Miguel Enrique Iglesias Martínez

Orcid: 0000-0003-1405-5099

According to our database1, Miguel Enrique Iglesias Martínez authored at least 10 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Automatic classification of the physical surface in sound uroflowmetry using machine learning methods.
EURASIP J. Audio Speech Music. Process., December, 2024

Spectral Analysis of Anomalous Capacitance Measurements in Interleaving Structures: Study of Frequency Distribution in Photomultipliers.
Symmetry, 2024

Bispectral Wavelet Time-Frequency Analysis for The Detection of Field Winding Faults in Synchronous Motors.
Proceedings of the 33rd IEEE International Symposium on Industrial Electronics, 2024

2023
Influence of the Flux Sensor Position for Rotor Fault Detection in WRIM: A Power Spectral Entropy Analysis.
Proceedings of the 32nd IEEE International Symposium on Industrial Electronics, 2023

Classification of the Physical Surface in Sound-based Uroflowmetry.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

2021
Envelope Spectral Kurtosis and SVD Analysis of Stray Flux Signals for the Diagnosis of Field Winding Faults in Synchronous Motors.
Proceedings of the 30th IEEE International Symposium on Industrial Electronics, 2021

Multifractal Spectrum and Higher Order Statistics for the Detection of Field Winding Faults in Wound Field Synchronous Motors.
Proceedings of the 19th IEEE International Conference on Industrial Informatics, 2021

Bispectrum and Kurtosis Analysis of Rotor Currents for the Detection of Field Winding Faults in Synchronous Motors.
Proceedings of the 22nd IEEE International Conference on Industrial Technology, 2021

2018
Feature Extraction and Similarity of Movement Detection during Sleep, Based on Higher Order Spectra and Entropy of the Actigraphy Signal: Results of the Hispanic Community Health Study/Study of Latinos.
Sensors, 2018

2013
Detection of Periodic Signals in Noise Based on Higher-Order Statistics Joined to Convolution Process and Spectral Analysis.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2013


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