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Record W2996111009 · doi:10.1109/tmtt.2019.2953604

Tunable Diplexer With Identical Passband and Constant Absolute Bandwidth

2019· article· en· W2996111009 on OpenAlexfundno aff
Zhiyou Li, Xiao Tang, Di Lu, Ming Yu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPassbandBand-pass filterDiplexerCenter frequencyBandwidth (computing)ResonatorElectronic engineeringPhysicsComputer scienceTopology (electrical circuits)AcousticsEngineeringOptoelectronicsTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

Frequency-adaptive bandpass diplexer (FA-BPD) with constant absolute bandwidths (ABWs) and two identical passbands at every state is the most useful duplexing tunable filter for the smart frequency-division duplex (FDD) system. However, it is widely considered a challenge. In this article, we propose a new synchronously tuned trimode resonator (STTR) to achieve such a type of FA-BPDs. The proposed STTR has three flexible resonant modes that are able to be synchronously tuned by only one bias with the predefined frequency spacings. The spacings between every two tunable resonant frequencies can be precisely controlled as desired. Consequently, the resultant tunable filters can be implemented with different constant ABWs and at the different center frequency, which makes it possible to achieve two frequency-adaptive bandpass filters (FA-BPFs) at different frequency ranges but with the same ABW. Based on the proposed STTR, a three-pole FA-BPF with nearly 250-MHz ABW is examined. Using the FA-BPF as basic building blocks, a 1.5-3.5-GHz FA-BPD with identical passband and constant ABW is developed finally. The simulation and measurement results confirm the proposed design method and also exhibit the merits, such as low loss, simple design, wide frequency tuning range with synchronous passband.

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 score0.553
Threshold uncertainty score0.833

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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