An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode ...An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.展开更多
In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the ...In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.展开更多
In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless ...In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.展开更多
Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the pu...Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the purpose of diagnosis. An adaptive signal enhancer (ASE) based on the Least Mean Square (LMS) algorithm is presented to detect transient evoked OAEs (TEOAEs). The TEOAEs detection results from 106 ears show that ASE reaches better estimation of TEOAEs than a conventional ensemble averaging (EA) technique. With the ASE, the improvement of SNR was increased faster than that with the EA and the number of sweeps required can be markedly reduced. The detection time with ASE could be shortened by about 50% in comparison with that of EA.展开更多
基金Project(61573381)supported by the National Natural Science Foundation of ChinaProject(2012AA051601)supported by the National High-tech Research and Development Program of China
文摘An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
文摘In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.
基金supported by the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore’s Agency for Science,Technology and Research
文摘In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.
基金This work was supported by the National Natural Science Foundation of China (No.39870212)
文摘Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the purpose of diagnosis. An adaptive signal enhancer (ASE) based on the Least Mean Square (LMS) algorithm is presented to detect transient evoked OAEs (TEOAEs). The TEOAEs detection results from 106 ears show that ASE reaches better estimation of TEOAEs than a conventional ensemble averaging (EA) technique. With the ASE, the improvement of SNR was increased faster than that with the EA and the number of sweeps required can be markedly reduced. The detection time with ASE could be shortened by about 50% in comparison with that of EA.
