, 2016 A spectrally sparse signal of order $r$ is a mixture of $r$ damped or undamped complex sinusoids. This paper investigates the problem of reconstructing spectrally sparse signals from a random subset of $n$ regular time domain samples, which can be ...
Cai, Jian-Feng, Wang, Tianming, Wei, Ke
core +3 more sources
Assessment of reconstruction accuracy for under-sampled 31P-MRS data using compressed sensing and a low rank Hankel matrix completion approach. [PDF]
Front Endocrinol (Lausanne)García JA +2 more
europepmc +2 more sources
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction. [PDF]
Appl Comput Harmon Anal, 2016 Cai JF, Qu X, Xu W, Ye GB.
europepmc +2 more sources
Hankel matrix normalization for robust damage detection
, 2019 In the context of detecting changes in structural systems, multiple vibration-based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. One of the popular schemes is based on a robust subspace-based residual and enjoys favorable statistical and computational properties, like ...
Szymon Greś +4 more
openalex +4 more sources
Epileptiform Activity and Seizure Risk Follow Long-Term Non-Linear Attractor Dynamics. [PDF]
Adv Sci (Weinh)This study leverages the HAVOK framework to model long‐term, nonlinear attractor dynamics underlying epileptiform activity and seizure risk in epilepsy patients. By identifying key forcing mechanisms driving chaotic transitions, the findings improve seizure risk forecasting over multi‐day cycles and provide a pathway for personalized, data‐driven ...
Rosch RE +4 more
europepmc +3 more sources
Generalized Data–Driven Predictive Control: Merging Subspace and Hankel Predictors
Mathematics, 2023 Data–driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems.
M. Lazar, P. C. N. Verheijen
doaj +1 more source
H-representation method for solving reduced biquaternion matrix equation
Mathematical Modelling and Control, 2022 In this paper, we study the Hankel and Toeplitz solutions of reduced biquaternion matrix equation (1.1). Using semi-tensor product of matrices, the reduced biquaternion matrix equation (1.1) can be transformed into a general matrix equation of the form ...
Xueling Fan +3 more
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On Low-Rank Hankel Matrix Denoising
IFAC-PapersOnLine, 2021 The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise ...
Roy S. Smith, Mingzhou Yin
openaire +2 more sources
Singular Spectrum Analysis for Modal Estimation from Stationary Response Only
Sensors, 2022 Conventional experimental modal analysis uses excitation and response information to estimate the frequency response function. However, many engineering structures face excitation signals that are difficult to measure, so output-only modal estimation is ...
Chang-Sheng Lin, Yi-Xiu Wu
doaj +1 more source
Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA. [PDF]
Adv Sci (Weinh)The proposed spatial‐temporal principal component analysis (stPCA) method analytically reduces high‐dimensional time‐series data to a single latent variable by transforming spatial information into temporal dynamics. By preserving the temporal properties of the original data, stPCA effectively identifies critical transitions and tipping points.
Chen P +6 more
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