Optimising Time-Frequency Distributions: A Surface Metrology Approach
Time-frequency signal processing offers a significant advantage over temporal or frequency-only methods, but representations require optimisation for a given signal. Standard practice includes choosing the appropriate time-frequency distribution and fine-tuning its parameters, usually via visual ins...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/13/5804 |
| _version_ | 1797590859678482432 |
|---|---|
| author | Damir Malnar Miroslav Vrankic |
| author_facet | Damir Malnar Miroslav Vrankic |
| author_sort | Damir Malnar |
| collection | DOAJ |
| description | Time-frequency signal processing offers a significant advantage over temporal or frequency-only methods, but representations require optimisation for a given signal. Standard practice includes choosing the appropriate time-frequency distribution and fine-tuning its parameters, usually via visual inspection and various measures—the most commonly used ones are based on the Rényi entropies or energy concentration by Stanković. However, a discrepancy between the observed representation quality and reported numerical value may arise when the filter kernel has greater adaptability. Herein, a performance measure derived from the Abbot–Firestone curve similar to the volume parameters in surface metrology is proposed as the objective function to be minimised by the proposed minimalistic differential evolution variant that is parameter-free and uses a population of five members. Tests were conducted on two synthetic signals of different frequency modulations and one real-life signal. The multiform tiltable exponential kernel was optimised according to the Rényi entropy, Stanković’s energy concentration and the proposed measure. The resulting distributions were mutually evaluated using the same measures and visual inspection. The optimiser demonstrated a reliable convergence for all considered measures and signals, while the proposed measure showed consistent alignment of reported numerical values and visual assessments. |
| first_indexed | 2024-03-11T01:29:27Z |
| format | Article |
| id | doaj.art-7ec3f479a1484e8d9cab2403516cb3d8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ec3f479a1484e8d9cab2403516cb3d82023-11-18T17:27:01ZengMDPI AGSensors1424-82202023-06-012313580410.3390/s23135804Optimising Time-Frequency Distributions: A Surface Metrology ApproachDamir Malnar0Miroslav Vrankic1Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaTime-frequency signal processing offers a significant advantage over temporal or frequency-only methods, but representations require optimisation for a given signal. Standard practice includes choosing the appropriate time-frequency distribution and fine-tuning its parameters, usually via visual inspection and various measures—the most commonly used ones are based on the Rényi entropies or energy concentration by Stanković. However, a discrepancy between the observed representation quality and reported numerical value may arise when the filter kernel has greater adaptability. Herein, a performance measure derived from the Abbot–Firestone curve similar to the volume parameters in surface metrology is proposed as the objective function to be minimised by the proposed minimalistic differential evolution variant that is parameter-free and uses a population of five members. Tests were conducted on two synthetic signals of different frequency modulations and one real-life signal. The multiform tiltable exponential kernel was optimised according to the Rényi entropy, Stanković’s energy concentration and the proposed measure. The resulting distributions were mutually evaluated using the same measures and visual inspection. The optimiser demonstrated a reliable convergence for all considered measures and signals, while the proposed measure showed consistent alignment of reported numerical values and visual assessments.https://www.mdpi.com/1424-8220/23/13/5804optimisationtime-frequencydistributionsurfacemetrologyevolutionary |
| spellingShingle | Damir Malnar Miroslav Vrankic Optimising Time-Frequency Distributions: A Surface Metrology Approach Sensors optimisation time-frequency distribution surface metrology evolutionary |
| title | Optimising Time-Frequency Distributions: A Surface Metrology Approach |
| title_full | Optimising Time-Frequency Distributions: A Surface Metrology Approach |
| title_fullStr | Optimising Time-Frequency Distributions: A Surface Metrology Approach |
| title_full_unstemmed | Optimising Time-Frequency Distributions: A Surface Metrology Approach |
| title_short | Optimising Time-Frequency Distributions: A Surface Metrology Approach |
| title_sort | optimising time frequency distributions a surface metrology approach |
| topic | optimisation time-frequency distribution surface metrology evolutionary |
| url | https://www.mdpi.com/1424-8220/23/13/5804 |
| work_keys_str_mv | AT damirmalnar optimisingtimefrequencydistributionsasurfacemetrologyapproach AT miroslavvrankic optimisingtimefrequencydistributionsasurfacemetrologyapproach |