The limit conditions the pilots are submitted to during flight missions make monitoring neuromuscular fatigue ( NMF ) extremely important , as NMF significantly increases the risk of accident and affects the pilot ’s efficiency . We developed a noninvasive approach of monitoring the NMF , based on the compression of the Power Spectral Density of the SEMG ( surface electromyogram ) toward lower frequencies with increasing NMF , quantified by the Instantaneous Median Scale ( IMedS ) computed from SEMG via the Wavelet Transform . The Raa parameter ( Area/Amplitude Ratio ) computed on the SEMG was concurrently used . A novel algorithm to detect the skew angle of a scanned track image is proposed . The proposed algorithm is based on wavelet transform and linear least square fitting method . First , a skew feature image of the original track image , which preserves the track 's horizontal feature , is extracted by the wavelet transform . The symmetrical and unsymmetrical faults are created and the fault current in the system is given to the wavelet transform . Energy values are extracted from wavelet transform and it used to identify the fault . The proposed methodology is verified by using MATLAB/simulink model . The realization of the system components , however , di ers considerably . 1.1 The Master Module The master module depicted in g.1 consists of a wavelet transformation module ( WTM ) , an event detector and a partial tracker module controller . WTM s(k ) generate/ delete PTMs It is shown formally that secure aggregation and wavelet-based multi-resolution representation are compatible . Furthermore , it is shown that that the wavelet transformation is compatible with existing privacy-preserving protocols and can be used to extend them with additional degrees of freedom . An implementation of the proposed approach is used for evaluation of feasibility in a low-cost embedded environment . This paper proposes a parameter-embedding technique in MJP2 and constructs a standard stream which consists both of picture data and the parameters . The parameters are embedded into the LH1 component of wavelet transform domain through the ROI ( region of interest ) function of JPEG2000 without significant loss in the performance of comb-tooth suppression . Some experimental results show the feasibility of our proposed scheme . Abstract In the present paper , we investigate a stability-guaranteed adaptive algorithm for an adaptive feedforward controller , which is implemented by three kinds of approach : a time-domain approach using an IIR type of adaptive filter , a frequency-domain approach using an adaptive frequency sampling filter ( FSF ) , and a wavelet transform domain approach using an adaptive wavelet packet futer bank ( WPFB ) . In a case when an external signal has power spectrum localized in low frequency range as in active noise control , the adaptive WPFB based algorithm can attain fast and stable convergency . The guided wave signals were measured continuously using the pitch-catch method at regular intervals . The wavelet transform process was performed to improve the quality of the signal . The guided wave 's velocity of each measurement time was varied by extracting the time of flight . In order to reduce the error and improve the image visual quality in the wavelet transform process , a new method based on adaptive lifting scheme wavelet transform is presented . The gradient information is introduced to construct the decision-making function so as to gain the update and prediction operator due to grasp the optimized wavelet base . The results in this paper showed that it ’s difficult to use conventional infrared spectra and second derivative spectra directly to distinguish them . But it ’s quite easy to use two-dimensional infrared ( 2D IR ) correlation spectra , especially after applying wavelet transform process , to discriminate them . The resolution of the 2D IR spectrum is improved obviously after wavelet transform process , more peaks appear and the peaks become quite clear and separate . The closed-form expression resulted from a frequency domain interpretation of the smoothness priors procedure . The trade-off parameter determines the amount of frequency components that the procedure allows to pass . We show that the trade-off parameter is related to the arbitrary choice of cutoff frequency . This paper focuses on block-level detection , which is applicable for detecting transmitted control information , particularly when this information is selected or chosen from a finite set of information that are known at both transmitting and receiving devices . Using an orthogonal frequency division multiplexing architecture , this paper investigates and evaluates the performance of a time-domain decision criterion in comparison with a form of Maximum Likelihood ( ML ) estimation method . Unlike the ML method , the proposed time-domain detection technique requires no channel estimation as it uses the correlation ( in the time-domain ) that exists between the received and the transmitted selective information as a means of detection . However , due to occlusions , some parts of the scene are invisible in the high-resolution views and can not be synthesized from the reference images . In this paper , a novel high-frequency extrapolation method is proposed , utilizing additional sampling points pre-estimated by a low-resolution block-matching approach . Averaged over all considered test sets , the proposed method achieves a PSNR gain of 0.94 dB with respect to the unrefined signal extrapolation algorithm . A Digital-to-frequency converter ( DFC ) based on the concept of time-average frequency and a flying-adder frequency synthesizer is a novel circuit component that enables many system-level innovations . The clock output of the DFC has unique characteristics in both time and frequency domains . Although deep residual learning has already been applied to MI detection and localization , I propose a more accurate system that distinguishes among a higher number ( i.e. , six ) of MI locations . Inspired by speech waveform processing with neural networks , I found a more robust front-end than directly arranging the multi-lead ECG signal into an input matrix consisting of the use of a single one-dimensional convolutional layer per ECG lead to extract a pseudo-time-frequency representation and create a compact and discriminative input feature volume . As a result , I end up with a system achieving an MI detection and localization accuracy of 99.99 % on the well-known Physikalisch-Technische Bundesanstalt ( PTB ) database . The speech deficits associated with lingual dystonia were analyzed using acoustic analyses . Acoustic analysis focusing on lingual-fractive spectra was conducted using spectrographic displays by the time frequency response ( TFR ) software program to investigate the hypothesis of abnormal lingual fronting and elevation . A prominent phonetic error was the misperception of the palatial fricative for the alveolar fricative . A method of reproducing audio signals comprises : transforming audio signals into a plurality of frequency components ; seeking the plurality of the frequency components for a reference frequency component domain ; attenuating power of at least one of reference components in the reference frequency component domain to interpolate the frequency component in a frequency band higher than the reference frequency component domain , and transforming the interpolated frequency component into a time component . It is shown that the nonresponse period is nearly equal to one divided period ( TDO ) . The proposed scheme shows much better jitter suppression characteristics than some other systems , e.g. , the clock frequency duplication system , the phase correcting-restriction system and the system using a random walk filter . Finally , experimental jitter suppression characteristics of multistage connection of new modules with a nonresponse period of 0 , 0,5TDO and 1.0TDO , respectively , are shown . Based on the distributing character of Doppler frequency shift , swing frequency style of Doppler frequency shift search is employed . At last , a whole acquisition scheme are combined from optimized coarse acquisition、fine frequency algorithms and swing frequency style of Doppler search . Simulation results show that the designed scheme is of the advantage of less calculation、shorter acquisition time and the acquisition results can satisfy tracking needs . The dotted line is a fitted line to show how close the power spectrum follows a power law . SFRDeadLeaves ( f ) = √ PSimage ( f ) PStarget ( f ) ( 1 ) DeadLeaves direct The first approach clearly misses an important point : Camera do not only remove ( high ) spatial frequencies as part of the spatial frequency transfer , they also add noise to the image . This noise will therefore also add high spatial frequencies which will interfere with the measurement . An equivalent statement also holds for the inverse DFT operator . This allows us to quickly assess how an input is affected by any distortion we might make to its frequency representation . 2.1 Conjugate symmetry constraints ( 3 ) Thus , intuitively , given the left half of our frequency map , the diminished number of degrees of freedom allows us to reconstruct the right . In effect , this allows us to store approximately half the parameters that would otherwise be necessary . We apply this stochastically by assigning a distribution pR ( · ) on the frequency truncation radius ( for simplicity we apply the same truncation on both axes ) , sampling from this a random radius at each iteration , and wiping out all frequencies outside the square of that size . Note that this can be regarded as an application of nested dropout ( Rippel et al . , 2014 ) on both dimensions of the frequency decomposition of our input . In practice , we have had success choosing pR ( · ) This setting leads to the highest reported single - scale score of 53.12 % on validation data so far , significantly outperforming the LSUN 2017 segmentation winner ’s single - scale approach of 51.59 % . As also listed in Table [ reference ] , their approach additionally used hybrid dilated convolutions , applied an inverse frequency weighting for correcting training data class imbalance as well as pretrained on Cityscapes . subsection : Timing analyses We report precision at 1 . We consider a frequency - based baseline which predicts the most frequent tag . We also compare with Tagspace , which is a tag prediction model similar to ours , but based on the Wsabie model of weston2011wsabie . The main motivation is that filters spaced linearly at low frequencies and logarithmically at high frequencies make it possible to capture phonetically important acoustic correlates . Early evidence was provided in showing that mel frequency scaled cepstral coefficients ( MFCCs ) are effective in capturing the acoustic information required to recognize syllables in continuous speech . Motivated by these analysis , a small number of MFCCs ( usually ) with their first and second time - derivatives , as proposed in , have been found suited for statistical and neural ASR systems . The use of historical information can greatly help language models deal with long range dependencies as shown by . Finally , in a recent paper , achieve improved performance for language modeling by using frequency agnostic word embeddings , a technique orthogonal to and combinable with PDR . bibliography : References 3 , bottom - left , shows the decorrelation filters for each channel obtained by reshaping the largest eigenvectors of Σ. The largest eigenvectors are smoothing filters while the smaller ones resemble increasingly higher - frequency filters . The corresponding eigenvalues decay rapidly and in practice we use the top k = 4 filters . It is able to segment both small and large classes well . We remark here that we used median frequency class balancing in training SegNet - Basic and SegNet . In addition , there is an overall smooth quality of segmentation much like what is typically obtained with CRF post - processing . This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network ( CNN ) where a modified ECG signal is given as an input signal to the network . Each ECG signal is first decomposed through Empirical Mode Decomposition ( EMD ) and higher order Intrinsic Mode Functions ( IMFs ) are combined to form a modified ECG signal . It is believed that the use of EMD would provide a broader range of information and can provide denoising performance . These IMFs are transformed into analytic representations by applying the Hilbert transform . From these analytic IMFs , various features namely mean , kurtosis , skewness , entropy , and negentropy are extracted ; these features are used as input to least squares support vector machines ( LS-SVMs ) classifier with radial basis function ( RBF ) kernel and polynomial kernel . The accuracy results achieved for LS-SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92 % , respectively , which are found to be better as compared with other state-of-the-art methods . The Hilbert transformation of IMFs helps to represent the signals in analytical form . The two bandwidths , namely amplitude modulated bandwidth and frequency modulated bandwidth calculated from the analytic IMFs , have been used as input to artificial neural networks ( ANN ) for classifying normal and abnormal EEG signals . Available EEG signals are used to show the effectiveness of the proposed method . The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs . The two bandwidths , namely amplitude modulation bandwidth ( BAM ) and frequency modulation bandwidth ( BFM ) , computed from the analytic IMFs , have been used as an input to least squares support vector machine ( LS-SVM ) for classifying seizure and nonseizure EEG signals . The proposed method for classification of EEG signals based on the bandwidth features ( BAM and BFM ) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010 . The results show that errors increase as the gap length increases . In addition , errors in the high-frequency IMFs are less than the low-frequency IMFs . The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints . Both agents cause an increase of power density and a decrease of nonlinearity on β-band neuronal oscillation accompany with a loss of consciousness . Moreover , anesthesia agents cause the decreases of power density and nonlinearity ( i.e. DWD ) for the low-frequency IMFs . To better analyze the time series of historical data , VMD decomposes time series data into an ensemble of components with different frequencies ; this improves the shortcomings of decomposition from Empirical Mode Decomposition ( EMD ) and Ensemble Empirical Mode Decomposition ( EEMD ) processes . Classification is achieved via the spectrum characteristics of modal components , the high-frequency Intrinsic Mode Functions ( IMFs ) components are predicted using the DBN , and the low-frequency IMFs components are predicted using the ARMA . Eventually , the forecasting result is generated by reconstructing the predicted component values . Indeed , more structure information gives more additional discriminant ability . Furthermore , a new discriminant IMFs dictionary is designed to enhance interclass difference via empirical mode decomposition . The proposed method is investigated on several HSI data sets . Both these features were fed to a Support Vector Machine ( SVM ) classifier , and their performance parameters were evaluated . It is concluded that statistical parameters , as well as nonlinear parameters of the EEG IMFs , are prospective potential features for automated diagnosis of encephalopathy . In order to deal with non-stationary and chaotic series , a hybrid forecasting approach is proposed in this study , which integrates ensemble empirical mode decomposition ( EEMD ) and optimal combined forecasting model ( CFM ) . The proposed approach introduces a new intrinsic mode functions ( IMFs ) reconstruction method by using evolutionary clustering algorithm , and utilizes optimal combined model to forecast sub-series . Firstly , the EEMD technique is employed to sift the IMFs and a residue . The HIF detection method is as follows : First , perform the VMD on transient zero sequence currents to obtain the intrinsic mode functions ( IMFs ) and select the IMFs with the largest kurtosis value as the characteristic IMFs . Second , calculate the characteristic IMFs to obtain TKEOs and divide into subintervals of TKEOs waveform to calculate the time entropy values . Finally , construct HIF detection criterion as follows : when time entropy value is 0 , it is judged as CS or LS . To overcome this limitation , this paper uses empirical mode decomposition ( EMD ) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR ( Support Vector Regression ) to forecast . The approach contains four steps : ( 1 ) using ARIMA to analyze the linear part of the original time series ; ( 2 ) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function ( IMF ) components and one residual component ; ( 3 ) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part ; ( 4 ) combining the forecasting results of linear model and nonlinear model . To verify the effectiveness of the proposed approach , four stock indices are chosen as the forecasting targets . The HHT consists two steps namely empirical mode decomposition ( EMD ) and the Hilbert transform ( HT ) . The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions ( IMFs ) , and the Hilbert transformation of these IMFs provide instantaneous frequency estimation of the IMFs . The instantaneous frequency estimation of IMFs have been used as a feature to identify the IMFs in order to separate rhythms of EEG signal . First , decompose the acoustic signals with Doppler shift into IMFs by EMD . Then , calculate the 8 VFs of IMFs and 10 TFs of original signals , take the 18 features as the input of BPN . After training the BPN , the result of test samples shows the proposed approach can discriminate different fault conditions of train bearing reliably and accurately . A time-frequency analysis method based on S-transform is proposed , which describes the local characteristics of flicker signal in both time and frequency domains . The row vector of S-transform matrix is the distribution of amplitude along with time at a certain frequency and its column vector is the distribution of amplitude along with frequency at a certain sampling time , i.e.,the results of S-transform contain the information of frequency-time , amplitude-time and amplitude-frequency , according to which , the fluctuation amplitude and modulation frequency of voltage flicker signal can be obtained , as well as its beginning and ending moments . Because the time resolution is incompatible with the frequency resolution in time-frequency analysis , the relationship between Gaussian window width in S-transform and the timefrequency resolution is studied . With the time , they may lead to a catastrophic failure . In order to avoid this situation , S-transform technique is proposed to extract the suitable features of incipient fault in this chapter . A least square support vector machine ( LS-SVM ) classifier is developed utilizing the features so that incipient fault is distinguished from the normal disturbances . This brings a big challenge to the common long-time coherent integration based target detection methods . To solve this problem , a novel hypersonic maneuvering target detection method called Radon-S transform is proposed in this paper on the basis of Radon transform and S-transform . It performs the coherent integration along the target track on the time – range plane , and then performs the non-coherent integration along the time – frequency curve of target echo on the time – frequency plane . This paper presents S-Transform based detection technique to detect symmetrical fault with high resistance up to 200 Ω during power swing . The proposed scheme is based on S-Transform analysis on the current and voltage waveforms . The effectiveness of the proposed scheme was verified with the IEEE 14 bus test system and simulations results show that the proposed technique can detect high resistance symmetrical fault occurring during power swing from 5 Ω up to 200 Ω. This study presents a novel PQ disturbance-recognition algorithm , based on time-frequency ( TF ) analysis and a decision tree ( DT ) classifier . The proposed method requires fewer feature statistics compared to the S-transform-based approach for PQ disturbance identification . In this study , feature statistics extracted using TF analysis are trained by a DT classifier to perform the automatic classification of PQ disturbances . In order to solve the 0 - 1 knapsack problem effectively , hybrid binary differential evolution algorithm is proposed . Firstly , the algorithm are added to the original differential evolution based on the original differential evolution with binary coding and three new operations which are mapping operation , S transform operation and inverse mapping operation , The three new operations extend the original differential evolution from the continuous field to discrete field by successfully solving the no closure problem which the original differential evolution leads . Additionally , the greedy transform method is used to change the infeasible solution which violates the constraints into the feasible one . The first is a static calculation by a finite difference method and a power iteration algorithm for providing a database of the required neutronic parameters . The second is the neutron noise calculations in the time-frequency domain and the extraction of the noise characteristics by using the Wigner-Ville distribution . The third is the categorization of the neutron noise sources based on their extracted characteristics such as the source type , the source location , the number of sources , the time of the occurrence , the instance frequency and the noise amplitude . The aircraft targets are modeled by perfectly conducting straight thin wire structures and the electromagnetic fields back-scattered from targets are numerically generated . This technique uses the Wigner-Ville distribution ( WD ) and the principal component analysis ( PCA ) . The technique is applied to perfectly conducting targets of moderately complicated geometries for the first time . In the analysis of the existing time-frequency analyzing method , it found that the signal of background noise was assumed Gaussian distribution , so the performance of the time-frequency domain analysis for signal of noise in such non-Gaussian distribution as FLOA was in severe degradation . Focus on this problem , this paper proposed a novel Wigner-Ville distribution analysis method of fractional lower-order alpha stable distribution based on conventional Wigner-Ville distribution . Simulations show that the proposed method has more excellent performance than conventional Wigner-Ville distribution in time-frequency analyzing of alpha stable distribution process and is a new one in time-frequency domain research of alpha stable distribution . In order to find the distribution relationship between the frequency and time , we adopt some time-frequency analysis methods to deal with ENPEMF signals . In this paper , an improved algorithm named Wigner-Ville Distribute based on Dual Ensemble Empirical Mode Decomposition ( DEEMD-WVD ) and the Hilbert-Huang Transform ( HHT ) are used to process ENPEMF data obtained before the Lushan earthquake ( MS 7.0 , 2013 - 04 - 20 ) , it reveals clear preseismic time-frequency characteristics of ENPEMF , namely , a strong-weak-strong pattern . Among them , its frequency chart analysis and design function of the filter with the analysis is very strong , make arithmetic digital signal handled to become very brief , intuitionistic . This text introduced time-frequency analysis foundation theories and some applied , make use of the MATLAB language to construct a kind of time and frequency density function , aims at explaining signal containing frequency weight and its evolve characteristic by wigner-ville distribute . Several time-frequency analysis methods are compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk . The representative Wigner-Ville distribution may suffer from severe interferences by cross terms even though it gives good resolution both in time and frequency . The smoothing process improves the interference problem at the expense of resolution . We present the two-dimensional quaternion Wigner-Ville distribution ( QWVD ) . The transform is constructed by substituting the Fourier transform kernel with the quaternion Fourier transform ( QFT ) kernel in the classical Wigner-Ville distribution definition . Based on the properties of quaternions and the QFT kernel we obtain three types of the QWVD . To do end , EEG activities recorded from f py andpparticipants while listening to three categories of light , ha uralesad music . Wigner-Ville Distribution feature and artificial n classes network are used for feature extraction and classify of three of mood states . Experimental results shown effectiveness of eproposed system for recognizing different influenced cognitiv as states , and this results can be used in BCI ap-plication such users virtual reality , games and digital entertainment , or allowing to play a musical note by brain signals . To make a balance between both , we propose a parameter estimation algorithm of PPS based on particle swarm optimization . The parameter estimation algorithm includes two steps , the previous is rough estimation based on Wigner-Ville distribution slice and the latter is accurate estimation based on polynomial-kernel Fourier transform . To search for the compatible kernel parameters , we propose a modified particle swarm optimization . An efficient method to eliminate redundant frequencies present in one of the existing multi-frequency methods for analog fault diagnosis is proposed in this paper . First the two-dimensional fault dictionary is constructed where entries are gain signatures of all faults and frequencies . This is achieved by presenting a new ambiguity set partition method based on hierarchical clustering and an improved entropy index method . Besides , multi-frequency analysis is also incorporated in the described method . The proposed method is tested and compared with exhaustive search through its application to two benchmark circuits . We present novel algorithms using multi-frequency analysis for full-field profilometry . The technique provides absolute order of interference in an interferogram . It consists of two parts , the first part is a condensed process that use software to extract parasitic while saving great efforts on environment setup . The second part is a multi-frequency analysis method that superimpose several simulation results to form single waveform . This method could retain response from higher harmonics of certain signal compared to conventional single-frequency simulation . Multi- system and multi-frequency are two key factors that determine the performance of precise point positioning . Both multi-frequency and multi-system lead to new biases , which are not solved systematically . This paper concentrates on mathematical models of biases , influences of these biases , and positioning performance analysis of different observation models . It is difficult to mitigate the LFM interferences in the GPS signals only in the time or frequency domain . In this paper , a new time-frequency tool based on the fractional Fourier transform ( FRFT ) is proposed to cancel the LFM interference which has clear time-frequency signatures . Finally , the simulation results show that this method is valid . Doppler signal is researched in time-frequency domain in this paper . The Time-Frequency Toolbox ( TFTB ) in MATLAB is used . Wigner-Ville distribution , Wigner-Hough transformation , Choi-Williams distribution , spectrogram and other distributions are used to analyze Doppler signal . Time-frequency distribution of signal is researched and several rearrangements are compared . The Time-Frequency Toolbox ( TFTB ) in MATLAB is used . Simulation results show that rearrangement can restrain cross-terms and improve time-frequent aggregation . The efficacy of the proposed method is verified by applying it to computer simulated and real-world cases provided by National Renewable Energy Laboratory ( NREL ) in the US . Time-frequency analysis is performed utilizing Time-Frequency Toolbox ( TFTB ) in MATLAB ® developed for the analysis of non-stationary signals . We present a method to quantitatively characterize voice abnormalities using wavelet analysis . The proposed method uses wavelets to decompose acoustic signals , acquired clinically from patients with normal and pathological voices , into their designated frequency/time components . These components contain valuable information on the unique dynamic properties of the vocal system and correlate with specific voice conditions . This paper presents the concept of the Stochastic Multi-dimensional Harmonic Balance Method ( Stochastic-MHBM ) in order to solve dynamical problems with non-regular non linearities in presence of uncertainties . To treat the nonlinearity in the stochastic and frequency domains , the Alternate Frequency-Time method with Probabilistic Collocation ( AFTPC ) is proposed . The approach is demonstrated using nonlinear two-degree-of-freedom model with different types of nonlinearities ( cubic nonlinearity , contact/no contact , friction ) . One cardiac cycle of ECG signal consists of characteristic waves P-QRS-T. The amplitudes and intervals values of P-QRS-T segment determine the functioning of heart of every human . In this paper a novel methodology of Frequency-Time based approach is used to identify P-QRS-T waves . R-peak detection is the first step in characteristic waves detection , for identifying R-peak , wavelet transform ( sym4 ) decomposition method ( Frequency domain ) is used . An apparatus for decoding a coded audio data in the audio decoder , comprising : error detecting means for detecting an error of the audio data has been encoded ; error concealing method determining means , for error detection by said error detected by the means information for determining the audio frame and weight of window function hide the error weight ; frequency-time converter for converting audio data into frequency components into a time component audio frame ; audio frame buffer for storing converted by the frequency-time time components of the audio output by the frame ; and a windowing processing device , means for determining the window function according to the determined method of error concealment by the weight of component audio frame by the frequency time converter output and the time / or audio frame stored in the audio frame buffer are weighted .
The limit conditions the pilots are submitted to during flight missions make monitoring neuromuscular fatigue ( NMF ) extremely important , as NMF significantly increases the risk of accident and affects the pilot ’s efficiency . We developed a noninvasive approach of monitoring the NMF , based on the compression of the Power Spectral Density of the SEMG ( surface electromyogram ) toward lower frequencies with increasing NMF , quantified by the Instantaneous Median Scale ( IMedS ) computed from SEMG via the Wavelet Transform . The Raa parameter ( Area/Amplitude Ratio ) computed on the SEMG was concurrently used .
A novel algorithm to detect the skew angle of a scanned track image is proposed . The proposed algorithm is based on wavelet transform and linear least square fitting method . First , a skew feature image of the original track image , which preserves the track 's horizontal feature , is extracted by the wavelet transform .
The symmetrical and unsymmetrical faults are created and the fault current in the system is given to the wavelet transform . Energy values are extracted from wavelet transform and it used to identify the fault . The proposed methodology is verified by using MATLAB/simulink model .
The realization of the system components , however , di ers considerably . 1.1 The Master Module The master module depicted in g.1 consists of a wavelet transformation module ( WTM ) , an event detector and a partial tracker module controller . WTM s(k ) generate/ delete PTMs
It is shown formally that secure aggregation and wavelet-based multi-resolution representation are compatible . Furthermore , it is shown that that the wavelet transformation is compatible with existing privacy-preserving protocols and can be used to extend them with additional degrees of freedom . An implementation of the proposed approach is used for evaluation of feasibility in a low-cost embedded environment .
This paper proposes a parameter-embedding technique in MJP2 and constructs a standard stream which consists both of picture data and the parameters . The parameters are embedded into the LH1 component of wavelet transform domain through the ROI ( region of interest ) function of JPEG2000 without significant loss in the performance of comb-tooth suppression . Some experimental results show the feasibility of our proposed scheme .
Abstract In the present paper , we investigate a stability-guaranteed adaptive algorithm for an adaptive feedforward controller , which is implemented by three kinds of approach : a time-domain approach using an IIR type of adaptive filter , a frequency-domain approach using an adaptive frequency sampling filter ( FSF ) , and a wavelet transform domain approach using an adaptive wavelet packet futer bank ( WPFB ) . In a case when an external signal has power spectrum localized in low frequency range as in active noise control , the adaptive WPFB based algorithm can attain fast and stable convergency .
The guided wave signals were measured continuously using the pitch-catch method at regular intervals . The wavelet transform process was performed to improve the quality of the signal . The guided wave 's velocity of each measurement time was varied by extracting the time of flight .
In order to reduce the error and improve the image visual quality in the wavelet transform process , a new method based on adaptive lifting scheme wavelet transform is presented . The gradient information is introduced to construct the decision-making function so as to gain the update and prediction operator due to grasp the optimized wavelet base .
The results in this paper showed that it ’s difficult to use conventional infrared spectra and second derivative spectra directly to distinguish them . But it ’s quite easy to use two-dimensional infrared ( 2D IR ) correlation spectra , especially after applying wavelet transform process , to discriminate them . The resolution of the 2D IR spectrum is improved obviously after wavelet transform process , more peaks appear and the peaks become quite clear and separate .
The closed-form expression resulted from a frequency domain interpretation of the smoothness priors procedure . The trade-off parameter determines the amount of frequency components that the procedure allows to pass . We show that the trade-off parameter is related to the arbitrary choice of cutoff frequency .
This paper focuses on block-level detection , which is applicable for detecting transmitted control information , particularly when this information is selected or chosen from a finite set of information that are known at both transmitting and receiving devices . Using an orthogonal frequency division multiplexing architecture , this paper investigates and evaluates the performance of a time-domain decision criterion in comparison with a form of Maximum Likelihood ( ML ) estimation method . Unlike the ML method , the proposed time-domain detection technique requires no channel estimation as it uses the correlation ( in the time-domain ) that exists between the received and the transmitted selective information as a means of detection .
However , due to occlusions , some parts of the scene are invisible in the high-resolution views and can not be synthesized from the reference images . In this paper , a novel high-frequency extrapolation method is proposed , utilizing additional sampling points pre-estimated by a low-resolution block-matching approach . Averaged over all considered test sets , the proposed method achieves a PSNR gain of 0.94 dB with respect to the unrefined signal extrapolation algorithm .
A Digital-to-frequency converter ( DFC ) based on the concept of time-average frequency and a flying-adder frequency synthesizer is a novel circuit component that enables many system-level innovations . The clock output of the DFC has unique characteristics in both time and frequency domains .
Although deep residual learning has already been applied to MI detection and localization , I propose a more accurate system that distinguishes among a higher number ( i.e. , six ) of MI locations . Inspired by speech waveform processing with neural networks , I found a more robust front-end than directly arranging the multi-lead ECG signal into an input matrix consisting of the use of a single one-dimensional convolutional layer per ECG lead to extract a pseudo-time-frequency representation and create a compact and discriminative input feature volume . As a result , I end up with a system achieving an MI detection and localization accuracy of 99.99 % on the well-known Physikalisch-Technische Bundesanstalt ( PTB ) database .
The speech deficits associated with lingual dystonia were analyzed using acoustic analyses . Acoustic analysis focusing on lingual-fractive spectra was conducted using spectrographic displays by the time frequency response ( TFR ) software program to investigate the hypothesis of abnormal lingual fronting and elevation . A prominent phonetic error was the misperception of the palatial fricative for the alveolar fricative .
A method of reproducing audio signals comprises : transforming audio signals into a plurality of frequency components ; seeking the plurality of the frequency components for a reference frequency component domain ; attenuating power of at least one of reference components in the reference frequency component domain to interpolate the frequency component in a frequency band higher than the reference frequency component domain , and transforming the interpolated frequency component into a time component .
It is shown that the nonresponse period is nearly equal to one divided period ( TDO ) . The proposed scheme shows much better jitter suppression characteristics than some other systems , e.g. , the clock frequency duplication system , the phase correcting-restriction system and the system using a random walk filter . Finally , experimental jitter suppression characteristics of multistage connection of new modules with a nonresponse period of 0 , 0,5TDO and 1.0TDO , respectively , are shown .
Based on the distributing character of Doppler frequency shift , swing frequency style of Doppler frequency shift search is employed . At last , a whole acquisition scheme are combined from optimized coarse acquisition、fine frequency algorithms and swing frequency style of Doppler search . Simulation results show that the designed scheme is of the advantage of less calculation、shorter acquisition time and the acquisition results can satisfy tracking needs .
The dotted line is a fitted line to show how close the power spectrum follows a power law . SFRDeadLeaves ( f ) = √ PSimage ( f ) PStarget ( f ) ( 1 ) DeadLeaves direct The first approach clearly misses an important point : Camera do not only remove ( high ) spatial frequencies as part of the spatial frequency transfer , they also add noise to the image . This noise will therefore also add high spatial frequencies which will interfere with the measurement .
An equivalent statement also holds for the inverse DFT operator . This allows us to quickly assess how an input is affected by any distortion we might make to its frequency representation . 2.1 Conjugate symmetry constraints
( 3 ) Thus , intuitively , given the left half of our frequency map , the diminished number of degrees of freedom allows us to reconstruct the right . In effect , this allows us to store approximately half the parameters that would otherwise be necessary .
We apply this stochastically by assigning a distribution pR ( · ) on the frequency truncation radius ( for simplicity we apply the same truncation on both axes ) , sampling from this a random radius at each iteration , and wiping out all frequencies outside the square of that size . Note that this can be regarded as an application of nested dropout ( Rippel et al . , 2014 ) on both dimensions of the frequency decomposition of our input . In practice , we have had success choosing pR ( · )
This setting leads to the highest reported single - scale score of 53.12 % on validation data so far , significantly outperforming the LSUN 2017 segmentation winner ’s single - scale approach of 51.59 % . As also listed in Table [ reference ] , their approach additionally used hybrid dilated convolutions , applied an inverse frequency weighting for correcting training data class imbalance as well as pretrained on Cityscapes . subsection : Timing analyses
We report precision at 1 . We consider a frequency - based baseline which predicts the most frequent tag . We also compare with Tagspace , which is a tag prediction model similar to ours , but based on the Wsabie model of weston2011wsabie .
The main motivation is that filters spaced linearly at low frequencies and logarithmically at high frequencies make it possible to capture phonetically important acoustic correlates . Early evidence was provided in showing that mel frequency scaled cepstral coefficients ( MFCCs ) are effective in capturing the acoustic information required to recognize syllables in continuous speech . Motivated by these analysis , a small number of MFCCs ( usually ) with their first and second time - derivatives , as proposed in , have been found suited for statistical and neural ASR systems .
The use of historical information can greatly help language models deal with long range dependencies as shown by . Finally , in a recent paper , achieve improved performance for language modeling by using frequency agnostic word embeddings , a technique orthogonal to and combinable with PDR . bibliography : References
3 , bottom - left , shows the decorrelation filters for each channel obtained by reshaping the largest eigenvectors of Σ. The largest eigenvectors are smoothing filters while the smaller ones resemble increasingly higher - frequency filters . The corresponding eigenvalues decay rapidly and in practice we use the top k = 4 filters .
It is able to segment both small and large classes well . We remark here that we used median frequency class balancing in training SegNet - Basic and SegNet . In addition , there is an overall smooth quality of segmentation much like what is typically obtained with CRF post - processing .
This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network ( CNN ) where a modified ECG signal is given as an input signal to the network . Each ECG signal is first decomposed through Empirical Mode Decomposition ( EMD ) and higher order Intrinsic Mode Functions ( IMFs ) are combined to form a modified ECG signal . It is believed that the use of EMD would provide a broader range of information and can provide denoising performance .
These IMFs are transformed into analytic representations by applying the Hilbert transform . From these analytic IMFs , various features namely mean , kurtosis , skewness , entropy , and negentropy are extracted ; these features are used as input to least squares support vector machines ( LS-SVMs ) classifier with radial basis function ( RBF ) kernel and polynomial kernel . The accuracy results achieved for LS-SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92 % , respectively , which are found to be better as compared with other state-of-the-art methods .
The Hilbert transformation of IMFs helps to represent the signals in analytical form . The two bandwidths , namely amplitude modulated bandwidth and frequency modulated bandwidth calculated from the analytic IMFs , have been used as input to artificial neural networks ( ANN ) for classifying normal and abnormal EEG signals . Available EEG signals are used to show the effectiveness of the proposed method .
The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs . The two bandwidths , namely amplitude modulation bandwidth ( BAM ) and frequency modulation bandwidth ( BFM ) , computed from the analytic IMFs , have been used as an input to least squares support vector machine ( LS-SVM ) for classifying seizure and nonseizure EEG signals . The proposed method for classification of EEG signals based on the bandwidth features ( BAM and BFM ) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010 .
The results show that errors increase as the gap length increases . In addition , errors in the high-frequency IMFs are less than the low-frequency IMFs . The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints .
Both agents cause an increase of power density and a decrease of nonlinearity on β-band neuronal oscillation accompany with a loss of consciousness . Moreover , anesthesia agents cause the decreases of power density and nonlinearity ( i.e. DWD ) for the low-frequency IMFs .
To better analyze the time series of historical data , VMD decomposes time series data into an ensemble of components with different frequencies ; this improves the shortcomings of decomposition from Empirical Mode Decomposition ( EMD ) and Ensemble Empirical Mode Decomposition ( EEMD ) processes . Classification is achieved via the spectrum characteristics of modal components , the high-frequency Intrinsic Mode Functions ( IMFs ) components are predicted using the DBN , and the low-frequency IMFs components are predicted using the ARMA . Eventually , the forecasting result is generated by reconstructing the predicted component values .
Indeed , more structure information gives more additional discriminant ability . Furthermore , a new discriminant IMFs dictionary is designed to enhance interclass difference via empirical mode decomposition . The proposed method is investigated on several HSI data sets .
Both these features were fed to a Support Vector Machine ( SVM ) classifier , and their performance parameters were evaluated . It is concluded that statistical parameters , as well as nonlinear parameters of the EEG IMFs , are prospective potential features for automated diagnosis of encephalopathy .
In order to deal with non-stationary and chaotic series , a hybrid forecasting approach is proposed in this study , which integrates ensemble empirical mode decomposition ( EEMD ) and optimal combined forecasting model ( CFM ) . The proposed approach introduces a new intrinsic mode functions ( IMFs ) reconstruction method by using evolutionary clustering algorithm , and utilizes optimal combined model to forecast sub-series . Firstly , the EEMD technique is employed to sift the IMFs and a residue .
The HIF detection method is as follows : First , perform the VMD on transient zero sequence currents to obtain the intrinsic mode functions ( IMFs ) and select the IMFs with the largest kurtosis value as the characteristic IMFs . Second , calculate the characteristic IMFs to obtain TKEOs and divide into subintervals of TKEOs waveform to calculate the time entropy values . Finally , construct HIF detection criterion as follows : when time entropy value is 0 , it is judged as CS or LS .
To overcome this limitation , this paper uses empirical mode decomposition ( EMD ) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR ( Support Vector Regression ) to forecast . The approach contains four steps : ( 1 ) using ARIMA to analyze the linear part of the original time series ; ( 2 ) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function ( IMF ) components and one residual component ; ( 3 ) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part ; ( 4 ) combining the forecasting results of linear model and nonlinear model . To verify the effectiveness of the proposed approach , four stock indices are chosen as the forecasting targets .
The HHT consists two steps namely empirical mode decomposition ( EMD ) and the Hilbert transform ( HT ) . The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions ( IMFs ) , and the Hilbert transformation of these IMFs provide instantaneous frequency estimation of the IMFs . The instantaneous frequency estimation of IMFs have been used as a feature to identify the IMFs in order to separate rhythms of EEG signal .
First , decompose the acoustic signals with Doppler shift into IMFs by EMD . Then , calculate the 8 VFs of IMFs and 10 TFs of original signals , take the 18 features as the input of BPN . After training the BPN , the result of test samples shows the proposed approach can discriminate different fault conditions of train bearing reliably and accurately .
A time-frequency analysis method based on S-transform is proposed , which describes the local characteristics of flicker signal in both time and frequency domains . The row vector of S-transform matrix is the distribution of amplitude along with time at a certain frequency and its column vector is the distribution of amplitude along with frequency at a certain sampling time , i.e.,the results of S-transform contain the information of frequency-time , amplitude-time and amplitude-frequency , according to which , the fluctuation amplitude and modulation frequency of voltage flicker signal can be obtained , as well as its beginning and ending moments . Because the time resolution is incompatible with the frequency resolution in time-frequency analysis , the relationship between Gaussian window width in S-transform and the timefrequency resolution is studied .
With the time , they may lead to a catastrophic failure . In order to avoid this situation , S-transform technique is proposed to extract the suitable features of incipient fault in this chapter . A least square support vector machine ( LS-SVM ) classifier is developed utilizing the features so that incipient fault is distinguished from the normal disturbances .
This brings a big challenge to the common long-time coherent integration based target detection methods . To solve this problem , a novel hypersonic maneuvering target detection method called Radon-S transform is proposed in this paper on the basis of Radon transform and S-transform . It performs the coherent integration along the target track on the time – range plane , and then performs the non-coherent integration along the time – frequency curve of target echo on the time – frequency plane .
This paper presents S-Transform based detection technique to detect symmetrical fault with high resistance up to 200 Ω during power swing . The proposed scheme is based on S-Transform analysis on the current and voltage waveforms . The effectiveness of the proposed scheme was verified with the IEEE 14 bus test system and simulations results show that the proposed technique can detect high resistance symmetrical fault occurring during power swing from 5 Ω up to 200 Ω.
This study presents a novel PQ disturbance-recognition algorithm , based on time-frequency ( TF ) analysis and a decision tree ( DT ) classifier . The proposed method requires fewer feature statistics compared to the S-transform-based approach for PQ disturbance identification . In this study , feature statistics extracted using TF analysis are trained by a DT classifier to perform the automatic classification of PQ disturbances .
In order to solve the 0 - 1 knapsack problem effectively , hybrid binary differential evolution algorithm is proposed . Firstly , the algorithm are added to the original differential evolution based on the original differential evolution with binary coding and three new operations which are mapping operation , S transform operation and inverse mapping operation , The three new operations extend the original differential evolution from the continuous field to discrete field by successfully solving the no closure problem which the original differential evolution leads . Additionally , the greedy transform method is used to change the infeasible solution which violates the constraints into the feasible one .
The first is a static calculation by a finite difference method and a power iteration algorithm for providing a database of the required neutronic parameters . The second is the neutron noise calculations in the time-frequency domain and the extraction of the noise characteristics by using the Wigner-Ville distribution . The third is the categorization of the neutron noise sources based on their extracted characteristics such as the source type , the source location , the number of sources , the time of the occurrence , the instance frequency and the noise amplitude .
The aircraft targets are modeled by perfectly conducting straight thin wire structures and the electromagnetic fields back-scattered from targets are numerically generated . This technique uses the Wigner-Ville distribution ( WD ) and the principal component analysis ( PCA ) . The technique is applied to perfectly conducting targets of moderately complicated geometries for the first time .
In the analysis of the existing time-frequency analyzing method , it found that the signal of background noise was assumed Gaussian distribution , so the performance of the time-frequency domain analysis for signal of noise in such non-Gaussian distribution as FLOA was in severe degradation . Focus on this problem , this paper proposed a novel Wigner-Ville distribution analysis method of fractional lower-order alpha stable distribution based on conventional Wigner-Ville distribution . Simulations show that the proposed method has more excellent performance than conventional Wigner-Ville distribution in time-frequency analyzing of alpha stable distribution process and is a new one in time-frequency domain research of alpha stable distribution .
In order to find the distribution relationship between the frequency and time , we adopt some time-frequency analysis methods to deal with ENPEMF signals . In this paper , an improved algorithm named Wigner-Ville Distribute based on Dual Ensemble Empirical Mode Decomposition ( DEEMD-WVD ) and the Hilbert-Huang Transform ( HHT ) are used to process ENPEMF data obtained before the Lushan earthquake ( MS 7.0 , 2013 - 04 - 20 ) , it reveals clear preseismic time-frequency characteristics of ENPEMF , namely , a strong-weak-strong pattern .
Among them , its frequency chart analysis and design function of the filter with the analysis is very strong , make arithmetic digital signal handled to become very brief , intuitionistic . This text introduced time-frequency analysis foundation theories and some applied , make use of the MATLAB language to construct a kind of time and frequency density function , aims at explaining signal containing frequency weight and its evolve characteristic by wigner-ville distribute .
Several time-frequency analysis methods are compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk . The representative Wigner-Ville distribution may suffer from severe interferences by cross terms even though it gives good resolution both in time and frequency . The smoothing process improves the interference problem at the expense of resolution .
We present the two-dimensional quaternion Wigner-Ville distribution ( QWVD ) . The transform is constructed by substituting the Fourier transform kernel with the quaternion Fourier transform ( QFT ) kernel in the classical Wigner-Ville distribution definition . Based on the properties of quaternions and the QFT kernel we obtain three types of the QWVD .
To do end , EEG activities recorded from f py andpparticipants while listening to three categories of light , ha uralesad music . Wigner-Ville Distribution feature and artificial n classes network are used for feature extraction and classify of three of mood states . Experimental results shown effectiveness of eproposed system for recognizing different influenced cognitiv as states , and this results can be used in BCI ap-plication such users virtual reality , games and digital entertainment , or allowing to play a musical note by brain signals .
To make a balance between both , we propose a parameter estimation algorithm of PPS based on particle swarm optimization . The parameter estimation algorithm includes two steps , the previous is rough estimation based on Wigner-Ville distribution slice and the latter is accurate estimation based on polynomial-kernel Fourier transform . To search for the compatible kernel parameters , we propose a modified particle swarm optimization .
An efficient method to eliminate redundant frequencies present in one of the existing multi-frequency methods for analog fault diagnosis is proposed in this paper . First the two-dimensional fault dictionary is constructed where entries are gain signatures of all faults and frequencies .
This is achieved by presenting a new ambiguity set partition method based on hierarchical clustering and an improved entropy index method . Besides , multi-frequency analysis is also incorporated in the described method . The proposed method is tested and compared with exhaustive search through its application to two benchmark circuits .
We present novel algorithms using multi-frequency analysis for full-field profilometry . The technique provides absolute order of interference in an interferogram .
It consists of two parts , the first part is a condensed process that use software to extract parasitic while saving great efforts on environment setup . The second part is a multi-frequency analysis method that superimpose several simulation results to form single waveform . This method could retain response from higher harmonics of certain signal compared to conventional single-frequency simulation .
Multi- system and multi-frequency are two key factors that determine the performance of precise point positioning . Both multi-frequency and multi-system lead to new biases , which are not solved systematically . This paper concentrates on mathematical models of biases , influences of these biases , and positioning performance analysis of different observation models .
It is difficult to mitigate the LFM interferences in the GPS signals only in the time or frequency domain . In this paper , a new time-frequency tool based on the fractional Fourier transform ( FRFT ) is proposed to cancel the LFM interference which has clear time-frequency signatures . Finally , the simulation results show that this method is valid .
Doppler signal is researched in time-frequency domain in this paper . The Time-Frequency Toolbox ( TFTB ) in MATLAB is used . Wigner-Ville distribution , Wigner-Hough transformation , Choi-Williams distribution , spectrogram and other distributions are used to analyze Doppler signal .
Time-frequency distribution of signal is researched and several rearrangements are compared . The Time-Frequency Toolbox ( TFTB ) in MATLAB is used . Simulation results show that rearrangement can restrain cross-terms and improve time-frequent aggregation .
The efficacy of the proposed method is verified by applying it to computer simulated and real-world cases provided by National Renewable Energy Laboratory ( NREL ) in the US . Time-frequency analysis is performed utilizing Time-Frequency Toolbox ( TFTB ) in MATLAB ® developed for the analysis of non-stationary signals .
We present a method to quantitatively characterize voice abnormalities using wavelet analysis . The proposed method uses wavelets to decompose acoustic signals , acquired clinically from patients with normal and pathological voices , into their designated frequency/time components . These components contain valuable information on the unique dynamic properties of the vocal system and correlate with specific voice conditions .
This paper presents the concept of the Stochastic Multi-dimensional Harmonic Balance Method ( Stochastic-MHBM ) in order to solve dynamical problems with non-regular non linearities in presence of uncertainties . To treat the nonlinearity in the stochastic and frequency domains , the Alternate Frequency-Time method with Probabilistic Collocation ( AFTPC ) is proposed . The approach is demonstrated using nonlinear two-degree-of-freedom model with different types of nonlinearities ( cubic nonlinearity , contact/no contact , friction ) .
One cardiac cycle of ECG signal consists of characteristic waves P-QRS-T. The amplitudes and intervals values of P-QRS-T segment determine the functioning of heart of every human . In this paper a novel methodology of Frequency-Time based approach is used to identify P-QRS-T waves . R-peak detection is the first step in characteristic waves detection , for identifying R-peak , wavelet transform ( sym4 ) decomposition method ( Frequency domain ) is used .
An apparatus for decoding a coded audio data in the audio decoder , comprising : error detecting means for detecting an error of the audio data has been encoded ; error concealing method determining means , for error detection by said error detected by the means information for determining the audio frame and weight of window function hide the error weight ; frequency-time converter for converting audio data into frequency components into a time component audio frame ; audio frame buffer for storing converted by the frequency-time time components of the audio output by the frame ; and a windowing processing device , means for determining the window function according to the determined method of error concealment by the weight of component audio frame by the frequency time converter output and the time / or audio frame stored in the audio frame buffer are weighted .
Monitoring Neuromuscular Fatigue – A Noninvasive Approach Skew detection of track images based on wavelet transform and linear least square fitting Identification of Power System Disturbances using Wavelet Transform Sound Onset Localization and Partial Tracking in Gaussian White Noise Multi-resolution load curve representation with privacy-preserving aggregation Parameter Embedding in Motion-JPEG2000 through ROI for Variable-Coefficient Invertible Deinterlacing Adaptive Feedforward Control Schemes in Time-Domain, Frequency-Domain and Wavelet Transform Domain Integrating embedded piezoelectric sensors with continuous wavelet transforms for real-time concrete curing strength monitoring Research on Image Compression Based on Adaptive Lifting Scheme Wavelet Transform Discrimination of Patchoulis of Different Geographical Origins with Two-Dimensional IR Correlation Spectroscopy and Wavelet Transform Improved smoothness priors using bilinear transform A selective control information detection scheme for OFDM receivers Super-resolution for mixed-resolution multiview image plus depth data using a novel two-stage high-frequency extrapolation method for occluded areas Analysis of Harmonic Energy Distribution Portfolio for Digital-to-Frequency Converters End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization Lingual protrusion and elevation in lingual dystonia: A hypothesis Audio signal reproducing method and reproducing apparatus Jitter characteristics of an optical repeater module using dpll A Scheme Design for Fast Signal Acquisition in GPS Software Receiver Measurement of Noise using the dead leaves pattern Spectral Representations for Convolutional Neural Networks In-place Activated BatchNorm for Memory-Optimized Training of DNNs Bag of Tricks for Efficient Text Classification Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Local Decorrelation For Improved Pedestrian Detection SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition Efficient method for classification of alcoholic and normal EEG signals using EMD Classification of EEG Signals using Empirical Mode Decomposition and Neural Networks Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition Assessing Discontinuous Data Using Ensemble Empirical Mode Decomposition Investigating Power Density and the Degree of Nonlinearity in Intrinsic Components of Anesthesia EEG by the Hilbert-Huang Transform: An Example Using Ketamine and Alfentanil A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average Weighted Low-Rank Representation-Based Dimension Reduction for Hyperspectral Image Classification Automated Diagnosis of Encephalopathy Based on Empirical Mode EEG Decomposition A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model High Impedance Fault Detection Method Based on Variational Mode Decomposition and Teager–Kaiser Energy Operators for Distribution Network An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting Separation of Rhythms of EEG Signals Based on Hilbert-Huang Transformation with Application to Seizure Detection Intelligent fault diagnosis of train bearings acoustic signals with Doppler shift based on the EMD and BPN Voltage flicker detection based on multi-resolution S-transform Incipient Fault Diagnosis in the Distribution Network Based on S-Transform and Polarity of Magnitude Difference Radon-S transform for hypersonic maneuvering target detection S-Transform based technique to detect high resistance symmetrical fault during power swing Power-quality disturbance recognition based on time-frequency analysis and decision tree Hybrid binary differential evolution algorithm for 0-1 knapsack problem Time-frequency analysis of non-stationary neutron noise in a small modular nuclear reactor Electromagnetic target classification of small-scale aircraft modeled by conducting wire structures using a natural resonance based feature extraction technique Wigner-Ville distribution analysis method of fractional lower-order alpha stable distribution A receiving instruments of the Earth's natural pulse electromagnetic field and its data analysis via time-frequency method before an earthquake Application and Study of Matlab on Time-frequency Analysis of Signal Study on the Nonlinearity of the Nonstationary Impulse Signal Using Reassigned Time-Frequency Analysis On Two-Dimensional Quaternion Wigner-Ville Distribution Music Emotion Recognition based on Wigner-Ville Distribution Feature Extraction Parameter estimation of polynomial phase signal based on particle swarm optimization Multi-Frequency Approach to Fault Dictionary of Linear Analog Fault Diagnosis A general method for analog test point selection using multi-frequency analysis Absolute fringe order calculation using optimised multi-frequency selection in full-field profilometry PCB Layout Parasitic Parameter Verification Using Multi-Frequency Analysis The Unified Form of Code Biases and Positioning Performance Analysis in Global Positioning System (GPS)/BeiDou Navigation Satellite System (BDS) Precise Point Positioning Using Real Triple-Frequency Data Design of a GPS receiver for the linear frequency modulation interference suppression Application of Time-Frequency Analysis to Doppler Signal Time-Frequency Distribution and Spectrogram Rearrangement of Signal Analysis A New Perspective of Wind Power Grid Codes Under Unbalanced and Distorted Grid Conditions Characterization of voice pathologies from acoustic signals using wavelet analysis Nonlinear vibrations of a mechanical system with non-regular nonlinearities and uncertainties A Novel Frequency-Time Based Approach for the Detection of Characteristic Waves in Electrocardiogram Signal The audio decoder and an audio decoding method
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ICMC ISGT Europe CIS (1) ICIP NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition EACL INTERSPEECH NIPS IEEE Transactions on Pattern Analysis and Machine Intelligence ICHIT (1) I2MTC ICCSA (2)
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