Front-End Receiver Architecture for Miniaturised Ultrasound Imaging
Bibliographic record
Abstract
The design and measured results for an I/Q synthetic aperture beamforming front-end are presented.The system targets a highly portable ultrasound imaging applications such as wearable/portable devices and capsule endoscopes.Synthetic aperture beamforming is carried out in the baseband in order to minimise the bandwidth and power consumption.A single-channel analogue front-end (AFE) demodulates RF signals into I/Q components.The FPGA-based beamformer dynamically apodises and focuses the data by interpolating and applying complex phase rotations to the I/Q samples.The entire system is pipelined using a synthetic aperture protocol through a single, multiplexed channel in order to reduce the cost and complexity of the system and minimise the area.The AFE consumes 7.8mW and occupies 1.5 mm × 1.5 mm in AMS 0.35µm CMOS.The digital beamformer is implemented on a Kintex-7 TM FPGA and consumes 262mW for a frame rate of 4Hz.Measured results using real ultrasound data reveal that comparable image quality may be attained to the case when full RF beamforming is used.Future work includes integration of analogue/digital components on a single chip.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".