Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

An architecture for the vlsi design of systems for time-frequency analysis and time-varying filtering

Abstract: A flexible system for time-frequency signal analysis is presented. It is based on the S-method, which has a significant advantage in implementation since it can involve, as a key intermediate step, the Short-time Fourier transform or the Hartley transform, each widely studied and commonly used in practice. Signal invariant and signal dependent system forms are presented. Hardware design, for a fixed-point arithmetic, is well-structured and suitable for vlsi implementation. The same hardware, without additional time requirements, may be shared for the simultaneous realization of the fourth order L-Wigner distribution, as well as for the realization of the cross-terms free fourth order polynomial Wigner-Ville distribution. This possibility makes the designed hardware suitable for wide range of the applications. The proposed hardware is applied to the realization of time-varying filtering, as well. Finally, it has been implemented with fpga chips (Field Programmable Gate Array) in order to verify the results on real devices.

Instantaneous parameter estimation in cardiovascular time series by harmonic and time-frequency analysis

Abstract: Time-frequency distributions, such as smoothed pseudo Wigner-Ville distribution (SPWVD) and complex demodulation (CDM) provide useful time-varying spectral parameter estimators. However, each of these methods has limitations that a joint utilization could largely reduce, due to their interesting complementary features. The aim of this paper is to validate the joint SPWVD-CDM method on synthetic and real cardiovascular time series with normal and reduced variability such as in autonomic blockade or autonomic deficiency. We propose two indexes related to the noise present in the signal and to the dispersion of the power spectrum in order to validate instantaneous parameter estimation. In the low-frequency band, the interpretation of the instantaneous frequency and phase of cardiovascular time-series should be discarded in many real-life situations. Conversely, in the high frequency band, under paced breathing, the reliability of the instantaneous parameters is demonstrated even in conditions of reduced cardiovascular variability.

Fast implementations of generalized discrete time-frequency distributions

Abstract: Cohen's class of time-frequency distributions (TFDs) have significant potential for the analysis of complex signals. In order to evaluate the TFD of a signal using its samples, discrete-time TFDs (DTFDs) have been defined as the Fourier transform of a smoothed discrete autocorrelation. Existing algorithms evaluate real-valued DTFDs using FFTs of the conjugate-symmetric autocorrelation. Although the computation required to smooth the autocorrelation is often greater than that for the FFT, there are no widely applicable fast algorithms for this part of the processing. Since the FFT is relatively inexpensive, downsampling is ineffective for reducing computation. If the DTFD needs only to be evaluated at a few frequencies for each time instant, the cost per time-frequency sample can be extremely high. The authors introduce two approaches for reducing the computation time of DTFDs. First, they define approximations to real-valued DTFDs, using spectrograms, that admit fast, space-saving evaluations. Frequency downsampling reduces the computation time of these approximations. Next, they define DTFDs that admit fast evaluations over sparse sets of time-frequency samples. A single short time Fourier transform is calculated in order for DTPD time-frequency samples to be evaluated at an additional, fixed cost per sample. >

Advances in time-frequency analysis of biomedical signals.

Abstract: The frequency content of many biomedical signals can change rapidly with time. Conventional Fourier spectral analysis techniques are insufficient for analyzing the time-varying spectral content of these signals. By mapping a one-dimensional function of time (or frequency), the time-frequency representation can localize the signal energy in both the time and frequency directions. It has been shown that many biomedical signal problems may benefit from time-frequency analysis. The objective of this paper is to review the advances in time-frequency analysis of biomedical signals. Relevant theoretical methodologies and practical considerations are introduced, and five application areas are reviewed: electroencephalography (EEG), electrocardiography, phonocardiography, electrogastrography, and electromyography.

Detection of stator incipient faults and identification of faulty phase in three-phase induction motor – simulation and experimental verification

Abstract: Motor current signature analysis is a well-known method for the diagnosis of stator incipient faults on a three-phase induction motor (IM). In classical motor current signature analysis the fault feature is extracted by analysing the frequency spectrum obtained from the Fourier analysis. However, for proper fault diagnosis, time–frequency domain analysis is required. This study proposes an algorithm based on wavelet analysis for detection of stator incipient faults and identification of faulty phase in three-phase IM. A turn level distributed parameter model of a 3-hp IM is considered for the simulation of inter-turn faults. The parameters used in the simulated model are calculated by conducting experiments on a 3-hp IM. This model is validated by comparing the frequency response of the simulated model with the frequency response measured on practical machine. The proposed algorithm uses an adaptive threshold-based logic for detecting the inter-turn faults and identifying the faulty phase. The algorithm is validated with data generated by the specially designed 3-hp IM. The experimental and simulation results show that the proposed algorithm is effective in detecting the inter-turn faults and identifying the faulty phase even in the presence of supply unbalance conditions.