Michael Taroudakis is a Professor Emeritus of the Department of Mathematics and Applied Mathematics of the University of Crete. He is also a member of the Institute of Applied and Computational Mathematics (IACM) of the Foundation for Research & Technology-Hellas (FORTH).
Taroudakis M.I., Tzagkarakis G., Tsakalidis P.: “Classification of acoustic signals using the statistics of the 1-D wavelet transform coefficients” Journal of the Acoustical Society of America Vol. 119, pp 1396-1405 (2006).
Taroudakis M.I. and Smaragdakis C. "On the use of Genetic Algorithms and a statistical characterization of the acoustic signal for tomographic and bottom geoacoustic inversions” Acta Acustica united with Acustica Vol. 95, No 5, pp 814-822 (2009).
Taroudakis M.I. and Smaragdakis C. " Inversions of statistical parameters of an acoustic signal in range-dependent environments with applications in ocean acoustic tomography" Journal of the Acoustical Society of America Vol. 134, pp 2814-2822 (2013).
Taroudakis M.I., Smaragdakis C. and Chapman, N.R. " Inversion of acoustical data from the `Shallow Water 06' experiment, using a statistical method for signal characterization " Journal of the Acoustical Society of America Vol. 136, pp. EL336-EL342 (2014).
Taroudakis M. “Towards a silent marine environment: The role of passive acoustic observatories” Rivista Italiana di Acustica Vol 39, pp 51-62 (2015).
Taroudakis M.I.: "Statistical Characterization of Acoustic Signals Using 1D Wavelet Transforms with Applications in Acoustical Oceanography" J. Th. Comp. Acous. Vol. 26, No. 4, 1850047 DOI: 10.1142/S2591728518500470 (2018).
Mathematical modelling of physical phenomena with emphasis in wave propagation
Underwater Acoustics
Direct problems. Helmholtz equation - Parabolic approximation. Normal-mode theory in range-independent and range-dependent environments. Broad-band propagation
Inverse problems. Ocean Acoustic Tomography. Wave-theoretic approaches. Linear and non-linear schemes. Matched-field processing. Neural Networks. Genetic Algrithms. Hybrid schemes.
Geoacoustic Inversions. Bottom recognition.
Statistical Signal Processing for inversions, Machine learning techniques.
Ambient noise in the sea. Prediction models. Monitoring processes