Sparse representations and compressive sampling approaches in engineering mechanics: A review of theoretical concepts and diverse applications
A review of theoretical concepts and diverse applications of sparse representations and
compressive sampling (CS) approaches in engineering mechanics problems is provided …
compressive sampling (CS) approaches in engineering mechanics problems is provided …
SparseTFNet: A physically informed autoencoder for sparse time–frequency analysis of seismic data
The time–frequency (TF) analysis is an effective tool in seismic signal processing. The
sparsity-based TF transforms have been widely used to obtain high localized TF …
sparsity-based TF transforms have been widely used to obtain high localized TF …
Matching Pursuit Network: An Interpretable Sparse Time–Frequency Representation Method Toward Mechanical Fault Diagnosis
Rotatory machinery commonly operates in complex environments with strong noise and
variable working conditions. Time–frequency representation offers a valuable method for …
variable working conditions. Time–frequency representation offers a valuable method for …
Digital twin for electric vehicle battery management with incremental learning
NDKM Eaty, P Bagade - Expert Systems with Applications, 2023 - Elsevier
The current Industry 4.0 revolution promotes the use of cyber–physical systems to enhance
manufacturing and other industrial processes via automation, real-time analysis, etc. Data …
manufacturing and other industrial processes via automation, real-time analysis, etc. Data …
Reversible image hiding algorithm based on compressive sensing and deep learning
Compressive sensing (CS) can realize compression and encryption simultaneously.
However, the current sampling-reconstruction algorithms based on CS are time-consuming …
However, the current sampling-reconstruction algorithms based on CS are time-consuming …
A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines
F Liu, S Gao, Z Tian, D Liu - Marine Structures, 2020 - Elsevier
Abstract The Hilbert-Huang transform (HHT) has been widely applied and recognised as a
powerful time-frequency analysis method for nonlinear and non-stationary signals in …
powerful time-frequency analysis method for nonlinear and non-stationary signals in …
Development of flexible electronic biosensors for healthcare engineering
Wearable biosensors have attracted considerable attention because of their potential use in
real-time monitoring and personalized disease diagnosis. The good deformability and …
real-time monitoring and personalized disease diagnosis. The good deformability and …
Data-driven time-frequency method and its application in detection of free gas beneath a gas hydrate deposit
The time-frequency (TF) analysis method plays a significant role in the detection of natural
gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in …
gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in …
SPARCS: A sparse recovery approach for integrated communication and human sensing in mmWave systems
A well established method to detect and classify human movements using Millimeter-Wave
(mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed …
(mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed …
Ridge-aware weighted sparse time-frequency representation
The ideal time-frequency (TF) representation which distributes the total energy along the
instantaneous frequency (IF) of a signal is essentially sparse. Motivated by the weighted …
instantaneous frequency (IF) of a signal is essentially sparse. Motivated by the weighted …