A Self-Adaptive Wavelet-Based Algorithm for Wave Measurement Using Nautical Radar
Bibliographic record
Abstract
In this paper, a self-adaptive 2-D continuous-wavelet-transform-based algorithm for extracting wave information from X-band nautical radar images is presented. After investigating the 2-D continuous wavelet transform and its application for radar image processing, it is found that the wavelet scaling parameters will affect the results of wave field analysis. The relation of the scaling parameters to the minimum distinguishable wavenumber is developed using a calibration factor. Optimal empirical values of such calibration factors are determined from a series of simulation data tests for variable wave conditions. An iterative algorithm is then proposed that enables the system to automatically select the optimal calibration factor without requiring a reference to other instrumentation. The algorithm is evaluated using dual-polarized radar data collected on the east coast of Canada. Results of the proposed algorithm are analyzed and compared with in situ TRIAXYS wave buoy data as well as that obtained from the conventional 3-D fast Fourier transform (FFT)-based method. The impact of signal polarization on the results is explored. The agreement between the buoy and FFT results indicates that the proposed algorithm is practical and effective as an alternative to the classic 3-D FFT-based method for retrieving ocean wave information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".