scholarly journals Association between P3 event-related potential amplitude and externalizing disorders: A time-domain and time-frequency investigation of 29-year-old adults

2013 ◽  
Vol 50 (7) ◽  
pp. 595-609 ◽  
Author(s):  
Henry H. Yoon ◽  
Stephen M. Malone ◽  
Scott J. Burwell ◽  
Edward M. Bernat ◽  
William G. Iacono
Keyword(s):  
Time Domain ◽  
Externalizing Disorders ◽  
Event Related Potential ◽  
Old Adults ◽  
Time Frequency ◽  
Potential Amplitude

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

Methods to isolate retrograde and prograde Rayleigh-wave signals

2019 ◽  
Vol 219 (2) ◽  
pp. 975-994 ◽  
Author(s):  
Gabriel Gribler ◽  
T Dylan Mikesell
Keyword(s):  
Rayleigh Wave ◽  
Time Domain ◽  
Fundamental Mode ◽  
Poisson Ratio ◽  
Low Frequency ◽  
Wave Dispersion ◽  
Low Frequencies ◽  
Retrograde Motion ◽  
Mode Identification ◽  
Time Frequency

SUMMARY Estimating shear wave velocity with depth from Rayleigh-wave dispersion data is limited by the accuracy of fundamental and higher mode identification and characterization. In many cases, the fundamental mode signal propagates exclusively in retrograde motion, while higher modes propagate in prograde motion. It has previously been shown that differences in particle motion can be identified with multicomponent recordings and used to separate prograde from retrograde signals. Here we explore the domain of existence of prograde motion of the fundamental mode, arising from a combination of two conditions: (1) a shallow, high-impedance contrast and (2) a high Poisson ratio material. We present solutions to isolate fundamental and higher mode signals using multicomponent recordings. Previously, a time-domain polarity mute was used with limited success due to the overlap in the time domain of fundamental and higher mode signals at low frequencies. We present several new approaches to overcome this low-frequency obstacle, all of which utilize the different particle motions of retrograde and prograde signals. First, the Hilbert transform is used to phase shift one component by 90° prior to summation or subtraction of the other component. This enhances either retrograde or prograde motion and can increase the mode amplitude. Secondly, we present a new time–frequency domain polarity mute to separate retrograde and prograde signals. We demonstrate these methods with synthetic and field data to highlight the improvements to dispersion images and the resulting dispersion curve extraction.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

scholarly journals Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua Zhang ◽  
Chengyu Liu ◽  
Zhimin Zhang ◽  
Yujie Xing ◽  
Xinwen Liu ◽  
...  
Keyword(s):  
Cardiac Arrhythmia ◽  
Time Domain ◽  
Original Data ◽  
Classification Problem ◽  
Recurrence Plot ◽  
Two Dimensions ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
The Time Domain

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.

scholarly journals Orthogonal Time Sequency Multiplexing Modulation

2021 ◽  
Author(s):  
Tharaj Thaj ◽  
Emanuele Viterbo
Keyword(s):  
Time Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
High Mobility ◽  
Low Complexity ◽  
Modulation Scheme ◽  
Frequency Space ◽  
Time Frequency ◽  
Single Carrier ◽  
The Time Domain

This paper proposes <i>orthogonal time sequency multiplexing</i> (OTSM), a novel single carrier modulation scheme based on the well known Walsh-Hadamard transform (WHT) combined with row-column interleaving, and zero padding (ZP) between blocks in the time-domain. The information symbols in OTSM are multiplexed in the delay and sequency domain using a cascade of time-division and Walsh-Hadamard (sequency) multiplexing. By using the WHT for transmission and reception, the modulation and demodulation steps do not require any complex multiplications. We then propose two low-complexity detectors: (i) a simpler non-iterative detector based on a single tap minimum mean square time-frequency domain equalizer and (ii) an iterative time-domain detector. We demonstrate, via numerical simulations, that the proposed modulation scheme offers high performance gains over orthogonal frequency division multiplexing (OFDM) and exhibits the same performance of orthogonal time frequency space (OTFS) modulation, but with lower complexity. In proposing OTSM, along with simple detection schemes, we offer the lowest complexity solution to achieving reliable communication in high mobility wireless channels, as compared to the available schemes published so far in the literature.

Spectrum analysis of moving average operator and construction of time-frequency hybrid sequence operator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Changhai Lin ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingjie Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Moving Average ◽  
Spectral Characteristics ◽  
Complex Data ◽  
Time Data ◽  
Average Operator ◽  
Content Type ◽  
Time Frequency ◽  
Hybrid Sequence

PurposeThe purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator.Design/methodology/approachFirstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise.FindingsThrough the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified.Practical implicationsThe real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data.Originality/valueFirstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.

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