Evidence map›Paper›PMID 41541844›Full record

ArticleIEEE transactions on signal processing : a publication of the IEEE Signal Processing Society2025

Peak Persistence Diagrams for Shape-Based Signal Estimation.

Woo Min Kim, Sutanoy Dasgupta, Pavan Turaga, Anuj Srivastava

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, 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

4 authors.

Woo Min KimDepartment of Statistics, Florida State University, Tallahassee, FL 32306, USA.
Sutanoy DasguptaDepartment of Statistics, Texas A&M University, College Station, TX 77840, USA.
Pavan TuragaThe GAME School, Arizona State University, Tempe, AZ 85287, USA.
Anuj SrivastavaDepartment of Statistics, Florida State University, Tallahassee, FL 32306, USA.

Funding

Dense Life-log Health Analytics from Wearable Sensors using Functional Analysis and Riemannian GeometryR01GM135927 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI TURAGA, PAVAN · 2019 to 2022
$1.3M
NIGMS NIH HHS R01 GM135927
6 · The paper itself

Abstract

Signal estimation from noisy data is a fundamental problem in signal processing and data analysis. Existing literature offers various estimators based on different model choices and estimation criteria. This paper uses an innovative framework that leverages topological and geometric features of the data for signal estimation. The proposed approach builds on penalized elastic signal alignment (PESA) framework and introduces a topological tool -

Indexed as

dynamic time warpingelastic alignmentpeak persistenceshape-constrained signalshape estimationsignal estimation

Identifiers

PMID41541844
PMCPMC12803482

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.