ArticleScientific reports2025
Novel sparse decomposition with adaptive evolutionary atoms for nonstationary signal extraction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Design, Implementation and Calibration of Analog Gyro-Based Angular Rate Data Acquisition System for High-Spinning Rotation Rate Applications.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Existing sparse decomposition techniques often rely on over-complete dictionaries and extensive prior knowledge, leading to slow computation and low efficiency. This paper introduces a novel sparse decomposition method, Adaptive Evolutionary Atomic Sparse Decomposition (AEA-SD). A general atom (g-atom) is designed to accommodate various typical signals, accompanied by an adaptive atom-based automatic target signal localization algorithm. To accelerate convergence, a fast sparse decomposition method based on Bat algorithm is developed, which eliminates the reliance on redundant dictionaries. The performance of AEA-SD is verified by using magnetotelluric (MT) data. Experimental results demonstrate that AEA-SD reduces CPU usage by over 30% compared to traditional algorithms and enhances signal extraction accuracy by at least 58%, 46%, and 58% in low, middle, and high frequency bands, respectively. Furthermore, the method's adaptability makes it applicable to diverse scenarios requiring the removal of complex background noise from sensor signals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.