Signal processing - Revision history

Kvng: review: rm already mentioned. link improvement.

2025-05-27T18:29:50Z

review: rm already mentioned. link improvement.

← Previous revision		Revision as of 18:29, 27 May 2025
Line 131:		Line 131:
	* [[Recurrence relation]]s<ref name="Engelberg2008">{{cite book\|author=Shlomo Engelberg\|title=Digital Signal Processing: An Experimental Approach\|url=https://books.google.com/books?id=z3CpcCHbtgIC\|date=8 January 2008\|publisher=Springer Science & Business Media\|isbn=978-1-84800-119-0}}</ref>		* [[Recurrence relation]]s<ref name="Engelberg2008">{{cite book\|author=Shlomo Engelberg\|title=Digital Signal Processing: An Experimental Approach\|url=https://books.google.com/books?id=z3CpcCHbtgIC\|date=8 January 2008\|publisher=Springer Science & Business Media\|isbn=978-1-84800-119-0}}</ref>
	* [[Transform theory]]		* [[Transform theory]]
	* [[Time-frequency analysis]]{{spaced ndash}} for processing non-stationary signals<ref>{{cite book\|title=Time frequency signal analysis and processing a comprehensive reference\|year=2003\|publisher=Elsevier\|location=Amsterdam\|isbn=0-08-044335-4\|edition=1\|editor=Boashash, Boualem}}</ref~~><!--[[User:Kvng/RTH]]--~~>		* [[Time-frequency analysis]]{{spaced ndash}} for processing non-stationary signals<ref>{{cite book\|title=Time frequency signal analysis and processing a comprehensive reference\|year=2003\|publisher=Elsevier\|location=Amsterdam\|isbn=0-08-044335-4\|edition=1\|editor=Boashash, Boualem}}</ref>
	* [[Linear canonical transformation]]		* [[Linear canonical transformation]]
	* [[Spectral estimation]]{{spaced ndash}} for determining the spectral content (i.e., the distribution of power over frequency) of a [[time series]]<ref>{{cite book\|first1=Petre\|last1=Stoica\|first2=Randolph\|last2=Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref>		* [[Spectral estimation]]{{spaced ndash}} for determining the spectral content (i.e., the distribution of power over frequency) of a set of [[time series]] data points<ref>{{cite book\|first1=Petre\|last1=Stoica\|first2=Randolph\|last2=Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref>
	*[[Statistical signal processing]]{{spaced ndash}} analyzing and extracting information from signals and noise based on their stochastic properties		*[[Statistical signal processing]]{{spaced ndash}} analyzing and extracting information from signals and noise based on their stochastic properties
	*[[Linear time-invariant system]] theory, and [[transform theory]]		*[[Linear time-invariant system]] theory, and [[transform theory]]
Line 139:		Line 139:
	*[[System identification]]<ref name="Billings"/> and classification		*[[System identification]]<ref name="Billings"/> and classification
	*[[Calculus]]		*[[Calculus]]
	*[[~~Code~~]]		*[[Coding theory]]
	*[[Complex analysis]]<ref name="SchreierScharf2010">{{cite book\|author1=Peter J. Schreier\|author2=Louis L. Scharf\|title=Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals\|url=https://books.google.com/books?id=HBaxLfDsAHoC&q=%22complex+analysis%22\|date=4 February 2010\|publisher=Cambridge University Press\|isbn=978-1-139-48762-7}}</ref>		*[[Complex analysis]]<ref name="SchreierScharf2010">{{cite book\|author1=Peter J. Schreier\|author2=Louis L. Scharf\|title=Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals\|url=https://books.google.com/books?id=HBaxLfDsAHoC&q=%22complex+analysis%22\|date=4 February 2010\|publisher=Cambridge University Press\|isbn=978-1-139-48762-7}}</ref>
	*[[Vector spaces]] and [[Linear algebra]]<ref name="Little2019">{{cite book\|author=Max A. Little\|title=Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics\|url=https://books.google.com/books?id=ejGoDwAAQBAJ&q=%22vector+space%22\|date=13 August 2019\|publisher=OUP Oxford\|isbn=978-0-19-102431-3}}</ref>		*[[Vector spaces]] and [[Linear algebra]]<ref name="Little2019">{{cite book\|author=Max A. Little\|title=Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics\|url=https://books.google.com/books?id=ejGoDwAAQBAJ&q=%22vector+space%22\|date=13 August 2019\|publisher=OUP Oxford\|isbn=978-0-19-102431-3}}</ref>
Line 148:		Line 148:
	*[[Optimization]]<ref name="PalomarEldar2010">{{cite book\|author1=Daniel P. Palomar\|author2=Yonina C. Eldar\|title=Convex Optimization in Signal Processing and Communications\|url=https://books.google.com/books?id=UOpnvPJ151gC\|year=2010\|publisher=Cambridge University Press\|isbn=978-0-521-76222-9}}</ref>		*[[Optimization]]<ref name="PalomarEldar2010">{{cite book\|author1=Daniel P. Palomar\|author2=Yonina C. Eldar\|title=Convex Optimization in Signal Processing and Communications\|url=https://books.google.com/books?id=UOpnvPJ151gC\|year=2010\|publisher=Cambridge University Press\|isbn=978-0-521-76222-9}}</ref>
	*[[Numerical methods]]		*[[Numerical methods]]
		⚫	*[[Data mining]]{{spaced ndash}} for statistical analysis of relations between large quantities of variables (in this context representing many physical signals), to extract previously unknown interesting patterns<!--[[User:Kvng/RTH]]-->
	*[[Time series]]
⚫	*[[Data mining]]{{spaced ndash}} for statistical analysis of relations between large quantities of variables (in this context representing many physical signals), to extract previously unknown interesting patterns

	==See also==		==See also==

OAbot: Open access bot: url-access updated in citation with #oabot.

2025-05-24T13:07:14Z

Open access bot: url-access updated in citation with #oabot.

← Previous revision		Revision as of 13:07, 24 May 2025
Line 93:		Line 93:

	===Graph ===		===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.\|volume=68 \|pages=2272–2286 \|doi=10.1109/TSP.2020.2982325 \|arxiv=1905.04441 \|bibcode=2020ITSP...68.2272T }}</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.\|volume=10 \|pages=277–293 \|doi=10.1109/TSIPN.2024.3375593 }}</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.\|volume=8 \|pages=201–214 \|doi=10.1109/TSIPN.2022.3156886 \|arxiv=2207.06439 }}</ref> Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite book\|title=2020 IEEE International Conference on Image Processing (ICIP)\|date=October 2020\|chapter-url=https://ieeexplore.ieee.org/document/9190887\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter= Semi-Supervised Background Subtraction of Unseen Videos: Minimization of the Total Variation of Graph Signals\|pages= 3224–3228\|doi= 10.1109/ICIP40778.2020.9190887\|isbn= 978-1-7281-6395-6}}</ref>		'''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.\|volume=68 \|pages=2272–2286 \|doi=10.1109/TSP.2020.2982325 \|arxiv=1905.04441 \|bibcode=2020ITSP...68.2272T }}</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.\|volume=10 \|pages=277–293 \|doi=10.1109/TSIPN.2024.3375593 \|url-access=subscription}}</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.\|volume=8 \|pages=201–214 \|doi=10.1109/TSIPN.2022.3156886 \|arxiv=2207.06439 }}</ref> Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite book\|title=2020 IEEE International Conference on Image Processing (ICIP)\|date=October 2020\|chapter-url=https://ieeexplore.ieee.org/document/9190887\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter= Semi-Supervised Background Subtraction of Unseen Videos: Minimization of the Total Variation of Graph Signals\|pages= 3224–3228\|doi= 10.1109/ICIP40778.2020.9190887\|isbn= 978-1-7281-6395-6}}</ref>
	<ref name="Giraldo2">{{cite book\|title=2020 25th International Conference on Pattern Recognition (ICPR)\|date=2020\|chapter-url=https://ieeexplore.ieee.org/document/9412999\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter=GraphBGS: Background Subtraction via Recovery of Graph Signals \|pages=6881–6888 \|doi=10.1109/ICPR48806.2021.9412999 \|arxiv=2001.06404 \|isbn=978-1-7281-8808-9 }}</ref>		<ref name="Giraldo2">{{cite book\|title=2020 25th International Conference on Pattern Recognition (ICPR)\|date=2020\|chapter-url=https://ieeexplore.ieee.org/document/9412999\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter=GraphBGS: Background Subtraction via Recovery of Graph Signals \|pages=6881–6888 \|doi=10.1109/ICPR48806.2021.9412999 \|arxiv=2001.06404 \|isbn=978-1-7281-8808-9 }}</ref>
	<ref name="Giraldo3">{{cite book\|title=Frontiers of Computer Vision\|date=February 2021\|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.\|chapter=The Emerging Field of Graph Signal Processing for Moving Object Segmentation \|series=Communications in Computer and Information Science \|volume=1405 \|pages=31–45 \|doi=10.1007/978-3-030-81638-4_3 \|isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.\|pages=161–165 \|doi=10.23919/EUSIPCO63174.2024.10715291 \|isbn=978-9-4645-9361-7 }}</ref>		<ref name="Giraldo3">{{cite book\|title=Frontiers of Computer Vision\|date=February 2021\|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.\|chapter=The Emerging Field of Graph Signal Processing for Moving Object Segmentation \|series=Communications in Computer and Information Science \|volume=1405 \|pages=31–45 \|doi=10.1007/978-3-030-81638-4_3 \|isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.\|pages=161–165 \|doi=10.23919/EUSIPCO63174.2024.10715291 \|isbn=978-9-4645-9361-7 }}</ref>

Citation bot: Added doi. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 416/525

2025-05-10T11:33:30Z

Added doi. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 416/525

← Previous revision		Revision as of 11:33, 10 May 2025
Line 169:		Line 169:

	==Further reading==		==Further reading==
	* {{cite book \|first=Charles \|last=Byrne \|title=Signal Processing: A Mathematical Approach \|publisher=[[Taylor & Francis]] \|year=2014 \|isbn=9780429158711 \|url=https://www.taylorfrancis.com/books/oa-mono/10.1201/b17672/signal-processing-charles-byrne}}		* {{cite book \|first=Charles \|last=Byrne \|title=Signal Processing: A Mathematical Approach \|publisher=[[Taylor & Francis]] \|year=2014 \|doi=10.1201/b17672 \|isbn=9780429158711 \|url=https://www.taylorfrancis.com/books/oa-mono/10.1201/b17672/signal-processing-charles-byrne}}
	* {{cite book\|last=P Stoica\|first=R Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=https://user.it.uu.se/%7Eps/SAS-new.pdf}}		* {{cite book\|last=P Stoica\|first=R Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=https://user.it.uu.se/%7Eps/SAS-new.pdf}}
	* {{cite book \|first=Athanasios \|last=Papoulis \|title=Probability, Random Variables, and Stochastic Processes \|year=1991 \|edition=third \|publisher=McGraw-Hill \|isbn=0-07-100870-5}}		* {{cite book \|first=Athanasios \|last=Papoulis \|title=Probability, Random Variables, and Stochastic Processes \|year=1991 \|edition=third \|publisher=McGraw-Hill \|isbn=0-07-100870-5}}

Սահակ: /* Further reading */ replace a non-open access book with an open access one

2025-05-08T21:45:14Z

Further reading: replace a non-open access book with an open access one

← Previous revision		Revision as of 21:45, 8 May 2025
Line 169:		Line 169:

	==Further reading==		==Further reading==
			* {{cite book \|first=Charles \|last=Byrne \|title=Signal Processing: A Mathematical Approach \|publisher=[[Taylor & Francis]] \|year=2014 \|isbn=9780429158711 \|url=https://www.taylorfrancis.com/books/oa-mono/10.1201/b17672/signal-processing-charles-byrne}}
	* {{cite book\|last=P Stoica\|first=R Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}
	* {{cite book \|~~first~~=~~Steven M.~~ \|~~last~~=~~Kay~~ \|title=~~Fundamentals~~ of ~~Statistical Signal Processing~~ \|publisher=[[Prentice Hall]] \|location=~~[[Upper Saddle River, New Jersey]]~~ \|~~year~~=~~1993 \|isbn=0~~-~~13-345711-7 \|oclc=26504848~~}}		* {{cite book\|last=P Stoica\|first=R Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=https://user.it.uu.se/%7Eps/SAS-new.pdf}}
	* {{cite book \|first=Athanasios \|last=Papoulis \|title=Probability, Random Variables, and Stochastic Processes \|year=1991 \|edition=third \|publisher=McGraw-Hill \|isbn=0-07-100870-5}}		* {{cite book \|first=Athanasios \|last=Papoulis \|title=Probability, Random Variables, and Stochastic Processes \|year=1991 \|edition=third \|publisher=McGraw-Hill \|isbn=0-07-100870-5}}
	* Kainam Thomas Wong [http://www.eie.polyu.edu.hk/~enktwong/]: Statistical Signal Processing lecture notes at the University of Waterloo, Canada.		* Kainam Thomas Wong [http://www.eie.polyu.edu.hk/~enktwong/]: Statistical Signal Processing lecture notes at the University of Waterloo, Canada.

Kvng: review: missing article. consistent formatting.

2025-04-28T02:35:35Z

review: missing article. consistent formatting.

← Previous revision		Revision as of 02:35, 28 April 2025
Line 125:		Line 125:
	* [[Filter (signal processing)\|Filters]]{{spaced ndash}} for example analog (passive or active) or digital ([[FIR filter\|FIR]], [[IIR filter\|IIR]], frequency domain or [[stochastic filter]]s, etc.)		* [[Filter (signal processing)\|Filters]]{{spaced ndash}} for example analog (passive or active) or digital ([[FIR filter\|FIR]], [[IIR filter\|IIR]], frequency domain or [[stochastic filter]]s, etc.)
	* [[Sampling (signal processing)\|Samplers]] and [[analog-to-digital converter]]s for [[signal acquisition]] and reconstruction, which involves measuring a physical signal, storing or transferring it as digital signal, and possibly later rebuilding the original signal or an approximation thereof.		* [[Sampling (signal processing)\|Samplers]] and [[analog-to-digital converter]]s for [[signal acquisition]] and reconstruction, which involves measuring a physical signal, storing or transferring it as digital signal, and possibly later rebuilding the original signal or an approximation thereof.
	* [[Digital signal processor]]s (DSPs)~~<!--[[User:Kvng/RTH]]-->~~		* [[Digital signal processor]]s (DSPs)

	==Mathematical methods applied==		==Mathematical methods applied==
	* [[Differential equations]]<ref name="Gaydecki2004">{{cite book\|author=Patrick Gaydecki\|title=Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design\|url=https://books.google.com/books?id=6Qo7NvX3vz4C&q=%22differential+equation%22+OR+%22differential+equations%22&pg=PA40\|year=2004\|publisher=IET\|isbn=978-0-85296-431-6\|pages=40–}}</ref> - for modeling system behavior, connecting input and output relations in linear time-invariant systems. For instance, a low-pass filter such as an [[RC circuit]] can be modeled as a differential equation in signal processing, which allows one to compute the continuous output signal as function of the input or initial conditions.		* [[Differential equations]]<ref name="Gaydecki2004">{{cite book\|author=Patrick Gaydecki\|title=Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design\|url=https://books.google.com/books?id=6Qo7NvX3vz4C&q=%22differential+equation%22+OR+%22differential+equations%22&pg=PA40\|year=2004\|publisher=IET\|isbn=978-0-85296-431-6\|pages=40–}}</ref>{{spaced ndash}} for modeling system behavior, connecting input and output relations in linear time-invariant systems. For instance, a low-pass filter such as an [[RC circuit]] can be modeled as a differential equation in signal processing, which allows one to compute the continuous output signal as a function of the input or initial conditions.
	* [[Recurrence relation]]s<ref name="Engelberg2008">{{cite book\|author=Shlomo Engelberg\|title=Digital Signal Processing: An Experimental Approach\|url=https://books.google.com/books?id=z3CpcCHbtgIC\|date=8 January 2008\|publisher=Springer Science & Business Media\|isbn=978-1-84800-119-0}}</ref>		* [[Recurrence relation]]s<ref name="Engelberg2008">{{cite book\|author=Shlomo Engelberg\|title=Digital Signal Processing: An Experimental Approach\|url=https://books.google.com/books?id=z3CpcCHbtgIC\|date=8 January 2008\|publisher=Springer Science & Business Media\|isbn=978-1-84800-119-0}}</ref>
	* [[Transform theory]]		* [[Transform theory]]
	* [[Time-frequency analysis]]{{spaced ndash}} for processing non-stationary signals<ref>{{cite book\|title=Time frequency signal analysis and processing a comprehensive reference\|year=2003\|publisher=Elsevier\|location=Amsterdam\|isbn=0-08-044335-4\|edition=1\|editor=Boashash, Boualem}}</ref>		* [[Time-frequency analysis]]{{spaced ndash}} for processing non-stationary signals<ref>{{cite book\|title=Time frequency signal analysis and processing a comprehensive reference\|year=2003\|publisher=Elsevier\|location=Amsterdam\|isbn=0-08-044335-4\|edition=1\|editor=Boashash, Boualem}}</ref><!--[[User:Kvng/RTH]]-->
	* [[Linear canonical transformation]]		* [[Linear canonical transformation]]
	* [[Spectral estimation]]{{spaced ndash}} for determining the spectral content (i.e., the distribution of power over frequency) of a [[time series]]<ref>{{cite book\|first1=Petre\|last1=Stoica\|first2=Randolph\|last2=Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref>		* [[Spectral estimation]]{{spaced ndash}} for determining the spectral content (i.e., the distribution of power over frequency) of a [[time series]]<ref>{{cite book\|first1=Petre\|last1=Stoica\|first2=Randolph\|last2=Moses\|title=Spectral Analysis of Signals\|year=2005\|publisher=Prentice Hall\|location=NJ\|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref>

Citation bot: Alter: title, template type. Add: chapter, chapter-url. Removed or converted URL. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1883/2277

2025-04-18T19:34:56Z

Alter: title, template type. Add: chapter, chapter-url. Removed or converted URL. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1883/2277

← Previous revision		Revision as of 19:34, 18 April 2025
Line 93:		Line 93:

	===Graph ===		===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.\|volume=68 \|pages=2272–2286 \|doi=10.1109/TSP.2020.2982325 \|arxiv=1905.04441 \|bibcode=2020ITSP...68.2272T }}</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.\|volume=10 \|pages=277–293 \|doi=10.1109/TSIPN.2024.3375593 }}</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.\|volume=8 \|pages=201–214 \|doi=10.1109/TSIPN.2022.3156886 \|arxiv=2207.06439 }}</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.\|pages= 3224–3228\|doi= 10.1109/ICIP40778.2020.9190887\|isbn= 978-1-7281-6395-6}}</ref>		'''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.\|volume=68 \|pages=2272–2286 \|doi=10.1109/TSP.2020.2982325 \|arxiv=1905.04441 \|bibcode=2020ITSP...68.2272T }}</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.\|volume=10 \|pages=277–293 \|doi=10.1109/TSIPN.2024.3375593 }}</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.\|volume=8 \|pages=201–214 \|doi=10.1109/TSIPN.2022.3156886 \|arxiv=2207.06439 }}</ref> Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite book\|title=2020 IEEE International Conference on Image Processing (ICIP)\|date=October 2020\|chapter-url=https://ieeexplore.ieee.org/document/9190887\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter= Semi-Supervised Background Subtraction of Unseen Videos: Minimization of the Total Variation of Graph Signals\|pages= 3224–3228\|doi= 10.1109/ICIP40778.2020.9190887\|isbn= 978-1-7281-6395-6}}</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.\|pages=6881–6888 \|doi=10.1109/ICPR48806.2021.9412999 \|arxiv=2001.06404 \|isbn=978-1-7281-8808-9 }}</ref>		<ref name="Giraldo2">{{cite book\|title=2020 25th International Conference on Pattern Recognition (ICPR)\|date=2020\|chapter-url=https://ieeexplore.ieee.org/document/9412999\|last1=Giraldo\|first1=J.\|last2=Bouwmans\|first2=T.\|chapter=GraphBGS: Background Subtraction via Recovery of Graph Signals \|pages=6881–6888 \|doi=10.1109/ICPR48806.2021.9412999 \|arxiv=2001.06404 \|isbn=978-1-7281-8808-9 }}</ref>
	<ref name="Giraldo3">{{cite ~~journal~~\|title=~~The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on~~ Frontiers of Computer Vision~~, IW-FCV 2021~~\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.\|series=Communications in Computer and Information Science \|volume=1405 \|pages=31–45 \|doi=10.1007/978-3-030-81638-4_3 \|isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.\|pages=161–165 \|doi=10.23919/EUSIPCO63174.2024.10715291 \|isbn=978-9-4645-9361-7 }}</ref>		<ref name="Giraldo3">{{cite book\|title=Frontiers of Computer Vision\|date=February 2021\|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.\|chapter=The Emerging Field of Graph Signal Processing for Moving Object Segmentation \|series=Communications in Computer and Information Science \|volume=1405 \|pages=31–45 \|doi=10.1007/978-3-030-81638-4_3 \|isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.\|pages=161–165 \|doi=10.23919/EUSIPCO63174.2024.10715291 \|isbn=978-9-4645-9361-7 }}</ref>

	==Application fields==		==Application fields==

Citation bot: Altered journal. Add: series, isbn, bibcode, arxiv, doi, pages, volume. Upgrade ISBN10 to 13. | Use this bot. Report bugs. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1883/2277

2025-04-17T07:51:27Z

Altered journal. Add: series, isbn, bibcode, arxiv, doi, pages, volume. Upgrade ISBN10 to 13. | Use this bot. Report bugs. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1883/2277

← Previous revision		Revision as of 07:51, 17 April 2025
Line 93:		Line 93:

	===Graph ===		===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>		'''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.\|volume=68 \|pages=2272–2286 \|doi=10.1109/TSP.2020.2982325 \|arxiv=1905.04441 \|bibcode=2020ITSP...68.2272T }}</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.\|volume=10 \|pages=277–293 \|doi=10.1109/TSIPN.2024.3375593 }}</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.\|volume=8 \|pages=201–214 \|doi=10.1109/TSIPN.2022.3156886 \|arxiv=2207.06439 }}</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.\|pages= 3224–3228\|doi= 10.1109/ICIP40778.2020.9190887\|isbn= 978-1-7281-6395-6}}</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>		<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.\|pages=6881–6888 \|doi=10.1109/ICPR48806.2021.9412999 \|arxiv=2001.06404 \|isbn=978-1-7281-8808-9 }}</ref>
	<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.}}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.}}</ref>		<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.\|series=Communications in Computer and Information Science \|volume=1405 \|pages=31–45 \|doi=10.1007/978-3-030-81638-4_3 \|isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.\|pages=161–165 \|doi=10.23919/EUSIPCO63174.2024.10715291 \|isbn=978-9-4645-9361-7 }}</ref>

	==Application fields==		==Application fields==

WikiCleanerBot: v2.05b - Bot T20 CW#61 - Fix errors for CW project (Reference before punctuation)

2025-04-09T10:22:33Z

v2.05b - Bot T20 CW#61 - Fix errors for CW project (Reference before punctuation)

← Previous revision		Revision as of 10:22, 9 April 2025
Line 90:		Line 90:

	===Statistical ===		===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.		'''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 ===
	'''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>		'''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>		<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>
	<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.}}</ref> and sound anomaly detection <ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.}}</ref>.		<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.}}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.}}</ref>

	==Application fields==		==Application fields==

Constant314: /* Categories */ per WP:MOS

2025-04-05T22:33:55Z

Categories: per WP:MOS

← Previous revision		Revision as of 22:33, 5 April 2025
Line 61:		Line 61:
	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.		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 ~~Time~~===		===Continuous time===
	[[Continuous signal\|Continuous-time signal]] processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points).		[[Continuous signal\|Continuous-time signal]] processing is for signals that vary with the change of continuous domain (without considering some individual interrupted points).

Line 72:		Line 72:
	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.		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 ~~Time~~===		===Discrete time===
	[[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.		[[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.

Line 89:		Line 89:
	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>		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 ~~Signal Processing~~ ===		===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.		'''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 ~~Signal Processing~~ ===		===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>		'''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>		<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>

Davemck: /* Graph Signal Processing */ Clean up duplicate template arguments using findargdups

2025-04-05T17:51:30Z

Graph Signal Processing: Clean up duplicate template arguments using findargdups

← Previous revision		Revision as of 17:51, 5 April 2025
Line 93:		Line 93:

	===Graph Signal Processing ===		===Graph Signal Processing ===
	'''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=Javed\|first2=S~~.\|last2=Bouwmans\|first2=T.}}</ref>		'''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>		<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>
	<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.}}</ref> and sound anomaly detection <ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.}}</ref>.		<ref name="Giraldo3">{{cite journal\|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation\|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021\|date=February 2021\|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3\|last1=Giraldo\|first1=J.\|last2=Javed\|first2=S.\|last3=Sultana\|first3=M.\|last4=Jung\|first4=S.\|last5=Bouwmans\|first5=T.}}</ref> and sound anomaly detection <ref name="Bouwmans1">{{cite journal\|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing\|journal=European Conference on Signal Processing, EUSIPCO 2024\|date=2024\|url=https://ieeexplore.ieee.org/document/10715291\|last1=Mnasri\|first1=Z.\|last2=Giraldo\|first2=H. \|last3=Bouwmans\|first3=T.}}</ref>.