Novel low-power ultrasound digital preprocessing architecture for wireless display
Bibliographic record
Abstract
A complete hardware-based ultrasound preprocessing unit (PPU) is presented as an alternative to available power-hungry devices. Intended to expand the ultrasonic applications, the proposed unit allows replacement of the cable of the ultrasonic probe by a wireless link to transfer data from the probe to a remote monitor. The digital back-end architecture of this PPU is fully pipelined, which permits sampling of ultrasonic signals at a frequency equal to the field-programmable gate array-based system clock, up to 100 MHz. Experimental results show that the proposed processing unit has an excellent performance, an equivalent 53.15 Dhrystone 2.1 MIPS/ MHz (DMIPS/MHz), compared with other software-based architectures that allow a maximum of 1.6 DMIPS/MHz. In addition, an adaptive subsampling method is proposed to operate the pixel compressor, which allows real-time image zooming and, by removing high-frequency noise, the lateral and axial resolutions are enhanced by 25% and 33%, respectively. Realtime images, acquired from a reference phantom, validated the feasibility of the proposed architecture. For a display rate of 15 frames per second, and a 5-MHz single-element piezoelectric transducer, the proposed digital PPU requires a dynamic power of only 242 mW, which represents around 20% of the best-available software-based system. Furthermore, composed by the ultrasound processor and the image interpolation unit, the digital processing core of the PPU presents good power-performance ratios of 26 DMIPS/mW and 43.9 DMIPS/mW at a 20-MHz and 100-MHz sample frequency, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".