This letter describe target classification from the synthesized active sonar returns from targets. A fractional Fourier transform is applied to the sonar returns to extract shape variation in the fractional Fourier domain depending on the highlight points and aspects of the target. With the proposed features, four different targets are classified using two neural network classifiers.

This letter deals with angular position classification using the synthesized active sonar returns from targets. For the synthesis of active sonar returns, we synthesized active sonar returns based on ray tracing algorithm for 3D highlight models. Then, a fractional Fourier transform (FrFT) was applied to the sonar returns to extract the angular position information depending on the target aspect by utilizing separation capability of the time-delayed combination of linear frequency modulated (LFM) signals in the FrFT domain. With the FrFT-based features, three different target angular positions were classified using neural networks.

The simplicity is a type of measurement that represents visual simplicity of a signal, regardless of its amplitude and frequency variation. We propose an algorithm that can detect major components of heart sound using Gaussian regression to the smoothed simplicity profile of a heart sound signal. The weight and spread of the Gaussians are used as features to discriminate cardiac murmurs from major components of a heart sound signal. Experimental results show that the proposed method is very promising for robust and accurate detection of major heart sound components as well as cardiac murmurs.

Canonical correlation analysis (CCA) is applied to extract features for microphone classification. We utilized the coherence between near-silence regions. Experimental results show the promise of canonical correlation features for microphone classification.