{
  "abstract": "Synchronizing heterogeneous multirate sensor streams is a prerequisite for valid event-related inference, yet practical multimodal systems combine independent clocks, unequal native sampling rates, missing synchronization anchors, timestamp jitter, and occasional gross timing errors. This study introduces MATH-SYNC, a robust affine-clock alignment framework that estimates a common temporal coordinate without forcing signals onto a shared sampling grid. An inverse affine-clock reparameterization yields a linear residual model after fixing a temporal gauge, and Huber M-estimation is implemented by iteratively reweighted least squares with a MAD-based robust scale update; conditional on a fixed robust scale, the resulting Huber objective is convex. We characterize formulation-specific structural properties, including fixed-scale convexity, exact rank-based identifiability, a sufficient anchor condition, conditional uniqueness, and fixed-weight conditioning. Validation combines controlled Monte Carlo experiments with semi-synthetic perturbation-and-replay on real public timing schedules while preserving native sampling rates. Within-participant replication on Harvard S0201 showed minimal session-to-session variation. In a frozen 10-participant Harvard cohort, Joint Huber reduced participant-level median macro-RMSE relative to Joint OLS by 73.7% under nominal contamination (4.052 to 1.064 ms) and 62.4% under stress contamination (15.190 to 5.713 ms), with improvements in all participants. In the external 15-participant WESAD dataset, the corresponding reductions were 82.1% and 67.5%, again with improvements in all participants. Under nearly clean timing, OLS and Huber were practically indistinguishable, with median differences below 1 microsecond. These findings support robust, identifiable, and native-rate-preserving affine-clock estimation for heterogeneous multimodal sensor alignment under controlled timestamp contamination in synthetic and semi-synthetic schedule replay.",
  "abstract_shown": true,
  "affiliations": [
    "Research Department, Universidad Ecotec, Samborondon 092302, Ecuador"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jesus Rafael Hechavarria-Hernandez"
    },
    {
      "affs": [
        0
      ],
      "name": "Ana María Padrón-Medina"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniel Oleas",
      "orcid": "0000-0002-7347-814X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/94hmqfgw1r2gg.bib",
  "cited_by": 1,
  "doi": "10.3390/math14203720",
  "fulltext_url": null,
  "id": "94hmqfgw1r2gg",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3720",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1705|Network Time Synchronization Technologies"
    ]
  ],
  "openalex": "W7221633688",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/94hmqfgw1r2gg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203720",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3720"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "MATH-SYNC: Robust and Identifiable Affine-Clock Alignment of Heterogeneous Multirate Sensor Streams",
  "title_text": "MATH-SYNC: Robust and Identifiable Affine-Clock Alignment of Heterogeneous Multirate Sensor Streams",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/94hmqfgw1r2gg",
  "venue": "Mathematics",
  "versions": []
}