Transmission of side-scan sonar snippets from an underway unmanned underwater vehicle
Bibliographic record
Abstract
An unique vector quantization compression methodology was applied to compress and encode side-scan sonar snippets of mine-like objects generated by automated target recognition tools on-board underway unmanned underwater vehicles (UUV). These compressed and encoded images were then further formed into acoustic packets. The objective was to transmit these acoustic packets, underwater, as representations of the sonar snippets (mugshots). The ability to transmit sonar snippets underwater while the UUV is underway is important as it allows the above-water operator to examine an image of mine-like objects, without recovering the UUV, for a timely decision on whether the object is actually a mine. This vector quantization method was used because of its terseness and thus it could be transmitted by WHOI underwater micromodems integrated on IVER3 UUVs. This presentation describes the algorithm, its implementation, and its initial in-water validation in local waters. This capability was also validated and demonstrated during the Royal Navy Unmanned Warrior 2016 exercise. Results from this will also be presented and discussed.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".