Spectral estimation—What is new? What is next?
JB Tary, RH Herrera, J Han… - Reviews of …, 2014 - Wiley Online Library
Spectral estimation, and corresponding time‐frequency representation for nonstationary
signals, is a cornerstone in geophysical signal processing and interpretation. The last 10–15 …
signals, is a cornerstone in geophysical signal processing and interpretation. The last 10–15 …
The state-of-the-art on time-frequency signal processing techniques for high-resolution representation of nonlinear systems in engineering
One of the serious issues of traditional signal processing techniques in analyzing the
responses of real-life structures is related to the presentation of fundamental information of …
responses of real-life structures is related to the presentation of fundamental information of …
A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery
Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is
a key but challenging issue in the applications of battery health diagnosis and management …
a key but challenging issue in the applications of battery health diagnosis and management …
Improved complete ensemble EMD: A suitable tool for biomedical signal processing
The empirical mode decomposition (EMD) decomposes non-stationary signals that may
stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted …
stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted …
A novel machine learning‐based algorithm to detect damage in high‐rise building structures
A novel model is presented for global health monitoring of large structures such as high‐rise
building structures through adroit integration of 2 signal processing techniques …
building structures through adroit integration of 2 signal processing techniques …
Signal processing techniques for vibration-based health monitoring of smart structures
JP Amezquita-Sanchez, H Adeli - Archives of Computational Methods in …, 2016 - Springer
Signal processing is the key component of any vibration-based structural health monitoring
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …
Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis
A Bossman - Complexity, 2021 - Wiley Online Library
With the steady growth in the data set on the COVID‐19 pandemic, empirical works that
employ novel and yet appropriate statistical techniques to corroborate previous findings of …
employ novel and yet appropriate statistical techniques to corroborate previous findings of …
Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: A model steel truss bridge case study
Signal processing is one of the essential components in vibration-based approaches and
damage detection for structural health monitoring. Since signals in the real world are often …
damage detection for structural health monitoring. Since signals in the real world are often …
Seismic time–frequency analysis via empirical wavelet transform
Time-frequency analysis is able to reveal the useful information hidden in the seismic data.
The high resolution of the time-frequency representation is of great importance to depict …
The high resolution of the time-frequency representation is of great importance to depict …
Applications of variational mode decomposition in seismic time-frequency analysis
We have introduced a novel time-frequency decomposition approach for analyzing seismic
data. This method is inspired by the newly developed variational mode decomposition …
data. This method is inspired by the newly developed variational mode decomposition …