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Record W2963884830 · doi:10.1109/mwc.2017.1600308

SMIET: Simultaneous Molecular Information and Energy Transfer

2017· article· en· W2963884830 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

VenueIEEE Wireless Communications · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsYork University
Fundersnot available
KeywordsMolecular communicationComputer scienceEnergy (signal processing)Energy harvestingTransmission (telecommunications)TelecommunicationsCommunications systemRelayInformation transferPoint (geometry)Efficient energy useElectrical engineeringPhysicsPower (physics)Transmitter

Abstract

fetched live from OpenAlex

The performance of communication systems is fundamentally limited by the loss of energy through propagation and circuit inefficiencies. The emergence of the Internet of Nano Things ecosystem means there is a need to design and build nanoscale energy efficient communication subsystems. In this article, we show that it is possible to achieve ultra low energy communications at the nanoscale, if diffusive molecules are used for carrying data. While the energy of electromagnetic waves will inevitably decay as a function of transmission distance and time, the energy in individual molecules does not. Over time, the receiver has an opportunity to recover some, if not all, of the molecular energy transmitted. The article demonstrates the potential of ultra-low energy SMIET through point-to-point systems, two different nano-relay systems, and multiple access systems. It also discusses the benefits of crowd energy harvesting compared to traditional wave-based systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.604

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 studies0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.214 · 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