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Record W1963498099 · doi:10.5539/mas.v9n4p328

Multi-Frequency Modulation Laser Range Finding System

2015· article· en· W1963498099 on OpenAlexvenueno aff
Yu Chen, Chunyang Wang, Huan Gao, Huan Liu

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsRangingComputer scienceModulation (music)Frequency modulationRange (aeronautics)LaserPhase (matter)AlgorithmRotation (mathematics)Sampling (signal processing)JammingOpticsAcousticsPhysicsComputer visionTelecommunicationsBandwidth (computing)Materials scienceDetector

Abstract

fetched live from OpenAlex

Traditional laser ranging system has a poor phase measuring accuracy, low anti-jamming capability and time-consuming measurement. A multi-frequency modulation laser range finder method is proposed in this paper. System uses phase detection algorithm to calculate the sine of ranging phase for noisy environments, and the angle is calculated by Coordinate Rotation Digital Computer(CORDIC) angle solver algorithm. When the sampling frequency is 500MHz, the word length is 16-bits, the SNR is 12dB, the measurement range is 100m, the phase difference resolution is higher than 0.0213°, and the distance accuracy is 0.10mm. Experiments proved that the system can meet the need of high-accuracy and low computational complexity.

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)0.000
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 score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.056
GPT teacher head0.295
Teacher spread0.239 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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