Academic Journal
Temporal Super-Resolution of Non-Stationary Signals: Mixed-Domain Training and a Hybrid Wavelet–Superlet Pilot.
| Τίτλος: | Temporal Super-Resolution of Non-Stationary Signals: Mixed-Domain Training and a Hybrid Wavelet–Superlet Pilot. |
|---|---|
| Συγγραφείς: | Ibarra-Fiallo, Julio, Agudelo-Moreno, D'hamar, Lara, Juan A. |
| Πηγή: | Algorithms; Jul2026, Vol. 19 Issue 7, p599, 25p |
| Θεματικοί όροι: | Signal reconstruction, Signal processing, Wavelet transforms, Electroencephalography, Spectrum analysis |
| Περίληψη: | Temporal super-resolution (SR) aims to reconstruct a high-resolution signal from a low-resolution observation. When hardware limits force low sampling rates, this problem becomes critical for non-stationary signals with abrupt transients and rapid spectral changes. This manuscript reports a deterministic case study using pretrained 1D convolutional models and deterministic evaluation on paired real, synthetic, and mixed EEG-like signals. A compact encoder–linear upsampler–refinement architecture is evaluated at 5× upsampling under four training regimes: synthetic-only, real-only, tuned-real, and mixed. Performance is assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Normalized Mean Absolute Error (NMAE), Log Spectral Distance (LSD), and spectral correlation (SCORR). Across 12 model–dataset combinations, mixed-domain training yields the most robust cross-domain behavior, outperforming single-domain checkpoints on real and mixed evaluation subsets. These findings support the practical value of training corpus composition for temporal SR under distribution shift. A focused morphological event analysis further shows that reconstruction error concentrates at abrupt amplitude and frequency boundaries, confirming that these transient regions are the dominant local challenge. An exploratory hybrid wavelet–superlet pilot is also reported; it achieves competitive pointwise error on selected domains but exhibits a substantial spectral-fidelity gap, indicating that frequency-aware inputs alone do not guarantee spectral reconstruction without auxiliary spectral losses. [ABSTRACT FROM AUTHOR] |
| Copyright of Algorithms is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19994893&ISBN=&volume=19&issue=7&date=20260701&spage=599&pages=599-623&title=Algorithms&atitle=Temporal%20Super-Resolution%20of%20Non-Stationary%20Signals%3A%20Mixed-Domain%20Training%20and%20a%20Hybrid%20Wavelet%E2%80%93Superlet%20Pilot.&aulast=Ibarra-Fiallo%2C%20Julio&id=DOI:10.3390/a19070599 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 195811122 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42175292969 |
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| Items | – Name: Title Label: Title Group: Ti Data: Temporal Super-Resolution of Non-Stationary Signals: Mixed-Domain Training and a Hybrid Wavelet–Superlet Pilot. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ibarra-Fiallo%2C+Julio%22">Ibarra-Fiallo, Julio</searchLink><br /><searchLink fieldCode="AR" term="%22Agudelo-Moreno%2C+D'hamar%22">Agudelo-Moreno, D'hamar</searchLink><br /><searchLink fieldCode="AR" term="%22Lara%2C+Juan+A%2E%22">Lara, Juan A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Algorithms; Jul2026, Vol. 19 Issue 7, p599, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Temporal super-resolution (SR) aims to reconstruct a high-resolution signal from a low-resolution observation. When hardware limits force low sampling rates, this problem becomes critical for non-stationary signals with abrupt transients and rapid spectral changes. This manuscript reports a deterministic case study using pretrained 1D convolutional models and deterministic evaluation on paired real, synthetic, and mixed EEG-like signals. A compact encoder–linear upsampler–refinement architecture is evaluated at 5× upsampling under four training regimes: synthetic-only, real-only, tuned-real, and mixed. Performance is assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Normalized Mean Absolute Error (NMAE), Log Spectral Distance (LSD), and spectral correlation (SCORR). Across 12 model–dataset combinations, mixed-domain training yields the most robust cross-domain behavior, outperforming single-domain checkpoints on real and mixed evaluation subsets. These findings support the practical value of training corpus composition for temporal SR under distribution shift. A focused morphological event analysis further shows that reconstruction error concentrates at abrupt amplitude and frequency boundaries, confirming that these transient regions are the dominant local challenge. An exploratory hybrid wavelet–superlet pilot is also reported; it achieves competitive pointwise error on selected domains but exhibits a substantial spectral-fidelity gap, indicating that frequency-aware inputs alone do not guarantee spectral reconstruction without auxiliary spectral losses. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Algorithms is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/a19070599 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 599 Subjects: – SubjectFull: Signal reconstruction Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Wavelet transforms Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Spectrum analysis Type: general Titles: – TitleFull: Temporal Super-Resolution of Non-Stationary Signals: Mixed-Domain Training and a Hybrid Wavelet–Superlet Pilot. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ibarra-Fiallo, Julio – PersonEntity: Name: NameFull: Agudelo-Moreno, D'hamar – PersonEntity: Name: NameFull: Lara, Juan A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19994893 Numbering: – Type: volume Value: 19 – Type: issue Value: 7 Titles: – TitleFull: Algorithms Type: main |
| ResultId | 1 |