An Investigation of Binary Coded Excitation Methods Using Golay Code Pairs for High Frequency Ultrasound Imaging
Bibliographic record
Abstract
We are investigating the feasibility of binary coded excitation methods using Golay code pairs for high frequency ultrasound imaging as a way to increase the signal to noise ratio. I present some theoretical models used to simulate the coded excitation method and results generated from the models. A new coded excitation high frequency ultrasound prototype system was built to verify the simulation results. Both the simulation and the experimental results show that binary coded excitation can improve the signal to noise ratio in high frequency ultrasound backscatter signals. These results are confirmed in phantoms and excised bovine liver. If just white noise is considered, the encoding gain is 15dB for a Golay pair of length 4. We find the system to be very sensitive to motion (i.e. phase shift) and frequency dependent (FD) attenuation, creating sidelobes and degrading axial resolution and encoding gain. Methods to address these issues are discussed.
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How this classification was reachedexpand
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".