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Record W2638534188 · doi:10.1109/ccece.2017.7946592

IOT: Advance physical signaling methods for Power Line networking system

2017· article· en· W2638534188 on OpenAlexaff
Jason Falbo, Ahmed Refaey, Raveendra K. Rao, Khaled Loukhaoukha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsWestern University
Fundersnot available
KeywordsFirmwareComputer sciencePower-line communicationPhysical layerCover (algebra)Software deploymentWirelessSynchronization (alternating current)Communications systemEmbedded systemArchitectureComputer networkComputer architectureTelecommunicationsComputer hardwarePower (physics)EngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

IoT tends to focus on a variety of wireless technologies and approaches to enable communication over the air, however, powerline networking may also be considered as an alternative reliable and robust signaling option. Methods and devices described in this letter form the basis of modern communication system. A powerline alternative deployment covering Hardware, Embedded System Architecture, proposed System Firmware, Physical Layer Architecture, Robust Mode Correlation (Bit Masking), Communication Synchronization Facility will be presented. This is not intended to cover an exhaustive discussion of apparatus, techniques and mechanisms, though it does provide a complete low level overview of a proposed IoT architecture based on powerline communications.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score0.398

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 science0.0000.000
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 head0.033
GPT teacher head0.367
Teacher spread0.335 · 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
GenreMethods

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

Citations0
Published2017
Admission routes1
Has abstractyes

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