A Concise Tutorial on Functional Analysis for Applications to Signal Processing
Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sci |
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| Online Access: | https://www.mdpi.com/2413-4155/4/4/40 |
| _version_ | 1797455374137163776 |
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| author | Najah F. Ghalyan Asok Ray William Kenneth Jenkins |
| author_facet | Najah F. Ghalyan Asok Ray William Kenneth Jenkins |
| author_sort | Najah F. Ghalyan |
| collection | DOAJ |
| description | Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format. |
| first_indexed | 2024-03-09T15:53:29Z |
| format | Article |
| id | doaj.art-5bfe63e323e143998577ac34af36eeb2 |
| institution | Directory Open Access Journal |
| issn | 2413-4155 |
| language | English |
| last_indexed | 2024-03-09T15:53:29Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sci |
| spelling | doaj.art-5bfe63e323e143998577ac34af36eeb22023-11-24T17:51:13ZengMDPI AGSci2413-41552022-10-01444010.3390/sci4040040A Concise Tutorial on Functional Analysis for Applications to Signal ProcessingNajah F. Ghalyan0Asok Ray1William Kenneth Jenkins2Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USAFunctional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format.https://www.mdpi.com/2413-4155/4/4/40statistical signal processingadaptive signal processingfunctional analysis |
| spellingShingle | Najah F. Ghalyan Asok Ray William Kenneth Jenkins A Concise Tutorial on Functional Analysis for Applications to Signal Processing Sci statistical signal processing adaptive signal processing functional analysis |
| title | A Concise Tutorial on Functional Analysis for Applications to Signal Processing |
| title_full | A Concise Tutorial on Functional Analysis for Applications to Signal Processing |
| title_fullStr | A Concise Tutorial on Functional Analysis for Applications to Signal Processing |
| title_full_unstemmed | A Concise Tutorial on Functional Analysis for Applications to Signal Processing |
| title_short | A Concise Tutorial on Functional Analysis for Applications to Signal Processing |
| title_sort | concise tutorial on functional analysis for applications to signal processing |
| topic | statistical signal processing adaptive signal processing functional analysis |
| url | https://www.mdpi.com/2413-4155/4/4/40 |
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