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Record W3207454634 · doi:10.1109/tim.2021.3119131

A Dual-Band Rat-Race Coupler for High Band Ratio Wireless Applications

2021· article· en· W3207454634 on OpenAlexaff
Aijaz M. Zaidi, Mirza Tariq Beg, Binod Kumar Kanaujia, Karun Rawat, Sachin Kumar, Karumudi Rambabu, Satya P. Singh, A. Lay-Ekuakille

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research Board
KeywordsRat-race couplerMulti-band deviceHybrid couplerReturn lossMicrostripWirelessFrequency bandTransmission lineRadio spectrumLine (geometry)Electrical engineeringElectronic engineeringPower dividers and directional couplersPhysicsEngineeringBandwidth (computing)TelecommunicationsMathematicsAntenna (radio)

Abstract

fetched live from OpenAlex

This paper presents a dual-band rat-race coupler for high band ratio wireless applications. In this paper, a dual-band transmission line has also been proposed to design the rat-race coupler. The proposed rat-race coupler has been developed by substituting each transmission line segment of a single-band rat-race coupler with the dual-band transmission line. To validate the design method, a rat-race coupler for 0.7 GHz and 4.9 GHz operating frequencies. has been developed. Maximum 3.3 dB insertion loss and 20 dB return loss and isolation have been attained for both the frequency bands. Compared with the existing dual-band rat-race couplers, the proposed coupler offers dual-band operation with a high band ratio. In addition, the first time a dual-band rat-race coupler compatible with to microstrip transmission line and having input and output on different sides has been developed. This feature makes simpler connections when connected to other circuits.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.716

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 head0.021
GPT teacher head0.224
Teacher spread0.204 · 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 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

Citations15
Published2021
Admission routes1
Has abstractyes

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