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Record W2953549290 · doi:10.1109/access.2018.2889683

An Experimentally Validated Channel Model for Molecular Communication Systems

2019· article· en· W2953549290 on OpenAlexaff
Narae Kim, Nariman Farsad, Changmin Lee, Andrew W. Eckford, Chan‐Byoung Chae

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsYork University
FundersNational Research FoundationNational Research Foundation of KoreaMinistry of Education
KeywordsMolecular communicationComputer scienceChannel (broadcasting)Focus (optics)Noise (video)Communications systemFunction (biology)Sensitivity (control systems)Modulation (music)Experimental dataData modelingElectronic engineeringAcousticsTelecommunicationsArtificial intelligencePhysicsOpticsMathematics

Abstract

fetched live from OpenAlex

In this paper, we present an experimentally validated end-to-end channel model for molecular communication systems with metal-oxide sensors. In particular, we focus on the recently developed tabletop molecular communication platform. Unlike previous work, this paper separates the system into two parts-the propagation and the sensing. Based on this separation, a more realistic channel model is derived. The coefficients in the derived models are estimated using a large collection of experimental data. It is shown how the coefficients change as a function of different system parameters such as distance, spraying duration, and an initial condition. Finally, a noise model is derived for the system to complete an end-to-end system model for the tabletop platform that can be utilized with various system variables. Using this new channel model, we propose a multi-level modulation technique that represents different symbols with different spraying durations while still providing more feasibility and less computational complexity in practice.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.546

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 integrity0.0000.000
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 head0.031
GPT teacher head0.304
Teacher spread0.273 · 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 designSimulation or modeling
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

Citations26
Published2019
Admission routes1
Has abstractyes

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