A systematic method for isolating, tracking and discriminating time-frequency components of bat echolocation calls

2018 ◽  
Vol 143 (2) ◽  
pp. 716-726 ◽  
Author(s):  
Yanqing Fu ◽  
Laura N. Kloepper
Keyword(s):  
Systematic Method ◽  
Echolocation Calls ◽  
Time Frequency ◽  
Frequency Components

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

Improved EEMD in the Application of Signal Singularity Detection

2012 ◽  
Vol 518-523 ◽  
pp. 3847-3851
Author(s):  
Mei Jun Zhang ◽  
Chuang Wang ◽  
Hao Chen ◽  
Qun Zhang Tu
Keyword(s):  
Frequency Spectrum ◽  
Original Data ◽  
Singularity Detection ◽  
Time Frequency ◽  
Local Characteristics ◽  
Cross Terms ◽  
Marginal Spectrum ◽  
Frequency Components ◽  
Detection Signal ◽  
Improved Eemd

In order to solve the endpoint effect and modal aliasing phenomenon in EMD and EEMD,Improved EEMD is put forward, and the application in signal singularity detection is researched in this paper. The improved EEMD will signal drops down into a series of different IMF to highlight the different local characteristics of original data, and then calculate Hilbert marginal spectrum and time-frequency spectrum to determine the frequency of these mutations and mutations position. To compared with FT, STFT, WVD,WT, EMD and EEMD etc, No cross-terms and no false IMF components are produced in the Hilbert time-frequency spectrum of the improved EEMD. Different frequency components and frequency mutations position are clearly distinguished at the same time. The Hilbert time-frequency spectrum of the improved EEMD has more superior detection signal singularity ability.

scholarly journals Soft missing-feature mask generation for Robot Audition

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Toru Takahashi ◽  
Kazuhiro Nakadai ◽  
Kazunori Komatani ◽  
Tetsuya Ogata ◽  
Hiroshi G. Okuno
Keyword(s):  
Human Robot Interaction ◽  
Sigmoid Function ◽  
Robot Interaction ◽  
Time Frequency ◽  
Sound Source Separation ◽  
Robot Audition ◽  
Frequency Components ◽  
Interaction Task ◽  
Missing Feature Theory ◽  
Missing Feature

AbstractThis paper describes an improvement in automatic speech recognition (ASR) for robot audition by introducing Missing Feature Theory (MFT) based on soft missing feature masks (MFM) to realize natural human-robot interaction. In an everyday environment, a robot’s microphones capture various sounds besides the user’s utterances. Although sound-source separation is an effective way to enhance the user’s utterances, it inevitably produces errors due to reflection and reverberation. MFT is able to cope with these errors. First, MFMs are generated based on the reliability of time-frequency components. Then ASR weighs the time-frequency components according to the MFMs. We propose a new method to automatically generate soft MFMs, consisting of continuous values from 0 to 1 based on a sigmoid function. The proposed MFM generation was implemented for HRP-2 using HARK, our open-sourced robot audition software. Preliminary results show that the soft MFM outperformed a hard (binary) MFM in recognizing three simultaneous utterances. In a human-robot interaction task, the interval limitations between two adjacent loudspeakers were reduced from 60 degrees to 30 degrees by using soft MFMs.

Constant-frequency and frequency-modulated components in the echolocation calls of three species of small bats (Emballonuridae, Thyropteridae, and Vespertilionidae)

1999 ◽  
Vol 77 (12) ◽  
pp. 1891-1900 ◽  
Author(s):  
M B Fenton ◽  
J Rydell ◽  
M J Vonhof ◽  
J Eklöf ◽  
W C Lancaster
Keyword(s):  
Single Frequency ◽  
Constant Frequency ◽  
Field Station ◽  
Echolocation Calls ◽  
Low Intensity ◽  
Low Duty Cycle ◽  
Frequency Components ◽  
Ca 50 ◽  
Higher Harmonic ◽  
Thyroptera Tricolor

The echolocation calls of Rhychonycteris naso (Emballonuridae), Thyroptera tricolor (Thyropteridae), and Myotis riparius (Vespertilionidae) were recorded at the Cãno Palma Field Station in Costa Rica in February 1998. All three species produced echolocation calls at low duty cycle (signal on ~10% of the time). While T. tricolor produced low-intensity echolocation calls that were barely detectable when the bats were <0.5 m from the microphone, the other two species produced high-intensity calls, readily detectable at distances >5 m. Myotis riparius produced calls that swept from about 120 kHz to just over 50 kHz in about 2 ms. We found no evidence of harmonics in these calls. Rhynchonycteris naso and T. tricolor produced multiharmonic echolocation calls. In R. naso the calls included narrowband and broadband components and varied in bandwidth, sweeping from just under 100 kHz to around 75 kHz in over 5 ms. Most calls were dominated by the higher harmonic (ca. 100 kHz), but some also included a lower one (ca. 50 kHz). The calls of T. tricolor were 5-10 ms long and dominated by a single frequency (ca. 45 kHz), sometimes with a ca. 25 kHz component. The echolocation calls of all three species included frequency-modulated and constant-frequency components. While these terms describe the components of the echolocation calls, they do not necessarily describe the bats' echolocation behaviour.

Health Monitoring of PCB’s Under Mechanical Shock Loads

2019 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Jeff Suhling ◽  
Ken Blecker
Keyword(s):  
Health Monitoring ◽  
Failure Strain ◽  
Mechanical Shock ◽  
Frequency Data ◽  
Time Frequency ◽  
Feature Vectors ◽  
Damping Characteristics ◽  
Before And After ◽  
Frequency Components ◽  
Electronic Assemblies

Abstract Feature vectors for health monitoring of electronic assemblies under repetitive mechanical shock have been developed for assemblies subject to 3,000g acceleration levels. The resistance and strain measurements of the PCB are acquired during each drop to analyze the changes in the values during the experiment. Analysis on the progression of failure was carried out using frequency-based techniques on the strain signals from different locations of the board and failure of the package was identified from the increase in the resistance values of the package during the drop. Feature vectors selected were based on the time-frequency data as well as the logarithmic decrement of the strain signals during the different drops. Different statistical approaches on identifying the changes in the damping characteristics of the package during drop were also carried out. Statistical analysis on the changes in the resistance values were quantified in accordance with the changes in the strain and correlation of the both were attempted. The dependence on position of the strain gauge on the PCB were also studied by comparing the variation of the feature vectors of the corresponding strain signals. The before and after failure strain signals were compared on the frequency components and as well as the changes in the damping characteristics of the strain signals.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

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