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Record W2232231960 · doi:10.5539/apr.v8n1p47

Increasing Ultrasonic Array Data Acquisition Rate through the use of Kasami Codes and the Maximum Entropy Method

2016· article· en· W2232231960 on OpenAlexvenueno aff
Eugeny Bazulin, A. E. Bazulin

Bibliographic record

VenueApplied Physics Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceDecoding methodsAlgorithmEntropy (arrow of time)AcousticsOpticsPhysics

Abstract

fetched live from OpenAlex

<p class="1Body">We suggest using sets of pseudo-orthogonal code with antenna arrays working in Full Matrix Capture (FMC) mode, to increase the rate of data acquisition. This allows the use of signals comprised of phase-manipulated Kasami sequences, specifically developed for CMDA technology. The use of the Maximum Entropy Method (MEM) for decoding signals in lieu of matched filtration allows us to reduce noise level and increase time resolution in reflectors’ image. Additionally, to reduce noise level by more than 6 dB we suggest the use of various Kasami sequences for each position of an antenna array. Numerical and model experiments demonstrate the efficacy of the proposed approach.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.795
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.160
GPT teacher head0.358
Teacher spread0.198 · 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 teacher head, 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

Citations1
Published2016
Admission routes1
Has abstractyes

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