A classification procedure based on time-frequency decomposition of the signal is presented. Parametric spectral ESPRIT method is used for estimation of relevant parameters of signal components and specific areas of the time-frequency plane are chosen, where the signal is expected to show most characteristic patterns. Classification is based on time-domain correlation of reconstructed signals. ...

Few fast statistical signal processing algorithms exist for large problems involving non-stationary processes and irregular measurements. A recently introduced class of multiscale autoregressive models indexed by trees admits signal processing algorithms which can efficiently deal with problems of this type. In this paper we provide a novel and efficient algorithm for translating any secondorde...

Vibration problems in rotors can be extremely frustrating and may lead to greatly reduced reliability. By utilizing the proper data collection and analysis techniques, the faults because of vibration can be discovered and predicted. The signal analysis is important in extracting fault characteristics in fault diagnosis of machinery. The traditional signal analysis can not settle for non-station...

The purpose of the present study was to analyze the electromyographic (EMG) signals of the rectus femoris (RF), vastus lateralis (VL), semitendinosus (ST) and short head of the biceps femoris (BF) during the performance of stationary running at different intensities in aquatic and dry land environments. The sample consisted of 12 female volunteers who performed the stationary running exercise i...

This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information ...

A class of random non-stationary signals termed timbre×dynamics is introduced and studied. These signals are obtained by non-linear transformations of stationary random gaussian signals, in such a way that the transformation can be approximated by translations in an appropriate representation domain. In such situations, approximate maximum likelihood estimation techniques can be derived, which ...

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally...