Corpus ID: 202770273

Time-Frequency Signal Analysis and Processing

@inproceedings{Boashash2002TimeFrequencySA,
  title={Time-Frequency Signal Analysis and Processing},
  author={B. Boashash},
  year={2002}
}
The emergence of phase asynchrony and frequency modulation in metacommunities
TLDR
It is shown that phase asynchrony in a homogeneous metacommunity requires a minimum of three communities and is characterized by the emergence of regional frequency modulation of population fluctuations, which results in spectral signatures in local time series that can be used to infer the causes and properties of metacomunity dynamics. Expand
A local entropy-based algorithm for information content extraction from time-frequency distributions of noisy signals
TLDR
It is shown that the local Renyi entropy (LRE) can accurately distinguish classes containing noise from classes with the useful information content, as a consequence of their basic structural differences in the time–frequency plane. Expand
The application of signal processing techniques for real-time monitoring of a ship ’ s dynamic stability via a ship ’ s motion responses
Significant changes of stability at sea can lead to dangerous situations and eventually stability failure. Despite its importance, the current intact stability (IS) criteria do not evaluate theExpand
Sparse Time-Frequency Distribution Calculation with an Adaptive Thresholding Algorithm
TLDR
A sparse reconstruction algorithm is applied, which combines the fast intersection of the confidence intervals (FICI) rule and the two step iterative shrinkage/thresholding algorithm (TwIST), denoted as the FICI-TwIST algorithm, to modeled and real-life teleseismic signals to achieve a high resolution sparse TFD with heavily suppressed cross-terms. Expand
On the L0-Norm Based Sparse Time-Frequency Distribution Reconstruction
  • Ivan Volaric, V. Sucic
  • Computer Science
  • 2018 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)
  • 2018
TLDR
This paper investigates the application of the $\ell_{0}$ -norm based reconstruction algorithms for the sparse TFD calculation, and employs the adaptive CS scheme in order to improve the quality of the resulting sparse TFDs, while reducing the reconstruction algorithm execution time. Expand
Aggregation of gravitational wave detections by means of T-norms
We propose the application of triangular norms in the time-frequency domain, as a general framework for implementing consistency tests between the data gathered by different non collocated GWExpand
Enhancement of the spikes attributes in the time-frequency representations of real EEG signals
TLDR
The objective of this work is to provide a methodology to enhance this characteristic in the time-frequency domain of EEG signals by amplifying the spike components in the raw EEG signal using the differential window, then a modified version of adaptive directional time- frequency distribution of the amplified signal is computed. Expand
Instantaneous Frequency Estimation of Nonlinear Frequency-Modulated Signals Under Strong Noise Environment
TLDR
First, a modified S-method is proposed to represent the time–frequency (TF) characteristic and the Viterbi algorithm is used to extract the IF from the TF plane, which is proposed here as the IF estimator. Expand
Experiments on Electromagnetic Pulse (EMP) generated by laser-plasma interaction in nanosecond regime
Transient electromagnetic pulses in the radiofrequency-microwave regime and with very high intensities are produced because of the interaction of high energy and high power lasers with targets. ThisExpand
Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure
TLDR
MBD and SPWVD outperform other TFDs in terms of time-frequency resolution, cross-terms suppression, and number of TF components to represent the HRV response during AFT. Expand
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References

SHOWING 1-10 OF 24 REFERENCES
8. Summary and conclusions
On rotated time-frequency kernels
TLDR
It is shown that the resulting time-frequency representations show a better reduction of cross-terms without too severely degrading the autoterms than the corresponding original time- frequency representations. Expand
Spectrogram segmentation by means of statistical features for non-stationary signal interpretation
TLDR
This paper investigates the use of TFR statistical properties for classification or recognition purposes, focusing on a particular TFR: the spectrogram, and proposes a method of segmentation that is relevant for the signal understanding. Expand
Time-frequency distributions with complex argument
  • L. Stanković
  • Mathematics, Computer Science
  • IEEE Trans. Signal Process.
  • 2002
TLDR
It is shown that the estimation can be improved with respect to the Wigner distribution based one since the bias can be significantly reduced with only a slight increase of the variance. Expand
A wavelet tour of signal processing
Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour.Expand
Time-Frequency Signal Analysis: Methods and Applications.
TLDR
Partial table of contents: FUNDAMENTALS Instantaneous Frequency and Time-Frequency Distributions, Techniques and Limitations of Spectrum Analysis with the DFT, Applications of Time-Varying Filtering and Signal Synthesis. Expand
Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals
TLDR
The concept of instantaneous frequency, its definitions, and the correspondence between the various mathematical models formulated for representation of IF are discussed and it is shown that these notions are well described in the context of the theory presented. Expand
Kernel design for reduced interference distributions
TLDR
The authors present a class of time-frequency signal representations called the reduced interference distribution (RID), and a systematic procedure to create RID kernels, (or, equivalently, compute RIDs) is proposed. Expand
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