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Record W4401379722 · doi:10.1109/mmm.2024.3412448

Design Techniques for Passive Planar Reconfigurable RF Circuits: Reconfigurable RF Circuits

2024· article· en· W4401379722 on OpenAlexaff
Aijaz M. Zaidi, Binod Kumar Kanaujia, Jugul Kishor, Sumer Singh Singhwal, Vikrant Kaim, Amit Kumar, Karumudi Rambabu, S.R. Rengarajan

Bibliographic record

VenueIEEE Microwave Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectronic circuitPlanarRadio frequencyElectronic engineeringElectrical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Reconfigurable systems play an important role in the design of reconfigurable wireless communication systems. In the traditional approach, separate transmit and receive pathways are required for every supported communication standard/frequency band for an RF front-end architecture. However, this approach increases the complexity and size of the system. This problem can be solved by employing subsystems, or blocks, that can function across multiple frequency bands and standards or that can reconfigure themselves based on the spectrum. This flexibility can free up finite spectrum resources to enable a miniaturized system. Therefore, microwave subsystems should ideally be reconfigurable and frequency agile in order to handle the vast frequency allocation of the regulated communication bands and the multitude of standards by which these radios must function. Such subsystems would allow for the implementation of new architectures with fewer functional components. Therefore, reconfigurable circuits can be useful for several wireless applications, such as telecommunication and military applications, where new frequency bands are coming or are expected to come. A flow graph for designing reconfigurable circuits is in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref>.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.978
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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