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Record W1989868490 · doi:10.1109/glocom.2014.7037234

Bounds on distance estimation via diffusive molecular communication

2014· preprint· en· W1989868490 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMolecular communicationCramér–Rao boundEstimatorUpper and lower boundsMaximum likelihoodImpulse (physics)Variance (accounting)MathematicsChannel (broadcasting)Estimation theoryStatisticsAlgorithmMaximum likelihood sequence estimationComputer scienceApplied mathematicsMathematical analysisPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper studies distance estimation for diffusive molecular communication. The strength of the channel impulse response generally decreases with distance, so it is measured to estimate the distance. The Cramer-Rao lower bound on the variance of the distance estimation error is derived. The lower bound is derived for a physically unbounded environment with molecule degradation and steady uniform flow. The maximum likelihood distance estimator is derived and its accuracy is shown via simulation to perform very close to the Cramer-Rao lower bound. An existing protocol is shown to be equivalent to the maximum likelihood distance estimator if only one observation is made. Simulation results also show the accuracy of existing protocols with respect to the Cramer-Rao lower bound.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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

Quick stats

Citations36
Published2014
Admission routes1
Has abstractyes

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