scholarly journals Noise-Assisted Instantaneous Coherence Analysis of Brain Connectivity

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Meng Hu ◽  
Hualou Liang
Keyword(s):  
High Resolution ◽  
Hilbert Transform ◽  
Brain Connectivity ◽  
Random Noise ◽  
Power Spectra ◽  
Macaque Monkey ◽  
Frequency Resolution ◽  
Neural Data ◽  
Time Frequency ◽  
Multivariate Mode

Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

High-resolution seismic complex trace analysis by adaptive fast sparse S-transform

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V51-V67 ◽  
Author(s):  
Hamid Sattari
Keyword(s):  
High Resolution ◽  
Hilbert Transform ◽  
Trace Analysis ◽  
Spectral Decomposition ◽  
Decomposition Method ◽  
Random Noise ◽  
Real Data ◽  
Mixed Norm ◽  
Time Frequency ◽  
S Transform

Complex trace analysis provides seismic interpreters with a view to identify the nature of challenging subsurface geologic features. However, the conventional procedure based on the Hilbert transform (HT) is highly sensitive to random noise and sudden frequency variations in seismic data. Generally, conventional filtering methods reduce the spectral bandwidth while stabilizing complex trace analysis, whereas obtaining high-resolution images of multiple thin-bed layers requires wideband data. It is thus a challenging problem to reconcile the conflict between the two purposes, and a powerful signal processing device is required. To overcome the issue, I first introduced the fast sparse S-transform (ST) as a powerful time-frequency decomposition method to improve the windowed Hilbert transform (WHT). Then, in addition to the mixed-norm higher resolution provided by the fast sparse ST, I have developed a novel sparsity-based optimization for window parameters. The process adaptively regularizes sudden changes in frequency content of nonstationary signals with the same computational complexity of the nonoptimized algorithm. The performance of the proposed windowing optimization is compared with those of available methods that have so far been used for adaptivity enhancement of Fourier-based spectral decomposition methods. The final adaptive and sparse version of WHT is used to achieve high-resolution complex trace analysis and address the above-mentioned conflict. The instantaneous complex attributes obtained by the proposed method for several synthetic and real data sets of which multiple thin-bed layers contain wedges, trapped gas reservoirs, and faults are superior to those obtained by WHT via adaptive sparse STFT, robust adaptive WHT, and conventional HT. Potential applications of the adaptive double-sparse ST as a new spectral decomposition method were also evaluated.

High-resolution time-frequency hilbert transform using sparsity-aware weighting function

2021 ◽  
Author(s):  
Mohsen Kazemnia Khakhki ◽  
Peyman Poor Moghaddam ◽  
Hamed Yazdanpanah ◽  
Webe J. Mansur
Keyword(s):  
High Resolution ◽  
Hilbert Transform ◽  
Weighting Function ◽  
Resolution Time ◽  
Time Frequency

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

scholarly journals Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5025
Author(s):  
Xuegong Zhao ◽  
Hao Wu ◽  
Xinyan Li ◽  
Zhenming Peng ◽  
Yalin Li
Keyword(s):  
High Resolution ◽  
Reflection Coefficient ◽  
Objective Function ◽  
Frequency Domain ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Frequency Resolution ◽  
Basis Pursuit ◽  
Time Frequency

Seismic reflection coefficient inversion in the joint time-frequency domain is a method for inverting reflection coefficients using time domain and frequency domain information simultaneously. It can effectively improve the time-frequency resolution of seismic data. However, existing research lacks an analysis of the factors that affect the resolution of inversion results. In this paper, we analyze the influence of parameters, such as the length of the time window, the size of the sliding step, the dominant frequency band, and the regularization factor of the objective function on inversion results. The SPGL1 algorithm for basis pursuit denoising was used to solve our proposed objective function. The applied geological model and experimental field results show that our method can obtain a high-resolution seismic reflection coefficient section, thus providing a potential avenue for high-resolution seismic data processing and seismic inversion, especially for thin reservoir inversion and prediction.

Dynamic Acoustic Measurement Techniques Considering Human Perception

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Klaus Genuit ◽  
Wade Bray
Keyword(s):  
High Resolution ◽  
Time Resolution ◽  
Internal Combustion Engines ◽  
Dynamic Measurement ◽  
Frequency Resolution ◽  
Sound Perception ◽  
Time Frequency ◽  
The Absolute ◽  
Human Hearing ◽  
Short Time

Dynamic measurement implies determining the content of signals having spectral structure and energy changing with time, sometimes on very short time scales. Dynamic measurements can present challenges to determine sufficient information in both the time and frequency domains. High resolution in frequency prevents finding short-term peak levels and recognizing true crest factors, and vice versa. The human ear/brain system exceeds the simultaneous time and frequency recognition of conventional measurement methods, further complicating the challenge. People have at least three times better time/frequency resolution than the familiar Fourier transform moved across the time axis, although quite often a compromise block size can be found that gives time/frequency measurement agreeing with human sound perception of both factors. Unlike technical measuring systems, human hearing is also very sensitive to patterns. The presence of tones, varying tones (amplitude and/or frequency), clicks, rattles, splashing sounds, etc., even at low levels in the presence of other less structured noise of considerably higher level, can dominate perception. Human consciousness effectively performs the opposite of averaging, ignoring the absolute value of slowly varying or stationary signals and focusing on things differing at short time bases from their surroundings in both time and frequency. In dynamic measurement, it can be difficult to withdraw an important pattern from the absolute whole. Case studies will be given comparing conventional techniques with three high-resolution time/frequency methods useful in general engineering although developed to model the processes of human sound perception: a hearing model with very rapid time resolution at all frequencies (Sottek, R., 1993, “Modelle zur Signalverarbeitung im menschlichen Gehör,” dissertation, RWTH Aachen), a relative (pattern) measurement technique subtracting a sliding average in both time and frequency from a running instantaneous spectrum (Genuit, K., 1996, “A New Approach to Objective Determination of Noise Quality Based on Relative Parameters,” Proceedings of InterNoise, Liverpool, UK), and a Fourier-based window deconvolution method giving pure spectral lines regardless of signal-to-block synchronization and permitting multiplication of frequency resolution for a given block length and time resolution (Sottek, R., 1993, “Modelle zur Signalverarbeitung im menschlichen Gehör,” dissertation, RWTH Aachen;Bray, W. R., 2004, “Perceptually Related Analysis of Time-Frequency Patterns via a Hearing Model (Sottek), a Pattern-Measurement Algorithm (“Relative Approach”) and a Window-Deconvolution Algorithm,” 147th Meeting, New York, May, Acoustical Society of America, 5aPPb7). Types of noise which particularly benefit from the techniques we will discuss include, but are by no means limited to, time-varying emissions from information technology devices (printers, hard disk drives, servosystems), appliances, HVAC (compressors and controls), hydraulic systems including direct high-pressure fuel injection internal combustion engines, tonal orders from rotating machinery, and environmental noise in workplaces and residences. The three analytic tools presented here are well suited in matching the time-frequency, tonal, and pattern recognition capabilities of human hearing, and offer general engineering capabilities especially involving the fine time-structured behavior of transient and tonal events.

Dynamic Acoustic Measurement Techniques Considering Human Perception

2006 ◽  
Author(s):  
Klaus Genuit ◽  
Wade Bray
Keyword(s):  
High Resolution ◽  
Time Resolution ◽  
Dynamic Measurement ◽  
Spectral Lines ◽  
Frequency Resolution ◽  
Spectral Structure ◽  
Sound Perception ◽  
Time Frequency ◽  
The Absolute ◽  
Short Time

Dynamic measurement implies determining the content of signals having spectral structure and energy changing with time, sometimes on very short time scales. Dynamic measurements can present challenges to determine sufficient information in both the time and frequency domains. High resolution in frequency prevents finding short-term peak levels and recognizing true crest factors, and vice versa. If the dynamic measurement concerns sound, the much better simultaneous recognition of time and frequency information by the ear/brain than by conventional measurement methods can further complicate the challenge. People have at least three times better simultaneous time/frequency resolution than the familiar Fourier transform moved across the time axis, although quite often a compromise block size can be found that gives time/frequency measurement agreeing with human sound perception of both factors. Unlike technical measuring systems, human hearing is also very sensitive to patterns. The presence of tones, varying tones (amplitude and/or frequency), clicks, rattles, splashing sounds, etc., even at low levels in the presence of other less structured noise of considerably higher level, can dominate perception. Human consciousness effectively performs the opposite of averaging, ignoring the absolute value of slowly varying or stationary signals and focusing on things differing at short time bases from their surroundings in both time and frequency. In dynamic measurement it can be difficult to withdraw the important pattern from the absolute whole. Case studies will be given comparing conventional techniques with three high-resolution time/frequency methods useful in general engineering although developed to model the processes of human sound perception: a hearing model with very rapid time resolution at all frequencies [1], a relative (pattern) measurement technique subtracting a sliding average in both time and frequency from a running instantaneous spectrum [2], and a Fourier-based window deconvolution method giving pure spectral lines regardless of signal-to-block synchronization and permitting multiplication of frequency resolution for a given block length and time resolution [1], [3].

scholarly journals Frequency bandwidth enhancement using continuous wavelet transform and random noise attenuation by f-xy filtering technique – a case study from Badin oilfield, Indus basin, Pakistan

2019 ◽  
Vol 133 ◽  
pp. 01007
Author(s):  
Asad Taimur ◽  
Akinniyi Akinsunmade ◽  
Sylwia Tomecka-Suchon ◽  
Fahad Mehmood
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Random Noise ◽  
Resolving Power ◽  
Indus Basin ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Bandwidth Extension ◽  
Time Frequency

Routine seismic data processing does not always meet the quantitative interpreters’ expectations especially in areas like Badin, where prospective thin bed B – sand interval is ambiguous throughout the seismic volume. Continuous Wavelet Transform (CWT) provides detailed description of seismic signal in both time and frequency without compromising on window length and a fixed time-frequency resolution over time-frequency spectrum. We present enhancement of seismic data for effective interpretation using the bandwidth extension technique. Implementing bandwidth extension, the dominant frequency increases from 18 Hz to 30 Hz and the frequency content boosted from 40 Hz to 60 Hz. Noise inclusion by the technique was suppressed by F-XY predictive filter and F-XY deconvolution with edge preserve smoothing. Phase and spectral balancing were applied to partial angle stacks to stabilize the phase rotation across the 3D survey, particularly for far offset stack. Frequency was balanced using surface consistent spectrum balancing, and subjected to trace scaling for amplitudes balance and preservation. Results of the techniques yielded unique improvement on the data resolution and subtle information about the thin sand beds were better delineated. Tuning thickness analysis reveals the usefulness of bandwidth extension, with an increase of 30% in the resolving power of thin beds.

High-resolution characterization of geologic structures using the synchrosqueezing transform

2017 ◽  
Vol 5 (1) ◽  
pp. T75-T85 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Zhuosheng Zhang ◽  
Xiudi Jiang ◽  
Qi Lv
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Proper Time ◽  
Frequency Resolution ◽  
Heisenberg Uncertainty Principle ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Basic Wavelet ◽  
Synchrosqueezing Transform

The main factors responsible for the nonstationarity of seismic signals are the nonstationarity of the geologic structural sequences and the complex pore structure. Time-frequency analysis can identify various frequency components of seismic data and reveal their time-variant features. Choosing a proper time-frequency decomposition algorithm is the key to analyze these nonstationarity signals and reveal the geologic information contained in the seismic data. According to the Heisenberg uncertainty principle, we cannot obtain the finest time location and the best frequency resolution at the same time, which results in the trade-off between the time resolution and the frequency resolution. For instance, the most commonly used approach is the short-time Fourier transform, in which the predefined window length limits the flexibility to adjust the temporal and spectral resolution at the same time. The continuous wavelet transform (CWT) produces an “adjustable” resolution of time-frequency map using dilation and translation of a basic wavelet. However, the CWT has limitations in dealing with fast varying instantaneous frequencies. The synchrosqueezing transform (SST) can improve the quality and readability of the time-frequency representation. We have developed a high-resolution and effective time-frequency analysis method to characterize geologic bodies contained in the seismic data. We named this method the SST, and the basic wavelet is the three-parameter wavelet (SST-TPW). The TPW is superior in time-frequency resolution than those of the Morlet and Ricker wavelets. Experiments on synthetic and field data determined its validity and effectiveness, which can be used in assisting in oil/gas reservoir identification.

scholarly journals On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter

2020 ◽  
Vol 64 (4) ◽  
pp. 352-365
Author(s):  
Sara Seninete ◽  
Mansour Abed ◽  
Azeddine Bendiabdellah ◽  
Malika Mimi ◽  
Adel Belouchrani ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Circuit ◽  
Frequency Resolution ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Insulated Gate Bipolar Transistor ◽  
Concentration Measure ◽  
Standard Tool ◽  
Time Frequency Localization ◽  
Frequency Components

Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.

scholarly journals Electrophysiological aftereffects of high-frequency transcranial random noise stimulation (hf-tRNS): an EEG investigation

2021 ◽  
Author(s):  
Filippo Ghin ◽  
Louise O’Hare ◽  
Andrea Pavan
Keyword(s):  
High Frequency ◽  
Discrimination Task ◽  
Temporal Dynamics ◽  
Random Noise ◽  
Decomposition Analysis ◽  
Oscillatory Activity ◽  
Motion Direction ◽  
Time Frequency ◽  
Direction Discrimination ◽  
Transcranial Random Noise Stimulation

AbstractThere is evidence that high-frequency transcranial random noise stimulation (hf-tRNS) is effective in improving behavioural performance in several visual tasks. However, so far there has been limited research into the spatial and temporal characteristics of hf-tRNS-induced facilitatory effects. In the present study, electroencephalogram (EEG) was used to investigate the spatial and temporal dynamics of cortical activity modulated by offline hf-tRNS on performance on a motion direction discrimination task. We used EEG to measure the amplitude of motion-related VEPs over the parieto-occipital cortex, as well as oscillatory power spectral density (PSD) at rest. A time–frequency decomposition analysis was also performed to investigate the shift in event-related spectral perturbation (ERSP) in response to the motion stimuli between the pre- and post-stimulation period. The results showed that the accuracy of the motion direction discrimination task was not modulated by offline hf-tRNS. Although the motion task was able to elicit motion-dependent VEP components (P1, N2, and P2), none of them showed any significant change between pre- and post-stimulation. We also found a time-dependent increase of the PSD in alpha and beta bands regardless of the stimulation protocol. Finally, time–frequency analysis showed a modulation of ERSP power in the hf-tRNS condition for gamma activity when compared to pre-stimulation periods and Sham stimulation. Overall, these results show that offline hf-tRNS may induce moderate aftereffects in brain oscillatory activity.

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