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Analog signal processing is for signals that have not been digitized, as in most 20th-century [[radio]], telephone, and television systems. This involves linear electronic circuits as well as nonlinear ones. The former are, for instance, [[passive filter]]s, [[active filter]]s, [[Electronic mixer|additive mixers]], [[integrator]]s, and [[Analog delay line|delay line]]s. Nonlinear circuits include [[compandor]]s, multipliers ([[frequency mixer]]s, [[voltage-controlled amplifier]]s), [[voltage-controlled filter]]s, [[voltage-controlled oscillator]]s, and [[phase-locked loop]]s.
===Continuous
[[Continuous signal|Continuous-time signal]] processing is for signals that vary with the change of continuous ___domain (without considering some individual interrupted points).
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In some contexts, <math>h(t)</math> is referred to as the impulse response of the system. The above [[convolution]] operation is conducted between the input and the system.
===Discrete
[[Discrete-time signal]] processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude.
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Polynomial signal processing is a type of non-linear signal processing, where [[polynomial]] systems may be interpreted as conceptually straightforward extensions of linear systems to the nonlinear case.<ref>{{cite book |author1=V. John Mathews |author2=Giovanni L. Sicuranza |title=Polynomial Signal Processing |date=May 2000 |isbn=978-0-471-03414-8 |publisher=Wiley}}</ref>
===Statistical
'''Statistical signal processing''' is an approach which treats signals as [[stochastic process]]es, utilizing their [[statistical]] properties to perform signal processing tasks <ref name ="Scharf">{{cite book |first=Louis L. |last=Scharf |title=Statistical signal processing: detection, estimation, and time series analysis |publisher=[[Addison–Wesley]] |___location=[[Boston]] |year=1991 |isbn=0-201-19038-9 |oclc=61160161}}</ref>. Statistical techniques are widely used in signal processing applications. For example, one can model the [[probability distribution]] of noise incurred when photographing an image, and construct techniques based on this model to [[noise reduction|reduce the noise]] in the resulting image.
===Graph
'''Graph signal processing''' generalizes signal processing tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph <ref name ="Ortega">{{cite book |first=A. |last=Ortega |title=Introduction to Graph Signal Processing |publisher=[[Cambridge University Press]] |___location=[[Cambridge]] |year=2022 |isbn=9781108552349}}</ref>. Graph signal processing presents several key points such as sampling signal techniques <ref name="Tanaka">{{cite journal|title=Generalized Sampling on Graphs with Subspace and Smoothness Prior|journal=IEEE Transactions on Signal Processing|date=2020|url=https://ieeexplore.ieee.org/document/9043719|last1=Tanaka|first1=Y.|last2=Eldar|first2=Y.}}</ref>, recovery techniques <ref name="Fascista">{{cite journal|title=Graph Signal Reconstruction under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification|journal=IEEE Transactions on Signal and Information Processing over Networks|date=2024|url=https://ieeexplore.ieee.org/document/10465260|last1=Fascista|first1=A.|last2=Coluccia|first2=A.|last3=Ravazzi|first3=C.}}</ref> and time-varying techiques <ref name="Giraldo">{{cite journal|title=Reconstruction of Time-varying Graph Signals via Sobolev Smoothness|journal=IEEE Transactions on Signal and Information Processing Over Networks|date=March 2022|url=https://ieeexplore.ieee.org/document/9730033|last1=Giraldo|first1=J.|last2=Mahmood|first2=A. |last3=Garcia-Garcia|first3=B.|last4=Thanou|first4=D.|last5=Bouwmans|first5=T.}}</ref>. Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite journal|title=Semi-Supervised Background Subtraction of Unseen Videos: Minimization of The Total Variation of Graph Signals|journal= IEEE International Conference on Image Processing, ICIP 2020|date=October 2020|url=https://ieeexplore.ieee.org/document/9190887|last1=Giraldo|first1=J.|last2=Bouwmans|first2=T.}}</ref>
<ref name="Giraldo2">{{cite journal|title=GraphBGS: Background Subtraction via Recovery of Graph Signals|journal=IInternational Conference on Pattern Recognition, ICPR 2020|date=2020|url=https://ieeexplore.ieee.org/document/9412999|last1=Giraldo|first1=J.|last2=Bouwmans|first2=T.}}</ref>
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