Resumen
Classifying bubbles in liquids is a crucial problem that is demanded within multiple fields. This paper discusses a new method for classifying bubble sizes in non-contact and inexpensive approach using ultrasound analysis. Exploiting the principle of buoyancy, free rising bubbles with larger volumes elevate faster to the surface compared to the smaller ones, given that they have the same densities. An envelope detector is proposed which tracks the changes in the ultrasound signals reflected by the bubbles when they cross the ultrasound field. These changes in the reflected signals are distinctive for the sizes under consideration. Relevant spectral and linear predictive coding features that represent the distinct characteristics are extracted. These features are fed to a feed-forward artificial neural network to successfully classify air bubbles according to their sizes with an accuracy of 98.8%. This method provides promising applications to be implemented in industrial, biomedical and environmental fields.