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A Compact Microstrip Band-pass Filter for S/C-band Applications

2024· article· en· W4408701758 on OpenAlexaff
Anil Kumar Nayak, I.M. Filanovsky, Kambiz Moez, Amalendu Patnaik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrostripBand-pass filterC bandComputer scienceFilter (signal processing)Electronic engineeringOptoelectronicsElectrical engineeringTelecommunicationsMaterials scienceEngineering

Abstract

fetched live from OpenAlex

This paper presents a compact planar ultrawideband (UWB) band-pass filter (BPF). Initially, it employs a compact BPF with a reversible notched band to reject unwanted signals. The second phase entails determining and establishing the conventional microstrip feed’s input impedance at a fixed value of 50 ohms. Then, to eliminate the unwanted signal, a triangleshaped patch is etched from an L-shaped parallel linked transmission line. The bottom part of the BPF receives grounding. The BPF is designed, simulated, and fabricated in the laboratory. The measured fractional impedance bandwidth of 4.42 and 25.83%, minimum insertion loss of 1.40 and 0.32 dB, and minimum total loss of 15 and 8%, along with flat group delay at first and second resonant frequencies, respectively. Notably, BPF exhibits minimal loss, exceptionally low distortion, and acceptable speed. It demonstrates that the simulation and experimental results support the conclusions. The proposed BPF is suitable for S/C-band applications.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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
Published2024
Admission routes1
Has abstractyes

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