Evidence map›Paper›PMID 41461760›Full record

ArticleScientific reports2025

Machine learning-optimized compact wearable frequency reconfigurable antenna for sub-6 GHz/mm-wave 5G integration.

Abubakar Salisu, Mahmud Abd Elwanis, Issa Elfergani, Umar Musa, Abdulgafor Alfares, Ibrahim Gharbia, Jonathan Rodriguez, Chan H See, Raed Abd-Alhameed

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Abubakar SalisuDepartment of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK. a.salisu@bradford.ac.uk.
Mahmud Abd ElwanisDepartment of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK.
Issa ElferganiDepartment of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK.
Umar MusaDepartment of Electrical Engineering, Bayero University Kano, Kano, 700006, Nigeria. umusa.ele@buk.edu.ng.
Abdulgafor AlfaresDepartment Department of Electrical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin, 39524, Saudi Arabia.
Ibrahim GharbiaDepartment of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK.
Jonathan RodriguezInstituto de Telecomunicações, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal.
Chan H SeeSchool of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK.
Raed Abd-AlhameedDepartment of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK. r.a.a.abd@bradford.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Future 5G wireless systems will have substantial challenges in integrating the sub-6 GHz and millimeter-wave (mm-wave) bands due to their massive frequency ratios. This paper proposes a machine learning-optimized compact wearable frequency-reconfigurable antenna for sub-6 GHz/mm-wave 5G integration. Fabricated on a flexible Rogers Duroid substrate (27.8 × 14 × 0.508 mm

Indexed as

Bending investigationFrequency reconfigurabilityMachine learningPIN diodeSAR

Identifiers

PMID41461760
PMCPMC12749218

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.