Nonstationary ultrasound Doppler signals, those are changing with time and frequency simultaneously, are widely observed in biological and speech signals. A Cohen’s class timefrequency (TF) analysis can analyze nonstationary signals with high resolution in time and frequency at a same time. A time-frequency distribution (TFD) is largely affected by a kernel function. Thus, there is sometimes a ...

The Fourier transform has been a major tool in analysis for over 100 years. However, it solely provides frequency information, and hides (in its phases) information concerning the moment of emission and duration of a signal. D. Gabor resolved this problem in 1946 [93] by introducing a fundamental new approach to signal decomposition. Gabor’s approach quickly became the paradigm for this area, b...

In this paper, we report the efficiency of Fourier Bessel transform and time-frequency based method in conjunction with the fractional Fourier transform, for extracting micro-Doppler radar signatures from the rotating targets. This approach comprises mainly of two processes; the first being decomposition of the radar return, in order to extract micro-Doppler (m-D) features and the second being,...

We developed a novel movement-imagery-based braincomputer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, timefrequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were ...

In this paper, we propose a mask estimation method for a computational auditory scene analysis (CASA) based speech recognition front-end using speech obtained from two microphones. The proposed mask estimation method incorporates the observation that the mask information should be correlated over contiguous analysis time frames and adjacent frequency channels. To this end, two different hidden ...

Data filtering methods are so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. In particular, the time-frequency domain filters describe the fractal structure of financial markets better than the t...

Magnetoencephalography (MEG) is a brain imaging technique that non-invasively measures neurallygenerated magnetic fields. Earlier MEG studies have focused on the neural responses to amplitude modulated (AM) auditory signals near 40Hz. Speech signals, however, contain a wide range of modulation rates, most of which are well below 40 Hz. Therefore we seek to characterize the modulation transfer f...