OSCR

Performance Studies on machine learning based channel modelling for vehicular visible light communication.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match
  1. [1] § Results and discussion ↔ train.py, lines 8–29 · score 0.57 · AdamW, PyTorch, batch, optimization, training, loss

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 29 lines · 894 B · MIT · 1 match

  1. import torch
  2. from torch.utils.data import DataLoader
  3. import torch.optim as optim
  4. from dataset import VLCDataset
  5. from model import HybridModel
  6. from utils import compute_rmse
  8. def train(data_path, epochs=50):
  9. dataset = VLCDataset(data_path)
  10. loader = DataLoader(dataset, batch_size=256, shuffle=True)
  12. model = HybridModel(input_dim=dataset.X.shape[1])
  13. optimizer = optim.AdamW(model.parameters(), lr=1e-3)
  14. criterion = torch.nn.MSELoss()
  16. for epoch in range(epochs):
  17. total_loss = 0
  18. for x, y in loader:
  19. optimizer.zero_grad()
  20. pred = model(x)
  21. loss = criterion(pred.squeeze(), y)
  22. loss.backward()
  23. optimizer.step()
  24. total_loss += loss.item()
  26. print(f"Epoch {epoch+1}, Loss: {total_loss:.4f}")
  28. torch.save(model.state_dict(), "model.pth")
  29. return model

train.py at commit af5310f, under MIT · at the source

Overview

Authors: L Ramya1, K Umadevi2
  1. Department of Electronics and Communication Engineering, Sri Ramakrishna College of Engineering, Perambalur, 621 113, TamilNadu India
  2. Department of Electrical and Electronics Engineering, Sengunthar Engineering College (Autonomous), Tiruchengode, Namakkal, 637 205 TamilNadu India
Journal: Scientific reports, volume 16, issue 1, article 22182
Dates: received 28 January 2026; accepted 11 May 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-53143-w · PMID 42141007 · PMCID PMC13369992 · OpenAlex W7161276940
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, Machine learning, Preprocessing, Physiology & signal measures
Keywords: Vehicular visible light communication, Machine learning channel modelling, Hybrid prediction framework, Optical wireless communication, Intelligent transportation systems, V2LC performance evaluation, Engineering, Mathematics and computing
Topic: Optical Wireless Communication Technologies (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Citations: not cited yet (Europe PMC); 29 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

woven-visionai/wts-dataset

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 34a1c6d65710f95b6599edb6d90d84dd3dcc21b1, 13 May 2026
Languages: Python (12)
Size: 41 files, 12 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (evaluation/eval-metrics-AIC-Track2/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (3 files), OpenCV (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/woven-visionai/wts-dataset

Zenodo 19975161

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (4 files), NumPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
9 files
At the source:

micro4cloud/my-paper-code

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: af5310f91149f092dbe4e7a660c937edbc15ca59, 2 May 2026
Languages: Python (6)
Size: 12 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (4 files), NumPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
9 files
At the source: github.com/micro4cloud/my-paper-code/tree/v1.0

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-53143-w.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 24 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-53143-w.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 keywords, 3 references.

Cite

This paper

Ramya, L., & Umadevi, K. (2026). Performance Studies on machine learning based channel modelling for vehicular visible light communication. Scientific reports, 16(1), 22182. https://doi.org/10.1038/s41598-026-53143-w

BibTeX

@article{ramya2026performance,
author = {Ramya, L and Umadevi, K},
title = {{Performance Studies on machine learning based channel modelling for vehicular visible light communication}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {22182},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-53143-w},
url = {https://doi.org/10.1038/s41598-026-53143-w},
pmid = {42141007},
pmcid = {PMC13369992}
}

RIS

TY - JOUR
AU - Ramya, L
AU - Umadevi, K
TI - Performance Studies on machine learning based channel modelling for vehicular visible light communication
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/15
VL - 16
IS - 1
SP - 22182
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-53143-w
UR - https://doi.org/10.1038/s41598-026-53143-w
LA - en
ER -

CSL-JSON

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"container-title": "Scientific reports",
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"family": "Ramya",
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],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "22182",
"DOI": "10.1038/s41598-026-53143-w",
"PMID": "42141007",
"PMCID": "PMC13369992",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-53143-w",
"language": "en",
"issued": {
"date-parts": [
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]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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