scholarly journals A Novel Method for Designing General Window Functions with Flexible Spectral Characteristics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3081 ◽  
Author(s):  
Yinghao Sun ◽  
Quanhua Liu ◽  
Jinjian Cai ◽  
Teng Long
Keyword(s):  
Spectral Characteristics ◽  
Measured Data ◽  
Sidelobe Level ◽  
Time Frequency ◽  
Window Functions ◽  
Trade Offs ◽  
Novel Method ◽  
Cyclic Algorithm ◽  
Conventional Methods ◽  
Domain Weighting

In the field of sensor signal processing, windows are time-/frequency-domain weighting functions that are widely applied to reduce the well-known Gibbs oscillations. Conventional methods generally control the spectral characteristics of windows by adjusting several of the parameters of closed-form expressions. Designers must make trade-offs among the mainlobe width (MW), the peak sidelobe level (PSL), and sometimes the sidelobe fall-off rate (SLFOR) of windows by carefully adjusting these parameters. Generally, not all sidelobes need to be suppressed in specified applications. In this paper, a novel method, i.e., the inverse of the shaped output using the cyclic algorithm (ISO-CA), for designing window functions with flexible spectral characteristics is proposed. Simulations are conducted to test the effectiveness, flexibility and versatility of the method. Some experiments based on real measured data are also presented to demonstrate the practicability. The results show that the window functions generated using the cyclic algorithm (CA) yield better performance overall than the windows of conventional methods, achieving a narrower MW, a lower PSL, and a controllable SLFOR. In addition, steerable sidelobes over specified regions can be acquired both easily and flexibly while maintaining the original properties of the initial window as much as possible.

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Haitham Issa ◽  
Sali Issa ◽  
Wahab Shah
Keyword(s):  
Cross Validation ◽  
Image Feature ◽  
Emotional States ◽  
Time Frequency ◽  
Brain Signals ◽  
Average Accuracy ◽  
Gender And Age ◽  
Novel Method ◽  
Fold Cross Validation ◽  
Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

scholarly journals Minimally-modeled search of higher multipole gravitational-wave radiation in compact binary coalescences.

2021 ◽  
Author(s):  
G. Vedovato ◽  
Edoardo Milotti ◽  
Giovanni Andrea Prodi ◽  
Sophie Bini ◽  
Marco Drago ◽  
...  
Keyword(s):  
Wave Radiation ◽  
Full Detail ◽  
Parameter Estimates ◽  
Spectral Features ◽  
Operating Characteristics ◽  
Fast Analysis ◽  
Time Frequency ◽  
Compact Binary ◽  
Frequency Representation ◽  
Novel Method

Abstract As the Advanced LIGO and Advanced Virgo interferometers, soon to be joined by the KAGRA interferometer, increase their sensitivity, they detect an ever-larger number of gravitational waves with a significant presence of higher multipoles in addition to the dominant (2, 2) multipole. These higher multipoles can be detected with different approaches, such as the minimally-modeled burst search methods, and here we discuss one such approach based on the coherent WaveBurst pipeline (cWB). During the inspiral phase the higher multipoles produce chirps whose instantaneous frequency is a multiple of the dominant (2, 2) multipole, and here we describe how cWB can be used to detect these spectral features. The search is performed within suitable regions of the time-frequency representation; their shape is determined by optimizing the Receiver Operating Characteristics. This novel method has already been used in the GW190814 discovery paper (Astrophys. J. Lett. 896 L44) and is very fast and flexible. Here we describe in full detail the procedure used to detect the (3, 3) multipole in GW190814 as well as searches for other higher multipoles during the inspiral phase, and apply it to another event that displays higher multipoles, GW190412, replicating the results obtained with different methods. The procedure described here can be used for the fast analysis of higher multipoles and to support the findings obtained with the model-based Bayesian parameter estimates.

Variable Speed Estimation for Wheel-Bearings with STFT and BP Neural Network

2014 ◽  
Vol 945-949 ◽  
pp. 1112-1115
Author(s):  
Yuan Zhou ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Si Jie Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Background Noise ◽  
Speed Estimation ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Novel Method ◽  
Instantaneous Frequencies ◽  
Short Time

For the variable speed estimation of wheel-bearings in strong background noise, a novel method with the short-time Fourier transform and BP neural network (STFT-BPNN) is proposed. In the method, it calculates the time-frequency spectrum with STFT technique. Then the instantaneous frequency is estimated by peak detection. Taking the instantaneous frequencies as the input vectors, the BP neural network is trained to fit the discrete instantaneous frequencies. The effectiveness of proposed method is demonstrated by simulation. Experimental results show that proposed method provides better performance on variable speed estimation for wheel-bearings.

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.

scholarly journals Analytic wavelet packets for robust ultrasonic flow measurement

2020 ◽  
Vol 87 (1) ◽  
pp. 45-54
Author(s):  
Matthias Bächle ◽  
Daniel Alexander Schwär ◽  
Fernando Puente León
Keyword(s):  
Transit Time ◽  
Flow Measurement ◽  
Principal Component ◽  
Time Difference ◽  
Frequency Resolution ◽  
Wavelet Packets ◽  
Accurate Estimation ◽  
Time Frequency ◽  
Analytic Wavelet ◽  
Conventional Methods

AbstractA key element in robust transit-time ultrasonic flow measurement is the accurate estimation of the transit-time difference. Conventional methods, such as cross-correlation or the estimation in the phase domain, are limited in their robustness against signal distortions, interfering signals or noise. In this work, we present a novel method to estimate the transit-time difference through the fusion of selected analytic wavelet packet coefficients. The combination of the complex coefficients, which represent a projection of the signal on analytic wavelets, with a configurable time-frequency resolution allows a sub-sample estimation at the frequency of interest. After giving an introduction into the fundamentals of analytic wavelet packets based on multi-scale filtering, we introduce two features that correlate strongly with the transit-time difference. The selection and fusion of these features is done by using correlation coefficients with a calibration set and principal component analysis. Finally, using a clamp-on flow measurement system, the robustness against temperature variation and measurement noise is shown and compared with conventional methods.

scholarly journals A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijian Wang ◽  
Likang Zheng ◽  
Wenhua Du ◽  
Wenan Cai ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Weight Coefficient ◽  
Convergence Condition ◽  
Intelligent Fault Diagnosis ◽  
Wind Turbine Gearbox ◽  
Time Frequency ◽  
Proposed Model ◽  
Novel Method ◽  
Relationship Of

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

scholarly journals Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4034 ◽  
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Jie Chen ◽  
Chunsheng Li
Keyword(s):  
Radio Frequency ◽  
Pulse Compression ◽  
Radio Frequency Interference ◽  
Single Shot ◽  
Separation Problem ◽  
Underdetermined Blind Source Separation ◽  
Time Frequency ◽  
Interference Signals ◽  
Novel Method ◽  
Short Time

Radio frequency interference (RFI) is known to jam synthetic aperture radar (SAR) measurements, severely degrading the SAR imaging quality. The suppression of RFI in SAR echo signals is usually an underdetermined blind source separation problem. In this paper, we propose a novel method for multiclass RFI detection and suppression based on the single shot multibox detector (SSD). First, an echo-interference dataset is established by randomly combining the target signal with various types of RFI in a simulation, and the time–frequency form of the dataset is obtained by utilizing the short-time Fourier transform (STFT). Next, the time–frequency dataset acts as input data to train the SSD and obtain a network that is capable of detecting, identifying and estimating the interference. Finally, all of the interference signals are exactly reconstructed based on the prediction results of the SSD and mitigated by an adaptive filter. The proposed method can effectively increase the signal-to-interference-noise ratio (SINR) of RFI-contaminated SAR echoes and improve the peak sidelobe ratio (PSLR) after pulse compression. The simulated experimental results validate the effectiveness of the proposed method.

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