Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multifractal formalism for functions part I: results valid for all functions
SIAM Journal on Mathematical Analysis
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Engineering Applications of Artificial Intelligence
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Today, the pace of progress in fermentation is fast and furious, particularly since the advent of genetic engineering and the recent advances in computer sciences and process control. The high cost associated with many fermentation processes makes optimization of bioreactor performance trough command control very desirable. Clearly, control of fermentation is recognized as a vital component in the operation and successful production of many industries. Today's advances in measurement, data acquisition and handling technologies provide a wealth of new data which can be used to improve existing models. In this article we propose a method of physiological state identification based on segmentation of bioreactor sensors signals. The underlying of this method is based on the detection of signals singularities by the Maximum of Modulus of Wavelets Transform and their characterization by Holder exponent evaluation. The physiological states identification is based on the correlation product between biochemical signals. The efficiency of the method has been tested in a fed-batch fermentation having the goal to increase the biomass production.