Peran Dan Fungsi Humas Dalam Meningkatkan Pelayanan PT. Garuda Indonesia Medan
Bibliographic record
Abstract
Clear visualization of spine structures in ultrasound imaging is difficult due to factors such as specular reflection, off-axis energy, and reverberation artifacts. The received channel data from the spine are often tilted even after delay correction, resulting in signal cancellation during the beamforming process. Conventional beamformers are not designed to tackle this issue. This paper proposes a closed-loop beamforming method which feeds back the location of the spine to the beamforming process so that backscattered bone signals can be aligned prior to the beamforming. To suppress the weak soft tissue and reverberation artifacts and increase the contrast of bones, a tensor-based filtering is employed prior to the cross-correlation-based alignment. Directional filtering is also employed to improve the bone surface detection. Phantom studies show improvement on the sharpness of the spine without shape distortion. In vivo results confirm significant contrast improvement of spinal structures. Compared with the conventional delay-and-sum beamforming, the proposed method improves the contrast ratio (CR) of the spine from 0.56 to 0.96. The 6-dB width of bone surfaces is also reduced by 51%.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".