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Record W4402747093 · doi:10.4314/sa.v23i3.24

Interference cancellation system of a microwave resonator filter for 4G and 5G applications

2024· article· en· W4402747093 on OpenAlexfundno aff
F. Onaifo, K.O. Ogbeide, E.L. Omoze

Bibliographic record

VenueScientia Africana · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersUniversity of North TexasLondon Metropolitan UniversityUniversity of WaterlooCochin University of Science and TechnologyPurdue University
KeywordsMicrowaveResonatorInterference (communication)Single antenna interference cancellationFilter (signal processing)TelecommunicationsElectronic engineeringComputer scienceAcousticsOptoelectronicsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper aimed at designing a microwave resonator filter for use in 4G and 5G applications. A band resonator filter was designed and implemented using microstrip technology. Poor performance of a microstrip microwave resonator filter is due to spurious harmonics and unwanted frequencies which are always present and cause interferences of signals hindering effective communication. The coupling matrix was first obtained using polynomial functions of transmission and reflection coefficients. A cost function for optimization was developed and particle swarm optimization techniques were used to optimize the coupling matrix. The transversal matrix was developed and converted to the folded matrix for practical filter implementation. A bandpass filter was designed and simulated using high- frequency structure simulator software. Unwanted frequencies are eliminated by employing a novel square open loop resonator structure. The filter was designed to specification and the results show that unwanted frequencies were eliminated corresponding to the frequency of the outer resonator and therefore performs better than comparable filter.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.292

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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