SensorAI: A Machine Learning Framework for Sensor Data.

Stephen Coshatt, He Yang, Shushan Wu, Jin Ye, Ping Ma, Wenzhan Song

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

6 authors.

Stephen CoshattThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0003-2156-1440

He YangThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.

Shushan WuThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.

Jin YeThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.

Ping MaThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.

Wenzhan SongThe Center for Cyber-Physcial Systems, University of Georgia, Athens, GA 30602, USA.

Funding

Department of Defense DOD-FA8571-21-C-0020Department of Energy DOE-EE0009026National Science Foundation NSF-1940864, NSF-2019311, NSF-2324389, NSF-2312974National Science Foundation NSF-2102032National Science Foundation NSF-2318809National Science Foundation NSF-2414706NIH HHS NIH-1R01HL172291-01

6 · The paper itself

Abstract

As machine learning and artificial intelligence are being integrated into cyber-physical systems, it is becoming important for engineers to know and understand these topics. In particular, sensor data is on the rise in these systems and therefore engineers need to understand which models are appropriate to time-series sensor data and how signal processing can be used with them. The Center for Cyber-Physical Systems (CCPS) at the University of Georgia (UGA) is addressing these issues. Student researchers in the CCPS require skills in these areas. This paper demonstrates a machine learning framework for time-series sensor data that can be used to quickly build, train, and test multiple models on CCPS testbed data. The framework is also a tool that can be used as a tutorial to help student researchers understand the concepts required to be successful in the CCPS.

Indexed as

artificial intelligencedigital signal processingfalse data injection attackmachine learningsensor datatime series

Identifiers

PMID41095045

DOI10.3390/s25196223

PMCPMC12526965

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.