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Record W2563264513 · doi:10.1109/tuffc.2016.2646925

A Sub-Nyquist, Variable Sampling, High-Frequency Phased Array Beamformer

2016· article· en· W2563264513 on OpenAlexafffund
Christopher Samson, Andre Bezanson, Jeremy A. Brown

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2016
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities Agency
KeywordsPhased arrayNyquist–Shannon sampling theoremNyquist frequencyBeamformingPhased-array opticsSampling (signal processing)PhysicsAcousticsElectronic engineeringComputer scienceOpticsEngineeringTelecommunicationsBandwidth (computing)Antenna (radio)Detector

Abstract

fetched live from OpenAlex

A digital receive beamformer implementing a "one sample per pixel" variable sampling technique is described. The sampling method reduces the required sampling rates by a factor of 3, and reduces the data capture rate by a factor of 2, in comparison with the previous systems based on variable sampling. The sampling method is capable of estimating broadband pulse envelopes accurate for bandwidths up to 83.0%. This beamforming method has been implemented on a field-programmable gate array with maximum transmit and receive delay errors measured to be less than ±1.0 ns. The beamformer was tested and verified on a previously described 45-MHz 64-element phased array. The system generates images with 128 lines, 512 pixels per RF line, and 2 transmit focal zones. The system generates images with approximately 55 dB of dynamic range and was tested by imaging wire targets submersed in a water bath, wire targets embedded in a tissue phantom, and real-time in vivo imaging of a human wrist.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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.

Opus teacher head0.008
GPT teacher head0.195
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2016
Admission routes2
Has abstractyes

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