Results 31 to 40 of about 1,681 (208)

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

IEEE Access, 2019
Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns.
Hamed Azami, Steven E. Arnold, Saeid Sanei, Zhuoqing Chang, Guillermo Sapiro, Javier Escudero, Anoopum S. Gupta   +6 more
doaj   +1 more source

Fault Diagnosis of Gearbox Multi-channel Vibration Signal based on Improved Multivariate Multiscale Dispersion Entropy

Jixie chuandong, 2021
When the gearbox fails， its vibration signal is unstable and nonlinear. The commonly applied gearbox fault diagnosis methods are almost all based on the single-channel vibration signal analysis，which is easy to cause the loss of fault information，so the ...
Fuming Zhou, Jinxing Shen, Xiaoqiang Yang, Wuqiang Liu, Xiaolin Liu   +4 more
doaj  

Cumulative Paired φ-Entropy

Entropy, 2016
A new kind of entropy will be introduced which generalizes both the differential entropy and the cumulative (residual) entropy. The generalization is twofold.
Ingo Klein, Benedikt Mangold, Monika Doll   +2 more
doaj   +1 more source

A Novel Denoising Method for Ship-Radiated Noise

Journal of Marine Science and Engineering, 2023
Ship-radiated noise (SN) is one of the most critical signals in the complex marine environment; however, it is inevitably contaminated by the marine environment’s noise as well as noise from other equipment.
Yuxing Li, Chunli Zhang, Yuhan Zhou
doaj   +1 more source

Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

Earth, Planets and Space, 2021
Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT data denoising methods are usually applied to entire MT time-series, which results in the loss of useful MT signals and a decrease of imaging accuracy of ...
Xian Zhang, Jin Li, Diquan Li, Yong Li, Bei Liu, Yanfang Hu   +5 more
doaj   +1 more source

RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells

FEBS Open Bio, EarlyView.
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour, Christine Volmert, Valentina Piano   +2 more
wiley   +1 more source

An Evaluation of Entropy Measures for Microphone Identification

Entropy, 2020
Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream.
Gianmarco Baldini, Irene Amerini
doaj   +1 more source

Complexity Analysis of Three-Dimensional Fractional-Order Chaotic System Based on Entropy Theory

IEEE Access, 2021
Complexity analysis of fractional-order chaotic system is an interesting topic in recent years. How to measure the complexity of fractional-order chaotic system correctly and effectively is the basis of its theoretical analysis and engineering ...
Guohui Li, Xiangyu Zhang, Hong Yang
doaj   +1 more source

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler, Vivian Maas, Arian Taheri Amin, Jennifer Faber, Tamara Schaprian, Katharina Hill, Matthis Synofzik, Lisa H. Graf, Heike Jacobi, Manuela Lima, Ana F. Ferreira, Bart P. van de Warrenburg, Ilse H. J. Willemse, Dagmar Timmann, Andreas Thieme, Paola Giunti, Hector Garcia‐Moreno, Magda M. Santana, Jeannette Hübener‐Schmid, Elin H. Davies, Thomas Klockgether   +20 more
wiley   +1 more source

An Explainable Machine Learning Network for Classification of Autism Spectrum Disorder Using Optimal Frequency Band Identification From Brain EEG

IEEE Access
Autism Spectrum Disorder (ASD) is characterized by repetitive and restricted behavior, involving challenges for subjective detection methods. There is a need for an objective diagnostic technique that maximizes accuracy while utilizing a minimum of ...
S. Saranya, R. Menaka
doaj   +1 more source