Impulse response-based actuation of ultrasound transducer for enhanced axial resolution in A-mode ultrasound scanner
Bibliographic record
Abstract
This work proposes a novel method for adaptively acquiring the impulse response of an ultrasound scanner and using it to drive the transducer, thereby optimizing the pulse-echo response. Specifically, this work introduces a new technique for obtaining the equivalent impulse response of the entire system, including the ultrasound transmitter (TX) circuit, transducer, medium, and receiver (RX) circuit. Compared to conventional transducer actuation using pulse waves or sine waves, the proposed method achieves efficient acoustic energy transmission and reception by fully utilizing the system bandwidth through impulse response-based transducer re-actuation. The proposed impulse response acquisition algorithm and transducer actuation method were implemented on a field-programmable gate array (FPGA) device and validated using an A-mode ultrasound scanner circuit. Experimental results demonstrated that, under the same TX power condition, the axial resolution of the pulse-echo response was improved by at least 1.42 times compared to conventional pulse sine-wave, bipolar, and unipolar actuation methods. Adaptive system-wide impulse response acquisition and activation of ultrasonic transducer with the acquired impulse response for enhanced axial resolution in a real-time ultrasound scanner. • A novel method for adaptively utilizing the system-wide impulse response of an ultrasound scanner to optimize pulse-echo performance.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".