The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wand...The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.展开更多
The dynamic responses of suspension system of a vehicle travelling at varying speeds are generally nonstationary random processes,and the non-stationary random analysis has become an important and complex problem in v...The dynamic responses of suspension system of a vehicle travelling at varying speeds are generally nonstationary random processes,and the non-stationary random analysis has become an important and complex problem in vehicle ride dynamics in the past few years.This paper proposes a new concept,called dynamic frequency domain(DFD),based on the fact that the human body holds different sensitivities to vibrations at different frequencies,and applies this concept to the dynamic assessment on non-stationary vehicles.The study mainly includes two parts,the first is the input numerical calculation of the front and the rear wheels,and the second is the dynamical response analysis of suspension system subjected to non-stationary random excitations.Precise time integration method is used to obtain the vertical acceleration of suspension barycenter and the pitching angular acceleration,both root mean square(RMS)values of which are illustrated in different accelerating cases.The results show that RMS values of non-stationary random excitations are functions of time and increase as the speed increases at the same time.The DFD of vertical acceleration is finally analyzed using time-frequency analysis technique,and the conclusion is obviously that the DFD has a trend to the low frequency region,which would be significant reference for active suspension design under complex driving conditions.展开更多
Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under cond...Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.展开更多
Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.Th...Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.展开更多
文摘The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.
基金This work was supported by the National Natural Science Foundation of China(No.51705205)。
文摘The dynamic responses of suspension system of a vehicle travelling at varying speeds are generally nonstationary random processes,and the non-stationary random analysis has become an important and complex problem in vehicle ride dynamics in the past few years.This paper proposes a new concept,called dynamic frequency domain(DFD),based on the fact that the human body holds different sensitivities to vibrations at different frequencies,and applies this concept to the dynamic assessment on non-stationary vehicles.The study mainly includes two parts,the first is the input numerical calculation of the front and the rear wheels,and the second is the dynamical response analysis of suspension system subjected to non-stationary random excitations.Precise time integration method is used to obtain the vertical acceleration of suspension barycenter and the pitching angular acceleration,both root mean square(RMS)values of which are illustrated in different accelerating cases.The results show that RMS values of non-stationary random excitations are functions of time and increase as the speed increases at the same time.The DFD of vertical acceleration is finally analyzed using time-frequency analysis technique,and the conclusion is obviously that the DFD has a trend to the low frequency region,which would be significant reference for active suspension design under complex driving conditions.
文摘Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.
基金supported by the Project supported by the Jiangsu Province Carbon Peak Carbon Neutral Technology Innovation Project in China(BE2022034)the Sinopec Science and Technology Research Project(No.P21079-3 and No.P22090)the Shengli Oilfi eld Company Science and Technology Research Project(No.YKS2201).
基金National Natural Science Foundation of China(61672032)National Key Research and Development Program of China(2016YFD0800904)+1 种基金Anhui Provincial Science and Technology Project(16030701091)The Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(AE2018009).
文摘Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.
