MétaCan
Menu
Back to cohort
Record W3184424992 · doi:10.36227/techrxiv.14974560.v1

Lightweight Low-Noise Linear Isolator Integrating Phase-Engineered Temporal Loops

2021· preprint· en· W3184424992 on OpenAlexafffund
Sajjad Taravati, George V. Eleftheriades

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicNeural Networks and Reservoir Computing
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsolatorNoise (video)Phase (matter)AcousticsPhase noiseComputer scienceMaterials scienceElectronic engineeringEngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The quest for efficient and versatile microwave and optical isolators has recently spawned several novel space-time-modulated isolator structures.However, such space-time isolators suffer from a large profile and complex architecture caused by progressive nonreciprocal space-time coupling properties.To overcome these limitations, we propose a nonmagnetic phase-engineered temporal-loop-based isolator featuring large isolation levels, weak undesired time harmonics, and a low profile.The proposed isolator is composed of two temporal loops that provide desired constructive and destructive interferences of different time harmonics.Furthermore, these two loops are designed in a way to assure that the circulation and reflection of different time harmonics strengthen a low insertion loss unidirectional signal transmission.An experimental demonstration of the proposed time-modulated isolator at microwave frequencies is provided, featuring strong unidirectional wave transmission through the isolator with more than 27 dB contrast between the forward and backward waves across a fractional bandwidth of 14.3%.The proposed isolator outperforms the nonlinear-based and transistor-based isolators by featuring a highly linear response with OP 1dB of higher than 31 dBm, high power rating of more than 47 dBm, and a low noise figure of 3.4 dB.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicNeural Networks and Reservoir ComputingFrench-language works237,207