Data Reconstruction Using Smart Sensor Placement.

Farnaz Boudaghi, Danial Waleed, Luis A Duffaut Espinosa

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Farnaz BoudaghiDepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA.ORCID 0009-0008-7058-1337

Danial WaleedDepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA.ORCID 0000-0003-2034-5404

Luis A Duffaut EspinosaDepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA.ORCID 0000-0003-4363-5375

Funding

Broad Agency Announcement Program and the Cold Regions Research and Engineering Laboratory W913E521C0003

6 · The paper itself

Abstract

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator.

Indexed as

Kriging Kalman Filtermodel-free controloptimal number of sensorsQR placement methodsensor placement

Identifiers

PMID39338753

DOI10.3390/s24186008

PMCPMC11436107

What OpenQuestion holds

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Registered trials

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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.