Video Lectures[edit]

- Signals and Systems by Alan V. Oppenheim
- Signals and Systems by Dennis Freeman
- Signal and Systems by K S Venkatesh
- Signals and Systems by Suhash Chandra Dutta Roy
- ECE 2610: Introduction to Signals and Systems by Mark Wickert

### Lecture Notes[edit]

- EE102: Introduction to Signals & Systems by Stephen Boyd
- Signals and Systems - NPTEL
- ELEC 301 - Introduction to Signals and Systems by Richard Baraniuk
- Signals and Systems by Tania Stathaki
- Signals and Linear Systems by Peter Y. K. Cheung
- Signals and Systems by Rafaello D'Andrea
- Introduction to Communication, Control, and Signal Processing by Alan V. Oppenheim and George Verghese

## Books[edit]

- Oppenheim, A. V., & Verghese, G. C. (2015).
*Signals, systems and inference*. Pearson. - Haykin, S. S. (2013).
*Digital communications*. John Wiley & Sons. - Proakis, J. G., & Salehi, M. (2013).
*Fundamentals of communication systems*. Pearson. - Couch, L. W., Kulkarni, M., & Acharya, U. S. (2012).
*Digital and analog communication systems*. 8th Ed. Prentice Hall. - Haykin, S. & Moher M. (2009).
*Communication systems*. 5th Ed. International Student Version. John Wiley & Sons. - Lathi, B. P. (2011).
*Modern digital and analog communication systems*. 4th Ed. Oxford University Press. - Haykin, S. S., Moher, M., & Song, T. (2007).
*An introduction to analog and digital communications*. 2nd Ed. John Wiley & Sons. - Lathi, B. P. (2004).
*Linear Systems and Signals*. 2nd Ed. Oxford University Press. - Haykin, S., & Van Veen, B. (2002).
*Signals and systems*. 2nd Ed. John Wiley & Sons. - Proakis, J. G., Salehi, M., Zhou, N., & Li, X. (2001).
*Communication systems engineering*. 2nd Ed. Prentice Hall. - Lathi, B. P. (2000).
*Signal Processing and Linear Systems*. Oxford University Press. - Oppenheim, A. V., & Willsky, A. S. (1997).
*Signals and Systems*. Prentice Hall.

## Software[edit]

- Signal Processing Toolbox - MATLAB
- Python for Signal Processing using IPython
- Signal Processing (SciPy.Signal) - Python
- Bob - Python, C++
- GSPBox - MATLAB
- PyGSP - Python
- Information Theory and Signal Processing Library (libit) - C

## See also[edit]

## Other Resources[edit]

- Signal Processing - Google Scholar Metrics (Top Publications)
- Signal Processing - Nature
- Signal Analysis/Processing Software - a list of software packages
- MATLAB Vs Python for Signal Processing - a discussion why MATLAB is essential for this field
- Signals and Systems by Wikibooks
- Signal Processing by Wikibooks